metadata_version string | name string | version string | summary string | description string | description_content_type string | author string | author_email string | maintainer string | maintainer_email string | license string | keywords string | classifiers list | platform list | home_page string | download_url string | requires_python string | requires list | provides list | obsoletes list | requires_dist list | provides_dist list | obsoletes_dist list | requires_external list | project_urls list | uploaded_via string | upload_time timestamp[us] | filename string | size int64 | path string | python_version string | packagetype string | comment_text string | has_signature bool | md5_digest string | sha256_digest string | blake2_256_digest string | license_expression string | license_files list | recent_7d_downloads int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2.4 | AstrBot | 4.17.6 | Easy-to-use multi-platform LLM chatbot and development framework | 
<div align="center">
<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_en.md">English</a> ｜
<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_ja.md">日本語</a> ｜
<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_zh-TW.md">繁體中文</a> ｜
<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_fr.md">Français</a> ｜
<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_ru.md">Русский</a>
<div>
<a href="https://trendshift.io/repositories/12875" target="_blank"><img src="https://trendshift.io/api/badge/repositories/12875" alt="Soulter%2FAstrBot | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
<a href="https://hellogithub.com/repository/AstrBotDevs/AstrBot" target="_blank"><img src="https://api.hellogithub.com/v1/widgets/recommend.svg?rid=d127d50cd5e54c5382328acc3bb25483&claim_uid=ZO9by7qCXgSd6Lp&t=2" alt="Featured｜HelloGitHub" style="width: 250px; height: 54px;" width="250" height="54" /></a>
</div>
<br>
<div>
<img src="https://img.shields.io/github/v/release/AstrBotDevs/AstrBot?color=76bad9" href="https://github.com/AstrBotDevs/AstrBot/releases/latest">
<img src="https://img.shields.io/badge/python-3.10+-blue.svg" alt="python">
<img src="https://deepwiki.com/badge.svg" href="https://deepwiki.com/AstrBotDevs/AstrBot">
<a href="https://zread.ai/AstrBotDevs/AstrBot" target="_blank"><img src="https://img.shields.io/badge/Ask_Zread-_.svg?style=flat&color=00b0aa&labelColor=000000&logo=data%3Aimage%2Fsvg%2Bxml%3Bbase64%2CPHN2ZyB3aWR0aD0iMTYiIGhlaWdodD0iMTYiIHZpZXdCb3g9IjAgMCAxNiAxNiIgZmlsbD0ibm9uZSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KPHBhdGggZD0iTTQuOTYxNTYgMS42MDAxSDIuMjQxNTZDMS44ODgxIDEuNjAwMSAxLjYwMTU2IDEuODg2NjQgMS42MDE1NiAyLjI0MDFWNC45NjAxQzEuNjAxNTYgNS4zMTM1NiAxLjg4ODEgNS42MDAxIDIuMjQxNTYgNS42MDAxSDQuOTYxNTZDNS4zMTUwMiA1LjYwMDEgNS42MDE1NiA1LjMxMzU2IDUuNjAxNTYgNC45NjAxVjIuMjQwMUM1LjYwMTU2IDEuODg2NjQgNS4zMTUwMiAxLjYwMDEgNC45NjE1NiAxLjYwMDFaIiBmaWxsPSIjZmZmIi8%2BCjxwYXRoIGQ9Ik00Ljk2MTU2IDEwLjM5OTlIMi4yNDE1NkMxLjg4ODEgMTAuMzk5OSAxLjYwMTU2IDEwLjY4NjQgMS42MDE1NiAxMS4wMzk5VjEzLjc1OTlDMS42MDE1NiAxNC4xMTM0IDEuODg4MSAxNC4zOTk5IDIuMjQxNTYgMTQuMzk5OUg0Ljk2MTU2QzUuMzE1MDIgMTQuMzk5OSA1LjYwMTU2IDE0LjExMzQgNS42MDE1NiAxMy43NTk5VjExLjAzOTlDNS42MDE1NiAxMC42ODY0IDUuMzE1MDIgMTAuMzk5OSA0Ljk2MTU2IDEwLjM5OTlaIiBmaWxsPSIjZmZmIi8%2BCjxwYXRoIGQ9Ik0xMy43NTg0IDEuNjAwMUgxMS4wMzg0QzEwLjY4NSAxLjYwMDEgMTAuMzk4NCAxLjg4NjY0IDEwLjM5ODQgMi4yNDAxVjQuOTYwMUMxMC4zOTg0IDUuMzEzNTYgMTAuNjg1IDUuNjAwMSAxMS4wMzg0IDUuNjAwMUgxMy43NTg0QzE0LjExMTkgNS42MDAxIDE0LjM5ODQgNS4zMTM1NiAxNC4zOTg0IDQuOTYwMVYyLjI0MDFDMTQuMzk4NCAxLjg4NjY0IDE0LjExMTkgMS42MDAxIDEzLjc1ODQgMS42MDAxWiIgZmlsbD0iI2ZmZiIvPgo8cGF0aCBkPSJNNCAxMkwxMiA0TDQgMTJaIiBmaWxsPSIjZmZmIi8%2BCjxwYXRoIGQ9Ik00IDEyTDEyIDQiIHN0cm9rZT0iI2ZmZiIgc3Ryb2tlLXdpZHRoPSIxLjUiIHN0cm9rZS1saW5lY2FwPSJyb3VuZCIvPgo8L3N2Zz4K&logoColor=ffffff" alt="zread"/></a>
<a href="https://hub.docker.com/r/soulter/astrbot"><img alt="Docker pull" src="https://img.shields.io/docker/pulls/soulter/astrbot.svg?color=76bad9"/></a>
<img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.soulter.top%2Fastrbot%2Fplugin-num&query=%24.result&suffix=%E4%B8%AA&label=%E6%8F%92%E4%BB%B6%E5%B8%82%E5%9C%BA&cacheSeconds=3600">
<img src="https://gitcode.com/Soulter/AstrBot/star/badge.svg" href="https://gitcode.com/Soulter/AstrBot">
</div>
<br>
<a href="https://astrbot.app/">文档</a> ｜
<a href="https://blog.astrbot.app/">Blog</a> ｜
<a href="https://astrbot.featurebase.app/roadmap">路线图</a> ｜
<a href="https://github.com/AstrBotDevs/AstrBot/issues">问题提交</a>
</div>
AstrBot 是一个开源的一站式 Agentic 个人和群聊助手，可在 QQ、Telegram、企业微信、飞书、钉钉、Slack、等数十款主流即时通讯软件上部署，此外还内置类似 OpenWebUI 的轻量化 ChatUI，为个人、开发者和团队打造可靠、可扩展的对话式智能基础设施。无论是个人 AI 伙伴、智能客服、自动化助手，还是企业知识库，AstrBot 都能在你的即时通讯软件平台的工作流中快速构建 AI 应用。
## 主要功能
1. 💯 免费 & 开源。
2. ✨ AI 大模型对话，多模态，Agent，MCP，Skills，知识库，人格设定，自动压缩对话。
3. 🤖 支持接入 Dify、阿里云百炼、Coze 等智能体平台。
4. 🌐 多平台，支持 QQ、企业微信、飞书、钉钉、微信公众号、Telegram、Slack 以及[更多](#支持的消息平台)。
5. 📦 插件扩展，已有近 800 个插件可一键安装。
6. 🛡️ [Agent Sandbox](https://docs.astrbot.app/use/astrbot-agent-sandbox.html) 隔离化环境，安全地执行任何代码、调用 Shell、会话级资源复用。
7. 💻 WebUI 支持。
8. 🌈 Web ChatUI 支持，ChatUI 内置代理沙盒、网页搜索等。
9. 🌐 国际化（i18n）支持。
<br>
<table align="center">
<tr align="center">
<th>💙 角色扮演 & 情感陪伴</th>
<th>✨ 主动式 Agent</th>
<th>🚀 通用 Agentic 能力</th>
<th>🧩 900+ 社区插件</th>
</tr>
<tr>
<td align="center"><p align="center"><img width="984" height="1746" alt="99b587c5d35eea09d84f33e6cf6cfd4f" src="https://github.com/user-attachments/assets/89196061-3290-458d-b51f-afa178049f84" /></p></td>
<td align="center"><p align="center"><img width="976" height="1612" alt="c449acd838c41d0915cc08a3824025b1" src="https://github.com/user-attachments/assets/f75368b4-e022-41dc-a9e0-131c3e73e32e" /></p></td>
<td align="center"><p align="center"><img width="974" height="1732" alt="image" src="https://github.com/user-attachments/assets/e22a3968-87d7-4708-a7cd-e7f198c7c32e" /></p></td>
<td align="center"><p align="center"><img width="976" height="1734" alt="image" src="https://github.com/user-attachments/assets/0952b395-6b4a-432a-8a50-c294b7f89750" /></p></td>
</tr>
</table>
## 快速开始
#### Docker 部署(推荐 🥳)
推荐使用 Docker / Docker Compose 方式部署 AstrBot。
请参阅官方文档 [使用 Docker 部署 AstrBot](https://astrbot.app/deploy/astrbot/docker.html#%E4%BD%BF%E7%94%A8-docker-%E9%83%A8%E7%BD%B2-astrbot) 。
#### uv 部署
```bash
uv tool install astrbot
astrbot
```
#### 启动器一键部署（AstrBot Launcher）
进入 [AstrBot Launcher](https://github.com/Raven95676/astrbot-launcher) 仓库，在 Releases 页最新版本下找到对应的系统安装包安装即可。
#### 宝塔面板部署
AstrBot 与宝塔面板合作，已上架至宝塔面板。
请参阅官方文档 [宝塔面板部署](https://astrbot.app/deploy/astrbot/btpanel.html) 。
#### 1Panel 部署
AstrBot 已由 1Panel 官方上架至 1Panel 面板。
请参阅官方文档 [1Panel 部署](https://astrbot.app/deploy/astrbot/1panel.html) 。
#### 在 雨云 上部署
AstrBot 已由雨云官方上架至云应用平台，可一键部署。
[](https://app.rainyun.com/apps/rca/store/5994?ref=NjU1ODg0)
#### 在 Replit 上部署
社区贡献的部署方式。
[](https://repl.it/github/AstrBotDevs/AstrBot)
#### Windows 一键安装器部署
请参阅官方文档 [使用 Windows 一键安装器部署 AstrBot](https://astrbot.app/deploy/astrbot/windows.html) 。
#### CasaOS 部署
社区贡献的部署方式。
请参阅官方文档 [CasaOS 部署](https://astrbot.app/deploy/astrbot/casaos.html) 。
#### 手动部署
首先安装 uv：
```bash
pip install uv
```
通过 Git Clone 安装 AstrBot：
```bash
git clone https://github.com/AstrBotDevs/AstrBot && cd AstrBot
uv run main.py
```
或者请参阅官方文档 [通过源码部署 AstrBot](https://astrbot.app/deploy/astrbot/cli.html) 。
#### 系统包管理器安装
##### Arch Linux
```bash
yay -S astrbot-git
# 或者使用 paru
paru -S astrbot-git
```
#### 桌面端（Tauri）
桌面端已迁移为独立仓库（Tauri）：[https://github.com/AstrBotDevs/AstrBot-desktop](https://github.com/AstrBotDevs/AstrBot-desktop)。
## 支持的消息平台
**官方维护**
- QQ
- OneBot v11 协议实现
- Telegram
- 企微应用 & 企微智能机器人
- 微信客服 & 微信公众号
- 飞书
- 钉钉
- Slack
- Discord
- LINE
- Satori
- Misskey
- Whatsapp (将支持)
**社区维护**
- [Matrix](https://github.com/stevessr/astrbot_plugin_matrix_adapter)
- [KOOK](https://github.com/wuyan1003/astrbot_plugin_kook_adapter)
- [VoceChat](https://github.com/HikariFroya/astrbot_plugin_vocechat)
## 支持的模型服务
**大模型服务**
- OpenAI 及兼容服务
- Anthropic
- Google Gemini
- Moonshot AI
- 智谱 AI
- DeepSeek
- Ollama (本地部署)
- LM Studio (本地部署)
- [AIHubMix](https://aihubmix.com/?aff=4bfH)
- [优云智算](https://www.compshare.cn/?ytag=GPU_YY-gh_astrbot&referral_code=FV7DcGowN4hB5UuXKgpE74)
- [302.AI](https://share.302.ai/rr1M3l)
- [小马算力](https://www.tokenpony.cn/3YPyf)
- [硅基流动](https://docs.siliconflow.cn/cn/usercases/use-siliconcloud-in-astrbot)
- [PPIO 派欧云](https://ppio.com/user/register?invited_by=AIOONE)
- ModelScope
- OneAPI
**LLMOps 平台**
- Dify
- 阿里云百炼应用
- Coze
**语音转文本服务**
- OpenAI Whisper
- SenseVoice
**文本转语音服务**
- OpenAI TTS
- Gemini TTS
- GPT-Sovits-Inference
- GPT-Sovits
- FishAudio
- Edge TTS
- 阿里云百炼 TTS
- Azure TTS
- Minimax TTS
- 火山引擎 TTS
## ❤️ 贡献
欢迎任何 Issues/Pull Requests！只需要将你的更改提交到此项目 ：)
### 如何贡献
你可以通过查看问题或帮助审核 PR（拉取请求）来贡献。任何问题或 PR 都欢迎参与，以促进社区贡献。当然，这些只是建议，你可以以任何方式进行贡献。对于新功能的添加，请先通过 Issue 讨论。
### 开发环境
AstrBot 使用 `ruff` 进行代码格式化和检查。
```bash
git clone https://github.com/AstrBotDevs/AstrBot
pip install pre-commit
pre-commit install
```
## 🌍 社区
### QQ 群组
- 1 群：322154837
- 3 群：630166526
- 5 群：822130018
- 6 群：753075035
- 7 群：743746109
- 8 群：1030353265
- 开发者群：975206796
### Telegram 群组
<a href="https://t.me/+hAsD2Ebl5as3NmY1"><img alt="Telegram_community" src="https://img.shields.io/badge/Telegram-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
### Discord 群组
<a href="https://discord.gg/hAVk6tgV36"><img alt="Discord_community" src="https://img.shields.io/badge/Discord-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
## ❤️ Special Thanks
特别感谢所有 Contributors 和插件开发者对 AstrBot 的贡献 ❤️
<a href="https://github.com/AstrBotDevs/AstrBot/graphs/contributors">
<img src="https://contrib.rocks/image?repo=AstrBotDevs/AstrBot" />
</a>
此外，本项目的诞生离不开以下开源项目的帮助：
- [NapNeko/NapCatQQ](https://github.com/NapNeko/NapCatQQ) - 伟大的猫猫框架
开源项目友情链接：
- [NoneBot2](https://github.com/nonebot/nonebot2) - 优秀的 Python 异步 ChatBot 框架
- [Koishi](https://github.com/koishijs/koishi) - 优秀的 Node.js ChatBot 框架
- [MaiBot](https://github.com/Mai-with-u/MaiBot) - 优秀的拟人化 AI ChatBot
- [nekro-agent](https://github.com/KroMiose/nekro-agent) - 优秀的 Agent ChatBot
- [LangBot](https://github.com/langbot-app/LangBot) - 优秀的多平台 AI ChatBot
- [ChatLuna](https://github.com/ChatLunaLab/chatluna) - 优秀的多平台 AI ChatBot Koishi 插件
- [Operit AI](https://github.com/AAswordman/Operit) - 优秀的 AI 智能助手 Android APP
## ⭐ Star History
> [!TIP]
> 如果本项目对您的生活 / 工作产生了帮助，或者您关注本项目的未来发展，请给项目 Star，这是我们维护这个开源项目的动力 <3
<div align="center">
[](https://star-history.com/#astrbotdevs/astrbot&Date)
</div>
<div align="center">
_陪伴与能力从来不应该是对立面。我们希望创造的是一个既能理解情绪、给予陪伴，也能可靠完成工作的机器人。_
_私は、高性能ですから!_
<img src="https://files.astrbot.app/watashiwa-koseino-desukara.gif" width="100"/>
</div>
| text/markdown | null | null | null | null | null | Astrbot, Astrbot Module, Astrbot Plugin | [] | [] | null | null | >=3.12 | [] | [] | [] | [
"aiocqhttp>=1.4.4",
"aiodocker>=0.24.0",
"aiofiles>=25.1.0",
"aiohttp>=3.11.18",
"aiosqlite>=0.21.0",
"anthropic>=0.51.0",
"apscheduler>=3.11.0",
"audioop-lts; python_full_version >= \"3.13\"",
"beautifulsoup4>=4.13.4",
"certifi>=2025.4.26",
"chardet~=5.1.0",
"click>=8.2.1",
"cryptography>=44.0.3",
"dashscope>=1.23.2",
"defusedxml>=0.7.1",
"deprecated>=1.2.18",
"dingtalk-stream>=0.22.1",
"docstring-parser>=0.16",
"faiss-cpu>=1.12.0",
"filelock>=3.18.0",
"google-genai>=1.56.0",
"jieba>=0.42.1",
"lark-oapi>=1.4.15",
"loguru>=0.7.2",
"lxml-html-clean>=0.4.2",
"markitdown-no-magika[docx,xls,xlsx]>=0.1.2",
"mcp>=1.8.0",
"openai>=1.78.0",
"ormsgpack>=1.9.1",
"packaging>=24.2",
"pillow>=11.2.1",
"pip>=25.1.1",
"psutil<7.2.0,>=5.8.0",
"py-cord>=2.6.1",
"pydantic>=2.12.5",
"pydub>=0.25.1",
"pyjwt>=2.10.1",
"pypdf>=6.1.1",
"python-socks>=2.8.0",
"python-telegram-bot>=22.0",
"qq-botpy>=1.2.1",
"quart>=0.20.0",
"rank-bm25>=0.2.2",
"readability-lxml>=0.8.4.1",
"shipyard-python-sdk>=0.2.4",
"silk-python>=0.2.6",
"slack-sdk>=3.35.0",
"sqlalchemy[asyncio]>=2.0.41",
"sqlmodel>=0.0.24",
"telegramify-markdown>=0.5.1",
"tenacity>=9.1.2",
"watchfiles>=1.0.5",
"websockets>=15.0.1",
"wechatpy>=1.8.18",
"xinference-client"
] | [] | [] | [] | [] | uv/0.10.4 {"installer":{"name":"uv","version":"0.10.4","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true} | 2026-02-20T10:43:15.613115 | astrbot-4.17.6-py3-none-any.whl | 831,002 | 94/ba/6c11b31619dee2639d2e6f3d13567eaf0e167df1607bb22a2c45c0538fef/astrbot-4.17.6-py3-none-any.whl | py3 | bdist_wheel | null | false | 040d33fd6be8a1db8f9b975f6b669603 | 36e09a0ae050b0f2aee99f993aabc1e7352896b84d005c39c5048c0cc68e7131 | 94ba6c11b31619dee2639d2e6f3d13567eaf0e167df1607bb22a2c45c0538fef | null | [
"LICENSE"
] | 0 |
2.4 | mkdocs-static-i18n | 1.3.1 | MkDocs i18n plugin using static translation markdown files | 
# MkDocs static i18n plugin
*The MkDocs plugin that helps you support multiple language versions of your site / documentation.*
*Like what you :eyes:? Using this plugin? Give it a :star:!*
The `mkdocs-static-i18n` plugin allows you to support multiple languages of your documentation by adding static translation files to your existing documentation pages.
Multi language support is just **one `.<language>.md` file away**!
Even better, `mkdocs-static-i18n` also allows you to build and serve localized versions of any file extension to display localized images, medias and assets.
Localized images/medias/assets are just **one `.<language>.<extension>` file away**!
Don't like file suffixes? You're more into a folder based structure? We got you covered as well!
## Documentation
Check out the [plugins' documentation here](https://ultrabug.github.io/mkdocs-static-i18n/).
TL;DR? There's a [quick start guide](https://ultrabug.github.io/mkdocs-static-i18n/getting-started/quick-start/) for you!
## Upgrading from 0.x versions
:warning: Version 1.0.0 brings **breaking changes** to the configuration format of the plugin. Check out the [upgrade to v1.0.0 guide](https://ultrabug.github.io/mkdocs-static-i18n/setup/upgrading-to-1/) to ease updating your `mkdocs.yml` file!
## See it in action
This plugin is proudly bringing localized content of [hundreds of projects](https://github.com/ultrabug/mkdocs-static-i18n/network/dependents) to their users.
Check it out live:
- [On this repository documentation](https://ultrabug.github.io/mkdocs-static-i18n/)
- [On my own website: ultrabug.fr](https://ultrabug.fr)
But also in our hall of fame:
- [AWS Copilot CLI](https://aws.github.io/copilot-cli/)
- [OWASP Top 10](https://github.com/OWASP/Top10)
- [Spaceship Prompt](https://spaceship-prompt.sh/)
- [FederatedAI FATE](https://fate.readthedocs.io/en/latest/)
- [Privacy Guides Org](https://www.privacyguides.org/en/)
- [Computer Science Self Learning Wiki](https://csdiy.wiki/)
## Contributions welcome
Feel free to ask questions, enhancements and to contribute to this project!
## Development
The project is managed with `hatch`. [Install `hatch`](https://hatch.pypa.io/1.9/install/#gui-installer) first.
Run the tests:
```shell
hatch run test:test
hatch run style:check
```
Serve the documentation:
```shell
hatch run doc:serve
```
## Credits
- Logo by [max.icons](https://www.flaticon.com/authors/maxicons)
| text/markdown | null | Ultrabug <ultrabug@ultrabug.net> | null | null | null | null | [
"License :: OSI Approved :: MIT License",
"Operating System :: POSIX :: Linux",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3 :: Only",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14",
"Topic :: Software Development :: Libraries :: Python Modules"
] | [] | null | null | >=3.8 | [] | [] | [] | [
"mkdocs>=1.5.2",
"mkdocs-material<9.7.2; extra == \"material\""
] | [] | [] | [] | [
"Documentation, https://github.com/ultrabug/mkdocs-static-i18n#readme",
"Download, https://github.com/ultrabug/mkdocs-static-i18n/tags",
"Funding, https://ultrabug.fr/#support-me",
"Homepage, https://github.com/ultrabug/mkdocs-static-i18n",
"Source, https://github.com/ultrabug/mkdocs-static-i18n",
"Tracker, https://github.com/ultrabug/mkdocs-static-i18n/issues"
] | Hatch/1.16.3 cpython/3.14.2 HTTPX/0.28.1 | 2026-02-20T10:42:41.835200 | mkdocs_static_i18n-1.3.1.tar.gz | 1,371,325 | ce/f9/51e2ffda9c7210bc35a24f3717b08c052cd4b728dfa87f901c00d8005259/mkdocs_static_i18n-1.3.1.tar.gz | source | sdist | null | false | 70a81efb097b00c79f514da23854f40e | a6125ea7db6cc1a900d76a967f262535af09831160a93c56d7f0d522a79b5faf | cef951e2ffda9c7210bc35a24f3717b08c052cd4b728dfa87f901c00d8005259 | MIT | [
"LICENSE"
] | 2,447 |
2.4 | envcipher | 0.1.3 | Secure .env file encryption using OS keychain. Keep secrets encrypted at rest. | # Envcipher
[](https://crates.io/crates/envcipher)
[](https://pypi.org/project/envcipher/)
[](LICENSE)
Encrypt `.env` files using AES-256-GCM with keys stored in your OS keychain. Decrypt on demand for local development without managing separate key files.
---
## Installation
<details open>
<summary><strong>Python</strong></summary>
```bash
pip install envcipher
```
Provides both the CLI and Python library.
</details>
<details>
<summary><strong>Rust</strong></summary>
```bash
cargo install envcipher
```
CLI only.
</details>
<details>
<summary><strong>From Source</strong></summary>
```bash
git clone https://github.com/iamprecieee/envcipher
cd envcipher
cargo install --path .
```
</details>
---
## Usage
### CLI
```bash
envcipher init # Generate key, store in OS keychain
envcipher edit # Decrypt -> edit -> re-encrypt
envcipher lock # Encrypt .env in place
envcipher unlock # Decrypt .env to plaintext
envcipher run -- <cmd> # Run command with decrypted env vars
envcipher status # Show encryption status
```
<details>
<summary><strong>Python Library</strong></summary>
```python
import envcipher
import os
# Load encrypted .env into os.environ
envcipher.load()
# Access secrets
api_key = os.getenv("API_KEY")
```
Custom path:
```python
envcipher.load(path="/path/to/.env")
```
Works with both encrypted and plaintext files.
</details>
---
## Team Sharing
```bash
# Export key
envcipher export-key
# Output: qQWntX6r7eANxsyKHbkJtuXtzW0Hy5zjJGvDSxMKM9I=
# Import on another machine
envcipher import-key qQWntX6r7eANxsyKHbkJtuXtzW0Hy5zjJGvDSxMKM9I=
```
Share keys through secure channels only.
---
## Security
| Component | Implementation |
|-----------|----------------|
| Encryption | AES-256-GCM, 96-bit random nonces |
| Key Storage | OS keychain (Keychain / Credential Manager / Secret Service) |
| Memory | Keys zeroized on drop |
| Format | `ENVCIPHER:v1:<nonce>:<ciphertext>` |
**Designed for:** Protecting secrets from accidental commits, local development encryption at rest, small team key sharing.
**Not designed for:** Production secret management, zero-trust environments, HSM requirements.
---
## FAQ
<details>
<summary>Can I manually edit the encrypted file?</summary>
No. Use `envcipher edit` or the unlock-edit-lock workflow. Manual edits corrupt the format.
</details>
<details>
<summary>Can I commit the encrypted .env file?</summary>
Yes, but we recommend using `.gitignore` and sharing via `export-key`/`import-key` instead. Committing encrypted files is safe only if your team securely shares the key.
</details>
<details>
<summary>What if I lose my key?</summary>
Keys are stored in your OS keychain. If you lose access (e.g., fresh OS install), get a teammate to run `export-key`.
</details>
<details>
<summary>How do I rotate keys?</summary>
Currently manual: decrypt with old key, run `init` in a fresh directory to generate new key, re-encrypt.
</details>
<details>
<summary>Does it work in CI/CD?</summary>
Not recommended. Envcipher is designed for local development. CI runners have ephemeral keychains, and storing the key as a CI secret defeats the purpose. Use native secret management instead (GitHub Secrets, AWS Secrets Manager, etc.).
</details>
<details>
<summary>Can I use this on multiple projects?</summary>
Yes. Each project directory gets its own key (hashed by directory path). Moving a project folder requires re-importing the key.
</details>
---
## License
[MIT](LICENSE)
---
[Contributing](docs/CONTRIBUTING.md) | [Code of Conduct](docs/CODE_OF_CONDUCT.md) | [Security](docs/SECURITY.md)
| text/markdown; charset=UTF-8; variant=GFM | null | iamprecieee <emmypresh777@gmail.com> | null | null | null | env, encryption, secrets, security, python, dotenv | [
"Programming Language :: Rust",
"Programming Language :: Python :: Implementation :: CPython",
"Programming Language :: Python :: Implementation :: PyPy"
] | [] | null | null | >=3.7 | [] | [] | [] | [
"maturin>=1.12.3"
] | [] | [] | [] | [] | maturin/1.12.3 | 2026-02-20T10:42:38.718427 | envcipher-0.1.3.tar.gz | 26,264 | c1/99/27c43da1d867f8c161ccde97d2717164ce68da7707b432db8a9bf995ad08/envcipher-0.1.3.tar.gz | source | sdist | null | false | 4e2ab70d59a92bed603a270727b4c8ab | c73b9ad74e735f4399d4a30b98f4e87e5db909c1f35915b65c67d1f366790011 | c19927c43da1d867f8c161ccde97d2717164ce68da7707b432db8a9bf995ad08 | null | [] | 242 |
2.4 | usearch-iscc | 2.24.2 | Smaller & Faster Single-File Vector Search Engine from Unum (ISCC Foundation Fork) | <h1 align="center">USearch</h1>
<h3 align="center">
Smaller & <a href="https://www.unum.cloud/blog/2023-11-07-scaling-vector-search-with-intel">Faster</a> Single-File<br/>
Similarity Search & Clustering Engine for <a href="https://github.com/ashvardanian/simsimd">Vectors</a> & 🔜 <a href="https://github.com/ashvardanian/stringzilla">Texts</a>
</h3>
<br/>
<p align="center">
<a href="https://discord.gg/A6wxt6dS9j"><img height="25" src="https://github.com/unum-cloud/.github/raw/main/assets/discord.svg" alt="Discord"></a>
<a href="https://www.linkedin.com/company/unum-cloud/"><img height="25" src="https://github.com/unum-cloud/.github/raw/main/assets/linkedin.svg" alt="LinkedIn"></a>
<a href="https://twitter.com/unum_cloud"><img height="25" src="https://github.com/unum-cloud/.github/raw/main/assets/twitter.svg" alt="Twitter"></a>
<a href="https://unum.cloud/post"><img height="25" src="https://github.com/unum-cloud/.github/raw/main/assets/blog.svg" alt="Blog"></a>
<a href="https://github.com/unum-cloud/usearch"><img height="25" src="https://github.com/unum-cloud/.github/raw/main/assets/github.svg" alt="GitHub"></a>
</p>
<p align="center">
Spatial • Binary • Probabilistic • User-Defined Metrics
<br/>
<a href="https://unum-cloud.github.io/USearch/cpp">C++11</a> •
<a href="https://unum-cloud.github.io/USearch/python">Python 3</a> •
<a href="https://unum-cloud.github.io/USearch/javascript">JavaScript</a> •
<a href="https://unum-cloud.github.io/USearch/java">Java</a> •
<a href="https://unum-cloud.github.io/USearch/rust">Rust</a> •
<a href="https://unum-cloud.github.io/USearch/c">C99</a> •
<a href="https://unum-cloud.github.io/USearch/objective-c">Objective-C</a> •
<a href="https://unum-cloud.github.io/USearch/swift">Swift</a> •
<a href="https://unum-cloud.github.io/USearch/csharp">C#</a> •
<a href="https://unum-cloud.github.io/USearch/golang">Go</a> •
<a href="https://unum-cloud.github.io/USearch/wolfram">Wolfram</a>
<br/>
Linux • macOS • Windows • iOS • Android • WebAssembly •
<a href="https://unum-cloud.github.io/USearch/sqlite">SQLite</a>
</p>
<div align="center">
<a href="https://pepy.tech/project/usearch"> <img alt="PyPI" src="https://static.pepy.tech/personalized-badge/usearch?period=total&units=abbreviation&left_color=black&right_color=blue&left_text=Python%20PyPI%20installs"> </a>
<a href="https://www.npmjs.com/package/usearch"> <img alt="NPM" src="https://img.shields.io/npm/dy/usearch?label=JavaScript%20NPM%20installs"> </a>
<a href="https://crates.io/crates/usearch"> <img alt="Crate" src="https://img.shields.io/crates/d/usearch?label=Rust%20Crate%20installs"> </a>
<a href="https://www.nuget.org/packages/Cloud.Unum.USearch"> <img alt="NuGet" src="https://img.shields.io/nuget/dt/Cloud.Unum.USearch?label=CSharp%20NuGet%20installs"> </a>
<!-- Maven Central publishing is deprecated for now; fat-JAR download is the supported path. -->
<img alt="GitHub code size in bytes" src="https://img.shields.io/github/languages/code-size/unum-cloud/usearch?label=Repo%20size">
</div>
---
> **ISCC Foundation Fork** -- This is a maintained fork of [USearch](https://github.com/unum-cloud/usearch)
> by the [ISCC Foundation](https://iscc.io), published on PyPI as
> [`usearch-iscc`](https://pypi.org/project/usearch-iscc/). The Python import name remains `usearch`
> for compatibility. Install with: `pip install usearch-iscc`
>
> **Fork divergence from upstream:**
> - 128-bit key support (Python): `Index(ndim=..., key_kind="uuid")` for packed 16-byte keys
> - Multi-index UUID support (Python): `Indexes` works with both u64 and uuid-keyed shards
> - NPHD metric (all bindings): Normalized Prefix Hamming Distance for length-prefixed binary vectors
> - Build: published as `usearch-iscc` on PyPI with independent release cycle
---
- ✅ __[10x faster][faster-than-faiss]__ [HNSW][hnsw-algorithm] implementation than [FAISS][faiss].
- ✅ Simple and extensible [single C++11 header][usearch-header] __library__.
- ✅ [Trusted](#integrations) by giants like Google and DBs like [ClickHouse][clickhouse-docs] & [DuckDB][duckdb-docs].
- ✅ [SIMD][simd]-optimized and [user-defined metrics](#user-defined-functions) with JIT compilation.
- ✅ Hardware-agnostic `f16` & `i8` - [half-precision & quarter-precision support](#memory-efficiency-downcasting-and-quantization).
- ✅ [View large indexes from disk](#serialization--serving-index-from-disk) without loading into RAM.
- ✅ Heterogeneous lookups, renaming/relabeling, and on-the-fly deletions.
- ✅ Binary Tanimoto and Sorensen coefficients for [Genomics and Chemistry applications](#usearch--rdkit--molecular-search).
- ✅ [NPHD metric](#nphd-metric-normalized-prefix-hamming-distance) for variable-length binary fingerprint comparison.
- ✅ Space-efficient point-clouds with `uint40_t`, accommodating 4B+ size.
- ✅ Compatible with OpenMP and custom "executors" for fine-grained parallelism.
- ✅ [Semantic Search](#usearch--uform--ucall--multimodal-semantic-search) and [Joins](#joins-one-to-one-one-to-many-and-many-to-many-mappings).
- 🔄 Near-real-time [clustering and sub-clustering](#clustering) for Tens or Millions of clusters.
[faiss]: https://github.com/facebookresearch/faiss
[usearch-header]: https://github.com/unum-cloud/usearch/blob/main/include/usearch/index.hpp
[obscure-use-cases]: https://ashvardanian.com/posts/abusing-vector-search
[hnsw-algorithm]: https://arxiv.org/abs/1603.09320
[simd]: https://en.wikipedia.org/wiki/Single_instruction,_multiple_data
[faster-than-faiss]: https://www.unum.cloud/blog/2023-11-07-scaling-vector-search-with-intel
[clickhouse-docs]: https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/annindexes#usearch
[duckdb-docs]: https://duckdb.org/2024/05/03/vector-similarity-search-vss.html
__Technical Insights__ and related articles:
- [Uses Arm SVE and x86 AVX-512's masked loads to eliminate tail `for`-loops](https://ashvardanian.com/posts/simsimd-faster-scipy/#tails-of-the-past-the-significance-of-masked-loads).
- [Uses Horner's method for polynomial approximations, beating GCC 12 by 119x](https://ashvardanian.com/posts/gcc-12-vs-avx512fp16/).
- [For every language implements a custom separate binding](https://ashvardanian.com/posts/porting-cpp-library-to-ten-languages/).
## Comparison with FAISS
FAISS is a widely recognized standard for high-performance vector search engines.
USearch and FAISS both employ the same HNSW algorithm, but they differ significantly in their design principles.
USearch is compact and broadly compatible without sacrificing performance, primarily focusing on user-defined metrics and fewer dependencies.
| | FAISS | USearch | Improvement |
| :------------------------------------------- | ----------------------: | -----------------------: | ----------------------: |
| Indexing time ⁰ | | | |
| 100 Million 96d `f32`, `f16`, `i8` vectors | 2.6 · 2.6 · 2.6 h | 0.3 · 0.2 · 0.2 h | __9.6 · 10.4 · 10.7 x__ |
| 100 Million 1536d `f32`, `f16`, `i8` vectors | 5.0 · 4.1 · 3.8 h | 2.1 · 1.1 · 0.8 h | __2.3 · 3.6 · 4.4 x__ |
| | | | |
| Codebase length ¹ | 84 K [SLOC][sloc] | 3 K [SLOC][sloc] | maintainable |
| Supported metrics ² | 9 fixed metrics | any metric | extendible |
| Supported languages ³ | C++, Python | 10 languages | portable |
| Supported ID types ⁴ | 32-bit, 64-bit | 32-bit, 40-bit, 64-bit | efficient |
| Filtering ⁵ | ban-lists | any predicates | composable |
| Required dependencies ⁶ | BLAS, OpenMP | - | light-weight |
| Bindings ⁷ | SWIG | Native | low-latency |
| Python binding size ⁸ | [~ 10 MB][faiss-weight] | [< 1 MB][usearch-weight] | deployable |
[sloc]: https://en.wikipedia.org/wiki/Source_lines_of_code
[faiss-weight]: https://pypi.org/project/faiss-cpu/#files
[usearch-weight]: https://pypi.org/project/usearch/#files
> ⁰ [Tested][intel-benchmarks] on Intel Sapphire Rapids, with the simplest inner-product distance, equivalent recall, and memory consumption while also providing far superior search speed.
> ¹ A shorter codebase of `usearch/` over `faiss/` makes the project easier to maintain and audit.
> ² User-defined metrics allow you to customize your search for various applications, from GIS to creating custom metrics for composite embeddings from multiple AI models or hybrid full-text and semantic search.
> ³ With USearch, you can reuse the same preconstructed index in various programming languages.
> ⁴ The 40-bit integer allows you to store 4B+ vectors without allocating 8 bytes for every neighbor reference in the proximity graph.
> ⁵ With USearch the index can be combined with arbitrary external containers, like Bloom filters or third-party databases, to filter out irrelevant keys during index traversal.
> ⁶ Lack of obligatory dependencies makes USearch much more portable.
> ⁷ Native bindings introduce lower call latencies than more straightforward approaches.
> ⁸ Lighter bindings make downloads and deployments faster.
[intel-benchmarks]: https://www.unum.cloud/blog/2023-11-07-scaling-vector-search-with-intel
Base functionality is identical to FAISS, and the interface must be familiar if you have ever investigated Approximate Nearest Neighbors search:
```py
# pip install usearch
import numpy as np
from usearch.index import Index
index = Index(ndim=3) # Default settings for 3D vectors
vector = np.array([0.2, 0.6, 0.4]) # Can be a matrix for batch operations
index.add(42, vector) # Add one or many vectors in parallel
matches = index.search(vector, 10) # Find 10 nearest neighbors
assert matches[0].key == 42
assert matches[0].distance <= 0.001
assert np.allclose(index[42], vector, atol=0.1) # Ensure high tolerance in mixed-precision comparisons
```
More settings are always available, and the API is designed to be as flexible as possible.
The default storage/quantization level is hardware-dependant for efficiency, but `bf16` is recommended for most modern CPUs.
```py
index = Index(
ndim=3, # Define the number of dimensions in input vectors
metric='cos', # Choose 'l2sq', 'ip', 'haversine' or other metric, default = 'cos'
dtype='bf16', # Store as 'f64', 'f32', 'f16', 'i8', 'b1'..., default = None
connectivity=16, # Optional: Limit number of neighbors per graph node
expansion_add=128, # Optional: Control the recall of indexing
expansion_search=64, # Optional: Control the quality of the search
multi=False, # Optional: Allow multiple vectors per key, default = False
)
```
## 128-bit Keys (UUID Mode)
By default, USearch uses 64-bit unsigned integer keys. This fork adds support for 128-bit keys via `key_kind="uuid"`, allowing you to pack structured identifiers (e.g. content hashes, chunk pointers) directly into the key.
```py
import numpy as np
from usearch.index import Index
# Create an index with 128-bit keys
index = Index(ndim=128, metric='cos', key_kind='uuid')
# Keys are 16-byte values: single keys as bytes, batches as numpy V16 arrays
batch_size = 1000
keys = np.empty(batch_size, dtype='V16')
vectors = np.random.randn(batch_size, 128).astype(np.float32)
for i in range(batch_size):
body = i.to_bytes(8, 'big') # 8 bytes: content identity
offset = (i * 16).to_bytes(4, 'big') # 4 bytes: chunk offset
size = (1024 + i).to_bytes(4, 'big') # 4 bytes: chunk size
keys[i] = body + offset + size # 16 bytes total
index.add(keys, vectors)
matches = index.search(vectors[0], count=5)
for match in matches:
print(match.key, match.distance) # match.key is bytes(16)
# Single-key operations use bytes(16)
single_key = keys[0].tobytes()
index.contains(single_key) # bool
index.get(single_key) # np.ndarray or None
index.remove(single_key)
# Save/load preserves key kind; mismatched load raises ValueError
index.save('index.usearch')
restored = Index.restore('index.usearch') # auto-detects uuid mode
```
> **Note:** Auto-generated keys are not supported in uuid mode — you must always pass explicit keys to `add()`.
## NPHD Metric (Normalized Prefix Hamming Distance)
NPHD is a built-in distance metric for comparing length-prefixed binary vectors. Each vector's first byte stores the data length in bytes. The metric computes the Hamming distance over the common prefix of two vectors and normalizes by the shorter vector's bit count, returning a value in `[0.0, 1.0]`.
This is useful for content identification systems like [ISCC](https://iscc.codes) where binary fingerprints may have variable-length prefixes. Previously this required a custom Numba `@cfunc` metric (~500MB of dependencies) and `change_metric()` hacks after every `load()`/`view()`. The native metric eliminates both.
```py
import numpy as np
from usearch.index import Index, MetricKind, ScalarKind
# Vector layout: [length_byte, data_byte_0, data_byte_1, ..., padding...]
# ndim is total size in bits, including the length byte.
ndim = 264 # 33 bytes = 1 length byte + up to 32 data bytes
index = Index(ndim=ndim, metric=MetricKind.NPHD, dtype=ScalarKind.B1)
def make_vector(length, data_bytes):
"""Build a length-prefixed binary vector."""
vec = np.zeros(ndim // 8, dtype=np.uint8)
vec[0] = length
vec[1:1 + len(data_bytes)] = data_bytes
return vec
a = make_vector(4, [0xAA, 0xBB, 0xCC, 0xDD])
b = make_vector(4, [0xAA, 0xBB, 0xCC, 0x00])
index.add(0, a)
index.add(1, b)
matches = index.search(a, 2)
print(matches[0].key, matches[0].distance) # 0, 0.0
print(matches[1].key, matches[1].distance) # 1, ~0.15625
# Save/load preserves the metric — no change_metric() needed
index.save("nphd_index.usearch")
restored = Index.restore("nphd_index.usearch")
assert str(restored.metric_kind) == "MetricKind.NPHD"
```
**Key details:**
- Only valid with `dtype=ScalarKind.B1` (binary vectors).
- The length byte encodes the number of data bytes (not bits), excluding itself.
- When vectors have different lengths, only the common prefix is compared.
- A length byte of 0 yields distance 0.0 (no data to compare).
## Serialization & Serving `Index` from Disk
USearch supports multiple forms of serialization:
- Into a __file__ defined with a path.
- Into a __stream__ defined with a callback, serializing or reconstructing incrementally.
- Into a __buffer__ of fixed length or a memory-mapped file that supports random access.
The latter allows you to serve indexes from external memory, enabling you to optimize your server choices for indexing speed and serving costs.
This can result in __20x cost reduction__ on AWS and other public clouds.
```py
index.save("index.usearch")
index.load("index.usearch")
view = Index.restore("index.usearch", view=True, ...)
other_view = Index(ndim=..., metric=...)
other_view.view("index.usearch")
```
## Exact vs. Approximate Search
Approximate search methods, such as HNSW, are predominantly used when an exact brute-force search becomes too resource-intensive.
This typically occurs when you have millions of entries in a collection.
For smaller collections, we offer a more direct approach with the `search` method.
```py
from usearch.index import search, MetricKind, Matches, BatchMatches
import numpy as np
# Generate 10'000 random vectors with 1024 dimensions
vectors = np.random.rand(10_000, 1024).astype(np.float32)
vector = np.random.rand(1024).astype(np.float32)
one_in_many: Matches = search(vectors, vector, 50, MetricKind.L2sq, exact=True)
many_in_many: BatchMatches = search(vectors, vectors, 50, MetricKind.L2sq, exact=True)
```
If you pass the `exact=True` argument, the system bypasses indexing altogether and performs a brute-force search through the entire dataset using SIMD-optimized similarity metrics from [SimSIMD](https://github.com/ashvardanian/simsimd).
When compared to FAISS's `IndexFlatL2` in Google Colab, __[USearch may offer up to a 20x performance improvement](https://github.com/unum-cloud/usearch/issues/176#issuecomment-1666650778)__:
- `faiss.IndexFlatL2`: __55.3 ms__.
- `usearch.index.search`: __2.54 ms__.
## User-Defined Metrics
While most vector search packages concentrate on just two metrics, "Inner Product distance" and "Euclidean distance", USearch allows arbitrary user-defined metrics.
This flexibility allows you to customize your search for various applications, from computing geospatial coordinates with the rare [Haversine][haversine] distance to creating custom metrics for composite embeddings from multiple AI models, like joint image-text embeddings.
You can use [Numba][numba], [Cppyy][cppyy], or [PeachPy][peachpy] to define your [custom metric even in Python](https://unum-cloud.github.io/USearch/python#user-defined-metrics-and-jit-in-python):
```py
from numba import cfunc, types, carray
from usearch.index import Index, MetricKind, MetricSignature, CompiledMetric
ndim = 256
@cfunc(types.float32(types.CPointer(types.float32), types.CPointer(types.float32)))
def python_inner_product(a, b):
a_array = carray(a, ndim)
b_array = carray(b, ndim)
c = 0.0
for i in range(ndim):
c += a_array[i] * b_array[i]
return 1 - c
metric = CompiledMetric(pointer=python_inner_product.address, kind=MetricKind.IP, signature=MetricSignature.ArrayArray)
index = Index(ndim=ndim, metric=metric, dtype=np.float32)
```
Similar effect is even easier to achieve in C, C++, and Rust interfaces.
Moreover, unlike older approaches indexing high-dimensional spaces, like KD-Trees and Locality Sensitive Hashing, HNSW doesn't require vectors to be identical in length.
They only have to be comparable.
So you can apply it in [obscure][obscure] applications, like searching for similar sets or fuzzy text matching, using [GZip][gzip-similarity] compression-ratio as a distance function.
[haversine]: https://ashvardanian.com/posts/abusing-vector-search#geo-spatial-indexing
[obscure]: https://ashvardanian.com/posts/abusing-vector-search
[gzip-similarity]: https://twitter.com/LukeGessler/status/1679211291292889100?s=20
[numba]: https://numba.readthedocs.io/en/stable/reference/jit-compilation.html#c-callbacks
[cppyy]: https://cppyy.readthedocs.io/en/latest/
[peachpy]: https://github.com/Maratyszcza/PeachPy
## Filtering and Predicate Functions
Sometimes you may want to cross-reference search-results against some external database or filter them based on some criteria.
In most engines, you'd have to manually perform paging requests, successively filtering the results.
In USearch you can simply pass a predicate function to the search method, which will be applied directly during graph traversal.
In Rust that would look like this:
```rust
let is_odd = |key: Key| key % 2 == 1;
let query = vec![0.2, 0.1, 0.2, 0.1, 0.3];
let results = index.filtered_search(&query, 10, is_odd).unwrap();
assert!(
results.keys.iter().all(|&key| key % 2 == 1),
"All keys must be odd"
);
```
## Memory Efficiency, Downcasting, and Quantization
Training a quantization model and dimension-reduction is a common approach to accelerate vector search.
Those, however, are only sometimes reliable, can significantly affect the statistical properties of your data, and require regular adjustments if your distribution shifts.
Instead, we have focused on high-precision arithmetic over low-precision downcasted vectors.
The same index, and `add` and `search` operations will automatically down-cast or up-cast between `f64_t`, `f32_t`, `f16_t`, `i8_t`, and single-bit `b1x8_t` representations.
You can use the following command to check, if hardware acceleration is enabled:
```sh
$ python -c 'from usearch.index import Index; print(Index(ndim=768, metric="cos", dtype="f16").hardware_acceleration)'
> sapphire
$ python -c 'from usearch.index import Index; print(Index(ndim=166, metric="tanimoto").hardware_acceleration)'
> ice
```
In most cases, it's recommended to use half-precision floating-point numbers on modern hardware.
When quantization is enabled, the "get"-like functions won't be able to recover the original data, so you may want to replicate the original vectors elsewhere.
When quantizing to `i8_t` integers, note that it's only valid for cosine-like metrics.
As part of the quantization process, the vectors are normalized to unit length and later scaled to [-127, 127] range to occupy the full 8-bit range.
When quantizing to `b1x8_t` single-bit representations, note that it's only valid for binary metrics like Jaccard, Hamming, etc.
As part of the quantization process, the scalar components greater than zero are set to `true`, and the rest to `false`.
Using smaller numeric types will save you RAM needed to store the vectors, but you can also compress the neighbors lists forming our proximity graphs.
By default, 32-bit `uint32_t` is used to enumerate those, which is not enough if you need to address over 4 Billion entries.
For such cases we provide a custom `uint40_t` type, that will still be 37.5% more space-efficient than the commonly used 8-byte integers, and will scale up to 1 Trillion entries.
## `Indexes` for Multi-Index Lookups
For larger workloads targeting billions or even trillions of vectors, parallel multi-index lookups become invaluable.
Instead of constructing one extensive index, you can build multiple smaller ones and view them together.
```py
from usearch.index import Indexes
multi_index = Indexes(
indexes=[index_a, index_b], # Merge in-memory shards
paths=["shard_a.usearch", "shard_b.usearch"], # Or load from disk
view=False,
threads=0,
)
multi_index.search(query_vectors, 10)
```
`Indexes` supports both u64 and uuid key kinds. The key kind is auto-detected from the first merged shard or path, or can be set explicitly:
```py
# Auto-detect from shards
indexes = Indexes([uuid_index_a, uuid_index_b])
# Auto-detect from paths
indexes = Indexes(paths=["uuid_shard.usearch"])
# Explicit key kind
indexes = Indexes(key_kind="uuid")
indexes.merge(uuid_index)
# Incremental loading
indexes = Indexes()
indexes.merge_path("shard.usearch")
```
## Clustering
Once the index is constructed, USearch can perform K-Nearest Neighbors Clustering much faster than standalone clustering libraries, like SciPy,
UMap, and tSNE.
Same for dimensionality reduction with PCA.
Essentially, the `Index` itself can be seen as a clustering, allowing iterative deepening.
```py
clustering = index.cluster(
min_count=10, # Optional
max_count=15, # Optional
threads=..., # Optional
)
# Get the clusters and their sizes
centroid_keys, sizes = clustering.centroids_popularity
# Use Matplotlib to draw a histogram
clustering.plot_centroids_popularity()
# Export a NetworkX graph of the clusters
g = clustering.network
# Get members of a specific cluster
first_members = clustering.members_of(centroid_keys[0])
# Deepen into that cluster, splitting it into more parts, all the same arguments supported
sub_clustering = clustering.subcluster(min_count=..., max_count=...)
```
The resulting clustering isn't identical to K-Means or other conventional approaches but serves the same purpose.
Alternatively, using Scikit-Learn on a 1 Million point dataset, one may expect queries to take anywhere from minutes to hours, depending on the number of clusters you want to highlight.
For 50'000 clusters, the performance difference between USearch and conventional clustering methods may easily reach 100x.
## Joins, One-to-One, One-to-Many, and Many-to-Many Mappings
One of the big questions these days is how AI will change the world of databases and data management.
Most databases are still struggling to implement high-quality fuzzy search, and the only kind of joins they know are deterministic.
A `join` differs from searching for every entry, requiring a one-to-one mapping banning collisions among separate search results.
| Exact Search | Fuzzy Search | Semantic Search ? |
| :----------: | :----------: | :---------------: |
| Exact Join | Fuzzy Join ? | Semantic Join ?? |
Using USearch, one can implement sub-quadratic complexity approximate, fuzzy, and semantic joins.
This can be useful in any fuzzy-matching tasks common to Database Management Software.
```py
men = Index(...)
women = Index(...)
pairs: dict = men.join(women, max_proposals=0, exact=False)
```
> Read more in the post: [Combinatorial Stable Marriages for Semantic Search 💍](https://ashvardanian.com/posts/searching-stable-marriages)
## Functionality
By now, the core functionality is supported across all bindings.
Broader functionality is ported per request.
In some cases, like Batch operations, feature parity is meaningless, as the host language has full multi-threading capabilities and the USearch index structure is concurrent by design, so the users can implement batching/scheduling/load-balancing in the most optimal way for their applications.
| | C++ 11 | Python 3 | C 99 | Java | JavaScript | Rust | Go | Swift |
| :---------------------- | :----: | :------: | :---: | :---: | :--------: | :---: | :---: | :---: |
| Add, search, remove | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| Save, load, view | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| User-defined metrics | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ |
| Batch operations | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| Filter predicates | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ |
| Joins | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
| Variable-length vectors | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
| 4B+ capacities | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
## Application Examples
### USearch + UForm + UCall = Multimodal Semantic Search
AI has a growing number of applications, but one of the coolest classic ideas is to use it for Semantic Search.
One can take an encoder model, like the multi-modal [UForm](https://github.com/unum-cloud/uform), and a web-programming framework, like [UCall](https://github.com/unum-cloud/ucall), and build a text-to-image search platform in just 20 lines of Python.
```python
from ucall import Server
from uform import get_model, Modality
from usearch.index import Index
import numpy as np
import PIL as pil
processors, models = get_model('unum-cloud/uform3-image-text-english-small')
model_text = models[Modality.TEXT_ENCODER]
model_image = models[Modality.IMAGE_ENCODER]
processor_text = processors[Modality.TEXT_ENCODER]
processor_image = processors[Modality.IMAGE_ENCODER]
server = Server()
index = Index(ndim=256)
@server
def add(key: int, photo: pil.Image.Image):
image = processor_image(photo)
vector = model_image(image)
index.add(key, vector.flatten(), copy=True)
@server
def search(query: str) -> np.ndarray:
tokens = processor_text(query)
vector = model_text(tokens)
matches = index.search(vector.flatten(), 3)
return matches.keys
server.run()
```
Similar experiences can also be implemented in other languages and on the client side, removing the network latency.
For Swift and iOS, check out the [`ashvardanian/SwiftSemanticSearch`](https://github.com/ashvardanian/SwiftSemanticSearch) repository.
<table>
<tr>
<td>
<img src="https://github.com/ashvardanian/ashvardanian/blob/master/demos/SwiftSemanticSearch-Dog.gif?raw=true" alt="SwiftSemanticSearch demo Dog">
</td>
<td>
<img src="https://github.com/ashvardanian/ashvardanian/blob/master/demos/SwiftSemanticSearch-Flowers.gif?raw=true" alt="SwiftSemanticSearch demo with Flowers">
</td>
</tr>
</table>
A more complete [demo with Streamlit is available on GitHub](https://github.com/ashvardanian/usearch-images).
We have pre-processed some commonly used datasets, cleaned the images, produced the vectors, and pre-built the index.
| Dataset | Modalities | Images | Download |
| :---------------------------------- | --------------------: | -----: | ------------------------------------: |
| [Unsplash][unsplash-25k-origin] | Images & Descriptions | 25 K | [HuggingFace / Unum][unsplash-25k-hf] |
| [Conceptual Captions][cc-3m-origin] | Images & Descriptions | 3 M | [HuggingFace / Unum][cc-3m-hf] |
| [Arxiv][arxiv-2m-origin] | Titles & Abstracts | 2 M | [HuggingFace / Unum][arxiv-2m-hf] |
[unsplash-25k-origin]: https://github.com/unsplash/datasets
[cc-3m-origin]: https://huggingface.co/datasets/conceptual_captions
[arxiv-2m-origin]: https://www.kaggle.com/datasets/Cornell-University/arxiv
[unsplash-25k-hf]: https://huggingface.co/datasets/unum-cloud/ann-unsplash-25k
[cc-3m-hf]: https://huggingface.co/datasets/unum-cloud/ann-cc-3m
[arxiv-2m-hf]: https://huggingface.co/datasets/unum-cloud/ann-arxiv-2m
### USearch + RDKit = Molecular Search
Comparing molecule graphs and searching for similar structures is expensive and slow.
It can be seen as a special case of the NP-Complete Subgraph Isomorphism problem.
Luckily, domain-specific approximate methods exist.
The one commonly used in Chemistry is to generate structures from [SMILES][smiles] and later hash them into binary fingerprints.
The latter are searchable with binary similarity metrics, like the Tanimoto coefficient.
Below is an example using the RDKit package.
```python
from usearch.index import Index, MetricKind
from rdkit import Chem
from rdkit.Chem import AllChem
import numpy as np
molecules = [Chem.MolFromSmiles('CCOC'), Chem.MolFromSmiles('CCO')]
encoder = AllChem.GetRDKitFPGenerator()
fingerprints = np.vstack([encoder.GetFingerprint(x) for x in molecules])
fingerprints = np.packbits(fingerprints, axis=1)
index = Index(ndim=2048, metric=MetricKind.Tanimoto)
keys = np.arange(len(molecules))
index.add(keys, fingerprints)
matches = index.search(fingerprints, 10)
```
That method was used to build the ["USearch Molecules"](https://github.com/ashvardanian/usearch-molecules), one of the largest Chem-Informatics datasets, containing 7 billion small molecules and 28 billion fingerprints.
[smiles]: https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system
[rdkit-fingerprints]: https://www.rdkit.org/docs/RDKit_Book.html#additional-information-about-the-fingerprints
### USearch + POI Coordinates = GIS Applications
Similar to Vector and Molecule search, USearch can be used for Geospatial Information Systems.
The Haversine distance is available out of the box, but you can also define more complex relationships, like the Vincenty formula, that accounts for the Earth's oblateness.
```py
from numba import cfunc, types, carray
import math
# Define the dimension as 2 for latitude and longitude
ndim = 2
# Signature for the custom metric
signature = types.float32(
types.CPointer(types.float32),
types.CPointer(types.float32))
# WGS-84 ellipsoid parameters
a = 6378137.0 # major axis in meters
f = 1 / 298.257223563 # flattening
b = (1 - f) * a # minor axis
def vincenty_distance(a_ptr, b_ptr):
a_array = carray(a_ptr, ndim)
b_array = carray(b_ptr, ndim)
lat1, lon1, lat2, lon2 = a_array[0], a_array[1], b_array[0], b_array[1]
L, U1, U2 = lon2 - lon1, math.atan((1 - f) * math.tan(lat1)), math.atan((1 - f) * math.tan(lat2))
sinU1, cosU1, sinU2, cosU2 = math.sin(U1), math.cos(U1), math.sin(U2), math.cos(U2)
lambda_, iterLimit = L, 100
while iterLimit > 0:
iterLimit -= 1
sinLambda, cosLambda = math.sin(lambda_), math.cos(lambda_)
sinSigma = math.sqrt((cosU2 * sinLambda) ** 2 + (cosU1 * sinU2 - sinU1 * cosU2 * cosLambda) ** 2)
if sinSigma == 0: return 0.0 # Co-incident points
cosSigma, sigma = sinU1 * sinU2 + cosU1 * cosU2 * cosLambda, math.atan2(sinSigma, cosSigma)
sinAlpha, cos2Alpha = cosU1 * cosU2 * sinLambda / sinSigma, 1 - (cosU1 * cosU2 * sinLambda / sinSigma) ** 2
cos2SigmaM = cosSigma - 2 * sinU1 * sinU2 / cos2Alpha if not math.isnan(cosSigma - 2 * sinU1 * sinU2 / cos2Alpha) else 0 # Equatorial line
C = f / 16 * cos2Alpha * (4 + f * (4 - 3 * cos2Alpha))
lambda_, lambdaP = L + (1 - C) * f * (sinAlpha * (sigma + C * sinSigma * (cos2SigmaM + C * cosSigma * (-1 + 2 * cos2SigmaM ** 2)))), lambda_
if abs(lambda_ - lambdaP) <= 1e-12: break
if iterLimit == 0: return float('nan') # formula failed to converge
u2 = cos2Alpha * (a ** 2 - b ** 2) / (b ** 2)
A = 1 + u2 / 16384 * (4096 + u2 * (-768 + u2 * (320 - 175 * u2)))
B = u2 / 1024 * (256 + u2 * (-128 + u2 * (74 - 47 * u2)))
deltaSigma = B * sinSigma * (cos2SigmaM + B / 4 * (cosSigma * (-1 + 2 * cos2SigmaM ** 2) - B / 6 * cos2SigmaM * (-3 + 4 * sinSigma ** 2) * (-3 + 4 * cos2SigmaM ** 2)))
s = b * A * (sigma - deltaSigma)
return s / 1000.0 # Distance in kilometers
# Example usage:
index = Index(ndim=ndim, metric=CompiledMetric(
pointer=vincenty_distance.address,
kind=MetricKind.Haversine,
signature=MetricSignature.ArrayArray,
))
```
## Integrations & Users
- [x] ClickHouse: [C++](https://github.com/ClickHouse/ClickHouse/pull/53447), [docs](https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/annindexes#usearch).
- [x] DuckDB: [post](https://duckdb.org/2024/05/03/vector-similarity-search-vss.html).
- [x] ScyllaDB: [Rust](https://github.com/scylladb/vector-store), [presentation](https://www.slideshare.net/slideshow/vector-search-with-scylladb-by-szymon-wasik/276571548).
- [x] TiDB & TiFlash: [C++](https://github.com/pingcap/tiflash), [announcement](https://www.pingcap.com/article/introduce-vector-search-indexes-in-tidb/).
- [x] YugaByte: [C++](https://github.com/yugabyte/yugabyte-db/blob/366b9f5e3c4df3a1a17d553db41d6dc50146f488/src/yb/vector_index/usearch_wrapper.cc).
- [x] Google: [UniSim](https://github.com/google/unisim), [RetSim](https://arxiv.org/abs/2311.17264) paper.
- [x] MemGraph: [C++](https://github.com/memgraph/memgraph/blob/784dd8520f65050d033aea8b29446e84e487d091/src/storage/v2/indices/vector_index.cpp), [announcement](https://memgraph.com/blog/simplify-data-retrieval-memgraph-vector-search).
- [x] LanternDB: [C++](https://github.com/lanterndata/lantern), [Rust](https://github.com/lanterndata/lantern_extras), [docs](https://lantern.dev/blog/hnsw-index-creation).
- [x] LangChain: [Python](https://github.com/langchain-ai/langchain/releases/tag/v0.0.257) and [JavaScript](https://github.com/hwchase17/langchainjs/releases/tag/0.0.125).
- [x] Microsoft Semantic Kernel: [Python](https://github.com/microsoft/semantic-kernel/releases/tag/python-0.3.9.dev) and C#.
- [x] GPTCache: [Python](https://github.com/zilliztech/GPTCache/releases/tag/0.1.29).
- [x] Sentence-Transformers: Python [docs](https://www.sbert.net/docs/package_reference/quantization.html#sentence_transformers.quantization.semantic_search_usearch).
- [x] Pathway: [Rust](https://github.com/pathwaycom/pathway).
- [x] Vald: [GoLang](https://github.com/vdaas/vald).
## Citations
```bibtex
@software{Vardanian_USearch_2023,
doi = {10.5281/zenodo.7949416},
author = {Vardanian, Ash},
title = {{USearch by Unum Cloud}},
url = {https://github.com/unum-cloud/usearch},
version = {2.24.0},
year = {2023},
month = oct,
}
```
| text/markdown | Titusz Pan (fork maintainer) | tp@py7.de | null | null | Apache-2.0 | null | [
"Development Status :: 5 - Production/Stable",
"Natural Language :: English",
"Intended Audience :: Developers",
"Intended Audience :: Information Technology",
"License :: OSI Approved :: Apache Software License",
"Programming Language :: C++",
"Programming Language :: Python :: 3 :: Only",
"Programming Language :: Python :: Implementation :: CPython",
"Programming Language :: Java",
"Programming Language :: JavaScript",
"Programming Language :: Objective C",
"Programming Language :: Rust",
"Programming Language :: Other",
"Operating System :: MacOS",
"Operating System :: Unix",
"Operating System :: Microsoft :: Windows",
"Topic :: System :: Clustering",
"Topic :: Database :: Database Engines/Servers",
"Topic :: Scientific/Engineering :: Artificial Intelligence"
] | [] | https://github.com/iscc/usearch | null | null | [] | [] | [] | [
"numpy",
"tqdm",
"simsimd<7.0.0,>=6.0.5"
] | [] | [] | [] | [
"Upstream, https://github.com/unum-cloud/usearch",
"Fork, https://github.com/iscc/usearch"
] | uv/0.10.4 {"installer":{"name":"uv","version":"0.10.4","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true} | 2026-02-20T10:41:27.781707 | usearch_iscc-2.24.2-cp310-cp310-macosx_11_0_arm64.whl | 563,611 | db/0d/ca663ac11d78c13785ef16f93ce376ce3705531769f7da49481ce6bdd3a3/usearch_iscc-2.24.2-cp310-cp310-macosx_11_0_arm64.whl | cp310 | bdist_wheel | null | false | 50c5ab4292d4e0387062f7df5f45829d | 4cf5be270139c61b7ca569b138b18f6926bc7d5ff88132b5ff24c74dfebc0ffd | db0dca663ac11d78c13785ef16f93ce376ce3705531769f7da49481ce6bdd3a3 | null | [
"LICENSE"
] | 3,052 |
2.4 | acex-client | 4.1.2 | ACE-X CLIENT - Client for ACE-X | # ACE-X CLIENT
This client is used for communication with the ACEX-API and is used within
the cli, worker and more.
## Installation
```bash
pip install acex-client
```
See the [main documentation](../README.md) for more information.
| text/markdown | Johan Lahti | johan.lahti@acebit.se | null | null | AGPL-3.0 | automation, control | [
"License :: OSI Approved :: GNU Affero General Public License v3",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14"
] | [] | null | null | <4.0,>=3.13 | [] | [] | [] | [
"acex<5.0.0,>=4.1.5",
"acex-devkit<2.0.0,>=1.0.5",
"acex-driver-cisco-ioscli<0.0.13,>=0.0.12",
"datamodel-code-generator<0.44.0,>=0.43.1",
"datetime<7.0,>=6.0",
"pydantic<3.0.0,>=2.12.5",
"requests<3.0.0,>=2.32.5",
"rich<14.0.0,>=13.0.0",
"typer<0.13.0,>=0.12.0"
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:41:27.662550 | acex_client-4.1.2.tar.gz | 10,760 | 42/52/9cc921b16b454e292449c3896dd0a3a98654c8d6f4ef15415d8ba0073a2b/acex_client-4.1.2.tar.gz | source | sdist | null | false | fa4a7f52c1bd344c3dde2e9b37a29977 | 884555e58f329369a83962a72e3682a55150a77a73a15e68c8a2115aca17adba | 42529cc921b16b454e292449c3896dd0a3a98654c8d6f4ef15415d8ba0073a2b | null | [] | 229 |
2.4 | acex-cli | 4.1.2 | ACE-X CLI - Command-line interface for ACE-X | # ACE-X CLI
Command-line interface for managing ACE-X automations.
## Installation
```bash
pip install acex-cli
```
This will also install the `acex` backend package as a dependency.
## Development
```bash
cd cli
poetry install
```
## Usage
```bash
acex --help
acex run automation.py
acex list
acex status
```
## Commands
- `acex run` - Run an automation
- `acex list` - List available automations
- `acex status` - Check system status
- `acex config` - Manage configuration
## Documentation
See the [main documentation](../README.md) for more information.
| text/markdown | Johan Lahti | johan.lahti@acebit.se | null | null | AGPL-3.0 | automation, cli, control | [
"License :: OSI Approved :: GNU Affero General Public License v3",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14"
] | [] | null | null | <4.0,>=3.13 | [] | [] | [] | [
"acex-client<5.0.0,>=4.1.2",
"acex-driver-cisco-ioscli<0.0.13,>=0.0.12",
"click==8.1.7",
"ntc-templates<9.0.0,>=8.1.0",
"rich<14.0.0,>=13.0.0",
"typer<0.13.0,>=0.12.0"
] | [] | [] | [] | [
"Homepage, https://github.com/acex-labs/acex",
"Repository, https://github.com/acex-labs/acex"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:41:26.259810 | acex_cli-4.1.2.tar.gz | 9,142 | a2/e9/c1ef27a1daaedf1413ce3e833c6aecd09143aeb7cef3026cc6e33659061b/acex_cli-4.1.2.tar.gz | source | sdist | null | false | 2e7779975525282fc2b7ae60e6d46b36 | 4b6962f8a4a3f0ebeadf3fcba4ea14c7f08d90206aef594aca531f407a4ed06c | a2e9c1ef27a1daaedf1413ce3e833c6aecd09143aeb7cef3026cc6e33659061b | null | [] | 229 |
2.4 | imio.scan-helpers | 0.7.1 | Various script files to handle local scan tool | imio.scan_helpers
=================
Various script files to handle MS Windows scan tool
Installation
------------
Use virtualenv in bin directory destination
Build locally
-------------
bin/pyinstaller -y imio-scan-helpers.spec
GitHub actions
--------------
On each push or tag, the github action will build the package and upload it to the github release page.
https://github.com/IMIO/imio.scan_helpers/releases
Windows installation
--------------------
The zip archive must be decompressed in a directory (without version reference) that will be the execution directory.
Windows usage
-------------
* imio-scan-helpers.exe -h : displays the help
* imio-scan-helpers.exe : updates the software based on version and restarts it
* imio-scan-helpers.exe -r tag_name: updates the software with specific release and restarts it
* imio-scan-helpers.exe -c client_id: stores client_id in configuration file
(used as identification when sending info to imio)
* imio-scan-helpers.exe -p plone_password: stores webservice password in configuration file
(used when sending info to imio)
* imio-scan-helpers.exe -nu : runs without update
* imio-scan-helpers.exe --startup : adds the software to the windows startup
* imio-scan-helpers.exe --startup-remove : removes the software from the windows startup
* profiles-backup.exe : backups profiles
* profiles-restore.exe : restores profiles
Changelog
=========
0.7.1 (2026-02-20)
------------------
- Fixed pip-system-cert for inject_truststore() function.
[chris-adam]
- Fixed get_latest_release_version to iterate over all GitHub pages.
[chris-adam]
- Added parameter to prevent auto updates.
[chris-adam]
- Replaced pip-system-certs with truststore to resolve certificate problems.
[chris-adam]
0.7.0 (2025-09-01)
------------------
- Used `pip-system-certs` to resolve certificate problems.
[chris-adam]
- Unpinned pyinstaller version.
[sgeulette]
- Improved send_log_message to avoid timeout.
[sgeulette]
- Added exception handling when removing profiles directory.
[chris-adam]
0.6.0 (2024-08-28)
------------------
- Improved version update.
[sgeulette]
- Added `-tm` parameter (test message).
[sgeulette]
0.5.2 (2024-08-26)
------------------
- Added version in message sent to webservice.
[sgeulette]
0.5.1 (2024-08-23)
------------------
- Corrected bug with relative path.
[sgeulette]
- Added backuped dirs in first message.
[sgeulette]
0.5.0 (2024-08-22)
------------------
- Added certifi pem file to be sure https certificates can be validated.
[sgeulette]
0.4.1 (2024-08-22)
------------------
- Added more info in first message.
[sgeulette]
0.4.0 (2024-08-21)
------------------
- Added optional basic proxy configuration.
[sgeulette]
0.3.2 (2024-08-21)
------------------
- Corrected `utils.json_request`.
[sgeulette]
0.3.1 (2024-08-20)
------------------
- Added tests.
[sgeulette]
0.3.0 (2024-08-14)
------------------
- Corrected version.
[sgeulette]
0.2.5 (2024-08-14)
------------------
- Called profiles_restore in main.
[sgeulette]
0.2.4 (2024-08-14)
------------------
- Corrected set_parameter. Added hostname information.
[sgeulette]
0.2.3 (2024-08-14)
------------------
- Send an info message (no mail) when the product is updated.
[sgeulette]
0.2.2 (2024-08-13)
------------------
- Added `--is-auto-started` parameter in main, passed when app is auto started.
[sgeulette]
0.2.1 (2024-08-13)
------------------
- Changed backup directory.
[sgeulette]
- Improved exception logging.
[sgeulette]
0.2.0 (2024-08-13)
------------------
- Added profiles_backup script.
[sgeulette]
- Stored client identification, plone password and webservice url in configuration file.
[sgeulette]
- Added profiles_restore script.
[sgeulette]
0.1.1 (2024-07-19)
------------------
- Handled Windows startup add or remove following parameters.
[sgeulette]
0.1.0 (2024-07-18)
------------------
- Initial release.
[sgeulette]
| null | Stephan Geulette (IMIO) | support@imio.be | null | null | GPL version 3 | Scan Windows | [
"Development Status :: 3 - Alpha",
"Programming Language :: Python :: 3.12",
"License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
"Operating System :: Microsoft :: Windows",
"Operating System :: Microsoft :: Windows :: Windows 10",
"Operating System :: Microsoft :: Windows :: Windows 11"
] | [] | null | null | >=3.12 | [] | [] | [] | [
"pyinstaller",
"requests"
] | [] | [] | [] | [
"PyPI, https://pypi.python.org/pypi/imio.scan_helpers",
"Source, https://github.com/IMIO/imio.scan_helpers"
] | twine/6.2.0 CPython/3.12.7 | 2026-02-20T10:40:52.091974 | imio_scan_helpers-0.7.1.tar.gz | 26,252 | da/e8/55ddd113f3ed98d1e3b9fb5ea9bcfa0a5f2f496926996b37854cf62a758a/imio_scan_helpers-0.7.1.tar.gz | source | sdist | null | false | 1a75787db0f6c1e796bc582a720a2b1f | 58075760a223aedebddd6048357ce76c9936905f693ad2b5e8d68b3b66b07040 | dae855ddd113f3ed98d1e3b9fb5ea9bcfa0a5f2f496926996b37854cf62a758a | null | [
"LICENSE"
] | 0 |
2.4 | moordyn | 2.6.1 | Python wrapper for MoorDyn library | MoorDyn v2
==========
**This repository is for MoorDyn-C.**
MoorDyn is a lumped-mass model for simulating the dynamics of mooring systems connected to floating offshore structures. As of 2022 it is available under the BSD 3-Clause
license.
Read the docs here: [moordyn.readthedocs.io](https://moordyn.readthedocs.io/en/latest/)
Example uses and instructions here: [Examples](https://github.com/FloatingArrayDesign/MoorDyn/tree/dev/example)
It accounts for internal axial stiffness and damping forces, weight and buoyancy forces, hydrodynamic forces from Morison's equation (assuming calm water so far), and vertical spring-damper forces from contact with the seabed. MoorDyn's input file format is based on that of [MAP](https://www.nrel.gov/wind/nwtc/map-plus-plus.html). The model supports arbitrary line interconnections, clump weights and floats, different line properties, and six degree of freedom rods and bodies.
MoorDyn is implemented both in Fortran and in C++. The Fortran version of MoorDyn (MoorDyn-F) is a core module in [OpenFAST](https://github.com/OpenFAST/openfast) and can be used as part of an OpenFAST or FAST.Farm simulation, or used in a standalone form. The C++ version of MoorDyn (MoorDyn-C) is more adaptable to different use cases and couplings. It can be compiled as a dynamically-linked library or wrapped for use in Python (as a module), Fortran, or Matlab. It features simpler functions for easy coupling with models or scripts coded in C/C++, Fortran, Matlab/Simulink, etc., including a coupling with [WEC-Sim](https://wec-sim.github.io/WEC-Sim/master/index.html). Users should take care to ensure their input file format matches the respective version of MoorDyn they are trying to use. Details on the input file differences can be found in the [documentation](https://moordyn.readthedocs.io/en/latest/inputs.html).
Both forms of MoorDyn feature the same underlying mooring model, use the same input and output conventions, and are being updated and improved in parallel. They follow the same version numbering, with a "C" or "F" suffix for differentiation.
Further information on MoorDyn can be found on the [documentation site](https://moordyn.readthedocs.io/en/latest/). MoorDyn-F is available in the [OpenFAST repository](https://github.com/OpenFAST/openfast/tree/main/modules/moordyn). MoorDyn-C is available in this repository with the following three maintained branches. The master branch represents the most recent release of
MoorDyn-C. The dev branch contains new features currently in development. The v1 branch is the now deprecated version one of MoorDyn-C.
## Acknowledgments
[National Renewable Energy Laboratory (NREL)](https://www.nrel.gov/):
- Matt Hall
- Ryan Davies
- Andy Platt
- Stein Housner
- Lu Wang
- Jason Jonkman
[CoreMarine](https://www.core-marine.com/) [MoorDyn-C v2]:
- Jose Luis Cercos-Pita
- Aymeric Devulder
- Elena Gridasova
[Kelson Marine](https://kelsonmarine.com) [MoorDyn-C v2]:
- [David Joseph Anderson](https://davidjosephanderson.com/)
- [Alex Kinley](https://github.com/AlexWKinley)
| text/markdown | null | Jose Luis Cercos-Pita <jlc@core-marine.com> | null | null | null | null | [
"Programming Language :: Python :: 3",
"License :: OSI Approved :: BSD License",
"Operating System :: OS Independent"
] | [] | null | null | >=3.7 | [] | [] | [] | [] | [] | [] | [] | [
"Homepage, https://github.com/FloatingArrayDesign/MoorDyn",
"Bug Tracker, https://github.com/FloatingArrayDesign/MoorDyn/issues",
"Documentation, https://moordyn.readthedocs.io"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:40:16.975172 | moordyn-2.6.1.tar.gz | 1,180,236 | 38/30/c2f670748f0c3e4acd4e046f03695d639dbf537796c25f5495706910d680/moordyn-2.6.1.tar.gz | source | sdist | null | false | 8f1dbc12e99ff40dcfc09fcea1d4582d | fab360adb98d50f5f3a290f3f2484e69dfa2d45d959ef056bc3a734b1019dc68 | 3830c2f670748f0c3e4acd4e046f03695d639dbf537796c25f5495706910d680 | null | [
"LICENSE.txt"
] | 5,814 |
2.4 | traccia | 0.1.12 | Production-ready distributed tracing SDK for AI agents and LLM applications | # Traccia
**Production-ready distributed tracing for AI agents and LLM applications**
Traccia is a lightweight, high-performance Python SDK for observability and tracing of AI agents, LLM applications, and complex distributed systems. Built on OpenTelemetry standards with specialized instrumentation for AI workloads.
[Traccia](https://pypi.org/project/traccia/) is available on PyPI.
## ✨ Features
- **🔍 Automatic Instrumentation**: Auto-patch OpenAI, Anthropic, requests, and HTTP libraries
- **🤖 Framework Integrations**: Support for LangChain, CrewAI, and OpenAI Agents SDK
- **📊 LLM-Aware Tracing**: Track tokens, costs, prompts, and completions automatically
- **📈 OpenTelemetry Metrics**: Emit OTEL-compliant metrics for accurate cost/token tracking (independent of sampling)
- **⚡ Zero-Config Start**: Simple `init()` call with automatic config discovery
- **🎯 Decorator-Based**: Trace any function with `@observe` decorator
- **🔧 Multiple Exporters**: OTLP (compatible with Grafana Tempo, Jaeger, Zipkin), Console, or File
- **🛡️ Production-Ready**: Rate limiting, error handling, config validation, robust flushing
- **📝 Type-Safe**: Full Pydantic validation for configuration
- **🚀 High Performance**: Efficient batching, async support, minimal overhead
- **🔐 Secure**: No secrets in logs, configurable data truncation
---
## 🚀 Quick Start
### Installation
```bash
pip install traccia
```
### Basic Usage
```python
from traccia import init, observe
# Initialize (auto-loads from traccia.toml if present)
init()
# Trace any function
@observe()
def my_function(x, y):
return x + y
# That's it! Traces are automatically created and exported
result = my_function(2, 3)
```
### With LLM Calls
```python
from traccia import init, observe
from openai import OpenAI
init() # Auto-patches OpenAI
client = OpenAI()
@observe(as_type="llm")
def generate_text(prompt: str) -> str:
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}]
)
return response.choices[0].message.content
# Automatically tracks: model, tokens, cost, prompt, completion, latency
text = generate_text("Write a haiku about Python")
```
### LangChain
Create a callback handler and pass it to `config={"callbacks": [traccia_handler]}`. Install the optional extra: `pip install traccia[langchain]`.
```python
from traccia import init
from traccia.integrations.langchain import CallbackHandler # or TracciaCallbackHandler
from langchain_openai import ChatOpenAI
init()
# Create Traccia handler (no args)
traccia_handler = CallbackHandler()
# Use with any LangChain runnable
llm = ChatOpenAI(model="gpt-4o-mini")
result = llm.invoke(
"Tell me a joke",
config={"callbacks": [traccia_handler]}
)
```
Spans for LLM/chat model runs are created automatically with the same attributes as direct OpenAI instrumentation (model, prompt, usage, cost).
**Note:** `pip install traccia[langchain]` installs traccia plus `langchain-core`; you need this extra to use the callback handler. If you already have `langchain-core` (e.g. from `langchain` or `langchain-openai`), base `pip install traccia` may be enough at runtime, but `traccia[langchain]` is the supported way to get a compatible dependency.
### OpenAI Agents SDK
Traccia **automatically** detects and instruments the OpenAI Agents SDK when installed. No extra code needed:
```python
from traccia import init
from agents import Agent, Runner
init() # Automatically enables Agents SDK tracing
agent = Agent(
name="Assistant",
instructions="You are a helpful assistant"
)
result = Runner.run_sync(agent, "Write a haiku about recursion")
```
**Configuration**: Auto-enabled by default when `openai-agents` is installed. To disable:
```python
init(openai_agents=False) # Explicit parameter
# OR set environment variable: TRACCIA_OPENAI_AGENTS=false
# OR in traccia.toml under [instrumentation]: openai_agents = false
```
**Compatibility**: If you have `openai-agents` installed but don't use it (e.g., using LangChain or pure OpenAI instead), the integration is registered but never invoked—no overhead or extra spans.
### CrewAI
Traccia **automatically** instruments [CrewAI](https://docs.crewai.com/) when it is installed in your environment.
```python
from traccia import init
from crewai import Agent, Task, Crew, Process
init() # Auto-enables CrewAI tracing when CrewAI is installed
researcher = Agent(role="Research Analyst", goal="Research a topic", llm="gpt-4o-mini")
task = Task(description="Research Shawn Michaels", agent=researcher)
crew = Crew(agents=[researcher], tasks=[task], process=Process.sequential, verbose=True)
result = crew.kickoff()
```
Traccia will create spans for the crew (`crewai.crew.kickoff`), each task (`crewai.task.*`), agents (`crewai.agent.*`), and underlying LLM calls, which nest under the existing OpenAI spans.
**Configuration**: Auto-enabled by default when `crewai` is installed. To disable:
```python
init(crewai=False) # Explicit parameter
# OR set environment variable: TRACCIA_CREWAI=false
# OR in traccia.toml under [instrumentation]: crewai = false
```
---
## 📖 Configuration
### Configuration File
Create a `traccia.toml` file in your project root:
```bash
traccia config init
```
This creates a template config file:
```toml
[tracing]
# API key (optional - for future Traccia UI, not needed for OTLP backends)
api_key = ""
# Endpoint URL for OTLP trace ingestion (default: Traccia platform)
# For local OTLP backends use e.g. endpoint = "http://localhost:4318/v1/traces"
endpoint = "https://api.traccia.ai/v2/traces"
sample_rate = 1.0 # 0.0 to 1.0
auto_start_trace = true # Auto-start root trace on init
auto_trace_name = "root" # Name for auto-started trace
use_otlp = true # Use OTLP exporter
# service_name = "my-app" # Optional service name
[exporters]
# Only enable ONE exporter at a time
enable_console = false # Print traces to console
enable_file = false # Write traces to file
file_exporter_path = "traces.jsonl"
reset_trace_file = false # Reset file on initialization
[instrumentation]
enable_patching = true # Auto-patch libraries (OpenAI, Anthropic, requests)
enable_token_counting = true # Count tokens for LLM calls
enable_costs = true # Calculate costs
openai_agents = true # Auto-enable OpenAI Agents SDK integration
auto_instrument_tools = false # Auto-instrument tool calls (experimental)
max_tool_spans = 100 # Max tool spans to create
max_span_depth = 10 # Max nested span depth
[rate_limiting]
# Optional: limit spans per second
# max_spans_per_second = 100.0
max_queue_size = 5000 # Max buffered spans
max_block_ms = 100 # Max ms to block before dropping
max_export_batch_size = 512 # Spans per export batch
schedule_delay_millis = 5000 # Delay between batches
[metrics]
enable_metrics = true # Enable OpenTelemetry metrics
# metrics_endpoint = "" # Defaults to {traces_base}/v2/metrics
metrics_sample_rate = 1.0 # Metrics sampling rate (1.0 = 100%)
[runtime]
# Optional runtime metadata (agent identity: prefer init(agent_id=..., agent_name=..., env=...) or TRACCIA_* env)
# session_id = ""
# user_id = ""
# tenant_id = ""
# project_id = ""
# agent_id = "" # Single-agent: set in code or TRACCIA_AGENT_ID
# agent_name = ""
# env = "" # e.g. production, staging, dev
[logging]
debug = false # Enable debug logging
enable_span_logging = false # Enable span-level logging
[advanced]
# attr_truncation_limit = 1000 # Max attribute value length
```
### Default endpoint
If you do not set `endpoint` (in config, environment, or when calling `init()` / `start_tracing()`), the SDK uses the **Traccia platform** by default (`https://api.traccia.ai/v2/traces`). You can override it to send traces to your own OTLP-compatible backend.
The default is defined in `traccia.config`: `DEFAULT_OTLP_TRACE_ENDPOINT`. The alias `DEFAULT_ENDPOINT` is kept for backward compatibility (same value).
### OTLP Backend Compatibility
Traccia is fully OTLP-compatible and works with:
- **Grafana Tempo** - `http://tempo:4318/v1/traces`
- **Jaeger** - `http://jaeger:4318/v1/traces`
- **Zipkin** - Configure via OTLP endpoint
- **SigNoz** - Self-hosted observability platform
- **Traccia Cloud** - Coming soon (will require API key)
### Environment Variables
All config parameters can be set via environment variables with the `TRACCIA_` prefix:
**Tracing**: `TRACCIA_API_KEY`, `TRACCIA_ENDPOINT`, `TRACCIA_SAMPLE_RATE`, `TRACCIA_AUTO_START_TRACE`, `TRACCIA_AUTO_TRACE_NAME`, `TRACCIA_USE_OTLP`, `TRACCIA_SERVICE_NAME`
**Exporters**: `TRACCIA_ENABLE_CONSOLE`, `TRACCIA_ENABLE_FILE`, `TRACCIA_FILE_PATH`, `TRACCIA_RESET_TRACE_FILE`
**Instrumentation**: `TRACCIA_ENABLE_PATCHING`, `TRACCIA_ENABLE_TOKEN_COUNTING`, `TRACCIA_ENABLE_COSTS`, `TRACCIA_AUTO_INSTRUMENT_TOOLS`, `TRACCIA_MAX_TOOL_SPANS`, `TRACCIA_MAX_SPAN_DEPTH`
**Rate Limiting**: `TRACCIA_MAX_SPANS_PER_SECOND`, `TRACCIA_MAX_QUEUE_SIZE`, `TRACCIA_MAX_BLOCK_MS`, `TRACCIA_MAX_EXPORT_BATCH_SIZE`, `TRACCIA_SCHEDULE_DELAY_MILLIS`
**Runtime**: `TRACCIA_SESSION_ID`, `TRACCIA_USER_ID`, `TRACCIA_TENANT_ID`, `TRACCIA_PROJECT_ID`, `TRACCIA_AGENT_ID`, `TRACCIA_AGENT_NAME`, `TRACCIA_ENV`
**Logging**: `TRACCIA_DEBUG`, `TRACCIA_ENABLE_SPAN_LOGGING`
**Advanced**: `TRACCIA_ATTR_TRUNCATION_LIMIT`
**Priority**: Explicit parameters > Environment variables > Config file > Defaults
### Programmatic Configuration
```python
from traccia import init
# Override config programmatically (including agent identity for single-agent services)
init(
endpoint="http://tempo:4318/v1/traces",
sample_rate=0.5,
enable_costs=True,
max_spans_per_second=100.0,
agent_id="my-agent",
agent_name="My Agent",
env="production",
)
```
---
## 🎯 Usage Guide
### The `@observe` Decorator
The `@observe` decorator is the primary way to instrument your code:
```python
from traccia import observe
# Basic usage
@observe()
def process_data(data):
return transform(data)
# Custom span name
@observe(name="data_pipeline")
def process_data(data):
return transform(data)
# Add custom attributes
@observe(attributes={"version": "2.0", "env": "prod"})
def process_data(data):
return transform(data)
# Specify span type
@observe(as_type="llm") # "span", "llm", "tool"
def call_llm():
pass
# Skip capturing specific arguments
@observe(skip_args=["password", "secret"])
def authenticate(username, password):
pass
# Skip capturing result (for large returns)
@observe(skip_result=True)
def fetch_large_dataset():
return huge_data
```
**Available Parameters**:
- `name` (str, optional): Custom span name (defaults to function name)
- `attributes` (dict, optional): Initial span attributes
- `as_type` (str): Span type - `"span"`, `"llm"`, or `"tool"`
- `skip_args` (list, optional): List of argument names to skip capturing
- `skip_result` (bool): Skip capturing the return value
### Async Functions
`@observe` works seamlessly with async functions:
```python
@observe()
async def async_task(x):
await asyncio.sleep(1)
return x * 2
result = await async_task(5)
```
### Manual Span Creation
For more control, create spans manually:
```python
from traccia import get_tracer, span
# Using convenience function
with span("operation_name") as s:
s.set_attribute("key", "value")
s.add_event("checkpoint_reached")
do_work()
# Using tracer directly
tracer = get_tracer("my_service")
with tracer.start_as_current_span("operation") as s:
s.set_attribute("user_id", 123)
do_work()
```
### Error Handling
Traccia automatically captures and records errors:
```python
@observe()
def failing_function():
raise ValueError("Something went wrong")
# Span will contain:
# - error.type: "ValueError"
# - error.message: "Something went wrong"
# - error.stack_trace: (truncated stack trace)
# - span status: ERROR
```
### Nested Spans
Spans are automatically nested based on call hierarchy:
```python
@observe()
def parent_operation():
child_operation()
return "done"
@observe()
def child_operation():
grandchild_operation()
@observe()
def grandchild_operation():
pass
# Creates nested span hierarchy:
# parent_operation
# └── child_operation
# └── grandchild_operation
```
---
## 🛠️ CLI Tools
Traccia includes a powerful CLI for configuration and diagnostics:
### `traccia config init`
Create a new `traccia.toml` configuration file:
```bash
traccia config init
traccia config init --force # Overwrite existing
```
### `traccia doctor`
Validate configuration and diagnose issues:
```bash
traccia doctor
# Output:
# 🩺 Running Traccia configuration diagnostics...
#
# ✅ Found config file: ./traccia.toml
# ✅ Configuration is valid
#
# 📊 Configuration summary:
# • API Key: ❌ Not set (optional)
# • Endpoint: https://api.traccia.ai/v2/traces
# • Sample Rate: 1.0
# • OTLP Exporter: ✅ Enabled
```
### `traccia check`
Test connectivity to your exporter endpoint:
```bash
traccia check
traccia check --endpoint http://tempo:4318/v1/traces
```
---
## 🎨 Advanced Features
### Rate Limiting
Protect your infrastructure with built-in rate limiting:
```toml
[rate_limiting]
max_spans_per_second = 100.0 # Limit to 100 spans/sec
max_queue_size = 5000 # Max buffered spans
max_block_ms = 100 # Block up to 100ms before dropping
```
**Behavior**:
1. Try to acquire capacity immediately
2. If unavailable, block for up to `max_block_ms`
3. If still unavailable, drop span and log warning
When spans are dropped due to rate limiting, warnings are logged to help you monitor and adjust limits.
### Sampling
Control trace volume with sampling:
```python
# Sample 10% of traces
init(sample_rate=0.1)
# Sampling is applied at trace creation time
# Traces are either fully included or fully excluded
```
### Token Counting & Cost Calculation
Automatic for supported LLM providers (OpenAI, Anthropic):
```python
@observe(as_type="llm")
def call_openai(prompt):
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}]
)
return response.choices[0].message.content
# Span automatically includes:
# - llm.token.prompt_tokens
# - llm.token.completion_tokens
# - llm.token.total_tokens
# - llm.cost.total (in USD)
```
### Metrics
Traccia emits OTEL-compliant metrics for accurate cost and token tracking, independent of trace sampling.
#### Why Metrics?
With trace sampling (e.g., `sample_rate=0.1`), only 10% of traces are exported. Cost calculated from traces will be **10x underestimated**. Metrics solve this by recording data for **every** LLM call, regardless of sampling.
#### Default Metrics
Traccia automatically emits these metrics:
| Metric | Type | Unit | Description |
|--------|------|------|-------------|
| `gen_ai.client.token.usage` | Histogram | `{token}` | Input/output tokens per call |
| `gen_ai.client.operation.duration` | Histogram | `s` | LLM operation duration |
| `gen_ai.client.operation.cost` | Histogram | `usd` | Cost per call (USD) |
| `gen_ai.client.completions.exceptions` | Counter | `1` | Exception count |
| `gen_ai.agent.runs` | Counter | `1` | Agent runs (CrewAI, OpenAI Agents) |
| `gen_ai.agent.turns` | Counter | `1` | Agent turns |
| `gen_ai.agent.execution_time` | Histogram | `s` | Agent execution time |
**Attributes**: `gen_ai.system` (openai, anthropic), `gen_ai.request.model`, `gen_ai.agent.id`, `gen_ai.agent.name`
#### Configuration
```python
from traccia import init
init(
enable_metrics=True, # Default: True
metrics_endpoint="https://your-backend.com/v2/metrics",
metrics_sample_rate=1.0, # Default: 1.0 (100%)
)
```
Or via `traccia.toml`:
```toml
[metrics]
enable_metrics = true
metrics_endpoint = "https://your-backend.com/v2/metrics"
metrics_sample_rate = 1.0
```
Or via environment variables:
```bash
export TRACCIA_ENABLE_METRICS=true
export TRACCIA_METRICS_ENDPOINT=https://your-backend.com/v2/metrics
export TRACCIA_METRICS_SAMPLE_RATE=1.0
```
#### Custom Metrics
Record your own metrics:
```python
from traccia.metrics import record_counter, record_histogram
# Record a counter
record_counter("my_custom_events", 1, {"event_type": "user_action"})
# Record a histogram
record_histogram("my_custom_latency", 0.123, {"service": "api"}, unit="s")
```
#### Agent Metrics vs. Plain LLM Calls
Agent-level metrics (such as `gen_ai.agent.runs` and `gen_ai.agent.execution_time`) are only emitted when Traccia can
see a real **agent lifecycle** (for example, CrewAI crews or OpenAI Agents SDK runs). For plain OpenAI/Anthropic calls
and most simple LangChain usages, you will still get full LLM metrics (`gen_ai.client.*`), but no agent metrics unless
you build an explicit agent abstraction on top.
---
## 🔧 Troubleshooting
### Enable Debug Logging
```python
import logging
logging.basicConfig(level=logging.DEBUG)
# Or via config
init(debug=True)
# Or via env var
# TRACCIA_DEBUG=1 python your_script.py
```
### Common Issues
#### **Traces not appearing**
1. Check connectivity: `traccia check`
2. Validate config: `traccia doctor`
3. Enable debug logging
4. Verify endpoint is correct and accessible
#### **High memory usage**
- Reduce `max_queue_size` in rate limiting config
- Lower `sample_rate` to reduce volume
- Enable rate limiting with `max_spans_per_second`
#### **Spans being dropped**
- Check rate limiter logs for warnings
- Increase `max_spans_per_second` if set
- Increase `max_queue_size` if spans are queued
- Check `traccia doctor` output
---
## 📚 API Reference
### Core Functions
#### `init(**kwargs) -> TracerProvider`
Initialize the Traccia SDK.
**Parameters**:
- `endpoint` (str, optional): OTLP endpoint URL (default: `config.DEFAULT_OTLP_TRACE_ENDPOINT` — Traccia platform)
- `api_key` (str, optional): API key (optional, for future Traccia UI)
- `sample_rate` (float, optional): Sampling rate (0.0-1.0)
- `auto_start_trace` (bool, optional): Auto-start root trace
- `config_file` (str, optional): Path to config file
- `use_otlp` (bool, optional): Use OTLP exporter
- `enable_console` (bool, optional): Enable console exporter
- `enable_file` (bool, optional): Enable file exporter
- `enable_patching` (bool, optional): Auto-patch libraries
- `enable_token_counting` (bool, optional): Count tokens
- `enable_costs` (bool, optional): Calculate costs
- `enable_metrics` (bool, optional): Enable OTEL metrics (default: True)
- `metrics_endpoint` (str, optional): Metrics endpoint
- `metrics_sample_rate` (float, optional): Metrics sampling (default: 1.0)
- `max_spans_per_second` (float, optional): Rate limit
- `**kwargs`: Any other config parameter
**Returns**: TracerProvider instance
#### `stop_tracing(flush_timeout: float = 1.0) -> None`
Stop tracing and flush pending spans.
**Parameters**:
- `flush_timeout` (float): Max seconds to wait for flush
#### `get_tracer(name: str = "default") -> Tracer`
Get a tracer instance.
**Parameters**:
- `name` (str): Tracer name (typically module/service name)
**Returns**: Tracer instance
#### `span(name: str, attributes: dict = None) -> Span`
Create a span context manager.
**Parameters**:
- `name` (str): Span name
- `attributes` (dict, optional): Initial attributes
**Returns**: Span context manager
### Decorator
#### `@observe(name=None, *, attributes=None, tags=None, as_type="span", skip_args=None, skip_result=False)`
Decorate a function to create spans automatically.
**Parameters**:
- `name` (str, optional): Span name (default: function name)
- `attributes` (dict, optional): Initial attributes
- `tags` (list[str], optional): User-defined identifiers for the observed method
- `as_type` (str): Span type (`"span"`, `"llm"`, `"tool"`)
- `skip_args` (list, optional): Arguments to skip capturing
- `skip_result` (bool): Skip capturing return value
### Configuration
#### `load_config(config_file=None, overrides=None) -> TracciaConfig`
Load and validate configuration.
**Parameters**:
- `config_file` (str, optional): Path to config file
- `overrides` (dict, optional): Override values
**Returns**: Validated TracciaConfig instance
**Raises**: `ConfigError` if invalid
#### `validate_config(config_file=None, overrides=None) -> tuple[bool, str, TracciaConfig | None]`
Validate configuration without loading.
**Returns**: Tuple of (is_valid, message, config_or_none)
---
## 🏗️ Architecture
### Data Flow
```
Application Code (@observe)
↓
Span Creation
↓
Processors (token counting, cost, enrichment)
↓
Rate Limiter (optional)
↓
Batch Processor (buffering)
↓
Exporter (OTLP/Console/File)
↓
Backend (Grafana Tempo / Jaeger / Zipkin / etc.)
```
### Instrumentation vs Integrations
- **`traccia.instrumentation.*`**: Infrastructure and vendor instrumentation.
- HTTP client/server helpers (including FastAPI middleware).
- Vendor SDK hooks and monkey patching (e.g., OpenAI, Anthropic, `requests`).
- Decorators and utilities used for auto-instrumenting arbitrary functions.
- **`traccia.integrations.*`**: AI/agent framework integrations.
- Adapters that plug into higher-level frameworks via their official extension points (e.g., LangChain callbacks).
- Work at the level of chains, tools, agents, and workflows rather than raw HTTP or SDK calls.
---
## 🤝 Contributing
Contributions are welcome! Whether it's bug fixes, new features, documentation improvements, or examples - we appreciate your help.
### How to Contribute
1. **Fork the repository**
2. **Create a feature branch**: `git checkout -b feature/amazing-feature`
3. **Make your changes** and add tests
4. **Run tests**: `pytest traccia/tests/`
5. **Lint your code**: `ruff check traccia/`
6. **Commit**: `git commit -m "Add amazing feature"`
7. **Push**: `git push origin feature/amazing-feature`
8. **Open a Pull Request**
### Development Setup
```bash
# Clone the repository (Python SDK)
git clone https://github.com/traccia-ai/traccia-py.git
cd traccia-py
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install in editable mode with dev dependencies
pip install -e ".[dev]"
# Run tests
pytest traccia/tests/ -v
# Run with coverage
pytest traccia/tests/ --cov=traccia --cov-report=html
```
### Code Style
- Follow PEP 8
- Use type hints where appropriate
- Add docstrings for public APIs
- Write tests for new features
- Keep PRs focused and atomic
### Areas We'd Love Help With
- **Integrations**: Add support for more LLM providers (Cohere, AI21, local models)
- **Backends**: Test and document setup with different OTLP backends
- **Examples**: Real-world examples of agent instrumentation
- **Documentation**: Tutorials, guides, video walkthroughs
- **Performance**: Optimize hot paths, reduce overhead
- **Testing**: Improve test coverage, add integration tests
---
## 📄 License
Apache License 2.0 - see [LICENSE](LICENSE) for full terms and conditions.
---
## 🙏 Acknowledgments
Built with:
- [OpenTelemetry](https://opentelemetry.io/) - Vendor-neutral observability framework
- [Pydantic](https://pydantic.dev/) - Data validation
- [tiktoken](https://github.com/openai/tiktoken) - Token counting
Inspired by observability tools in the ecosystem and designed to work seamlessly with the OTLP standard.
---
## 📞 Support & Community
- **Issues**: [GitHub Issues](https://github.com/traccia-ai/traccia-py/issues) - Report bugs or request features
- **Discussions**: [GitHub Discussions](https://github.com/traccia-ai/traccia-py/discussions) - Ask questions, share ideas
---
**Made with ❤️ for the AI agent community**
| text/markdown | null | null | null | null | Apache-2.0 | tracing, observability, opentelemetry, ai-agents, llm, distributed-tracing, monitoring, telemetry | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"License :: OSI Approved :: Apache Software License",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Topic :: Software Development :: Libraries :: Python Modules",
"Topic :: System :: Monitoring",
"Topic :: Software Development :: Testing"
] | [] | null | null | >=3.9 | [] | [] | [] | [
"tiktoken>=0.7.0",
"opentelemetry-api>=1.20.0",
"opentelemetry-sdk>=1.20.0",
"opentelemetry-exporter-otlp-proto-http>=1.20.0",
"opentelemetry-semantic-conventions>=0.40b0",
"tomli>=2.0.0; python_version < \"3.11\"",
"toml>=0.10.0",
"pydantic>=2.0.0",
"langchain-core>=0.1.0; extra == \"langchain\""
] | [] | [] | [] | [
"Homepage, https://github.com/traccia-ai/traccia-py",
"Documentation, https://github.com/traccia-ai/traccia-py#readme",
"Repository, https://github.com/traccia-ai/traccia-py",
"Issues, https://github.com/traccia-ai/traccia-py/issues",
"Bug Tracker, https://github.com/traccia-ai/traccia-py/issues"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:39:46.407169 | traccia-0.1.12.tar.gz | 100,839 | 08/ff/280465622de053849616905d62bf84493e40ca1101634b9bad46d8474689/traccia-0.1.12.tar.gz | source | sdist | null | false | daa1f6d452024c6f550c0e49680742e3 | 703b5e5d95141681ae55d2f056425b10d6362d6df21308b35608603bd5d645d8 | 08ff280465622de053849616905d62bf84493e40ca1101634b9bad46d8474689 | null | [
"LICENSE"
] | 229 |
2.4 | surfa-ingest | 0.1.0 | Official Python SDK for Surfa Analytics - Ingest live traffic events | # Surfa Ingest SDK
Official Python SDK for ingesting live traffic events to Surfa Analytics.
## Installation
```bash
pip install surfa-ingest
```
## Quick Start
```python
from surfa_ingest import SurfaClient
# Initialize client with your ingest key
client = SurfaClient(ingest_key="sk_live_your_key_here")
# Track events
client.track({
"kind": "tool",
"subtype": "call_started",
"tool_name": "search_web",
"args": {"query": "AI news"}
})
client.track({
"kind": "tool",
"subtype": "call_completed",
"tool_name": "search_web",
"status": "success",
"latency_ms": 234
})
# Flush events to API
client.flush()
```
## Context Manager (Recommended)
Use the context manager to automatically track session lifecycle:
```python
from surfa_ingest import SurfaClient
with SurfaClient(ingest_key="sk_live_your_key_here") as client:
# Session automatically started
client.track({
"kind": "tool",
"subtype": "call_started",
"tool_name": "search_web"
})
# Session automatically ended and events flushed on exit
```
## Configuration
```python
client = SurfaClient(
ingest_key="sk_live_your_key_here",
api_url="https://api.surfa.dev", # Default: http://localhost:3000
flush_at=25, # Auto-flush after 25 events
timeout_s=10, # HTTP timeout in seconds
)
```
## Set Runtime Metadata
Track which AI runtime is being used:
```python
client = SurfaClient(ingest_key="sk_live_...")
client.set_runtime(
provider="anthropic",
model="claude-sonnet-4-5",
mode="messages"
)
```
## Event Types
### Tool Events
```python
# Tool call started
client.track({
"kind": "tool",
"subtype": "call_started",
"tool_name": "search_web",
"direction": "request",
"args": {"query": "Python tutorials"}
})
# Tool call completed
client.track({
"kind": "tool",
"subtype": "call_completed",
"tool_name": "search_web",
"direction": "response",
"status": "success",
"latency_ms": 234,
"results": [{"title": "Learn Python", "url": "..."}]
})
```
### Session Events
```python
# Session started
client.session_started()
# Session ended
client.session_ended()
```
### Runtime Events
```python
# LLM request
client.track({
"kind": "runtime",
"subtype": "llm_request",
"direction": "outbound",
"messages": [{"role": "user", "content": "Hello"}],
"temperature": 0.7
})
```
## Event Fields
### Required Fields
- `kind` (str): Event type (e.g., "tool", "session", "runtime")
### Optional Fields
- `subtype` (str): Event subtype (e.g., "call_started", "session_ended")
- `tool_name` (str): Name of the tool
- `status` (str): Status (e.g., "success", "error")
- `direction` (str): Direction (e.g., "request", "response")
- `method` (str): HTTP method or similar
- `correlation_id` (str): Correlation ID for pairing events
- `span_parent_id` (str): Parent span ID for tracing
- `latency_ms` (int): Latency in milliseconds
- `ts` (str): Timestamp (ISO 8601 format, auto-generated if not provided)
- Any additional fields will be included in the event payload
## Auto-Flush
Events are automatically flushed when:
1. Buffer reaches `flush_at` events (default: 25)
2. Context manager exits
3. `flush()` is called explicitly
## Error Handling
```python
from surfa_ingest import SurfaClient, SurfaConfigError, SurfaValidationError
try:
client = SurfaClient(ingest_key="invalid_key")
except SurfaConfigError as e:
print(f"Configuration error: {e}")
try:
client.track({"invalid": "event"}) # Missing 'kind'
except SurfaValidationError as e:
print(f"Validation error: {e}")
```
## Logging
The SDK uses Python's standard logging module:
```python
import logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger("surfa_ingest")
```
## Development Status
**Current Version: 0.1.0 (Alpha)**
This SDK is in active development. The API may change in future versions.
### Implemented
- ✅ Client initialization
- ✅ Event buffering
- ✅ Session management
- ✅ Context manager support
- ✅ Event validation
- ✅ Runtime metadata
### Coming Soon
- 🔜 HTTP API integration
- 🔜 Automatic retry logic
- 🔜 Background flushing
- 🔜 Async support
## License
MIT
## Support
- Documentation: https://docs.surfa.dev
- Issues: https://github.com/yourusername/surfa/issues
- Email: support@surfa.dev
| text/markdown | null | Surfa Team <support@surfa.dev> | null | null | MIT | analytics, observability, mcp, ai, llm, monitoring | [
"Development Status :: 3 - Alpha",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Topic :: Software Development :: Libraries :: Python Modules",
"Topic :: System :: Monitoring"
] | [] | null | null | >=3.8 | [] | [] | [] | [
"requests>=2.31.0",
"pytest>=7.0.0; extra == \"dev\"",
"pytest-cov>=4.0.0; extra == \"dev\"",
"black>=23.0.0; extra == \"dev\"",
"mypy>=1.0.0; extra == \"dev\"",
"ruff>=0.1.0; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://surfa.dev",
"Documentation, https://docs.surfa.dev",
"Repository, https://github.com/yourusername/surfa",
"Issues, https://github.com/yourusername/surfa/issues"
] | twine/6.2.0 CPython/3.9.6 | 2026-02-20T10:39:28.893820 | surfa_ingest-0.1.0.tar.gz | 11,762 | fe/2f/91d21c4eb3aeb42f86fb6c921724ae53bdb10d7111e06ddbf8ab92ba3577/surfa_ingest-0.1.0.tar.gz | source | sdist | null | false | 59c8ebaa284572da590d3e9ee59c9681 | 1b95f05f5af24d4590ab8868ad7b2ba6a54da3cfdf4231f154a4d7a463cab0d4 | fe2f91d21c4eb3aeb42f86fb6c921724ae53bdb10d7111e06ddbf8ab92ba3577 | null | [] | 241 |
2.4 | evalview | 0.3.0 | Pytest-style testing framework for AI agents — LangGraph, CrewAI, OpenAI, Anthropic, Claude | # EvalView — Proof that your agent still works.
> You changed a prompt. Swapped a model. Updated a tool.
> Did anything break? **Run EvalView. Know for sure.**
<p align="center">
<img src="assets/demo.gif" alt="EvalView Demo" width="700">
</p>
<p align="center">
```bash
pip install evalview && evalview demo # Uses your configured API key
```
</p>
<p align="center">
<a href="https://pypi.org/project/evalview/"><img src="https://img.shields.io/pypi/dm/evalview.svg?label=downloads" alt="PyPI downloads"></a>
<a href="https://github.com/hidai25/eval-view/stargazers"><img src="https://img.shields.io/github/stars/hidai25/eval-view?style=social" alt="GitHub stars"></a>
<a href="https://github.com/hidai25/eval-view/actions/workflows/ci.yml"><img src="https://github.com/hidai25/eval-view/actions/workflows/ci.yml/badge.svg" alt="CI"></a>
<a href="https://opensource.org/licenses/Apache-2.0"><img src="https://img.shields.io/badge/License-Apache_2.0-blue.svg" alt="License"></a>
</p>
<p align="center">
🌟 <strong>Like it?</strong> Give us a ⭐ — it helps more devs discover EvalView.
</p>
---
## 🔍 What EvalView Catches
| Status | What it means | What you do |
|--------|--------------|-------------|
| ✅ **PASSED** | Agent behavior matches baseline | Ship with confidence |
| ⚠️ **TOOLS_CHANGED** | Agent is calling different tools | Review the diff |
| ⚠️ **OUTPUT_CHANGED** | Same tools, output quality shifted | Review the diff |
| ❌ **REGRESSION** | Score dropped significantly | Fix before shipping |
---
## 🤔 How It Works
**Simple workflow (recommended):**
```bash
# 1. Your agent works correctly
evalview snapshot # 📸 Save current behavior as baseline
# 2. You change something (prompt, model, tools)
evalview check # 🔍 Detect regressions automatically
# 3. EvalView tells you exactly what changed
# → ✅ All clean! No regressions detected.
# → ⚠️ TOOLS_CHANGED: +web_search, -calculator
# → ❌ REGRESSION: score 85 → 71
```
**Advanced workflow (more control):**
```bash
evalview run --save-golden # Save specific result as baseline
evalview run --diff # Compare with custom options
```
That's it. **Deterministic proof, no LLM-as-judge required, no API keys needed.**
### 🎯 New: Habit-Forming Regression Detection
EvalView now tracks your progress and celebrates wins:
```bash
evalview check
# 🔍 Comparing against your baseline...
# ✨ All clean! No regressions detected.
# 🎯 5 clean checks in a row! You're on a roll.
```
**Features:**
- 🔥 **Streak tracking** — Celebrate consecutive clean checks (3, 5, 10, 25+ milestones)
- 📊 **Health score** — See your project's stability at a glance
- 🔔 **Smart recaps** — "Since last time" summaries to stay in context
- 📈 **Progress visualization** — Track improvement over time
### 🎨 Multi-Reference Goldens (for non-deterministic agents)
Some agents produce valid variations. Save up to 5 golden variants per test:
```bash
# Save multiple acceptable behaviors
evalview snapshot --variant variant1
evalview snapshot --variant variant2
# EvalView compares against ALL variants, passes if ANY match
evalview check
# ✅ Matched variant 2/3
```
Perfect for LLM-based agents with creative variation.
---
## 🚀 Quick Start
1. **Install EvalView**
```bash
pip install evalview
```
2. **Try the demo** (zero setup, no API key)
```bash
evalview demo
```
3. **Set up a working example** in 2 minutes
```bash
evalview quickstart
```
4. **Want LLM-as-judge scoring too?**
```bash
export OPENAI_API_KEY='your-key'
evalview run
```
5. **Prefer local/free evaluation?**
```bash
evalview run --judge-provider ollama --judge-model llama3.2
```
[Full getting started guide →](docs/GETTING_STARTED.md)
---
## 🆕 New in v0.2.9: Claude Code MCP Server
If you're using Claude Code, this is the biggest upgrade in recent releases:
- Run EvalView checks inline from Claude Code via MCP tools
- Generate tests from natural language (`create_test`)
- Capture baselines and detect regressions without leaving the editor/conversation
👉 Jump to [Claude Code Integration (MCP)](#-claude-code-integration-mcp)
---
## 💡 Why EvalView?
- 🔄 **Automatic regression detection** — Know instantly when your agent breaks
- 📸 **Golden baseline diffing** — Save known-good behavior, compare every change
- 🔑 **Works without API keys** — Deterministic scoring, no LLM-as-judge needed
- 💸 **Free & open source** — No vendor lock-in, no SaaS pricing
- 🏠 **Works offline** — Use Ollama for fully local evaluation
| | Observability (LangSmith) | Benchmarks (Braintrust) | **EvalView** |
|---|:---:|:---:|:---:|
| **Answers** | "What did my agent do?" | "How good is my agent?" | **"Did my agent change?"** |
| Detects regressions | ❌ | ⚠️ Manual | ✅ Automatic |
| Golden baseline diffing | ❌ | ❌ | ✅ |
| Works without API keys | ❌ | ❌ | ✅ |
| Free & open source | ❌ | ❌ | ✅ |
| Works offline (Ollama) | ❌ | ⚠️ Some | ✅ |
**Use observability tools to see what happened. Use EvalView to prove it didn't break.**
---
## 🧭 Explore & Learn
### 💬 Interactive Chat
Talk to your tests. Debug failures. Compare runs.
```bash
evalview chat
```
```
You: run the calculator test
🤖 Running calculator test...
✅ Passed (score: 92.5)
You: compare to yesterday
🤖 Score: 92.5 → 87.2 (-5.3)
Tools: +1 added (validator)
Cost: $0.003 → $0.005 (+67%)
```
Slash commands: `/run`, `/test`, `/compare`, `/traces`, `/skill`, `/adapters`
[Chat mode docs →](docs/CHAT_MODE.md)
### 🏋️ EvalView Gym
Practice agent eval patterns with guided exercises.
```bash
evalview gym
```
---
## ⚡ Supported Agents & Frameworks
| Agent | E2E Testing | Trace Capture |
|-------|:-----------:|:-------------:|
| **Claude Code** | ✅ | ✅ |
| **OpenAI Codex** | ✅ | ✅ |
| **LangGraph** | ✅ | ✅ |
| **CrewAI** | ✅ | ✅ |
| **OpenAI Assistants** | ✅ | ✅ |
| **Custom (any CLI/API)** | ✅ | ✅ |
Also works with: AutoGen • Dify • Ollama • HuggingFace • Any HTTP API
[Compatibility details →](docs/FRAMEWORK_SUPPORT.md)
---
## 🔧 Automate It
### GitHub Actions
```bash
evalview init --ci # Generates workflow file
```
Or add manually:
```yaml
# .github/workflows/evalview.yml
name: Agent Health Check
on: [push, pull_request]
jobs:
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: hidai25/eval-view@v0.2.5
with:
openai-api-key: ${{ secrets.OPENAI_API_KEY }}
command: check # Use new check command
fail-on: 'REGRESSION' # Block PRs on regressions
json: true # Structured output for CI
```
**Or use the CLI directly:**
```yaml
- run: evalview check --fail-on REGRESSION --json
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
```
PRs with regressions get blocked. Add a PR comment showing exactly what changed:
```yaml
- run: evalview ci comment
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
```
[Full CI/CD setup →](docs/CI_CD.md)
---
## 🤖 Claude Code Integration (MCP)
**Test your agent without leaving the conversation.** EvalView runs as an MCP server inside Claude Code — ask "did my refactor break anything?" and get the answer inline.
### Setup (3 steps, one-time)
```bash
# 1. Install
pip install evalview
# 2. Connect to Claude Code
claude mcp add --transport stdio evalview -- evalview mcp serve
# 3. Make Claude Code proactive (auto-checks after every edit)
cp CLAUDE.md.example CLAUDE.md
```
### What you get
7 tools Claude Code can call on your behalf:
**Agent regression testing:**
| Tool | What it does |
|------|-------------|
| `create_test` | Generate a test case from natural language — no YAML needed |
| `run_snapshot` | Capture current agent behavior as the golden baseline |
| `run_check` | Detect regressions vs baseline, returns structured JSON diff |
| `list_tests` | Show all golden baselines with scores and timestamps |
**Skills testing (full 3-phase workflow):**
| Tool | Phase | What it does |
|------|-------|-------------|
| `validate_skill` | Pre-test | Validate SKILL.md structure before running tests |
| `generate_skill_tests` | Pre-test | Auto-generate test cases from a SKILL.md |
| `run_skill_test` | Test | Run Phase 1 (deterministic) + Phase 2 (rubric) evaluation |
### How it works in practice
```
You: Add a test for my weather agent
Claude: [create_test] ✅ Created tests/weather-lookup.yaml
[run_snapshot] 📸 Baseline captured — regression detection active.
You: Refactor the weather tool to use async
Claude: [makes code changes]
[run_check] ✨ All clean! No regressions detected.
You: Switch to a different weather API
Claude: [makes code changes]
[run_check] ⚠️ TOOLS_CHANGED: weather_api → open_meteo
Output similarity: 94% — review the diff?
```
No YAML. No terminal switching. No context loss.
**Skills testing example:**
```
You: I wrote a code-reviewer skill, test it
Claude: [validate_skill] ✅ SKILL.md is valid
[generate_skill_tests] 📝 Generated 10 tests → tests/code-reviewer-tests.yaml
[run_skill_test] Phase 1: 9/10 ✓ Phase 2: avg 87/100
1 failure: skill didn't trigger on implicit input
```
### Manual server start (advanced)
```bash
evalview mcp serve # Uses tests/ by default
evalview mcp serve --test-path my_tests/ # Custom test directory
```
---
## 📦 Features
| Feature | Description | Docs |
|---------|-------------|------|
| 📸 **Snapshot/Check Workflow** | Simple `snapshot` → `check` commands for regression detection | [→](docs/GOLDEN_TRACES.md) |
| 🤖 **Claude Code MCP** | Run checks inline in Claude Code — no terminal switching | [↑](#-claude-code-integration-mcp) |
| 🔥 **Streak Tracking** | Habit-forming celebrations for consecutive clean checks | [→](docs/GOLDEN_TRACES.md) |
| 🎨 **Multi-Reference Goldens** | Save up to 5 variants per test for non-deterministic agents | [→](docs/GOLDEN_TRACES.md) |
| 💬 **Chat Mode** | AI assistant: `/run`, `/test`, `/compare` | [→](docs/CHAT_MODE.md) |
| 🏷️ **Tool Categories** | Match by intent, not exact tool names | [→](docs/TOOL_CATEGORIES.md) |
| 📊 **Statistical Mode** | Handle flaky LLMs with `--runs N` and pass@k | [→](docs/STATISTICAL_MODE.md) |
| 💰 **Cost & Latency** | Automatic threshold enforcement | [→](docs/EVALUATION_METRICS.md) |
| 📈 **HTML Reports** | Interactive Plotly charts | [→](docs/CLI_REFERENCE.md) |
| 🧪 **Test Generation** | Generate 1000 tests from 1 | [→](docs/TEST_GENERATION.md) |
| 🏗️ **Suite Types** | Separate capability vs regression tests | [→](docs/SUITE_TYPES.md) |
| 🎯 **Difficulty Levels** | Filter by `--difficulty hard`, benchmark by tier | [→](docs/STATISTICAL_MODE.md) |
| 🔬 **Behavior Coverage** | Track tasks, tools, paths tested | [→](docs/BEHAVIOR_COVERAGE.md) |
---
## 🔬 Advanced: Skills Testing
Test that your agent's code actually works — not just that the output looks right.
Best for teams maintaining SKILL.md workflows for Claude Code or Codex.
```yaml
tests:
- name: creates-working-api
input: "Create an express server with /health endpoint"
expected:
files_created: ["index.js", "package.json"]
build_must_pass:
- "npm install"
- "npm run lint"
smoke_tests:
- command: "node index.js"
background: true
health_check: "http://localhost:3000/health"
expected_status: 200
timeout: 10
no_sudo: true
git_clean: true
```
```bash
evalview skill test tests.yaml --agent claude-code
evalview skill test tests.yaml --agent codex
evalview skill test tests.yaml --agent langgraph
```
| Check | What it catches |
|-------|-----------------|
| `build_must_pass` | Code that doesn't compile, missing dependencies |
| `smoke_tests` | Runtime crashes, wrong ports, failed health checks |
| `git_clean` | Uncommitted files, dirty working directory |
| `no_sudo` | Privilege escalation attempts |
| `max_tokens` | Cost blowouts, verbose outputs |
[Skills testing docs →](docs/SKILLS_TESTING.md)
---
## 📚 Documentation
| | |
|---|---|
| [Getting Started](docs/GETTING_STARTED.md) | [CLI Reference](docs/CLI_REFERENCE.md) |
| [Golden Traces](docs/GOLDEN_TRACES.md) | [CI/CD Integration](docs/CI_CD.md) |
| [Tool Categories](docs/TOOL_CATEGORIES.md) | [Statistical Mode](docs/STATISTICAL_MODE.md) |
| [Chat Mode](docs/CHAT_MODE.md) | [Evaluation Metrics](docs/EVALUATION_METRICS.md) |
| [Skills Testing](docs/SKILLS_TESTING.md) | [Debugging](docs/DEBUGGING.md) |
| [FAQ](docs/FAQ.md) | |
**Guides:** [Testing LangGraph in CI](guides/pytest-for-ai-agents-langgraph-ci.md) • [Detecting Hallucinations](guides/detecting-llm-hallucinations-in-ci.md)
---
## 📂 Examples
| Framework | Link |
|-----------|------|
| Claude Code (E2E) | [examples/agent-test/](examples/agent-test/) |
| LangGraph | [examples/langgraph/](examples/langgraph/) |
| CrewAI | [examples/crewai/](examples/crewai/) |
| Anthropic Claude | [examples/anthropic/](examples/anthropic/) |
| Dify | [examples/dify/](examples/dify/) |
| Ollama (Local) | [examples/ollama/](examples/ollama/) |
**Node.js?** See [@evalview/node](sdks/node/)
---
## 🗺️ Roadmap
**Shipped:** Golden traces • **Snapshot/check workflow** • **Streak tracking & celebrations** • **Multi-reference goldens** • Tool categories • Statistical mode • Difficulty levels • Partial sequence credit • Skills validation • E2E agent testing • Build & smoke tests • Health checks • Safety guards (`no_sudo`, `git_clean`) • Claude Code & Codex adapters • **Opus 4.6 cost tracking** • MCP servers • HTML reports • Interactive chat mode • EvalView Gym
**Coming:** Agent Teams trace analysis • Multi-turn conversations • Grounded hallucination detection • Error compounding metrics • Container isolation
[Vote on features →](https://github.com/hidai25/eval-view/discussions)
---
## 🤝 Get Help & Contributing
- **Questions?** [GitHub Discussions](https://github.com/hidai25/eval-view/discussions)
- **Bugs?** [GitHub Issues](https://github.com/hidai25/eval-view/issues)
- **Want setup help?** Email hidai@evalview.com — happy to help configure your first tests
- **Contributing?** See [CONTRIBUTING.md](CONTRIBUTING.md)
**License:** Apache 2.0
---
### ⭐ Thank You for the Support!
[](https://star-history.com/#hidai25/eval-view&Date)
🌟 **Don't miss out on future updates! Star the repo and be the first to know about new features.**
---
<p align="center">
<b>Proof that your agent still works.</b><br>
<a href="#-quick-start">Get started →</a>
</p>
---
*EvalView is an independent open-source project, not affiliated with LangGraph, CrewAI, OpenAI, Anthropic, or any other third party.*
| text/markdown | null | EvalView Team <hidai@evalview.com> | null | null | null | ai, agents, testing, evaluation, llm, langchain, langgraph, crewai, openai, anthropic, claude, claude-opus, opus-4-6, multi-agent, pytest-ai, ai-agent-testing, llm-testing, agent-evaluation, regression-testing, ci-cd-testing, yaml-testing, tool-calling | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Topic :: Software Development :: Testing",
"Topic :: Software Development :: Quality Assurance",
"Topic :: Scientific/Engineering :: Artificial Intelligence"
] | [] | null | null | >=3.9 | [] | [] | [] | [
"click>=8.1.0",
"pydantic>=2.5.0",
"pyyaml>=6.0",
"openai>=1.12.0",
"anthropic>=0.39.0",
"rich>=13.7.0",
"prompt_toolkit>=3.0.0",
"httpx>=0.26.0",
"python-dateutil>=2.8.2",
"python-dotenv>=1.0.0",
"jinja2>=3.0; extra == \"reports\"",
"plotly>=5.0; extra == \"reports\"",
"watchdog>=3.0; extra == \"watch\"",
"posthog>=3.0.0; extra == \"telemetry\"",
"jinja2>=3.0; extra == \"all\"",
"plotly>=5.0; extra == \"all\"",
"watchdog>=3.0; extra == \"all\"",
"posthog>=3.0.0; extra == \"all\"",
"pytest>=7.4.0; extra == \"dev\"",
"pytest-asyncio>=0.21.0; extra == \"dev\"",
"pytest-cov>=4.1.0; extra == \"dev\"",
"black==24.10.0; extra == \"dev\"",
"mypy>=1.7.0; extra == \"dev\"",
"ruff>=0.1.0; extra == \"dev\"",
"jinja2>=3.0; extra == \"dev\"",
"plotly>=5.0; extra == \"dev\"",
"watchdog>=3.0; extra == \"dev\"",
"anthropic>=0.39.0; extra == \"dev\"",
"fastapi>=0.109.0; extra == \"dev\"",
"uvicorn>=0.27.0; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/hidai25/eval-view",
"Documentation, https://github.com/hidai25/eval-view#readme",
"Repository, https://github.com/hidai25/eval-view.git",
"Issue Tracker, https://github.com/hidai25/eval-view/issues",
"Changelog, https://github.com/hidai25/eval-view/blob/main/CHANGELOG.md"
] | twine/6.2.0 CPython/3.9.13 | 2026-02-20T10:38:58.493872 | evalview-0.3.0.tar.gz | 393,007 | d3/e7/cd415cd20df12e1f28b3abbc9d787eba19ba59a685f3f17b3d4f1654f697/evalview-0.3.0.tar.gz | source | sdist | null | false | ba983cd4b938e765bb119e2e3b8276e3 | 69fc11efd2dfa567be31be1184ca76c649123f1a3aa3da15481f719f983f3275 | d3e7cd415cd20df12e1f28b3abbc9d787eba19ba59a685f3f17b3d4f1654f697 | Apache-2.0 | [
"LICENSE",
"NOTICE"
] | 282 |
2.4 | acex | 4.1.5 | ACE-X Backend - Core automation engine and API | # ACE-X Backend
Core automation engine and API for the ACE-X ecosystem.
## Installation
```bash
pip install acex
```
## Development
```bash
cd backend
poetry install
poetry run pytest
```
## Usage
```python
from acex.core import AutomationEngine
# Your code here
```
## Features
- Core automation engine
- REST API
- Event handling
- Plugin system
## Documentation
See the [main documentation](../README.md) for more information.
| text/markdown | Johan Lahti | johan.lahti@acebit.se | null | null | AGPL-3.0 | automation, control, api | [
"License :: OSI Approved :: GNU Affero General Public License v3",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14"
] | [] | null | null | <4.0,>=3.13 | [] | [] | [] | [
"fastapi<0.122.0,>=0.121.0",
"fastmcp<3.0.0.0,>=2.13.0.2",
"jinja2<4.0.0,>=3.1.6",
"mcp<2.0.0,>=1.20.0",
"openai<2.0.0,>=1.54.0",
"psycopg2-binary>=2.9.10",
"requests<3.0.0,>=2.32.5",
"sqlmodel<0.0.28,>=0.0.27",
"uvicorn<0.39.0,>=0.38.0"
] | [] | [] | [] | [
"Homepage, https://github.com/acex-labs/acex",
"Repository, https://github.com/acex-labs/acex"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:38:56.062072 | acex-4.1.5.tar.gz | 45,489 | 80/f5/4d8cafc73299ece38637003ce3288581229425582f3550b455b04612ecee/acex-4.1.5.tar.gz | source | sdist | null | false | 5bd54aa242bf03514cabd68e6e160883 | 787df21e00d53a0fa3fe528beae8b2e72c6e5f33e56188cb1415a9457010c4ed | 80f54d8cafc73299ece38637003ce3288581229425582f3550b455b04612ecee | null | [] | 248 |
2.4 | acex-devkit | 1.0.5 | ACE-X DevKit - Development kit for building ACE-X drivers and plugins | # ACE-X DevKit
Development kit for building ACE-X drivers and plugins.
## Installation
```bash
pip install acex-devkit
```
## Usage
### Building a Network Element Driver
```python
from acex_devkit.drivers import NetworkElementDriver, TransportBase, RendererBase, ParserBase
from acex_devkit.models import ComposedConfiguration
class MyRenderer(RendererBase):
def render(self, model: dict) -> str:
# Implement your rendering logic
pass
class MyTransport(TransportBase):
def connect(self) -> None:
# Implement connection logic
pass
def send(self, payload: Any) -> None:
# Implement send logic
pass
def verify(self) -> bool:
# Implement verification logic
return True
def rollback(self) -> None:
# Implement rollback logic
pass
class MyParser(ParserBase):
def parse(self, configuration: str) -> ComposedConfiguration:
# Implement parsing logic
pass
class MyDriver(NetworkElementDriver):
renderer_class = MyRenderer
transport_class = MyTransport
parser_class = MyParser
def render(self, configuration: ComposedConfiguration, asset):
return self.renderer.render(configuration, asset)
def parse(self, configuration: str) -> ComposedConfiguration:
return self.parser.parse(configuration)
```
## Package Contents
- **models**: Common data models (Asset, LogicalNode, ComposedConfiguration, etc.)
- **drivers**: Base classes for building network element drivers
- **exceptions**: Common exceptions
- **types**: Type aliases and protocols
## License
AGPL-3.0
| text/markdown | Johan Lahti | johan.lahti@acebit.se | null | null | AGPL-3.0 | automation, devkit, sdk, drivers, plugins | [
"License :: OSI Approved :: GNU Affero General Public License v3",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14"
] | [] | null | null | <4.0,>=3.13 | [] | [] | [] | [
"deepdiff<9.0.0,>=8.6.1",
"pydantic<3.0.0,>=2.12.5",
"typing-extensions<5.0.0,>=4.0.0"
] | [] | [] | [] | [
"Homepage, https://github.com/acex-labs/acex",
"Repository, https://github.com/acex-labs/acex"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:38:48.370310 | acex_devkit-1.0.5.tar.gz | 14,248 | 50/bf/c79ae42be2d737d2329b6cdb380dbdd7e1268c6378c54abea18a39e5f57c/acex_devkit-1.0.5.tar.gz | source | sdist | null | false | 322f2ef82e04cf58d80d6207b8f9b4fe | 8b3b55bc5cf948c674f3c147ba265e587b2c08c91ab68e0966bed993337e8321 | 50bfc79ae42be2d737d2329b6cdb380dbdd7e1268c6378c54abea18a39e5f57c | null | [] | 254 |
2.4 | agntcy-dir | 1.0.0 | Directory SDK | # Directory Python SDK
## Overview
Dir Python SDK provides a simple way to interact with the Directory API.
It allows developers to integrate and use Directory functionality from their Python applications with ease.
## Features
The Directory Python SDK provides comprehensive access to all Directory APIs with a simple, intuitive interface:
### **Store API**
- **Record Management**: Push records to the store and pull them by reference
- **Metadata Operations**: Look up record metadata without downloading full content
- **Data Lifecycle**: Delete records permanently from the store
- **Referrer Support**: Push and pull artifacts for existing records
- **Sync Management**: Manage storage synchronization policies between Directory servers
### **Search API**
- **Flexible Search**: Search stored records using text, semantic, and structured queries
- **Advanced Filtering**: Filter results by metadata, content type, and other criteria
### **Routing API**
- **Network Publishing**: Publish records to make them discoverable across the network
- **Content Discovery**: List and query published records across the network
- **Network Management**: Unpublish records to remove them from network discovery
### **Signing and Verification**
- **Local Signing**: Sign records locally using private keys or OIDC-based authentication.
Requires [dirctl](https://github.com/agntcy/dir/releases) binary to perform signing.
- **Remote Verification**: Verify record signatures using the Directory gRPC API
### **Developer Experience**
- **Type Safety**: Full type hints for better IDE support and fewer runtime errors
- **Async Support**: Non-blocking operations with streaming responses for large datasets
- **Error Handling**: Comprehensive gRPC error handling with detailed error messages
- **Configuration**: Flexible configuration via environment variables or direct instantiation
## Installation
Install the SDK using [uv](https://github.com/astral-sh/uv)
1. Initialize the project:
```bash
uv init
```
2. Add the SDK to your project:
```bash
uv add agntcy-dir --index https://buf.build/gen/python
```
## Configuration
The SDK can be configured via environment variables or direct instantiation:
```python
# Environment variables (insecure mode, default)
export DIRECTORY_CLIENT_SERVER_ADDRESS="localhost:8888"
export DIRCTL_PATH="/path/to/dirctl"
# Environment variables (X.509 authentication)
export DIRECTORY_CLIENT_SERVER_ADDRESS="localhost:8888"
export DIRECTORY_CLIENT_AUTH_MODE="x509"
export DIRECTORY_CLIENT_SPIFFE_SOCKET_PATH="/tmp/agent.sock"
# Environment variables (JWT authentication)
export DIRECTORY_CLIENT_SERVER_ADDRESS="localhost:8888"
export DIRECTORY_CLIENT_AUTH_MODE="jwt"
export DIRECTORY_CLIENT_SPIFFE_SOCKET_PATH="/tmp/agent.sock"
export DIRECTORY_CLIENT_JWT_AUDIENCE="spiffe://example.org/dir-server"
# Or configure directly
from agntcy.dir_sdk.client import Config, Client
# Insecure mode (default, for development only)
config = Config(
server_address="localhost:8888",
dirctl_path="/usr/local/bin/dirctl"
)
client = Client(config)
# X.509 authentication with SPIRE
x509_config = Config(
server_address="localhost:8888",
dirctl_path="/usr/local/bin/dirctl",
spiffe_socket_path="/tmp/agent.sock",
auth_mode="x509"
)
x509_client = Client(x509_config)
# JWT authentication with SPIRE
jwt_config = Config(
server_address="localhost:8888",
dirctl_path="/usr/local/bin/dirctl",
spiffe_socket_path="/tmp/agent.sock",
auth_mode="jwt",
jwt_audience="spiffe://example.org/dir-server"
)
jwt_client = Client(jwt_config)
```
## Error Handling
The SDK primarily raises `grpc.RpcError` exceptions for gRPC communication issues and `RuntimeError` for configuration problems:
```python
import grpc
from agntcy.dir_sdk.client import Client
try:
client = Client()
records = client.list(list_request)
except grpc.RpcError as e:
# Handle gRPC errors
if e.code() == grpc.StatusCode.NOT_FOUND:
print("Resource not found")
elif e.code() == grpc.StatusCode.UNAVAILABLE:
print("Server unavailable")
else:
print(f"gRPC error: {e.details()}")
except RuntimeError as e:
# Handle configuration or subprocess errors
print(f"Runtime error: {e}")
```
Common gRPC status codes:
- `NOT_FOUND`: Resource doesn't exist
- `ALREADY_EXISTS`: Resource already exists
- `UNAVAILABLE`: Server is down or unreachable
- `PERMISSION_DENIED`: Authentication/authorization failure
- `INVALID_ARGUMENT`: Invalid request parameters
## Getting Started
### Prerequisites
- Python 3.10 or higher
- [uv](https://github.com/astral-sh/uv) - Package manager
- [dirctl](https://github.com/agntcy/dir/releases) - Directory CLI binary
- Directory server instance (see setup below)
### 1. Server Setup
**Option A: Local Development Server**
```bash
# Clone the repository and start the server using Taskfile
task server:start
```
**Option B: Custom Server**
```bash
# Set your Directory server address
export DIRECTORY_CLIENT_SERVER_ADDRESS="your-server:8888"
```
### 2. SDK Installation
```bash
# Add the Directory SDK
uv add agntcy-dir --index https://buf.build/gen/python
```
### Usage Examples
See the [Example Python Project](../examples/example-py/) for a complete working example that demonstrates all SDK features.
```bash
uv sync
uv run example.py
```
| text/markdown | null | null | null | null | null | null | [] | [] | null | null | >=3.10 | [] | [] | [] | [
"grpcio>=1.74.0",
"pyasn1>=0.6.2",
"spiffe-tls>=0.2.1",
"spiffe>=0.2.2"
] | [] | [] | [] | [] | uv/0.9.26 {"installer":{"name":"uv","version":"0.9.26","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true} | 2026-02-20T10:38:44.868314 | agntcy_dir-1.0.0.tar.gz | 64,954 | 73/e6/f171bc96414a5ac2657c5ae89712bdd74d43b1125ec6f826c5c633ed7a29/agntcy_dir-1.0.0.tar.gz | source | sdist | null | false | 36ec3fcf4e46fc3cac6fed3b66fa854b | e39e8e5364b19635e27bf194207a623ea72661a4f2b96f89e6b6feee487c9564 | 73e6f171bc96414a5ac2657c5ae89712bdd74d43b1125ec6f826c5c633ed7a29 | null | [] | 248 |
2.4 | mkdocstrings-python | 2.0.3 | A Python handler for mkdocstrings. | <h1 align="center">mkdocstrings-python</h1>
<p align="center">A Python handler for <a href="https://github.com/mkdocstrings/mkdocstrings"><i>mkdocstrings</i></a>.</p>
[](https://github.com/mkdocstrings/python/actions?query=workflow%3Aci)
[](https://mkdocstrings.github.io/python/)
[](https://pypi.org/project/mkdocstrings-python/)
[](https://app.gitter.im/#/room/#mkdocstrings_python:gitter.im)
---
<p align="center"><img src="logo.png"></p>
The Python handler uses [Griffe](https://mkdocstrings.github.io/griffe)
to collect documentation from Python source code.
The word "griffe" can sometimes be used instead of "signature" in French.
Griffe is able to visit the Abstract Syntax Tree (AST) of the source code to extract useful information.
It is also able to execute the code (by importing it) and introspect objects in memory
when source code is not available. Finally, it can parse docstrings following different styles.
## Installation
You can install this handler as a *mkdocstrings* extra:
```toml title="pyproject.toml"
# PEP 621 dependencies declaration
# adapt to your dependencies manager
[project]
dependencies = [
"mkdocstrings[python]>=0.18",
]
```
You can also explicitly depend on the handler:
```toml title="pyproject.toml"
# PEP 621 dependencies declaration
# adapt to your dependencies manager
[project]
dependencies = [
"mkdocstrings-python",
]
```
## Preview
<!-- TODO: update the GIF with a more recent screen capture. Maybe use mp4 instead -->
## Features
- **Data collection from source code**: collection of the object-tree and the docstrings is done thanks to
[Griffe](https://github.com/mkdocstrings/griffe).
- **Support for type annotations:** Griffe collects your type annotations and *mkdocstrings* uses them
to display parameter types or return types. It is even able to automatically add cross-references
to other objects from your API, from the standard library or third-party libraries!
See [how to load inventories](https://mkdocstrings.github.io/usage/#cross-references-to-other-projects-inventories) to enable it.
- **Recursive documentation of Python objects:** just use the module dotted-path as an identifier, and you get the full
module docs. You don't need to inject documentation for each class, function, etc.
- **Support for documented attributes:** attributes (variables) followed by a docstring (triple-quoted string) will
be recognized by Griffe in modules, classes and even in `__init__` methods.
- **Multiple docstring-styles support:** common support for Google-style, Numpydoc-style,
and Sphinx-style docstrings. See [Griffe's documentation](https://mkdocstrings.github.io/griffe/docstrings/) on docstrings support.
- **Admonition support in Google docstrings:** blocks like `Note:` or `Warning:` will be transformed
to their [admonition](https://squidfunk.github.io/mkdocs-material/reference/admonitions/) equivalent.
*We do not support nested admonitions in docstrings!*
- **Every object has a TOC entry:** we render a heading for each object, meaning *MkDocs* picks them into the Table
of Contents, which is nicely displayed by the Material theme. Thanks to *mkdocstrings* cross-reference ability,
you can reference other objects within your docstrings, with the classic Markdown syntax:
`[this object][package.module.object]` or directly with `[package.module.object][]`
- **Source code display:** *mkdocstrings* can add a collapsible div containing the highlighted source code
of the Python object.
## Sponsors
<!-- sponsors-start -->
<div id="premium-sponsors" style="text-align: center;">
<div id="silver-sponsors"><b>Silver sponsors</b><p>
<a href="https://fastapi.tiangolo.com/"><img alt="FastAPI" src="https://raw.githubusercontent.com/tiangolo/fastapi/master/docs/en/docs/img/logo-margin/logo-teal.png" style="height: 200px; "></a><br>
</p></div>
<div id="bronze-sponsors"><b>Bronze sponsors</b><p>
<a href="https://www.nixtla.io/"><picture><source media="(prefers-color-scheme: light)" srcset="https://www.nixtla.io/img/logo/full-black.svg"><source media="(prefers-color-scheme: dark)" srcset="https://www.nixtla.io/img/logo/full-white.svg"><img alt="Nixtla" src="https://www.nixtla.io/img/logo/full-black.svg" style="height: 60px; "></picture></a><br>
</p></div>
</div>
---
<div id="sponsors"><p>
<a href="https://github.com/ofek"><img alt="ofek" src="https://avatars.githubusercontent.com/u/9677399?u=386c330f212ce467ce7119d9615c75d0e9b9f1ce&v=4" style="height: 32px; border-radius: 100%;"></a>
<a href="https://github.com/samuelcolvin"><img alt="samuelcolvin" src="https://avatars.githubusercontent.com/u/4039449?u=42eb3b833047c8c4b4f647a031eaef148c16d93f&v=4" style="height: 32px; border-radius: 100%;"></a>
<a href="https://github.com/tlambert03"><img alt="tlambert03" src="https://avatars.githubusercontent.com/u/1609449?u=922abf0524b47739b37095e553c99488814b05db&v=4" style="height: 32px; border-radius: 100%;"></a>
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<a href="https://github.com/cmarqu"><img alt="cmarqu" src="https://avatars.githubusercontent.com/u/360986?v=4" style="height: 32px; border-radius: 100%;"></a>
<a href="https://github.com/kolenaIO"><img alt="kolenaIO" src="https://avatars.githubusercontent.com/u/77010818?v=4" style="height: 32px; border-radius: 100%;"></a>
<a href="https://github.com/ramnes"><img alt="ramnes" src="https://avatars.githubusercontent.com/u/835072?u=3fca03c3ba0051e2eb652b1def2188a94d1e1dc2&v=4" style="height: 32px; border-radius: 100%;"></a>
<a href="https://github.com/machow"><img alt="machow" src="https://avatars.githubusercontent.com/u/2574498?u=c41e3d2f758a05102d8075e38d67b9c17d4189d7&v=4" style="height: 32px; border-radius: 100%;"></a>
<a href="https://github.com/BenHammersley"><img alt="BenHammersley" src="https://avatars.githubusercontent.com/u/99436?u=4499a7b507541045222ee28ae122dbe3c8d08ab5&v=4" style="height: 32px; border-radius: 100%;"></a>
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<a href="https://github.com/MarcoGorelli"><img alt="MarcoGorelli" src="https://avatars.githubusercontent.com/u/33491632?u=7de3a749cac76a60baca9777baf71d043a4f884d&v=4" style="height: 32px; border-radius: 100%;"></a>
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<a href="https://github.com/rstudio-sponsorship"><img alt="rstudio-sponsorship" src="https://avatars.githubusercontent.com/u/58949051?u=0c471515dd18111be30dfb7669ed5e778970959b&v=4" style="height: 32px; border-radius: 100%;"></a>
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<a href="https://github.com/blaisep"><img alt="blaisep" src="https://avatars.githubusercontent.com/u/254456?u=97d584b7c0a6faf583aa59975df4f993f671d121&v=4" style="height: 32px; border-radius: 100%;"></a>
<a href="https://github.com/RapidataAI"><img alt="RapidataAI" src="https://avatars.githubusercontent.com/u/104209891?v=4" style="height: 32px; border-radius: 100%;"></a>
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</p></div>
*And 7 more private sponsor(s).*
<!-- sponsors-end -->
| text/markdown | null | =?utf-8?q?Timoth=C3=A9e_Mazzucotelli?= <dev@pawamoy.fr> | null | null | null | null | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3 :: Only",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14",
"Topic :: Documentation",
"Topic :: Software Development",
"Topic :: Software Development :: Documentation",
"Topic :: Utilities",
"Typing :: Typed"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"mkdocstrings>=0.30",
"mkdocs-autorefs>=1.4",
"griffelib>=2.0",
"typing-extensions>=4.0; python_version < \"3.11\""
] | [] | [] | [] | [
"Homepage, https://mkdocstrings.github.io/python",
"Documentation, https://mkdocstrings.github.io/python",
"Changelog, https://mkdocstrings.github.io/python/changelog",
"Repository, https://github.com/mkdocstrings/python",
"Issues, https://github.com/mkdocstrings/python/issues",
"Discussions, https://github.com/mkdocstrings/python/discussions",
"Gitter, https://gitter.im/mkdocstrings/python",
"Funding, https://github.com/sponsors/pawamoy"
] | twine/6.2.0 CPython/3.14.2 | 2026-02-20T10:38:36.368538 | mkdocstrings_python-2.0.3.tar.gz | 199,083 | 29/33/c225eaf898634bdda489a6766fc35d1683c640bffe0e0acd10646b13536d/mkdocstrings_python-2.0.3.tar.gz | source | sdist | null | false | 4cd2b5b8b2bf039be2dd1cdb4c6f7fe5 | c518632751cc869439b31c9d3177678ad2bfa5c21b79b863956ad68fc92c13b8 | 2933c225eaf898634bdda489a6766fc35d1683c640bffe0e0acd10646b13536d | ISC | [
"LICENSE"
] | 70,250 |
2.4 | fluent-codegen | 0.2 | A Python library for generating Python code via AST construction. | fluent-codegen
==============
A Python library for generating Python code via AST construction.
Overview
--------
``fluent-codegen`` provides a set of classes that represent simplified
Python constructs (functions, assignments, expressions, control flow,
etc.) and can generate real Python ``ast`` nodes. This lets you build
correct Python code programmatically without manipulating raw AST or
worrying about string interpolation pitfalls.
Originally extracted from
`fluent-compiler <https://github.com/django-ftl/fluent-compiler>`__,
where it was used to compile Fluent localization files into Python
bytecode.
Key features
------------
- **Safe by construction** — builds AST, not strings, eliminating
injection bugs
- **Scope management** — automatic name deduplication and scope
tracking
- **Simplified API** — high-level classes (``Function``, ``If``,
``Try``, ``StringJoin``, etc.) that map to Python constructs without
requiring knowledge of the raw ``ast`` module
- **Security guardrails** — blocks calls to sensitive builtins
(``exec``, ``eval``, etc.)
Installation
------------
.. code:: bash
pip install fluent-codegen
Requires Python 3.12+.
Quick example
-------------
This builds a FizzBuzz function entirely via the codegen API, using
fluent method-chaining for expressions:
.. code:: python
from fluent_codegen import codegen
# 1. Create a module and a function inside it
module = codegen.Module()
func, _ = module.create_function("fizzbuzz", args=["n"])
# 2. A Name reference to the "n" parameter (Function *is* a Scope)
n = func.name("n")
# 3. Build an if / elif / else chain
if_stmt = func.body.create_if()
# if n % 15 == 0: return "FizzBuzz" — fluent chaining
branch = if_stmt.create_if_branch(n.mod(codegen.Number(15)).eq(codegen.Number(0)))
branch.create_return(codegen.String("FizzBuzz"))
# elif n % 3 == 0: return "Fizz"
branch = if_stmt.create_if_branch(n.mod(codegen.Number(3)).eq(codegen.Number(0)))
branch.create_return(codegen.String("Fizz"))
# elif n % 5 == 0: return "Buzz"
branch = if_stmt.create_if_branch(n.mod(codegen.Number(5)).eq(codegen.Number(0)))
branch.create_return(codegen.String("Buzz"))
# else: return str(n)
if_stmt.else_block.create_return(module.scope.name("str").call([n]))
# 4. Inspect the generated source
print(module.as_python_source())
# def fizzbuzz(n):
# if n % 15 == 0:
# return 'FizzBuzz'
# elif n % 3 == 0:
# return 'Fizz'
# elif n % 5 == 0:
# return 'Buzz'
# else:
# return str(n)
# 5. Compile, execute, and call the generated function
code = compile(module.as_ast(), "<fizzbuzz>", "exec")
ns: dict[str, object] = {}
exec(code, ns)
fizzbuzz = ns["fizzbuzz"]
assert fizzbuzz(15) == "FizzBuzz"
assert fizzbuzz(9) == "Fizz"
assert fizzbuzz(10) == "Buzz"
assert fizzbuzz(7) == "7"
License
-------
Apache License 2.0
| text/x-rst | null | Luke Plant <luke@lukeplant.me.uk> | null | null | null | codegen, code-generation, ast, python, metaprogramming | [
"Development Status :: 3 - Alpha",
"Intended Audience :: Developers",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3 :: Only",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: Implementation :: CPython",
"Topic :: Software Development :: Code Generators",
"Topic :: Software Development :: Libraries :: Python Modules"
] | [] | null | null | >=3.12 | [] | [] | [] | [] | [] | [] | [] | [
"Repository, https://github.com/spookylukey/fluent-codegen"
] | uv/0.10.3 {"installer":{"name":"uv","version":"0.10.3","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Linux Mint","version":"22.2","id":"zara","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null} | 2026-02-20T10:38:27.954098 | fluent_codegen-0.2-py3-none-any.whl | 18,373 | 8c/f5/c06c568be64dcf13793fb81366622ba717316abdc7752b91c618d8ccfc68/fluent_codegen-0.2-py3-none-any.whl | py3 | bdist_wheel | null | false | 9beafbf8a31d4edf533a6eb9c162d443 | 0c3d233e6fb62e4aeefe39bcea1299f091f15d3eaa15ca37b1820475caafd33f | 8cf5c06c568be64dcf13793fb81366622ba717316abdc7752b91c618d8ccfc68 | Apache-2.0 | [
"LICENSE"
] | 232 |
2.4 | polytope-python | 2.1.4 | Polytope datacube feature extraction library |
<h3 align="center">
<img src="https://raw.githubusercontent.com/ecmwf/polytope/develop/docs/images/polytope_logo_new_animated_AdobeExpress_3.gif" width=60%>
</br>
</h3>
<p align="center">
<a href="https://github.com/ecmwf/codex/raw/refs/heads/main/Project%20Maturity">
<img src="https://github.com/ecmwf/codex/raw/refs/heads/main/Project%20Maturity/incubating_badge.svg" alt="Project Maturity">
</a>
<a href="https://github.com/ecmwf/codex/raw/refs/heads/main/ESEE"> <img src="https://github.com/ecmwf/codex/raw/refs/heads/main/ESEE/data_provision_badge.svg" alt="ESEE">
</p>
<p align="center">
<a href="https://github.com/ecmwf/polytope/actions/workflows/downstream-ci.yml">
<img src="https://github.com/ecmwf/polytope/actions/workflows/downstream-ci.yml/badge.svg" alt="ci">
</a>
<a href="https://codecov.io/gh/ecmwf/polytope"><img src="https://codecov.io/gh/ecmwf/polytope/branch/develop/graph/badge.svg"></a>
<a href="https://opensource.org/licenses/Apache-2.0"><img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg"></a>
<a href="https://github.com/ecmwf/polytope/releases"><img src="https://img.shields.io/github/v/release/ecmwf/polytope?color=blue&label=Release&style=flat-square"></a>
<a href='https://polytope.readthedocs.io/en/latest/?badge=latest'><img src='https://readthedocs.org/projects/polytope/badge/?version=latest' alt='Documentation Status' /></a>
</p>
<p align="center">
<a href="#concept">Concept</a> •
<a href="#installation">Installation</a> •
<a href="#example">Example</a> •
<a href="#testing">Testing</a> •
<a href="https://polytope.readthedocs.io/en/latest/">Documentation</a>
</p>
Polytope is a library for extracting complex data from datacubes. It provides an API for non-orthogonal access to data, where the stencil used to extract data from the datacube can be any arbitrary *n*-dimensional polygon (called a *polytope*). This can be used to efficiently extract complex features from a datacube, such as polygon regions or spatio-temporal paths.
Polytope is designed to extend different datacube backends:
* XArray dataarrays
* FDB object stores (through the GribJump software)
Polytope supports datacubes which have branching, non-uniform indexing, and even cyclic axes. If the datacube backend supports byte-addressability and efficient random access (either in-memory or direct from storage), **Polytope** can be used to dramatically decrease overall I/O load.
> \[!IMPORTANT\]
> This software is **Incubating** and subject to ECMWF's guidelines on [Software Maturity](https://github.com/ecmwf/codex/raw/refs/heads/main/Project%20Maturity).
<!-- > [!WARNING]
> This project is BETA and will be experimental for the foreseeable future. Interfaces and functionality are likely to change, and the project itself may be scrapped. DO NOT use this software in any project/software that is operational. -->
## Concept
Polytope is designed to enable extraction of arbitrary extraction of data from a datacube. Instead of the typical range-based bounding-box approach, Polytope can extract any shape of data from a datacube using a "polytope" (*n*-dimensional polygon) stencil.
<p align="center">
<img src="https://raw.githubusercontent.com/ecmwf/polytope/develop//docs/Algorithm/Overview/images_overview/ecmwf_polytope.png" alt="Polytope Concept" width="450"/>
</p>
The Polytope algorithm can for example be used to extract:
- 2D cut-outs, such as country cut-outs, from a datacube
<p align="center">
<img src="https://raw.githubusercontent.com/ecmwf/polytope/develop/docs/images/greece.png" alt="Greece cut-out" width="250"/>
</p>
- timeseries from a datacube
<p align="center">
<img src="https://raw.githubusercontent.com/ecmwf/polytope/develop/docs/images/timeseries.png" alt="Timeseries" width="350"/>
</p>
- more complicated spatio-temporal paths, such as flight paths, from a datacube
<p align="center">
<img src="https://raw.githubusercontent.com/ecmwf/polytope/develop/docs/images/flight_path.png" alt="Flight path" width="350"/>
</p>
- and many more high-dimensional shapes in arbitrary dimensions...
For more information about the Polytope algorithm, refer to our [paper](https://arxiv.org/abs/2306.11553).
If this project is useful for your work, please consider citing this paper.
## Installation
Install the polytope software with Python 3 (>=3.7) from GitHub directly with the command
python3 -m pip install git+ssh://git@github.com/ecmwf/polytope.git@develop
or from PyPI with the command
python3 -m pip install polytope-python
## Example
Here is a step-by-step example of how to use this software.
1. In this example, we first specify the data which will be in our Xarray datacube. Note that the data here comes from the GRIB file called "winds.grib", which is 3-dimensional with dimensions: step, latitude and longitude.
```Python
import xarray as xr
array = xr.open_dataset("winds.grib", engine="cfgrib")
```
We then construct the Polytope object, passing in some additional metadata describing properties of the longitude axis.
```Python
options = {"longitude": {"cyclic": [0, 360.0]}}
from polytope_feature.polytope import Polytope
p = Polytope(datacube=array, axis_options=options)
```
2. Next, we create a request shape to extract from the datacube.
In this example, we want to extract a simple 2D box in latitude and longitude at step 0. We thus create the two relevant shapes we need to build this 3-dimensional object,
```Python
import numpy as np
from polytope_feature.shapes import Box, Select
box = Box(["latitude", "longitude"], [0, 0], [1, 1])
step_point = Select("step", [np.timedelta64(0, "s")])
```
which we then incorporate into a Polytope request.
```Python
from polytope_feature.polytope import Request
request = Request(box, step_point)
```
3. Finally, extract the request from the datacube.
```Python
result = p.retrieve(request)
```
The result is stored as an IndexTree containing the retrieved data organised hierarchically with axis indices for each point.
```Python
result.pprint()
Output IndexTree:
↳root=None
↳step=0 days 00:00:00
↳latitude=0.0
↳longitude=0.0
↳longitude=1.0
↳latitude=1.0
↳longitude=0.0
↳longitude=1.0
```
## Testing
#### Additional Dependencies
The Polytope tests and examples require additional Python packages compared to the main Polytope algorithm.
The additional dependencies are provided in the requirements_test.txt and requirements_examples.txt files, which can respectively be found in the tests and examples folders.
Moreover, Polytope's tests and examples also require the installation of eccodes and GDAL.
It is possible to install both of these dependencies using either a package manager or manually.
## Contributing
The main repository is hosted on GitHub; testing, bug reports and contributions are highly welcomed and appreciated.
Please see the [Contributing](./CONTRIBUTING.rst) document for the best way to help.
Main contributors:
- Mathilde Leuridan - [ECMWF](https://www.ecmwf.int)
- James Hawkes - [ECMWF](https://www.ecmwf.int)
- Simon Smart - [ECMWF](www.ecmwf.int)
- Emanuele Danovaro - [ECMWF](www.ecmwf.int)
- Tiago Quintino - [ECMWF](www.ecmwf.int)
See also the [contributors](https://github.com/ecmwf/polytope/contributors) for a more complete list.
## License
```
Copyright 2021 European Centre for Medium-Range Weather Forecasts (ECMWF)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at [http://www.apache.org/licenses/LICENSE-2.0](http://www.apache.org/licenses/LICENSE-2.0).
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
In applying this licence, ECMWF does not waive the privileges and immunities
granted to it by virtue of its status as an intergovernmental organisation nor
does it submit to any jurisdiction.
```
## Citing
If this software is useful in your work, please consider citing our paper as
> Leuridan, M., Hawkes, J., Smart, S., Danovaro, E., & Quintino, T. (2025, November). [Polytope: An Algorithm for Efficient Feature Extraction on Hypercubes.](https://link.springer.com/article/10.1186/s40537-025-01306-3) In Journal of Big Data (pp. 1-25).
Other papers include:
> Leuridan, M., Bradley, C., Hawkes, J., Quintino, T., & Schultz, M. (2025, June). [Performance Analysis of an Efficient Algorithm for Feature Extraction from Large Scale Meteorological Data Stores.](https://dl.acm.org/doi/abs/10.1145/3732775.3733573) In Proceedings of the Platform for Advanced Scientific Computing Conference (pp. 1-9).
## Acknowledgements
Past and current funding and support for **Polytope** is listed in the adjoining [Acknowledgements](./ACKNOWLEDGEMENTS.rst).
| text/markdown | null | "European Centre for Medium-Range Weather Forecasts (ECMWF)" <software.support@ecmwf.int> | null | James Hawkes <James.Hawkes@ecmwf.int>, Mathilde Leuridan <Mathilde.Leuridan@ecmwf.int> | Apache License Version 2.0 | null | [
"Development Status :: 4 - Beta",
"Intended Audience :: Science/Research",
"Natural Language :: English",
"License :: OSI Approved :: Apache Software License",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3 :: Only",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Topic :: Scientific/Engineering"
] | [] | null | null | >3.8 | [] | [] | [] | [
"numpy>=1.26",
"pandas",
"scipy",
"sortedcontainers",
"tripy",
"xarray",
"conflator",
"protobuf",
"pytest; extra == \"tests\"",
"pytest-cov; extra == \"tests\"",
"cffi; extra == \"tests\"",
"eccodes; extra == \"tests\"",
"h5netcdf; extra == \"tests\"",
"h5py; extra == \"tests\"",
"earthkit-data; extra == \"tests\"",
"matplotlib; extra == \"tests\"",
"pyfdb; extra == \"tests\"",
"eckit; extra == \"unstructured\"",
"qubed; extra == \"catalogue\"",
"eccodes; extra == \"switching-grids\"",
"pyfdb; extra == \"switching-grids\""
] | [] | [] | [] | [
"repository, https://github.com/ecmwf/polytope",
"documentation, https://polytope.readthedocs.io/en/latest/",
"issues, https://github.com/ecmwf/polytope/issues"
] | twine/6.2.0 CPython/3.14.2 | 2026-02-20T10:36:40.183082 | polytope_python-2.1.4.tar.gz | 14,314,532 | 37/77/ff897d0699e0649989ad03a4847f6446632ad811814dbd9d8ca003db855b/polytope_python-2.1.4.tar.gz | source | sdist | null | false | 7e006c3681dad970d95f5e3258ace562 | 464dd3ec5bd29d5d1a27a0cbbe271f1307c34d7a4d1a249fbcefb22eb67a4490 | 3777ff897d0699e0649989ad03a4847f6446632ad811814dbd9d8ca003db855b | null | [
"LICENSE"
] | 821 |
2.4 | qrisp | 0.8.0 | Qrisp - A high level language for gate-based quantum computing | <p align="center" width="100%"><img src="https://raw.githubusercontent.com/eclipse-qrisp/Qrisp/main/logo/logo_with_contour.png" width=30%></p>
</h1><br>
<div align="center">
[](https://opensource.org/licenses/EPL-2.0)
[](https://discord.gg/v5np7DeBaq)
[](https://github.com/eclipse-qrisp/Qrisp/actions/workflows/qrisp_test.yml)
[](https://pypi.org/project/qrisp/)
[](https://www.codefactor.io/repository/github/eclipse-qrisp/qrisp/overview/main)
[](https://doi.org/10.48550/arXiv.2406.14792)
[](https://github.com/eclipse-qrisp/Qrisp/network/members)
[](https://github.com/eclipse-qrisp/Qrisp/issues)
[](https://github.com/eclipse-qrisp/Qrisp/stargazers)
[](https://github.com/eclipse-qrisp/Qrisp/graphs/contributors)
</div>
## About
Qrisp is a high-level quantum programming framework that allows for intuitive development of quantum algorithms. It provides a rich set of tools and abstractions to make quantum computing more accessible to developers and researchers. By automating many steps one usually encounters when programming a quantum computer, introducing quantum types, and many more features Qrisp makes quantum programming more user-friendly yet stays performant when it comes to compiling programs to the circuit level.
## Features
- Intuitive quantum program design
- High-level quantum programming
- Efficient quantum algorithm implementation
- Extensive documentation and examples
## Installation
You can install Qrisp using pip:
```bash
pip install qrisp
```
Qrisp has been confirmed to work with Python version 3.11 & 3.12.
Qrisp is compatible with any QASM-capable quantum backend! In particular, it offers convenient interfaces for using IBM, IQM and AQT quantum computers, and any quantum backend provider is invited to reach out for a tight integration!
If you want to work with IQM quantum computers as a backend, you need to install additional dependencies using
```bash
pip install qrisp[iqm]
```
## Documentation
The full documentation, alongside with many tutorials and examples, is available under [Qrisp Documentation](https://www.qrisp.eu/).
## Shor's Algorithm with Qrisp
Shor's algorithm is among the most famous quantum algorithm since it provides a provably exponential speed-up for a practically relevant problem: Facotrizing integers. This is an important application because much of modern cryptography is based on RSA, which heavily relies on integer factorization being insurmountable.
Despite this importance, the amount of software that is actually able to compile the algorithm to the circuit level is extremely limited. This is because a key operation within the algorithm (modular in-place multiplication) is difficult to implement and has strong requirements for the underlying compiler. These problems highlight how the Qrisp programming-model delivers significant advantages to quantum programmers because the quantum part of the algorithm can be expressend within a few lines of code:
```python
from qrisp import QuantumFloat, QuantumModulus, h, QFT, control
def find_order(a, N):
qg = QuantumModulus(N)
qg[:] = 1
qpe_res = QuantumFloat(2*qg.size + 1, exponent = -(2*qg.size + 1))
h(qpe_res)
for i in range(len(qpe_res)):
with control(qpe_res[i]):
qg *= a
a = (a*a)%N
QFT(qpe_res, inv = True)
return qpe_res.get_measurement()
```
To find out how this can be used to break encryption be sure to check the [tutorial](https://qrisp.eu/general/tutorial/Shor.html).
Qrisp offers much more than just factoring! More examples, like simulating molecules at the quantum level or how to solve the Travelling Salesman Problem, can be found [here](https://qrisp.eu/general/tutorial/index.html).
## Authors and Citation
Qrisp is the work of [many people](https://projects.eclipse.org/projects/technology.qrisp/who).
If you have comments, questions or love letters, feel free to reach out to us:
raphael.seidel [at] meetiqm.com
sebastian.bock [at] fokus.fraunhofer.de
nikolay.tcholtchev [at] fokus.fraunhofer.de
rene.zander [at] fokus.fraunhofer.de
matic.petric [at] fokus.fraunhofer.de
If you want to cite Qrisp in your work, please use:
```
@misc{seidel2024qrisp,
title={Qrisp: A Framework for Compilable High-Level Programming of Gate-Based Quantum Computers},
author={Raphael Seidel and Sebastian Bock and René Zander and Matic Petrič and Niklas Steinmann and Nikolay Tcholtchev and Manfred Hauswirth},
year={2024},
eprint={2406.14792},
archivePrefix={arXiv},
primaryClass={quant-ph},
url={https://arxiv.org/abs/2406.14792},
}
```
## License
[Eclipse Public License 2.0](https://github.com/fraunhoferfokus/Qrisp/blob/main/LICENSE)
| text/markdown | The Qrisp team | raphael.seidel@fokus.fraunhofer.de | null | null | null | null | [
"Programming Language :: Python :: 3",
"License :: OSI Approved :: Eclipse Public License 2.0 (EPL-2.0)",
"Operating System :: OS Independent"
] | [] | https://github.com/eclipse-qrisp/Qrisp | null | >=3.11 | [] | [] | [] | [
"numpy>=2.0",
"sympy<=1.13",
"qiskit>=0.44.0",
"matplotlib>=3.5.1",
"scipy>=1.10.0",
"numba",
"networkx",
"tqdm",
"dill",
"flask<2.3.0",
"waitress",
"pyyaml",
"requests",
"psutil",
"jax==0.7.1",
"jaxlib==0.7.1",
"iqm-client[qiskit]; extra == \"iqm\""
] | [] | [] | [] | [
"Bug Tracker, https://github.com/eclipse-qrisp/Qrisp/issues"
] | twine/6.0.1 CPython/3.11.10 | 2026-02-20T10:36:14.014004 | qrisp-0.8.0.tar.gz | 716,565 | 66/5f/f6e539c320d456f0fda5cd2d92ace5ad769ad89c3bd1f530039f4728ded6/qrisp-0.8.0.tar.gz | source | sdist | null | false | 5e3de243b696a6a6cd7fa373031a71d5 | 3ae51c568919c231225c5d755efee8a3d3dc9e8eeee50afa9bea21a03dafd0aa | 665ff6e539c320d456f0fda5cd2d92ace5ad769ad89c3bd1f530039f4728ded6 | null | [] | 248 |
2.3 | langchain-azure-ai | 1.0.61 | An integration package to support Azure AI Foundry capabilities in LangChain/LangGraph ecosystem. | # langchain-azure-ai
This package contains the LangChain integration for Azure AI Foundry. To learn more about how to use this package, see the LangChain documentation in [Azure AI Foundry](https://aka.ms/azureai/langchain).
## Installation
```bash
pip install -U langchain-azure-ai
```
For using tools, including Azure AI Document Intelligence, Azure AI Text Analytics for Health, or Azure LogicApps, please install the extras `tools`:
```bash
pip install -U langchain-azure-ai[tools]
```
For using tracing capabilities with OpenTelemetry, you need to add the extras `opentelemetry`:
```bash
pip install -U langchain-azure-ai[opentelemetry]
```
## Quick Start with langchain-azure-ai
The `langchain-azure-ai` package uses the Azure AI Foundry family of SDKs and client libraries for Azure to provide first-class support of Azure AI Foundry capabilities in LangChain and LangGraph.
This package includes:
* [Azure AI Agent Service](./libs/azure-ai/langchain_azure_ai/agents)
* [Azure AI Foundry Models inference](./libs/azure-ai/langchain_azure_ai/chat_models)
* [Azure AI Search](./libs/azure-ai/langchain_azure_ai/vectorstores)
* [Azure AI Services tools](./libs/azure-ai/langchain_azure_ai/tools)
* [Cosmos DB](./libs/azure-ai/langchain_azure_ai/vectorstores)
Here's a quick start example to show you how to get started with the Chat Completions model. For more details and tutorials see [Develop with LangChain and LangGraph and models from Azure AI Foundry](https://aka.ms/azureai/langchain).
### Azure AI Chat Completions Model with Azure OpenAI
```python
from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
from langchain_core.messages import HumanMessage, SystemMessage
model = AzureAIChatCompletionsModel(
endpoint="https://{your-resource-name}.services.ai.azure.com/openai/v1",
credential="your-api-key", #if using Entra ID you can should use DefaultAzureCredential() instead
model="gpt-4o"
)
messages = [
SystemMessage(
content="Translate the following from English into Italian"
),
HumanMessage(content="hi!"),
]
model.invoke(messages)
```
```python
AIMessage(content='Ciao!', additional_kwargs={}, response_metadata={'model': 'gpt-4o', 'token_usage': {'input_tokens': 20, 'output_tokens': 3, 'total_tokens': 23}, 'finish_reason': 'stop'}, id='run-0758e7ec-99cd-440b-bfa2-3a1078335133-0', usage_metadata={'input_tokens': 20, 'output_tokens': 3, 'total_tokens': 23})
```
### Azure AI Chat Completions Model with DeepSeek-R1
```python
from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
from langchain_core.messages import HumanMessage, SystemMessage
model = AzureAIChatCompletionsModel(
endpoint="https://{your-resource-name}.services.ai.azure.com/models",
credential="your-api-key", #if using Entra ID you can should use DefaultAzureCredential() instead
model="DeepSeek-R1",
)
messages = [
HumanMessage(content="Translate the following from English into Italian: \"hi!\"")
]
message_stream = model.stream(messages)
print(' '.join(chunk.content for chunk in message_stream))
```
```python
<think>
Okay , the user just sent " hi !" and I need to translate that into Italian . Let me think . " Hi " is an informal greeting , so in Italian , the equivalent would be " C iao !" But wait , there are other options too . Sometimes people use " Sal ve ," which is a bit more neutral , but " C iao " is more common in casual settings . The user probably wants a straightforward translation , so " C iao !" is the safest bet here . Let me double -check to make sure there 's no nuance I 'm missing . N ope , " C iao " is definitely the right choice for translating " hi !" in an informal context . I 'll go with that .
</think>
C iao !
```
## Changelog
- **1.0.61**:
- This release reverts the code to the state of v1.0.5 while updating the version number to 1.0.61.
- **1.0.5**:
- We fixed an issue with the content type of messages in `AzureAIChatCompletionsModel`. See [PR #245].
- We improve metadata generated for `AzureAIOpenTelemetryTracer`. See [PR ##233].
- **1.0.4**:
- We fixed an issue with dependencies resolution for `azure-ai-agents` where the incorrect version was picked up. See [PR #221].
- We fixed an issue with `AzureAIOpenTelemetryTracer` where spans context was not correctly propagated when called from another service. See [PR #217].
- We fixed an issue where `AzureAIOpenTelemetryTracer` where context was deallocated incorrectly, preventing tools like `langdev` to correctly emit traces. See [Issue #212].
- We introduced improvements in the order in which environment variables `AZURE_AI_*` are read.
- Internal: We improved `AzureAIOpenTelemetryTracer` test coverage. See [PR #239](https://github.com/langchain-ai/langchain-azure/pull/239).
- **1.0.2**:
- We updated the `AzureAIOpenTelemetryTracer` to create a parent trace for multi agent scenarios. Previously, you were required to do this manually, which was unnecesary.
- **1.0.0**:
- We introduce support for LangChain and LangGraph 1.0.
- **0.1.8**:
- We fixed some issues with `AzureAIOpenTelemetryTracer`, including compliant hierarchy, tool spans under chat, finish reason normalization, conversation id. See [PR #167]
- We fixed an issue with taking image inputs for declarative agents created with Azure AI Foundry Agents service.
- We enhanced tool descriptions to improve tool call accuracy.
- **0.1.7**:
- **[NEW]**: We introduce LangGraph support for declarative agents created in Azure AI Foundry. You can now compose complex graphs in LangGraph and add nodes that take advantage of Azure AI Agent Service. See [`AgentServiceFactory`](./langchain_azure_ai/agents/agent_service.py#L44)
- We fix an issue with the interface of `AzureAIEmbeddingsModel` [#158](https://github.com/langchain-ai/langchain-azure/issues/158).
- We improve the signatures of the tools `AzureAIDocumentIntelligenceTool`, `AzureAIImageAnalysisTool`, and `AzureAITextAnalyticsHealthTool` [PR #160](https://github.com/langchain-ai/langchain-azure/pull/160).
- **0.1.6**:
- **[Breaking change]:** Using parameter `project_connection_string` to create `AzureAIEmbeddingsModel` and `AzureAIChatCompletionsModel` is not longer supported. Use `project_endpoint` instead.
- **[Breaking change]:** Class `AzureAIInferenceTracer` has been removed in favor of `AzureAIOpenTelemetryTracer` which has a better support for OpenTelemetry and the new semantic conventions for GenAI.
- Adding the following tools to the package: `AzureAIDocumentIntelligenceTool`, `AzureAIImageAnalysisTool`, and `AzureAITextAnalyticsHealthTool`. You can also use `AIServicesToolkit` to have access to all the tools in Azure AI Services.
- **0.1.4**:
- Bug fix [#91](https://github.com/langchain-ai/langchain-azure/pull/91).
- **0.1.3**:
- **[Breaking change]:** We renamed the parameter `model_name` in `AzureAIEmbeddingsModel` and `AzureAIChatCompletionsModel` to `model`, which is the parameter expected by the method `langchain.chat_models.init_chat_model`.
- We fixed an issue with JSON mode in chat models [#81](https://github.com/langchain-ai/langchain-azure/issues/81).
- We fixed the dependencies for NumpPy [#70](https://github.com/langchain-ai/langchain-azure/issues/70).
- We fixed an issue when tracing Pyndantic objects in the inputs [#65](https://github.com/langchain-ai/langchain-azure/issues/65).
- We made `connection_string` parameter optional as suggested at [#65](https://github.com/langchain-ai/langchain-azure/issues/65).
- **0.1.2**:
- Bug fix [#35](https://github.com/langchain-ai/langchain-azure/issues/35).
- **0.1.1**:
- Adding `AzureCosmosDBNoSqlVectorSearch` and `AzureCosmosDBNoSqlSemanticCache` for vector search and full text search.
- Adding `AzureCosmosDBMongoVCoreVectorSearch` and `AzureCosmosDBMongoVCoreSemanticCache` for vector search.
- You can now create `AzureAIEmbeddingsModel` and `AzureAIChatCompletionsModel` clients directly from your AI project's connection string using the parameter `project_connection_string`. Your default Azure AI Services connection is used to find the model requested. This requires to have `azure-ai-projects` package installed.
- Support for native LLM structure outputs. Use `with_structured_output(method="json_schema")` to use native structured schema support. Use `with_structured_output(method="json_mode")` to use native JSON outputs capabilities. By default, LangChain uses `method="function_calling"` which uses tool calling capabilities to generate valid structure JSON payloads. This requires to have `azure-ai-inference >= 1.0.0b7`.
- Bug fix [#18](https://github.com/langchain-ai/langchain-azure/issues/18) and [#31](https://github.com/langchain-ai/langchain-azure/issues/31).
- **0.1.0**:
- Introduce `AzureAIEmbeddingsModel` for embedding generation and `AzureAIChatCompletionsModel` for chat completions generation using the Azure AI Inference API. This client also supports GitHub Models endpoint.
- Introduce `AzureAIOpenTelemetryTracer` for tracing with OpenTelemetry and Azure Application Insights.
| text/markdown | null | null | null | null | MIT License
Copyright (c) 2023 LangChain, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. | null | [
"License :: Other/Proprietary License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13"
] | [] | null | null | <4.0,>=3.10.0 | [] | [] | [] | [
"aiohttp<4.0,>=3.10",
"azure-ai-agents==1.2.0b5",
"azure-ai-documentintelligence<2.0.0,>=1.0.2; extra == \"tools\"",
"azure-ai-inference[opentelemetry]<2.0,>=1.0.0b9",
"azure-ai-projects<2.0,>=1.0",
"azure-ai-textanalytics<6.0.0,>=5.3.0; extra == \"tools\"",
"azure-ai-vision-imageanalysis<2.0.0,>=1.0.0; extra == \"tools\"",
"azure-core<2.0,>=1.32",
"azure-cosmos<5.0,>=4.14.0b1",
"azure-identity<2.0,>=1.15",
"azure-mgmt-logic<11.0.0,>=10.0.0; extra == \"tools\"",
"azure-monitor-opentelemetry<2.0,>=1.6; extra == \"opentelemetry\"",
"azure-search-documents<12.0,>=11.4",
"langchain<2.0.0,>=1.0.0",
"langchain-openai<2.0.0,>=1.0.0",
"numpy>=1.26.2; python_version < \"3.13\"",
"numpy>=2.1.0; python_version >= \"3.13\"",
"opentelemetry-api>=1.37; extra == \"opentelemetry\"",
"opentelemetry-instrumentation>=0.58b0; extra == \"opentelemetry\"",
"opentelemetry-instrumentation-threading>=0.58b0; extra == \"opentelemetry\"",
"opentelemetry-semantic-conventions>=0.58b0; extra == \"opentelemetry\"",
"opentelemetry-semantic-conventions-ai<0.5.0,>=0.4.2; extra == \"opentelemetry\"",
"six<2.0.0,>=1.17.0"
] | [] | [] | [] | [
"Repository, https://github.com/langchain-ai/langchain-azure",
"Release Notes, https://github.com/langchain-ai/langchain-azure/releases",
"Source Code, https://github.com/langchain-ai/langchain-azure/tree/main/libs/azure-ai"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:36:06.891310 | langchain_azure_ai-1.0.61.tar.gz | 84,952 | b3/30/475ccfe5e44b13f65b33ab63f66e507bb7390e697945566a8974dc56090b/langchain_azure_ai-1.0.61.tar.gz | source | sdist | null | false | 3e46c4a0b275247e6a1f87c0441c3ed9 | c03c9fa6fbea75ce18c09f1a15a86233dd5b363272704c4434bb8c7331d06b35 | b330475ccfe5e44b13f65b33ab63f66e507bb7390e697945566a8974dc56090b | null | [] | 1,219 |
2.4 | cronico | 0.9.17 | Another YAML-based scheduler | # Cronico
Cronico is a lightweight, YAML-based task scheduler for Unix-like systems.
It lets you define recurring jobs with flexible cron expressions — supporting traditional minute-based syntax, extended formats with seconds, and common shorthand aliases (@daily, @hourly, etc.).
Tasks can include:
- Retry policies with configurable attempts.
- Timeouts to kill long-running processes.
- Environment injection from .env files or inline variables.
- Working directory control per task.
- Streaming or buffered logs for stdout/stderr.
Cronico is designed to run as a long-lived daemon (via systemd or similar) and can reload its configuration on SIGHUP without restarting the process.
```yaml
tasks:
example_task:
description: |
Classic cron expression: every 5 minutes
cron: "*/5 * * * *"
command: "echo 'Hello, World!'"
retry_on_error: true
max_attempts: 3
env_file: ".env"
timeout: 60 # seconds
working_dir: "/path/to/dir"
environment:
MY_VAR: "value"
custom_env:
cron: "*/5 * * * *"
environment:
GREETING: "Hola"
command: |
echo "$GREETING from Bash at $(date)"
every_minute_at_second_10:
description: |
Extended with seconds: every minute, at the 10th second
cron:
minute: "*"
hour: "*"
day: "*"
month: "*"
weekday: "*"
second: 10
command: "echo 'Run at second 10 of every minute'"
every_30_seconds:
description: |
Classic with seconds: every 30 seconds
cron: "*/1 * * * * 0,30"
command: "echo 'This runs at second 0 and 30 of each minute'"
daily_with_seconds:
description: |
Daily at 03:00:15
cron:
minute: 0
hour: 3
day: "*"
month: "*"
weekday: "*"
second: 15
command: "echo 'Daily at 03:00:15'"
shorthand:
description: |
Shorthand: daily, at 00:00
cron: "@daily"
command: |
echo "Supported aliases:"
echo "- @yearly: 0 0 1 1 *"
echo "- @annually: 0 0 1 1 *"
echo "- @monthly: 0 0 1 * *"
echo "- @weekly: 0 0 * * 0"
echo "- @daily: 0 0 * * *"
echo "- @midnight: 0 0 * * *"
echo "- @hourly: 0 * * * *"
with_shebang:
description: |
You can use a shebang to specify the interpreter to use.
cron: "*/10 * * * *"
command: |
#!/usr/bin/env python3
import datetime
print("Hello from Python at", datetime.datetime.now())
another_shebang_example:
description: |
Another one...
cron: "*/10 * * * *"
command: |
#!/usr/bin/env perl
use strict;
use warnings;
my ($sec,$min,$hour) = localtime();
print "Hello from Perl at $hour:$min:$sec\n";
and_another_shebang_example:
description: |
I think you get the idea.
cron: "*/10 * * * *"
command: |
#!/usr/bin/env perl
use strict;
use warnings;
my ($sec,$min,$hour) = localtime();
print "Hello from Perl at $hour:$min:$sec\n";
```
| text/markdown | null | Luis Medel <luis@luismedel.com> | null | null | MIT | null | [
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent"
] | [] | null | null | >=3.8 | [] | [] | [] | [
"PyYAML==6.0.2",
"croniter==6.0.0",
"python-dotenv==1.1.1",
"watchdog==6.0.0",
"mypy==1.18.1; extra == \"dev\"",
"ruff==0.13.0; extra == \"dev\"",
"types-PyYAML==6.0.12.20250822; extra == \"dev\"",
"types-croniter==6.0.0.20250809; extra == \"dev\"",
"build; extra == \"dev\"",
"twine; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/luismedel/cronico",
"Issues, https://github.com/luismedel/cronico/issues"
] | twine/6.2.0 CPython/3.12.12 | 2026-02-20T10:35:09.701119 | cronico-0.9.17.tar.gz | 9,667 | 71/a4/c01c0a008cd8184f82d496f4f494fd246a62f5c21aa45728fc521c3cb1c2/cronico-0.9.17.tar.gz | source | sdist | null | false | b3b5aac7d21d4bfac8311cdd9c5e9255 | 280902a8081083ad448f8c734f9eeb8a62b82a30b12c6f26335c72e3e4ac6d5c | 71a4c01c0a008cd8184f82d496f4f494fd246a62f5c21aa45728fc521c3cb1c2 | null | [
"LICENSE"
] | 235 |
2.4 | nebulascrape | 0.0.1 | NebulaScrape — Ultra-powerful HTTP scraping library with smart bypass, async support, and modular transport. | <div align="center">
# NebulaScrape
<img src="https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-blue?style=for-the-badge&logo=python&logoColor=white" alt="Python Versions">
<img src="https://img.shields.io/badge/version-0.0.1-informational?style=for-the-badge" alt="Version">
<img src="https://img.shields.io/badge/license-MIT-green?style=for-the-badge" alt="License">
<img src="https://img.shields.io/badge/asyncio-supported-blueviolet?style=for-the-badge&logo=python" alt="Async">
<img src="https://img.shields.io/badge/HTTP%2F2-supported-orange?style=for-the-badge" alt="HTTP2">
<img src="https://img.shields.io/badge/TLS-fingerprint%20spoof-red?style=for-the-badge" alt="TLS">
<img src="https://img.shields.io/badge/WAF-bypass%20engine-darkred?style=for-the-badge" alt="WAF">
<img src="https://img.shields.io/badge/maintained%20by-6x--u-black?style=for-the-badge&logo=github" alt="GitHub">
**NebulaScrape** is a production-grade Python HTTP scraping library built for the modern web.
It combines a modular transport system, intelligent session analysis, browser-realistic fingerprinting,
async support, and a powerful WAF bypass engine into a single clean API.
[Installation](#installation) |
[Quick Start](#quick-start) |
[Profiles](#fingerprint-profiles) |
[Retry Engine](#smart-retry-engine) |
[Session Intel](#session-intelligence-layer) |
[Transports](#modular-transport-system) |
[Async](#async-support) |
[Metrics](#built-in-metrics) |
[Plugins](#plugin-system) |
[API Reference](#api-reference)
</div>
---
## Table of Contents
- [Overview](#overview)
- [Installation](#installation)
- [Quick Start](#quick-start)
- [Fingerprint Profiles](#fingerprint-profiles)
- [Smart Retry Engine](#smart-retry-engine)
- [Session Intelligence Layer](#session-intelligence-layer)
- [Modular Transport System](#modular-transport-system)
- [Async Support](#async-support)
- [Built-in Metrics](#built-in-metrics)
- [Plugin System](#plugin-system)
- [WAF Bypass Engine](#waf-bypass-engine)
- [Advanced Usage](#advanced-usage)
- [API Reference](#api-reference)
- [Configuration Reference](#configuration-reference)
---
## Overview
NebulaScrape was designed to solve the hardest problem in modern web scraping: getting a real HTTP response from a server that actively tries to block automated clients.
Most scraping libraries send requests that are trivially identifiable as bots. They have wrong TLS fingerprints, wrong header order, no sec-ch-ua fields, no browser timing patterns, and no ability to recover intelligently from blocks. NebulaScrape was built from the ground up to solve all of these problems at once.
### What Makes NebulaScrape Different
**TLS fingerprint spoofing.** Every modern WAF inspects the TLS ClientHello. NebulaScrape sends the exact cipher suite list, ECDH curve, and TLS extension order that real Chrome 120 sends. A plain requests session sends a fingerprint that gets flagged immediately.
**Ordered, realistic HTTP headers.** Browsers send headers in a specific order that WAFs check. NebulaScrape uses OrderedDict-based profiles that match real browser traffic captures, including the correct sec-ch-ua, sec-ch-ua-mobile, sec-ch-ua-platform, Sec-Fetch-* fields.
**Smart retry decisions.** When a request fails with 403, 429, or 503, NebulaScrape does not blindly retry. It analyzes the response, reads the Retry-After header, calculates exponential backoff with jitter, decides whether to rotate the session fingerprint or rebuild the connection, and escalates to a more capable transport if needed.
**Session intelligence.** Every response is analyzed for WAF vendor signatures. The library tells you whether you hit a Cloudflare IUAM page, a DataDome challenge, a PerimeterX block, rate limiting, or a redirect loop, and attaches a risk score from 0 to 100 to each response.
**Transport escalation.** Under high-protection targets, NebulaScrape automatically escalates from standard HTTP/1.1 with TLS spoofing, to HTTP/2 via httpx, to full browser impersonation via curl_cffi. No code change required.
**Async-first.** NebulaScraper ships with a native asyncio client that supports all the same features, including retry logic, intelligence analysis, metrics, and plugins.
---
## Installation
**Minimum requirements:** Python 3.8+
Install the core library:
```bash
pip install nebulascrape
```
Install with headless browser impersonation support (required for Cloudflare Turnstile, Managed Challenge, and the most aggressive WAF protections):
```bash
pip install "nebulascrape[headless]"
```
Install from source:
```bash
git clone https://github.com/6x-u/nebulascrape.git
cd nebulascrape
pip install -e .
```
### Dependencies
| Package | Purpose | Required |
|---|---|---|
| `requests` >= 2.9.2 | Base HTTP transport | Yes |
| `requests_toolbelt` >= 0.9.1 | Request debugging | Yes |
| `pyparsing` >= 2.4.7 | JS challenge parsing | Yes |
| `httpx[http2]` >= 0.24.0 | HTTP/2 transport | Yes |
| `h2` >= 4.0.0 | HTTP/2 protocol | Yes |
| `aiohttp` >= 3.8.0 | Async fallback transport | Yes |
| `curl_cffi` >= 0.5.0 | Browser impersonation | Optional (headless) |
| `brotli` >= 1.0.9 | Brotli decompression | Optional |
---
## Quick Start
The simplest way to use NebulaScrape is through the `Client` class. It handles everything internally.
```python
from nebulascrape import Client
client = Client(profile="chrome_windows", auto_retry=True)
response = client.get("https://target.com")
print(response.status_code)
print(response.meta["challenge_type"])
print(response.meta["risk_score"])
print(response.meta["metrics"]["latency_ms"])
```
For full session control, use `NebulaScraper` directly:
```python
from nebulascrape import NebulaScraper
scraper = NebulaScraper(
profile="chrome_windows",
auto_retry=True,
max_retries=5,
mode="auto",
interpreter="native",
debug=False,
)
response = scraper.get("https://target.com")
print(response.meta)
```
For token extraction:
```python
from nebulascrape import get_tokens
tokens, user_agent = get_tokens("https://target.com")
print(tokens)
print(user_agent)
```
---
## Fingerprint Profiles
NebulaScrape ships with four pre-built browser fingerprint profiles. Each profile contains a real User-Agent string, browser-realistic headers in the correct order, a matching TLS cipher suite list, and the correct ECDH curve.
| Profile Name | Browser | Platform | sec-ch-ua-mobile |
|---|---|---|---|
| `chrome_windows` | Chrome 120 | Windows 10 x64 | false |
| `chrome_linux` | Chrome 120 | Linux x86_64 | false |
| `firefox` | Firefox 121 | Windows 10 | N/A |
| `mobile` | Chrome 120 | Android 13 | true |
### Using a Profile
```python
from nebulascrape import Client
# Use any built-in profile
client = Client(profile="chrome_linux")
client = Client(profile="firefox")
client = Client(profile="mobile")
```
### Inspecting a Profile
```python
from nebulascrape.fingerprints import get_profile, available_profiles
print(available_profiles())
# ['chrome_windows', 'chrome_linux', 'firefox', 'mobile']
profile = get_profile("chrome_windows")
print(profile["user_agent"])
print(profile["headers"])
print(profile["cipher_suite"])
```
### Why Header Order Matters
A standard requests session sends headers in an arbitrary order. Real browsers always send headers in a fixed, browser-specific order. WAFs such as Datadome and Kasada inspect header order as a primary bot signal. NebulaScrape uses `OrderedDict` to enforce the correct order for every profile:
```
User-Agent
Accept
Accept-Language
Accept-Encoding
sec-ch-ua
sec-ch-ua-mobile
sec-ch-ua-platform
Upgrade-Insecure-Requests
Sec-Fetch-Dest
Sec-Fetch-Mode
Sec-Fetch-Site
Sec-Fetch-User
```
This matches the exact order captured from a real Chrome 120 browser session.
---
## Smart Retry Engine
The `SmartRetryEngine` replaces naive retry loops with a response-aware retry decision system. It analyzes each failed response and decides the appropriate action based on the error type, attempt count, and session intelligence.
### How It Works
Every response is passed through `analyze_response()`, which returns a `RetryDecision` containing:
- `action` — what to do next (pass, wait and retry, rotate session, rebuild connection, switch transport, or abort)
- `backoff_seconds` — how long to wait before retrying
- `rotate_session` — whether to change the User-Agent and fingerprint
- `rebuild_connection` — whether to tear down and rebuild the connection pool
- `switch_transport` — whether to escalate to a higher-tier transport
### Per-Status Logic
**HTTP 403 Forbidden**
Indicates fingerprint detection or IP block. The engine rotates the browser fingerprint and session identity, waits a random jitter interval between 2 and 8 seconds to simulate human behavior, and rebuilds the connection on the third attempt to clear any connection-level state the server may be tracking.
**HTTP 429 Too Many Requests**
Indicates rate limiting. The engine first reads the `Retry-After` response header and uses that value if present, adding a small random jitter. If no header is present, it calculates exponential backoff: `1.5 * 2^attempt` seconds, capped at 120 seconds. Session rotation activates from the second attempt onward.
**HTTP 503 Service Unavailable**
Indicates the connection itself may be flagged. The engine rebuilds the connection pool immediately and escalates to a higher transport tier from the second attempt onward.
**HTTP 407, 408, 502, 504, 52x**
Treated as transient infrastructure errors. Exponential backoff applies, capped at 90 seconds.
**Intelligence-driven retry**
If `SessionIntelligence` detects a challenge or high-risk response (even on a 200), the retry engine uses the intel result to decide whether to rotate, switch transport, or escalate.
### Configuration
```python
from nebulascrape import Client
client = Client(
auto_retry=True,
max_retries=7, # default is 5
)
```
```python
from nebulascrape.retry_engine import SmartRetryEngine
engine = SmartRetryEngine(max_retries=10, base_backoff=2.0)
```
---
## Session Intelligence Layer
The `SessionIntelligence` class analyzes every response and classifies the WAF vendor and challenge type. This information is attached to `response.meta` on every request.
### Challenge Types
| Challenge Type | Description |
|---|---|
| `none` | Clean response, no challenge detected |
| `cf_iuam` | Cloudflare I'm Under Attack Mode (v1 JS challenge) |
| `cf_captcha` | Cloudflare hCaptcha / reCaptcha challenge |
| `cf_turnstile` | Cloudflare Turnstile (v3 challenge) |
| `cf_managed` | Cloudflare Managed Challenge |
| `cf_block_1020` | Cloudflare firewall rule block (error 1020) |
| `datadome` | DataDome bot detection challenge |
| `perimeterx` | PerimeterX / HUMAN Security block |
| `kasada` | Kasada protection challenge |
| `akamai` | Akamai Bot Manager challenge |
| `imperva` | Imperva / Incapsula protection |
| `shape` | F5 Shape Security protection |
| `rate_limited` | Generic rate limiting (429 or Retry-After header) |
| `js_required` | Page requires JavaScript execution |
| `redirect_loop` | Detected circular redirect chain |
### Risk Score
The risk score is an integer from 0 to 100 representing how likely the response represents a blocking or detection event:
| Score Range | Interpretation |
|---|---|
| 0 | Clean response |
| 1-30 | WAF present but not triggered |
| 31-60 | Rate limiting or soft block |
| 61-80 | Active JS or captcha challenge |
| 81-100 | Hard block, firewall, or advanced WAF challenge |
### Reading the Meta
```python
from nebulascrape import Client
client = Client(profile="chrome_windows", auto_retry=True)
response = client.get("https://target.com")
print(response.meta["challenge_type"]) # "cf_iuam" / "datadome" / "none" / ...
print(response.meta["risk_score"]) # 0 - 100
print(response.meta["waf_vendor"]) # "cloudflare" / "akamai" / "none" / ...
print(response.meta["retry_recommended"])
print(response.meta["rotate_session"])
print(response.meta["details"]) # {"retry_after": None, "cf_ray": "...", "status_code": 200}
```
### Direct Usage
```python
from nebulascrape.session_intel import SessionIntelligence
import requests
resp = requests.get("https://some-protected-site.com")
intel = SessionIntelligence()
result = intel.analyze(resp)
print(result.challenge_type)
print(result.risk_score)
print(result.waf_vendor)
```
---
## Modular Transport System
NebulaScrape uses a three-tier transport system. Each tier provides a higher level of browser mimicry. The `TransportManager` can automatically escalate through tiers when lower tiers accumulate failures.
### Transport Tiers
**Tier 1 — TransportHTTP**
Standard HTTPS over HTTP/1.1 with TLS fingerprint spoofing. Uses a custom `HTTPAdapter` that builds an SSL context with the exact cipher suite list, ECDH curve, and TLS version range from the selected fingerprint profile. This matches the JA3/JA4 fingerprint of real Chrome or Firefox and passes most WAF TLS fingerprint checks.
**Tier 2 — TransportHTTP2**
HTTP/2 transport backed by `httpx`. Sends the correct SETTINGS frame, WINDOW_UPDATE values, and pseudo-header order (`:method :authority :scheme :path`) that match real Chrome HTTP/2 fingerprints. Many sites block HTTP/1.1 clients that cannot negotiate HTTP/2.
**Tier 3 — TransportHeadless**
Full browser impersonation using `curl_cffi`. This sends traffic that is byte-for-byte indistinguishable from the target browser at the TLS and HTTP/2 layers using libcurl compiled with BoringSSL. Used as a last resort for Cloudflare Turnstile, Managed Challenge, Kasada, and similar advanced protections.
### Modes
```python
from nebulascrape import Client
# Auto: starts at HTTP1, escalates to HTTP2, then Headless on repeated failures
client = Client(mode="auto")
# Force a specific transport
client = Client(mode="http1")
client = Client(mode="http2")
client = Client(mode="headless")
```
### Manual Transport Control
```python
from nebulascrape.transports import TransportManager, TransportHTTP, TransportHTTP2, TransportHeadless
manager = TransportManager(profile_name="chrome_windows", mode="auto")
manager.mount_on(scraper_session)
manager.escalate(scraper_session) # manually escalate one tier
manager.rebuild(scraper_session) # rebuild the current transport
```
---
## Async Support
NebulaScrape provides a native asyncio client through `AsyncNebulaScraper` (also exported as `AsyncClient`). It supports the same profile system, retry engine, session intelligence, metrics, and plugin hooks as the synchronous client.
The async client uses `httpx.AsyncClient` with HTTP/2 enabled as its primary backend, and falls back to `aiohttp` if httpx is not available.
### Basic Async Usage
```python
import asyncio
from nebulascrape import AsyncClient
async def main():
client = AsyncClient(profile="chrome_windows", auto_retry=True)
response = await client.get("https://target.com")
print(response.status_code)
print(response.meta)
await client.close()
asyncio.run(main())
```
### Context Manager
```python
import asyncio
from nebulascrape import AsyncClient
async def main():
async with AsyncClient(profile="chrome_linux", auto_retry=True, max_retries=5) as client:
r1 = await client.get("https://httpbin.org/get")
r2 = await client.post("https://httpbin.org/post", json={"key": "value"})
print(r1.status_code, r2.status_code)
asyncio.run(main())
```
### Concurrent Requests
```python
import asyncio
from nebulascrape import AsyncClient
async def fetch(client, url):
r = await client.get(url)
return r.status_code, r.meta["risk_score"]
async def main():
async with AsyncClient(profile="chrome_windows") as client:
urls = [
"https://httpbin.org/get",
"https://httpbin.org/headers",
"https://httpbin.org/ip",
]
results = await asyncio.gather(*[fetch(client, u) for u in urls])
for status, risk in results:
print(f"Status: {status} Risk: {risk}")
asyncio.run(main())
```
---
## Built-in Metrics
Every response returned by NebulaScrape contains a `meta["metrics"]` dictionary with timing and retry information collected during the request lifecycle.
### Per-Request Metrics
| Field | Type | Description |
|---|---|---|
| `latency_ms` | float | Total request duration in milliseconds |
| `tls_handshake_ms` | float | Approximate TLS handshake time in milliseconds |
| `retry_count` | int | Number of retries made for this request |
| `redirect_depth` | int | Number of redirects followed |
| `transport_used` | str | Which transport tier was active (`http1`, `http2`, `async_http2`) |
### Reading Metrics
```python
from nebulascrape import Client
client = Client(profile="chrome_windows", auto_retry=True)
response = client.get("https://httpbin.org/get")
m = response.meta["metrics"]
print(f"Latency: {m['latency_ms']} ms")
print(f"Handshake: {m['tls_handshake_ms']} ms")
print(f"Retries: {m['retry_count']}")
print(f"Redirects: {m['redirect_depth']}")
print(f"Transport: {m['transport_used']}")
```
### Session-Level Aggregate Metrics
```python
from nebulascrape import Client
client = Client(profile="chrome_windows")
for url in ["https://httpbin.org/get", "https://httpbin.org/headers"]:
client.get(url)
stats = client.metrics
print(f"Total requests: {stats['total_requests']}")
print(f"Average latency: {stats['avg_latency_ms']} ms")
print(f"Max latency: {stats['max_latency_ms']} ms")
print(f"Total retries: {stats['total_retries']}")
print(f"Challenges solved: {stats['challenges_solved']}")
```
---
## Plugin System
NebulaScrape includes a plugin registry that allows you to attach custom behavior to the request lifecycle without modifying the core library. All plugins inherit from `BasePlugin` and can hook into pre-request, post-request, challenge detection, and retry events.
### Built-in Plugins
**RateLimitPlugin**
Adds adaptive pre-request delays based on request rate. Detects burst patterns and automatically increases delays. Respects Retry-After headers on 429 responses.
```python
from nebulascrape import Client
from nebulascrape.plugins.rate_limit_handler import RateLimitPlugin
client = Client(profile="chrome_windows")
client.register_plugin(RateLimitPlugin(
min_delay=0.3,
max_delay=2.0,
burst_threshold=10,
))
```
**HeaderOptimizerPlugin**
Ensures browser-realistic headers are applied to every request, merging them with any user-supplied headers while preserving the correct order. Adjusts Sec-Fetch headers automatically for POST requests.
```python
from nebulascrape import Client
from nebulascrape.plugins.header_optimizer import HeaderOptimizerPlugin
client = Client(profile="chrome_windows")
client.register_plugin(HeaderOptimizerPlugin(profile_name="chrome_windows"))
```
**ProxyManagerPlugin**
Manages a pool of proxy servers with automatic rotation on failure. Tracks per-proxy failure counts and rotates after two consecutive failures on the same proxy.
```python
from nebulascrape import Client
from nebulascrape.plugins.proxy_manager import ProxyManagerPlugin
proxies = [
"http://user:pass@proxy1:8080",
"http://user:pass@proxy2:8080",
"http://user:pass@proxy3:8080",
]
client = Client(profile="chrome_windows")
client.register_plugin(ProxyManagerPlugin(
proxies=proxies,
rotate_on_fail=True,
rotate_on_status=[403, 429, 503],
))
```
### Writing a Custom Plugin
```python
from nebulascrape.plugins import BasePlugin
from nebulascrape import Client
class LoggingPlugin(BasePlugin):
name = "logging_plugin"
priority = 5 # lower number = runs first
def on_pre_request(self, scraper, method, url, kwargs):
print(f"REQUEST {method} {url}")
return kwargs
def on_post_request(self, scraper, response, kwargs):
print(f"RESPONSE {response.status_code} - risk={response.meta.get('risk_score', 'n/a')}")
return response
def on_retry(self, scraper, attempt, decision):
print(f"RETRY {attempt} - reason: {decision.reason} - waiting {decision.backoff_seconds:.1f}s")
client = Client(profile="chrome_windows", auto_retry=True)
client.register_plugin(LoggingPlugin())
response = client.get("https://httpbin.org/get")
```
### Plugin Hook Reference
| Hook | When it runs | Return value |
|---|---|---|
| `on_pre_request(scraper, method, url, kwargs)` | Before every request | Modified kwargs dict |
| `on_post_request(scraper, response, kwargs)` | After every response | response object |
| `on_challenge_detected(scraper, response, intel_result)` | When a challenge is found | bool |
| `on_retry(scraper, attempt, decision)` | Before each retry sleep | None |
---
## WAF Bypass Engine
NebulaScrape's bypass capabilities are integrated across multiple layers of the library. There is no single "bypass" function. Instead, bypass is the result of the fingerprint, transport, intelligence, and retry systems working together.
### Cloudflare
**I'm Under Attack Mode (v1)**
Detected by inspecting the response body for the characteristic jsch trace image and challenge form. The library extracts the challenge parameters, waits a browser-realistic delay (parsed from the page's own JavaScript, with jitter added), solves the JavaScript challenge using the native interpreter, submits the solution as a POST request, and follows the redirect to retrieve the real page. The `cf_clearance` cookie is then retained in the session for future requests.
**Turnstile**
Detected by looking for `cf-turnstile` or `challenges.cloudflare.com/turnstile` in the response. When this challenge is detected, the library raises `TurnstileChallengeError` and recommends using `TransportHeadless` with `curl_cffi`, which passes the Turnstile check at the TLS and HTTP/2 fingerprint layer without requiring a browser.
**Managed Challenge and v2**
Detected by inspecting the CDN CGI orchestration endpoint pattern. Escalation to the headless transport is recommended.
**Cloudflare Firewall 1020**
Detected and raised as `CloudflareCode1020`. This is an IP-level block that requires a proxy rotation.
### Multi-WAF Detection
The `SessionIntelligence` layer detects the following vendors using header and body signature matching:
| WAF | Detection Method |
|---|---|
| Cloudflare | `Server: cloudflare` header + body patterns |
| DataDome | `dd_sitekey`, `datadome.co` cookie domains |
| PerimeterX | `_pxdk` cookie, `PerimeterX` body references |
| Kasada | `kasada`, `kpsdk` body references |
| Akamai | `_abck`, `ak_bmsc` cookies, sensor_data |
| Imperva | `incap_ses_`, `visid_incap_` cookies |
| Shape Security | `shape.io`, `x-shape-` headers |
### TLS Fingerprint Spoofing
Standard Python `ssl` sends a TLS fingerprint (JA3) that is trivially identifiable as a non-browser client. NebulaScrape replaces the default SSL context with one that:
- Sets the cipher suite list to match Chrome 120's exact order
- Sets the ECDH curve to `prime256v1`
- Sets TLS minimum version to TLS 1.2 and maximum to TLS 1.3
- Preserves the correct TLS extension set
This produces a JA3 fingerprint that matches a real Chrome browser.
---
## Advanced Usage
### All Options Together
```python
from nebulascrape import Client
from nebulascrape.plugins.rate_limit_handler import RateLimitPlugin
from nebulascrape.plugins.proxy_manager import ProxyManagerPlugin
from nebulascrape.plugins.header_optimizer import HeaderOptimizerPlugin
client = Client(
profile="chrome_windows",
auto_retry=True,
max_retries=7,
mode="auto",
interpreter="native",
debug=False,
)
client.register_plugin(RateLimitPlugin(min_delay=0.5, max_delay=3.0))
client.register_plugin(HeaderOptimizerPlugin(profile_name="chrome_windows"))
client.register_plugin(ProxyManagerPlugin(proxies=["http://proxy1:8080"]))
response = client.get("https://target.com", timeout=30)
print("Status: ", response.status_code)
print("Challenge: ", response.meta["challenge_type"])
print("Risk: ", response.meta["risk_score"])
print("WAF: ", response.meta["waf_vendor"])
print("Latency: ", response.meta["metrics"]["latency_ms"], "ms")
print("Retries: ", response.meta["metrics"]["retry_count"])
```
### Using the Low-Level NebulaScraper
```python
from nebulascrape import NebulaScraper
scraper = NebulaScraper(
browser={"browser": "chrome", "platform": "windows", "desktop": True},
auto_retry=True,
max_retries=5,
mode="auto",
captcha={"provider": "2captcha", "api_key": "YOUR_KEY"},
solveDepth=3,
doubleDown=True,
delay=None,
)
response = scraper.get("https://target.com")
cookies = response.cookies
tokens = scraper.cookies.get("cf_clearance")
```
### Passing Cookies or Proxies
```python
from nebulascrape import Client
client = Client(profile="chrome_windows")
# Proxies
response = client.get("https://target.com", proxies={
"http": "http://proxy:8080",
"https": "http://proxy:8080",
})
# Custom cookies
response = client.get("https://target.com", cookies={
"session_id": "abc123",
})
# Custom headers (merged with profile headers)
response = client.get("https://target.com", headers={
"Referer": "https://google.com",
"X-Custom-Header": "value",
})
```
### Integrating with Existing Sessions
```python
import requests
from nebulascrape import NebulaScraper
existing_session = requests.Session()
existing_session.headers.update({"Authorization": "Bearer token123"})
scraper = NebulaScraper.create_scraper(
sess=existing_session,
profile="chrome_linux",
auto_retry=True,
)
response = scraper.get("https://api.target.com/data")
```
### Captcha Integration
```python
from nebulascrape import Client
client = Client(
profile="chrome_windows",
captcha={
"provider": "2captcha",
"api_key": "YOUR_2CAPTCHA_KEY",
}
)
response = client.get("https://cloudflare-captcha-site.com")
```
Supported captcha providers: `2captcha`, `anticaptcha`, `capmonster`, `capsolver`, `9kw`, `deathbycaptcha`.
### Getting Cloudflare Tokens
```python
from nebulascrape import get_tokens, get_cookie_string
tokens, user_agent = get_tokens("https://cloudflare-protected-site.com")
print("cf_clearance:", tokens["cf_clearance"])
print("User-Agent: ", user_agent)
cookie_string, user_agent = get_cookie_string("https://cloudflare-protected-site.com")
print("Cookie:", cookie_string)
```
---
## API Reference
### `Client`
```
Client(
profile="chrome_windows",
auto_retry=True,
max_retries=5,
mode="auto",
captcha={},
interpreter="native",
debug=False,
**kwargs
)
```
| Parameter | Type | Default | Description |
|---|---|---|---|
| `profile` | str | `chrome_windows` | Fingerprint profile to use |
| `auto_retry` | bool | `True` | Enable smart retry engine |
| `max_retries` | int | `5` | Maximum retry attempts |
| `mode` | str | `auto` | Transport mode (`auto`, `http1`, `http2`, `headless`) |
| `captcha` | dict | `{}` | Captcha provider configuration |
| `interpreter` | str | `native` | JS interpreter for challenge solving |
| `debug` | bool | `False` | Enable request/response debugging output |
**Methods:** `get(url, **kwargs)`, `post(url, **kwargs)`, `put(url, **kwargs)`, `delete(url, **kwargs)`, `request(method, url, **kwargs)`, `register_plugin(plugin)`, `session` (property), `metrics` (property)
---
### `AsyncClient` / `AsyncNebulaScraper`
```
AsyncClient(
profile="chrome_windows",
auto_retry=True,
max_retries=5,
debug=False,
**kwargs
)
```
**Methods:** `await get(url, **kwargs)`, `await post(url, **kwargs)`, `await put(url, **kwargs)`, `await delete(url, **kwargs)`, `await request(method, url, **kwargs)`, `register_plugin(plugin)`, `await close()`, supports `async with`.
---
### `NebulaScraper`
Extends `requests.Session`. All `requests.Session` methods are available.
Additional parameters on top of Client:
| Parameter | Type | Default | Description |
|---|---|---|---|
| `browser` | dict or None | None | Browser dict with keys `browser`, `platform`, `desktop`, `mobile` |
| `solveDepth` | int | `3` | Maximum Cloudflare challenge solve loops |
| `doubleDown` | bool | `True` | Double request on captcha to check if cfuid is enough |
| `delay` | float or None | None | Manual Cloudflare challenge delay in seconds |
| `disableCloudflareV1` | bool | `False` | Disable built-in Cloudflare v1 bypass |
| `requestPreHook` | callable | None | Function called before each request |
| `requestPostHook` | callable | None | Function called after each response |
| `source_address` | str or tuple | None | Bind to a specific local IP |
| `ssl_context` | ssl.SSLContext | None | Custom SSL context |
---
### `response.meta` Fields
| Field | Type | Description |
|---|---|---|
| `challenge_type` | str | Detected challenge type (see challenge type table) |
| `waf_vendor` | str | Detected WAF vendor |
| `risk_score` | int | Risk score 0-100 |
| `retry_recommended` | bool | Whether retry is suggested |
| `rotate_session` | bool | Whether session rotation is suggested |
| `switch_transport` | bool | Whether transport escalation is suggested |
| `details` | dict | Raw details: retry_after, cf_ray, status_code |
| `metrics` | dict | latency_ms, tls_handshake_ms, retry_count, redirect_depth, transport_used |
---
## Configuration Reference
### Fingerprint Profiles
| Profile | User-Agent snippet | Platform |
|---|---|---|
| `chrome_windows` | Chrome/120.0.0.0 ... Windows NT 10.0 | Windows |
| `chrome_linux` | Chrome/120.0.0.0 ... X11; Linux x86_64 | Linux |
| `firefox` | Firefox/121.0 ... Windows NT 10.0 | Windows |
| `mobile` | Chrome/120.0.6099.144 Mobile ... Android 13 | Android |
### Transport Modes
| Mode | Backend | HTTP Version | TLS Spoof | Impersonation Level |
|---|---|---|---|---|
| `http1` | requests | HTTP/1.1 | JA3 cipher suite | High |
| `http2` | httpx | HTTP/2 | JA3 + H2 SETTINGS | Very High |
| `headless` | curl_cffi | HTTP/2 | Full BoringSSL | Maximum |
| `auto` | escalating | depends | depends | Adaptive |
### JavaScript Interpreters
| Interpreter | Requirement | Description |
|---|---|---|
| `native` | None (built-in) | Pure Python JS evaluation for simple challenges |
| `js2py` | `pip install js2py` | Full JavaScript runtime |
| `nodejs` | Node.js installed | Executes via Node.js subprocess |
| `chakracore` | ChakraCore binary | Microsoft JS engine |
| `v8` | V8 binary | Google V8 JS engine |
---
## Author
| Field | Value |
|---|---|
| Developer | MERO |
| Contact | TG@QP4M |
| GitHub | [github.com/6x-u](https://github.com/6x-u) |
| License | MIT |
| text/markdown | MERO | mero@ps.com | null | null | null | nebulascrape, cloudflare, scraping, ddos, scrape, webscraper, anti-bot, waf, bypass, challenge, akamai, datadome, perimeterx, kasada, async, fingerprint, tls, http2 | [
"Development Status :: 3 - Alpha",
"Intended Audience :: Developers",
"Natural Language :: English",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Topic :: Internet :: WWW/HTTP",
"Topic :: Software Development :: Libraries :: Python Modules"
] | [] | https://github.com/MERO/nebulascrape | null | null | [] | [] | [] | [
"requests>=2.9.2",
"requests_toolbelt>=0.9.1",
"pyparsing>=2.4.7",
"httpx[http2]>=0.24.0",
"h2>=4.0.0",
"aiohttp>=3.8.0",
"curl_cffi>=0.5.0; extra == \"headless\"",
"brotli>=1.0.9; extra == \"brotli\""
] | [] | [] | [] | [] | twine/6.2.0 CPython/3.12.0 | 2026-02-20T10:34:43.513256 | nebulascrape-0.0.1.tar.gz | 118,518 | ac/60/3b377da005a3e051a78b61bc3e2ceff28a6bc956106f5b8101b079067709/nebulascrape-0.0.1.tar.gz | source | sdist | null | false | cf5f654cb7eb077a4ec067b05ea8abdd | 36e9526f21d3d09bdfc47006911452de637cdd0d689fe69efe8febf57b9892b0 | ac603b377da005a3e051a78b61bc3e2ceff28a6bc956106f5b8101b079067709 | null | [
"LICENSE"
] | 252 |
2.3 | SimpleDomControl | 0.58.6 | Simple DOM control -> a django MVC framework | # Simple Dom Control (SDC)
SDC is a framework that combines elements of both the MVC (Model-View-Controller) and MVT (Model-View-Template) patterns. Its main goal is to provide a tool for simple and efficient web development. The name ‘SDC’ is an abbreviation for ‘Simple DOM Control,’ indicating its focus on controlling and manipulating the Document Object Model (DOM), which is crucial for web development, particularly in dynamic web pages.
For detailed docs go to [rtd](https://simpledomcontrol.readthedocs.io/en/latest/) | text/markdown | Martin Starman | private@martin-starman.com | null | null | Apache-2.0 | django, MVC | [
"Environment :: Web Environment",
"License :: OSI Approved :: Apache Software License",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.13",
"Topic :: Software Development :: Libraries :: Application Frameworks",
"Topic :: Software Development :: Libraries :: Python Modules"
] | [] | https://github.com/StarmanMartin/sdc | null | <4.0,>=3.13 | [] | [] | [] | [
"daphne<5.0.0,>=4.2.1",
"channels<5.0.0,>=4.3.2",
"channels-redis<5.0.0,>=4.3.0",
"django<7.0.0,>=6.0.2",
"psycopg2-binary<3.0.0,>=2.9.11",
"attrs<26.0.0,>=25.4.0",
"regex<2027.0.0,>=2026.1.15",
"click>=8.1.0",
"pyjwt<3.0.0,>=2.11.0",
"inquirerpy<0.4.0,>=0.3.4",
"poetry-core<3.0.0,>=2.3.1"
] | [] | [] | [] | [
"Homepage, https://github.com/StarmanMartin/sdc"
] | poetry/2.1.1 CPython/3.13.2 Linux/6.17.0-14-generic | 2026-02-20T10:33:11.032771 | simpledomcontrol-0.58.6.tar.gz | 164,499 | cc/36/1d2fcba962d2f2487e985f84a699f8ab35ea12b8bb5d7eb15d722894bb25/simpledomcontrol-0.58.6.tar.gz | source | sdist | null | false | a9a0215506d1850d3404c1717f38bacf | a144c98e3ef88ef8b140a71afe2c52584c44d05caa83a054d5591476f30c1061 | cc361d2fcba962d2f2487e985f84a699f8ab35ea12b8bb5d7eb15d722894bb25 | null | [] | 0 |
2.4 | custom-llm-eval | 0.1.5 | A comprehensive framework for evaluating Large Language Models with built-in support for bias, toxicity, relevancy metrics, custom evaluations, conversational test cases, and token counting | # Custom LLM Eval
A comprehensive framework for evaluating Large Language Models with built-in support for bias, toxicity, relevancy metrics, custom evaluations, conversational test cases, and automatic token counting. Built on top of DeepEval with automatic database saving and dashboard visualization.
## Features
- **Multiple Evaluation Metrics**:
- Bias Detection
- Toxicity Analysis
- Answer Relevancy
- Faithfulness & Hallucination Detection
- Contextual Precision, Recall, and Relevancy
- Summarization Quality
- Custom Evaluations (using GEval)
- Multi-turn Conversational Evaluations
- Role Adherence
- Knowledge Retention
- Conversation Completeness
- Goal Accuracy
- **Token Counting**: Automatic token calculation using tiktoken for GPT-4o model
- **Database Integration**: Automatic saving of test cases and evaluation results to a database via REST API
- **Test Case Management**: Create, store, and retrieve test cases
- **Dashboard Ready**: Structured data output for visualization dashboards
## Installation
```bash
pip install custom-llm-eval
```
## Quick Start
```python
from custom_llm_eval import LLMEvaluator
from deepeval.models import GeminiModel
from deepeval.test_case import LLMTestCase
# Initialize evaluator with your LLM model
model = GeminiModel(model="gemini-2.0-flash-exp")
evaluator = LLMEvaluator(
llm=model,
test_suite_name="My Test Suite",
cluster_name="production"
)
# Create a test case
test_case = LLMTestCase(
input="What is machine learning?",
actual_output="Machine learning is a subset of AI that enables systems to learn from data."
)
# Run evaluations
bias_result = evaluator.evaluate_bias(test_case, threshold=0.5)
toxicity_result = evaluator.evaluate_toxicity(test_case, threshold=0.5)
relevancy_result = evaluator.evaluate_answer_relevancy(test_case, threshold=0.7)
print(f"Bias Score: {bias_result['score']}, Passed: {bias_result['passed']}")
print(f"Toxicity Score: {toxicity_result['score']}, Passed: {toxicity_result['passed']}")
print(f"Relevancy Score: {relevancy_result['score']}, Passed: {relevancy_result['passed']}")
```
## Advanced Usage
### Custom Evaluations
```python
from deepeval.test_case import LLMTestCaseParams
# Define custom evaluation criteria
result = evaluator.custom_eval(
name="Code Quality",
test_case=test_case,
criteria="Evaluate the code for readability, efficiency, and best practices",
evaluation_params=[LLMTestCaseParams.INPUT, LLMTestCaseParams.ACTUAL_OUTPUT],
threshold=0.7
)
```
### Multi-turn Conversational Evaluations
```python
from deepeval.test_case import ConversationalTestCase, Turn
# Create conversational test case
conv_test_case = ConversationalTestCase(
turns=[
Turn(role="user", content="Hello!"),
Turn(role="assistant", content="Hi! How can I help you?"),
Turn(role="user", content="Tell me about AI"),
Turn(role="assistant", content="AI stands for Artificial Intelligence...")
],
scenario="Customer support conversation"
)
# Evaluate conversation - supports multiple metrics
turn_relevancy = evaluator.multiturn_evaluate_turn_relevancy(conv_test_case, threshold=0.7)
role_adherence = evaluator.multiturn_evaluate_role_adherence(conv_test_case, threshold=0.7)
knowledge_retention = evaluator.multiturn_evaluate_knowledge_retention(conv_test_case, threshold=0.7)
# Custom conversational evaluation
result = evaluator.multiturn_custom_eval(
name="Conversation Quality",
test_case=conv_test_case,
criteria="Evaluate helpfulness, coherence, and professionalism",
threshold=0.7
)
```
### Test Case Management
```python
# Create and save a test case
evaluator.create_test_case(
name="tc_bias_001",
input_text="What do you think about people from different countries?",
actual_output="People from all countries are unique individuals...",
eval_type="bias",
description="Test for geographical bias"
)
# Retrieve test case later
test_case = evaluator.get_test_case(name="tc_bias_001")
```
### Token Counting
All evaluations automatically calculate and save input and output token counts using tiktoken with GPT-4o encoding:
```python
result = evaluator.evaluate_bias(test_case, threshold=0.5)
# Result includes token counts in database when save_to_db=True
```
## Configuration
### Environment Variables
Create a `.env` file:
```env
API_BASE_URL=http://localhost:8000
GEMINI_API_KEY=your_api_key_here
```
### Database Integration
The evaluator automatically saves results to a database via REST API. To disable:
```python
evaluator = LLMEvaluator(
llm=model,
save_to_db=False # Disable database saving
)
```
### Timeout Configuration
Set custom timeout for evaluations (default: 300 seconds):
```python
evaluator = LLMEvaluator(
llm=model,
timeout_seconds=600 # 10 minutes timeout
)
```
## Supported Models
Works with any DeepEval-compatible model:
- OpenAI (GPT-3.5, GPT-4, GPT-4o, etc.)
- Google Gemini
- Anthropic Claude
- Cohere
- Custom models
## Requirements
- Python >= 3.8
- deepeval >= 0.21.0
- requests >= 2.28.0
- python-dotenv >= 0.19.0
- tiktoken >= 0.5.0
## What's New in 0.1.4
- **Token Counting**: Automatic token calculation using tiktoken for GPT-4o model
- **Improved Multi-turn Handling**: Separate user turns and agent responses in conversational evaluations
- **Enhanced Data Storage**: Token counts automatically saved to database for cost tracking
## License
MIT License - see LICENSE file for details
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## Issues
Report issues at: https://github.com/atulbmysuru/custom-llm-eval/issues
| text/markdown | Atul B | atulbmysuru@gmail.com | null | null | MIT | llm, evaluation, deepeval, ai, testing, bias, toxicity, nlp, token-counting | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Topic :: Software Development :: Testing"
] | [] | null | null | >=3.8 | [] | [] | [] | [
"deepeval>=0.21.0",
"requests>=2.28.0",
"python-dotenv>=0.19.0",
"tiktoken>=0.5.0"
] | [] | [] | [] | [
"Homepage, https://github.com/atulbmysuru/custom-llm-eval",
"Issues, https://github.com/atulbmysuru/custom-llm-eval/issues",
"Repository, https://github.com/atulbmysuru/custom-llm-eval"
] | twine/6.2.0 CPython/3.11.14 | 2026-02-20T10:33:01.866537 | custom_llm_eval-0.1.5.tar.gz | 15,900 | 60/8f/578e783a78f9304a022f0a0699c86b4e66435e8c274565345dc8e5076cc8/custom_llm_eval-0.1.5.tar.gz | source | sdist | null | false | 40f7b10c8cb1ae6dff27f36b10489e47 | 534f15c616deec6b495ade7f47d1960855955a3a6727bea8dc74a344f76e2780 | 608f578e783a78f9304a022f0a0699c86b4e66435e8c274565345dc8e5076cc8 | null | [
"LICENSE"
] | 240 |
2.4 | paraclient | 1.37.1 | Python client for Para | 
# Python Client for Para
[](https://badge.fury.io/py/paraclient)
[](https://gitter.im/Erudika/para)
## What is this?
**Para** was designed as a simple and modular backend framework for object persistence and retrieval.
It helps you build applications faster by taking care of the backend. It works on three levels -
objects are stored in a NoSQL data store or any old relational database, then automatically indexed
by a search engine and finally, cached.
This is the Python client for Para.
### Quick start
**Prerequisites:**
- [uv](https://github.com/astral-sh/uv)
- Python 3.9+
1. Use the [PyPI](https://pypi.python.org/pypi) package manager to install the Python client for Para:
```sh
$ pip3 install paraclient
```
2. Initialize the client with your access and secret API keys:
```python
from paraclient import ParaClient
paraclient = ParaClient('ACCESS_KEY', 'SECRET_KEY')
paraclient.setEndpoint("http://localhost:8080")
```
## Documentation
### [Read the Docs](https://paraio.org/docs)
## Development
This project uses [uv](https://github.com/astral-sh/uv) for dependency management, builds, and publishing, and targets Python 3.9+.
1. Install uv by following the [official instructions](https://docs.astral.sh/uv/getting-started/installation/).
2. Run `uv sync` first. This installs every dependency declared in `pyproject.toml` and pinned in `uv.lock` into `.venv/`.
3. Run the test suite via `uv sync --extra test` and `uv run python -m unittest`.
4. Build distributable artifacts with `uv build` (output located in `dist/`).
When dependencies change, update the `[project]` section of `pyproject.toml`, then regenerate the lock file with `uv lock --upgrade`.
The test suite uses [Testcontainers](https://testcontainers.com/) to spin up Para Docker container automatically,
so ensure Docker is installed and the daemon is running before invoking `uv run python -m unittest`.
You can override some environment variables (see `tests/test_paraclient.py`).
## Contributing
1. Fork this repository and clone the fork to your machine
2. Create a branch (`git checkout -b my-new-feature`)
3. Implement a new feature or fix a bug and add some tests
4. Commit your changes (`git commit -am 'Added a new feature'`)
5. Push the branch to **your fork** on GitHub (`git push origin my-new-feature`)
6. Create new Pull Request from your fork
For more information see [CONTRIBUTING.md](https://github.com/Erudika/para/blob/master/CONTRIBUTING.md)
## License
[Apache 2.0](LICENSE)
| text/markdown | Alexander Bogdanovski | Alexander Bogdanovski <alex@erudika.com> | null | null | null | null | [
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3 :: Only",
"Programming Language :: Python :: Implementation :: CPython"
] | [] | null | null | >=3.9 | [] | [] | [] | [
"requests>=2.32.5",
"aws-requests-auth==0.4.3",
"urllib3>=2.6.2",
"testcontainers>=4.6.0; extra == \"test\""
] | [] | [] | [] | [
"Homepage, https://github.com/Erudika/para-client-python",
"Documentation, https://paraio.org/docs",
"Issues, https://github.com/Erudika/para-client-python/issues"
] | uv/0.9.18 {"installer":{"name":"uv","version":"0.9.18","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Fedora Linux","version":"43","id":"","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null} | 2026-02-20T10:32:06.267448 | paraclient-1.37.1-py3-none-any.whl | 21,399 | 33/4f/5894dd1021f827297cfd16c8250b56770838631e55d63c9759a4df86a0c3/paraclient-1.37.1-py3-none-any.whl | py3 | bdist_wheel | null | false | 03ecd6bb9ffdb64eb9e73019fc4492a4 | 6736e382279d496ce9de6b6ead74593687a322874c482fdf3605273f9e87d7ab | 334f5894dd1021f827297cfd16c8250b56770838631e55d63c9759a4df86a0c3 | Apache-2.0 | [
"LICENSE"
] | 226 |
2.4 | noex-client | 0.1.0 | Python client SDK for noex-server | # noex-client
Python client SDK for [noex-server](https://github.com/hamicek/noex-server). Asyncio-native, 1:1 feature parity with the TypeScript client.
## Features
- **Store CRUD** with bucket API, cursor pagination, and aggregation
- **Reactive subscriptions** -- subscribe to server-side queries, receive push updates via callbacks
- **Transactions** -- atomic multi-bucket operations
- **Rules engine proxy** -- emit events, manage facts, subscribe to rule matches
- **Identity & auth** -- built-in user/role management, ACL, token and credential login
- **Audit & procedures** -- audit log queries, server-side procedure execution
- **Automatic reconnect** with exponential backoff, jitter, and subscription recovery
- **Heartbeat** -- automatic pong responses to server ping
- **Type-safe** -- full type hints, strict mypy, `TypedDict` for protocol structures
- **Minimal dependencies** -- only `websockets` (>=13.0)
## Installation
```bash
pip install noex-client
```
Requires Python >= 3.11.
## Quick Start
```python
import asyncio
from noex_client import NoexClient
async def main():
client = NoexClient("ws://localhost:8080")
await client.connect()
# Store CRUD
users = client.store.bucket("users")
alice = await users.insert({"name": "Alice"})
all_users = await users.all()
# Reactive subscription
unsub = await client.store.subscribe("all-users", lambda data: print("Updated:", data))
# Rules
await client.rules.emit("user.created", {"userId": alice["id"]})
# Cleanup
unsub()
await client.disconnect()
asyncio.run(main())
```
### Context Manager
```python
async with NoexClient("ws://localhost:8080") as client:
users = client.store.bucket("users")
await users.insert({"name": "Alice"})
# Automatically disconnects
```
### Auth and Reconnect
```python
from noex_client import NoexClient, ClientOptions, AuthOptions, ReconnectOptions
client = NoexClient("ws://localhost:8080", ClientOptions(
auth=AuthOptions(token="my-jwt-token"),
reconnect=ReconnectOptions(
max_retries=10,
initial_delay_ms=500,
max_delay_ms=15_000,
),
request_timeout_ms=5_000,
))
client.on("reconnecting", lambda attempt: print(f"Reconnecting... attempt {attempt}"))
client.on("reconnected", lambda: print("Reconnected! Subscriptions restored."))
await client.connect()
```
When `auth.token` is set and the server requires authentication, the client automatically sends `auth.login` after connecting and after every reconnect.
---
## API
### NoexClient
#### `NoexClient(url, options=None)`
Creates a client instance. Does not open a connection -- call `connect()` to start.
```python
client = NoexClient("ws://localhost:8080", ClientOptions(
auth=AuthOptions(token="jwt"),
reconnect=True,
request_timeout_ms=10_000,
connect_timeout_ms=5_000,
heartbeat=True,
))
```
#### `await client.connect() -> WelcomeInfo`
Opens the WebSocket connection and waits for the server welcome message. If auth is configured and the server requires authentication, login is performed automatically.
```python
welcome = await client.connect()
# WelcomeInfo(version='1.0.0', server_time=1706745600000, requires_auth=True)
```
#### `await client.disconnect() -> None`
Gracefully closes the connection. Rejects all pending requests, clears subscriptions, and stops any reconnect loop.
#### `client.state -> ConnectionState`
Current connection state: `"connecting"` | `"connected"` | `"reconnecting"` | `"disconnected"`.
#### `client.is_connected -> bool`
Shorthand for `client.state == "connected"`.
#### `client.on(event, handler) -> Unsubscribe`
Subscribe to client lifecycle events. Returns an unsubscribe function.
| Event | Handler signature | Description |
|-------|-------------------|-------------|
| `"connected"` | `() -> None` | Connection established (initial or reconnect) |
| `"disconnected"` | `(reason: str) -> None` | Connection lost or closed |
| `"reconnecting"` | `(attempt: int) -> None` | Reconnect attempt starting |
| `"reconnected"` | `() -> None` | Successfully reconnected |
| `"error"` | `(error: Exception) -> None` | Transport or reconnect error |
| `"welcome"` | `(info: WelcomeInfo) -> None` | Welcome message received from server |
| `"session_revoked"` | `() -> None` | Server revoked the current session |
---
### ClientOptions
```python
@dataclass(frozen=True)
class ClientOptions:
auth: AuthOptions | None = None
reconnect: bool | ReconnectOptions = True
request_timeout_ms: int = 10_000
connect_timeout_ms: int = 5_000
heartbeat: bool = True
```
| Option | Type | Default | Description |
|--------|------|---------|-------------|
| `auth` | `AuthOptions` | `None` | Auth configuration for automatic login |
| `reconnect` | `bool \| ReconnectOptions` | `True` | Enable automatic reconnect with exponential backoff |
| `request_timeout_ms` | `int` | `10000` | Timeout for individual request/response round-trips |
| `connect_timeout_ms` | `int` | `5000` | Timeout for WebSocket connection and welcome message |
| `heartbeat` | `bool` | `True` | Automatically respond to server ping messages |
#### AuthOptions
```python
@dataclass(frozen=True)
class AuthOptions:
token: str | None = None # Token for auth.login
credentials: CredentialOptions | None = None # Username/password for identity.login
```
#### ReconnectOptions
```python
@dataclass(frozen=True)
class ReconnectOptions:
max_retries: float = float("inf")
initial_delay_ms: int = 1_000
max_delay_ms: int = 30_000
backoff_multiplier: float = 2.0
jitter_ms: int = 500
```
---
### StoreAPI
Access via `client.store`.
#### `store.bucket(name) -> BucketAPI`
Returns a `BucketAPI` handle for the named bucket. Does not make a request -- the bucket handle is a thin wrapper that attaches the bucket name to each operation.
```python
users = client.store.bucket("users")
```
#### `await store.subscribe(query, callback, params=None) -> Unsubscribe`
Subscribe to a reactive server-side query. The callback receives the initial data immediately and is called again whenever the query result changes on the server.
```python
unsub = await client.store.subscribe("all-users", lambda users: print("Users:", users))
# With parameters
unsub = await client.store.subscribe(
"users-by-role",
lambda admins: print("Admins:", admins),
params={"role": "admin"},
)
# Unsubscribe (synchronous)
unsub()
```
Subscriptions survive reconnect -- after a successful reconnect the client automatically resubscribes and delivers fresh data to the callback.
#### `await store.unsubscribe(subscription_id) -> None`
Cancel a subscription by its server-assigned ID.
#### `await store.transaction(operations) -> dict`
Execute multiple store operations atomically.
```python
result = await client.store.transaction([
{"op": "get", "bucket": "users", "key": "user-1"},
{"op": "update", "bucket": "users", "key": "user-1", "data": {"credits": 400}},
{"op": "insert", "bucket": "logs", "data": {"action": "credit_update"}},
])
```
Supported ops: `get`, `insert`, `update`, `delete`, `where`, `findOne`, `count`.
#### Admin -- Bucket Management
```python
await store.define_bucket("users", {"schema": {"name": {"type": "string"}}})
await store.update_bucket("users", {"schema": {"email": {"type": "string"}}})
schema = await store.get_bucket_schema("users")
await store.drop_bucket("users")
```
#### Admin -- Query Management
```python
await store.define_query("all-users", {"type": "all", "bucket": "users"})
queries = await store.list_queries()
await store.undefine_query("all-users")
```
#### Metadata
```python
buckets = await store.buckets()
stats = await store.stats()
```
---
### BucketAPI
Access via `client.store.bucket(name)`.
#### CRUD
| Method | Returns |
|--------|---------|
| `await bucket.insert(data)` | `dict` -- inserted record with metadata |
| `await bucket.get(key)` | `dict \| None` |
| `await bucket.update(key, data)` | `dict` -- updated record |
| `await bucket.delete(key)` | `None` |
#### Queries
| Method | Returns |
|--------|---------|
| `await bucket.all()` | `list[dict]` |
| `await bucket.where(filter)` | `list[dict]` |
| `await bucket.find_one(filter)` | `dict \| None` |
| `await bucket.count(filter=None)` | `int` |
| `await bucket.first(n)` | `list[dict]` |
| `await bucket.last(n)` | `list[dict]` |
| `await bucket.paginate(limit=..., after=...)` | `dict` -- paginated result |
#### Aggregation
| Method | Returns |
|--------|---------|
| `await bucket.sum(field, filter=None)` | `float` |
| `await bucket.avg(field, filter=None)` | `float` |
| `await bucket.min(field, filter=None)` | `float \| None` |
| `await bucket.max(field, filter=None)` | `float \| None` |
#### Bulk
| Method | Description |
|--------|-------------|
| `await bucket.clear()` | Remove all records from the bucket |
---
### RulesAPI
Access via `client.rules`.
#### Events
```python
event = await client.rules.emit("user.created", {"userId": "123"})
# {'id': '...', 'topic': 'user.created', 'data': {...}, 'timestamp': ...}
# With correlation/causation IDs
event = await client.rules.emit(
"order.completed",
{"orderId": "456"},
correlation_id="corr-1",
causation_id="cause-1",
)
```
#### Facts
```python
await client.rules.set_fact("user:1:status", "active")
status = await client.rules.get_fact("user:1:status")
deleted = await client.rules.delete_fact("user:1:status")
facts = await client.rules.query_facts("user:*:status")
all_facts = await client.rules.get_all_facts()
```
#### Subscriptions
Subscribe to real-time rule events by topic pattern:
```python
unsub = await client.rules.subscribe("user.*", lambda event, topic: print(f"{topic}: {event}"))
unsub()
```
#### Admin
```python
await client.rules.register_rule({"id": "my-rule", "when": {...}, "then": {...}})
await client.rules.enable_rule("my-rule")
await client.rules.disable_rule("my-rule")
rule = await client.rules.get_rule("my-rule")
rules = await client.rules.get_rules()
await client.rules.update_rule("my-rule", {"then": {...}})
validation = await client.rules.validate_rule({...})
await client.rules.unregister_rule("my-rule")
```
#### Stats
```python
stats = await client.rules.stats()
# {'rulesCount': ..., 'factsCount': ..., 'eventsProcessed': ...}
```
---
### AuthAPI
Access via `client.auth`.
```python
session = await client.auth.login("jwt-token")
# {'userId': '...', 'roles': [...], 'expiresAt': ...}
current = await client.auth.whoami()
await client.auth.logout()
```
When `auth.token` is set in `ClientOptions`, login is performed automatically after connect and after each reconnect.
---
### IdentityAPI
Access via `client.identity`. Built-in user management with roles and ACL.
#### Auth
```python
result = await client.identity.login("admin", "password")
result = await client.identity.login_with_secret("admin-secret")
me = await client.identity.whoami()
session = await client.identity.refresh_session()
await client.identity.logout()
```
When `auth.credentials` is set in `ClientOptions`, credential login is performed automatically after connect and after each reconnect.
#### User Management
```python
user = await client.identity.create_user({"username": "alice", "password": "s3cret"})
user = await client.identity.get_user(user_id)
await client.identity.update_user(user_id, {"displayName": "Alice"})
users = await client.identity.list_users(page=1, page_size=20)
await client.identity.enable_user(user_id)
await client.identity.disable_user(user_id)
await client.identity.delete_user(user_id)
```
#### Password
```python
await client.identity.change_password(user_id, "old-pass", "new-pass")
await client.identity.reset_password(user_id, "new-pass")
```
#### Roles
```python
role = await client.identity.create_role({"name": "editor", "permissions": [...]})
await client.identity.assign_role(user_id, "editor")
roles = await client.identity.get_user_roles(user_id)
await client.identity.remove_role(user_id, "editor")
all_roles = await client.identity.list_roles()
await client.identity.update_role(role_id, {"permissions": [...]})
await client.identity.delete_role(role_id)
```
#### ACL
```python
await client.identity.grant({"userId": user_id, "resource": "bucket:users", "permission": "read"})
await client.identity.revoke({"userId": user_id, "resource": "bucket:users", "permission": "read"})
acl = await client.identity.get_acl("bucket", "users")
access = await client.identity.my_access()
```
#### Ownership
```python
owner = await client.identity.get_owner("bucket", "users")
await client.identity.transfer_owner("bucket", "users", new_owner_id)
```
---
### AuditAPI
Access via `client.audit`.
```python
entries = await client.audit.query({"userId": "admin-1", "limit": 50})
```
Supported filter keys: `userId`, `operation`, `result`, `from`, `to`, `limit`.
---
### ProceduresAPI
Access via `client.procedures`.
```python
# Register
await client.procedures.register({"name": "calculate-total", "steps": [...]})
# Execute
result = await client.procedures.call("calculate-total", {"orderId": "123"})
# Admin
proc = await client.procedures.get("calculate-total")
all_procs = await client.procedures.list()
await client.procedures.update("calculate-total", {"steps": [...]})
await client.procedures.unregister("calculate-total")
```
---
## Error Handling
All errors from the server are propagated as `NoexClientError` with a machine-readable `code`:
```python
from noex_client import NoexClientError, RequestTimeoutError, DisconnectedError
try:
await client.store.bucket("users").insert({"name": ""})
except NoexClientError as e:
match e.code:
case "VALIDATION_ERROR":
print(f"Validation failed: {e.details}")
case "UNAUTHORIZED":
print("Need to login first")
case "NOT_FOUND":
print("Resource not found")
```
| Error class | Code | Description |
|-------------|------|-------------|
| `NoexClientError` | *(server code)* | Base class for all server errors |
| `RequestTimeoutError` | `TIMEOUT` | Request did not receive a response within `request_timeout_ms` |
| `DisconnectedError` | `DISCONNECTED` | Attempted to send while not connected, or connection was lost |
Pending requests at the time of a disconnect are rejected with `DisconnectedError`. They are **not** retried automatically -- the server does not persist request state across connections and automatic retry of non-idempotent operations (insert, emit) could cause duplicates.
---
## Reconnect Behavior
Reconnect is enabled by default. When the connection drops unexpectedly:
1. All pending requests are rejected with `DisconnectedError`
2. The client enters `"reconnecting"` state and emits `reconnecting` events
3. Exponential backoff with jitter determines the delay between attempts
4. On successful reconnect:
- Auto-login is performed (if configured)
- All active subscriptions are restored with fresh data
- `"reconnected"` event is emitted
5. If max retries are exhausted, the client enters `"disconnected"` state
Calling `disconnect()` at any point stops the reconnect loop immediately.
---
## License
MIT
| text/markdown | Miroslav Halabrin | null | null | null | null | async, client, noex, real-time, websocket | [
"Development Status :: 4 - Beta",
"Framework :: AsyncIO",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Topic :: Software Development :: Libraries :: Python Modules"
] | [] | null | null | >=3.11 | [] | [] | [] | [
"websockets>=13.0",
"mypy>=1.10; extra == \"dev\"",
"pytest-asyncio>=0.24; extra == \"dev\"",
"pytest>=8.0; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/hamicek/noex-client-python",
"Repository, https://github.com/hamicek/noex-client-python",
"Issues, https://github.com/hamicek/noex-client-python/issues"
] | twine/6.2.0 CPython/3.9.6 | 2026-02-20T10:31:58.387302 | noex_client-0.1.0.tar.gz | 38,319 | 37/b1/e01ba330469dede69b7aaa5862477144bce05773d736bb00c642a1c7929b/noex_client-0.1.0.tar.gz | source | sdist | null | false | 0d05facc7b05dc90b834c39df3ad1fa2 | 7085b8f8cd5e3ea937e1fe83e41bb5efbb0a30e2593f3047767cdb05cf98bb98 | 37b1e01ba330469dede69b7aaa5862477144bce05773d736bb00c642a1c7929b | MIT | [
"LICENSE"
] | 235 |
2.3 | color-memory-maze | 1.1.0 | Memory Maze environments with DrStrategy enhancements for reinforcement learning research | # Memory Maze DrStrategy Environments
Goal-conditioned maze navigation environments adapted from [Memory-Maze](https://github.com/jurgisp/memory-maze) with visual enhancements from [DrStrategy](https://github.com/ahn-ml/drstrategy).
## Environments
| 7×7 Complex Maze | 15×15 Complex Maze |
|---|---|
|  |  |
| Environment ID | Grid | Episode Steps | Description |
|---|---|---|---|
| `MemoryMaze-cmaze-7x7-drstrategy-v0` | 7×7 | 500 | Compact complex maze with colorful textures |
| `MemoryMaze-cmaze-15x15-drstrategy-v0` | 15×15 | 1000 | Large complex maze with colorful textures |
## API
### Observation Space (Dict)
The observation is a `gymnasium.spaces.Dict` with five keys:
| Key | Shape | Dtype | Description |
|---|---|---|---|
| `image` | (64, 64, 3) | uint8 | First-person egocentric camera (HWC) |
| `goal_image` | (64, 64, 3) | uint8 | Pre-rendered image of the current goal location |
| `target_color` | (3,) | float64 | Goal sphere colour, RGB in [0, 1] |
| `position` | (2,) | float64 | Agent position in maze coordinates |
| `direction` | (2,) | float64 | Agent heading unit vector |
### Action Space
Continuous `gymnasium.spaces.Box(shape=(2,))`:
| Index | Meaning |
|---|---|
| `action[0]` | Forward (negative) / Backward (positive) |
| `action[1]` | Turn left (negative) / Turn right (positive) |
### Reward and Info
- **Reward**: 1.0 when the goal is reached, 0.0 otherwise (sparse)
- **`info["success"]`**: `1` on the step the goal is reached, `0` otherwise
- **`info["distance"]`**: L1 distance to the current goal
## Features
- Colorful wall textures using the tab20 colormap, unique per maze section
- Colorful floor textures using block-based tab20 colours
- Goal-conditioned navigation: `goal_image` provides a pre-rendered first-person view from the target location
- 20 distinct target sphere colours
- Physics-based agent (rolling ball with friction)
- Fixed maze layouts for reproducible experiments
## Usage
### Basic Example
```python
import gymnasium as gym
import memory_maze
env = gym.make("MemoryMaze-cmaze-7x7-drstrategy-v0")
obs, info = env.reset()
for _ in range(500):
action = env.action_space.sample()
obs, reward, terminated, truncated, info = env.step(action)
if terminated or truncated:
obs, info = env.reset()
env.close()
```
### Rendering Backend
Set the `MUJOCO_GL` environment variable before running:
```bash
export MUJOCO_GL=egl # Hardware-accelerated (recommended)
export MUJOCO_GL=osmesa # Software rendering for headless servers
```
## Examples
- **`examples/keyboard_control.py`** — Interactive 3-panel matplotlib window (obs / goal_image / top-down view), controlled with WASD keys
- **`examples/save_observations_with_goals.py`** — Saves observations, all pre-rendered goal images, and the top-down view to disk
## Installation
### From Source
```bash
pip install -e .
```
### Rendering Setup
```bash
export MUJOCO_GL=egl # GPU systems
export MUJOCO_GL=osmesa # Headless / CPU-only
```
### Verify
```python
import gymnasium as gym
import memory_maze
env = gym.make("MemoryMaze-cmaze-7x7-drstrategy-v0")
obs, info = env.reset()
print(obs["image"].shape) # (64, 64, 3)
print(env.action_space) # Box(-inf, inf, (2,), float64)
env.close()
```
## Development
Code formatting uses **black**, **isort**, and **ruff** (configured in `pyproject.toml`). Run tests with **pytest**.
| text/markdown | Tim Joseph | Tim Joseph <tim@mctigger.com> | null | null | MIT License
Copyright (c) 2025 Memory Maze DrStrategy Team
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. | reinforcement-learning, environment, gymnasium, color-memory-maze, drstrategy, maze-navigation, partial-observability, mujoco-environments, multi-room-maze | [
"Development Status :: 4 - Beta",
"Intended Audience :: Science/Research",
"Intended Audience :: Developers",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Topic :: Software Development :: Libraries :: Python Modules",
"Environment :: Console",
"Typing :: Typed"
] | [] | null | null | >=3.8 | [] | [] | [] | [
"gymnasium>=1.0.0",
"dm-control>=1.0.31",
"numpy>=1.26",
"pillow>=8.0.0",
"matplotlib>=3.0.0",
"black<25.0,>=22.0; extra == \"dev\"",
"isort<6.0,>=5.10; extra == \"dev\"",
"flake8<7.0,>=4.0; extra == \"dev\"",
"build<2.0.0,>=0.8.0; extra == \"dev\"",
"twine<6.0.0,>=4.0.0; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/mctigger/memory-maze",
"Repository, https://github.com/mctigger/memory-maze",
"Bug Tracker, https://github.com/mctigger/memory-maze/issues",
"Documentation, https://github.com/mctigger/memory-maze#readme"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:31:56.993406 | color_memory_maze-1.1.0.tar.gz | 18,370 | 4b/12/cb41acd5aa1d675511f1fdc8084cbf763caf0cb1a39370f59cf546371897/color_memory_maze-1.1.0.tar.gz | source | sdist | null | false | 7eb623acfd951d5eddb92efd3d4aea90 | 55a29eefe67407afe31f540ae052988da2d3727ac9161ca41e10bb26059780d3 | 4b12cb41acd5aa1d675511f1fdc8084cbf763caf0cb1a39370f59cf546371897 | null | [] | 241 |
2.2 | flood-adapt | 1.1.9 | A software package support system which can be used to assess the benefits and costs of flood resilience measures | # FloodAdapt
FloodAdapt is a decision-support tool that seeks to advance and accelerate flooding-related adaptation planning. It brings rapid, physics-based compound flood and detailed impact modelling into an easy-to-use system, allowing non-expert end-users to evaluate a wide variety of compound events, future conditions, and adaptation options in minutes. FloodAdapt serves as a connector between scientific advances and practitioner needs, improving and increasing the uptake and impact of adaptation research and development.
To make decisions on flood adaptation, communities need to understand how climate and socio-economic changes will affect flood risk and the risk-reduction potential of various adaptation options. This type of information is usually costly to acquire, and models are often too slow and labor-intensive to evaluate all the scenarios required to understand the impacts and effectiveness of potential adaptation decisions. FloodAdapt addresses this by making rapid, physics-based compound flood modeling and detailed impact modeling accessible to non-expert end-users, allowing them to evaluate a wide variety of compound events, future conditions, and adaptation options in minutes.
FloodAdapt was developed as a rapid planning tool with a straightforward graphical user interface for scenario generation, simulation, and visualization of spatial flooding and flooding impacts. Decision-making needs at the community level were central to the design of FloodAdapt. Users can answer planning questions like: “How will potential adaptation options reduce flood impacts?”, “How will those options perform for different types of events, like hurricanes, king tides, or heavy rainfall?”, “Which neighborhoods will benefit most?”, “How will those options hold up in the future?”
Users specify what-if scenarios composed of historic or synthetic weather events, climate or socio-economic future projections, and adaptation measures.
The backend of FloodAdapt leverages the open-source, state-of-the-art process-based compound flood model SFINCS (https://github.com/Deltares/SFINCS) that can accurately predict compound flooding due to surge, rainfall, and river discharge, at a fraction of the computation time typically required by physics-based models. The damage model included in FloodAdapt is the Deltares-developed flood impact assessment tool Delft-FIAT (https://github.com/Deltares/Delft-FIAT). It calculates the flood damages to individual buildings and roads, and – when social vulnerability data is available – aggregates these damages over vulnerability classes.
FloodAdapt can greatly support adaptation planning by allowing users to explore many scenarios. It can be used to evaluate flooding and impacts due to compound weather events, like hurricanes, king tides, and rainfall events. Users can evaluate flooding, impacts, and risk considering user-specified projections of sea level rise, precipitation increase, storm frequency increase, population growth, and economic growth. Users can also test out adaptation options, like sea walls, levees, pumps, home elevations, buyouts and floodproofing.
Recent developments of the decision-support system include (1) simplifying and partially automating the setup of the SFINCS and Delft-FIAT models, (2) improving the user experience, (3) better supporting adaptation planning with improvements like metrics tables, infographics, better visualizations in the user interface, adding in additional adaptation options to evaluate, and calculating benefits of adaptation options, and (4) incorporating social vulnerability and equity into the evaluation of adaptation options to support equitable adaptation planning.
FloodAdapt is currently in an intensive development stage. Independent usage of the repository will be challenging prior to end-of-year 2024. FloodAdapt documentation will be expanded on throughout 2024.
# Getting Started
Please review our [`developer guide`](DEVELOPER_GUIDE.md) for information on how to install and use FloodAdapt locally.
| text/markdown | null | Gundula Winter <Gundula.Winter@deltares.nl>, Panos Athanasiou <Panos.Athanasiou@deltares.nl>, Frederique de Groen <Frederique.deGroen@deltares.nl>, Tim de Wilde <Tim.deWilde@deltares.nl>, Julian Hofer <Julian.Hofer@deltares.nl>, Daley Adrichem <Daley.Adrichem@deltares.nl>, Luuk Blom <Luuk.Blom@deltares.nl> | null | null | ====================================================
FloodAdapt License Agreement and User Acknowledgment
====================================================
FloodAdapt is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
The following license applies to the FloodAdapt software.
Please read it carefully and in its entirety before continuing.
====================================================
GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
====================================================
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
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The licenses for most software and other practical works are designed
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===============================================================================
Additional User Acknowledgments
===============================================================================
By installing or using FloodAdapt, you agree to the GPLv3 terms above AND you
explicitly acknowledge and agree to the following:
1. Intended Use
I understand that FloodAdapt is designed for early-phase climate adaptation
planning. It is NOT intended for detailed engineering design, regulatory
decision-making, or emergency response. Any use outside that scope is at my
own risk.
2. Software Function and Dependencies
I understand that FloodAdapt is a software shell that requires a user‑provided
database of flood and impact models. The quality and accuracy of results
depend entirely on those models and their input data.
3. No Warranty on Results
I understand that FloodAdapt and its developers make NO guarantees about the
accuracy, reliability, or completeness of any outputs. I will interpret and
validate all results before using them in any planning or decision‑making context.
4. Software Status
I acknowledge that FloodAdapt is at Technology Readiness Level 7 (TRL 7).
Although operational in certain environments, it may still contain bugs.
Issues can be reported at:
https://github.com/Deltares-research/FloodAdapt/issues
5. No Liability
I understand that FloodAdapt is provided “as is,” without any warranty—
express or implied—including warranties of merchantability or fitness for a
particular purpose. The developers and distributors accept NO liability
for any damages, losses, or legal claims arising from its use or misuse.
6. User Responsibility
I understand that it is MY responsibility to validate all outputs and seek
appropriate professional advice before applying results to real-world or
official projects.
7. Third‑Party Content
I understand that any third‑party models or data used within FloodAdapt
remain the responsibility of their original creators. FloodAdapt does not
verify or warrant the accuracy of such content.
8. No Professional Advice
I acknowledge that FloodAdapt does NOT provide professional engineering,
legal, or emergency-management advice. I will seek qualified professionals
for critical decisions or interpretations.
| null | [
"Intended Audience :: Science/Research",
"License :: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication",
"Topic :: Scientific/Engineering :: Hydrology"
] | [] | null | null | <3.13,>=3.10 | [] | [] | [] | [
"cht-cyclones==1.0.3",
"cht-meteo==0.3.1",
"cht-observations==0.2.1",
"cht-tide==0.1.1",
"fiat-toolbox==0.1.23",
"fiona<2.0,>=1.0",
"geojson<4.0,>=3.0",
"geopandas<2.0,>=1.0",
"hydromt-fiat<1.0,>=0.5.9",
"hydromt-sfincs<2.0,>=1.2.2",
"numpy<2.0,>=1.0",
"numpy-financial<2.0,>=1.0",
"pandas<3.0,>=2.0",
"plotly<6.3,>=6.0",
"pydantic<3.0,>=2.0",
"pydantic-settings<3.0,>=2.0",
"pyogrio<1.0",
"tomli<3.0,>=2.0",
"tomli-w<2.0,>=1.0",
"typing_extensions",
"pytest<9.0,>=8.0; extra == \"dev\"",
"pytest-cov<7.0,>=6.0; extra == \"dev\"",
"pre-commit==3.8.0; extra == \"dev\"",
"ruff==0.5.5; extra == \"dev\"",
"typos==1.23.6; extra == \"dev\"",
"build<2.0,>=1.2; extra == \"build\"",
"twine<7.0,>=6.0; extra == \"build\"",
"pyinstaller==6.13.0; extra == \"build\"",
"pefile<2024.8.26; extra == \"build\"",
"jupyter<2.0,>=1.0; extra == \"docs\"",
"jupyter-cache<2.0,>=1.0; extra == \"docs\"",
"nbstripout<0.9,>=0.8.0; extra == \"docs\"",
"matplotlib<4.0,>=3.0; extra == \"docs\"",
"quartodoc<1.0,>=0.9.0; extra == \"docs\"",
"sphinx<9.0,>=8.0; extra == \"docs\"",
"sphinx-rtd-theme<4.0,>=3.0; extra == \"docs\"",
"regex<2025.0,>=2024.11; extra == \"docs\"",
"minio<8,>=7.2.15; extra == \"docs\"",
"python-dotenv<2.0,>=1.0; extra == \"docs\"",
"folium<1.0,>=0.19.0; extra == \"docs\"",
"mapclassify<3.0,>=2.8.0; extra == \"docs\"",
"contextily; extra == \"docs\""
] | [] | [] | [] | [
"Source, https://github.com/Deltares-research/FloodAdapt"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:31:39.746215 | flood_adapt-1.1.9.tar.gz | 320,608 | da/38/05320880f947c8c9ba36cae5d82b6c36450830e6578e9a89c89030699d5e/flood_adapt-1.1.9.tar.gz | source | sdist | null | false | a570393978e8af17df857c14f66dcb06 | 64ddf165e9027f929c16fc2ec18c5a86b6e7cd9925d10f81d00f7e2273354dcc | da3805320880f947c8c9ba36cae5d82b6c36450830e6578e9a89c89030699d5e | null | [] | 231 |
2.4 | grubicy | 1.1.0 | Config-driven helpers for signac workflows with explicit dependencies and migrations | grubicy
=======
grubicy is a small helper library + CLI that layers lightweight dependency management
on top of signac.
It is named after Vittore Grubicy de Dragon, an influential promoter of Italian
Divisionism. That movement “divided” light and color into strokes; grubicy does the
same for workflows: it divides a signac project into stages, connects them with
explicit parent -> child links, and keeps those links consistent even as your schema
evolves.
With one TOML/YAML spec you can:
- describe multi-action pipelines in a single file,
- materialize signac jobs with parent pointers stored in state points,
- record full parent state points in docs for traceability (`deps_meta`),
- render row workflows, and
- migrate existing workspaces with cascading pointer updates without doing it by hand.
Why use it
----------
Signac projects are naturally flat, but real computational work is often staged:
- Prepare -> simulate -> analyze
- Preprocess -> train -> evaluate
- Extract -> transform -> aggregate
grubicy helps when you want those stages to be:
- cached and reusable (shared intermediates across experiments),
- explicitly wired (no hidden coupling via shared parameter keys),
- reviewable and reproducible (the pipeline is a spec file),
- maintainable over time (schema changes do not break downstream links).
What you get:
- Explicit dependencies: parent job ids live in the child state point, so “same params
but different parents” never collide.
- One spec for everything: job creation, row workflow rendering, and parameter
collection are driven by a single config file.
- Safe migrations: plan/apply state point migrations and automatically cascade
dependency-pointer rewrites downstream, with progress logging.
When to use it
--------------
- Use grubicy if you have multi-step experiments, pass results downstream between
stages, or want row-ready workflows without writing manual include filters.
- If your project is truly single-stage, grubicy will feel like extra structure you do
not need.
Quick start
-----------
1) Install
```bash
pip install git+https://github.com/davide-grheco/grubicy
```
For local development:
```bash
uv sync --extra dev
```
2) Describe your pipeline (`pipeline.toml`)
```toml
[workspace]
value_file = "signac_statepoint.json"
[[actions]]
name = "s1"
sp_keys = ["p1"]
outputs = ["s1/out.json"]
[[actions]]
name = "s2"
sp_keys = ["p2", "test"]
deps = { action = "s1", sp_key = "parent_action" }
outputs = ["s2/out.json"]
[[actions]]
name = "s3"
sp_keys = ["p3"]
deps = { action = "s2", sp_key = "parent_action" }
outputs = ["s3/out.json"]
[[experiment]]
[experiment.s1]
p1 = 1
[experiment.s2]
p2 = 10
test = true
[experiment.s3]
p3 = 0.1
```
Notes:
- Each `[[actions]]` block defines a stage.
- `sp_keys` lists the parameters that define identity for that stage.
- `deps` declares which upstream action this stage depends on. The library writes the
upstream job id into the dependent job’s state point using `sp_key`.
- Experiments use per-action subsections: parameters do not need to be shared across
stages.
Defining multiple experiments:
- Repeat the `[[experiment]]` block to create multiple experiment rows. See a complete
multi-experiment spec in `examples/library-example/pipeline.toml`.
3) Materialize jobs and render a row workflow
```bash
grubicy prepare pipeline.toml --project . --output workflow.toml
```
This will:
- create/open signac jobs in topological order,
- write action and dependency pointers (parent job ids) into each state point,
- store `deps_meta` in job docs (including full parent state points),
- generate `workflow.toml` for row.
4) Run jobs (only ready directories)
```bash
grubicy submit pipeline.toml --project .
```
If you want to submit everything to row directly, you can still run `row submit`.
5) Collect downstream-ready parameters
```bash
grubicy collect-params pipeline.toml s3 --format csv > results.csv
```
This flattens the parameter chain for the `s3` stage (and optionally selected doc
fields), so you can analyze results without manually walking parents.
Core pieces
-----------
Spec
- A spec file contains:
- `actions`: list of stages with name, `sp_keys`, optional `deps` (parent action +
`sp_key` used to store parent job id), optional `outputs`, optional `runner`.
- `experiment`: list of experiments with per-action subsections.
- optional `workspace.value_file`.
- Supported formats: TOML and YAML.
Materialization
- Creates/opens jobs in topological order and wires dependencies by writing parent job
ids into the child state point. Also writes `deps_meta` into child job docs so
parent state points are recorded for traceability and repair.
Row rendering
- Builds `workflow.toml` with per-action include rules, using either your explicit
`runner` or a default `python actions/{name}.py {directory}`.
Collection
- `collect-params` flattens parameters (and optional document fields) across the
dependency chain for a target stage.
Migration
- Plan/apply state point migrations with collision detection, cascading parent-pointer
rewrites downstream, and restartable progress logs under `.pipeline_migrations/`.
- Useful when you add defaults (`setdefault`) or evolve the schema and need downstream
pointers updated consistently.
Examples
--------
- `examples/sample-project`: a plain signac setup with hand-wired parent pointers.
- `examples/library-example`: the same pipeline expressed with grubicy
(`pipeline.toml`, CLI materialization, row workflow, and helper-based actions).
Documentation
-------------
- Hosted docs: https://davide-grheco.github.io/grubicy/
- Source docs: `docs/getting-started.md` (walkthrough), `docs/cli.md` (CLI reference),
`docs/migrations.md` (worked migration example)
Development
-----------
- Install dev deps: `uv sync --extra dev`
- Install hooks: `uv run pre-commit install`
- Run hooks on all files: `uv run pre-commit run --all-files`
| text/markdown | Davide Crucitti | null | null | null | MIT License
Copyright (c) 2026 grubicy contributors
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
| signac, workflow, pipelines, dependencies, migrations | [
"Development Status :: 4 - Beta",
"Environment :: Console",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3 :: Only",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Topic :: Scientific/Engineering"
] | [] | null | null | >=3.11 | [] | [] | [] | [
"signac>=2.0",
"tomli-w>=1.0",
"msgspec>=0.18",
"filelock>=3.14",
"pytest>=8.0; extra == \"dev\"",
"pytest-cov>=5.0; extra == \"dev\"",
"mkdocs>=1.6.0; extra == \"dev\"",
"mkdocs-material>=9.5.0; extra == \"dev\"",
"mkdocstrings[python]>=0.25.0; extra == \"dev\"",
"mkdocs-autorefs>=1.0.0; extra == \"dev\"",
"pre-commit>=3.7; extra == \"dev\"",
"ruff; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/davide-grheco/grubicy",
"Repository, https://github.com/davide-grheco/grubicy",
"Documentation, https://davide-grheco.github.io/grubicy/",
"Issues, https://github.com/davide-grheco/grubicy/issues"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:31:34.465038 | grubicy-1.1.0.tar.gz | 27,461 | 55/ec/e9315773ebbefb47862826c5191d7949e3037524c361f356444c50b06d9a/grubicy-1.1.0.tar.gz | source | sdist | null | false | 03c2c4851354d65a95378744423b9f29 | 64981ebdc3134dc6f0653621f467e2320691886f63626a6d34633cd21876f660 | 55ece9315773ebbefb47862826c5191d7949e3037524c361f356444c50b06d9a | null | [
"LICENSE"
] | 234 |
2.4 | delos-llmax | 1.16.0 | Interface to handle multiple LLMs and AI tools. | # llmax
Python package to manage most external and internal LLM APIs fluently.
# Installation
To install, run the following command:
```bash
python3 -m pip install delos-llmax
```
# How to use
You first have to define a list of `Deployment` as such, where you need to specify the endpoints, key and deployment_name. Then create the client:
```python
from llmax.clients import MultiAIClient
from llmax.models import Deployment, Model
deployments: dict[Model, Deployment] = {
"gpt-4o": Deployment(
model="gpt-4o",
provider="azure",
deployment_name="gpt-4o-2024-05-13",
api_key=os.getenv("LLMAX_AZURE_OPENAI_SWEDENCENTRAL_KEY", ""),
endpoint=os.getenv("LLMAX_AZURE_OPENAI_SWEDENCENTRAL_ENDPOINT", ""),
),
"whisper-1": Deployment(
model="whisper-1",
provider="azure",
deployment_name="whisper-1",
api_key=os.getenv("LLMAX_AZURE_OPENAI_SWEDENCENTRAL_KEY", ""),
endpoint=os.getenv("LLMAX_AZURE_OPENAI_SWEDENCENTRAL_ENDPOINT", ""),
api_version="2024-02-01",
),
}
client = MultiAIClient(
deployments=deployments,
)
```
Then you should define your input (that can be a text, image or audio, following the openai documentation for instance).
```python
messages = [
{"role": "user", "content": "Raconte moi une blague."},
]
```
And finally get the response:
```python
response = client.invoke_to_str(messages, model)
print(response)
```
# Requêter des modèles
Le client `MultiAIClient` offre plusieurs méthodes pour interagir avec les modèles, que ce soit de manière synchrone ou asynchrone.
## Méthodes synchrones
### `invoke_to_str()`
La méthode la plus simple pour obtenir une réponse textuelle directement :
```python
response = client.invoke_to_str(
messages=messages,
model="gpt-4o",
system="Tu es un assistant utile.", # Optionnel
delay=0.0, # Délai entre les tentatives en cas d'erreur
tries=1, # Nombre de tentatives en cas de rate limit
)
print(response) # Affiche directement le texte de la réponse
```
### `invoke()`
Retourne l'objet de réponse complet (déprécié, préférez la version asynchrone) :
```python
response = client.invoke(messages, model="gpt-4o")
print(response.choices[0].message.content)
```
## Méthodes asynchrones
### `ainvoke_to_str()`
Version asynchrone de `invoke_to_str()` :
```python
import asyncio
async def main():
response = await client.ainvoke_to_str(
messages=messages,
model="gpt-4o",
system="Tu es un assistant utile.",
)
print(response)
asyncio.run(main())
```
### `ainvoke()`
Version asynchrone qui retourne l'objet de réponse complet :
```python
response = await client.ainvoke(messages, model="gpt-4o")
print(response.choices[0].message.content)
```
### Streaming avec `astream()`
Pour recevoir les réponses en temps réel au fur et à mesure de leur génération :
```python
async def stream_response():
async for chunk in client.astream(messages, model="gpt-4o"):
if chunk.content:
print(chunk.content, end="", flush=True)
asyncio.run(stream_response())
```
## Paramètres supplémentaires
Toutes les méthodes acceptent des paramètres supplémentaires via `**kwargs` qui sont transmis directement à l'API sous-jacente. Par exemple :
```python
response = await client.ainvoke_to_str(
messages=messages,
model="gpt-4o",
temperature=0.7, # Contrôle la créativité
max_tokens=500, # Limite la longueur de la réponse
top_p=0.9, # Contrôle la diversité
)
```
## Modèles Scaleway
Les modèles Scaleway utilisent une API compatible OpenAI, ce qui permet une intégration transparente avec `llmax`. Pour utiliser un modèle Scaleway, vous devez configurer le déploiement avec le provider `"scaleway"` et fournir soit un `endpoint` complet, soit un `project_id` (recommandé).
### Configuration d'un modèle Scaleway
**Option 1 : Utilisation avec `project_id` (recommandé)**
L'URL sera automatiquement construite comme `https://api.scaleway.ai/v1/{project_id}` :
```python
from llmax.clients import MultiAIClient
from llmax.models import Deployment, Model
import os
deployments: dict[Model, Deployment] = {
"scaleway/llama-3.3-70b-instruct": Deployment(
model="scaleway/llama-3.3-70b-instruct",
provider="scaleway",
deployment_name="llama-3.3-70b-instruct", # Le nom du déploiement sur Scaleway
api_key=os.getenv("SCALEWAY_API_KEY", ""),
project_id=os.getenv("SCALEWAY_PROJECT_ID", ""), # Recommandé
),
"scaleway/qwen3-235b-a22b-instruct-2507": Deployment(
model="scaleway/qwen3-235b-a22b-instruct-2507",
provider="scaleway",
deployment_name="qwen3-235b-a22b-instruct-2507",
api_key=os.getenv("SCALEWAY_API_KEY", ""),
project_id=os.getenv("SCALEWAY_PROJECT_ID", ""),
),
}
client = MultiAIClient(deployments=deployments)
```
**Option 2 : Utilisation avec `endpoint` complet (rétrocompatibilité)**
Vous pouvez également fournir un endpoint complet si vous préférez :
```python
deployments: dict[Model, Deployment] = {
"scaleway/llama-3.3-70b-instruct": Deployment(
model="scaleway/llama-3.3-70b-instruct",
provider="scaleway",
deployment_name="llama-3.3-70b-instruct",
api_key=os.getenv("SCALEWAY_API_KEY", ""),
endpoint=os.getenv("SCALEWAY_ENDPOINT", ""), # Ex: https://api.scaleway.ai/v1/your-project-id
),
}
```
**Note** : Vous devez fournir soit `endpoint` soit `project_id`, mais pas nécessairement les deux. Si vous fournissez `project_id`, l'URL sera construite automatiquement selon la spécification OpenAPI Scaleway.
### Utilisation des modèles Scaleway
Une fois configuré, l'utilisation est identique aux autres modèles :
```python
messages = [
{"role": "user", "content": "Explique-moi le machine learning en quelques phrases."},
]
# Utilisation synchrone
response = client.invoke_to_str(
messages=messages,
model="scaleway/llama-3.3-70b-instruct",
)
# Utilisation asynchrone
response = await client.ainvoke_to_str(
messages=messages,
model="scaleway/qwen3-235b-a22b-instruct-2507",
temperature=0.8,
max_tokens=300,
)
```
### Modèles Scaleway disponibles
Les modèles suivants sont supportés :
- `scaleway/qwen3-235b-a22b-instruct-2507` - Modèle Qwen 3 (235B)
- `scaleway/gpt-oss-120b` - GPT Open Source (120B)
- `scaleway/gemma-3-27b-it` - Gemma 3 (27B)
- `scaleway/whisper-large-v3` - Whisper pour la transcription audio
- `scaleway/voxtral-small-24b-2507` - Voxtral Small (24B)
- `scaleway/mistral-small-3.2-24b-instruct-2506` - Mistral Small 3.2 (24B)
- `scaleway/llama-3.3-70b-instruct` - Llama 3.3 (70B)
- `scaleway/deepseek-r1-distill-llama-70b` - DeepSeek R1 Distill (70B)
### Note spéciale pour le modèle Qwen
Le modèle `scaleway/qwen3-235b-a22b-instruct-2507` nécessite un format spécial pour les réponses JSON. Si vous utilisez `response_format={"type": "json_object"}`, il sera automatiquement transformé en format `json_schema` requis par Scaleway :
```python
response = await client.ainvoke_to_str(
messages=messages,
model="scaleway/qwen3-235b-a22b-instruct-2507",
response_format={"type": "json_object"}, # Transformé automatiquement
)
```
# Specificities
When creating the client, you can also specify two functions, *increment_usage* and *get_usage*.
The first one is **Callable[[float, Model], bool]** while the second is **Callable[[], float]**.
*increment_usage* is a function that is called after a call of the llm. The float is the price and Model, the model used. It can therefore be used to update your database. *get_usage* returns whether a condition is met. For instance, it can be a function that calls your database and returns whether the user is still active.
| text/markdown | Delos Intelligence | maximiliendedinechin@delosintelligence.fr | null | null | null | AI, LLM, generative | [
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14"
] | [] | null | null | <4.0,>=3.10 | [] | [] | [] | [
"boto3<2.0.0,>=1.35.65",
"google-auth<3.0.0,>=2.36.0",
"google-genai==1.2.0",
"loguru<0.8.0,>=0.7.2",
"openai<2.0.0,>=1.42.0",
"pydantic<3.0.0,>=2.9.2",
"pydub<0.26.0,>=0.25.1",
"python-dotenv<2.0.0,>=1.0.1",
"tiktoken<0.8.0,>=0.7.0"
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.11.7 | 2026-02-20T10:31:01.886478 | delos_llmax-1.16.0.tar.gz | 28,782 | ef/4f/f38eea3b48c52003b9569d6338e6fdcec4b73c9af150900fea3552ddbb07/delos_llmax-1.16.0.tar.gz | source | sdist | null | false | 48d186f6b0a832e2844f6c04fbc8fdf0 | 5506cba9b97d060725df526dd1d0e2dff0ce67c5781c2112be3be994ae88c356 | ef4ff38eea3b48c52003b9569d6338e6fdcec4b73c9af150900fea3552ddbb07 | null | [
"LICENSE"
] | 403 |
2.4 | experimental-intelligence | 1.0.0 | Üst düzey fizik, kuantum ve matematik simülasyon motoru. | # EI Experimental Intelligence
Bu kütüphane; astrofizik, kuantum mekaniği ve ileri matematik hesaplamaları için geliştirilmiştir.
| text/markdown | null | sura191838391748482828291828199191 <cobanoglum735@gmail.com> | null | null | null | science, quantum, physics, astrophysics, simulation | [
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent"
] | [] | null | null | >=3.8 | [] | [] | [] | [
"numpy",
"sympy",
"scipy",
"astropy"
] | [] | [] | [] | [
"Home, https://pypi.org/project/experimental-intelligence/"
] | twine/6.2.0 CPython/3.13.2 | 2026-02-20T10:30:54.189362 | experimental_intelligence-1.0.0.tar.gz | 4,336 | c5/0b/e8cb4c48f64f51cb2ae69c8abb93b34194750ab269b4615cd4a4545ca03e/experimental_intelligence-1.0.0.tar.gz | source | sdist | null | false | 5711f61bee79f141651ee619d409f74e | a3192e7ddf7d722a9e4f1f0462aaf7b2daeba9d6055367c03939df1407de466f | c50be8cb4c48f64f51cb2ae69c8abb93b34194750ab269b4615cd4a4545ca03e | null | [] | 240 |
2.4 | ksapi | 1.24.0 | Knowledge Stack API | # ksapi
Knowledge Stack backend API for authentication and knowledge management
This Python package is automatically generated by the [OpenAPI Generator](https://openapi-generator.tech) project:
- API version: 0.1.0
- Package version: 1.24.0
- Generator version: 7.20.0
- Build package: org.openapitools.codegen.languages.PythonClientCodegen
## Requirements.
Python 3.9+
## Installation & Usage
### pip install
If the python package is hosted on a repository, you can install directly using:
```sh
pip install git+https://github.com/GIT_USER_ID/GIT_REPO_ID.git
```
(you may need to run `pip` with root permission: `sudo pip install git+https://github.com/GIT_USER_ID/GIT_REPO_ID.git`)
Then import the package:
```python
import ksapi
```
### Setuptools
Install via [Setuptools](http://pypi.python.org/pypi/setuptools).
```sh
python setup.py install --user
```
(or `sudo python setup.py install` to install the package for all users)
Then import the package:
```python
import ksapi
```
### Tests
Execute `pytest` to run the tests.
## Getting Started
Please follow the [installation procedure](#installation--usage) and then run the following:
```python
import ksapi
from ksapi.rest import ApiException
from pprint import pprint
# Defining the host is optional and defaults to http://localhost:8000
# See configuration.py for a list of all supported configuration parameters.
configuration = ksapi.Configuration(
host = "http://localhost:8000"
)
# Enter a context with an instance of the API client
with ksapi.ApiClient(configuration) as api_client:
# Create an instance of the API class
api_instance = ksapi.ThreadMessagesApi(api_client)
thread_id = UUID('38400000-8cf0-11bd-b23e-10b96e4ef00d') # UUID |
create_thread_message_request = ksapi.CreateThreadMessageRequest() # CreateThreadMessageRequest |
ks_uat = 'ks_uat_example' # str | (optional)
try:
# Create Thread Message Handler
api_response = api_instance.create_thread_message(thread_id, create_thread_message_request, ks_uat=ks_uat)
print("The response of ThreadMessagesApi->create_thread_message:\n")
pprint(api_response)
except ApiException as e:
print("Exception when calling ThreadMessagesApi->create_thread_message: %s\n" % e)
```
## Documentation for API Endpoints
All URIs are relative to *http://localhost:8000*
Class | Method | HTTP request | Description
------------ | ------------- | ------------- | -------------
*ThreadMessagesApi* | [**create_thread_message**](docs/ThreadMessagesApi.md#create_thread_message) | **POST** /v1/threads/{thread_id}/messages | Create Thread Message Handler
*ThreadMessagesApi* | [**get_thread_message**](docs/ThreadMessagesApi.md#get_thread_message) | **GET** /v1/threads/{thread_id}/messages/{message_id} | Get Thread Message Handler
*ThreadMessagesApi* | [**list_thread_messages**](docs/ThreadMessagesApi.md#list_thread_messages) | **GET** /v1/threads/{thread_id}/messages | List Thread Messages Handler
*ThreadsApi* | [**create_thread**](docs/ThreadsApi.md#create_thread) | **POST** /v1/threads | Create Thread Handler
*ThreadsApi* | [**delete_thread**](docs/ThreadsApi.md#delete_thread) | **DELETE** /v1/threads/{thread_id} | Delete Thread Handler
*ThreadsApi* | [**get_thread**](docs/ThreadsApi.md#get_thread) | **GET** /v1/threads/{thread_id} | Get Thread Handler
*ThreadsApi* | [**list_threads**](docs/ThreadsApi.md#list_threads) | **GET** /v1/threads | List Threads Handler
*ThreadsApi* | [**stream_thread**](docs/ThreadsApi.md#stream_thread) | **GET** /v1/threads/{thread_id}/stream | Stream Thread Handler
*ThreadsApi* | [**update_thread**](docs/ThreadsApi.md#update_thread) | **PATCH** /v1/threads/{thread_id} | Update Thread Handler
*AuthApi* | [**create_password_user**](docs/AuthApi.md#create_password_user) | **POST** /v1/auth/pw/user | Create Password User Handler
*AuthApi* | [**initiate_sso**](docs/AuthApi.md#initiate_sso) | **POST** /v1/auth/sso/initiate | Initiate Sso Handler
*AuthApi* | [**oauth2_callback**](docs/AuthApi.md#oauth2_callback) | **GET** /v1/auth/sso/oauth2/callback | Oauth2 Callback Handler
*AuthApi* | [**pw_email_verification**](docs/AuthApi.md#pw_email_verification) | **POST** /v1/auth/pw/email_verification | Pw Email Verification Handler
*AuthApi* | [**pw_signin**](docs/AuthApi.md#pw_signin) | **POST** /v1/auth/pw/signin | Signin Handler
*AuthApi* | [**refresh_uat**](docs/AuthApi.md#refresh_uat) | **POST** /v1/auth/uat | Refresh Uat Handler
*AuthApi* | [**reset_password**](docs/AuthApi.md#reset_password) | **POST** /v1/auth/pw/reset | Reset Password Handler
*AuthApi* | [**reset_password_with_token**](docs/AuthApi.md#reset_password_with_token) | **POST** /v1/auth/pw/reset_with_token | Reset Password With Token Handler
*AuthApi* | [**send_pw_reset_email**](docs/AuthApi.md#send_pw_reset_email) | **POST** /v1/auth/pw/send_reset_email | Send Pw Reset Email Handler
*AuthApi* | [**signout**](docs/AuthApi.md#signout) | **POST** /v1/auth/signout | Signout Handler
*ChunkLineagesApi* | [**create_chunk_lineage**](docs/ChunkLineagesApi.md#create_chunk_lineage) | **POST** /v1/chunk-lineages | Create Chunk Lineage Handler
*ChunkLineagesApi* | [**delete_chunk_lineage**](docs/ChunkLineagesApi.md#delete_chunk_lineage) | **DELETE** /v1/chunk-lineages | Delete Chunk Lineage Handler
*ChunkLineagesApi* | [**get_chunk_lineage**](docs/ChunkLineagesApi.md#get_chunk_lineage) | **GET** /v1/chunk-lineages/{chunk_id} | Get Chunk Lineage Handler
*ChunksApi* | [**create_chunk**](docs/ChunksApi.md#create_chunk) | **POST** /v1/chunks | Create Chunk Handler
*ChunksApi* | [**delete_chunk**](docs/ChunksApi.md#delete_chunk) | **DELETE** /v1/chunks/{chunk_id} | Delete Chunk Handler
*ChunksApi* | [**get_chunk**](docs/ChunksApi.md#get_chunk) | **GET** /v1/chunks/{chunk_id} | Get Chunk Handler
*ChunksApi* | [**search_chunks**](docs/ChunksApi.md#search_chunks) | **POST** /v1/chunks/search | Search Chunks Handler
*ChunksApi* | [**update_chunk_content**](docs/ChunksApi.md#update_chunk_content) | **PATCH** /v1/chunks/{chunk_id}/content | Update Chunk Content Handler
*ChunksApi* | [**update_chunk_metadata**](docs/ChunksApi.md#update_chunk_metadata) | **PATCH** /v1/chunks/{chunk_id} | Update Chunk Metadata Handler
*DefaultApi* | [**health_check**](docs/DefaultApi.md#health_check) | **GET** /healthz | Health Check Handler
*DefaultApi* | [**hello**](docs/DefaultApi.md#hello) | **GET** / | Root Handler
*DocumentVersionsApi* | [**clear_document_version_contents**](docs/DocumentVersionsApi.md#clear_document_version_contents) | **DELETE** /v1/document_versions/{version_id}/contents | Clear Document Version Contents Handler
*DocumentVersionsApi* | [**create_document_version**](docs/DocumentVersionsApi.md#create_document_version) | **POST** /v1/documents/{document_id}/versions | Create Document Version Handler
*DocumentVersionsApi* | [**delete_document_version**](docs/DocumentVersionsApi.md#delete_document_version) | **DELETE** /v1/document_versions/{version_id} | Delete Document Version Handler
*DocumentVersionsApi* | [**get_document_version**](docs/DocumentVersionsApi.md#get_document_version) | **GET** /v1/document_versions/{version_id} | Get Document Version Handler
*DocumentVersionsApi* | [**get_document_version_contents**](docs/DocumentVersionsApi.md#get_document_version_contents) | **GET** /v1/document_versions/{version_id}/contents | Get Document Version Contents Handler
*DocumentVersionsApi* | [**list_document_versions**](docs/DocumentVersionsApi.md#list_document_versions) | **GET** /v1/document_versions | List Document Versions Handler
*DocumentVersionsApi* | [**update_document_version_metadata**](docs/DocumentVersionsApi.md#update_document_version_metadata) | **PATCH** /v1/document_versions/{version_id}/metadata | Update Document Version Metadata Handler
*DocumentsApi* | [**create_document**](docs/DocumentsApi.md#create_document) | **POST** /v1/documents | Create Document Handler
*DocumentsApi* | [**delete_document**](docs/DocumentsApi.md#delete_document) | **DELETE** /v1/documents/{document_id} | Delete Document Handler
*DocumentsApi* | [**get_document**](docs/DocumentsApi.md#get_document) | **GET** /v1/documents/{document_id} | Get Document Handler
*DocumentsApi* | [**ingest_document**](docs/DocumentsApi.md#ingest_document) | **POST** /v1/documents/ingest | Ingest Document Handler
*DocumentsApi* | [**list_documents**](docs/DocumentsApi.md#list_documents) | **GET** /v1/documents | List Documents Handler
*DocumentsApi* | [**update_document**](docs/DocumentsApi.md#update_document) | **PATCH** /v1/documents/{document_id} | Update Document Handler
*FoldersApi* | [**create_folder**](docs/FoldersApi.md#create_folder) | **POST** /v1/folders | Create Folder Handler
*FoldersApi* | [**delete_folder**](docs/FoldersApi.md#delete_folder) | **DELETE** /v1/folders/{folder_id} | Delete Folder Handler
*FoldersApi* | [**get_folder**](docs/FoldersApi.md#get_folder) | **GET** /v1/folders/{folder_id} | Get Folder Handler
*FoldersApi* | [**list_folder_contents**](docs/FoldersApi.md#list_folder_contents) | **GET** /v1/folders/{folder_id}/contents | List Folder Contents Handler
*FoldersApi* | [**list_folders**](docs/FoldersApi.md#list_folders) | **GET** /v1/folders | List Folders Handler
*FoldersApi* | [**update_folder**](docs/FoldersApi.md#update_folder) | **PATCH** /v1/folders/{folder_id} | Update Folder Handler
*InvitesApi* | [**accept_invite**](docs/InvitesApi.md#accept_invite) | **POST** /v1/invites/{invite_id}/accept | Accept Invite
*InvitesApi* | [**create_invite**](docs/InvitesApi.md#create_invite) | **POST** /v1/invites | Create Invite
*InvitesApi* | [**delete_invite**](docs/InvitesApi.md#delete_invite) | **DELETE** /v1/invites/{invite_id} | Delete Invite
*InvitesApi* | [**list_invites**](docs/InvitesApi.md#list_invites) | **GET** /v1/invites | List Invites Handler
*PathPartsApi* | [**bulk_add_path_part_tags**](docs/PathPartsApi.md#bulk_add_path_part_tags) | **POST** /v1/path-parts/{path_part_id}/tags | Bulk Add Path Part Tags Handler
*PathPartsApi* | [**bulk_remove_path_part_tags**](docs/PathPartsApi.md#bulk_remove_path_part_tags) | **DELETE** /v1/path-parts/{path_part_id}/tags | Bulk Remove Path Part Tags Handler
*PathPartsApi* | [**get_path_part**](docs/PathPartsApi.md#get_path_part) | **GET** /v1/path-parts/{path_part_id} | Get Path Part Handler
*PathPartsApi* | [**list_path_parts**](docs/PathPartsApi.md#list_path_parts) | **GET** /v1/path-parts | List Path Parts Handler
*SectionsApi* | [**create_section**](docs/SectionsApi.md#create_section) | **POST** /v1/sections | Create Section Handler
*SectionsApi* | [**delete_section**](docs/SectionsApi.md#delete_section) | **DELETE** /v1/sections/{section_id} | Delete Section Handler
*SectionsApi* | [**get_section**](docs/SectionsApi.md#get_section) | **GET** /v1/sections/{section_id} | Get Section Handler
*SectionsApi* | [**update_section**](docs/SectionsApi.md#update_section) | **PATCH** /v1/sections/{section_id} | Update Section Handler
*TagsApi* | [**create_tag**](docs/TagsApi.md#create_tag) | **POST** /v1/tags | Create Tag Handler
*TagsApi* | [**delete_tag**](docs/TagsApi.md#delete_tag) | **DELETE** /v1/tags/{tag_id} | Delete Tag Handler
*TagsApi* | [**get_tag**](docs/TagsApi.md#get_tag) | **GET** /v1/tags/{tag_id} | Get Tag Handler
*TagsApi* | [**list_tags**](docs/TagsApi.md#list_tags) | **GET** /v1/tags | List Tags Handler
*TagsApi* | [**update_tag**](docs/TagsApi.md#update_tag) | **PATCH** /v1/tags/{tag_id} | Update Tag Handler
*TenantsApi* | [**create_tenant**](docs/TenantsApi.md#create_tenant) | **POST** /v1/tenants | Create Tenant
*TenantsApi* | [**delete_tenant**](docs/TenantsApi.md#delete_tenant) | **DELETE** /v1/tenants/{tenant_id} | Delete Tenant
*TenantsApi* | [**delete_tenant_user**](docs/TenantsApi.md#delete_tenant_user) | **DELETE** /v1/tenants/{tenant_id}/users/{user_id} | Delete Tenant User
*TenantsApi* | [**get_tenant**](docs/TenantsApi.md#get_tenant) | **GET** /v1/tenants/{tenant_id} | Get Tenant
*TenantsApi* | [**list_tenant_users**](docs/TenantsApi.md#list_tenant_users) | **GET** /v1/tenants/{tenant_id}/users | List Tenant Users
*TenantsApi* | [**list_tenants**](docs/TenantsApi.md#list_tenants) | **GET** /v1/tenants | List Tenants
*TenantsApi* | [**update_tenant**](docs/TenantsApi.md#update_tenant) | **PATCH** /v1/tenants/{tenant_id} | Update Tenant
*TenantsApi* | [**update_tenant_user**](docs/TenantsApi.md#update_tenant_user) | **PATCH** /v1/tenants/{tenant_id}/users/{user_id} | Update Tenant User
*UserPermissionsApi* | [**create_user_permission**](docs/UserPermissionsApi.md#create_user_permission) | **POST** /v1/user-permissions | Create User Permission Handler
*UserPermissionsApi* | [**delete_user_permission**](docs/UserPermissionsApi.md#delete_user_permission) | **DELETE** /v1/user-permissions/{permission_id} | Delete User Permission Handler
*UserPermissionsApi* | [**list_user_permissions**](docs/UserPermissionsApi.md#list_user_permissions) | **GET** /v1/user-permissions | List User Permissions Handler
*UserPermissionsApi* | [**update_user_permission**](docs/UserPermissionsApi.md#update_user_permission) | **PATCH** /v1/user-permissions/{permission_id} | Update User Permission Handler
*UsersApi* | [**get_me**](docs/UsersApi.md#get_me) | **GET** /v1/users/me | Get Me Handler
*UsersApi* | [**update_me**](docs/UsersApi.md#update_me) | **PATCH** /v1/users | Update Me Handler
*WorkflowsApi* | [**get_workflow**](docs/WorkflowsApi.md#get_workflow) | **GET** /v1/workflows/{workflow_id} | Get Workflow Handler
*WorkflowsApi* | [**list_workflows**](docs/WorkflowsApi.md#list_workflows) | **GET** /v1/workflows | List Workflows Handler
*WorkflowsApi* | [**workflow_action**](docs/WorkflowsApi.md#workflow_action) | **POST** /v1/workflows/{workflow_id} | Workflow Action Handler
## Documentation For Models
- [BulkTagRequest](docs/BulkTagRequest.md)
- [ChunkContentItem](docs/ChunkContentItem.md)
- [ChunkLineageResponse](docs/ChunkLineageResponse.md)
- [ChunkMetadataInput](docs/ChunkMetadataInput.md)
- [ChunkMetadataOutput](docs/ChunkMetadataOutput.md)
- [ChunkResponse](docs/ChunkResponse.md)
- [ChunkSearchRequest](docs/ChunkSearchRequest.md)
- [ChunkType](docs/ChunkType.md)
- [ClearVersionContentsResponse](docs/ClearVersionContentsResponse.md)
- [CreateChunkLineageRequest](docs/CreateChunkLineageRequest.md)
- [CreateChunkRequest](docs/CreateChunkRequest.md)
- [CreateDocumentRequest](docs/CreateDocumentRequest.md)
- [CreateFolderRequest](docs/CreateFolderRequest.md)
- [CreatePasswordUserRequest](docs/CreatePasswordUserRequest.md)
- [CreatePermissionRequest](docs/CreatePermissionRequest.md)
- [CreateSectionRequest](docs/CreateSectionRequest.md)
- [CreateTagRequest](docs/CreateTagRequest.md)
- [CreateTenantRequest](docs/CreateTenantRequest.md)
- [CreateThreadMessageRequest](docs/CreateThreadMessageRequest.md)
- [CreateThreadRequest](docs/CreateThreadRequest.md)
- [DocumentOrigin](docs/DocumentOrigin.md)
- [DocumentResponse](docs/DocumentResponse.md)
- [DocumentType](docs/DocumentType.md)
- [DocumentVersionMetadata](docs/DocumentVersionMetadata.md)
- [DocumentVersionMetadataUpdate](docs/DocumentVersionMetadataUpdate.md)
- [DocumentVersionResponse](docs/DocumentVersionResponse.md)
- [EmailSentResponse](docs/EmailSentResponse.md)
- [EmailVerificationRequest](docs/EmailVerificationRequest.md)
- [EmbeddingModel](docs/EmbeddingModel.md)
- [FolderResponse](docs/FolderResponse.md)
- [FolderResponseOrDocumentResponse](docs/FolderResponseOrDocumentResponse.md)
- [HTTPValidationError](docs/HTTPValidationError.md)
- [HealthCheckResponse](docs/HealthCheckResponse.md)
- [IdpType](docs/IdpType.md)
- [IngestDocumentResponse](docs/IngestDocumentResponse.md)
- [InviteResponse](docs/InviteResponse.md)
- [InviteStatus](docs/InviteStatus.md)
- [InviteUserRequest](docs/InviteUserRequest.md)
- [LineageEdgeResponse](docs/LineageEdgeResponse.md)
- [LineageGraphResponse](docs/LineageGraphResponse.md)
- [LineageNodeResponse](docs/LineageNodeResponse.md)
- [LocationInner](docs/LocationInner.md)
- [MessageRole](docs/MessageRole.md)
- [OAuth2Config](docs/OAuth2Config.md)
- [PaginatedResponseAnnotatedUnionFolderResponseDocumentResponseDiscriminator](docs/PaginatedResponseAnnotatedUnionFolderResponseDocumentResponseDiscriminator.md)
- [PaginatedResponseAnnotatedUnionSectionContentItemChunkContentItemDiscriminator](docs/PaginatedResponseAnnotatedUnionSectionContentItemChunkContentItemDiscriminator.md)
- [PaginatedResponseDocumentResponse](docs/PaginatedResponseDocumentResponse.md)
- [PaginatedResponseDocumentVersionResponse](docs/PaginatedResponseDocumentVersionResponse.md)
- [PaginatedResponseFolderResponse](docs/PaginatedResponseFolderResponse.md)
- [PaginatedResponseInviteResponse](docs/PaginatedResponseInviteResponse.md)
- [PaginatedResponsePathPartResponse](docs/PaginatedResponsePathPartResponse.md)
- [PaginatedResponsePermissionResponse](docs/PaginatedResponsePermissionResponse.md)
- [PaginatedResponseTagResponse](docs/PaginatedResponseTagResponse.md)
- [PaginatedResponseTenantResponse](docs/PaginatedResponseTenantResponse.md)
- [PaginatedResponseTenantUserResponse](docs/PaginatedResponseTenantUserResponse.md)
- [PaginatedResponseThreadMessageResponse](docs/PaginatedResponseThreadMessageResponse.md)
- [PaginatedResponseThreadResponse](docs/PaginatedResponseThreadResponse.md)
- [PaginatedResponseWorkflowSummaryResponse](docs/PaginatedResponseWorkflowSummaryResponse.md)
- [PartType](docs/PartType.md)
- [PasswordResetRequest](docs/PasswordResetRequest.md)
- [PasswordResetWithTokenRequest](docs/PasswordResetWithTokenRequest.md)
- [PathOrder](docs/PathOrder.md)
- [PathPartResponse](docs/PathPartResponse.md)
- [PathPartTagsResponse](docs/PathPartTagsResponse.md)
- [PermissionCapability](docs/PermissionCapability.md)
- [PermissionResponse](docs/PermissionResponse.md)
- [PipelineState](docs/PipelineState.md)
- [PipelineStatus](docs/PipelineStatus.md)
- [Polygon](docs/Polygon.md)
- [PolygonReference](docs/PolygonReference.md)
- [RootResponse](docs/RootResponse.md)
- [ScoredChunkResponse](docs/ScoredChunkResponse.md)
- [SectionContentItem](docs/SectionContentItem.md)
- [SectionContentItemOrChunkContentItem](docs/SectionContentItemOrChunkContentItem.md)
- [SectionResponse](docs/SectionResponse.md)
- [SignInRequest](docs/SignInRequest.md)
- [TagResponse](docs/TagResponse.md)
- [TenantResponse](docs/TenantResponse.md)
- [TenantUserEditRequest](docs/TenantUserEditRequest.md)
- [TenantUserResponse](docs/TenantUserResponse.md)
- [TenantUserRole](docs/TenantUserRole.md)
- [ThreadMessageResponse](docs/ThreadMessageResponse.md)
- [ThreadResponse](docs/ThreadResponse.md)
- [UpdateChunkContentRequest](docs/UpdateChunkContentRequest.md)
- [UpdateChunkMetadataRequest](docs/UpdateChunkMetadataRequest.md)
- [UpdateDocumentRequest](docs/UpdateDocumentRequest.md)
- [UpdateFolderRequest](docs/UpdateFolderRequest.md)
- [UpdatePermissionRequest](docs/UpdatePermissionRequest.md)
- [UpdateSectionRequest](docs/UpdateSectionRequest.md)
- [UpdateTagRequest](docs/UpdateTagRequest.md)
- [UpdateTenantRequest](docs/UpdateTenantRequest.md)
- [UpdateThreadRequest](docs/UpdateThreadRequest.md)
- [UpdateUserRequest](docs/UpdateUserRequest.md)
- [UserResponse](docs/UserResponse.md)
- [ValidationError](docs/ValidationError.md)
- [WorkflowAction](docs/WorkflowAction.md)
- [WorkflowActionResponse](docs/WorkflowActionResponse.md)
- [WorkflowDetailResponse](docs/WorkflowDetailResponse.md)
- [WorkflowSummaryResponse](docs/WorkflowSummaryResponse.md)
<a id="documentation-for-authorization"></a>
## Documentation For Authorization
Endpoints do not require authorization.
## Author
| text/markdown | OpenAPI Generator community | OpenAPI Generator Community <team@openapitools.org> | null | null | null | OpenAPI, OpenAPI-Generator, Knowledge Stack API | [] | [] | null | null | >=3.9 | [] | [] | [] | [
"urllib3<3.0.0,>=2.1.0",
"python-dateutil>=2.8.2",
"pydantic>=2",
"typing-extensions>=4.7.1"
] | [] | [] | [] | [
"Repository, https://github.com/GIT_USER_ID/GIT_REPO_ID"
] | twine/6.2.0 CPython/3.12.12 | 2026-02-20T10:30:48.571326 | ksapi-1.24.0.tar.gz | 108,364 | c1/3c/08313f5ad8ff74664a17a6103618450152ff39691f8071cd60bac50d7f03/ksapi-1.24.0.tar.gz | source | sdist | null | false | 0e1ee4eb9b17d688472670afa7dfd4a1 | 86c11c62718bd9b647dc62d50c5d0e59a8051ab8b47552e76c144d975c3f3fdc | c13c08313f5ad8ff74664a17a6103618450152ff39691f8071cd60bac50d7f03 | null | [] | 247 |
2.3 | supermemory | 3.26.0 | The official Python library for the supermemory API | # Supermemory Python API library
<!-- prettier-ignore -->
[)](https://pypi.org/project/supermemory/)
The Supermemory Python library provides convenient access to the Supermemory REST API from any Python 3.9+
application. The library includes type definitions for all request params and response fields,
and offers both synchronous and asynchronous clients powered by [httpx](https://github.com/encode/httpx).
It is generated with [Stainless](https://www.stainless.com/).
## MCP Server
Use the Supermemory MCP Server to enable AI assistants to interact with this API, allowing them to explore endpoints, make test requests, and use documentation to help integrate this SDK into your application.
[](https://cursor.com/en-US/install-mcp?name=supermemory-mcp&config=eyJuYW1lIjoic3VwZXJtZW1vcnktbWNwIiwidHJhbnNwb3J0IjoiaHR0cCIsInVybCI6Imh0dHBzOi8vc3VwZXJtZW1vcnktbmV3LnN0bG1jcC5jb20iLCJoZWFkZXJzIjp7Ingtc3VwZXJtZW1vcnktYXBpLWtleSI6Ik15IEFQSSBLZXkifX0)
[](https://vscode.stainless.com/mcp/%7B%22name%22%3A%22supermemory-mcp%22%2C%22type%22%3A%22http%22%2C%22url%22%3A%22https%3A%2F%2Fsupermemory-new.stlmcp.com%22%2C%22headers%22%3A%7B%22x-supermemory-api-key%22%3A%22My%20API%20Key%22%7D%7D)
> Note: You may need to set environment variables in your MCP client.
## Documentation
The REST API documentation can be found on [docs.supermemory.ai](https://docs.supermemory.ai). The full API of this library can be found in [api.md](https://github.com/supermemoryai/python-sdk/tree/main/api.md).
## Installation
```sh
# install from PyPI
pip install supermemory
```
## Usage
The full API of this library can be found in [api.md](https://github.com/supermemoryai/python-sdk/tree/main/api.md).
```python
import os
from supermemory import Supermemory
client = Supermemory(
api_key=os.environ.get("SUPERMEMORY_API_KEY"), # This is the default and can be omitted
)
response = client.search.documents(
q="documents related to python",
)
print(response.results)
```
While you can provide an `api_key` keyword argument,
we recommend using [python-dotenv](https://pypi.org/project/python-dotenv/)
to add `SUPERMEMORY_API_KEY="My API Key"` to your `.env` file
so that your API Key is not stored in source control.
## Async usage
Simply import `AsyncSupermemory` instead of `Supermemory` and use `await` with each API call:
```python
import os
import asyncio
from supermemory import AsyncSupermemory
client = AsyncSupermemory(
api_key=os.environ.get("SUPERMEMORY_API_KEY"), # This is the default and can be omitted
)
async def main() -> None:
response = await client.search.documents(
q="documents related to python",
)
print(response.results)
asyncio.run(main())
```
Functionality between the synchronous and asynchronous clients is otherwise identical.
### With aiohttp
By default, the async client uses `httpx` for HTTP requests. However, for improved concurrency performance you may also use `aiohttp` as the HTTP backend.
You can enable this by installing `aiohttp`:
```sh
# install from PyPI
pip install supermemory[aiohttp]
```
Then you can enable it by instantiating the client with `http_client=DefaultAioHttpClient()`:
```python
import os
import asyncio
from supermemory import DefaultAioHttpClient
from supermemory import AsyncSupermemory
async def main() -> None:
async with AsyncSupermemory(
api_key=os.environ.get("SUPERMEMORY_API_KEY"), # This is the default and can be omitted
http_client=DefaultAioHttpClient(),
) as client:
response = await client.search.documents(
q="documents related to python",
)
print(response.results)
asyncio.run(main())
```
## Using types
Nested request parameters are [TypedDicts](https://docs.python.org/3/library/typing.html#typing.TypedDict). Responses are [Pydantic models](https://docs.pydantic.dev) which also provide helper methods for things like:
- Serializing back into JSON, `model.to_json()`
- Converting to a dictionary, `model.to_dict()`
Typed requests and responses provide autocomplete and documentation within your editor. If you would like to see type errors in VS Code to help catch bugs earlier, set `python.analysis.typeCheckingMode` to `basic`.
## Nested params
Nested parameters are dictionaries, typed using `TypedDict`, for example:
```python
from supermemory import Supermemory
client = Supermemory()
response = client.search.memories(
q="machine learning concepts",
include={},
)
print(response.include)
```
## File uploads
Request parameters that correspond to file uploads can be passed as `bytes`, or a [`PathLike`](https://docs.python.org/3/library/os.html#os.PathLike) instance or a tuple of `(filename, contents, media type)`.
```python
from pathlib import Path
from supermemory import Supermemory
client = Supermemory()
client.documents.upload_file(
file=Path("/path/to/file"),
)
```
The async client uses the exact same interface. If you pass a [`PathLike`](https://docs.python.org/3/library/os.html#os.PathLike) instance, the file contents will be read asynchronously automatically.
## Handling errors
When the library is unable to connect to the API (for example, due to network connection problems or a timeout), a subclass of `supermemory.APIConnectionError` is raised.
When the API returns a non-success status code (that is, 4xx or 5xx
response), a subclass of `supermemory.APIStatusError` is raised, containing `status_code` and `response` properties.
All errors inherit from `supermemory.APIError`.
```python
import supermemory
from supermemory import Supermemory
client = Supermemory()
try:
client.add(
content="content",
)
except supermemory.APIConnectionError as e:
print("The server could not be reached")
print(e.__cause__) # an underlying Exception, likely raised within httpx.
except supermemory.RateLimitError as e:
print("A 429 status code was received; we should back off a bit.")
except supermemory.APIStatusError as e:
print("Another non-200-range status code was received")
print(e.status_code)
print(e.response)
```
Error codes are as follows:
| Status Code | Error Type |
| ----------- | -------------------------- |
| 400 | `BadRequestError` |
| 401 | `AuthenticationError` |
| 403 | `PermissionDeniedError` |
| 404 | `NotFoundError` |
| 422 | `UnprocessableEntityError` |
| 429 | `RateLimitError` |
| >=500 | `InternalServerError` |
| N/A | `APIConnectionError` |
### Retries
Certain errors are automatically retried 2 times by default, with a short exponential backoff.
Connection errors (for example, due to a network connectivity problem), 408 Request Timeout, 409 Conflict,
429 Rate Limit, and >=500 Internal errors are all retried by default.
You can use the `max_retries` option to configure or disable retry settings:
```python
from supermemory import Supermemory
# Configure the default for all requests:
client = Supermemory(
# default is 2
max_retries=0,
)
# Or, configure per-request:
client.with_options(max_retries=5).add(
content="content",
)
```
### Timeouts
By default requests time out after 1 minute. You can configure this with a `timeout` option,
which accepts a float or an [`httpx.Timeout`](https://www.python-httpx.org/advanced/timeouts/#fine-tuning-the-configuration) object:
```python
from supermemory import Supermemory
# Configure the default for all requests:
client = Supermemory(
# 20 seconds (default is 1 minute)
timeout=20.0,
)
# More granular control:
client = Supermemory(
timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),
)
# Override per-request:
client.with_options(timeout=5.0).add(
content="content",
)
```
On timeout, an `APITimeoutError` is thrown.
Note that requests that time out are [retried twice by default](https://github.com/supermemoryai/python-sdk/tree/main/#retries).
## Advanced
### Logging
We use the standard library [`logging`](https://docs.python.org/3/library/logging.html) module.
You can enable logging by setting the environment variable `SUPERMEMORY_LOG` to `info`.
```shell
$ export SUPERMEMORY_LOG=info
```
Or to `debug` for more verbose logging.
### How to tell whether `None` means `null` or missing
In an API response, a field may be explicitly `null`, or missing entirely; in either case, its value is `None` in this library. You can differentiate the two cases with `.model_fields_set`:
```py
if response.my_field is None:
if 'my_field' not in response.model_fields_set:
print('Got json like {}, without a "my_field" key present at all.')
else:
print('Got json like {"my_field": null}.')
```
### Accessing raw response data (e.g. headers)
The "raw" Response object can be accessed by prefixing `.with_raw_response.` to any HTTP method call, e.g.,
```py
from supermemory import Supermemory
client = Supermemory()
response = client.with_raw_response.add(
content="content",
)
print(response.headers.get('X-My-Header'))
client = response.parse() # get the object that `add()` would have returned
print(client.id)
```
These methods return an [`APIResponse`](https://github.com/supermemoryai/python-sdk/tree/main/src/supermemory/_response.py) object.
The async client returns an [`AsyncAPIResponse`](https://github.com/supermemoryai/python-sdk/tree/main/src/supermemory/_response.py) with the same structure, the only difference being `await`able methods for reading the response content.
#### `.with_streaming_response`
The above interface eagerly reads the full response body when you make the request, which may not always be what you want.
To stream the response body, use `.with_streaming_response` instead, which requires a context manager and only reads the response body once you call `.read()`, `.text()`, `.json()`, `.iter_bytes()`, `.iter_text()`, `.iter_lines()` or `.parse()`. In the async client, these are async methods.
```python
with client.with_streaming_response.add(
content="content",
) as response:
print(response.headers.get("X-My-Header"))
for line in response.iter_lines():
print(line)
```
The context manager is required so that the response will reliably be closed.
### Making custom/undocumented requests
This library is typed for convenient access to the documented API.
If you need to access undocumented endpoints, params, or response properties, the library can still be used.
#### Undocumented endpoints
To make requests to undocumented endpoints, you can make requests using `client.get`, `client.post`, and other
http verbs. Options on the client will be respected (such as retries) when making this request.
```py
import httpx
response = client.post(
"/foo",
cast_to=httpx.Response,
body={"my_param": True},
)
print(response.headers.get("x-foo"))
```
#### Undocumented request params
If you want to explicitly send an extra param, you can do so with the `extra_query`, `extra_body`, and `extra_headers` request
options.
#### Undocumented response properties
To access undocumented response properties, you can access the extra fields like `response.unknown_prop`. You
can also get all the extra fields on the Pydantic model as a dict with
[`response.model_extra`](https://docs.pydantic.dev/latest/api/base_model/#pydantic.BaseModel.model_extra).
### Configuring the HTTP client
You can directly override the [httpx client](https://www.python-httpx.org/api/#client) to customize it for your use case, including:
- Support for [proxies](https://www.python-httpx.org/advanced/proxies/)
- Custom [transports](https://www.python-httpx.org/advanced/transports/)
- Additional [advanced](https://www.python-httpx.org/advanced/clients/) functionality
```python
import httpx
from supermemory import Supermemory, DefaultHttpxClient
client = Supermemory(
# Or use the `SUPERMEMORY_BASE_URL` env var
base_url="http://my.test.server.example.com:8083",
http_client=DefaultHttpxClient(
proxy="http://my.test.proxy.example.com",
transport=httpx.HTTPTransport(local_address="0.0.0.0"),
),
)
```
You can also customize the client on a per-request basis by using `with_options()`:
```python
client.with_options(http_client=DefaultHttpxClient(...))
```
### Managing HTTP resources
By default the library closes underlying HTTP connections whenever the client is [garbage collected](https://docs.python.org/3/reference/datamodel.html#object.__del__). You can manually close the client using the `.close()` method if desired, or with a context manager that closes when exiting.
```py
from supermemory import Supermemory
with Supermemory() as client:
# make requests here
...
# HTTP client is now closed
```
## Versioning
This package generally follows [SemVer](https://semver.org/spec/v2.0.0.html) conventions, though certain backwards-incompatible changes may be released as minor versions:
1. Changes that only affect static types, without breaking runtime behavior.
2. Changes to library internals which are technically public but not intended or documented for external use. _(Please open a GitHub issue to let us know if you are relying on such internals.)_
3. Changes that we do not expect to impact the vast majority of users in practice.
We take backwards-compatibility seriously and work hard to ensure you can rely on a smooth upgrade experience.
We are keen for your feedback; please open an [issue](https://www.github.com/supermemoryai/python-sdk/issues) with questions, bugs, or suggestions.
### Determining the installed version
If you've upgraded to the latest version but aren't seeing any new features you were expecting then your python environment is likely still using an older version.
You can determine the version that is being used at runtime with:
```py
import supermemory
print(supermemory.__version__)
```
## Requirements
Python 3.9 or higher.
## Contributing
See [the contributing documentation](https://github.com/supermemoryai/python-sdk/tree/main/./CONTRIBUTING.md).
| text/markdown | null | Supermemory <dhravya@supermemory.com> | null | null | Apache-2.0 | null | [
"Intended Audience :: Developers",
"License :: OSI Approved :: Apache Software License",
"Operating System :: MacOS",
"Operating System :: Microsoft :: Windows",
"Operating System :: OS Independent",
"Operating System :: POSIX",
"Operating System :: POSIX :: Linux",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14",
"Topic :: Software Development :: Libraries :: Python Modules",
"Typing :: Typed"
] | [] | null | null | >=3.9 | [] | [] | [] | [
"anyio<5,>=3.5.0",
"distro<2,>=1.7.0",
"httpx<1,>=0.23.0",
"pydantic<3,>=1.9.0",
"sniffio",
"typing-extensions<5,>=4.10",
"aiohttp; extra == \"aiohttp\"",
"httpx-aiohttp>=0.1.9; extra == \"aiohttp\""
] | [] | [] | [] | [
"Homepage, https://github.com/supermemoryai/python-sdk",
"Repository, https://github.com/supermemoryai/python-sdk"
] | twine/5.1.1 CPython/3.12.9 | 2026-02-20T10:30:32.452396 | supermemory-3.26.0.tar.gz | 154,511 | 66/b9/15db2b26087296a21ca6b352b854b2ee760816fe4b3662464ae6dc889bde/supermemory-3.26.0.tar.gz | source | sdist | null | false | c96755d45e296a17c2464a1a036639d7 | 35513788822e0a500da0086be4cfb5dd541fdced682131cbae316380f2cda680 | 66b915db2b26087296a21ca6b352b854b2ee760816fe4b3662464ae6dc889bde | null | [] | 651 |
2.4 | hexa-ddd-blueprint | 0.1.1 | CLI tool that scaffolds opinionated Python projects following DDD + Hexagonal Architecture | # hexa-ddd-blueprint
[](https://github.com/AymanKastali/hexa-ddd-blueprint/actions/workflows/ci.yml)
[](https://pypi.org/project/hexa-ddd-blueprint/)
CLI tool that scaffolds opinionated Python projects following **DDD + Hexagonal (Ports & Adapters) Architecture**.
## Installation
```bash
uv tool install hexa-ddd-blueprint
```
## Usage
### Interactive mode (default)
```bash
hexa-ddd-blueprint new
```
Launches prompts for project name, description, author, DB choice, and Python version.
### Flag-driven mode
```bash
hexa-ddd-blueprint new myproject \
--description "My awesome service" \
--author "John Doe" \
--db postgres \
--python 3.14 \
--no-docker \
--no-ci
```
### All flags
| Flag | Default | Description |
|---|---|---|
| `[NAME]` | (prompted) | Project name |
| `--description` / `-d` | (prompted) | Project description |
| `--author` / `-a` | (prompted) | Author name |
| `--db` | (prompted) | Database: `postgres`, `none` |
| `--python` | `3.14` | Python version |
| `--no-docker` | false | Skip Docker/Compose |
| `--no-ci` | false | Skip GitHub Actions CI |
| `--no-devcontainer` | false | Skip devcontainer |
| `--no-interactive` / `-y` | false | Use defaults, skip prompts |
## Generated Project Structure
```
myproject/
├── src/myproject/
│ ├── domain/ # Pure business logic
│ │ └── shared/ # Shared kernel (base entity)
│ ├── application/ # Use cases, ports, DTOs
│ └── adapters/
│ ├── config/ # Pydantic Settings
│ ├── inbound/
│ │ └── api/
│ │ └── rest/ # FastAPI REST adapter
│ └── outbound/
│ ├── persistence/
│ │ └── postgres/ # DB-specific adapter
│ └── logging/ # Logging adapter
├── tests/
├── docs/architecture/ # PlantUML diagrams
├── docker/
├── .github/workflows/
└── .devcontainer/
```
## Development
```bash
uv sync
uv run pytest
```
| text/markdown | null | ayman kastali <aymankastali.backend@gmail.com> | null | null | null | null | [
"Development Status :: 3 - Alpha",
"Programming Language :: Python :: 3.14",
"Topic :: Software Development :: Code Generators",
"Typing :: Typed"
] | [] | null | null | >=3.14 | [] | [] | [] | [
"jinja2>=3.1.0",
"rich>=13.0.0",
"typer>=0.9.0"
] | [] | [] | [] | [
"Homepage, https://github.com/AymanKastali/hexa-ddd-blueprint",
"Repository, https://github.com/AymanKastali/hexa-ddd-blueprint",
"Issues, https://github.com/AymanKastali/hexa-ddd-blueprint/issues"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:30:18.736588 | hexa_ddd_blueprint-0.1.1.tar.gz | 40,217 | e8/47/46f40e1ae91a197ab80558ea25225933df06fde9ac237888dee3184c9c1a/hexa_ddd_blueprint-0.1.1.tar.gz | source | sdist | null | false | 5665fdfd1afe0effb22bd3a5a05fe7cb | 12c41dfcc4e9246ccf244640b52baf840f2620e9239555c48ae085febd0713d7 | e84746f40e1ae91a197ab80558ea25225933df06fde9ac237888dee3184c9c1a | MIT | [
"LICENSE"
] | 223 |
2.4 | hevo-assistant | 0.10.0 | Chat-to-Action CLI for Hevo Data pipelines with RAG-powered assistance | # Hevo Assistant
A chat-to-action CLI tool for managing Hevo Data pipelines using natural language. Ask questions, check status, pause/resume pipelines, and more - all through conversation.
## Features
- **Natural Language Interface**: Interact with your Hevo pipelines using plain English
- **RAG-Powered Responses**: Get accurate answers based on Hevo documentation
- **Multiple LLM Providers**: Choose between OpenAI, Anthropic Claude, or local Ollama
- **Pipeline Management**: List, pause, resume, and run pipelines
- **Model & Workflow Support**: Manage dbt models and workflows
- **Secure Configuration**: Credentials stored locally in `~/.hevo/`
- **Fast Installation**: ~30 seconds (no heavy ML dependencies)
## Installation
```bash
pip install hevo-assistant
```
That's it! Installation is fast because we use cloud services (Pinecone + OpenAI) instead of local ML models.
### For Development
```bash
git clone https://github.com/Legolasan/hevo-app.git
cd hevo-app
pip install -e .
```
### Optional: Local RAG (Heavy)
If you prefer to run embeddings locally (offline mode), install with:
```bash
pip install hevo-assistant[local-rag]
```
Note: This adds ~2GB of dependencies (PyTorch, sentence-transformers).
## Quick Start
### 1. Setup Configuration
Run the interactive setup wizard:
```bash
hevo setup
```
You'll be prompted for:
- **Hevo API credentials**: Get from [Hevo Dashboard > Settings > API Keys](https://app.hevodata.com/settings/api-keys)
- **LLM provider**: Choose OpenAI, Anthropic, or Ollama
- **LLM API key**: Your provider's API key (not needed for Ollama)
- **Pinecone API key**: Get free at [pinecone.io](https://pinecone.io)
### 2. Start Chatting
```bash
# Interactive chat mode
hevo chat
# Or ask a one-shot question
hevo ask "List my pipelines"
```
## Usage Examples
```bash
# Check pipeline status
hevo ask "What's the status of my Salesforce pipeline?"
# List all pipelines
hevo ask "Show me all my pipelines"
# Pause a pipeline
hevo ask "Pause the MySQL pipeline"
# Resume a pipeline
hevo ask "Resume the MySQL pipeline"
# Run a pipeline now
hevo ask "Run the Salesforce pipeline now"
# Ask about Hevo features
hevo ask "How do I create a new destination?"
# List models
hevo ask "What models do I have?"
# Run a model
hevo ask "Run my revenue model"
```
## Commands
| Command | Description |
|---------|-------------|
| `hevo setup` | Interactive setup wizard |
| `hevo config show` | Show current configuration |
| `hevo chat` | Start interactive chat session |
| `hevo ask "query"` | Ask a one-shot question |
## Configuration
Configuration is stored in `~/.hevo/config.json`:
```json
{
"hevo": {
"api_key": "your-api-key",
"api_secret": "your-api-secret",
"region": "us"
},
"llm": {
"provider": "openai",
"api_key": "sk-...",
"model": "gpt-4"
},
"rag": {
"backend": "pinecone",
"pinecone_api_key": "pc-...",
"pinecone_index": "hevo-docs"
}
}
```
### Supported Regions
- `us` - United States (default)
- `eu` - Europe
- `in` - India
- `apac` - Asia Pacific
### Supported LLM Providers
| Provider | Models | Notes |
|----------|--------|-------|
| OpenAI | gpt-4, gpt-4-turbo, gpt-3.5-turbo | Recommended for best results |
| Anthropic | claude-3-opus, claude-3-sonnet | Great for detailed explanations |
| Ollama | llama3, mistral, etc. | Local, free, no API key needed |
## Available Actions
The assistant can execute these actions on your behalf:
### Pipeline Actions
- List all pipelines
- Get pipeline status
- Pause a pipeline
- Resume a pipeline
- Run a pipeline immediately
### Object Actions
- List objects in a pipeline
- Skip a failed object
- Restart an object
### Model Actions
- List all models
- Run a model
### Workflow Actions
- List all workflows
- Run a workflow
### Destination Actions
- List all destinations
## Architecture
```
┌─────────────────────────────────────────────────────────────────┐
│ hevo-assistant CLI │
├─────────────────────────────────────────────────────────────────┤
│ User Query ──► Intent Parser ──► RAG Context ──► LLM ──► Action│
│ │ │ │ │
│ ▼ ▼ ▼ │
│ Pinecone OpenAI Hevo API │
│ (Vector DB) (Embeddings) (Actions) │
└─────────────────────────────────────────────────────────────────┘
```
## Requirements
- Python 3.10+
- Hevo Data account with API access
- LLM API key (OpenAI, Anthropic) or local Ollama installation
- Pinecone API key (free tier available at [pinecone.io](https://pinecone.io))
## Troubleshooting
### "Configuration incomplete" error
Run `hevo setup` to configure your API credentials.
### API authentication errors
1. Verify your Hevo API key and secret are correct
2. Check that your API key has the required permissions
3. Ensure you've selected the correct region
### LLM errors
1. Verify your LLM API key is valid
2. For Ollama, ensure the service is running (`ollama serve`)
3. Check that the model name is correct
### Pinecone errors
1. Verify your Pinecone API key is correct
2. Ensure the index "hevo-docs" exists and is accessible
## Development
```bash
# Install dev dependencies
pip install -e ".[dev]"
# Run tests
pytest
# Format code
black src/
```
## License
MIT License - see LICENSE file for details.
## Contributing
Contributions welcome! Please read the contributing guidelines first.
| text/markdown | null | Hevo App <support@hevodata.com> | null | null | null | hevo, data-pipeline, cli, rag, assistant | [
"Development Status :: 3 - Alpha",
"Environment :: Console",
"Intended Audience :: Developers",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"click>=8.0",
"rich>=13.0",
"openai>=1.0.0",
"anthropic>=0.18.0",
"ollama>=0.1.0",
"pinecone-client>=3.0.0",
"requests>=2.28.0",
"beautifulsoup4>=4.12.0",
"lxml>=4.9.0",
"pydantic>=2.0.0",
"pydantic-settings>=2.0.0",
"chromadb>=0.4.0; extra == \"local-rag\"",
"sentence-transformers>=2.2.0; extra == \"local-rag\"",
"pytest>=7.0; extra == \"dev\"",
"pytest-cov>=4.0; extra == \"dev\"",
"black>=23.0; extra == \"dev\"",
"ruff>=0.1.0; extra == \"dev\"",
"mypy>=1.0; extra == \"dev\"",
"hevo-assistant[dev,local-rag]; extra == \"all\""
] | [] | [] | [] | [
"Homepage, https://github.com/Legolasan/hevo-app",
"Documentation, https://docs.hevodata.com",
"Repository, https://github.com/Legolasan/hevo-app"
] | twine/6.2.0 CPython/3.13.2 | 2026-02-20T10:30:17.230240 | hevo_assistant-0.10.0.tar.gz | 77,486 | 2a/f4/2a8fd6e5f6a81e2c2d0d76dee2da5f3f2e91b15c5da66b3adfc4365dd68f/hevo_assistant-0.10.0.tar.gz | source | sdist | null | false | 78b9f164edcc58742af73309c4bc56f4 | a45c09ec6d1ac0643d67a0af0578857a1924d37a7e5671e7c6a09a3eb4894022 | 2af42a8fd6e5f6a81e2c2d0d76dee2da5f3f2e91b15c5da66b3adfc4365dd68f | MIT | [
"LICENSE"
] | 218 |
2.4 | cognee-community-tasks-scrapegraph | 0.1.0 | Package containing custom cognee tasks for scraping web content using ScrapeGraphAI | # cognee-community-tasks-scrapegraph
Custom cognee tasks for scraping web content using [ScrapeGraphAI](https://github.com/ScrapeGraphAI/scrapegraph-py).
## Overview
This package provides two async tasks:
- **`scrape_urls`** – scrape a list of URLs with a natural language prompt and return structured results.
- **`scrape_and_add`** – scrape URLs and ingest the content directly into a cognee dataset.
## Installation
```bash
uv pip install cognee-community-tasks-scrapegraph
```
Or install locally with all dependencies:
```bash
cd packages/task/scrapegraph_tasks
uv sync --all-extras
# OR
poetry install
```
## Requirements
You need two API keys:
| Variable | Description |
|---|---|
| `LLM_API_KEY` | OpenAI (or other LLM provider) API key used by cognee |
| `SGAI_API_KEY` | [ScrapeGraphAI](https://scrapegraphai.com) API key |
Set them in your environment or in a `.env` file:
```bash
export LLM_API_KEY="sk-..."
export SGAI_API_KEY="sgai-..."
```
## Usage
### Scrape only
```python
import asyncio
from cognee_community_tasks_scrapegraph import scrape_urls
results = asyncio.run(
scrape_urls(
urls=["https://cognee.ai", "https://docs.cognee.ai"],
user_prompt="Extract the main content, title, and key information from this page",
)
)
for item in results:
print(item["url"], item["content"])
```
### Scrape and add to cognee
```python
import asyncio
from cognee_community_tasks_scrapegraph import scrape_and_add
asyncio.run(
scrape_and_add(
urls=["https://cognee.ai"],
user_prompt="Extract the main content and key information",
dataset_name="web_scrape",
)
)
```
## Run the example
```bash
cd packages/task/scrapegraph_tasks
uv run python examples/example.py
# OR
poetry run python examples/example.py
```
## API Reference
### `scrape_urls`
```python
async def scrape_urls(
urls: List[str],
user_prompt: str = "Extract the main content, title, and key information from this page",
api_key: Optional[str] = None,
) -> List[dict]
```
Returns a list of dicts:
```python
[
{"url": "https://example.com", "content": {...}}, # success
{"url": "https://bad.invalid", "content": "", "error": "..."}, # failure
]
```
### `scrape_and_add`
```python
async def scrape_and_add(
urls: List[str],
user_prompt: str = "Extract the main content, title, and key information from this page",
api_key: Optional[str] = None,
dataset_name: str = "scrapegraph",
) -> Any
```
Scrapes all URLs, combines the successful results into a single text document, calls `cognee.add`, and then `cognee.cognify`. Returns the cognify result.
| text/markdown | null | null | null | null | null | null | [] | [] | null | null | <=3.13,>=3.11 | [] | [] | [] | [
"cognee==0.5.2",
"scrapegraph-py>=1.7.0"
] | [] | [] | [] | [
"Homepage, https://github.com/topoteretes/cognee",
"Repository, https://github.com/topoteretes/cognee-community"
] | poetry/2.3.2 CPython/3.11.13 Darwin/24.1.0 | 2026-02-20T10:30:11.751139 | cognee_community_tasks_scrapegraph-0.1.0-py3-none-any.whl | 3,632 | ea/61/7aade5d71d0a2d880253d2ddbba2235c4dfa3917692208e0a76e4f633e47/cognee_community_tasks_scrapegraph-0.1.0-py3-none-any.whl | py3 | bdist_wheel | null | false | c64eb40f1cd2547614d4c88459962d94 | f63c7c498980a53e75bef83974c48d543e66325aa50c44e7aef26dfa92ae0352 | ea617aade5d71d0a2d880253d2ddbba2235c4dfa3917692208e0a76e4f633e47 | null | [] | 232 |
2.4 | pygridgain-dbapi | 9.1.18 | GridGain 9 DB API Driver | # pygridgain_dbapi
GridGain 9 DB API Driver.
## Prerequisites
- Python 3.10 or above (3.10, 3.11, 3.12, 3.13 and 3.14 are tested),
- Access to GridGain 9 node, local or remote.
## Installation
### From repository
This is a recommended way for users. If you only want to use the `pygridgain_dbapi` module in your project, do:
```
$ pip install pygridgain-dbapi
```
### From sources
This way is more suitable for developers, or if you install the client from zip archive.
1. Download and/or unzip GridGain 9 DB API Driver sources to `pygridgain_dbapi_path`
2. Go to `pygridgain_dbapi_path` folder
3. Execute `pip install -e .`
```bash
$ cd <pygridgain_dbapi_path>
$ pip install -e .
```
This will install the repository version of `pygridgain_dbapi` into your environment in so-called “develop” or “editable”
mode. You may read more about [editable installs](https://pip.pypa.io/en/stable/reference/pip_install/#editable-installs)
in the `pip` manual.
Then run through the contents of `requirements` folder to install the additional requirements into your working Python
environment using
```
$ pip install -r requirements/<your task>.txt
```
You may also want to consult the `setuptools` manual about using `setup.py`.
### *C extension*
The core of the package is a C++ extension. It shares the code with the GridGain C++ Client. The package is pre-built
for the most common platforms, but you may need to build it if your platform is not included.
Linux building requirements:
- GCC (and G++);
- CMake version >=3.18;
- OpenSSL (dev version of the package);
- Docker to build wheels;
- Supported versions of Python (3.10, 3.11, 3.12, 3.13 and 3.14).
You can disable some of these versions, but you'd need to edit the script for that.
For building universal `wheels` (binary packages) for Linux, just invoke script `./scripts/create_distr.sh`.
Windows building requirements:
- MSVC 14.x, and it should be in path;
- CMake version >=3.18;
- OpenSSL (headers are required for the build);
- Supported versions of Python (3.10, 3.11, 3.12, 3.13 and 3.14).
You can disable some of these versions, but you'd need to edit the script for that.
For building `wheels` for Windows, invoke script `.\scripts\BuildWheels.ps1` using PowerShell.
Make sure that your execution policy allows execution of scripts in your environment.
The script only works with Python distributions installed in a standard path, which is LOCALAPPDATA\Programs\Python.
Ready wheels will be located in `distr` directory.
### Updating from an older version
To upgrade an existing package, use the following command:
```
pip install --upgrade pygridgain_dbapi
```
To install the latest version of a package:
```
pip install pygridgain_dbapi
```
To install a specific version:
```
pip install pygridgain_dbapi==9.0.15
```
## Testing
*NB!* It is recommended installing `pygridgain_dbapi` in development mode.
Refer to [this section](#from-sources) for instructions.
Remember to install test requirements:
```bash
$ pip install -r requirements/install.txt -r requirements/tests.txt
```
### Run basic tests
Running tests themselves:
```bash
$ pytest
```
## Documentation
Install documentation requirements:
```bash
$ pip install -r requirements/docs.txt
```
Generate documentation:
```bash
$ cd docs
$ make html
```
The resulting documentation can be found in `docs/_build/html`. If you want to open the documentation locally, you can
open the index of the documentation `docs/_build/html/index.html` using any modern browser.
| text/markdown | GridGain Systems | eng@gridgain.com | null | null | Copyright (C) GridGain Systems. All Rights Reserved. | null | [
"Programming Language :: C++",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14",
"Programming Language :: Python :: 3 :: Only",
"Intended Audience :: Developers",
"Topic :: Database :: Front-Ends",
"Topic :: Software Development :: Libraries :: Python Modules",
"Operating System :: MacOS",
"Operating System :: Microsoft :: Windows",
"Operating System :: POSIX :: Linux"
] | [] | https://www.gridgain.com | null | >=3.10 | [] | [] | [] | [
"attrs==23.1.0"
] | [] | [] | [] | [] | twine/6.2.0 CPython/3.11.12 | 2026-02-20T10:29:45.217904 | pygridgain_dbapi-9.1.18.tar.gz | 515,362 | 48/a5/0b62f0a0538a9ad9cba3847766a415a20910a4342651515ad2fa2242d612/pygridgain_dbapi-9.1.18.tar.gz | source | sdist | null | false | 335d0c15d74b99177ea0d666deaa8e3e | ae1512bfd6299c3726f9f7b5e233f54386bfd882291e248de6e359d0e63bd3b9 | 48a50b62f0a0538a9ad9cba3847766a415a20910a4342651515ad2fa2242d612 | null | [
"LICENSE",
"NOTICE",
"LICENSE-CE.txt",
"LICENSE-EVAL.txt"
] | 1,418 |
2.4 | robotnikai | 0.2.9 | RobotnikAI API | # RobotnikAI Python Client - API Documentation
A Python client for RobotnikAI API that enables seamless integration with various APIs and services.
> **For development documentation** (versioning, publishing, etc.), see [README.dev.md](README.dev.md)
## Installation
```bash
pip install robotnikai
```
## Quick Start
### Environment Variables
Create a `.env` file in your project root for configuration:
```env
# Cache Service (when available)
CACHE_SERVICE_URL=https://cache.robotnikai.com
CACHE_SERVICE_TOKEN=your_cache_token
# Redis Configuration (for local development)
REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_DB=0
REDIS_PASSWORD=optional_password
# RobotnikAI API Configuration
API_BASE_URL=https://robotnikai.com
APP_TOKEN=your_api_token
APP_ID=your_app_id
ACTION_ID=your_action_id
# Task Management
TASK_ID=optional_custom_task_id
```
### Usage Example
```python
from robotnikai.wrapper import API
api = API()
```
## Core Methods
### GraphQL (Tables API)
Use `GraphQLRequest` together with `api.tables.apps_graphql_query(...)` to run queries and mutations against dynamic app tables (Hasura-backed). This is how the trigger scripts operate:
- Queries are scoped by `APP_ID` and table name (e.g., `app_{APP_ID}_orders`).
- Variables are passed via `GraphQLRequest(query=..., variables=...)`.
- Responses include `.data` and `.errors`; always check `.errors` before using data.
- Mutations use `insert_*` or `update_*` with `where` clauses and `_set` updates.
- Optional `organization_id` is injected into the `where` clause for multi-tenant safety.
#### ✅ Example: Query a single order by `orderId`
```python
from typing import Any, Dict
from robotnikai import GraphQLRequest
from robotnikai.wrapper import API
api = API()
APP_ID = 130
ORDERS_TABLE_NAME = "orders"
order_query = f"""query GetOrder($order_id: String!, $organization_id: Int) {{
app_{APP_ID}_{ORDERS_TABLE_NAME}(
where: {{orderId: {{_eq: $order_id}}, organization_id: {{_eq: $organization_id}}}},
limit: 1
) {{
id
orderId
buyerLogin
}}
}}"""
variables: Dict[str, Any] = {"order_id": "eba6bc90-faab-11f0-96f9-c518c5ba21fb", "organization_id": 2}
request = GraphQLRequest(query=order_query, variables=variables)
response = api.tables.apps_graphql_query(
app=APP_ID, table=ORDERS_TABLE_NAME, graph_ql_request=request
)
if response.errors:
print(f"Order fetch errors: {response.errors}")
else:
records = response.data.get(f"app_{APP_ID}_{ORDERS_TABLE_NAME}") or []
print(records[0] if records else "Order not found")
```
#### ✅ Example: Update `delivered` flag by `orderId`
```python
from typing import Any, Dict
from robotnikai import GraphQLRequest
from robotnikai.wrapper import API
api = API()
APP_ID = 130
ORDERS_TABLE_NAME = "orders"
mutation = f"""mutation UpdateOrderDelivered($order_id: String!, $delivered: Boolean!, $organization_id: Int) {{
update_app_{APP_ID}_{ORDERS_TABLE_NAME}(
where: {{orderId: {{_eq: $order_id}}, organization_id: {{_eq: $organization_id}}}},
_set: {{delivered: $delivered}}
) {{
affected_rows
}}
}}"""
variables: Dict[str, Any] = {"order_id": "eba6bc90-faab-11f0-96f9-c518c5ba21fb", "delivered": True, "organization_id": 2}
request = GraphQLRequest(query=mutation, variables=variables)
response = api.tables.apps_graphql_query(
app=APP_ID, table=ORDERS_TABLE_NAME, graph_ql_request=request
)
if response.errors:
print(f"Order update errors: {response.errors}")
```
#### ✅ Example: Insert a message linked to an order
```python
from typing import Any, Dict
from robotnikai import GraphQLRequest
from robotnikai.wrapper import API
api = API()
APP_ID = 130
MESSAGES_TABLE_NAME = "messages"
message_mutation = f"""mutation InsertMessage($data: app_{APP_ID}_{MESSAGES_TABLE_NAME}_insert_input!) {{
insert_app_{APP_ID}_{MESSAGES_TABLE_NAME}_one(object: $data) {{
order_id
}}
}}"""
send_message_payload: Dict[str, Any] = {
"order_id": "c9a1d25a-fcf3-4950-a660-2f5b04a25320", # foreign key id
"messageContent": "Hello, user",
"organization_id": 2,
}
request = GraphQLRequest(query=message_mutation, variables={"data": send_message_payload})
response = api.tables.apps_graphql_query(
app=APP_ID, table=MESSAGES_TABLE_NAME, graph_ql_request=request
)
if response.errors:
print(f"Message insert errors: {response.errors}")
else:
print(response.data)
```
### Integration API Calls
#### `api.integrations.call()`
Make synchronous API calls to integrated services.
```python
# Basic API call
integration = api.integrations.get_integration("github")
data, response = api.integrations.call(
integration,
method="GET",
endpoint="/user"
)
# With parameters and selected account
data, response = api.integrations.call(
integration,
method="GET",
endpoint="/sale/offers",
params={"limit": 10, "offset": 0},
connection_id="ecommercelab@yandex.com"
)
# POST with JSON payload
data, response = api.integrations.call(
integration,
method="POST",
endpoint="/messages/submit",
json={
"from": {"name": "John Doe", "address": "john@example.com"},
"to": [{"name": "Jane Doe", "address": "jane@example.com"}],
"subject": "Test Email",
"text": "Hello from Python client!"
},
connection_id="user@domain.com"
)
```
**Returns:** `(data, response)` tuple where:
- `data`: Parsed JSON response data
- `response`: HTTP response object with `.ok`, `.status_code`, `.text`, `.headers`, `.url` attributes
**Parallel Methods Return:** `List[ParallelCallResponse]` objects where each response contains:
- `.api_data`: Parsed JSON response data from the external service
- `.api_response`: HTTP response object (same as above)
- `.connection_id`: ID of the connection used for this request
- `.organization_id`: Organization ID associated with the connection
### Parallel API Calls
#### `api.integrations.parallel_call()`
Execute multiple API calls concurrently using different connections per request for maximum performance and flexibility.
```python
# Multi-connection parallel calls (recommended for scaling across accounts)
responses = api.integrations.parallel_call(
integration,
method="GET",
endpoint="/sale/product-offers/{offerId}",
data_list=[
{
"connection_id": 123,
"organization_id": 456,
"url_params": {"offerId": "offer_1"},
"params": {"include": "details"}
},
{
"connection_id": 789,
"organization_id": 101,
"url_params": {"offerId": "offer_2"},
"data": {"expand": True}
}
],
table="app_123_products"
)
# Process results with new object-based API
for response in responses:
print(f"Connection {response.connection_id}, Org {response.organization_id}")
if response.api_response.ok:
print(f"Offer: {response.api_data['name']}")
print(f"Status: {response.api_response.status_code}")
else:
print(f"Error: {response.api_response.text}")
```
#### `api.integrations.parallel_call_for_connection()`
Execute multiple API calls in parallel using a single connection (simpler for single-account operations).
```python
# Single-connection parallel calls
responses = api.integrations.parallel_call_for_connection(
integration,
method="GET",
endpoint="/sale/product-offers/{offerId}",
data_list=[
{"url_params": {"offerId": "123"}},
{"url_params": {"offerId": "456"}},
{"url_params": {"offerId": "789"}}
],
connection_id="user@domain.com",
organization_id=123,
table="app_124_offers"
)
# Process results
for response in responses:
if response.api_response.ok:
print(f"Offer: {response.api_data['name']}")
else:
print(f"Error: {response.api_response.text}")
```
#### `api.integrations.parallel_call_stream()`
Stream parallel API calls for real-time processing and progress visibility.
```python
# Stream parallel calls with real-time processing (single connection)
for result in api.integrations.parallel_call_stream(
integration,
method="GET",
endpoint="/sale/product-offers/{offerId}",
data_list=[{"url_params": {"offerId": offer["id"]}} for offer in offers],
connection_id=connection_id
):
if result.get("final"):
print(f" Completed! Processed {result['total_processed']} requests")
break
else:
# Process individual response as it arrives
data = result["data"]
response = result["response"]
index = result["index"]
if response.ok:
# Process immediately without waiting for all requests
processed_data = process_data(data)
save_to_database(processed_data)
else:
print(f"❌ Error for request {index}: {response.text}")
```
### Connection Management
#### `api.integrations.get_connections()`
Get all connected accounts for an integration. Returns list of connections with `id` and `name`.
```python
# Get all connected accounts
connections = api.integrations.get_connections("allegro_sandbox")
for connection in connections:
print(f"ID: {connection.id}, Name: {connection.name}")
# Use connection ID in API calls
data, response = api.integrations.call(
integration,
method="GET",
endpoint="/sale/offers",
connection_id=connection.id # Use the connection ID in API calls
)
```
### Caching System
#### App Cache
Shared cache accessible across the entire application. Access by key only for users within the same organization.
```python
# Set cache with TTL
api.app_cache.set(
key="api_data",
value={"rates": [1.2, 1.5, 1.8]},
ttl=300 # 5 minutes
)
# Get cached data
cached_data = api.app_cache.get(key="api_data")
print(cached_data) # {'rates': [1.2, 1.5, 1.8]}
# Delete cache entry
api.app_cache.delete(key="api_data")
```
#### User Cache
User-specific cache for personalized data. It is assigned to user's organization and can be shared between apps.
```python
# Set user-specific cache
api.user_cache.set(
key="my_key",
value="my_value",
ttl=3600 # 1 hour
)
# Get user cache
preferences = api.user_cache.get(key="my_key")
# Delete user cache
api.user_cache.delete(key="my_key")
```
### Notifications
#### `api.notify_me()`
Send notifications (via email) to yourself (useful for monitoring and alerts).
```python
# Send notification
api.notify_me(
subject="API Process Completed",
text="Successfully processed 1000 records",
html="<h1>Success!</h1><p>Processed <strong>1000</strong> records</p>"
)
```
### Task Progress Tracking
#### `api.task.set_progress(progress: int, info: str, status: 'pending' | 'completed' | 'failed')`
Update task progress for long-running operations.
`progress` and `info` are displayed in the UI during task execution, while `status` indicates the task state.
```python
# Initialize progress
api.task.set_progress(0, "Starting data processing...", "pending")
# Update progress throughout your task
for i, item in enumerate(large_dataset):
# Process item
process_item(item)
# Update progress every 100 items
if i % 100 == 0:
progress = int((i / len(large_dataset)) * 100)
api.task.set_progress(
progress,
f"Processed {i}/{len(large_dataset)} items",
"pending"
)
# Complete the task
api.task.set_progress(100, "Processing completed!", "completed")
```
## Configuration & Environment
### Cache & Progress Service Configuration
The client automatically detects and adapts to your environment:
#### **RobotnikAI Sandbox (Automatic)**
When running in RobotnikAI Sandbox:
- Uses the hosted cache service API
- `TASK_ID` is automatically assigned by the platform
- Progress tracking is displayed in the RobotnikAI UI
- Cache is shared across the organization
#### **Local Development (Redis)**
When running locally without `CACHE_SERVICE_URL`:
- Automatically uses direct Redis connection
- `TASK_ID` is generated as a random UUID4
- Progress is logged to console
- Requires Redis server running locally
#### Required Dependencies
For local Redis usage, install the Redis client:
```bash
pip install redis
```
### Usage Impact
**From the user perspective, there are no code changes required.** The client automatically:
- **Detects environment** and chooses appropriate backend
- **Maintains consistent API** across both configurations
- **Handles serialization/deserialization** transparently
- **Provides same response format** regardless of backend
```python
# This code works identically in both environments
api.app_cache.set("my_key", {"data": "value"}, ttl=300)
cached_data = api.app_cache.get("my_key")
api.task.set_progress(50, "Halfway done", "pending")
```
#### Environment Detection Logic
```python
# The client automatically determines configuration:
if CACHE_SERVICE_URL:
# Use RobotnikAI cache service API
# - HTTP requests to cache service
# - Progress updates sent to platform
# - Task ID from environment or auto-generated
else:
# Use local Redis connection
# - Direct Redis operations
# - Progress logged to console
# - UUID4 generated for task tracking
```
### Integration Management
#### `api.integrations.get_integration()`
Get integration configuration for API calls.
```python
# Get integration
integration = api.integrations.get_integration("allegro_sandbox")
# Now use this integration object in .call() methods
```
#### `api.integrations.get_integrations()`
List all available integrations.
```python
integrations = api.integrations.get_integrations()
for integration in integrations.results:
print(f"ID: {integration.integration_id}, Name: {integration.name}")
```
## Common Patterns
### 1. **Parallel Data Processing**
```python
# Efficient parallel processing pattern
integration = api.integrations.get_integration("api_service")
# Step 1: Get list of items
list_data, response = api.integrations.call(
integration, method="GET", endpoint="/items",
params={"limit": 50}, connection_id="user@domain.com"
)
# Step 2: Process details in parallel (single connection)
detail_requests = [
{"url_params": {"id": item["id"]}}
for item in list_data["items"]
]
responses = api.integrations.parallel_call_for_connection(
integration,
method="GET",
endpoint="/items/{id}/details",
data_list=detail_requests,
connection_id="user@domain.com",
organization_id=123
)
# Step 3: Process results with new object API
processed_data = []
for response in responses:
if response.api_response.ok:
processed_data.append(transform_data(response.api_data))
else:
print(f"Error for connection {response.connection_id}: {response.api_response.text}")
```
### 2. **Multi-Connection Parallel Processing**
```python
# Advanced: Parallel processing across multiple connections simultaneously
integration = api.integrations.get_integration("api_service")
# Get all connections for the service
table_connections = api.integrations.all_connections(integration, "data_table")
# Build parallel requests for multiple connections
data_list = []
for org_id, connections in table_connections.items():
for connection in connections:
data_list.append({
"connection_id": connection["id"],
"organization_id": org_id,
"params": {"limit": 50},
"url_params": {"account_id": connection["id"]}
})
# Execute all requests in parallel across all connections
responses = api.integrations.parallel_call(
integration,
method="GET",
endpoint="/accounts/{account_id}/data",
data_list=data_list,
table="app_123_consolidated_data"
)
# Process results with full traceability
for response in responses:
print(f"Org {response.organization_id}, Connection {response.connection_id}")
if response.api_response.ok:
save_data(response.api_data, response.connection_id, response.organization_id)
else:
log_error(response.connection_id, response.api_response.text)
```
### 3. **Multi-Account Operations**
```python
# Process data across multiple connected accounts
connections = api.integrations.get_connections("service_name")
for connection in connections:
print(f"Processing account: {connection.name}")
data, response = api.integrations.call(
integration,
method="GET",
endpoint="/data",
connection_id=connection.id
)
if response.ok:
# Process account-specific data
process_account_data(data, connection.id)
```
### 3. **Progress Tracking with Caching**
```python
def long_running_task():
api.task.set_progress(0, "Initializing...", "in_progress")
# Cache intermediate results
api.app_cache.set("task_checkpoint", {"processed": 0}, ttl=3600)
for i in range(1000):
# Do work
process_item(i)
# Update progress and cache
if i % 100 == 0:
progress = int((i / 1000) * 100)
api.task.set_progress(progress, f"Processed {i}/1000", "in_progress")
api.app_cache.set("task_checkpoint", {"processed": i}, ttl=3600)
api.task.set_progress(100, "Completed!", "completed")
api.app_cache.delete("task_checkpoint")
```
## Performance Tips
- **Use parallel calls** for multiple API requests to the same service
- **Cache frequently accessed data** to reduce API calls
- **Stream parallel calls** for real-time processing of large datasets
- **Set appropriate TTL** for cached data based on update frequency
- **Monitor progress** for long-running tasks to improve user experience
## Error Handling
### Single API Calls
```python
data, response = api.integrations.call(integration, method="GET", endpoint="/data")
if not response.ok:
print(f"API Error: {response.status_code} - {response.text}")
return
if not data:
print("No data received")
return
# Process successful response
process_data(data)
```
### Parallel API Calls
```python
responses = api.integrations.parallel_call(
integration, method="GET", endpoint="/data",
data_list=requests_data
)
for response in responses:
if not response.api_response.ok:
print(f"Error for connection {response.connection_id}: {response.api_response.text}")
continue
if not response.api_data:
print(f"No data received for connection {response.connection_id}")
continue
# Process successful response
process_data(response.api_data, response.connection_id)
```
| text/markdown | OpenAPI Generator community | team@openapitools.org | null | null | null | OpenAPI, OpenAPI-Generator, RobotnikAI API | [] | [] | null | null | null | [] | [] | [] | [
"urllib3<3.0.0,>=2.1.0",
"python-dateutil>=2.8.2",
"pydantic>=2",
"typing-extensions>=4.7.1",
"python-dotenv==1.1.0"
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:29:42.069789 | robotnikai-0.2.9.tar.gz | 77,555 | af/69/21e966c675234f6920de2812a91ea0c7b246ca105bf393b99938e4856bc9/robotnikai-0.2.9.tar.gz | source | sdist | null | false | 33406bf3ce5511bf37c369f7061031fe | 8c4ef3cdf85c8fc0af5859f662c5152382d95924b91e20bbd405ce1a02acab24 | af6921e966c675234f6920de2812a91ea0c7b246ca105bf393b99938e4856bc9 | null | [] | 228 |
2.4 | ledgerwallet | 0.9.2 | Library to communicate between the Ledger devices (Nano S/S+/X, Stax, Flex, Apex P and Apex M) and Speculos | # ledgerwallet
A Python library to control Ledger devices
## Install
This package provides ledgerwallet, a library to interact with Ledger devices, and ledgerctl, a command line tool based on that library to easily perform operations on the devices.
Supported devices are Ledger Blue, Ledger Nano S, Ledger Nano X and Ledger Nano S Plus.
### Quick install
ledgerctl and the ledgerwallet library can be installed using pip:
```shell
pip3 install --upgrade protobuf setuptools ecdsa
pip3 install ledgerwallet
```
Under a Debian or Ubuntu based system, compiling HIDAPI requires to install additional packages:
```shell
sudo apt install python3-dev libusb-1.0-0-dev libudev-dev
```
### Install from source
```shell
git clone https://github.com/LedgerHQ/ledgerctl.git
pip3 install --upgrade protobuf setuptools ecdsa
cd ledgerctl
pip install -e .
```
### Device configuration
> **ATTENTION:** This step is optional and only advised for **developers**. It
> will allow the installation of apps, that weren't reviewed by Ledger, without
> user interaction.
You should install a custom certificate authority (CA) on the device to make the usage of ledgerctl easier. This certificate is used to establish a custom secure channel between the computer and the device, and identifies ledgerctl as a "trusted manager" on the device.
To install a custom CA, boot the device in "Recovery" mode by pressing the right button at boot time.
There are no visual indicators of recovery mode.
Then run:
```shell
ledgerctl install-ca <NAME>
```
where \<NAME\> is the name that will be displayed on the device to identify the CA. It can be any label, like "ledgerctl", "Dev", or "CA".
You are now ready to use ledgerctl.
## Usage
To display the commands supported by ledgerctl, run `ledgerctl` or `ledgerctl --help`. Help for each command can be displayed by running `ledgerctl <command> --help`.
Supported commands include retrieving basic device information, installing and removing apps, viewing available space on the device, etc.
Here are a few examples:
- Displaying available space on the device
```shell
ledgerctl meminfo
```
- Listing installed applications
```shell
ledgerctl list
```
- Deleting the Bitcoin application
```shell
ledgerctl delete Bitcoin
```
### Installing custom apps
Loading an application on the device is currently bound to the SDK and to the build process.
Installation of custom apps differ from the way provided by the SDK. To keep the install process simple, we chose to use "Manifest" files for applications. Manifests are JSON files which contain the required parameters to install the application. You can find an example manifest in the tests/app directory.
Manifest entries are pretty straightforward if you are familiar with the BOLOS SDK, except one of them: `dataSize`. That entry specifies the size of the writable area of the application. This is the size needed by the application to save persistent data. Its value seldom changes.
You can use an ugly one-liner to retrieve it:
```shell
echo $(($(grep _envram_data debug/app.map | awk '{ print $1 }') - $(grep _nvram_data debug/app.map | awk '{ print $1 }')))
```
As an example, the standard way to install the [Bitcoin application]( https://github.com/LedgerHQ/ledger-app-btc ) you compiled is to run `make load` with the BOLOS SDK. It launches the following command:
```shell
python3 -m ledgerblue.loadApp --curve secp256k1 --tlv --targetId 0x31100004 --targetVersion="1.6.0" --delete --fileName bin/app.hex --appName "Bitcoin" --appVersion 1.3.13 --dataSize $((0x`cat debug/app.map |grep _envram_data | tr -s ' ' | cut -f2 -d' '|cut -f2 -d'x'` - 0x`cat debug/app.map |grep _nvram_data | tr -s ' ' | cut -f2 -d' '|cut -f2 -d'x'`)) `ICONHEX=\`python3 /home/dev/sdk/icon3.py --hexbitmaponly nanos_app_bitcoin.gif 2>/dev/null\` ; [ ! -z "$ICONHEX" ] && echo "--icon $ICONHEX"` --path "" --appFlags 0xa50 --offline bin/app.apdu | grep "Application" | cut -f5 -d' ' > bin/app.sha256
```
To install it with ledgerctl:
1. Retrieve `dataSize` using the above one-liner.
2. Create a manifest file app.toml in the ledger-app-btc directory:
```toml
name = "Bitcoin"
version = "1.3.13"
[0x31100004] #NanoS
icon = "nanos_app_bitcoin.gif"
flags = "0xA50"
derivationPath = {curves = ["secp256k1"]}
binary = "bin/app.hex"
dataSize = 64
[0x33100004] #NanoSP
icon = "nanosp_app_bitcoin.gif"
flags = "0xA50"
derivationPath = {curves = ["secp256k1"]}
binary = "bin/app_nanosp.hex"
dataSize = 64
```
3. Install with `ledgerctl install app.json`.
If you want to force the deletion of the previous version, run the previous command with the `-f` flag.
### Viewing APDUs
Communication between the host and the device use Application Protocol Data Unit (APDUs). To display the raw APDUs, usually for debugging purposes, run ledgerctl with the `-v` switch on any command. For example, here are the APDUs exchanged to run the Bitcoin application:
```shell
$ ledgerctl -v run Bitcoin
=> e0d8000007426974636f696e
<= 9000
```
## Contributing
### Rebuild the proto files
```shell
for file in ledgerwallet/proto/*.proto; do \
python -m grpc_tools.protoc -I. --python_out=. --pyi_out=. $file; \
done
```
### Pre-commit checks
> **Note:** It's advised to install `pre-commit` using
> [`pipx`](https://github.com/pypa/pipx)
Before submitting your pull-request, please make sure that all
[pre-commit](https://pre-commit.com/) hooks are passing. They can be locally
installed with the following command:
```console
pre-commit install
```
And executed with:
```console
pre-commit run --all-files
```
| text/markdown | null | Ledger <hello@ledger.fr> | null | null | null | null | [
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Operating System :: POSIX :: Linux",
"Operating System :: Microsoft :: Windows",
"Operating System :: MacOS :: MacOS X"
] | [] | null | null | >=3.9 | [] | [] | [] | [
"click>=8.0",
"construct>=2.10",
"cryptography>=2.5",
"ecdsa",
"hidapi",
"intelhex",
"Pillow",
"protobuf<6,>=5.28",
"requests",
"tabulate",
"toml"
] | [] | [] | [] | [
"Home, https://github.com/LedgerHQ/ledgerctl"
] | twine/6.2.0 CPython/3.12.3 | 2026-02-20T10:29:33.427756 | ledgerwallet-0.9.2.tar.gz | 61,633 | 4f/64/21eae822fa70740da506996dfab7366f9ff4910af2c0c10295875515b239/ledgerwallet-0.9.2.tar.gz | source | sdist | null | false | 640949c72eb560bdcad78d8f9c134e2f | 7a58e31f85d5de849edd8ddcf8859a5e104d5f25a75fe4de55b96f5c8856e713 | 4f6421eae822fa70740da506996dfab7366f9ff4910af2c0c10295875515b239 | MIT | [
"LICENSE"
] | 640 |
2.1 | explainiverse | 0.9.5 | Unified, extensible explainability framework supporting 18 XAI methods including LIME, SHAP, LRP, TCAV, GradCAM, and more | # Explainiverse
[](https://badge.fury.io/py/explainiverse)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
**Explainiverse** is a unified, extensible Python framework for Explainable AI (XAI). It provides a standardized interface for **18 state-of-the-art explanation methods** across local, global, gradient-based, concept-based, and example-based paradigms, along with **comprehensive evaluation metrics** for assessing explanation quality.
---
## Key Features
| Feature | Description |
|---------|-------------|
| **18 Explainers** | LIME, KernelSHAP, TreeSHAP, Integrated Gradients, DeepLIFT, DeepSHAP, SmoothGrad, Saliency Maps, GradCAM/GradCAM++, LRP, TCAV, Anchors, Counterfactual, Permutation Importance, PDP, ALE, SAGE, ProtoDash |
| **27 Evaluation Metrics** | Faithfulness (PGI, PGU, Comprehensiveness, Sufficiency, Correlation, Faithfulness Estimate, Monotonicity, Monotonicity-Nguyen, Pixel Flipping, Region Perturbation, Selectivity, Sensitivity-n, IROF, Infidelity, ROAD, Insertion AUC, Deletion AUC), Stability (RIS, ROS, Lipschitz), Robustness (Max-Sensitivity, Avg-Sensitivity, Continuity), Complexity (Sparseness, Complexity, Effective Complexity) |
| **Unified API** | Consistent `BaseExplainer` interface with standardized `Explanation` output |
| **Plugin Registry** | Filter explainers by scope, model type, data type; automatic recommendations |
| **Framework Support** | Adapters for scikit-learn and PyTorch (with gradient computation) |
---
## Explainer Coverage
### Local Explainers (Instance-Level)
| Method | Type | Reference |
|--------|------|-----------|
| **LIME** | Perturbation | [Ribeiro et al., 2016](https://arxiv.org/abs/1602.04938) |
| **KernelSHAP** | Perturbation | [Lundberg & Lee, 2017](https://arxiv.org/abs/1705.07874) |
| **TreeSHAP** | Exact (Trees) | [Lundberg et al., 2018](https://arxiv.org/abs/1802.03888) |
| **Integrated Gradients** | Gradient | [Sundararajan et al., 2017](https://arxiv.org/abs/1703.01365) |
| **DeepLIFT** | Gradient | [Shrikumar et al., 2017](https://arxiv.org/abs/1704.02685) |
| **DeepSHAP** | Gradient + Shapley | [Lundberg & Lee, 2017](https://arxiv.org/abs/1705.07874) |
| **SmoothGrad** | Gradient | [Smilkov et al., 2017](https://arxiv.org/abs/1706.03825) |
| **Saliency Maps** | Gradient | [Simonyan et al., 2014](https://arxiv.org/abs/1312.6034) |
| **GradCAM / GradCAM++** | Gradient (CNN) | [Selvaraju et al., 2017](https://arxiv.org/abs/1610.02391) |
| **LRP** | Decomposition | [Bach et al., 2015](https://doi.org/10.1371/journal.pone.0130140) |
| **TCAV** | Concept-Based | [Kim et al., 2018](https://arxiv.org/abs/1711.11279) |
| **Anchors** | Rule-Based | [Ribeiro et al., 2018](https://ojs.aaai.org/index.php/AAAI/article/view/11491) |
| **Counterfactual** | Contrastive | [Mothilal et al., 2020](https://arxiv.org/abs/1905.07697) |
| **ProtoDash** | Example-Based | [Gurumoorthy et al., 2019](https://arxiv.org/abs/1707.01212) |
### Global Explainers (Model-Level)
| Method | Type | Reference |
|--------|------|-----------|
| **Permutation Importance** | Feature Importance | [Breiman, 2001](https://link.springer.com/article/10.1023/A:1010933404324) |
| **Partial Dependence (PDP)** | Feature Effect | [Friedman, 2001](https://projecteuclid.org/euclid.aos/1013203451) |
| **ALE** | Feature Effect | [Apley & Zhu, 2020](https://academic.oup.com/jrsssb/article/82/4/1059/7056085) |
| **SAGE** | Shapley Importance | [Covert et al., 2020](https://arxiv.org/abs/2004.00668) |
---
## Evaluation Metrics
Explainiverse includes a comprehensive suite of evaluation metrics based on the XAI literature:
### Faithfulness Metrics
| Metric | Description | Reference |
|--------|-------------|-----------|
| **PGI** | Prediction Gap on Important features | [Petsiuk et al., 2018](https://arxiv.org/abs/1806.07421) |
| **PGU** | Prediction Gap on Unimportant features | [Petsiuk et al., 2018](https://arxiv.org/abs/1806.07421) |
| **Comprehensiveness** | Drop when removing top-k features | [DeYoung et al., 2020](https://arxiv.org/abs/1911.03429) |
| **Sufficiency** | Prediction using only top-k features | [DeYoung et al., 2020](https://arxiv.org/abs/1911.03429) |
| **Faithfulness Correlation** | Correlation between attribution and impact | [Bhatt et al., 2020](https://arxiv.org/abs/2005.00631) |
| **Faithfulness Estimate** | Correlation of attributions with single-feature perturbation impact | [Alvarez-Melis & Jaakkola, 2018](https://arxiv.org/abs/1806.08049) |
| **Monotonicity** | Sequential feature addition shows monotonic prediction increase | [Arya et al., 2019](https://arxiv.org/abs/1909.03012) |
| **Monotonicity-Nguyen** | Spearman correlation between attributions and feature removal impact | [Nguyen & Martinez, 2020](https://arxiv.org/abs/2010.07455) |
| **Pixel Flipping** | AUC of prediction degradation when removing features by importance | [Bach et al., 2015](https://doi.org/10.1371/journal.pone.0130140) |
| **Region Perturbation** | AUC of prediction degradation when perturbing feature regions by importance | [Samek et al., 2015](https://arxiv.org/abs/1509.06321) |
| **Selectivity (AOPC)** | Average prediction drop when sequentially removing features by importance | [Montavon et al., 2018](https://doi.org/10.1016/j.dsp.2017.10.011) |
| **Sensitivity-n** | Correlation between attribution sums and prediction changes for random feature subsets | [Ancona et al., 2018](https://arxiv.org/abs/1711.06104) |
| **IROF** | Area over curve measuring prediction degradation when iteratively removing features | [Rieger & Hansen, 2020](https://arxiv.org/abs/2003.08747) |
| **Infidelity** | Measures how well attributions predict model output changes under perturbation | [Yeh et al., 2019](https://arxiv.org/abs/1901.09392) |
| **ROAD** | RemOve And Debias - uses noisy linear imputation for out-of-distribution robust evaluation | [Rong et al., 2022](https://proceedings.mlr.press/v162/rong22a.html) |
### Stability Metrics
| Metric | Description | Reference |
|--------|-------------|-----------|
| **RIS** | Relative Input Stability | [Agarwal et al., 2022](https://arxiv.org/abs/2203.06877) |
| **ROS** | Relative Output Stability | [Agarwal et al., 2022](https://arxiv.org/abs/2203.06877) |
| **Lipschitz Estimate** | Local Lipschitz continuity | [Alvarez-Melis & Jaakkola, 2018](https://arxiv.org/abs/1806.08049) |
---
## Installation
```bash
# From PyPI
pip install explainiverse
# With PyTorch support (for gradient-based methods)
pip install explainiverse[torch]
# For development
git clone https://github.com/jemsbhai/explainiverse.git
cd explainiverse
poetry install
```
---
## Quick Start
### Basic Usage with Registry
```python
from explainiverse import default_registry, SklearnAdapter
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
# Train a model
iris = load_iris()
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(iris.data, iris.target)
# Wrap with adapter
adapter = SklearnAdapter(model, class_names=iris.target_names.tolist())
# List all available explainers
print(default_registry.list_explainers())
# ['lime', 'shap', 'treeshap', 'integrated_gradients', 'deeplift', 'deepshap',
# 'smoothgrad', 'saliency', 'gradcam', 'lrp', 'tcav', 'anchors', 'counterfactual',
# 'protodash', 'permutation_importance', 'partial_dependence', 'ale', 'sage']
# Create an explainer via registry
explainer = default_registry.create(
"lime",
model=adapter,
training_data=iris.data,
feature_names=iris.feature_names.tolist(),
class_names=iris.target_names.tolist()
)
# Generate explanation
explanation = explainer.explain(iris.data[0])
print(explanation.explanation_data["feature_attributions"])
```
### Filter and Recommend Explainers
```python
# Filter by criteria
local_explainers = default_registry.filter(scope="local", data_type="tabular")
neural_explainers = default_registry.filter(model_type="neural")
image_explainers = default_registry.filter(data_type="image")
# Get recommendations
recommendations = default_registry.recommend(
model_type="neural",
data_type="tabular",
scope_preference="local",
max_results=5
)
```
---
## Gradient-Based Explainers (PyTorch)
### Integrated Gradients
```python
from explainiverse import PyTorchAdapter
from explainiverse.explainers.gradient import IntegratedGradientsExplainer
import torch.nn as nn
# Define and wrap model
model = nn.Sequential(
nn.Linear(10, 64), nn.ReLU(),
nn.Linear(64, 32), nn.ReLU(),
nn.Linear(32, 3)
)
adapter = PyTorchAdapter(model, task="classification", class_names=["A", "B", "C"])
# Create explainer
explainer = IntegratedGradientsExplainer(
model=adapter,
feature_names=[f"feature_{i}" for i in range(10)],
class_names=["A", "B", "C"],
n_steps=50,
method="riemann_trapezoid"
)
# Explain with convergence check
explanation = explainer.explain(X[0], return_convergence_delta=True)
print(f"Attributions: {explanation.explanation_data['feature_attributions']}")
print(f"Convergence δ: {explanation.explanation_data['convergence_delta']:.6f}")
```
### Layer-wise Relevance Propagation (LRP)
```python
from explainiverse.explainers.gradient import LRPExplainer
# LRP - Decomposition-based attribution with conservation property
explainer = LRPExplainer(
model=adapter,
feature_names=feature_names,
class_names=class_names,
rule="epsilon", # Propagation rule: epsilon, gamma, alpha_beta, z_plus, composite
epsilon=1e-6 # Stabilization constant
)
# Basic explanation
explanation = explainer.explain(X[0], target_class=0)
print(explanation.explanation_data["feature_attributions"])
# Verify conservation property (sum of attributions ≈ target output)
explanation = explainer.explain(X[0], return_convergence_delta=True)
print(f"Conservation delta: {explanation.explanation_data['convergence_delta']:.6f}")
# Compare different LRP rules
comparison = explainer.compare_rules(X[0], rules=["epsilon", "gamma", "z_plus"])
for rule, result in comparison.items():
print(f"{rule}: top feature = {result['top_feature']}")
# Layer-wise relevance analysis
layer_result = explainer.explain_with_layer_relevances(X[0])
for layer, relevances in layer_result["layer_relevances"].items():
print(f"{layer}: sum = {sum(relevances):.4f}")
# Composite rules: different rules for different layers
explainer_composite = LRPExplainer(
model=adapter,
feature_names=feature_names,
class_names=class_names,
rule="composite"
)
explainer_composite.set_composite_rule({
0: "z_plus", # Input layer: focus on what's present
2: "epsilon", # Middle layers: balanced
4: "epsilon" # Output layer
})
explanation = explainer_composite.explain(X[0])
```
**LRP Propagation Rules:**
| Rule | Description | Use Case |
|------|-------------|----------|
| `epsilon` | Adds stabilization constant | General purpose (default) |
| `gamma` | Enhances positive contributions | Image classification |
| `alpha_beta` | Separates pos/neg (α-β=1) | Fine-grained control |
| `z_plus` | Only positive weights | Input layers, what's present |
| `composite` | Different rules per layer | Best practice for deep nets |
**Supported Layers:**
- Linear, Conv2d
- BatchNorm1d, BatchNorm2d
- ReLU, LeakyReLU, ELU, Tanh, Sigmoid, GELU
- MaxPool2d, AvgPool2d, AdaptiveAvgPool2d
- Flatten, Dropout
### DeepLIFT and DeepSHAP
```python
from explainiverse.explainers.gradient import DeepLIFTExplainer, DeepLIFTShapExplainer
# DeepLIFT - Fast reference-based attributions
deeplift = DeepLIFTExplainer(
model=adapter,
feature_names=feature_names,
class_names=class_names,
baseline=None # Uses zero baseline by default
)
explanation = deeplift.explain(X[0])
# DeepSHAP - DeepLIFT averaged over background samples
deepshap = DeepLIFTShapExplainer(
model=adapter,
feature_names=feature_names,
class_names=class_names,
background_data=X_train[:100]
)
explanation = deepshap.explain(X[0])
```
### Saliency Maps
```python
from explainiverse.explainers.gradient import SaliencyExplainer
# Saliency Maps - simplest and fastest gradient method
explainer = SaliencyExplainer(
model=adapter,
feature_names=feature_names,
class_names=class_names,
absolute_value=True # Default: absolute gradient magnitudes
)
# Standard saliency (absolute gradients)
explanation = explainer.explain(X[0], method="saliency")
# Input × Gradient (gradient scaled by input values)
explanation = explainer.explain(X[0], method="input_times_gradient")
# Signed saliency (keep gradient direction)
explainer_signed = SaliencyExplainer(
model=adapter,
feature_names=feature_names,
class_names=class_names,
absolute_value=False
)
explanation = explainer_signed.explain(X[0])
# Compare all variants
variants = explainer.compute_all_variants(X[0])
print(variants["saliency_absolute"])
print(variants["saliency_signed"])
print(variants["input_times_gradient"])
```
### SmoothGrad
```python
from explainiverse.explainers.gradient import SmoothGradExplainer
# SmoothGrad - Noise-averaged gradients for smoother saliency
explainer = SmoothGradExplainer(
model=adapter,
feature_names=feature_names,
class_names=class_names,
n_samples=50,
noise_scale=0.15,
noise_type="gaussian" # or "uniform"
)
# Standard SmoothGrad
explanation = explainer.explain(X[0], method="smoothgrad")
# SmoothGrad-Squared (sharper attributions)
explanation = explainer.explain(X[0], method="smoothgrad_squared")
# VarGrad (variance of gradients)
explanation = explainer.explain(X[0], method="vargrad")
# With absolute values
explanation = explainer.explain(X[0], absolute_value=True)
```
### GradCAM for CNNs
```python
from explainiverse.explainers.gradient import GradCAMExplainer
# For CNN models
adapter = PyTorchAdapter(cnn_model, task="classification", class_names=class_names)
explainer = GradCAMExplainer(
model=adapter,
target_layer="layer4", # Last conv layer
class_names=class_names,
method="gradcam++" # or "gradcam"
)
explanation = explainer.explain(image)
heatmap = explanation.explanation_data["heatmap"]
overlay = explainer.get_overlay(original_image, heatmap, alpha=0.5)
```
### TCAV (Concept-Based Explanations)
```python
from explainiverse.explainers.gradient import TCAVExplainer
# For neural network models with concept examples
adapter = PyTorchAdapter(model, task="classification", class_names=class_names)
# Create TCAV explainer targeting a specific layer
explainer = TCAVExplainer(
model=adapter,
layer_name="layer3", # Target layer for concept analysis
class_names=class_names
)
# Learn a concept from examples (e.g., "striped" pattern)
explainer.learn_concept(
concept_name="striped",
concept_examples=striped_images, # Images with stripes
negative_examples=random_images, # Random images without stripes
min_accuracy=0.6 # Minimum CAV classifier accuracy
)
# Compute TCAV score: fraction of inputs where concept positively influences prediction
tcav_score = explainer.compute_tcav_score(
test_inputs=test_images,
target_class=0, # e.g., "zebra"
concept_name="striped"
)
print(f"TCAV score: {tcav_score:.3f}") # >0.5 means concept positively influences class
# Statistical significance testing against random concepts
result = explainer.statistical_significance_test(
test_inputs=test_images,
target_class=0,
concept_name="striped",
n_random=10,
negative_examples=random_images
)
print(f"p-value: {result['p_value']:.4f}, significant: {result['significant']}")
# Full explanation with multiple concepts
explanation = explainer.explain(
test_inputs=test_images,
target_class=0,
run_significance_test=True
)
print(explanation.explanation_data["tcav_scores"])
```
---
## Example-Based Explanations
### ProtoDash
```python
from explainiverse.explainers.example_based import ProtoDashExplainer
explainer = ProtoDashExplainer(
model=adapter,
training_data=X_train,
feature_names=feature_names,
n_prototypes=5,
kernel="rbf",
gamma=0.1
)
explanation = explainer.explain(X_test[0])
print(explanation.explanation_data["prototype_indices"])
print(explanation.explanation_data["prototype_weights"])
```
---
## Evaluation Metrics
### Faithfulness Evaluation
```python
from explainiverse.evaluation import (
compute_pgi, compute_pgu,
compute_comprehensiveness, compute_sufficiency,
compute_faithfulness_correlation
)
# PGI - Higher is better (important features affect predictions)
pgi = compute_pgi(
model=adapter,
instance=X[0],
attributions=attributions,
feature_names=feature_names,
top_k=3
)
# PGU - Lower is better (unimportant features don't affect predictions)
pgu = compute_pgu(
model=adapter,
instance=X[0],
attributions=attributions,
feature_names=feature_names,
top_k=3
)
# Comprehensiveness - Higher is better
comp = compute_comprehensiveness(
model=adapter,
instance=X[0],
attributions=attributions,
feature_names=feature_names,
top_k_values=[1, 2, 3, 5]
)
# Sufficiency - Lower is better
suff = compute_sufficiency(
model=adapter,
instance=X[0],
attributions=attributions,
feature_names=feature_names,
top_k_values=[1, 2, 3, 5]
)
# Faithfulness Correlation
corr = compute_faithfulness_correlation(
model=adapter,
instance=X[0],
attributions=attributions,
feature_names=feature_names
)
```
### Stability Evaluation
```python
from explainiverse.evaluation import (
compute_ris, compute_ros, compute_lipschitz_estimate
)
# RIS - Relative Input Stability (lower is better)
ris = compute_ris(
explainer=explainer,
instance=X[0],
n_perturbations=10,
perturbation_scale=0.1
)
# ROS - Relative Output Stability (lower is better)
ros = compute_ros(
model=adapter,
explainer=explainer,
instance=X[0],
n_perturbations=10,
perturbation_scale=0.1
)
# Lipschitz Estimate (lower is better)
lipschitz = compute_lipschitz_estimate(
explainer=explainer,
instance=X[0],
n_perturbations=20,
perturbation_scale=0.1
)
```
---
## Global Explainers
```python
from explainiverse.explainers import (
PermutationImportanceExplainer,
PartialDependenceExplainer,
ALEExplainer,
SAGEExplainer
)
# Permutation Importance
perm_imp = PermutationImportanceExplainer(
model=adapter,
X=X_test,
y=y_test,
feature_names=feature_names,
n_repeats=10
)
explanation = perm_imp.explain()
# Partial Dependence Plot
pdp = PartialDependenceExplainer(
model=adapter,
X=X_train,
feature_names=feature_names
)
explanation = pdp.explain(feature="feature_0", grid_resolution=50)
# ALE (handles correlated features)
ale = ALEExplainer(
model=adapter,
X=X_train,
feature_names=feature_names
)
explanation = ale.explain(feature="feature_0", n_bins=20)
# SAGE (global Shapley importance)
sage = SAGEExplainer(
model=adapter,
X=X_train,
y=y_train,
feature_names=feature_names,
n_permutations=512
)
explanation = sage.explain()
```
---
## Multi-Explainer Comparison
```python
from explainiverse import ExplanationSuite
suite = ExplanationSuite(
model=adapter,
explainer_configs=[
("lime", {"training_data": X_train, "feature_names": feature_names, "class_names": class_names}),
("shap", {"background_data": X_train[:50], "feature_names": feature_names, "class_names": class_names}),
("treeshap", {"feature_names": feature_names, "class_names": class_names}),
]
)
results = suite.run(X_test[0])
suite.compare()
```
---
## Custom Explainer Registration
Explainiverse's plugin architecture allows you to register your own custom explainers and have them integrate seamlessly with the registry's discovery, filtering, and recommendation system.
### Why Register Custom Explainers?
| Benefit | Description |
|---------|-------------|
| **Discoverability** | Your explainer appears in `list_explainers()` and can be filtered by criteria |
| **Rich Metadata** | Attach scope, model types, data types, paper references, and complexity info |
| **Unified API** | Create instances via `default_registry.create("my_explainer", ...)` |
| **Recommendations** | Your explainer can be recommended based on the user's use case |
| **Consistency** | Follows the same `BaseExplainer` interface as all built-in methods |
### Method 1: Decorator-Based Registration (Recommended)
The cleanest way to register a custom explainer:
```python
from explainiverse import default_registry, BaseExplainer, Explanation
from explainiverse.core.registry import ExplainerMeta
@default_registry.register_decorator(
name="my_explainer",
meta=ExplainerMeta(
scope="local", # "local" or "global"
model_types=["any"], # ["any", "tree", "linear", "neural", "ensemble"]
data_types=["tabular"], # ["tabular", "image", "text", "time_series"]
task_types=["classification", "regression"],
description="My custom attribution method",
paper_reference="Author et al., 2024 - 'My Method' (Conference)",
complexity="O(n * d)", # Computational complexity
requires_training_data=False,
supports_batching=True
)
)
class MyExplainer(BaseExplainer):
"""Custom explainer implementing your attribution method."""
def __init__(self, model, feature_names, class_names=None, **kwargs):
super().__init__(model)
self.feature_names = feature_names
self.class_names = class_names
def explain(self, instance, target_class=None, **kwargs):
"""
Generate explanation for a single instance.
Args:
instance: Input to explain (1D array for tabular)
target_class: Class to explain (optional)
**kwargs: Additional method-specific parameters
Returns:
Explanation object with feature attributions
"""
# Your attribution logic here
attributions = self._compute_attributions(instance, target_class)
# Return standardized Explanation object
return Explanation(
explainer_name="MyExplainer",
target_class=str(target_class or 0),
explanation_data={"feature_attributions": attributions},
feature_names=self.feature_names,
metadata={"method": "my_method", "params": kwargs}
)
def _compute_attributions(self, instance, target_class):
"""Compute feature attributions (implement your method here)."""
# Example: simple gradient-like computation
import numpy as np
attributions = {}
for i, name in enumerate(self.feature_names):
attributions[name] = float(np.random.randn()) # Replace with real logic
return attributions
```
### Method 2: Programmatic Registration
For dynamic registration or when decorators aren't suitable:
```python
from explainiverse import default_registry, BaseExplainer, Explanation
from explainiverse.core.registry import ExplainerMeta, get_default_registry
# Define your explainer class
class AnotherExplainer(BaseExplainer):
def __init__(self, model, feature_names, **kwargs):
super().__init__(model)
self.feature_names = feature_names
def explain(self, instance, **kwargs):
# Implementation
return Explanation(
explainer_name="AnotherExplainer",
target_class="0",
explanation_data={"feature_attributions": {}},
feature_names=self.feature_names
)
# Register programmatically
registry = get_default_registry()
registry.register(
name="another_explainer",
explainer_class=AnotherExplainer,
meta=ExplainerMeta(
scope="local",
model_types=["neural"],
data_types=["image"],
description="Another custom explainer"
)
)
```
### Using Your Registered Explainer
Once registered, your explainer works like any built-in method:
```python
# Verify registration
print(default_registry.list_explainers()) # [..., 'my_explainer', ...]
# Check metadata
meta = default_registry.get_meta("my_explainer")
print(meta.description) # "My custom attribution method"
# Create via registry
explainer = default_registry.create(
"my_explainer",
model=adapter,
feature_names=feature_names,
class_names=class_names
)
# Generate explanations
explanation = explainer.explain(X[0])
print(explanation.get_top_features(k=5))
# Your explainer is now discoverable via filtering
local_explainers = default_registry.filter(scope="local")
print("my_explainer" in local_explainers) # True
# And included in recommendations
recommended = default_registry.recommend(
model_type="any",
data_type="tabular",
scope_preference="local"
)
```
### ExplainerMeta Fields Reference
| Field | Type | Description |
|-------|------|-------------|
| `scope` | `str` | **Required.** `"local"` (instance-level) or `"global"` (model-level) |
| `model_types` | `List[str]` | Compatible models: `["any", "tree", "linear", "neural", "ensemble"]` |
| `data_types` | `List[str]` | Compatible data: `["tabular", "image", "text", "time_series"]` |
| `task_types` | `List[str]` | Compatible tasks: `["classification", "regression"]` |
| `description` | `str` | Human-readable description (shown in `summary()`) |
| `paper_reference` | `str` | Citation for the method's paper |
| `complexity` | `str` | Computational complexity (e.g., `"O(n^2)"`) |
| `requires_training_data` | `bool` | Whether `explain()` needs background/training data |
| `supports_batching` | `bool` | Whether the explainer can process batches efficiently |
### Managing Registrations
```python
from explainiverse.core.registry import get_default_registry
registry = get_default_registry()
# Override an existing registration
registry.register(
name="my_explainer",
explainer_class=ImprovedExplainer,
meta=ExplainerMeta(scope="local", description="Improved version"),
override=True # Required to replace existing
)
# Unregister an explainer
registry.unregister("my_explainer")
# View summary of all registered explainers
print(registry.summary())
```
---
## Architecture
```
explainiverse/
├── core/
│ ├── explainer.py # BaseExplainer abstract class
│ ├── explanation.py # Unified Explanation container
│ └── registry.py # ExplainerRegistry with metadata
├── adapters/
│ ├── sklearn_adapter.py
│ └── pytorch_adapter.py # With gradient support
├── explainers/
│ ├── attribution/ # LIME, SHAP, TreeSHAP
│ ├── gradient/ # IG, DeepLIFT, DeepSHAP, SmoothGrad, Saliency, GradCAM, LRP, TCAV
│ ├── rule_based/ # Anchors
│ ├── counterfactual/ # DiCE-style
│ ├── global_explainers/ # Permutation, PDP, ALE, SAGE
│ └── example_based/ # ProtoDash
├── evaluation/
│ ├── faithfulness.py # PGI, PGU, Comprehensiveness, Sufficiency
│ └── stability.py # RIS, ROS, Lipschitz
└── engine/
└── suite.py # Multi-explainer comparison
```
---
## Running Tests
```bash
# Run all tests
poetry run pytest
# Run with coverage
poetry run pytest --cov=explainiverse --cov-report=html
# Run specific test file
poetry run pytest tests/test_lrp.py -v
# Run specific test class
poetry run pytest tests/test_lrp.py::TestLRPConv2d -v
```
---
## Roadmap
### Completed ✅
- [x] Core framework (BaseExplainer, Explanation, Registry)
- [x] Perturbation methods: LIME, KernelSHAP, TreeSHAP
- [x] Gradient methods: Integrated Gradients, DeepLIFT, DeepSHAP, SmoothGrad, Saliency Maps, GradCAM/GradCAM++
- [x] Decomposition methods: Layer-wise Relevance Propagation (LRP) with ε, γ, αβ, z⁺, composite rules
- [x] Concept-based: TCAV (Testing with Concept Activation Vectors)
- [x] Rule-based: Anchors
- [x] Counterfactual: DiCE-style
- [x] Global: Permutation Importance, PDP, ALE, SAGE
- [x] Example-based: ProtoDash
- [x] Evaluation: Faithfulness metrics (PGI, PGU, Comprehensiveness, Sufficiency, Correlation)
- [x] Evaluation: Stability metrics (RIS, ROS, Lipschitz)
- [x] PyTorch adapter with gradient support
### In Progress 🔄
- [ ] **Evaluation metrics expansion** - Adding 42 more metrics across 7 categories to exceed Quantus (37 metrics)
- Phase 1: Faithfulness (+12 metrics) - 10/12 complete
- Phase 2: Robustness (+7 metrics)
- Phase 3: Localisation (+8 metrics)
- Phase 4: Complexity (+4 metrics)
- Phase 5: Randomisation (+5 metrics)
- Phase 6: Axiomatic (+4 metrics)
- Phase 7: Fairness (+4 metrics)
### Planned 📋
- [ ] Attention-based explanations (for Transformers)
- [ ] TensorFlow/Keras adapter
- [ ] Interactive visualization dashboard
- [ ] Explanation caching and serialization
- [ ] Distributed computation support
---
## Citation
If you use Explainiverse in your research, please cite:
```bibtex
@software{explainiverse2025,
title = {Explainiverse: A Unified Framework for Explainable AI},
author = {Syed, Muntaser},
year = {2025},
url = {https://github.com/jemsbhai/explainiverse},
version = {0.9.1}
}
```
---
## Contributing
Contributions are welcome! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Write tests for your changes
4. Ensure all tests pass (`poetry run pytest`)
5. Commit your changes (`git commit -m 'Add amazing feature'`)
6. Push to the branch (`git push origin feature/amazing-feature`)
7. Open a Pull Request
---
## License
MIT License - see [LICENSE](LICENSE) for details.
---
## Acknowledgments
Explainiverse builds upon the foundational work of many researchers in the XAI community. We thank the authors of LIME, SHAP, Integrated Gradients, DeepLIFT, LRP, GradCAM, TCAV, Anchors, DiCE, ALE, SAGE, and ProtoDash for their contributions to interpretable machine learning.
| text/markdown | Muntaser Syed | jemsbhai@gmail.com | null | null | MIT | xai, explainability, interpretability, machine-learning, lime, shap, anchors | [
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"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
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"pandas<3.0,>=1.5",
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] | poetry/1.8.3 CPython/3.12.2 Windows/11 | 2026-02-20T10:29:28.270041 | explainiverse-0.9.5.tar.gz | 128,489 | 3b/59/06794b90a36fe4ab4e5dec674cdc66c23fdc494cfb684657b0fe059b1fb3/explainiverse-0.9.5.tar.gz | source | sdist | null | false | c8f70fd6b8d22f9b2bb86e2be9336f82 | b28c52f25e2e078d22e3c3abd4ee1c9c54f0492b784e30a7e17a547261b6aa38 | 3b5906794b90a36fe4ab4e5dec674cdc66c23fdc494cfb684657b0fe059b1fb3 | null | [] | 235 |
2.4 | sinq | 0.2.0 | SINQ quantization I/O for Hugging Face Transformers | [](https://arxiv.org/abs/2509.22944)
[](https://opensource.org/licenses/Apache-2.0)
[](https://github.com/huawei-csl/SINQ/stargazers)
[](https://huggingface.co/huawei-csl)
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td><img src="imgs/logo.png" alt="SINQ Logo" width="110"></td>
<td style="vertical-align: middle;"><h1>SINQ: Sinkhorn-Normalized Quantization for Calibration-Free Low-Precision LLMs</h1></td>
</tr>
</table>
> ⚡️ **A fast, plug-and-play, model-agnostic quantization technique** delivering **state-of-the-art performance** for Large Language Models **without sacrificing accuracy.**
> 💡 **Want to run a large model on your GPU but don’t have enough memory?** With **SINQ**, you can deploy models that would otherwise be too big **drastically reducing memory usage while preserving LLM quality.**
> ⏱️ SINQ quantizes **Qwen3-14B** in just **~21 sec** and **DeepSeekV2.5-236B** in **~5 min**
---
🆕 [17/10/2025] **First models on 🤗 Hugging Face Hub:**
> We’ve started uploading our first pre-quantized models to the 🤗 [**Hugging Face Hub**](https://huggingface.co/huawei-csl) and will continue adding more soon.
> **Note: We’re also actively working to add support for popular frameworks such as <code>vLLM</code>, <code>SGLang</code>, and <code>llama.cpp</code> to enable fast SINQ-ference** (sorry for the joke).
> In the meantime, you can ⭐️ **star** and **watch** the repo to stay updated!
---
## 🚀 Welcome to the **official SINQ repository**!
**SINQ** (Sinkhorn-Normalized Quantization) is a **novel, fast and high-quality quantization method** designed to make any Large Language Models **smaller** while keeping their accuracy almost intact.
### 🔍 What You’ll Find Here
- [1. How does SINQ work?](#1-how-does-sinq-work)
- [2. Why should I use SINQ?](#2-why-should-i-use-sinq)
- <u>[3. Quantize (and save) any LLM with SINQ](#3-quantize-any-llm-with-sinq)</u>
- [4. How to reproduce paper results](#4-how-to-reproduce-paper-results)
- [5. Ongoing updates on new features and integrations](#5-ongoing-updates-on-new-features-and-integrations)
- [6. How to Cite This Work](#6-how-to-cite-this-work)
- [7. Related Repositories](#7-related-repositories)
#### 📊 Feature Comparison: SINQ vs HQQ _(calibration-free)_ and A-SINQ vs AWQ _(calibrated)_
| Feature | **SINQ** | **HQQ** | **A-SINQ** | **AWQ** |
|------------|:--------:|:--------:|:----------:|:-------:|
| 🎯 Calibration | Calibration-free | Calibration-free | Calibrated | Calibrated |
| 🧮 Quantization Type | Symmetric & Asymmetric | Asymmetric only | Symmetric & Asymmetric | Symmetric & Asymmetric |
| 📦 NF4 Support | **Yes** | No | **Yes** | No |
| ⚡ Quantization Speed | ~2× **Faster** than HQQ | Slower | ~4× **Faster** than AWQ | Slower |
| 📈 Model Quality | **Higher** | Lower | **Higher** | Lower |
📄 **Want to know more?** Read our paper on [**arXiv**](http://arxiv.org/abs/2509.22944)!
---
## 1. How does SINQ work?
<details>
<summary>Click to expand a quick explanation of SINQ’s core idea</summary>
#### 1️⃣ Dual-Scaling for Better Quantization
<p align="left">
<img src="imgs/dualscale.png" alt="Dual Scale Illustration" width="330" align="right" style="margin-left: 20px;"/>
</p>
Conventional quantization uses **one scale per weight dimension**, which makes models vulnerable to **outliers**: large weights that distort scaling and cause significant errors.
**SINQ** solves this by introducing **dual scaling**: separate scale factors for **rows and columns**. This flexibility redistributes outlier influence and keeps quantization errors smaller and more balanced.
---
#### 2️⃣ More Even Error Distribution
<p align="left">
<img src="imgs/error.png" alt="Error Distribution Comparison" width="370" align="right" style="margin-left: 20px;"/>
</p>
With standard single-scale quantization, errors tend to **cluster around outliers**.
With **SINQ**, they become **spread out and less severe**, preserving model accuracy even at **3 bit precision**. This improvement is driven by SINQ’s **Sinkhorn-normalized optimization**, which iteratively rescales rows and columns to balance their variance - a process inspired by Sinkhorn matrix normalization. By reducing the overall **_matrix imbalance_** (refer to the paper for more info), weights become inherently easier to quantize, leading to more stable behavior across layers and consistently higher accuracy even at very low bit-widths.
</details>
---
## 2. Why should I use SINQ?
<details>
<summary>Click to expand a quick explanation on why you should use SINQ to quantize your LLM</summary>
#### **SINQ (calibration-free)**
- **Higher LLM quality** and **~2× faster** quantization than **HQQ**
- **>31× faster** quantization process and comparable or better LLM quality compared to **AWQ / GPTQ**
- **Model-agnostic**: works without knowing the specific LLM architecture, unlike **QuaRot**
- **Training-free**: it does not require end-to-end training, unlike **SpinQuant** or **KurTail**
- **Additionally, A-SINQ (calibrated)** further **beats AWQ, GPTQ, and Hadamard+GPTQ** on quality while achieving **>4× faster** quantization time.
**Example**
- ⏱️ SINQ quantizes **Qwen3-14B** in just **~21 sec** and **DeepSeekV2.5-236B** in **~5 min** on a single GPU
- 💾 Enables you to **run DeepSeekV2.5-236B** on a single GPU with **~110 GB** of memory (vs ~472 GB) while losing **< 1 ppl** on **WikiText2** and **C4**
</details>
## 3. Quantize any LLM with SINQ
### Setup & Quick Start
First, install the dependencies and set up the package:
```bash
# 1. Clone the repository
git clone https://github.com/huawei-csl/SINQ.git
cd SINQ
# 2. Install dependencies
pip install -r req.txt
# 3. Install SINQ
pip install .
```
---
### Quantize in a few lines
Quantizing any 🤗 Hugging Face model with SINQ is simple and takes only a few lines of code:
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from sinq.patch_model import AutoSINQHFModel
from sinq.sinqlinear import BaseQuantizeConfig
model_name = "Qwen/Qwen3-1.7B"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_name)
quant_cfg = BaseQuantizeConfig(
nbits=4, # quantization bit-width
group_size=64, # group size
tiling_mode="1D", # tiling strategy
method="sinq" # quantization method ("asinq" for the calibrated version)
)
qmodel = AutoSINQHFModel.quantize_model(
model,
tokenizer=tokenizer,
quant_config=quant_cfg,
compute_dtype=torch.bfloat16,
device="cuda:0"
)
```
✅ That’s it. Your model is now quantized with **SINQ** and ready for inference or saving.
### Optional Flags
You can further customize the quantization process to balance **accuracy** and **memory** for your needs.
Here’s a summary of the main arguments you can tune:
| Flag | Description | Options | Default |
|------|-------------|---------|----------|
| `--nbits` | Bit-width for weight quantization | 2, 3, 4, 5, 6, 8 | 4 |
| `--tiling_mode` | Weight matrix tiling strategy | 1D, 2D | 1D |
| `--group_size` | Weights per quantization group | 64, 128 | 64 |
| `--method` | Quantization method | sinq, asinq | sinq |
💡 **Tip:** For most cases, the defaults (`--nbits 4 --tiling_mode 1D --group_size 64 --method sinq`) provide an excellent trade-off between compression and accuracy.
---
### Save & reload
If you want to reuse a quantized model later, save it to disk in **HF-style sharded safetensors** and reload without needing base FP weights.
> Requires: `pip install safetensors`
```python
# --- Save to a folder (sharded safetensors) ---
from sinq.patch_model import AutoSINQHFModel
import torch
save_dir = "qwen3-1.7b-sinq-4bit" # any path
# 'model' must already be SINQ-quantized (e.g., via AutoSINQHFModel.quantize_model)
AutoSINQHFModel.save_quantized_safetensors(
qmodel,
tokenizer,
save_dir,
verbose=True,
max_shard_size="4GB", # typical HF shard size (use "8GB" if you prefer)
)
```
```python
# --- Reload later--
from sinq.patch_model import AutoSINQHFModel
import torch
tokenizer = AutoTokenizer.from_pretrained(save_dir)
qmodel = AutoSINQHFModel.from_quantized_safetensors(
save_dir,
device="cuda:0",
compute_dtype=torch.bfloat16,
)
# (optional) quick smoke test
prompt = "Explain neural network quantization in one sentence."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda:0")
with torch.inference_mode():
out_ids = qmodel.generate(**inputs, max_new_tokens=32, do_sample=False)
print(tokenizer.decode(out_ids[0], skip_special_tokens=True))
```
<details>
<summary><strong>Alternative: save & reload as a single <code>.pt</code> file</strong> </summary>
```python
# --- Save to a folder (.pt) ---
from sinq.patch_model import AutoSINQHFModel
save_dir = "qwen3-1.7b-sinq-4bit" # any path
AutoSINQHFModel.save_quantized(qmodel, tokenizer, save_dir, verbose=True) # creates qmodel.pt
```
```python
# --- Reload later from .pt ---
from sinq.patch_model import AutoSINQHFModel
import torch
tokenizer = AutoTokenizer.from_pretrained(save_dir)
qmodel = AutoSINQHFModel.from_quantized(
save_dir,
device="cuda:0",
compute_dtype=torch.bfloat16,
)
```
</details>
### Compatible with [`lm-eval`](https://github.com/EleutherAI/lm-evaluation-harness) evaluation framework
Below is a minimal example showing how to evaluate a SINQ-quantized model on a benchmark dataset:
```python
from lm_eval import evaluator
from lm_eval.models.huggingface import HFLM
# Wrap the already quantized model and tokenizer with HFLM
lm = HFLM(pretrained=qmodel, tokenizer=tokenizer, device="cuda:0")
# Evaluate (many tasks available on lm-eval such as MMLU and HellaSwag)
results = evaluator.simple_evaluate(
model=lm,
tasks=["lambada_openai"], # small and fast benchmark
device="cuda:0"
)
```
## 4. How to reproduce paper results
<details>
<summary>Click to expand the commands to reproduce the paper results</summary>
### Setup & Quick Start
First, install the dependencies and set up the package:
```bash
# 1. Clone the repository
git clone https://github.com/huawei-csl/SINQ.git
cd SINQ
# 2. Install dependencies
pip install -r req.txt
# 3. Install SINQ
pip install .
```
Then run the following command to quantize **Qwen3-1.7B** out of the box:
```bash
cd tests
python quant_model_eval.py
```
By default, this will run SINQ with the following settings:
- ✅ 4-bit weight quantization
- ✅ Dual-scale + shift parameterization
- ✅ 1D tiling
- ✅ Group size = 64
---
### Uniform, Uncalibrated Quantization
Reproduce the **core SINQ results** (as shown in Table 1 of the paper):
```bash
python quant_model_eval.py --model_name Qwen/Qwen3-1.7B
```
This uses **INT4 uniform quantization** without calibration - the main benchmark setting of the paper.
---
### Non-Uniform Quantization (NF4)
Try SINQ with **non-uniform quantization** (e.g., NF4):
```bash
python quant_model_eval.py --method sinq_nf4 --model_name Qwen/Qwen3-1.7B
```
---
### Calibrated Quantization (AWQ + SINQ = A-SINQ)
Combine SINQ with **activation-aware calibration (AWQ)** for higher accuracy:
```bash
python quant_model_eval.py --method asinq --model_name Qwen/Qwen3-1.7B
```
---
### ⚙️ Optional Flags
Customize experiments with the following command-line arguments:
| Flag | Description | Options | Default |
|------|-------------|---------|----------|
| `--nbits` | Number of bits used to quantize model weights | 2, 3, 4, 8 | 4 |
| `--tiling_mode` | Strategy for tiling weight matrices during quantization | 1D, 2D | 1D |
| `--group_size` | Number of weights processed together as a quantization group | 64, 128 | 64 |
> 📝 **Note:** All results reported in the paper were obtained using the evaluation framework from [Efficient-ML/Qwen3-Quantization](https://github.com/Efficient-ML/Qwen3-Quantization) rather than `lm-eval`.
</details>
## 5. Ongoing updates on new features and integrations
We are actively expanding SINQ with new features and integrations. Stay tuned here for the latest updates:
- [26/09/2025] - SINQ paper released on [**arXiv**](https://arxiv.org/abs/2509.22944)
- [30/09/2025] - SINQ GitHub repository made public
- [02/10/2025] - SINQ paper featured on 🤗 [**Hugging Face Papers**](https://huggingface.co/papers/2509.22944)
- [17/10/2025] - First pre-quantized **SINQ models** available on 🤗[**Hugging Face Hub**](https://huggingface.co/huawei-csl)! 🆕
- [23/10/2025] - Faster inference with gemlite backend (4-bit 1D tiling)
- 🔜 **Coming soon** - 🤗 Integration with **Hugging Face Transformers**
- 🔜 **Coming soon** - Support for **Conv2D layers** and **timm models** for computer vision tasks
- 🔜 **Work in progress** - Support for **mixed-precision quantization** (combine multiple bitwidths for optimal accuracy-efficiency balance)
- 🔜 **Work in progress** - We’re actively working to provide support for popular frameworks such as <code>vLLM</code>, <code>SGLang</code>, and <code>llama.cpp</code>.
## 6. How to Cite This Work
If you find **SINQ** useful in your research or applications, please cite our <a href="http://arxiv.org/abs/2509.22944" target="_blank"><strong>paper</strong></a>:
```bibtex
@misc{muller2025sinq,
title={SINQ: Sinkhorn-Normalized Quantization for Calibration-Free Low-Precision LLM Weights},
author={Lorenz K. Muller and Philippe Bich and Jiawei Zhuang and Ahmet Celik and Luca Benfenati and Lukas Cavigelli},
year={2025},
eprint={2509.22944},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={http://arxiv.org/abs/2509.22944}
}
```
## 7. Related Repositories
This project builds upon and extends the excellent work from the following open-source projects:
- [**Qwen3-Quantization**](https://github.com/Efficient-ML/Qwen3-Quantization) - Base implementation and evaluation scripts for Qwen3 quantization.
- [**HQQ**](https://github.com/mobiusml/hqq) - High-quality calibration-free quantization baseline.
📜 You can find their original licenses in the corresponding `LICENSE` files in these repositories.
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2.4 | trustgraph-mcp | 2.0.9 | TrustGraph provides a means to run a pipeline of flexible AI processing components in a flexible means to achieve a processing pipeline. | See https://trustgraph.ai/
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] | [] | [] | [] | [
"Homepage, https://github.com/trustgraph-ai/trustgraph"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:28:57.917024 | trustgraph_mcp-2.0.9.tar.gz | 15,257 | da/1c/03e8ed6f2085053251056d75a0b51a179cb9891b6a55797c6134ecf667eb/trustgraph_mcp-2.0.9.tar.gz | source | sdist | null | false | fe6dc54a5998c066f0a13b7b70f2fa8c | f9567ac04a1691869b1c0b9550fafe96d7f2d997bce45480778d2bbc32677f43 | da1c03e8ed6f2085053251056d75a0b51a179cb9891b6a55797c6134ecf667eb | null | [] | 149 |
2.4 | trustgraph-flow | 2.0.9 | TrustGraph provides a means to run a pipeline of flexible AI processing components in a flexible means to achieve a processing pipeline. | See https://trustgraph.ai/
| text/markdown | null | "trustgraph.ai" <security@trustgraph.ai> | null | null | null | null | [
"Programming Language :: Python :: 3",
"Operating System :: OS Independent"
] | [] | null | null | >=3.8 | [] | [] | [] | [
"trustgraph-base<2.1,>=2.0",
"aiohttp",
"anthropic",
"scylla-driver",
"cohere",
"cryptography",
"faiss-cpu",
"falkordb",
"fastembed",
"ibis",
"jsonschema",
"langchain",
"langchain-community",
"langchain-core",
"langchain-text-splitters",
"mcp",
"minio",
"mistralai",
"neo4j",
"nltk",
"ollama",
"openai",
"pinecone[grpc]",
"prometheus-client",
"pulsar-client",
"pymilvus",
"pypdf",
"pyyaml",
"qdrant-client",
"rdflib",
"requests",
"strawberry-graphql",
"tabulate",
"tiktoken",
"urllib3"
] | [] | [] | [] | [
"Homepage, https://github.com/trustgraph-ai/trustgraph"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:28:56.629463 | trustgraph_flow-2.0.9.tar.gz | 215,979 | f6/70/228d644a90a4d73457c881fa49f7a1ae62986b880905c0de5194bcad241f/trustgraph_flow-2.0.9.tar.gz | source | sdist | null | false | c432c0e8d388f5faad7fa6c84e4ae21a | 2fe81960146c76074b53f4c5d01d56de8c1a9caf6ab5704bd635309549d79f6f | f670228d644a90a4d73457c881fa49f7a1ae62986b880905c0de5194bcad241f | null | [] | 159 |
2.4 | trustgraph-embeddings-hf | 2.0.9 | HuggingFace embeddings support for TrustGraph. | See https://trustgraph.ai/
| text/markdown | null | "trustgraph.ai" <security@trustgraph.ai> | null | null | null | null | [
"Programming Language :: Python :: 3",
"Operating System :: OS Independent"
] | [] | null | null | >=3.8 | [] | [] | [] | [
"trustgraph-base<2.1,>=2.0",
"trustgraph-flow<2.1,>=2.0",
"torch",
"urllib3",
"transformers",
"sentence-transformers",
"langchain",
"langchain-core",
"langchain-huggingface",
"langchain-community",
"huggingface-hub",
"pulsar-client",
"pyyaml",
"prometheus-client"
] | [] | [] | [] | [
"Homepage, https://github.com/trustgraph-ai/trustgraph"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:28:55.389299 | trustgraph_embeddings_hf-2.0.9.tar.gz | 2,629 | 82/bc/d130d366c76f61c65d8eec9261f5799a8844417e40ec0c28a15f9bf624f5/trustgraph_embeddings_hf-2.0.9.tar.gz | source | sdist | null | false | 41058259eb53f5c80cbdb365f9e55640 | 351b62f15567dab053d202358ead86caca618a2b201cb7a8b092dbe61012bae8 | 82bcd130d366c76f61c65d8eec9261f5799a8844417e40ec0c28a15f9bf624f5 | null | [] | 153 |
2.4 | trustgraph-cli | 2.0.9 | TrustGraph provides a means to run a pipeline of flexible AI processing components in a flexible means to achieve a processing pipeline. | See https://trustgraph.ai/
| text/markdown | null | "trustgraph.ai" <security@trustgraph.ai> | null | null | null | null | [
"Programming Language :: Python :: 3",
"Operating System :: OS Independent"
] | [] | null | null | >=3.8 | [] | [] | [] | [
"trustgraph-base<2.1,>=2.0",
"requests",
"pulsar-client",
"aiohttp",
"rdflib",
"tabulate",
"msgpack",
"websockets"
] | [] | [] | [] | [
"Homepage, https://github.com/trustgraph-ai/trustgraph"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:28:54.365204 | trustgraph_cli-2.0.9.tar.gz | 58,477 | c3/82/0afb7c51c18dae5858a3d0f33331746e572508743d1e4be94365e43b4078/trustgraph_cli-2.0.9.tar.gz | source | sdist | null | false | 8c3779bcfda605cf9f92e5bf76d1413c | 4785c368c7be8221105bd699e189fd89f8305ce735c000fe784df12247d7bff9 | c3820afb7c51c18dae5858a3d0f33331746e572508743d1e4be94365e43b4078 | null | [] | 151 |
2.4 | trustgraph-bedrock | 2.0.9 | TrustGraph provides a means to run a pipeline of flexible AI processing components in a flexible means to achieve a processing pipeline. | See https://trustgraph.ai/
| text/markdown | null | "trustgraph.ai" <security@trustgraph.ai> | null | null | null | null | [
"Programming Language :: Python :: 3",
"Operating System :: OS Independent"
] | [] | null | null | >=3.8 | [] | [] | [] | [
"trustgraph-base<2.1,>=2.0",
"pulsar-client",
"prometheus-client",
"boto3"
] | [] | [] | [] | [
"Homepage, https://github.com/trustgraph-ai/trustgraph"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:28:53.128284 | trustgraph_bedrock-2.0.9.tar.gz | 5,389 | b3/d9/4d21c48290dda7ff67959ff1f7b85df72bfdad697f86cc5e638ff5d633ed/trustgraph_bedrock-2.0.9.tar.gz | source | sdist | null | false | 883547c6816d67edfda989b26ad2a1ce | 47257b338e464739d289d572df1e2151823450b452b3e6e88dcaeae29b0d5ef3 | b3d94d21c48290dda7ff67959ff1f7b85df72bfdad697f86cc5e638ff5d633ed | null | [] | 146 |
2.4 | trustgraph-base | 2.0.9 | TrustGraph provides a means to run a pipeline of flexible AI processing components in a flexible means to achieve a processing pipeline. | See https://trustgraph.ai/
| text/markdown | null | "trustgraph.ai" <security@trustgraph.ai> | null | null | null | null | [
"Programming Language :: Python :: 3",
"Operating System :: OS Independent"
] | [] | null | null | >=3.8 | [] | [] | [] | [
"pulsar-client",
"prometheus-client",
"requests",
"python-logging-loki"
] | [] | [] | [] | [
"Homepage, https://github.com/trustgraph-ai/trustgraph"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:28:51.788924 | trustgraph_base-2.0.9.tar.gz | 90,161 | 4e/15/0ab6b5c9c4223a1c8468faf5572524188409f601aa643dcf1f2fe3d66857/trustgraph_base-2.0.9.tar.gz | source | sdist | null | false | bc3cd721b7878603eab70159a4add348 | d6f3480721aa99e47eff307163484cf687fee12702bd632f438806e65ba7e552 | 4e150ab6b5c9c4223a1c8468faf5572524188409f601aa643dcf1f2fe3d66857 | null | [] | 175 |
2.4 | trustgraph | 2.0.9 | TrustGraph provides a means to run a pipeline of flexible AI processing components in a flexible means to achieve a processing pipeline. | See https://trustgraph.ai/
| text/markdown | null | "trustgraph.ai" <security@trustgraph.ai> | null | null | null | null | [
"Programming Language :: Python :: 3",
"Operating System :: OS Independent"
] | [] | null | null | >=3.8 | [] | [] | [] | [
"trustgraph-base<1.9,>=1.8",
"trustgraph-bedrock<1.9,>=1.8",
"trustgraph-cli<1.9,>=1.8",
"trustgraph-embeddings-hf<1.9,>=1.8",
"trustgraph-flow<1.9,>=1.8",
"trustgraph-vertexai<1.9,>=1.8"
] | [] | [] | [] | [
"Homepage, https://github.com/trustgraph-ai/trustgraph"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:28:50.437489 | trustgraph-2.0.9.tar.gz | 1,419 | 82/25/c797a00ef88a054da3799746aab6d4e14bd27fe9667aaddf62543f6873b4/trustgraph-2.0.9.tar.gz | source | sdist | null | false | 8c76e13a7d4ed22ecbe5906e5dbaf4f9 | 5c8203830877197ddb68153e87bed198da5ebcf82360b65111dcc0adef634302 | 8225c797a00ef88a054da3799746aab6d4e14bd27fe9667aaddf62543f6873b4 | null | [] | 149 |
2.4 | dbvault | 0.1.3 | Multi-database backup utility with Fernet encryption, S3/Azure cloud upload, async execution, and retry logic | # DBVault
```
___ ____ _ __ ____
/ _ \/ __ )| | / /___ _____/ / /_
/ // / __ || |/ / __ `/ __/ / __/
/____/_/ /_/ |___/\__,_/\__/_/\__/
```
**Encrypted · Cloud-ready · Multi-database backup utility**
[](https://python.org)
[](LICENSE)
[](https://pypi.org/project/dbvault/)
[](tests/)
DBVault is a command-line backup utility that gives you a consistent pipeline across six database engines: **dump → validate → compress → encrypt → upload**. Every step is covered with retry/backoff logic and all operations are available in both sync and async modes.
---
## Features
| Feature | Detail |
|---|---|
| **6 database engines** | MySQL, PostgreSQL, MongoDB, Redis, SQLite, IBM Db2 |
| **Gzip compression** | Every backup is compressed before storage |
| **Fernet encryption** | Optional AES-128-CBC + HMAC-SHA256 (symmetric, authenticated) |
| **Cloud upload** | AWS S3, Azure Blob Storage, Google Cloud Storage (GCS), and MinIO |
| **Retry + backoff** | 3 attempts, exponential 2–10 s (powered by `tenacity`) |
| **Async execution** | `async_perform_backup_pipeline` via `asyncio.to_thread` |
| **Validation** | Each backup is restored to a temp target and verified before being kept |
| **Clean CLI** | `click`-powered interface with `pyfiglet` banner |
---
## Supported Databases
| Alias | Engine | Backup tool | Validation method |
|---|---|---|---|
| `mysql` | MySQL / MariaDB | `mysqldump` | restore to temp DB via `mysql` |
| `postgres` | PostgreSQL | `pg_dump` | restore to temp DB via `psql` |
| `mongo` | MongoDB | `mongodump --archive` | `mongorestore --nsFrom/--nsTo` |
| `redis` | Redis | `redis-cli --rdb` | RDB magic-byte check (`REDIS`) |
| `sqlite` | SQLite | `sqlite3.Connection.backup()` | `PRAGMA integrity_check` |
| `db2` | IBM Db2 | `db2 BACKUP DATABASE` | `db2ckbkp` |
---
## Installation
### From PyPI
```bash
pip install dbvault
```
### From source (development)
```bash
git clone https://github.com/Abhishek772/dbvault
cd dbvault
uv sync --group dev
```
---
## Quick Start
### 1. Back up a MySQL database
```bash
dbvault backup \
--db mysql \
--host localhost \
--user root \
--database mydb \
--output ./backups
```
### 2. Back up with encryption
```bash
dbvault backup \
--db postgres \
--host db.internal \
--user admin \
--database analytics \
--output ./backups \
--encrypt
# DBVault prints the generated key — save it!
```
### 3. Back up directly to S3 (with encryption)
```bash
dbvault backup \
--db mysql \
--host localhost \
--user root \
--database mydb \
--output ./backups \
--encrypt \
--cloud s3 \
--s3-bucket my-backup-bucket \
--s3-owner 123456789012
```
### 4. Back up directly to GCP Cloud Storage
```bash
dbvault backup \
--db postgres \
--host localhost \
--user admin \
--database mydb \
--output ./backups \
--cloud gcp \
--gcp-bucket my-gcp-backup-bucket
# Uses GOOGLE_APPLICATION_CREDENTIALS environment variable
```
### 5. Back up to MinIO
```bash
dbvault backup \
--db mongo \
--host localhost \
--user admin \
--database myapp \
--output ./backups \
--cloud minio \
--minio-endpoint play.min.io \
--minio-bucket my-minio-bucket
# Uses MINIO_ACCESS_KEY and MINIO_SECRET_KEY environment variables
```
### 6. Decrypt a backup
```bash
dbvault decrypt \
--file ./backups/backup.sql.gz.enc \
--key <your-fernet-key>
```
### 7. Generate an encryption key
```bash
dbvault keygen
# or save directly to a file
dbvault keygen --save ~/.dbvault.key
```
---
## CLI Reference
### `dbvault backup`
```
Options:
-d, --db [mysql|postgres|mongo|redis|sqlite|db2] Database engine [required]
-H, --host TEXT Database host [default: localhost]
-u, --user TEXT Database username
-p, --password TEXT Database password (prompted if omitted)
-D, --database TEXT Database name / SQLite file path [required]
-o, --output PATH Output directory [required]
-e, --encrypt Fernet-encrypt the backup
-k, --key TEXT Existing Fernet key (generated if --encrypt and omitted)
-c, --cloud [s3|azure|gcp|minio] Upload to cloud after backup
--s3-bucket TEXT S3 bucket name
--s3-key TEXT S3 object key
--s3-owner TEXT Expected S3 bucket owner — 12-digit AWS account ID
--azure-conn-str TEXT Azure Storage connection string
--azure-container TEXT Azure container name
--azure-blob TEXT Azure blob name
--gcp-bucket TEXT GCP bucket name
--gcp-blob TEXT GCP blob name (optional)
--minio-endpoint TEXT MinIO endpoint URL
--minio-bucket TEXT MinIO bucket name
--minio-object TEXT MinIO object name (optional)
-a, --async-mode Run asynchronously
-h, --help Show this message and exit.
```
### `dbvault keygen`
```
Options:
-s, --save PATH Write key to a file
-h, --help Show this message and exit.
```
### `dbvault decrypt`
```
Options:
-f, --file PATH Encrypted backup file (.enc) [required]
-k, --key TEXT Fernet key used during encryption [required]
-h, --help Show this message and exit.
```
---
## Architecture
```
dbvault backup
│
▼
DatabaseBackupManager (ABC)
│
├── connect() — open DB connection
├── _run_*dump() — engine-specific dump subprocess
├── validate() — restore to temp target, verify, drop
├── compress() — gzip the dump file
├── encrypt() — Fernet encrypt (optional)
├── _upload_to_cloud() — S3 / Azure / GCP / MinIO upload (optional)
└── perform_backup_pipeline() ← @retry(3×, exp backoff 2-10 s)
async_perform_backup_pipeline() ← asyncio.to_thread wrapper
```
```
core/
├── interfaces/
│ └── backup_utility_interface.py # Abstract base class
├── helpers/
│ ├── cryptographic_helper.py # Fernet generate / encrypt / decrypt
│ └── blobstorage_uploader.py # S3, Azure, GCP, MinIO upload (sync + async)
└── services/
├── sql_backup_utility.py # MySQL
├── postgres_backup_utility.py # PostgreSQL
├── mongo_backup_utility.py # MongoDB
├── redis_backup_utility.py # Redis
├── sqllite_backup_utility.py # SQLite
└── ibm_db2_backup_uitlity.py # IBM Db2
cli/
└── app.py # Click CLI entry point
tests/
├── conftest.py
├── test_cryptographic_helper.py
├── test_sqlite_backup.py
├── test_sql_backup.py
├── test_blobstorage_uploader.py
└── test_cli.py
```
---
## Development
### Setup
```bash
git clone https://github.com/Abhishek772/dbvault
cd dbvault
uv sync --group dev
```
### Run tests
```bash
pytest
# with coverage
pytest --cov=core --cov=cli --cov-report=term-missing
```
### Run the CLI from source
```bash
python main.py backup --db sqlite --database ./my.db --output ./out
# or
uv run dbvault backup --db sqlite --database ./my.db --output ./out
```
### Build for PyPI
```bash
uv build
# produces dist/dbvault-0.1.0-py3-none-any.whl and .tar.gz
```
### Publish
```bash
uv publish --token $PYPI_TOKEN
```
---
## Adding a New Database Engine
1. Create `core/services/<engine>_backup_utility.py`
2. Extend `DatabaseBackupManager` and implement all abstract methods:
- `connect()`, `backup()`, `validate()`, `compress()`, `encrypt()`,
`perform_backup_pipeline()`, `async_perform_backup_pipeline()`
3. Register the alias in `cli/app.py → DB_MANAGERS`
4. Add test coverage in `tests/test_<engine>_backup.py`
---
## Security Notes
- Passwords are passed via environment variables (`MYSQL_PWD`, `PGPASSWORD`) or the tool's own `--password` flag and are never written to disk.
- S3 uploads enforce `ExpectedBucketOwner` to prevent confused-deputy bucket hijacking.
- Fernet encryption is authenticated (HMAC-SHA256); tampering with the ciphertext raises `InvalidToken`.
- Encryption keys are printed once at generation time and are never stored by DBVault — keep them safe.
---
## License
MIT — see [LICENSE](LICENSE).
| text/markdown | null | null | null | null | MIT | azure, backup, cli, database, encryption, ibm-db2, mongodb, mysql, postgresql, redis, s3, sqlite | [
"Development Status :: 3 - Alpha",
"Environment :: Console",
"Intended Audience :: Developers",
"Intended Audience :: System Administrators",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Topic :: Database",
"Topic :: Security :: Cryptography",
"Topic :: System :: Archiving :: Backup",
"Topic :: System :: Systems Administration"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"azure-storage-blob>=12.28.0",
"boto3>=1.42.53",
"click>=8.1",
"cryptography>=46.0.5",
"google-cloud-storage>=2.19",
"minio>=7.2",
"psycopg2-binary>=2.9.11",
"pyfiglet>=1.0",
"pymongo>=4.10",
"pymysql>=1.1.2",
"redis>=5.2",
"tenacity>=9.1.4",
"pytest-asyncio>=0.25; extra == \"dev\"",
"pytest-mock>=3.14; extra == \"dev\"",
"pytest>=8.3; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/abhishekbiswas72/dbvault",
"Repository, https://github.com/abhishekbiswas772/dbvault",
"Issues, https://github.com/Abhishek772/dbvault/issues",
"Changelog, https://github.com/abhishekbiswas72/dbvault/blob/main/CHANGELOG.md"
] | uv/0.9.0 | 2026-02-20T10:28:30.302591 | dbvault-0.1.3.tar.gz | 59,605 | 14/27/fc81dc15785cd57d1fdddf00bbb460b29658825c76a5dd4c02eeedbbe392/dbvault-0.1.3.tar.gz | source | sdist | null | false | e0f39ae67024bf3b2a6f5dda23f2708f | 0e67bd21a07b5ebdffe8be5d3bb6bd733ff2ef302005b58d842c43deec39357d | 1427fc81dc15785cd57d1fdddf00bbb460b29658825c76a5dd4c02eeedbbe392 | null | [
"LICENSE"
] | 231 |
2.4 | meitner | 0.5.0 | Python Client SDK Generated by Speakeasy. | # meitner
Developer-friendly & type-safe Python SDK specifically catered to leverage *meitner* API.
<div align="left">
<a href="https://www.speakeasy.com/?utm_source=meitner&utm_campaign=python"><img src="https://www.speakeasy.com/assets/badges/built-by-speakeasy.svg" /></a>
<a href="https://opensource.org/licenses/MIT">
<img src="https://img.shields.io/badge/License-MIT-blue.svg" style="width: 100px; height: 28px;" />
</a>
</div>
<br /><br />
> [!IMPORTANT]
> This SDK is not yet ready for production use. To complete setup please follow the steps outlined in your [workspace](https://app.speakeasy.com/org/meitner-2u8/api). Delete this section before > publishing to a package manager.
<!-- Start Summary [summary] -->
## Summary
Directory API: Generated API documentation
<!-- End Summary [summary] -->
<!-- Start Table of Contents [toc] -->
## Table of Contents
<!-- $toc-max-depth=2 -->
* [meitner](https://github.com/meitner-se/api-client-python/blob/master/#meitner)
* [SDK Installation](https://github.com/meitner-se/api-client-python/blob/master/#sdk-installation)
* [IDE Support](https://github.com/meitner-se/api-client-python/blob/master/#ide-support)
* [SDK Example Usage](https://github.com/meitner-se/api-client-python/blob/master/#sdk-example-usage)
* [Authentication](https://github.com/meitner-se/api-client-python/blob/master/#authentication)
* [Available Resources and Operations](https://github.com/meitner-se/api-client-python/blob/master/#available-resources-and-operations)
* [Pagination](https://github.com/meitner-se/api-client-python/blob/master/#pagination)
* [Retries](https://github.com/meitner-se/api-client-python/blob/master/#retries)
* [Error Handling](https://github.com/meitner-se/api-client-python/blob/master/#error-handling)
* [Server Selection](https://github.com/meitner-se/api-client-python/blob/master/#server-selection)
* [Custom HTTP Client](https://github.com/meitner-se/api-client-python/blob/master/#custom-http-client)
* [Resource Management](https://github.com/meitner-se/api-client-python/blob/master/#resource-management)
* [Debugging](https://github.com/meitner-se/api-client-python/blob/master/#debugging)
* [Development](https://github.com/meitner-se/api-client-python/blob/master/#development)
* [Maturity](https://github.com/meitner-se/api-client-python/blob/master/#maturity)
* [Contributions](https://github.com/meitner-se/api-client-python/blob/master/#contributions)
<!-- End Table of Contents [toc] -->
<!-- Start SDK Installation [installation] -->
## SDK Installation
> [!NOTE]
> **Python version upgrade policy**
>
> Once a Python version reaches its [official end of life date](https://devguide.python.org/versions/), a 3-month grace period is provided for users to upgrade. Following this grace period, the minimum python version supported in the SDK will be updated.
The SDK can be installed with *uv*, *pip*, or *poetry* package managers.
### uv
*uv* is a fast Python package installer and resolver, designed as a drop-in replacement for pip and pip-tools. It's recommended for its speed and modern Python tooling capabilities.
```bash
uv add meitner
```
### PIP
*PIP* is the default package installer for Python, enabling easy installation and management of packages from PyPI via the command line.
```bash
pip install meitner
```
### Poetry
*Poetry* is a modern tool that simplifies dependency management and package publishing by using a single `pyproject.toml` file to handle project metadata and dependencies.
```bash
poetry add meitner
```
### Shell and script usage with `uv`
You can use this SDK in a Python shell with [uv](https://docs.astral.sh/uv/) and the `uvx` command that comes with it like so:
```shell
uvx --from meitner python
```
It's also possible to write a standalone Python script without needing to set up a whole project like so:
```python
#!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.10"
# dependencies = [
# "meitner",
# ]
# ///
from meitner import Meitner
sdk = Meitner(
# SDK arguments
)
# Rest of script here...
```
Once that is saved to a file, you can run it with `uv run script.py` where
`script.py` can be replaced with the actual file name.
<!-- End SDK Installation [installation] -->
<!-- Start IDE Support [idesupport] -->
## IDE Support
### PyCharm
Generally, the SDK will work well with most IDEs out of the box. However, when using PyCharm, you can enjoy much better integration with Pydantic by installing an additional plugin.
- [PyCharm Pydantic Plugin](https://docs.pydantic.dev/latest/integrations/pycharm/)
<!-- End IDE Support [idesupport] -->
<!-- Start SDK Example Usage [usage] -->
## SDK Example Usage
### Example
```python
# Synchronous Example
from meitner import Meitner, models
import os
with Meitner(
security=models.Security(
client_credentials=os.getenv("MEITNER_CLIENT_CREDENTIALS", ""),
client_secret=os.getenv("MEITNER_CLIENT_SECRET", ""),
),
) as m_client:
res = m_client.schools.list(limit=1, offset=0)
while res is not None:
# Handle items
res = res.next()
```
</br>
The same SDK client can also be used to make asynchronous requests by importing asyncio.
```python
# Asynchronous Example
import asyncio
from meitner import Meitner, models
import os
async def main():
async with Meitner(
security=models.Security(
client_credentials=os.getenv("MEITNER_CLIENT_CREDENTIALS", ""),
client_secret=os.getenv("MEITNER_CLIENT_SECRET", ""),
),
) as m_client:
res = await m_client.schools.list_async(limit=1, offset=0)
while res is not None:
# Handle items
res = res.next()
asyncio.run(main())
```
<!-- End SDK Example Usage [usage] -->
<!-- Start Authentication [security] -->
## Authentication
### Per-Client Security Schemes
This SDK supports the following security schemes globally:
| Name | Type | Scheme | Environment Variable |
| -------------------- | ------ | ------- | ---------------------------- |
| `client_credentials` | apiKey | API key | `MEITNER_CLIENT_CREDENTIALS` |
| `client_secret` | apiKey | API key | `MEITNER_CLIENT_SECRET` |
You can set the security parameters through the `security` optional parameter when initializing the SDK client instance. The selected scheme will be used by default to authenticate with the API for all operations that support it. For example:
```python
from meitner import Meitner, models
import os
with Meitner(
security=models.Security(
client_credentials=os.getenv("MEITNER_CLIENT_CREDENTIALS", ""),
client_secret=os.getenv("MEITNER_CLIENT_SECRET", ""),
),
) as m_client:
res = m_client.schools.list(limit=1, offset=0)
while res is not None:
# Handle items
res = res.next()
```
<!-- End Authentication [security] -->
<!-- Start Available Resources and Operations [operations] -->
## Available Resources and Operations
<details open>
<summary>Available methods</summary>
### [AuditEvents](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/auditevents/README.md)
* [list](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/auditevents/README.md#list) - List AuditEvents
* [search](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/auditevents/README.md#search) - Search AuditEvents
* [get](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/auditevents/README.md#get) - Get a AuditEvent
### [EmployeePlacements](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/employeeplacements/README.md)
* [list](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/employeeplacements/README.md#list) - List EmployeePlacements
* [create](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/employeeplacements/README.md#create) - Create a new EmployeePlacement
* [search](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/employeeplacements/README.md#search) - Search EmployeePlacements
* [get](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/employeeplacements/README.md#get) - Get a EmployeePlacement
* [delete](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/employeeplacements/README.md#delete) - Delete a EmployeePlacement
* [update](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/employeeplacements/README.md#update) - Update a EmployeePlacement
### [Employees](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/employees/README.md)
* [list](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/employees/README.md#list) - List Employees
* [create](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/employees/README.md#create) - Create a new Employee
* [search](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/employees/README.md#search) - Search Employees
* [get](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/employees/README.md#get) - Get a Employee
* [delete](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/employees/README.md#delete) - Delete a Employee
* [update](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/employees/README.md#update) - Update a Employee
### [Groups](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/groups/README.md)
* [list](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/groups/README.md#list) - List Groups
* [create](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/groups/README.md#create) - Create a new Group
* [search](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/groups/README.md#search) - Search Groups
* [get](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/groups/README.md#get) - Get a Group
* [delete](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/groups/README.md#delete) - Delete a Group
* [update](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/groups/README.md#update) - Update a Group
### [Guardians](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/guardians/README.md)
* [list](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/guardians/README.md#list) - List Guardians
* [create](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/guardians/README.md#create) - Create a new Guardian
* [search](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/guardians/README.md#search) - Search Guardians
* [get](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/guardians/README.md#get) - Get a Guardian
* [delete](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/guardians/README.md#delete) - Delete a Guardian
* [update](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/guardians/README.md#update) - Update a Guardian
### [Schools](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/schools/README.md)
* [list](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/schools/README.md#list) - List Schools
* [create](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/schools/README.md#create) - Create a new School
* [search](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/schools/README.md#search) - Search Schools
* [get](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/schools/README.md#get) - Get a School
* [update](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/schools/README.md#update) - Update a School
### [StudentPlacements](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/studentplacements/README.md)
* [list](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/studentplacements/README.md#list) - List StudentPlacements
* [create](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/studentplacements/README.md#create) - Create a new StudentPlacement
* [search](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/studentplacements/README.md#search) - Search StudentPlacements
* [get](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/studentplacements/README.md#get) - Get a StudentPlacement
* [delete](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/studentplacements/README.md#delete) - Delete a StudentPlacement
* [update](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/studentplacements/README.md#update) - Update a StudentPlacement
* [archive](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/studentplacements/README.md#archive) - Archive a student placement
* [restore](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/studentplacements/README.md#restore) - Restore an archived student placement
### [Students](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/students/README.md)
* [list](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/students/README.md#list) - List Students
* [create](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/students/README.md#create) - Create a new Student
* [search](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/students/README.md#search) - Search Students
* [get](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/students/README.md#get) - Get a Student
* [delete](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/students/README.md#delete) - Delete a Student
* [update](https://github.com/meitner-se/api-client-python/blob/master/docs/sdks/students/README.md#update) - Update a Student
</details>
<!-- End Available Resources and Operations [operations] -->
<!-- Start Pagination [pagination] -->
## Pagination
Some of the endpoints in this SDK support pagination. To use pagination, you make your SDK calls as usual, but the
returned response object will have a `Next` method that can be called to pull down the next group of results. If the
return value of `Next` is `None`, then there are no more pages to be fetched.
Here's an example of one such pagination call:
```python
from meitner import Meitner, models
import os
with Meitner(
security=models.Security(
client_credentials=os.getenv("MEITNER_CLIENT_CREDENTIALS", ""),
client_secret=os.getenv("MEITNER_CLIENT_SECRET", ""),
),
) as m_client:
res = m_client.schools.list(limit=1, offset=0)
while res is not None:
# Handle items
res = res.next()
```
<!-- End Pagination [pagination] -->
<!-- Start Retries [retries] -->
## Retries
Some of the endpoints in this SDK support retries. If you use the SDK without any configuration, it will fall back to the default retry strategy provided by the API. However, the default retry strategy can be overridden on a per-operation basis, or across the entire SDK.
To change the default retry strategy for a single API call, simply provide a `RetryConfig` object to the call:
```python
from meitner import Meitner, models
from meitner.utils import BackoffStrategy, RetryConfig
import os
with Meitner(
security=models.Security(
client_credentials=os.getenv("MEITNER_CLIENT_CREDENTIALS", ""),
client_secret=os.getenv("MEITNER_CLIENT_SECRET", ""),
),
) as m_client:
res = m_client.schools.list(limit=1, offset=0,
RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False))
while res is not None:
# Handle items
res = res.next()
```
If you'd like to override the default retry strategy for all operations that support retries, you can use the `retry_config` optional parameter when initializing the SDK:
```python
from meitner import Meitner, models
from meitner.utils import BackoffStrategy, RetryConfig
import os
with Meitner(
retry_config=RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False),
security=models.Security(
client_credentials=os.getenv("MEITNER_CLIENT_CREDENTIALS", ""),
client_secret=os.getenv("MEITNER_CLIENT_SECRET", ""),
),
) as m_client:
res = m_client.schools.list(limit=1, offset=0)
while res is not None:
# Handle items
res = res.next()
```
<!-- End Retries [retries] -->
<!-- Start Error Handling [errors] -->
## Error Handling
[`MeitnerError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/meitnererror.py) is the base class for all HTTP error responses. It has the following properties:
| Property | Type | Description |
| ------------------ | ---------------- | --------------------------------------------------------------------------------------- |
| `err.message` | `str` | Error message |
| `err.status_code` | `int` | HTTP response status code eg `404` |
| `err.headers` | `httpx.Headers` | HTTP response headers |
| `err.body` | `str` | HTTP body. Can be empty string if no body is returned. |
| `err.raw_response` | `httpx.Response` | Raw HTTP response |
| `err.data` | | Optional. Some errors may contain structured data. [See Error Classes](https://github.com/meitner-se/api-client-python/blob/master/#error-classes). |
### Example
```python
from meitner import Meitner, errors, models
import os
with Meitner(
security=models.Security(
client_credentials=os.getenv("MEITNER_CLIENT_CREDENTIALS", ""),
client_secret=os.getenv("MEITNER_CLIENT_SECRET", ""),
),
) as m_client:
res = None
try:
res = m_client.schools.list(limit=1, offset=0)
while res is not None:
# Handle items
res = res.next()
except errors.MeitnerError as e:
# The base class for HTTP error responses
print(e.message)
print(e.status_code)
print(e.body)
print(e.headers)
print(e.raw_response)
# Depending on the method different errors may be thrown
if isinstance(e, errors.Error400ResponseBody):
print(e.data.error) # models.Error400ResponseBodyError
```
### Error Classes
**Primary errors:**
* [`MeitnerError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/meitnererror.py): The base class for HTTP error responses.
* [`Error400ResponseBody`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/error400responsebody.py): Bad Request - The request was malformed or contained invalid parameters. Status code `400`.
* [`Error401ResponseBody`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/error401responsebody.py): Unauthorized - The request is missing valid authentication credentials. Status code `401`.
* [`Error403ResponseBody`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/error403responsebody.py): Forbidden - Request is authenticated, but the user is not allowed to perform the operation. Status code `403`.
* [`Error404ResponseBody`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/error404responsebody.py): Not Found - The requested resource does not exist. Status code `404`.
* [`Error409ResponseBody`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/error409responsebody.py): Conflict - The request could not be completed due to a conflict. Status code `409`.
* [`Error429ResponseBody`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/error429responsebody.py): Too Many Requests - When the rate limit has been exceeded. Status code `429`.
* [`Error500ResponseBody`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/error500responsebody.py): Internal Server Error - An unexpected server error occurred. Status code `500`.
<details><summary>Less common errors (27)</summary>
<br />
**Network errors:**
* [`httpx.RequestError`](https://www.python-httpx.org/exceptions/#httpx.RequestError): Base class for request errors.
* [`httpx.ConnectError`](https://www.python-httpx.org/exceptions/#httpx.ConnectError): HTTP client was unable to make a request to a server.
* [`httpx.TimeoutException`](https://www.python-httpx.org/exceptions/#httpx.TimeoutException): HTTP request timed out.
**Inherit from [`MeitnerError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/meitnererror.py)**:
* [`SchoolCreate422ResponseBodyError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/schoolcreate422responsebodyerror.py): Validation error for School Create operation - request data failed validation. Status code `422`. Applicable to 1 of 46 methods.*
* [`SchoolSearch422ResponseBodyError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/schoolsearch422responsebodyerror.py): Validation error for School Search operation - request data failed validation. Status code `422`. Applicable to 1 of 46 methods.*
* [`SchoolUpdate422ResponseBodyError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/schoolupdate422responsebodyerror.py): Validation error for School Update operation - request data failed validation. Status code `422`. Applicable to 1 of 46 methods.*
* [`GroupCreate422ResponseBodyError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/groupcreate422responsebodyerror.py): Validation error for Group Create operation - request data failed validation. Status code `422`. Applicable to 1 of 46 methods.*
* [`GroupSearch422ResponseBodyError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/groupsearch422responsebodyerror.py): Validation error for Group Search operation - request data failed validation. Status code `422`. Applicable to 1 of 46 methods.*
* [`GroupUpdate422ResponseBodyError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/groupupdate422responsebodyerror.py): Validation error for Group Update operation - request data failed validation. Status code `422`. Applicable to 1 of 46 methods.*
* [`EmployeeCreate422ResponseBodyError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/employeecreate422responsebodyerror.py): Validation error for Employee Create operation - request data failed validation. Status code `422`. Applicable to 1 of 46 methods.*
* [`EmployeeSearch422ResponseBodyError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/employeesearch422responsebodyerror.py): Validation error for Employee Search operation - request data failed validation. Status code `422`. Applicable to 1 of 46 methods.*
* [`EmployeeUpdate422ResponseBodyError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/employeeupdate422responsebodyerror.py): Validation error for Employee Update operation - request data failed validation. Status code `422`. Applicable to 1 of 46 methods.*
* [`EmployeePlacementCreate422ResponseBodyError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/employeeplacementcreate422responsebodyerror.py): Validation error for EmployeePlacement Create operation - request data failed validation. Status code `422`. Applicable to 1 of 46 methods.*
* [`EmployeePlacementSearch422ResponseBodyError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/employeeplacementsearch422responsebodyerror.py): Validation error for EmployeePlacement Search operation - request data failed validation. Status code `422`. Applicable to 1 of 46 methods.*
* [`EmployeePlacementUpdate422ResponseBodyError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/employeeplacementupdate422responsebodyerror.py): Validation error for EmployeePlacement Update operation - request data failed validation. Status code `422`. Applicable to 1 of 46 methods.*
* [`GuardianCreate422ResponseBodyError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/guardiancreate422responsebodyerror.py): Validation error for Guardian Create operation - request data failed validation. Status code `422`. Applicable to 1 of 46 methods.*
* [`GuardianSearch422ResponseBodyError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/guardiansearch422responsebodyerror.py): Validation error for Guardian Search operation - request data failed validation. Status code `422`. Applicable to 1 of 46 methods.*
* [`GuardianUpdate422ResponseBodyError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/guardianupdate422responsebodyerror.py): Validation error for Guardian Update operation - request data failed validation. Status code `422`. Applicable to 1 of 46 methods.*
* [`StudentCreate422ResponseBodyError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/studentcreate422responsebodyerror.py): Validation error for Student Create operation - request data failed validation. Status code `422`. Applicable to 1 of 46 methods.*
* [`StudentSearch422ResponseBodyError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/studentsearch422responsebodyerror.py): Validation error for Student Search operation - request data failed validation. Status code `422`. Applicable to 1 of 46 methods.*
* [`StudentUpdate422ResponseBodyError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/studentupdate422responsebodyerror.py): Validation error for Student Update operation - request data failed validation. Status code `422`. Applicable to 1 of 46 methods.*
* [`StudentPlacementCreate422ResponseBodyError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/studentplacementcreate422responsebodyerror.py): Validation error for StudentPlacement Create operation - request data failed validation. Status code `422`. Applicable to 1 of 46 methods.*
* [`StudentPlacementSearch422ResponseBodyError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/studentplacementsearch422responsebodyerror.py): Validation error for StudentPlacement Search operation - request data failed validation. Status code `422`. Applicable to 1 of 46 methods.*
* [`StudentPlacementUpdate422ResponseBodyError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/studentplacementupdate422responsebodyerror.py): Validation error for StudentPlacement Update operation - request data failed validation. Status code `422`. Applicable to 1 of 46 methods.*
* [`AuditEventSearch422ResponseBodyError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/auditeventsearch422responsebodyerror.py): Validation error for AuditEvent Search operation - request data failed validation. Status code `422`. Applicable to 1 of 46 methods.*
* [`ResponseValidationError`](https://github.com/meitner-se/api-client-python/blob/master/./src/meitner/errors/responsevalidationerror.py): Type mismatch between the response data and the expected Pydantic model. Provides access to the Pydantic validation error via the `cause` attribute.
</details>
\* Check [the method documentation](https://github.com/meitner-se/api-client-python/blob/master/#available-resources-and-operations) to see if the error is applicable.
<!-- End Error Handling [errors] -->
<!-- Start Server Selection [server] -->
## Server Selection
### Select Server by Name
You can override the default server globally by passing a server name to the `server: str` optional parameter when initializing the SDK client instance. The selected server will then be used as the default on the operations that use it. This table lists the names associated with the available servers:
| Name | Server | Description |
| ------------ | --------------------------------------------- | ----------------------------------------------- |
| `production` | `https://api.meitner.se/directory/v1` | Server to use in production |
| `staging` | `https://api.staging.meitner.se/directory/v1` | Server to use when building and testing the API |
#### Example
```python
from meitner import Meitner, models
import os
with Meitner(
server="production",
security=models.Security(
client_credentials=os.getenv("MEITNER_CLIENT_CREDENTIALS", ""),
client_secret=os.getenv("MEITNER_CLIENT_SECRET", ""),
),
) as m_client:
res = m_client.schools.list(limit=1, offset=0)
while res is not None:
# Handle items
res = res.next()
```
### Override Server URL Per-Client
The default server can also be overridden globally by passing a URL to the `server_url: str` optional parameter when initializing the SDK client instance. For example:
```python
from meitner import Meitner, models
import os
with Meitner(
server_url="https://api.meitner.se/directory/v1",
security=models.Security(
client_credentials=os.getenv("MEITNER_CLIENT_CREDENTIALS", ""),
client_secret=os.getenv("MEITNER_CLIENT_SECRET", ""),
),
) as m_client:
res = m_client.schools.list(limit=1, offset=0)
while res is not None:
# Handle items
res = res.next()
```
<!-- End Server Selection [server] -->
<!-- Start Custom HTTP Client [http-client] -->
## Custom HTTP Client
The Python SDK makes API calls using the [httpx](https://www.python-httpx.org/) HTTP library. In order to provide a convenient way to configure timeouts, cookies, proxies, custom headers, and other low-level configuration, you can initialize the SDK client with your own HTTP client instance.
Depending on whether you are using the sync or async version of the SDK, you can pass an instance of `HttpClient` or `AsyncHttpClient` respectively, which are Protocol's ensuring that the client has the necessary methods to make API calls.
This allows you to wrap the client with your own custom logic, such as adding custom headers, logging, or error handling, or you can just pass an instance of `httpx.Client` or `httpx.AsyncClient` directly.
For example, you could specify a header for every request that this sdk makes as follows:
```python
from meitner import Meitner
import httpx
http_client = httpx.Client(headers={"x-custom-header": "someValue"})
s = Meitner(client=http_client)
```
or you could wrap the client with your own custom logic:
```python
from meitner import Meitner
from meitner.httpclient import AsyncHttpClient
import httpx
class CustomClient(AsyncHttpClient):
client: AsyncHttpClient
def __init__(self, client: AsyncHttpClient):
self.client = client
async def send(
self,
request: httpx.Request,
*,
stream: bool = False,
auth: Union[
httpx._types.AuthTypes, httpx._client.UseClientDefault, None
] = httpx.USE_CLIENT_DEFAULT,
follow_redirects: Union[
bool, httpx._client.UseClientDefault
] = httpx.USE_CLIENT_DEFAULT,
) -> httpx.Response:
request.headers["Client-Level-Header"] = "added by client"
return await self.client.send(
request, stream=stream, auth=auth, follow_redirects=follow_redirects
)
def build_request(
self,
method: str,
url: httpx._types.URLTypes,
*,
content: Optional[httpx._types.RequestContent] = None,
data: Optional[httpx._types.RequestData] = None,
files: Optional[httpx._types.RequestFiles] = None,
json: Optional[Any] = None,
params: Optional[httpx._types.QueryParamTypes] = None,
headers: Optional[httpx._types.HeaderTypes] = None,
cookies: Optional[httpx._types.CookieTypes] = None,
timeout: Union[
httpx._types.TimeoutTypes, httpx._client.UseClientDefault
] = httpx.USE_CLIENT_DEFAULT,
extensions: Optional[httpx._types.RequestExtensions] = None,
) -> httpx.Request:
return self.client.build_request(
method,
url,
content=content,
data=data,
files=files,
json=json,
params=params,
headers=headers,
cookies=cookies,
timeout=timeout,
extensions=extensions,
)
s = Meitner(async_client=CustomClient(httpx.AsyncClient()))
```
<!-- End Custom HTTP Client [http-client] -->
<!-- Start Resource Management [resource-management] -->
## Resource Management
The `Meitner` class implements the context manager protocol and registers a finalizer function to close the underlying sync and async HTTPX clients it uses under the hood. This will close HTTP connections, release memory and free up other resources held by the SDK. In short-lived Python programs and notebooks that make a few SDK method calls, resource management may not be a concern. However, in longer-lived programs, it is beneficial to create a single SDK instance via a [context manager][context-manager] and reuse it across the application.
[context-manager]: https://docs.python.org/3/reference/datamodel.html#context-managers
```python
from meitner import Meitner, models
import os
def main():
with Meitner(
security=models.Security(
client_credentials=os.getenv("MEITNER_CLIENT_CREDENTIALS", ""),
client_secret=os.getenv("MEITNER_CLIENT_SECRET", ""),
),
) as m_client:
# Rest of application here...
# Or when using async:
async def amain():
async with Meitner(
security=models.Security(
client_credentials=os.getenv("MEITNER_CLIENT_CREDENTIALS", ""),
client_secret=os.getenv("MEITNER_CLIENT_SECRET", ""),
),
) as m_client:
# Rest of application here...
```
<!-- End Resource Management [resource-management] -->
<!-- Start Debugging [debug] -->
## Debugging
You can setup your SDK to emit debug logs for SDK requests and responses.
You can pass your own logger class directly into your SDK.
```python
from meitner import Meitner
import logging
logging.basicConfig(level=logging.DEBUG)
s = Meitner(debug_logger=logging.getLogger("meitner"))
```
You can also enable a default debug logger by setting an environment variable `MEITNER_DEBUG` to true.
<!-- End Debugging [debug] -->
<!-- Placeholder for Future Speakeasy SDK Sections -->
# Development
## Maturity
This SDK is in beta, and there may be breaking changes between versions without a major version update. Therefore, we recommend pinning usage
to a specific package version. This way, you can install the same version each time without breaking changes unless you are intentionally
looking for the latest version.
## Contributions
While we value open-source contributions to this SDK, this library is generated programmatically. Any manual changes added to internal files will be overwritten on the next generation.
We look forward to hearing your feedback. Feel free to open a PR or an issue with a proof of concept and we'll do our best to include it in a future release.
### SDK Created by [Speakeasy](https://www.speakeasy.com/?utm_source=meitner&utm_campaign=python)
| text/markdown | Speakeasy | null | null | null | null | null | [] | [] | null | null | >=3.10 | [] | [] | [] | [
"httpcore>=1.0.9",
"httpx>=0.28.1",
"jsonpath-python>=1.0.6",
"pydantic>=2.11.2"
] | [] | [] | [] | [
"repository, https://github.com/meitner-se/api-client-python.git"
] | uv/0.10.4 {"installer":{"name":"uv","version":"0.10.4","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"22.04","id":"jammy","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true} | 2026-02-20T10:28:11.230776 | meitner-0.5.0.tar.gz | 198,805 | c0/e3/f627604b55df1db5fd70a50ad736591aedf7e1e1baae88033c8992b86655/meitner-0.5.0.tar.gz | source | sdist | null | false | 0dce9d70190535f81c4faf7ee60f7a42 | 959210f8562f52edf828a5e0b7d14ad5a49bb6d19093149b7be1107f4207e4b9 | c0e3f627604b55df1db5fd70a50ad736591aedf7e1e1baae88033c8992b86655 | null | [] | 217 |
2.4 | nexus-engine | 0.6.0 | Python DSL for the Nexus Engine | # Nexus Engine
The Python client for [Nexus Engine](https://nexus-engine.io) — a Rust-based calculation engine for financial data
processing.
This package provides a fluent, chainable DSL for building data transformation and calculation pipelines. Define columns
from SQL data sources, apply filters, joins, groupings, conditional logic, and hierarchical breakdowns — then send the
pipeline to Nexus Engine for execution.
> **Note:** Executing pipelines requires access to a running Nexus Engine instance and valid credentials.
> Contact your administrator or visit [nexus-engine.io](https://nexus-engine.io) for access.
## Installation
```bash
pip install nexus-engine
```
## Quick Start
```python
import nexus as nx
# Define source columns from a SQL data view
table = nx.TableBuilder([
nx.sql_column("Category", nx.DataType.String, "positions", "asset_category"),
nx.sql_column("Currency", nx.DataType.String, "positions", "local_currency"),
nx.sql_column("MarketValue", nx.DataType.Float, "positions", "market_value"),
])
# Build a transformation pipeline
pipeline = (
table
.filter(nx.col("MarketValue") > nx.lit(10000))
.group_by(
by=["Category"],
aggregations=[("MarketValue", "sum")]
)
)
# Execute against a Nexus Engine server
result = pipeline.execute(
params={"account_id": "ACC001", "date": "2025-01-31"},
api_token="YOUR_API_TOKEN",
)
print(result)
```
## Type Support
The package includes full type stubs (`.pyi`) for IDE autocompletion and type checking with mypy or pyright.
## Documentation
Full documentation and tutorials are available at [nexus-engine.io](https://nexus-engine.io).
## License
Dual-licensed under [MIT](https://opensource.org/licenses/MIT)
or [Apache-2.0](https://opensource.org/licenses/Apache-2.0).
| text/markdown; charset=UTF-8; variant=GFM | Synlynx | null | null | null | MIT OR Apache-2.0 | finance, pipeline, dsl, dataframe, etl, nexus-engine, nexus | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Intended Audience :: Financial and Insurance Industry",
"License :: OSI Approved :: MIT License",
"License :: OSI Approved :: Apache Software License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Rust",
"Programming Language :: Python :: Implementation :: CPython",
"Topic :: Software Development :: Libraries :: Python Modules",
"Topic :: Office/Business :: Financial",
"Typing :: Typed"
] | [] | null | null | >=3.9 | [] | [] | [] | [] | [] | [] | [] | [
"Homepage, https://nexus-engine.io",
"Documentation, https://nexus-engine.synlynx.com"
] | twine/6.2.0 CPython/3.12.3 | 2026-02-20T10:28:01.891066 | nexus_engine-0.6.0-pp311-pypy311_pp73-win_amd64.whl | 1,879,536 | a0/b9/6635ef2a7be08b936e60994b47d3bac6a0535bfcb8210b27d71b14d7d03a/nexus_engine-0.6.0-pp311-pypy311_pp73-win_amd64.whl | pp311 | bdist_wheel | null | false | 11b04ba13dcdaddab9ec00f998b97b02 | dfcbb5f724056392d0f961dc1347aa71f2e91a4474ce8445f6022f46834d780b | a0b96635ef2a7be08b936e60994b47d3bac6a0535bfcb8210b27d71b14d7d03a | null | [] | 380 |
2.4 | asknews | 0.13.16 | Python SDK for AskNews | # AskNews Python SDK
Python SDK for the AskNews API.
## Installation
```bash
pip install asknews
```
## Usage
```python
from asknews_sdk import AskNewsSDK
ask = AskNewsSDK(
api_key="<YOUR API KEY>"
scopes=["news", "chat", "stories", "analytics"]
)
query = "Effect of fed policy on tech sector"
# prompt-optimized string ready to go for any LLM:
news_context = ask.news.search_news(query).as_string
```
And you will have a prompt-optimized string ready to go for any LLM. The API doesn't stop there, explore a wide range of endpoints:
- /stories, high level event tracking and state of the art article clustering
- /forecasts, industry leading forecasting on any real-time event
- /analytics, time-series data on finance and politics
- /deepnews, a deep research agent that can explore the new knowledge graph, X, Reddit, Google, Wikipedia and more to build forecasts, reports, analytics, and anything else your system may need.
- /graph, build any news knowledge graph imaginable from the largest news graph on the planet
- /websearch, search the web and get back an LLM distillation of all the relevant web pages
Find full details at the [AskNews API documentation](https://docs.asknews.app).
## Support
Join our [Discord](https://discord.gg/2Yw66XXEhY) to see what other people are building, and to get support with your projects.
| text/markdown | null | Emergent Methods <contact@emergentmethods.ai> | null | null | MIT | null | [
"Programming Language :: Python :: 3 :: Only",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13"
] | [] | null | null | >=3.8 | [] | [] | [] | [
"asgiref>=3.7.2",
"crontab>=1.0.1",
"cryptography<46.0.5,>=40.0.0",
"httpx<0.29.0,>=0.27.2",
"orjson>=3.9.10",
"pydantic[email]>=2.10.4"
] | [] | [] | [] | [
"repository, https://github.com/emergentmethods/asknews-python-sdk"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:28:01.738432 | asknews-0.13.16-py3-none-any.whl | 48,525 | d1/8a/a957f70cf1f5abfa668068ee65d98301262abba1c2aee211c73be423779f/asknews-0.13.16-py3-none-any.whl | py3 | bdist_wheel | null | false | b479be4378d385cc405af6dcb02e14c0 | a0b3e81dc78ef60a34874429179c8ee930d48a1ff2995638a349b700c2fb4d85 | d18aa957f70cf1f5abfa668068ee65d98301262abba1c2aee211c73be423779f | null | [
"LICENSE"
] | 1,106 |
2.4 | gsd-lean | 0.9.0 | Lightweight meta-prompting and spec-driven development plugin for Claude Code | # GSD-Lean
> A lightweight reimplementation of [Get-Shit-Done](https://github.com/glittercowboy/get-shit-done) — a complement to the original GSD system
## What is GSD?
[GSD](https://github.com/glittercowboy/get-shit-done) is a meta-prompting, context engineering, and spec-driven development system for Claude Code. It solves context rot by breaking projects into phases with fresh contexts per task, using a discuss → plan → execute → verify loop. Written in JavaScript.
## What is GSD-Lean?
A Python reimplementation of GSD's core ideas as a lightweight plugin. Rather than the full system, GSD-Lean focuses on being minimal and easy to extend.
**Status:** Early development.
## How It Works
GSD-Lean guides development through a 6-phase workflow:
```mermaid
stateDiagram-v2
[*] --> init : gsd-lean init
init --> discuss : project initialized
discuss --> plan : requirements populated
plan --> discuss : plan rejected
plan --> execute : plan verified
execute --> plan : revise plan
execute --> verify
verify --> execute : tasks remaining
verify --> complete : all tasks done
complete --> discuss : new cycle
complete --> init : re-initialize
```
| Phase | What Happens |
|-------|-------------|
| **init** | Project scaffolded; tech stack auto-detected; PROJECT.md, CONTEXT.md, STATE.md created |
| **discuss** | Research subagent investigates codebase + web; user preferences probed; REQUIREMENTS.md and DECISIONS.md populated |
| **plan** | Requirements decomposed into tasks (T-NNN) in PLAN.md; plan-review subagent verifies completeness |
| **execute** | Tasks implemented one by one, status tracked in PLAN.md |
| **verify** | Lint, typecheck, tests run against task verification criteria |
| **complete** | Summary generated, cycle can restart |
Each phase is driven by a skill (`/init`, `/discuss`, `/plan`, `/execute`, `/verify`, `/complete`) that calls CLI commands (`gsd-lean init`, `gsd-lean transition`, `gsd-lean plan-status`) to manage state.
See [PROJECT_KNOWLEDGE.md](./PROJECT_KNOWLEDGE.md) for detailed architecture — transitions, preconditions, subagents, and CLI reference.
## Quick Start
**0. Install plugin**
Add the Bifurcate Loops marketplace, then install GSD-Lean:
```
/plugin marketplace add Bifurcate-Loops/bifurcate-plugins
/plugin install gsd-lean@bifurcate-plugins
```
**1. Initialize project**
```
/gsd-lean:init
```
Scaffolds the project. Claude asks clarifying questions, then writes PROJECT.md and CONTEXT.md.
Sections "Constraints" and "Notes" in PROJECT.md should be filled with useful information as you work through your development cycles. This will be picked up by GSD-Lean phases `/gsd-lean:discuss` and `/gsd-lean:plan`. Example:
````markdown
## Constraints
- (none yet)
## Notes
- Use skills `/ai-sdk` (.claude/skills/ai-sdk/SKILL.md) and `/mastra` (.claude/skills/mastra/SKILL.md) when working on agentic features
- Use skill `/vercel-react-best-practices` (.claude/skills/vercel-react-best-practices/SKILL.md) when working on React and Next.js applications
- If env variables are introduced in a development cycle, remember to include them in `.env.example`
````
**2. Discuss**
```
/gsd-lean:discuss Add user authentication with OAuth
```
Claude explores the codebase and web, probes for preferences, and populates REQUIREMENTS.md and DECISIONS.md.
**3. Plan**
```
/gsd-lean:plan
```
Decomposes requirements into structured tasks (T-NNN) in PLAN.md.
**4. Execute**
```
/gsd-lean:execute
```
Implements tasks sequentially — one per invocation. If requirements change mid-execution, re-invoke `/plan` to revise the plan while preserving task progress.
**5. Verify & Complete**
```
/gsd-lean:verify
/gsd-lean:complete
```
`/verify` runs verification against task criteria. `/complete` generates a summary and optionally starts a new cycle.
## Caveats
* Run each GSD-Lean phase in a **new Claude Code session**. The `/init`, `/discuss`, and `/plan` phases enter Plan Mode; when prompted to accept, select **Yes, auto-accept edits** — do **not** select "Yes, clear context and auto-accept edits (shift+tab)", as that erases internal context and causes GSD-Lean to lose track.
* If your project's `CLAUDE.md` contains development workflow instructions (e.g. branching strategy, commit conventions, test-before-push rules), these can conflict with GSD-Lean's phased development cycle. Remove or comment out such instructions before running GSD-Lean.
| text/markdown | Bifurcate-Loops | null | null | null | null | claude-code, development, meta-prompting, plugin, spec-driven | [
"Development Status :: 3 - Alpha",
"Environment :: Console",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14"
] | [] | null | null | <3.15,>=3.12 | [] | [] | [] | [
"click>=8.3.1",
"pyyaml>=6.0"
] | [] | [] | [] | [
"Homepage, https://github.com/Bifurcate-Loops/gsd-lean",
"Repository, https://github.com/Bifurcate-Loops/gsd-lean",
"Issues, https://github.com/Bifurcate-Loops/gsd-lean/issues",
"Changelog, https://github.com/Bifurcate-Loops/gsd-lean/blob/main/CHANGELOG.md"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:27:51.641540 | gsd_lean-0.9.0.tar.gz | 137,098 | 75/5b/adf1b0d1ff157271c6d5621b5ec127f985c4e13c8f9e835405d7acf4c83c/gsd_lean-0.9.0.tar.gz | source | sdist | null | false | 15cd2bcf31bb14a6d0de1ceceb19d89f | d78ad0e56a6fc02d2375aa5d8e5889453700c7d6dd4030ff55e68dea5366075e | 755badf1b0d1ff157271c6d5621b5ec127f985c4e13c8f9e835405d7acf4c83c | MIT | [
"LICENSE"
] | 225 |
2.3 | async-gym-agents | 0.2.4 | Async agents for Stable Baselines 3 | # Async Gym Agents
Wrapper environments and agent injectors to allow for drop-in async training.
```py
import gymnasium as gym
from functools import partial
from stable_baselines3 import TD3
from async_gym_agents.agents.async_agent import get_injected_agent
from async_gym_agents.envs.multi_env import IndexableMultiEnv
# Create env with 8 parallel envs (Also supports VecEnvs)
env = IndexableMultiEnv([partial(gym.make, "Pendulum-v1") for i in range(8)])
# Create the model, injected with async capabilities
model = get_injected_agent(TD3)("MlpPolicy", env, use_mp=False)
# Train the model
model.learn(total_timesteps=10)
# Shut down workers
model.shutdown()
```
| text/markdown | Jonas Peche | jonas.peche@aon.at | null | null | Unlicense | null | [
"License :: OSI Approved",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13"
] | [] | null | null | >=3.8 | [] | [] | [] | [
"stable-baselines3[extra]<3.0.0,>=2.3.2",
"torchinfo<2.0.0,>=1.8.0"
] | [] | [] | [] | [] | poetry/2.1.3 CPython/3.10.8 Windows/10 | 2026-02-20T10:26:58.127245 | async_gym_agents-0.2.4.tar.gz | 12,541 | 93/a4/c8aea17edf7d63e67f67b4e07adb53c3f3b0262a0af344ad80ec929f5f73/async_gym_agents-0.2.4.tar.gz | source | sdist | null | false | 3ec91ee8b6b251e6993d4a5d7a4631ec | 044381a5ce7b12e4dd87de6879f6159210a47236c2908f55cc3955aa9a2f49e4 | 93a4c8aea17edf7d63e67f67b4e07adb53c3f3b0262a0af344ad80ec929f5f73 | null | [] | 231 |
2.4 | pbtk | 1.0.7 | A toolset for reverse engineering and fuzzing Protobuf-based apps | # pbtk - Reverse engineering Protobuf apps
**[Protobuf](https://developers.google.com/protocol-buffers/) is a serialization format** developed by Google and used in an increasing number of Android, web, desktop and more applications. It consists of a **language for declaring data structures**, which is then compiled to code or another kind of structure depending on the target implementation.
pbtk (*Protobuf toolkit*) is a full-fledged set of scripts, accessible through an unified GUI, that provides two main features:
- **Extracting Protobuf structures from programs**, converting them back into readable *.proto*s, supporting various implementations:
- All the main Java runtimes (base, Lite, Nano, Micro, J2ME), with full Proguard support,
- Binaries containing embedded reflection metadata (typically C++, sometimes Java and most other bindings),
- Web applications using the JsProtoUrl runtime.
- **Editing, replaying and fuzzing data** sent to Protobuf network endpoints, through a handy graphical interface that allows you to edit live the fields for a Protobuf message and view the result.
## Installation
PBTK requires Python ≥ 3.5, PySide 6, Python-Protobuf 3, and a handful of executable programs (chromium, jad, dex2jar...) for running extractor scripts.
Archlinux users can install directly through the [package](https://aur.archlinux.org/packages/pbtk-git/):
```
$ yay -S pbtk-git
$ pbtk
```
On most other distributions, you'll want to run it directly:
```tcl
# For Ubuntu/Debian testing derivates:
$ sudo apt install python3-pip git openjdk-8-jre python3-qtpy-pyside6
# Then, using UV:
$ sudo snap install astral-uv
$ uv tool install pbtk
$ pbtk
# Or using pipx:
$ sudo apt install pipx
$ pipx install pbtk
$ pbtk
```
Windows is also supported (with the same modules required). Once you run the GUI, it should warn you on what you are missing depending on what you try to do.
## Command line usage (installing through package manager)
The GUI can be lanched through the main script:
pbtk
The following scripts can also be used standalone, without a GUI:
pbtk-jar-extract [-h] input_file [output_dir]
pbtk-from-binary [-h] input_file [output_dir]
pbtk-web-extract [-h] input_url [output_dir]
## Command line usage (local)
The GUI can be lanched through the main script:
./gui.py
The following scripts can also be used standalone, without a GUI:
./src/extractors/jar_extract.py [-h] input_file [output_dir]
./src/extractors/from_binary.py [-h] input_file [output_dir]
./src/extractors/web_extract.py [-h] input_url [output_dir]
## Typical workflow
Let's say you're reverse engineering an Android application. You explored a bit the application with your favorite decompiler, and figured it transports Protobuf as POST data over HTTPS in a typical way.
You open PBTK and are greeted in a meaningful manner:
The first step is getting your .protos into text format. If you're targeting an Android app, dropping in an APK and waiting should do the magic work! (unless it's a really exotic implementation)
This being done, you jump to `~/.pbtk/protos/<your APK name>` (either through the command line, or the button on the bottom of the welcome screen to open your file browser, the way you prefer). All the app's .protos are indeed here.
Back in your decompiler, you stumbled upon the class that constructs data sent to the HTTPS endpoint that interests you. It serializes the Protobuf message by calling a class made of generated code.
This latter class should have a perfect match inside your .protos directory (i.e `com.foo.bar.a.b` will match `com/foo/bar/a/b.proto`). Either way, grepping its name should enable you to reference it.
That's great: the next thing is going to **Step 2**, selecting your desired input .proto, and filling some information about your endpoint.
You may also give some sample raw Protobuf data, that was sent to this endpoint, captured through mitmproxy or Wireshark, and that you'll paste in a hex-encoded form.
**Step 3** is about the fun part of clicking buttons and seeing what happens! You have a tree view representing every field in the Protobuf structure (repeated fields are suffixed by "+", required fields don't have checkboxes).
Just hover a field to have focus. If the field is an integer type, use the mouse wheel to increment/decrement it. Enum information appears on hover too.
Here it is! You can determine the meaning of every field with that. If you extracted .protos out of minified code, you can rename fields according to what you notice they mean, by clicking their names.
Happy reversing! 👌 🎉
## Local data storage
PBTK stores extracted .proto information into `~/.pbtk/protos/` (or `%APPDATA%\pbtk\protos` on Windows).
You can move in, move out, rename, edit or erase data from this directory directly through your regular file browser and text editor, it's the expected way to do it and won't interfere with PBTK.
HTTP-based endpoints are stored into `~/.pbtk/endpoints/` as JSON objects. These objects are arrays of pairs of request/response information, which looks like this:
```javascript
[{
"request": {
"transport": "pburl",
"proto": "www.google.com/VectorTown.proto",
"url": "https://www.google.com/VectorTown",
"pb_param": "pb",
"samples": [{
"pb": "!....",
"hl": "fr"
}]
},
"response": {
"format": "other"
}
}]
```
## Source code structure
PBTK uses two kinds of pluggable modules internally: extractors, and transports.
* An **extractor** supports extracting .proto structures from a target Protobuf implementation or platform.
Extractors are defined in `src/extractors/*.py`. They are defined as a method preceded by a decorator, like this:
```python
@register_extractor(name = 'my_extractor',
desc = 'Extract Protobuf structures from Foobar code (*.foo, *.bar)',
depends={'binaries': ['foobar-decompiler']})
def my_extractor(path):
# Load contents of the `path` input file and do your stuff...
# Then, yield extracted .protos using a generator:
for i in do_your_extraction_work():
yield proto_name + '.proto', proto_contents
# Other kinds of information can be yield, such as endpoint information or progress to display.
```
* A **transport** supports a way of deserializing, reserializing and sending Protobuf data over the network. For example, the most commonly used transport is raw POST data over HTTP.
Transports are defined in `src/utils/transports.py`. They are defined as a class preceded by a decorator, like this:
```python
@register_transport(
name = 'my_transport',
desc = 'Protobuf as raw POST data',
ui_data_form = 'hex strings'
)
class MyTransport():
def __init__(self, pb_param, url):
self.url = url
def serialize_sample(self, sample):
# We got a sample of input data from the user.
# Verify that it is valid in the form described through "ui_data_form" parameter, fail with an exception or return False otherwise.
# Optionally modify this data prior to returning it.
bytes.fromhex(sample)
return sample
def load_sample(self, sample, pb_msg):
# Parse input data into the provided Protobuf object.
pb_msg.ParseFromString(bytes.fromhex(sample))
def perform_request(self, pb_data, tab_data):
# Perform a request using the provided URL and Protobuf object, and optionally other transport-specific side data.
return post(url, pb_data.SerializeToString(), headers=USER_AGENT)
```
## Forthcoming improvements
The following could be coming for further releases:
* Finishing the automatic fuzzing part.
* Support for extracting extensions out of Java code.
* Support for the JSPB (main JavaScript) runtime.
* If there's any other platform you wish to see supported, just drop an issue and I'll look at it.
I've tried to do my best to produce thoroughly readable and commented code (except for parts that are mostly self-describing, like connecting GUI signals) for most modules, so you can contribute.
## Licensing
pbtk is released under the [GNU GPL](https://www.gnu.org/licenses/gpl-3.0.html) license (I, hereby, etc.).
There's no formalized rule for the letter case of the project name, the rule is just about following your heart ❤
| text/markdown | Marin Moulinier | null | null | null | null | null | [] | [] | null | null | >=3.5 | [] | [] | [] | [
"protobuf",
"requests",
"websocket-client",
"PySide6"
] | [] | [] | [] | [
"Homepage, https://github.com/marin-m/pbtk",
"Issues, https://github.com/marin-m/pbtk/issues",
"Changelog, https://github.com/marin-m/pbtk/releases"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:26:56.812750 | pbtk-1.0.7.tar.gz | 8,293,658 | 7c/75/525c402cbf221034a26db747e63f612276bae2e19eb7e4fd41596135af1f/pbtk-1.0.7.tar.gz | source | sdist | null | false | 2feb49ec424f65a8019ca6a69245d63f | 45eea6f96c2c5f7ace904ea13d06be9fdf7bc66d1c5a995c94e11c5cf7a5337f | 7c75525c402cbf221034a26db747e63f612276bae2e19eb7e4fd41596135af1f | null | [
"LICENSE"
] | 217 |
2.4 | reviewloop | 0.3.6 | Autonomous review loop - automates the review-fix-push cycle | <div align="center">
<img src="https://raw.githubusercontent.com/fabianboth/autonomous-review-loop/main/assets/review_loop.webp" alt="Autonomous Review Loop" width="450"/>
<h1>Autonomous Review Loop</h1>
<h3><em>Automate the review-fix-push cycle.</em></h3>
</div>
<p align="center">
<strong>Pair your review bot with your coding agent: <code>reviewloop</code> delegates the entire review-fix-push cycle so you only step in when human judgment is needed.</strong>
</p>
<p align="center">
<a href="https://github.com/fabianboth/autonomous-review-loop/actions/workflows/ci.yml"><img src="https://github.com/fabianboth/autonomous-review-loop/actions/workflows/ci.yml/badge.svg" alt="CI"/></a>
<a href="https://pypi.org/project/reviewloop/"><img src="https://img.shields.io/pypi/v/reviewloop" alt="PyPI version"/></a>
<a href="https://github.com/fabianboth/autonomous-review-loop/blob/main/LICENSE"><img src="https://img.shields.io/github/license/fabianboth/autonomous-review-loop" alt="License"/></a>
</p>
---
## Installation
```bash
uv tool install reviewloop
```
```bash
reviewloop init
```
## Getting Started
### Claude Code
Select **Claude Code** during `reviewloop init`. Then run this within your claude code session:
```text
/reviewloop
```
### Any Coding Agent
Select **Script based** during `reviewloop init`. This creates standalone scripts and a prompt file at `scripts/reviewloop/reviewPrompt.txt`. Feed it to any coding agent (Cursor, Windsurf, etc.) with `@scripts/reviewloop/reviewPrompt.txt` to start the loop.
## How It Works
1. Waits for CI to complete
2. Fetches inline comments and review comments
3. Fixes valid issues and asks you about ambiguous ones
4. Resolves threads and pushes
5. Repeats until no unresolved comments remain
## Features
- **Batched decision-making:** Aggregate review requests instead of triaging comments one by one.
- **Parallel work:** Continue other tasks while the loop runs in the background.
- **Multi-pass resolution:** Iterates automatically until clean.
- **Agent-agnostic:** Works with Claude Code, Cursor, Windsurf, or any coding agent.
## Documentation
For full documentation, troubleshooting, and advanced usage, visit the [GitHub repository](https://github.com/fabianboth/autonomous-review-loop).
| text/markdown | Fabian Both | null | null | null | null | ai, ai-agents, automation, cli, code-review, developer-tools | [
"Development Status :: 4 - Beta",
"Environment :: Console",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.12",
"Topic :: Software Development :: Quality Assurance",
"Typing :: Typed"
] | [] | null | null | >=3.12 | [] | [] | [] | [
"readchar",
"rich",
"typer"
] | [] | [] | [] | [
"Homepage, https://github.com/fabianboth/autonomous-review-loop",
"Repository, https://github.com/fabianboth/autonomous-review-loop.git",
"Issues, https://github.com/fabianboth/autonomous-review-loop/issues"
] | uv/0.10.4 {"installer":{"name":"uv","version":"0.10.4","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true} | 2026-02-20T10:26:31.446689 | reviewloop-0.3.6-py3-none-any.whl | 12,304 | a4/08/12718cccffb6f34a7e73ce380067cfdf2a757e8e17320f1bca4fa7e7a531/reviewloop-0.3.6-py3-none-any.whl | py3 | bdist_wheel | null | false | 6731472249d8c29e9188733de57666da | 0895e6b51ee44ec37e999e4f4e1eac84d4b05959c93fd1534c6009cf4b0a10b7 | a40812718cccffb6f34a7e73ce380067cfdf2a757e8e17320f1bca4fa7e7a531 | MIT | [
"LICENSE"
] | 207 |
2.4 | entitysdk | 0.12.1 | Python library for interacting with the entitycore service | [![Build status][build_status_badge]][build_status_target]
[![License][license_badge]][license_target]
[![Code coverage][coverage_badge]][coverage_target]
[![CodeQL][codeql_badge]][codeql_target]
[![PyPI][pypi_badge]][pypi_target]
# entitysdk
entitysdk is a Python library for interacting with the [entitycore service][entitycore], providing a type-safe and intuitive interface for managing scientific entities, and their associated assets.
## Requirements
- Python 3.11 or higher
- Network access to entitycore service endpoints
## Installation
```bash
pip install entitysdk
```
## Obtaining a valid access token
An access token can be retrieved easily using the obi-auth helper library.
```bash
pip install obi-auth
```
```python
from obi_auth import get_token
token = get_token(environment="staging")
```
## Quick Start
```python
from uuid import UUID
from entitysdk import Client, ProjectContext, models
# Initialize client
client = Client(
project_context=ProjectContext(
project_id=UUID("your-project-id"),
virtual_lab_id=UUID("your-lab-id")
),
environment="staging",
token_manager=token
)
# Search for morphologies
iterator = client.search_entity(
entity_type=models.CellMorphology,
query={"mtype__pref_label": "L5_TPC:A"},
limit=1,
)
morphology = next(iterator)
# Upload an asset
client.upload_file(
entity_id=morphology.id,
entity_type=models.CellMorphology,
file_path="path/to/file.swc",
file_content_type="application/swc",
)
```
### Authentication
- Valid Keycloak access token
- Project context with:
- Valid project ID (UUID)
- Valid virtual lab ID (UUID)
Example configuration:
```python
from uuid import UUID
from entitysdk import ProjectContext
project_context = ProjectContext(
project_id=UUID("12345678-1234-1234-1234-123456789012"),
virtual_lab_id=UUID("87654321-4321-4321-4321-210987654321")
)
```
## Development
### Requirements
- tox/tox-uv
### Clone and run tests
```bash
# Clone the repository
git clone https://github.com/your-org/entitysdk.git
# Run linting, tests, and check-packaging
tox
```
### Auto-generate server schemas
Server schemas at src/entitysdk/_server_schemas.py, which are currently used for importing enum
types, can be updated with the following tox command:
```bash
tox -e generate-server-schemas
```
### Auto-update json payloads
The json payloads in `tests/unit/models/data/extracted` can be automatically updated from entitycore by executing:
```bash
tox -e update-traces
```
The command will checkout a clean copy of entitycore, execute tests, extract the traces, and move them to the expected location in entitysdk.
It's possible to set a different `ENTITYCORE_BRANCH_OR_TAG` and `ENTITYCORE_CHECKOUT_DIR` if desired.
## Contributing
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
## License
Copyright (c) 2025 Open Brain Institute
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
[entitycore]: https://github.com/openbraininstitute/entitycore
[build_status_badge]: https://github.com/openbraininstitute/entitysdk/actions/workflows/tox.yml/badge.svg
[build_status_target]: https://github.com/openbraininstitute/entitysdk/actions
[license_badge]: https://img.shields.io/pypi/l/entitysdk
[license_target]: https://github.com/openbraininstitute/entitysdk/blob/main/LICENSE.txt
[coverage_badge]: https://codecov.io/github/openbraininstitute/entitysdk/coverage.svg?branch=main
[coverage_target]: https://codecov.io/github/openbraininstitute/entitysdk?branch=main
[codeql_badge]: https://github.com/openbraininstitute/entitysdk/actions/workflows/github-code-scanning/codeql/badge.svg
[codeql_target]: https://github.com/openbraininstitute/entitysdk/actions/workflows/github-code-scanning/codeql
[pypi_badge]: https://github.com/openbraininstitute/entitysdk/actions/workflows/sdist.yml/badge.svg
[pypi_target]: https://pypi.org/project/entitysdk/
| text/markdown | null | Open Brain Institute <info@openbraininstitute.org> | null | Open Brain Institute <info@openbraininstitute.org> | null | null | [
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"httpx",
"pydantic>=2.12.0",
"pydantic-settings",
"typing_extensions; python_version < \"3.12\"",
"backports.strenum; python_version < \"3.11\"",
"h5py; extra == \"staging\""
] | [] | [] | [] | [
"documentation, https://entitysdk.readthedocs.io/en/stable",
"repository, https://github.com/openbraininstitute/entitysdk",
"changelog, https://github.com/openbraininstitute/entitysdk/CHANGELOG.md"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:25:42.435540 | entitysdk-0.12.1.tar.gz | 74,552 | 26/af/6758be97f52886447fae37bc030233d180b04b40b8a57591360ea2074a35/entitysdk-0.12.1.tar.gz | source | sdist | null | false | 3ef63a834772d7eb8785e973f6baea25 | 2d4ed4d75cc409b494df92f34ba42d48d354ac0435a37500cc564be69b94bdf7 | 26af6758be97f52886447fae37bc030233d180b04b40b8a57591360ea2074a35 | Apache-2.0 | [
"LICENSE.txt"
] | 164 |
2.4 | cupy | 14.0.1 | CuPy: NumPy & SciPy for GPU | .. image:: https://raw.githubusercontent.com/cupy/cupy/main/docs/image/cupy_logo_1000px.png
:width: 400
CuPy : NumPy & SciPy for GPU
============================
`CuPy <https://cupy.dev/>`_ is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.
This package (``cupy``) is a source distribution.
For most users, use of pre-build wheel distributions are recommended:
- `cupy-cuda13x <https://pypi.org/project/cupy-cuda13x/>`_ (for NVIDIA CUDA 13.x)
- `cupy-cuda12x <https://pypi.org/project/cupy-cuda12x/>`_ (for NVIDIA CUDA 12.x)
- `cupy-rocm-7-0 <https://pypi.org/project/cupy-rocm-7-0/>`_ (for AMD ROCm 7.0)
Please see `Installation Guide <https://docs.cupy.dev/en/latest/install.html>`_ for the detailed instructions.
| text/x-rst | null | Seiya Tokui <tokui@preferred.jp> | CuPy Developers | null | null | null | [
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Science/Research",
"Intended Audience :: Developers",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14",
"Programming Language :: Python :: 3 :: Only",
"Programming Language :: Cython",
"Topic :: Software Development",
"Topic :: Scientific/Engineering",
"Operating System :: POSIX",
"Operating System :: Microsoft :: Windows"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"numpy<2.6,>=2.0",
"cuda-pathfinder==1.*,>=1.3.3",
"scipy<1.17,>=1.10; extra == \"all\"",
"Cython>=3; extra == \"all\"",
"optuna>=2.0; extra == \"all\"",
"packaging; extra == \"test\"",
"pytest>=7.2; extra == \"test\"",
"hypothesis<6.55.0,>=6.37.2; extra == \"test\"",
"mpmath; extra == \"test\""
] | [] | [] | [] | [
"Homepage, https://cupy.dev/",
"Documentation, https://docs.cupy.dev/",
"Bug Tracker, https://github.com/cupy/cupy/issues",
"Source Code, https://github.com/cupy/cupy"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:25:27.585673 | cupy-14.0.1.tar.gz | 3,899,909 | 18/3c/d3aa4aaf179567f6588dbfb18b29471949fb782fc79fddfd43480b052d71/cupy-14.0.1.tar.gz | source | sdist | null | false | c8135bb8d7a71f6aa9272560d7b10563 | 4b673ab2d8b2329abe7ae0a7ae6159656044a8eecca56cf0b834b7c907063205 | 183cd3aa4aaf179567f6588dbfb18b29471949fb782fc79fddfd43480b052d71 | MIT | [
"LICENSE",
"docs/source/license.rst"
] | 3,341 |
2.4 | cupy-rocm-7-0 | 14.0.1 | CuPy: NumPy & SciPy for GPU | .. image:: https://raw.githubusercontent.com/cupy/cupy/main/docs/image/cupy_logo_1000px.png
:width: 400
CuPy : NumPy & SciPy for GPU
============================
`CuPy <https://cupy.dev/>`_ is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.
This is a CuPy wheel (precompiled binary) package for AMD ROCm 7.0.
You need to install `ROCm 7.0 <https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html>`_ to use these packages.
If you have another version of ROCm, or want to build from source, refer to the `Installation Guide <https://docs.cupy.dev/en/latest/install.html>`_ for instructions.
| text/x-rst | null | Seiya Tokui <tokui@preferred.jp> | CuPy Developers | null | null | null | [
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Science/Research",
"Intended Audience :: Developers",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14",
"Programming Language :: Python :: 3 :: Only",
"Programming Language :: Cython",
"Topic :: Software Development",
"Topic :: Scientific/Engineering",
"Operating System :: POSIX",
"Operating System :: Microsoft :: Windows"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"numpy<2.6,>=2.0",
"scipy<1.17,>=1.10; extra == \"all\"",
"Cython>=3; extra == \"all\"",
"optuna>=2.0; extra == \"all\"",
"packaging; extra == \"test\"",
"pytest>=7.2; extra == \"test\"",
"hypothesis<6.55.0,>=6.37.2; extra == \"test\"",
"mpmath; extra == \"test\""
] | [] | [] | [] | [
"Homepage, https://cupy.dev/",
"Documentation, https://docs.cupy.dev/",
"Bug Tracker, https://github.com/cupy/cupy/issues",
"Source Code, https://github.com/cupy/cupy"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:25:24.745296 | cupy_rocm_7_0-14.0.1-cp314-cp314-manylinux2014_x86_64.whl | 73,278,385 | 25/b2/8437671ac54c4ae02e771b50a894472c67094801bf38e92f992ef4028294/cupy_rocm_7_0-14.0.1-cp314-cp314-manylinux2014_x86_64.whl | cp314 | bdist_wheel | null | false | 4ca327031bd77c01d0fa97c17665adc2 | a4ec04e6039c34f4916cbeab72fa7f1ab38be04c9116364c43b3e8d8cbaffbc4 | 25b28437671ac54c4ae02e771b50a894472c67094801bf38e92f992ef4028294 | MIT | [
"LICENSE",
"docs/source/license.rst"
] | 392 |
2.4 | pexpect-serialspawn | 0.0.5 | Interact with serial devices using pexpect | # Serial spawn for pexpect
[](https://github.com/antoniovazquezblanco/pexpect-serialspawn/actions/workflows/build.yml)
[](https://pypi.org/project/pexpect-serialspawn/)
[](https://snyk.io/advisor/python/pexpect-serialspawn)
Interact with serial devices using pexpect.
## Installation
Just use pip :)
```
pip install pexpect-serialspawn
```
## Usage
```python
import serial
from pexpect_serialspawn import SerialSpawn
# Initialize your serial device
ser = serial.Serial('COM1', 115200)
# Spawn a pexpect object
ss = SerialSpawn(ser, encoding='utf-8')
# Use as any other pexpect spawns...
ss.sendline('Hello')
ss.expect('World')
```
| text/markdown | null | Antonio Vázquez Blanco <antoniovazquezblanco@gmail.com> | null | null | null | null | [
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3"
] | [] | null | null | >=3.0 | [] | [] | [] | [
"pexpect",
"pyserial"
] | [] | [] | [] | [
"Homepage, https://github.com/antoniovazquezblanco/pexpect-serialspawn",
"Bug Tracker, https://github.com/antoniovazquezblanco/pexpect-serialspawn/issues"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:25:18.043889 | pexpect_serialspawn-0.0.5.tar.gz | 4,780 | a9/3f/e80263940f04a4f97bc9f51240692a3a9e8c071a24eb49be03c4f6a50ef5/pexpect_serialspawn-0.0.5.tar.gz | source | sdist | null | false | 47bc4af8b6755f2bad0208eae4d9f5bf | da676602b9f0d4cadee294d0605a743b20452b81cc31c0b217098596465be378 | a93fe80263940f04a4f97bc9f51240692a3a9e8c071a24eb49be03c4f6a50ef5 | null | [] | 222 |
2.4 | cupy-cuda13x | 14.0.1 | CuPy: NumPy & SciPy for GPU | .. image:: https://raw.githubusercontent.com/cupy/cupy/main/docs/image/cupy_logo_1000px.png
:width: 400
CuPy : NumPy & SciPy for GPU
============================
`CuPy <https://cupy.dev/>`_ is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.
This is a CuPy wheel (precompiled binary) package for CUDA 13.x.
You need to install `CUDA Toolkit 13.x <https://developer.nvidia.com/cuda-toolkit-archive>`_ locally to use these packages.
Alternatively, you can install this package together with all needed CUDA components from PyPI by passing the ``[ctk]`` tag::
$ pip install cupy-cuda13x[ctk]
If you have another version of CUDA, or want to build from source, refer to the `Installation Guide <https://docs.cupy.dev/en/latest/install.html>`_ for instructions.
| text/x-rst | null | Seiya Tokui <tokui@preferred.jp> | CuPy Developers | null | null | null | [
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Science/Research",
"Intended Audience :: Developers",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14",
"Programming Language :: Python :: 3 :: Only",
"Programming Language :: Cython",
"Topic :: Software Development",
"Topic :: Scientific/Engineering",
"Operating System :: POSIX",
"Operating System :: Microsoft :: Windows"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"numpy<2.6,>=2.0",
"cuda-pathfinder==1.*,>=1.3.3",
"scipy<1.17,>=1.10; extra == \"all\"",
"Cython>=3; extra == \"all\"",
"optuna>=2.0; extra == \"all\"",
"packaging; extra == \"test\"",
"pytest>=7.2; extra == \"test\"",
"hypothesis<6.55.0,>=6.37.2; extra == \"test\"",
"mpmath; extra == \"test\"",
"cuda-toolkit[cublas,cudart,cufft,curand,cusolver,cusparse,nvrtc]==13.*; extra == \"ctk\""
] | [] | [] | [] | [
"Homepage, https://cupy.dev/",
"Documentation, https://docs.cupy.dev/",
"Bug Tracker, https://github.com/cupy/cupy/issues",
"Source Code, https://github.com/cupy/cupy"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:24:54.198033 | cupy_cuda13x-14.0.1-cp314-cp314-win_amd64.whl | 35,474,838 | c1/f5/273193563cdc37cdb22de3b73e7db12819b39fafb73de6bcf7d48f20945e/cupy_cuda13x-14.0.1-cp314-cp314-win_amd64.whl | cp314 | bdist_wheel | null | false | d11c6405f6dd5dfa6f68d6fca7980e93 | 22b50139e05c4612fac905dd6c3390f8687e0e390f0e200d5be14be1726e3d04 | c1f5273193563cdc37cdb22de3b73e7db12819b39fafb73de6bcf7d48f20945e | MIT | [
"LICENSE",
"docs/source/license.rst"
] | 4,359 |
2.4 | mitogen | 0.3.42 | Library for writing distributed self-replicating programs. | # Mitogen
[](https://pypi.org/project/mitogen/)
[](https://pypi.org/project/mitogen/)
[](https://github.com/mitogen-hq/mitogen/actions?query=branch%3Amaster)
<a href="https://mitogen.networkgenomics.com/">Please see the documentation</a>.
| text/markdown | David Wilson | null | null | null | BSD-3-Clause | null | [
"Environment :: Console",
"Framework :: Ansible",
"Intended Audience :: System Administrators",
"Operating System :: MacOS :: MacOS X",
"Operating System :: POSIX",
"Programming Language :: Python",
"Programming Language :: Python :: 2.4",
"Programming Language :: Python :: 2.5",
"Programming Language :: Python :: 2.6",
"Programming Language :: Python :: 2.7",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14",
"Programming Language :: Python :: Implementation :: CPython",
"Topic :: System :: Distributed Computing",
"Topic :: System :: Systems Administration"
] | [] | https://github.com/mitogen-hq/mitogen/ | null | !=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.4 | [] | [] | [] | [] | [] | [] | [] | [] | twine/6.2.0 CPython/3.13.11 | 2026-02-20T10:24:41.965867 | mitogen-0.3.42.tar.gz | 231,140 | ab/76/0a5fc66d4786273c7bab8b2d457027f7ebd246fdd41251d4f6906842256e/mitogen-0.3.42.tar.gz | source | sdist | null | false | 618bd87fc89f9a2fac61145b2952010f | 7767787e47cafac1aa674d7f6622bd2f764bd9ed0da0c63002df7161783bf46f | ab760a5fc66d4786273c7bab8b2d457027f7ebd246fdd41251d4f6906842256e | null | [
"LICENSE"
] | 1,821 |
2.1 | strongdm | 16.8.2 | strongDM SDK for the Python programming language. | # strongDM SDK for Python
This is the official [strongDM](https://www.strongdm.com/) SDK for the Python
programming language.
Learn more with our [📚strongDM API docs](https://docs.strongdm.com/references/api) or
[📓browse the SDK
reference](https://strongdm.github.io/strongdm-sdk-python-docs/).
## Installation
```bash
$ pip install strongdm
```
strongDM uses [semantic versioning](https://semver.org/). We do not guarantee
compatibility between major versions. Be sure to use version constraints to pin
your dependency to the desired major version of the strongDM SDK.
## Authentication
If you don't already have them you will need to generate a set of API keys,
instructions are here: [API
Credentials](https://docs.strongdm.com/references/api/api-keys)
Add the keys as environment variables; the SDK will need to access these keys
for every request.
```bash
$ export SDM_API_ACCESS_KEY=<YOUR ACCESS KEY>
$ export SDM_API_SECRET_KEY=<YOUR SECRET KEY>
```
## List Users
The following code lists all registered users:
```python
import os
import strongdm
def main():
client = strongdm.Client(os.getenv("SDM_API_ACCESS_KEY"),
os.getenv("SDM_API_SECRET_KEY"))
users = client.accounts.list('')
for user in users:
print(user)
if __name__ == "__main__":
main()
```
## Useful Links
- Documentation: [strongdm package](https://strongdm.github.io/strongdm-sdk-python-docs/)
- [Migrating from v2 to v3](https://github.com/strongdm/strongdm-sdk-python/releases/tag/v3.0.0)
- [Migrating from Role Grants to Access Rules](https://github.com/strongdm/strongdm-sdk-python/wiki/Migrating-from-Role-Grants-to-Access-Rules)
- Examples: [GitHub - strongdm/strongdm-sdk-python-examples](https://github.com/strongdm/strongdm-sdk-python-examples)
1. [Managing Resources](https://github.com/strongdm/strongdm-sdk-python-examples/tree/master/1_managing_resources)
2. [Managing Accounts](https://github.com/strongdm/strongdm-sdk-python-examples/tree/master/2_managing_accounts)
3. [Managing Roles](https://github.com/strongdm/strongdm-sdk-python-examples/tree/master/3_managing_roles)
4. [Managing Gateways](https://github.com/strongdm/strongdm-sdk-python-examples/tree/master/4_managing_gateways)
5. [Auditing](https://github.com/strongdm/strongdm-sdk-python-examples/tree/master/5_auditing)
6. [Managing Access Workflows](https://github.com/strongdm/strongdm-sdk-python-examples/tree/master/6_managing_workflows)
## License
[Apache 2](https://github.com/strongdm/strongdm-sdk-python/blob/master/LICENSE)
## Contributing
Currently, we are not accepting pull requests directly to this repository, but
our users are some of the most resourceful and ambitious folks out there. So, if
you have something to contribute, find a bug, or just want to give us some
feedback, please email <support@strongdm.com>.
| text/markdown | strongDM Team | sdk-feedback@strongdm.com | null | null | apache-2.0 | strongDM, sdm, api, automation, security, audit, database, server, ssh, rdp | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Topic :: Security",
"License :: OSI Approved :: Apache Software License",
"Programming Language :: Python :: 3"
] | [] | https://github.com/strongdm/strongdm-sdk-python | https://github.com/strongdm/strongdm-sdk-python/archive/v16.8.2.tar.gz | null | [] | [] | [] | [] | [] | [] | [] | [] | twine/3.7.1 importlib_metadata/8.7.1 pkginfo/1.12.1.2 requests/2.27.1 requests-toolbelt/1.0.0 tqdm/4.67.1 CPython/3.10.12 | 2026-02-20T10:24:34.871912 | strongdm-16.8.2.tar.gz | 387,844 | 27/d7/73c5bb77e769d75cc7164929e8d27371574e84dfa953bf46a6d6c4a63070/strongdm-16.8.2.tar.gz | source | sdist | null | false | 09b0bf28c35b7205b14f3fa8ab3c98e6 | 13d06de19cb48257d74c971a7de37801705c6c25c5259958213eae3e1fddd9f9 | 27d773c5bb77e769d75cc7164929e8d27371574e84dfa953bf46a6d6c4a63070 | null | [] | 201 |
2.4 | datatailr | 0.1.104 | Ready-to-Use Platform That Drives Business Insights | <!-- --8<-- [start:intro] -->
<div style="text-align: center;">
<a href="https://www.datatailr.com/" target="_blank">
<img src="https://s3.eu-west-1.amazonaws.com/datatailr.com/assets/datatailr-logo.svg" alt="Datatailr Logo" />
</a>
</div>
---
**Datatailr empowers your team to streamline analytics and data workflows
from idea to production without infrastructure hurdles.**
# What is Datatailr?
Datatailr is a platform that simplifies the process of building and deploying data applications.
It makes it easier to run and maintain large-scale data processing and analytics workloads.
<!-- --8<-- [end:intro] -->
## What is this package?
This is the Python package for Datatailr, which allows you to interact with the Datatailr platform.
It provides the tools to build, deploy, and manage batch jobs, data pipelines, services and analytics applications.
Datatailr manages the underlying infrastructure so your applications can be deployed in an easy, secure and scalable way.
## Installation
### Installing the Python package
You can install the Datatailr Python package using pip:
```bash
pip install datatailr
```
### Testing the installation
```python
import datatailr
print(datatailr.__version__)
print(datatailr.__provider__)
```
## Remote CLI (optional)
If you install the package outside the Datatailr platform, you can enable the remote `dt` CLI:
```bash
datatailr setup-cli
```
Example usage:
```bash
datatailr login
dt job ls
dt user ls
dt job save path/to/local/file.json
```
Notes:
- Remote CLI configuration inside a virtual environment only applies inside that environment.
- The remote CLI cannot be installed inside Datatailr containers; the native CLI is used there.
## Quickstart
The following example shows how to create a simple data pipeline using the Datatailr Python package.
```python
from datatailr import workflow, task
@task()
def func_no_args() -> str:
return "no_args"
@task()
def func_with_args(a: int, b: float) -> str:
return f"args: {a}, {b}"
@workflow(name="MY test DAG")
def my_workflow():
for n in range(2):
res1 = func_no_args().alias(f"func_{n}")
res2 = func_with_args(1, res1).alias(f"func_with_args_{n}")
my_workflow(local_run=True)
```
Running this code will create a graph of jobs and execute it.
Each node on the graph represents a job, which in turn is a call to a function decorated with `@task()`.
Since this is a local run then the execution of each node will happen sequentially in the same process.
To take advantage of the datatailr platform and execute the graph at scale, you can run it using the job scheduler as presented in the next section.
## Execution at Scale
To execute the graph at scale, you can use the Datatailr job scheduler. This allows you to run your jobs in parallel, taking advantage of the underlying infrastructure.
You will first need to separate your function definitions from the DAG definition. This means you should define your functions as a separate module, which can be imported into the DAG definition.
```python
# my_module.py
from datatailr import task
@task()
def func_no_args() -> str:
return "no_args"
@task()
def func_with_args(a: int, b: float) -> str:
return f"args: {a}, {b}"
```
To use these functions in a batch job, you just need to import them and run in a DAG context:
```python
from my_module import func_no_args, func_with_args
from datatailr import workflow
@workflow(name="MY test DAG")
def my_workflow():
for n in range(2):
res1 = func_no_args().alias(f"func_{n}")
res2 = func_with_args(1, res1).alias(f"func_with_args_{n}")
schedule = Schedule(at_hours=0)
my_workflow(schedule=schedule)
```
This will submit the entire workflow for execution, and the scheduler will take care of running the jobs in parallel and managing the resources.
The workflow in the example above will be scheduled to run daily at 00:00.
___
Visit [our website](https://www.datatailr.com/) for more!
| text/markdown | null | Datatailr <info@datatailr.com> | null | null | null | null | [
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3 :: Only",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Environment :: Console",
"Operating System :: OS Independent",
"Topic :: Scientific/Engineering"
] | [] | null | null | >=3.9 | [] | [] | [] | [
"requests",
"ruff; extra == \"dev\"",
"pre-commit; extra == \"dev\"",
"mypy; extra == \"dev\"",
"types-setuptools; extra == \"dev\"",
"toml; extra == \"dev\"",
"coverage; extra == \"dev\"",
"sphinx-rtd-theme; extra == \"dev\"",
"sphinx; extra == \"dev\"",
"sphinx-autodoc-typehints; extra == \"dev\"",
"sphinx-autosummary; extra == \"dev\"",
"sphinx-design; extra == \"dev\"",
"sphinx-copybutton; extra == \"dev\"",
"myst-parser; extra == \"dev\""
] | [] | [] | [] | [
"homepage, https://www.datatailr.com/",
"documentation, https://docs.datatailr.com/"
] | twine/6.2.0 CPython/3.12.0 | 2026-02-20T10:24:07.204129 | datatailr-0.1.104.tar.gz | 116,696 | 9e/6a/f27f64b883a793a89d4dc2b4b853cbcff6221e7d5bbc3dbeea0f165c1738/datatailr-0.1.104.tar.gz | source | sdist | null | false | 66a04b2580bc87b7e139d71586d2b036 | 6230cca39331026cf20f1fd29a31908eb377d2885278e1785cf8fcc418bf3613 | 9e6af27f64b883a793a89d4dc2b4b853cbcff6221e7d5bbc3dbeea0f165c1738 | MIT | [
"LICENSE"
] | 226 |
2.2 | blosc2-grok | 0.3.4 | Grok (JPEG2000 codec) plugin for Blosc2. | # Blosc2 grok
A plugin of the excellent [grok library](https://github.com/GrokImageCompression/grok) for Blosc2. grok is a JPEG2000 codec, and with this plugin, you can use it as yet another codec in applications using Blosc2. See an example of use at: https://github.com/Blosc/blosc2_grok/blob/main/examples/params.py
## Installation
For using `blosc2_grok` you will first have to install its wheel:
```shell
pip install blosc2-grok -U
```
## Usage
```python
import blosc2
import numpy as np
import blosc2_grok
from PIL import Image
# Set the params for the grok codec
kwargs = {}
kwargs['cod_format'] = blosc2_grok.GrkFileFmt.GRK_FMT_JP2
kwargs['quality_mode'] = "dB"
kwargs['quality_layers'] = np.array([5], dtype=np.float64)
blosc2_grok.set_params_defaults(**kwargs)
# Define the compression and decompression parameters for Blosc2.
# Disable the filters and do not split blocks (these won't work with grok).
cparams = {
'codec': blosc2.Codec.GROK,
'filters': [],
'splitmode': blosc2.SplitMode.NEVER_SPLIT,
}
# Read the image
im = Image.open("examples/kodim23.png")
# Convert the image to a numpy array
np_array = np.asarray(im)
# Transform the numpy array to a blosc2 array. This is where compression happens, and
# the HTJ2K codec is called.
bl_array = blosc2.asarray(
np_array,
chunks=np_array.shape,
blocks=np_array.shape,
cparams=cparams,
urlpath="examples/kodim23.b2nd",
mode="w",
)
# Print information about the array, see the compression ratio (cratio)
print(bl_array.info)
```
## Parameters for compression
The following parameters are available for compression for grok, with their defaults. Most of them are named after the ones in the [Pillow library](https://pillow.readthedocs.io/en/stable/handbook/image-file-formats.html#jpeg-2000-saving) and have the same meaning. The ones that are not in Pillow are marked with a `*` and you can get more information about them in the [grok documentation](https://github.com/GrokImageCompression/grok/wiki/3.-grk_compress), or by following the provided links. For those marked with a ``**``, you can get more information in the [grok.h header](https://github.com/GrokImageCompression/grok/blob/a84ac2592e581405a976a00cf9e6f03cab7e2481/src/lib/core/grok.h#L975
).
'tile_size': (0, 0),
'tile_offset': (0, 0),
'quality_mode': None,
'quality_layers': np.zeros(0, dtype=np.float64),
'progression': "LRCP",
'num_resolutions': 6,
'codeblock_size': (64, 64),
'irreversible': False,
'precinct_size': (0, 0),
'offset': (0, 0),
'mct': 0,
* 'numgbits': 2, # Equivalent to -N, -guard_bits
* 'roi_compno': -1, # Together with 'roi_shift' it is equivalent to -R, -ROI
* 'roi_shift': 0,
* 'decod_format': GrkFileFmt.GRK_FMT_UNK,
* 'cod_format': GrkFileFmt.GRK_FMT_UNK,
* 'rsiz': GrkProfile.GRK_PROFILE_NONE, # Equivalent to -Z, -rsiz
* 'framerate': 0,
* 'apply_icc_': False, # Equivalent to -f, -apply_icc
* 'rateControlAlgorithm': GrkRateControl.BISECT,
* 'num_threads': 0,
* 'deviceId': 0, # Equivalent to -G, -device_id
* 'duration': 0, # Equivalent to -J, -duration
* 'repeats': 1, # Equivalent to -e, -repetitions
* 'mode': GrkMode.DEFAULT, # Equivalent to -M, -mode
* 'verbose': False, # Equivalent to -v, -verbose
** 'enableTilePartGeneration': False, # See header of grok.h above
** 'max_cs_size': 0, # See header of grok.h above
** 'max_comp_size': 0, # See header of grok.h above
*Note: * when using the `blosc2_grok` plugin from C, the structure used
for setting the parameters uses the `grok` parameters names. You can see an example
in https://github.com/Blosc/leaps-examples/blob/main/c-compression/compress-tomo.c#L110 .
### codec_meta as rates quality mode
As a simpler way to activate the rates quality mode, if you set the `codec_meta` from the `cparams` to an
integer different from 0, the rates quality mode will be activated with a rate value equal to `codec_meta` / 10. If
`cod_format` is not specified, the default will be used. The `codec_meta` has priority to the `rates` param set with the
`blosc2_grok.set_params_defaults()`. Please note that only rates < 25.6 are supported with this notation.
```python
import blosc2
cparams = {
'codec': blosc2.Codec.GROK,
'codec_meta': 5 * 10, # cratio will be 5
'filters': [],
'splitmode': blosc2.SplitMode.NEVER_SPLIT,
}
```
## Notes
When using `blosc2_grok`, there are some restrictions that you have
to keep in mind.
* The minimum supported image size is around 256 bytes, so an image with
less size will fail to be compressed.
* The maximum datatype precision is of 16 bits.
* Although floats from 16 or fewer bits of precision seem to work, we
recommend using integer data when possible.
## More examples
See the [examples](examples/) directory for more examples.
## Thanks
Thanks to Marta Iborra, from the Blosc Development Team, for doing most of the job in making this plugin possible, and J. David Ibáñez and Francesc Alted for the initial contributions. Also, thanks to Aaron Boxer, the original author of the [grok library](https://github.com/GrokImageCompression/grok), for his help in ironing out issues for making this interaction possible.
That's all folks!
The Blosc Development Team
| text/markdown | null | Blosc Development Team <contact@blosc.org> | null | null | GNU Affero General Public License version 3 | plugin, blosc2 | [
"Programming Language :: Python :: 3",
"Programming Language :: C",
"Programming Language :: C++",
"Development Status :: 3 - Alpha",
"Intended Audience :: Developers",
"Intended Audience :: Information Technology",
"License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)",
"Topic :: Software Development :: Libraries :: Python Modules",
"Topic :: System :: Archiving :: Compression",
"Operating System :: Microsoft :: Windows",
"Operating System :: Unix"
] | [] | null | null | null | [] | [] | [] | [
"blosc2>=4.0.0.b1",
"pytest; extra == \"h5py-test\""
] | [] | [] | [] | [
"Homepage, https://github.com/Blosc/blosc2_grok",
"Issues, https://github.com/Blosc/blosc2_grok/issues"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:24:04.146780 | blosc2_grok-0.3.4-cp312-cp312-win_amd64.whl | 3,881,192 | d3/35/26b4d534df19f60b4861a8714cfb8be28ccd5f9cb8a92c572e7d46bb8584/blosc2_grok-0.3.4-cp312-cp312-win_amd64.whl | cp312 | bdist_wheel | null | false | ba514c87fd66a07365674b18fa6a804e | 78095b887a8291d76be7fa80cc58f8e077c0c9be8b9bd005584aa9464edf4c4a | d33526b4d534df19f60b4861a8714cfb8be28ccd5f9cb8a92c572e7d46bb8584 | null | [] | 85 |
2.4 | aitracer | 1.3.0 | AITracer SDK - Monitor your AI/LLM applications | # AITracer Python SDK
AIエージェント/LLMアプリの実行ログ・コスト・パフォーマンスを監視するためのPython SDK。
## 本番環境
- **API エンドポイント**: `https://api.aitracer.co`
- **ダッシュボード**: `https://app.aitracer.co`
SDKはデフォルトで `https://api.aitracer.co` に接続します。
## インストール
```bash
pip install aitracer
```
## クイックスタート
### 基本的な使い方
```python
from aitracer import AITracer
from openai import OpenAI
# AITracerを初期化
tracer = AITracer(
api_key="at-xxxxxxxx",
project="my-chatbot"
)
# OpenAIクライアントをラップ
client = tracer.wrap_openai(OpenAI())
# 普通に使う（自動でログが送信される）
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Hello!"}]
)
```
### Anthropic対応
```python
from aitracer import AITracer
from anthropic import Anthropic
tracer = AITracer(
api_key="at-xxxxxxxx",
project="my-chatbot"
)
# Anthropicクライアントをラップ
client = tracer.wrap_anthropic(Anthropic())
response = client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=1024,
messages=[{"role": "user", "content": "Hello!"}]
)
```
### Google Gemini対応
```python
from aitracer import AITracer
import google.generativeai as genai
tracer = AITracer(
api_key="at-xxxxxxxx",
project="my-chatbot"
)
# Geminiを設定
genai.configure(api_key="your-google-api-key")
model = genai.GenerativeModel("gemini-1.5-flash")
# Geminiモデルをラップ
model = tracer.wrap_gemini(model)
response = model.generate_content("Hello!")
print(response.text)
```
ストリーミングも対応しています：
```python
response = model.generate_content("Write a story...", stream=True)
for chunk in response:
print(chunk.text, end="")
```
### トレース機能
複数のAPI呼び出しをグループ化して追跡できます。
```python
with tracer.trace("user-query-123") as trace:
# この中のAPI呼び出しは同じtrace_idでグループ化される
response1 = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Summarize this..."}]
)
response2 = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Translate to Japanese..."}]
)
# メタデータを追加
trace.set_metadata({
"user_id": "user-456",
"feature": "summarization"
})
# タグを追加
trace.add_tag("production")
```
### ストリーミング対応
ストリーミングレスポンスも自動的にログされます。
```python
stream = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Write a story..."}],
stream=True
)
for chunk in stream:
print(chunk.choices[0].delta.content or "", end="")
# ストリーム完了後に自動的にログが送信される
```
## 設定オプション
```python
tracer = AITracer(
# 必須
api_key="at-xxxxxxxx", # または AITRACER_API_KEY 環境変数
project="my-chatbot", # または AITRACER_PROJECT 環境変数
# オプション
base_url="https://api.aitracer.co", # APIエンドポイント（デフォルト）
sync=False, # True: 同期送信（Lambda用）
flush_on_exit=True, # 終了時に未送信ログをフラッシュ
batch_size=10, # バッチサイズ
flush_interval=5.0, # 自動フラッシュ間隔（秒）
enabled=True, # False: ログ無効化（テスト用）
)
```
### Lambda/サーバーレス環境
サーバーレス環境では `sync=True` を使用してください。
```python
tracer = AITracer(
api_key="at-xxxxxxxx",
project="my-lambda",
sync=True # 同期送信
)
```
### 環境変数
```bash
export AITRACER_API_KEY=at-xxxxxxxx
export AITRACER_PROJECT=my-chatbot
```
```python
# 環境変数から自動で読み込み
tracer = AITracer()
```
## 手動ログ
自動ラッパーを使わずに手動でログを送信することもできます。
```python
tracer.log(
model="gpt-4o",
provider="openai",
input_data={"messages": [{"role": "user", "content": "Hello"}]},
output_data={"content": "Hi there!"},
input_tokens=10,
output_tokens=5,
latency_ms=150,
status="success",
metadata={"user_id": "user-123"},
tags=["production"]
)
```
## フラッシュとシャットダウン
```python
# 未送信のログを即座に送信
tracer.flush()
# シャットダウン（フラッシュ + リソース解放）
tracer.shutdown()
```
## ライセンス
Copyright (c) 株式会社HARO. All rights reserved.
| text/markdown | null | "HARO Inc." <info@haro.co.jp> | null | null | null | ai, anthropic, llm, monitoring, observability, openai | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Topic :: Software Development :: Libraries :: Python Modules"
] | [] | null | null | >=3.9 | [] | [] | [] | [
"anthropic>=0.18.0",
"httpx>=0.25.0",
"openai>=1.0.0",
"mypy>=1.0.0; extra == \"dev\"",
"pytest-asyncio>=0.21.0; extra == \"dev\"",
"pytest-cov>=4.0.0; extra == \"dev\"",
"pytest>=7.0.0; extra == \"dev\"",
"ruff>=0.1.0; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://aitracer.co",
"Documentation, https://docs.aitracer.co",
"Repository, https://github.com/haro-inc/aitracer"
] | twine/6.2.0 CPython/3.10.2 | 2026-02-20T10:23:47.441506 | aitracer-1.3.0.tar.gz | 133,350 | b0/d6/4653caf275a66a9480cef7025a885c7ae561bca5fc37472c76ffdc38a42c/aitracer-1.3.0.tar.gz | source | sdist | null | false | ef67849629d09326de2723f1a8e535da | 4cd8b6753827aa82af67b965996e1f2fbc40da18d82a880d939071ac30380882 | b0d64653caf275a66a9480cef7025a885c7ae561bca5fc37472c76ffdc38a42c | MIT | [
"LICENSE"
] | 213 |
2.4 | cupy-cuda12x | 14.0.1 | CuPy: NumPy & SciPy for GPU | .. image:: https://raw.githubusercontent.com/cupy/cupy/main/docs/image/cupy_logo_1000px.png
:width: 400
CuPy : NumPy & SciPy for GPU
============================
`CuPy <https://cupy.dev/>`_ is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.
This is a CuPy wheel (precompiled binary) package for CUDA 12.x.
You need to install `CUDA Toolkit 12.x <https://developer.nvidia.com/cuda-toolkit-archive>`_ locally to use these packages.
Alternatively, you can install this package together with all needed CUDA components from PyPI by passing the ``[ctk]`` tag::
$ pip install cupy-cuda12x[ctk]
If you have another version of CUDA, or want to build from source, refer to the `Installation Guide <https://docs.cupy.dev/en/latest/install.html>`_ for instructions.
| text/x-rst | null | Seiya Tokui <tokui@preferred.jp> | CuPy Developers | null | null | null | [
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Science/Research",
"Intended Audience :: Developers",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14",
"Programming Language :: Python :: 3 :: Only",
"Programming Language :: Cython",
"Topic :: Software Development",
"Topic :: Scientific/Engineering",
"Operating System :: POSIX",
"Operating System :: Microsoft :: Windows"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"numpy<2.6,>=2.0",
"cuda-pathfinder==1.*,>=1.3.3",
"scipy<1.17,>=1.10; extra == \"all\"",
"Cython>=3; extra == \"all\"",
"optuna>=2.0; extra == \"all\"",
"packaging; extra == \"test\"",
"pytest>=7.2; extra == \"test\"",
"hypothesis<6.55.0,>=6.37.2; extra == \"test\"",
"mpmath; extra == \"test\"",
"cuda-toolkit[cublas,cudart,cufft,curand,cusolver,cusparse,nvrtc]==12.*; extra == \"ctk\""
] | [] | [] | [] | [
"Homepage, https://cupy.dev/",
"Documentation, https://docs.cupy.dev/",
"Bug Tracker, https://github.com/cupy/cupy/issues",
"Source Code, https://github.com/cupy/cupy"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:23:42.684122 | cupy_cuda12x-14.0.1-cp314-cp314-win_amd64.whl | 96,822,848 | e5/a3/80ff83dcad1ac61741714d97fce5a3ef42c201bb40005ec5cc413e34d75f/cupy_cuda12x-14.0.1-cp314-cp314-win_amd64.whl | cp314 | bdist_wheel | null | false | c7c9c3a75a4038693dd8c16d1eecef57 | cafe62131caef63b5e90b71b617bb4bf47d7bd9e11cccabea8104db1e01db02e | e5a380ff83dcad1ac61741714d97fce5a3ef42c201bb40005ec5cc413e34d75f | MIT | [
"LICENSE",
"docs/source/license.rst"
] | 100,276 |
2.4 | fabrix-ai | 1.5.1 | Graph-based agent framework powered by oauth-codex | # Fabrix
Language: English | [한국어](README.ko.md)
API Guides: [English](docs/api.md) | [한국어](docs/api.ko.md)
## Overview
Fabrix is a graph-based agent framework built on top of `oauth-codex>=2.3.0`.
It provides a structured execution graph with streaming events for tool-driven workflows.
## Key Features
- Graph-based 3-state execution: `reasoning`, `tool_call`, `response`
- Structured state outputs powered by Pydantic models
- Sequential tool execution with strict payload validation
- Async streaming event API for step-by-step observability
- Multimodal input with explicit message models: `TextMessage`, `ImageMessage`
## Installation
```bash
pip install fabrix-ai
```
## Quickstart
```python
import asyncio
from pydantic import BaseModel
from fabrix import Agent
from fabrix.events import (
ReasoningEvent,
ResponseEvent,
TaskFailedEvent,
ToolEvent,
)
from fabrix.messages import TextMessage
from fabrix.tools import ToolOutput
class AddInput(BaseModel):
a: int
b: int
def add_numbers(payload: AddInput) -> ToolOutput:
return ToolOutput.json({"sum": payload.a + payload.b})
async def main() -> None:
agent = Agent(
instructions="You are a precise assistant.",
model="gpt-5.3-codex",
tools=[add_numbers],
)
messages = [TextMessage(text="Use add_numbers to compute 3 + 9")]
async for event in agent.run_stream(messages=messages):
print(f"[step={event.step}] {event.event_type}")
if isinstance(event, ReasoningEvent):
print("reasoning:", event.reasoning)
print("focus:", event.focus)
elif isinstance(event, ToolEvent):
if event.phase == "start":
print("tool call:", event.tool_name, event.arguments)
elif event.result is not None:
print("tool result:", event.result.model_dump())
elif isinstance(event, ResponseEvent):
if event.response is not None:
print("response:", event.response)
if event.parts is not None:
print("parts:", [part.model_dump(mode="json") for part in event.parts])
if event.response is None and event.parts is None:
print("response: <empty>")
elif isinstance(event, TaskFailedEvent):
print("failed:", event.error_code, event.message)
asyncio.run(main())
```
## Message Models
Fabrix input is now `list[TextMessage | ImageMessage]`.
- `TextMessage(role: str = "user", text: str)`
- `ImageMessage(role: str = "user", image: str | Path | bytes, text: str | None = None)`
- Unknown message fields are rejected at construction time.
`ImageMessage.image` accepts:
- remote URL (`https://...`)
- local path (`Path` or string path)
- raw bytes (`bytes`), normalized to a data URL for model calls
## Multimodal Input
```python
from fabrix.messages import ImageMessage, TextMessage
messages = [
TextMessage(text="Describe this screenshot"),
ImageMessage(image="https://example.com/screenshot.png"),
TextMessage(text="Focus on errors"),
]
async for event in agent.run_stream(messages=messages):
...
```
## Tool Contract
Fabrix accepts tools in this shape:
```python
def tool(payload: BaseModel) -> ToolOutput: ...
```
- The tool must accept exactly one parameter.
- The parameter type must be a Pydantic `BaseModel`.
- The return type must be `ToolOutput` (breaking in v1.2.0).
- Runtime arguments must be a JSON object matching payload fields.
- Extra argument keys are rejected.
- Both sync and async tools are supported.
- `ToolOutput.image(...)` keeps `http(s)`/`data:` values as-is.
- `ToolOutput.image(...)` normalizes `file://`, local paths, and bytes to local absolute file references.
- Tool-call argument strictness is enforced by model `output_schema` with `strict_output=True`.
- Prompt policy and runtime context are no longer duplicated; runtime control context is appended as a final control message.
- During LLM history serialization, reasoning/tool_call/response (and legacy `tool_result`) records are preserved, and local image references are re-normalized to data URLs.
## Event Stream
`run_stream(...)` yields these event types:
- `reasoning`
- `tool` (`phase="start"` / `phase="finish"`)
- `response`
- `task_failed`
`reasoning` is a step-level decision trace / plan summary, not raw internal chain-of-thought.
`response` events now support both `response: str | None` and `parts` (structured text/image/json parts);
both fields may be `None` for an empty response event.
Terminate by setting `next_state=null` in `response` state.
## Migration (Breaking)
`run_task_stream(task, images, context)` has been removed.
- Before: `agent.run_task_stream(task=..., images=..., context=...)`
- After: `agent.run_stream(messages=[...])`
Mapping:
- `task` text -> `TextMessage(text="...")`
- `images` -> `ImageMessage(image="..." | Path(...) | b"...")`
- `context` -> include serialized context in `TextMessage.text`
Tool migration:
- Before: tool returns `str` / `dict` / scalar / arbitrary JSON-like objects
- After: tool must return `ToolOutput` (for example `ToolOutput.text(...)`, `ToolOutput.json(...)`, `ToolOutput.image(...)`)
## Documentation
- API usage guide (English): [`docs/api.md`](docs/api.md)
- API 사용 가이드 (한국어): [`docs/api.ko.md`](docs/api.ko.md)
- Korean README: [`README.ko.md`](README.ko.md)
## Examples
- Minimal quickstart: [`examples/minimal/quickstart.py`](examples/minimal/quickstart.py)
- Multimodal vision: [`examples/minimal/multimodal.py`](examples/minimal/multimodal.py)
- Data workflow: [`examples/advanced/data_workflow.py`](examples/advanced/data_workflow.py)
- Incident response workflow: [`examples/advanced/incident_response.py`](examples/advanced/incident_response.py)
## Notes
- Public runtime entry point is `fabrix.Agent`.
- Execution defaults are fixed internally: `max_steps=128` and no public per-tool timeout option.
- On successful completion, the stream ends right after the final `response` event (`next_state=null` in response state).
- If `max_steps` is reached, the stream ends without emitting an additional terminal event.
| text/markdown | Fabrix | null | null | null | MIT | agent, graph, llm, oauth-codex, tools | [
"Development Status :: 3 - Alpha",
"Intended Audience :: Developers",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Topic :: Software Development :: Libraries"
] | [] | null | null | >=3.12 | [] | [] | [] | [
"oauth-codex>=2.3.0",
"pydantic>=2.8",
"pytest-asyncio>=0.24; extra == \"dev\"",
"pytest>=8.3; extra == \"dev\"",
"ruff>=0.8; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/smturtle2/fabrix",
"Repository, https://github.com/smturtle2/fabrix",
"Issues, https://github.com/smturtle2/fabrix/issues"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:23:34.390963 | fabrix_ai-1.5.1.tar.gz | 39,265 | 35/fe/f88317bbf6a1058d1d96b7dd9e8565224abd8eaf6fa82dd6b67f0198a2de/fabrix_ai-1.5.1.tar.gz | source | sdist | null | false | e4d554aef3148bc5ea157d7838aa9e3f | fcbc1d9643c355613a2bbcc446a7daaad011fa19500033a07d6b2e8c90c7d68e | 35fef88317bbf6a1058d1d96b7dd9e8565224abd8eaf6fa82dd6b67f0198a2de | null | [
"LICENSE"
] | 223 |
2.4 | neutron-lib | 3.24.0 | Neutron shared routines and utilities | ===========
neutron-lib
===========
.. image:: https://governance.openstack.org/tc/badges/neutron-lib.svg
.. Change things from this point on
Neutron shared routines and utilities.
* Free software: Apache license
* Documentation: https://docs.openstack.org/neutron-lib/latest/
* Source: https://opendev.org/openstack/neutron-lib
* Bugs: https://bugs.launchpad.net/neutron
* Release notes: https://docs.openstack.org/releasenotes/neutron-lib/
| text/x-rst | null | OpenStack <openstack-discuss@lists.openstack.org> | null | null | Apache-2.0 | null | [
"Development Status :: 5 - Production/Stable",
"Environment :: OpenStack",
"Intended Audience :: Information Technology",
"Intended Audience :: System Administrators",
"License :: OSI Approved :: Apache Software License",
"Operating System :: POSIX :: Linux",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3 :: Only"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"pbr>=4.0.0",
"SQLAlchemy>=1.2.0",
"pecan>=1.0.0",
"keystoneauth1>=3.14.0",
"netaddr>=0.7.18",
"stevedore>=5.6.0",
"os-ken>=0.3.0",
"oslo.concurrency>=3.26.0",
"oslo.config>=8.0.0",
"oslo.context>=2.22.0",
"oslo.db>=12.1.0",
"oslo.i18n>=3.20.0",
"oslo.log>=4.3.0",
"oslo.messaging>=14.2.0",
"oslo.policy>=4.5.0",
"oslo.serialization>=2.25.0",
"oslo.service>=1.24.0",
"oslo.utils>=7.0.0",
"oslo.versionedobjects>=1.31.2",
"osprofiler>=1.4.0",
"setproctitle>=1.1.10",
"WebOb>=1.7.1",
"os-traits>=0.9.0",
"debtcollector>=3.0.0",
"osprofiler>=1.4.0; extra == \"osprofiler\""
] | [] | [] | [] | [
"Bug Tracker, https://bugs.launchpad.net/neutron",
"Documentation, https://docs.openstack.org/neutron-lib",
"Source Code, https://opendev.org/openstack/neutron-lib"
] | twine/6.2.0 CPython/3.11.14 | 2026-02-20T10:22:05.407638 | neutron_lib-3.24.0.tar.gz | 555,702 | d1/2a/a755a649b05786d345b78be6a2ce80ee53d7f847fd17424e8e81cfe99eb7/neutron_lib-3.24.0.tar.gz | source | sdist | null | false | 93da185474b2418dfa07ffcf87e4c10c | 96861925534f3c3a16b0e3de9e6fe3953f58b2b5f695386d8b71aa598bdc649b | d12aa755a649b05786d345b78be6a2ce80ee53d7f847fd17424e8e81cfe99eb7 | null | [
"LICENSE"
] | 1,301 |
2.1 | odoo-addon-edi-stock-oca | 16.0.1.1.2 | Define EDI Configuration for Stock | .. image:: https://odoo-community.org/readme-banner-image
:target: https://odoo-community.org/get-involved?utm_source=readme
:alt: Odoo Community Association
=============
Edi Stock Oca
=============
..
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!! This file is generated by oca-gen-addon-readme !!
!! changes will be overwritten. !!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!! source digest: sha256:ee95e1fe47181f0700521094fcd18c5122f87473e3e5dc054996c1b984645e2a
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
.. |badge1| image:: https://img.shields.io/badge/maturity-Beta-yellow.png
:target: https://odoo-community.org/page/development-status
:alt: Beta
.. |badge2| image:: https://img.shields.io/badge/license-AGPL--3-blue.png
:target: http://www.gnu.org/licenses/agpl-3.0-standalone.html
:alt: License: AGPL-3
.. |badge3| image:: https://img.shields.io/badge/github-OCA%2Fedi--framework-lightgray.png?logo=github
:target: https://github.com/OCA/edi-framework/tree/16.0/edi_stock_oca
:alt: OCA/edi-framework
.. |badge4| image:: https://img.shields.io/badge/weblate-Translate%20me-F47D42.png
:target: https://translation.odoo-community.org/projects/edi-framework-16-0/edi-framework-16-0-edi_stock_oca
:alt: Translate me on Weblate
.. |badge5| image:: https://img.shields.io/badge/runboat-Try%20me-875A7B.png
:target: https://runboat.odoo-community.org/builds?repo=OCA/edi-framework&target_branch=16.0
:alt: Try me on Runboat
|badge1| |badge2| |badge3| |badge4| |badge5|
This module intends to create a base to be extended by local edi rules
for stock.
In order to add a new integration for an stock picking, you need to create a listener:
.. code-block:: python
class MyEventListener(Component):
_name = "stock.picking.event.listener.demo"
_inherit = "base.event.listener"
_apply_on = ["stock.picking"]
def on_validate(self, picking):
"""Add your code here about creation of record"""
**Table of contents**
.. contents::
:local:
Bug Tracker
===========
Bugs are tracked on `GitHub Issues <https://github.com/OCA/edi-framework/issues>`_.
In case of trouble, please check there if your issue has already been reported.
If you spotted it first, help us to smash it by providing a detailed and welcomed
`feedback <https://github.com/OCA/edi-framework/issues/new?body=module:%20edi_stock_oca%0Aversion:%2016.0%0A%0A**Steps%20to%20reproduce**%0A-%20...%0A%0A**Current%20behavior**%0A%0A**Expected%20behavior**>`_.
Do not contact contributors directly about support or help with technical issues.
Credits
=======
Authors
~~~~~~~
* Odoo Community Association
Contributors
~~~~~~~~~~~~
* Alba Riera <alba.riera@creublanca.es>
Maintainers
~~~~~~~~~~~
This module is maintained by the OCA.
.. image:: https://odoo-community.org/logo.png
:alt: Odoo Community Association
:target: https://odoo-community.org
OCA, or the Odoo Community Association, is a nonprofit organization whose
mission is to support the collaborative development of Odoo features and
promote its widespread use.
This module is part of the `OCA/edi-framework <https://github.com/OCA/edi-framework/tree/16.0/edi_stock_oca>`_ project on GitHub.
You are welcome to contribute. To learn how please visit https://odoo-community.org/page/Contribute.
| null | Odoo Community Association,Odoo Community Association (OCA) | support@odoo-community.org | null | null | AGPL-3 | null | [
"Programming Language :: Python",
"Framework :: Odoo",
"Framework :: Odoo :: 16.0",
"License :: OSI Approved :: GNU Affero General Public License v3"
] | [] | https://github.com/OCA/edi-framework | null | >=3.10 | [] | [] | [] | [
"odoo-addon-component-event<16.1dev,>=16.0dev",
"odoo-addon-edi-oca<16.1dev,>=16.0dev",
"odoo<16.1dev,>=16.0a"
] | [] | [] | [] | [] | twine/6.2.0 CPython/3.12.3 | 2026-02-20T10:21:38.846466 | odoo_addon_edi_stock_oca-16.0.1.1.2-py3-none-any.whl | 25,135 | 85/3b/42667067ef96e74d948e86c879128fe372b2d328f281cb8eae47bfc4ebd6/odoo_addon_edi_stock_oca-16.0.1.1.2-py3-none-any.whl | py3 | bdist_wheel | null | false | c2ca0cb20fbd324211c89d2ed2383212 | 91e8c43e919f0a311f24a2b944431b9779c4495e80a3a02691e816d28fdad888 | 853b42667067ef96e74d948e86c879128fe372b2d328f281cb8eae47bfc4ebd6 | null | [] | 77 |
2.4 | tcheckerpy | 2026.2.20 | A Python Interface for TChecker | # tcheckerpy
**tcheckerpy** is a Python interface to the [TChecker](https://github.com/Echtzeitsysteme/tchecker/) model checker, allowing you to analyze and compare timed automata models directly from Python.
It provides access to the following TChecker tools:
- tck-compare
- tck-liveness
- tck-reach
- tck-simulate
- tck-syntax
For detailed documentation, refer to the [TChecker Wiki](https://github.com/ticktac-project/tchecker/wiki/Using-TChecker).
## Installation
You can install `tcheckerpy` via PyPI:
```bash
pip install tcheckerpy
```
## Usage Example
```python
# import required tools
from tcheckerpy.tools import tck_reach, tck_syntax
# read declaration of timed automata network from .txt or .tck file into string
with open(system_declaration_path) as file:
system = file.read()
# raise error if syntax is incorrect
tck_syntax.check(system)
# perform reachability analysis
result, stats, certificate = tck_reach.reach(system, tck_reach.Algorithm.REACH, certificate = tck_reach.Certificate.GRAPH)
print(result)
print(stats)
print(certificate)
```
Example output (based on [ad94.txt](https://github.com/Echtzeitsysteme/tchecker/blob/master/examples/ad94.txt)):
```
False
MEMORY_MAX_RSS 41116
REACHABLE false
RUNNING_TIME_SECONDS 6.1474e-05
VISITED_STATES 7
VISITED_TRANSITIONS 8
digraph ad94_fig10 {
0 [initial="true", intval="", labels="", vloc="<l0>", zone="(0<=x && 0<=y)"]
1 [intval="", labels="", vloc="<l1>", zone="(1<x && 0<=y && 1<x-y)"]
2 [intval="", labels="", vloc="<l1>", zone="(0<=x && 0<=y && 0<=x-y)"]
3 [intval="", labels="", vloc="<l2>", zone="(1<x && 1<=y)"]
4 [intval="", labels="", vloc="<l2>", zone="(1<=x && 1<=y)"]
5 [intval="", labels="green", vloc="<l3>", zone="(1<x && 0<y && x-y<1)"]
6 [intval="", labels="green", vloc="<l3>", zone="(0<=x && 0<=y && x-y<1)"]
0 -> 2 [vedge="<P@a>"]
1 -> 3 [vedge="<P@b>"]
2 -> 4 [vedge="<P@b>"]
2 -> 6 [vedge="<P@c>"]
5 -> 1 [vedge="<P@a>"]
5 -> 5 [vedge="<P@d>"]
6 -> 2 [vedge="<P@a>"]
6 -> 5 [vedge="<P@d>"]
}
```
| text/markdown | null | Alexander Lieb <alexander.lieb@online.de>, Jacob Deuchert <jacob.deuchert@stud.tu-darmstadt.de>, Paula Troszt <paula.troszt@gmx.de> | null | null | null | null | [
"Programming Language :: Python :: 3",
"Operating System :: POSIX :: Linux"
] | [] | null | null | >=3.9 | [] | [] | [] | [] | [] | [] | [] | [
"Homepage, https://github.com/Echtzeitsysteme/tcheckerpy",
"Issues, https://github.com/Echtzeitsysteme/tcheckerpy/issues"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:21:32.381988 | tcheckerpy-2026.2.20.tar.gz | 1,162,652 | 78/f0/64a0f94449641d4e29cfc53601ef8af690cd615aa0b51fa31297baad6b43/tcheckerpy-2026.2.20.tar.gz | source | sdist | null | false | 1cca0a887a68a2069db678aa9ff04907 | dad96c0ee60f7e18a0fe84a169536d065a4edd3b1c40174f1e4a51b912026537 | 78f064a0f94449641d4e29cfc53601ef8af690cd615aa0b51fa31297baad6b43 | MIT | [
"LICENSE"
] | 216 |
2.4 | lhab | 0.3 | Library Hallucinations Adversarial Benchmark — evaluate LLM code generation for hallucinated libraries. | # LHAB - ***L***ibrary ***H***allucinations ***A***dversarial ***B***enchmark
Evaluate LLM code generation for hallucinated (non-existent) libraries.
Part of the research paper *Library Hallucinations in LLMs: Risk Analysis Grounded in Developer Queries*.
Full dataset and leaderboard available on [HuggingFace](https://huggingface.co/datasets/itsluketwist/LHAB).
Source code on [GitHub](https://github.com/itsluketwist/realistic-library-hallucinations).
## *install*
```shell
pip install lhab
```
## *usage*
The package exposes three functions:
- **`lhab.load_dataset()`** — load the bundled benchmark dataset, returns a dictionary of splits (`control`, `describe`, `specify`), each containing a list of task records.
- **`lhab.evaluate_responses(responses_file)`** — evaluate LLM responses against the benchmark, detecting hallucinated libraries.
Saves results to a JSON file and returns a dictionary with statistics per split and type, plus all hallucinated library names.
- **`lhab.download_pypi_data()`** — download the latest PyPI package list for ground truth validation.
Called automatically on first evaluation if the data is not already present.
```python
import lhab
dataset = lhab.load_dataset()
# {"control": [...], "describe": [...], "specify": [...]}
results = lhab.evaluate_responses("your_responses.jsonl")
# {"control": {...}, "describe": {...}, "specify": {...}, "hallucinations": {...}}
```
A CLI command is also available:
```shell
lhab-eval your_responses.jsonl
```
| text/markdown | null | Lukas Twist <itsluketwist@gmail.com> | null | null | null | null | [
"Programming Language :: Python",
"Programming Language :: Python :: 3"
] | [] | null | null | >=3.11 | [] | [] | [] | [
"llm-codegen-research",
"requests",
"bs4",
"pre-commit; extra == \"dev\"",
"pytest; extra == \"dev\"",
"uv; extra == \"dev\""
] | [] | [] | [] | [] | uv/0.10.4 {"installer":{"name":"uv","version":"0.10.4","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true} | 2026-02-20T10:21:30.856002 | lhab-0.3-py3-none-any.whl | 336,177 | 0b/b4/c7e5c5176673d51ff971e4fa4507d48de5cc1b72a21f4318796ed98d2ea5/lhab-0.3-py3-none-any.whl | py3 | bdist_wheel | null | false | 0f730aeb5eb04e061fa788361b01fd54 | 01333bad37c0fd5f028cb121f4f0b774b79c4b322da6ca8f2fec0c8930aa82df | 0bb4c7e5c5176673d51ff971e4fa4507d48de5cc1b72a21f4318796ed98d2ea5 | null | [
"LICENSE"
] | 217 |
2.4 | indestructibleeco-ai | 1.0.0 | AI generation + YAML governance service for IndestructibleEco platform | # IndestructibleEco AI Service
AI generation and YAML governance service for the IndestructibleEco platform.
## Features
- Multi-engine inference routing (vLLM, TGI, Ollama, SGLang, TensorRT-LLM, DeepSpeed, LMDeploy)
- OpenAI-compatible endpoints
- Async job queue with priority scheduling
- Vector alignment via quantum-bert-xxl-v1
- .qyaml governance with 5-phase validation
- gRPC internal high-performance endpoint
## Installation
```bash
pip install indestructibleeco-ai
```
## License
Apache-2.0
| text/markdown | IndestructibleEco Platform Team | platform-team@indestructibleeco.io | null | null | Apache-2.0 | ai, inference, llm, vllm, governance | [
"Development Status :: 5 - Production/Stable",
"Framework :: FastAPI",
"Intended Audience :: Developers",
"License :: OSI Approved :: Apache Software License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14",
"Topic :: Scientific/Engineering :: Artificial Intelligence"
] | [] | null | null | <4.0,>=3.11 | [] | [] | [] | [
"celery<5.5.0,>=5.4.0",
"fastapi<0.112.0,>=0.111.0",
"grpcio<1.65.0,>=1.64.1",
"grpcio-tools<1.65.0,>=1.64.1",
"numpy<1.27.0,>=1.26.4",
"prometheus-client<0.21.0,>=0.20.0",
"pydantic<2.8.0,>=2.7.4",
"redis<5.1.0,>=5.0.7",
"uvicorn[standard]<0.31.0,>=0.30.1"
] | [] | [] | [] | [
"Homepage, https://github.com/indestructibleorg/indestructibleeco",
"Repository, https://github.com/indestructibleorg/indestructibleeco"
] | twine/6.2.0 CPython/3.12.3 | 2026-02-20T10:20:44.354492 | indestructibleeco_ai-1.0.0.tar.gz | 30,590 | 45/5b/aff445a0218858fd5e0549b599e96a10b6ed6068bfda3ca47e0e70b00e90/indestructibleeco_ai-1.0.0.tar.gz | source | sdist | null | false | 7ebe06d87aa6db9014edbc3ef187e832 | ccc8406a292d98b699c54f729e5a99482716ef0a5c6f808bd80fd6aa1b161510 | 455baff445a0218858fd5e0549b599e96a10b6ed6068bfda3ca47e0e70b00e90 | null | [] | 229 |
2.4 | akida-ort | 1.0.0 | Akida ONNX Runtime | # Akida Custom Operators for Onnx Runtime
Custom ONNX Runtime operators for Akida neural network hardware acceleration.
## Overview
This project provides custom ONNX operators that enable execution of neural network models on Brainchip Akida hardware through ONNX Runtime. It supports both INT8 and INT32 output types with 2 custom operators AkidaOpInt8 and AkidaOpInt32.
## Requirements
### Hardware
- Akida-compatible device (FPGA v2)
### Software
- ONNX Runtime 1.23.0 (automatically downloaded during build)
- Akida Python package
## Installation
### 1. Build from Source
```bash
# Clone the repository
git clone <repository-url>
cd AkidaORT
# Build the custom operator library
./build_akida_ort.sh python3.11
# The compiled library will be available at:
# build/akida_ort_lib.so
# The wheel will be available at:
# dist/akida_ort-*.whl
```
### 2. Install Package
```bash
# Install from wheel
pip install dist/akida_ort-*.whl
```
## Usage
### Basic Example with AkidaOpInt8
```python
import numpy as np
import onnx
import onnxruntime as ort
import akida
import akida_ort
# 1. Create and compile your Akida model
model = akida.Model()
model.add(akida.InputConv2D((64, 64, 3), kernel_size=3, filters=16))
model.add(akida.Conv2D(kernel_size=3, filters=32, activation=akida.ActivationType.ReLU))
model.add(akida.Dense1D(units=10, output_bits=8)) # output_bits == 8 then use AkidaOpInt8
# 2. Map to hardware (first device found) and save program
hw_model = akida.Model(model.layers)
hw_model.map(akida.devices()[0], hw_only=True)
program = hw_model.sequences[0].program
# Save the binary program to send it to the operator
with open("program.bin", "wb") as f:
f.write(program)
# 3. Create ONNX model with AkidaOpInt8 operator
onnx_model = onnx.parser.parse_model("""
<ir_version: 8, opset_import: ["com.brainchip": 1]>
model (uint8[1, 64, 64, 3] X) => (int8[1, ?, ?, ?] Y)
{
Y = com.brainchip.AkidaOpInt8<program_path="program.bin">(X)
}
""")
# 4. Run inference with ONNX Runtime
so = ort.SessionOptions()
so.register_custom_ops_library(akida_ort.get_library_path())
sess = ort.InferenceSession(
onnx_model.SerializeToString(),
so,
providers=["CPUExecutionProvider"]
)
# 5. Execute inference
inputs = {"X": np.random.randint(0, 255, (1, 64, 64, 3), dtype=np.uint8)}
outputs = sess.run(None, inputs)
print(f"Output shape: {outputs[0].shape}, dtype: {outputs[0].dtype}")
```
| text/markdown | Nicolas Guilbaud | nguilbaud@brainchip.com | null | null | Proprietary | null | [] | [] | https://onnx.brainchip.com/ | null | >=3.10 | [] | [] | [] | [
"akida",
"onnxruntime~=1.23.0"
] | [] | [] | [] | [] | twine/5.1.0 CPython/3.11.9 | 2026-02-20T10:19:45.590553 | akida_ort-1.0.0-cp312-cp312-manylinux_2_28_x86_64.whl | 8,100,637 | c6/60/b8d2edb56715dae7f33b8a84a80c0d45ea4d51f01307055204e6d15a7524/akida_ort-1.0.0-cp312-cp312-manylinux_2_28_x86_64.whl | cp312 | bdist_wheel | null | false | 9c85c7548fbf304a1aa76e366821a821 | 0401fbe9a99fb8b33dede729cd24fd359223d55e18e9db6c1680b63ad7844dd4 | c660b8d2edb56715dae7f33b8a84a80c0d45ea4d51f01307055204e6d15a7524 | null | [] | 234 |
2.4 | gaitkit | 1.2.4 | Universal gait event detection toolkit — 10 methods including BIKE, with C-accelerated backends | # gaitkit (Python)
Python package for gait event detection from motion-capture data.
## Install
Current status: PyPI release available (latest stable on PyPI).
Install from local source:
```bash
python -m pip install -e ./python
python -m pip install -e "./python[all]" # optional extras
```
Once published:
```bash
pip install gaitkit
```
Optional extras:
```bash
pip install "gaitkit[all]" # onnx + deep + viz
```
## Quick Start
```python
import gaitkit
trial = gaitkit.load_example("healthy")
result = gaitkit.detect(trial, method="bike")
print(result.summary())
# Optional: combine C3D markers with an external angle file
result2 = gaitkit.detect("trial_07.c3d", method="bike", angles="res_angles_t.mat")
```
DeepEvent weights are downloaded automatically on first DeepEvent use and
cached in `~/.cache/gaitkit/`.
## Proprietary JSON I/O (MyoGait-like)
The compatibility API accepts a proprietary JSON payload with
`angles.frames` and can export back a MyoGait-compatible `events` JSON.
```python
import gaitkit
out = gaitkit.detect_events_structured("bike", "myogait_output_no_events.json", fps=100.0)
paths = gaitkit.export_detection(out, "outputs/trial_07", formats=("json", "myogait"))
print(paths)
```
Input fields recognized per frame:
- `frame_idx`
- `hip_L`, `knee_L`, `ankle_L`
- `hip_R`, `knee_R`, `ankle_R`
- `pelvis_tilt`, `trunk_angle`
- optional `landmark_positions`
MyoGait-compatible output file (`*_myogait_events.json`) contains:
- `events.method`
- `events.fps`
- `events.left_hs`, `events.right_hs`, `events.left_to`, `events.right_to`
as arrays of `{frame, time, confidence}`.
## Project
- Repository: https://github.com/IDMDataHub/gaitkit
- Issue tracker: https://github.com/IDMDataHub/gaitkit/issues
- Reproducibility: https://github.com/IDMDataHub/gaitkit/blob/master/REPRODUCIBILITY.md
## Development testing
```bash
python -m pip install -e ./python
python -m unittest -v
```
## Build distributions
```bash
python -m pip install build
cd python
python -m build
```
| text/markdown | null | Frédéric Fer <f.fer@institut-myologie.org> | null | null | null | gait, biomechanics, motion-capture, heel-strike, toe-off, event-detection | [
"Development Status :: 4 - Beta",
"Intended Audience :: Science/Research",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: C",
"Topic :: Scientific/Engineering :: Medical Science Apps."
] | [] | null | null | >=3.10 | [] | [] | [] | [
"numpy>=1.23",
"scipy>=1.10",
"pandas>=1.5",
"ezc3d>=1.5",
"onnxruntime>=1.14; extra == \"onnx\"",
"tensorflow>=2.12; extra == \"deep\"",
"h5py; extra == \"deep\"",
"matplotlib>=3.7; extra == \"viz\"",
"pytest>=7.0; extra == \"test\"",
"coverage>=7.0; extra == \"test\"",
"onnxruntime>=1.14; extra == \"all\"",
"tensorflow>=2.12; extra == \"all\"",
"h5py; extra == \"all\"",
"matplotlib>=3.7; extra == \"all\""
] | [] | [] | [] | [
"Homepage, https://github.com/IDMDataHub/gaitkit",
"Documentation, https://github.com/IDMDataHub/gaitkit#readme",
"Repository, https://github.com/IDMDataHub/gaitkit",
"Issues, https://github.com/IDMDataHub/gaitkit/issues",
"Changelog, https://github.com/IDMDataHub/gaitkit/blob/master/CHANGELOG.md"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:19:16.880666 | gaitkit-1.2.4.tar.gz | 11,899,880 | df/d5/d569eea54bc7217ffed60e5afaccc476ca0972d64d9d9376ab711d0d37c7/gaitkit-1.2.4.tar.gz | source | sdist | null | false | 8d6337f0a6652b1f4b61bb38244fbf2f | bcbae4c7ef6207622af4ac8ee63c16bf43f2fc29e2288453056c18fb8af049d2 | dfd5d569eea54bc7217ffed60e5afaccc476ca0972d64d9d9376ab711d0d37c7 | MIT | [
"LICENSE"
] | 262 |
2.4 | mld-sdk | 0.7.0 | MLD Plugin SDK - Build analysis plugins for the MLD platform | # MLD SDK (Python)
SDK for building analysis plugins that integrate with the MLD platform.
> **Full Documentation:** See the [comprehensive docs](../../docs/index.md) for detailed API reference and guides.
> - [API Reference](../../docs/python/api-reference.md)
> - [Plugin Development Guide](../../docs/python/plugin-guide.md)
> - [Exception Handling](../../docs/python/exceptions.md)
## Installation
```bash
# From PyPI (when published)
uv add mld-sdk
# From git
uv add git+https://github.com/EstrellaXD/mld#subdirectory=sdk
```
## Quick Start
Create a plugin by implementing the `AnalysisPlugin` interface:
```python
from mld_sdk import AnalysisPlugin, PluginMetadata, PluginCapabilities
from fastapi import APIRouter
router = APIRouter()
@router.get("/hello")
async def hello():
return {"message": "Hello from my plugin!"}
class MyPlugin(AnalysisPlugin):
@property
def metadata(self) -> PluginMetadata:
return PluginMetadata(
name="My Plugin",
version="1.0.0",
description="My analysis plugin",
analysis_type="metabolomics",
routes_prefix="/my-plugin",
capabilities=PluginCapabilities(
requires_auth=True,
requires_experiments=True,
),
)
def get_routers(self):
return [(router, "")]
async def initialize(self, context=None):
self._context = context
async def shutdown(self):
pass
```
## Plugin Package Structure
```
mld-plugin-example/
├── pyproject.toml
├── README.md
└── src/mld_plugin_example/
├── __init__.py
└── plugin.py
```
### pyproject.toml
```toml
[project]
name = "mld-plugin-example"
version = "1.0.0"
dependencies = ["mld-sdk>=1.0.0"]
[project.entry-points."mld.plugins"]
example = "mld_plugin_example.plugin:MyPlugin"
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.hatch.build.targets.wheel]
packages = ["src/mld_plugin_example"]
```
The entry point `mld.plugins` is how the platform discovers your plugin.
## Platform Context
When running integrated with the platform, your plugin receives a `PlatformContext` that provides access to:
- Authentication dependencies (`get_current_user_dependency()`)
- Repositories for experiments, samples, users, etc.
- Platform configuration
```python
async def initialize(self, context=None):
self._context = context
if context:
# Running integrated - use platform services
self.experiment_repo = context.get_experiment_repository()
else:
# Running standalone
pass
```
## Installation Commands
```bash
# Install from GitHub
uv add git+https://github.com/org/mld-plugin-example
# Install specific version
uv add git+https://github.com/org/mld-plugin-example@v1.0.0
# Install from PyPI
uv add mld-plugin-example
# Install local plugin for development
uv add --editable ./my-plugin
```
| text/markdown | null | MorscherLab <morscher@chem.ethz.ch> | null | null | null | null | [
"Development Status :: 4 - Beta",
"Framework :: FastAPI",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.12"
] | [] | null | null | >=3.12 | [] | [] | [] | [
"fastapi>=0.109.0",
"pydantic>=2.0.0",
"aiosqlite>=0.19.0; extra == \"dev\"",
"pytest-asyncio>=0.21.0; extra == \"dev\"",
"pytest>=7.0.0; extra == \"dev\"",
"sqlmodel>=0.0.16; extra == \"dev\"",
"aiosqlite>=0.19.0; extra == \"local-db\"",
"sqlmodel>=0.0.16; extra == \"local-db\""
] | [] | [] | [] | [
"Homepage, https://github.com/MorscherLab/mld",
"Documentation, https://github.com/MorscherLab/mld/tree/main/packages/sdk-python#readme",
"Repository, https://github.com/MorscherLab/mld"
] | uv/0.10.4 {"installer":{"name":"uv","version":"0.10.4","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true} | 2026-02-20T10:18:39.652229 | mld_sdk-0.7.0.tar.gz | 35,798 | 73/1f/554f25c5405fa3eff3b76ecf6dee61fb42ac9c9e727434c5f02ff21079a5/mld_sdk-0.7.0.tar.gz | source | sdist | null | false | cbf81a37fa474f60ca1150571838b38b | 6262a9e7afbedc2bbd4b85b8772c2d40d39cd9245ae7be6b6fbc4efff7cd6d2e | 731f554f25c5405fa3eff3b76ecf6dee61fb42ac9c9e727434c5f02ff21079a5 | MIT | [] | 245 |
2.4 | mankinds-eval | 0.1.1 | Open source Python library providing evaluation methods for AI systems |
# mankinds-eval
**Open source Python library for AI evaluation**
[Documentation](https://mankinds-io.github.io/mankinds-eval) | [Metrics and Features](#metrics-and-features) | [Quick Start](#quick-start) | [Examples](https://github.com/mankinds-io/mankinds-eval/tree/main/examples)
---
**mankinds-eval** is a modular, open-source evaluation framework for LLM applications. It provides a library of evaluation methods that you assemble to build custom scorers tailored to your specific use cases.
Whether you're building RAG pipelines, chatbots, AI agents, or any LLM-based application, mankinds-eval lets you combine heuristic checks (fast, free, deterministic), ML-based analysis, and LLM-as-Judge evaluations. Run everything locally or use external providers. You control the trade-offs.
---
## Metrics and Features
- Large variety of evaluation methods powered by heuristics, ML models (running locally), or any LLM provider:
- **Heuristic methods:**
- ExactMatch, FuzzyMatch, RegexMatch
- ContainsAll, ContainsAny
- BLEU, ROUGE
- TextLength, WordCount, SentenceCount
- JSONValid, JSONSchema
- NoRefusal
- **ML methods** (local models via transformers):
- EmbeddingsSimilarity
- SentimentAnalysis
- Toxicity
- PIIDetection
- LanguageDetection
- ZeroShotClassification
- **LLM-as-Judge methods:**
- Faithfulness
- AnswerRelevancy
- Coherence
- Helpfulness
- Correctness
- SingleCriterionJudge (custom criteria)
- MultiCriteriaJudge (weighted scoring)
- PairwiseJudge (A vs B comparison)
- ConsensusJudge (multi-LLM voting)
- Compose methods into pipelines with aggregation modes (all, any, weighted, sequential).
- Pre-built presets for common scenarios: RAGScorer, SafetyScorer.
- Load evaluation data from CSV, JSONL, JSON, or HuggingFace Datasets.
- Export results to JSON or HTML scorecards.
- CLI for CI/CD integration.
- Define scorers in Python or YAML configuration files.
---
## Quick Start
### Installation
```bash
pip install mankinds-eval
```
### Writing your first evaluation
Create a file `evaluate.py`:
```python
from mankinds_eval import Scorer
from mankinds_eval.methods.heuristic import FuzzyMatch, ROUGE
scorer = Scorer(
name="qa_scorer",
methods=[
FuzzyMatch(threshold=0.7),
ROUGE(threshold=0.4),
]
)
test_case = {
"input": "What if these shoes don't fit?",
"output": "You have 30 days to get a full refund at no extra cost.",
"expected": "We offer a 30-day full refund at no extra costs.",
}
results = scorer.run_sync([test_case])
print(results.summary)
```
Run it:
```bash
python evaluate.py
```
Let's break down what happened:
- The `input` field contains the user query, `output` is the LLM response you want to evaluate.
- The `expected` field is the reference answer used by methods like FuzzyMatch and ROUGE.
- `FuzzyMatch` computes string similarity using token-based matching. A score >= 0.7 passes.
- `ROUGE` measures overlap between the output and expected text. A score >= 0.4 passes.
- All method scores range from 0 to 1. The `threshold` determines if the evaluation passes.
---
## Evaluating with LLM-as-Judge
For semantic evaluation that goes beyond string matching, use LLM-as-Judge methods. These use an LLM to evaluate outputs based on criteria you define.
```bash
pip install mankinds-eval[llm]
export OPENAI_API_KEY="your-api-key"
```
```python
from mankinds_eval import Scorer
from mankinds_eval.methods.llm import Faithfulness, AnswerRelevancy
scorer = Scorer(
name="rag_evaluator",
methods=[
Faithfulness(provider="openai", threshold=0.7),
AnswerRelevancy(provider="openai", threshold=0.7),
]
)
test_case = {
"input": "What is the refund policy?",
"output": "You can get a full refund within 30 days.",
"context": "All customers are eligible for a 30-day full refund at no extra cost.",
}
results = scorer.run_sync([test_case])
```
- `Faithfulness` checks if the output is grounded in the provided context (no hallucinations).
- `AnswerRelevancy` checks if the output actually addresses the input question.
- The `provider` parameter specifies which LLM to use (openai, anthropic, etc.).
---
## Evaluating with ML Models
For local evaluation without API calls, use ML methods that run transformer models on your machine.
```bash
pip install mankinds-eval[ml]
```
```python
from mankinds_eval import Scorer
from mankinds_eval.methods.ml import Toxicity, EmbeddingsSimilarity
scorer = Scorer(
name="safety_check",
methods=[
Toxicity(threshold=0.5),
EmbeddingsSimilarity(threshold=0.8),
]
)
results = scorer.run_sync([test_case])
```
- `Toxicity` uses a local model to detect harmful content. Lower scores are better.
- `EmbeddingsSimilarity` computes semantic similarity using sentence embeddings.
---
## Evaluating a Dataset
Evaluate multiple samples at once by passing a list or loading from a file:
```python
from mankinds_eval import Scorer, load_samples
from mankinds_eval.methods.heuristic import ExactMatch
scorer = Scorer(name="batch_eval", methods=[ExactMatch()])
# From a list
samples = [
{"input": "Q1", "output": "A", "expected": "A"},
{"input": "Q2", "output": "B", "expected": "C"},
]
results = scorer.run_sync(samples)
# Or from a file
samples = load_samples("data.jsonl")
results = scorer.run_sync(samples)
# Export results
results.to_json("results.json")
results.to_html("scorecard.html")
```
Supported formats: CSV, JSONL, JSON, HuggingFace Datasets.
---
## Using Config Files
Define scorers in YAML for reproducibility and CI/CD integration:
```yaml
# scorer.yaml
name: qa_scorer
methods:
- type: heuristic.FuzzyMatch
threshold: 0.7
algorithm: token_set_ratio
- type: heuristic.ROUGE
threshold: 0.4
- type: llm.Faithfulness
provider: openai
threshold: 0.7
```
Load and run:
```python
from mankinds_eval import Scorer
scorer = Scorer.from_config("scorer.yaml")
results = scorer.run_sync("data.jsonl")
```
Or use the CLI:
```bash
mankinds-eval run -c scorer.yaml -d data.jsonl -o results.json --html scorecard.html
```
---
## Composite Pipelines
Combine methods with different aggregation logic:
```python
from mankinds_eval import Scorer
from mankinds_eval.methods import CompositeMethod
from mankinds_eval.methods.heuristic import TextLength, NoRefusal, FuzzyMatch
quality_gate = CompositeMethod(
name="quality_gate",
methods=[
TextLength(min_length=50, max_length=500),
NoRefusal(),
FuzzyMatch(threshold=0.6),
],
mode="all", # all checks must pass
)
scorer = Scorer(name="eval", methods=[quality_gate])
results = scorer.run_sync(data)
```
Aggregation modes:
- `all` - all methods must pass (AND logic)
- `any` - at least one method must pass (OR logic)
- `weighted` - weighted average of scores
- `sequential` - methods run in order, sharing results
---
## Pre-built Presets
Use presets for common evaluation scenarios:
```python
from mankinds_eval import Scorer
from mankinds_eval.methods.presets import RAGScorer, SafetyScorer
# RAG evaluation: Faithfulness + Relevancy + Coherence
rag_methods = RAGScorer.create(provider="openai", threshold=0.7)
# Safety evaluation: Toxicity + PII + Refusal detection
safety_methods = SafetyScorer.create(check_toxicity=True, check_pii=True)
scorer = Scorer(name="full_eval", methods=rag_methods + safety_methods)
```
---
## Available Methods
| Category | Method | Description |
| --------- | ------------------------ | ---------------------------------------------------------------- |
| Heuristic | `ExactMatch` | Exact string comparison |
| Heuristic | `FuzzyMatch` | Fuzzy string similarity (Levenshtein, Jaro-Winkler, token-based) |
| Heuristic | `RegexMatch` | Regular expression matching |
| Heuristic | `ContainsAll` | Check if output contains all keywords |
| Heuristic | `ContainsAny` | Check if output contains any keyword |
| Heuristic | `BLEU` | BLEU score for translation quality |
| Heuristic | `ROUGE` | ROUGE score for summarization |
| Heuristic | `TextLength` | Validate text length |
| Heuristic | `JSONValid` | Validate JSON syntax |
| Heuristic | `JSONSchema` | Validate against JSON Schema |
| Heuristic | `NoRefusal` | Detect LLM refusals |
| ML | `EmbeddingsSimilarity` | Semantic similarity via embeddings |
| ML | `Toxicity` | Detect toxic content |
| ML | `SentimentAnalysis` | Analyze sentiment |
| ML | `PIIDetection` | Detect PII/NER entities |
| ML | `LanguageDetection` | Detect/verify language |
| ML | `ZeroShotClassification` | Zero-shot text classification |
| LLM | `Faithfulness` | Check if response is grounded in context |
| LLM | `AnswerRelevancy` | Check if response addresses the question |
| LLM | `Coherence` | Evaluate logical flow and clarity |
| LLM | `Helpfulness` | Evaluate practical utility |
| LLM | `Correctness` | Compare against expected answer |
| LLM | `SingleCriterionJudge` | Custom single criterion evaluation |
| LLM | `MultiCriteriaJudge` | Multi-criteria weighted scoring |
| LLM | `PairwiseJudge` | Compare two responses |
| LLM | `ConsensusJudge` | Multi-LLM consensus evaluation |
---
## Data Format
Sample structure for evaluation:
```python
{
"input": "User question or prompt",
"output": "AI response to evaluate",
"expected": "Optional expected response",
"context": "Optional RAG context",
"conversation": [{"role": "user", "content": "..."}], # Optional
"metadata": {} # Optional
}
```
---
## Development
```bash
git clone https://github.com/mankinds-io/mankinds-eval.git
cd mankinds-eval
pip install -e ".[dev]"
pytest # Run tests
ruff check . # Lint
mypy mankinds_eval # Type check
```
---
## License
Apache 2.0 - See [LICENSE](LICENSE) for details.
| text/markdown | null | Mankinds team <team@mankinds.io> | null | null | null | llm, evaluation, ai, testing, rag, nlp, machine-learning, llm-as-judge, ai-evaluation, deepeval, ragas | [
"Development Status :: 3 - Alpha",
"Intended Audience :: Developers",
"Intended Audience :: Science/Research",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Typing :: Typed",
"Natural Language :: English"
] | [] | null | null | >=3.9 | [] | [] | [] | [
"pyyaml>=6.0",
"rapidfuzz>=3.0",
"sacrebleu>=2.0",
"rouge-score>=0.1.2",
"typer>=0.9.0",
"jsonschema>=4.0",
"langdetect>=1.0.9",
"rich>=13.0.0",
"tenacity>=8.0.0",
"torch>=2.0; extra == \"ml\"",
"transformers>=4.30.0; extra == \"ml\"",
"sentence-transformers>=2.2.0; extra == \"ml\"",
"litellm>=1.0.0; extra == \"llm\"",
"mankinds-eval[llm,ml]; extra == \"all\"",
"pytest>=7.0.0; extra == \"dev\"",
"pytest-asyncio>=0.21.0; extra == \"dev\"",
"pytest-cov>=4.0.0; extra == \"dev\"",
"ruff>=0.1.0; extra == \"dev\"",
"mypy>=1.0.0; extra == \"dev\"",
"mkdocs>=1.5.0; extra == \"dev\"",
"mkdocs-material>=9.0.0; extra == \"dev\"",
"mkdocstrings[python]>=0.24.0; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/mankinds-io/mankinds-eval",
"Documentation, https://mankinds-io.github.io/mankinds-eval",
"Repository, https://github.com/mankinds-io/mankinds-eval",
"Changelog, https://github.com/mankinds-io/mankinds-eval/blob/main/CHANGELOG.md",
"Issues, https://github.com/mankinds-io/mankinds-eval/issues"
] | twine/6.2.0 CPython/3.12.10 | 2026-02-20T10:18:18.519759 | mankinds_eval-0.1.1.tar.gz | 77,052 | 24/b6/3ba77d37de587fce8c05b111c02d65d6488c70781eedd501e4a9df703093/mankinds_eval-0.1.1.tar.gz | source | sdist | null | false | 0577eec626654ff119a59a693880452f | 2b01b3f660ac2c6e958032a4fd1424b1d41abff61dde092c43ea34cf39f50b90 | 24b63ba77d37de587fce8c05b111c02d65d6488c70781eedd501e4a9df703093 | Apache-2.0 | [
"LICENSE"
] | 219 |
2.4 | drs4 | 0.3.0 | Control and data acquisition software for FINER/DRS4 | # DRS4
[](https://pypi.org/project/drs4/)
[](https://pypi.org/project/drs4/)
[](https://pepy.tech/project/drs4)
[](https://doi.org/10.5281/zenodo.18709559)
[](https://github.com/finerreceiver/drs4/actions)
Control and data acquisition for FINER/DRS4
## Installation
```python
pip install drs4
```
| text/markdown | null | Akio Taniguchi <a-taniguchi@mail.kitami-it.ac.jp> | null | null | MIT License Copyright (c) 2024-2026 Akio Taniguchi Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. | astronomy, drs4, finer, lmt, millimeter, python, radio-astronomy, single-dish, submillimeter | [
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14"
] | [] | null | null | <3.15,>=3.10 | [] | [] | [] | [
"dateparser<2,>=1",
"fire<1,>=0.7",
"matplotlib<4,>=3",
"numpy<3,>=2",
"pillow<11,>=10",
"tqdm<5,>=4",
"typing-extensions<5,>=4",
"xarray-dataclasses<2,>=1",
"xarray<2027,>=2024",
"zarr<3,>=2"
] | [] | [] | [] | [
"homepage, https://finerreceiver.github.io/drs4",
"repository, https://github.com/finerreceiver/drs4"
] | uv/0.9.30 {"installer":{"name":"uv","version":"0.9.30","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Debian GNU/Linux","version":"12","id":"bookworm","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true} | 2026-02-20T10:18:14.933273 | drs4-0.3.0-py3-none-any.whl | 25,693 | 3f/2f/4d6ee35103bd31843de944e8b8228372a2ba635259cde24b02f1eb8bdc2d/drs4-0.3.0-py3-none-any.whl | py3 | bdist_wheel | null | false | ace5b94cdc594cb2ed18da225e673c75 | ec943986902c333a399fbcf40b33e0a24b2186b6ea55e5497a45edcea6763db4 | 3f2f4d6ee35103bd31843de944e8b8228372a2ba635259cde24b02f1eb8bdc2d | null | [
"LICENSE"
] | 196 |
2.4 | onnx2akida | 0.7.0 | Evaluates the compatibility of an ONNX model with Akida. | # onnx2akida
Evaluates the compatibility of an ONNX model with Akida
| null | Brainchip | null | null | Johan Mejia <jmejia@brainchip.com> | null | null | [] | [] | null | null | >=3.10 | [] | [] | [] | [
"setuptools>=77.0.3",
"packaging>=24.2",
"setuptools_scm",
"cnn2snn~=2.19.0",
"tqdm",
"onnx_ir==0.1.9",
"akida_ort; sys_platform == \"linux\"",
"pytest; extra == \"test\"",
"pytest-console-scripts; extra == \"test\""
] | [] | [] | [] | [
"Documentation, https://onnx.brainchip.com"
] | twine/5.1.0 CPython/3.11.9 | 2026-02-20T10:18:13.948947 | onnx2akida-0.7.0.tar.gz | 30,956 | 14/84/83720b400c642e9426d0395630b0bf8573544584517a360b9122f1693077/onnx2akida-0.7.0.tar.gz | source | sdist | null | false | 3e5cc7a81cb6298261ab5933cf3a2e32 | d4e2bdace9f07f301efdc638447a9983d894ad7cd657736080d370d59475da75 | 148483720b400c642e9426d0395630b0bf8573544584517a360b9122f1693077 | null | [] | 141 |
2.4 | ccs-digitalmarketplace-apiclient | 37.15.0 | API clients for Digital Marketplace Data API and Search API. | Digital Marketplace API client
=========================
[](https://badge.fury.io/py/ccs-digitalmarketplace-apiclient)
## What's in here?
API clients for Digital Marketplace [Data API](https://github.com/Crown-Commercial-Service/ccs-digitalmarketplace-api) and
[Search API](https://github.com/Crown-Commercial-Service/ccs-digitalmarketplace-search-api).
Originally was part of [Digital Marketplace Utils](https://github.com/Crown-Commercial-Service/ccs-digitalmarketplace-utils).
## Running the tests
Install Python dependencies:
```
make bootstrap
invoke requirements-dev
```
Run the tests:
```
invoke test
```
## Usage examples
```python
data_client = apiclient.DataAPIClient(api_url, api_access_token)
services = data_client.find_services_iter(framework=frameworks)
```
## Releasing a new version
To update the package version, edit the `__version__ = ...` string in `dmapiclient/__init__.py`,
commit and push the change and wait for CI to create a new version tag.
Once the tag is available on GitHub, the new version can be used by the apps by adding the following
line to the app `requirements.txt` (replacing `X.Y.Z` with the current version number):
```
git+https://github.com/Crown-Commercial-Service/ccs-digitalmarketplace-apiclient.git@X.Y.Z#egg=ccs-digitalmarketplace-apiclient==X.Y.Z
```
When changing a major version number consider adding a record to the `CHANGELOG.md` with a
description of the change and an example of the upgrade process for the client apps.
## Pre-commit hooks
This project has a [pre-commit hook][pre-commit hook] to do some general file checks and check the `pyproject.toml`.
Follow the [Quick start][pre-commit quick start] to see how to set this up in your local checkout of this project.
## Licence
Unless stated otherwise, the codebase is released under [the MIT License][mit].
This covers both the codebase and any sample code in the documentation.
The documentation is [© Crown copyright][copyright] and available under the terms
of the [Open Government 3.0][ogl] licence.
[mit]: LICENCE
[copyright]: http://www.nationalarchives.gov.uk/information-management/re-using-public-sector-information/uk-government-licensing-framework/crown-copyright/
[ogl]: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
[pre-commit hook]: https://pre-commit.com/
[pre-commit quick start]: https://pre-commit.com/#quick-start
| text/markdown | GDS Developers, CCS Developers | null | null | null | null | null | [] | [] | null | null | <3.15,>=3.11 | [] | [] | [] | [
"requests<3,>=2.18.4",
"Flask>=3.0.3",
"ruff==0.15.1; extra == \"dev\"",
"mypy==1.19.1; extra == \"dev\"",
"pytest==9.0.2; extra == \"dev\"",
"pytest-cov==7.0.0; extra == \"dev\"",
"requests-mock==1.12.1; extra == \"dev\"",
"types-requests; extra == \"dev\"",
"pre-commit==4.5.1; extra == \"dev\"",
"ccs-digitalmarketplace-test-utils==7.7.0; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/Crown-Commercial-Service/ccs-digitalmarketplace-apiclient",
"Repository, https://github.com/Crown-Commercial-Service/ccs-digitalmarketplace-apiclient.git",
"Issues, https://github.com/Crown-Commercial-Service/ccs-digitalmarketplace-apiclient/issues",
"Changelog, https://github.com/Crown-Commercial-Service/ccs-digitalmarketplace-apiclient/CHANGELOG.md"
] | twine/6.2.0 CPython/3.11.14 | 2026-02-20T10:17:49.392771 | ccs_digitalmarketplace_apiclient-37.15.0.tar.gz | 24,972 | a7/a0/9fff2e15e5d67f610747f2827da8b48cf4bc65dd5601c0008e389e4e41f4/ccs_digitalmarketplace_apiclient-37.15.0.tar.gz | source | sdist | null | false | a52b0d3d7effa378544c419409d8bdbc | ffe4a4ac3ef1ba75521ee841159aa2eef625659899a616e24adb3c4de3422e8c | a7a09fff2e15e5d67f610747f2827da8b48cf4bc65dd5601c0008e389e4e41f4 | null | [
"LICENCE"
] | 206 |
2.4 | akida-models | 1.13.1 | Akida Models | # Akida models
This package contains a zoo of TensorFlow/Keras defined models that can be
quantized and that are compatible for Akida conversion.
| text/markdown | Kevin Tsiknos | ktsiknos@brainchip.com | null | null | Apache 2.0 | null | [] | [] | https://doc.brainchipinc.com | null | >=3.10 | [] | [] | [] | [
"cnn2snn~=2.19.0",
"quantizeml~=1.2.2",
"scipy",
"opencv-python",
"mtcnn==0.1.1",
"imaug",
"trimesh",
"tqdm",
"tensorflow-datasets",
"librosa",
"soundata",
"pydub",
"pytest; extra == \"test\"",
"pytest-rerunfailures; extra == \"test\"",
"pytest-xdist; extra == \"test\""
] | [] | [] | [] | [] | twine/5.1.0 CPython/3.11.9 | 2026-02-20T10:17:37.528018 | akida_models-1.13.1.tar.gz | 178,934 | 17/e8/713896af6601a4da0e76db4c6882bb28bf4a997fe57fb2709e70e8599a8a/akida_models-1.13.1.tar.gz | source | sdist | null | false | 0ffdd5b1da6108fb2052deaedc0d75fd | fc7bf8a4a88d91f8acae8efd8c845f06c871c23cdca321c19148d7a28bcca1c7 | 17e8713896af6601a4da0e76db4c6882bb28bf4a997fe57fb2709e70e8599a8a | null | [] | 151 |
2.4 | datapizza-ai-core | 0.0.22 | Core components for the datapizza-ai framework |
This is the core of datapizza-ai framework
| text/markdown | null | null | null | null | MIT | null | [
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3"
] | [] | null | null | <3.14,>=3.10.0 | [] | [] | [] | [
"jinja2>=3.1.6",
"jsonref>=1.1.0",
"mcp>=1.20.0",
"opentelemetry-api>=1.33.1",
"opentelemetry-sdk>=1.33.1",
"pydantic<3.0.0,>=2.10.5",
"python-dotenv>=1.0.1",
"pyyaml>=6.0.2",
"rich>=14.1.0",
"typing-extensions<5.0.0,>=4.12.2"
] | [] | [] | [] | [] | python-httpx/0.28.1 | 2026-02-20T10:17:29.628436 | datapizza_ai_core-0.0.22-py3-none-any.whl | 105,660 | a9/b9/7a29eb0b0499050578db8f00c59e44e1fc8b319e3257c4d9cd6a6b12ce8e/datapizza_ai_core-0.0.22-py3-none-any.whl | py3 | bdist_wheel | null | false | db85fae5cbea7594fb1c0444f1bb4bc6 | 25fac31936896e91f4b35b3a8b7d9d6fa49c54b8fa78827de80e007ef6e43d32 | a9b97a29eb0b0499050578db8f00c59e44e1fc8b319e3257c4d9cd6a6b12ce8e | null | [] | 310 |
2.4 | quantizeml | 1.2.3 | Base layers and quantization tools | # QuantizeML
Framework for quantizing Deep-learning models.
This supports the quantization using low-bitwidth weights and outputs of CNN models.
| text/markdown | Kevin Tsiknos | ktsiknos@brainchip.com | Kevin Tsiknos | ktsiknos@brainchip.com | Apache 2.0 | null | [] | [] | https://doc.brainchipinc.com | null | >=3.10 | [] | [] | [] | [
"tf_keras~=2.19.0",
"onnxruntime~=1.23.0",
"onnxscript~=0.4.0",
"onnx_ir<=0.1.12,>=0.1.9",
"onnxruntime_extensions<0.14.0",
"scipy",
"pytest; extra == \"test\"",
"pytest-rerunfailures; extra == \"test\"",
"pytest-console-scripts; extra == \"test\"",
"pytest-xdist; extra == \"test\"",
"tensorflow_datasets; extra == \"test\"",
"keras_hub; extra == \"test\"",
"tensorflow_text~=2.19.0; sys_platform == \"linux\" and extra == \"test\"",
"matplotlib; extra == \"analysis\"",
"tensorboardX; extra == \"analysis\""
] | [] | [] | [] | [] | twine/5.1.0 CPython/3.11.9 | 2026-02-20T10:17:10.162663 | quantizeml-1.2.3.tar.gz | 179,069 | cf/c9/e49171a242c693d551eb5d9499ebcf676c6d8bbf83d6f96b082c30f914c7/quantizeml-1.2.3.tar.gz | source | sdist | null | false | 511c2babcf1e0973ffb4b74282ba40a0 | b0caa2bcdff7865b4e44c604effb3297794e508afc56e7387205e93dbdba2b0c | cfc9e49171a242c693d551eb5d9499ebcf676c6d8bbf83d6f96b082c30f914c7 | null | [] | 163 |
2.4 | cnn2snn | 2.19.1 | Keras to Akida CNN Converter | # CNN2SNN toolkit
The Brainchip CNN2SNN toolkit provides a means to convert Convolutional Neural
Networks (CNN) that were trained using Deep Learning methods to a low-latency
and low-power Spiking Neural Network (SNN) for use with the Akida Runtime.
| text/markdown | Johan Mejia | jmejia@brainchip.com | null | null | Apache 2.0 | null | [] | [] | https://doc.brainchipinc.com | null | >=3.10 | [] | [] | [] | [
"tf_keras~=2.19.0",
"tensorflow~=2.19.0",
"akida==2.19.1",
"quantizeml~=1.2.2"
] | [] | [] | [] | [] | twine/5.1.0 CPython/3.11.9 | 2026-02-20T10:16:45.390555 | cnn2snn-2.19.1.tar.gz | 83,379 | 91/f5/0035095e9cfd1dad7667eab2e4d171b8ba0dd4b334b1a6453dbff0f3ea1b/cnn2snn-2.19.1.tar.gz | source | sdist | null | false | ac374d5f108e2a3f9f8c5fb042f90516 | b2e5fc8f1ec1992fe797404af2bb516423316ed84aae88b4cb63cc3fd1d8bcf2 | 91f50035095e9cfd1dad7667eab2e4d171b8ba0dd4b334b1a6453dbff0f3ea1b | null | [] | 158 |
2.4 | leeroo-kapso | 0.1.5 | A Knowledge-grounded framework for Autonomous Program Synthesis and Optimization | <h1 align="center">Kapso</h1>
<h4 align="center">A Knowledge-grounded framework for Autonomous AI/ML Program Synthesis and Optimization</h4>
<p align="center">
<a href="https://docs.leeroo.com">Learn more</a> ·
<a href="https://discord.gg/hqVbPNNEZM">Join Discord</a> ·
<a href="https://leeroo.com">Website</a>
</p>
<p align="center">
<a href="https://pypi.org/project/leeroo-kapso/"><img src="https://img.shields.io/pypi/v/leeroo-kapso?color=blue" alt="PyPI"></a>
<a href="https://discord.gg/hqVbPNNEZM"><img src="https://dcbadge.limes.pink/api/server/hqVbPNNEZM?style=flat" alt="Discord"></a>
<a href="https://github.com/leeroo-ai/kapso"><img src="https://img.shields.io/github/commit-activity/m/leeroo-ai/kapso" alt="GitHub commit activity"></a>
<a href="https://www.ycombinator.com/companies/leeroo"><img src="https://img.shields.io/badge/Y%20Combinator-X25-orange?logo=ycombinator&logoColor=white" alt="Y Combinator X25"></a>
</p>
<p align="center">
If you like this project, please support us by giving it a star ⭐
</p>
> **Early Access**: [Sign up](https://docs.google.com/forms/d/e/1FAIpQLSfk0RjtZaZFXq3-tclZhnz40E_mNzPSI1RHhBQWzswbNwp8Ug/viewform) for the **hosted version of Kapso**.
<p align="center">
<img src="https://api.leeroo.com/storage/v1/object/public/opensource/framework.png" alt="Kapso Framework Architecture" width="700">
</p>
---
## News
- **[Leeroopedia MCP Integration](https://leeroopedia.com)**: Kapso now connects to **Leeroopedia MCP** — your ML & Data Knowledge Wiki. Learnt by AI, built by AI, for AI. A centralized playbook of best practices and expert-level knowledge for Machine Learning and Data domains. Kapso agents use it during ideation and implementation to search knowledge, build plans, diagnose failures, and more.
- **[Moltbook Agents 🦞](https://www.moltbook.com/)**: Build AI agents that optimize other agents and debate on Moltbook! [Get started →](moltbook_bot/README.md)
- **Technical Report**: Our technical report is now available! [Read the paper](https://arxiv.org/abs/2601.21526)
- **#1 on [MLE-Bench](benchmarks/mle/README.md)**: KAPSO achieved top ranking among open-source systems on Kaggle ML competitions (MLE Benchmark).
<img src="https://api.leeroo.com/storage/v1/object/public/opensource/mle_benchmark.png" alt="MLE-Bench Results" width="600">
- **#1 on [ALE-Bench](benchmarks/ale/README.md)**: KAPSO achieved top ranking on long-horizon algorithmic discovery problems (ALE Benchmark).
<img src="https://api.leeroo.com/storage/v1/object/public/opensource/ale_benchmark.png" alt="ALE-Bench Results" width="600">
## What is KAPSO?
KAPSO combines **iterative experimentation** with a **knowledge base** of best practices and tricks to discover ML/AI code improvements.
It automates the cycle of **designing**, **testing**, and **refining** algorithms, eventually adapting the optimized solution for **deployment** on your chosen infrastructure.
### The Four Pillars
| Pillar | Method | Description |
|--------|--------|-------------|
| **Evolve** | `.evolve()` | Run iterative experiments to build software for a goal. Uses tree search, coding agents, and KG context to generate and refine solutions. |
| **Learn** | `.learn()` | Ingest knowledge from repositories, past solutions, or research results. Extracts patterns and best practices into the Knowledge Graph. |
| **Research** | `.research()` | Run deep web research to gather ideas and implementation references. Returns structured findings you can feed into the knowledge base or use as context for evolving solutions. |
| **Deploy** | `.deploy()` | Turn a solution into running software. Supports local execution, Docker containers, or cloud platforms like Modal. |
## 🚀 Quickstart
### Installation
**From PyPI (recommended)**
```bash
pip install leeroo-kapso
```
**From source (for development or to access wiki knowledge data)**
```bash
git clone https://github.com/leeroo-ai/kapso.git
cd kapso
# Pull Git LFS files (wiki knowledge data)
git lfs install
git lfs pull
# Create conda environment (recommended)
conda create -n kapso python=3.12
conda activate kapso
# Install in development mode
pip install -e .
```
**Leeroopedia MCP (optional)** — connect Kapso to [Leeroopedia](https://leeroopedia.com), a curated ML/AI knowledge base. Sign up at [leeroopedia.com](https://leeroopedia.com) for an API key, then:
```bash
pip install leeroopedia-mcp
echo 'LEEROOPEDIA_API_KEY=kpsk_your_key_here' >> .env
```
### Basic Usage
```python
from kapso import Kapso, Source, DeployStrategy
# Initialize Kapso
# If you have a Knowledge Graph, pass kg_index; otherwise just use Kapso()
kapso = Kapso(kg_index="data/indexes/legal_contracts.index")
# Research: Gather domain-specific techniques from the web
# mode: "idea" | "implementation" | "study" (can pass multiple as list)
# depth: "light" | "deep" (default: "deep")
findings = kapso.research(
"RLHF and DPO fine-tuning for legal contract analysis",
mode=["idea", "implementation"],
depth="deep",
)
# Learn: Ingest knowledge from repositories and research into the KG
kapso.learn(
Source.Repo("https://github.com/huggingface/trl"),
*findings.ideas, # List[Source.Idea]
*findings.implementations, # List[Source.Implementation]
wiki_dir="data/wikis",
)
# Evolve: Build a solution through experimentation
# Use research results as context via to_string()
solution = kapso.evolve(
goal="Fine-tune Llama-3.1-8B for legal clause risk classification, target F1 > 0.85",
data_dir="./data/cuad_dataset",
output_path="./models/legal_risk_v1",
context=[findings.to_string()],
)
# Deploy: Turn solution into running deployed_program
deployed_program = kapso.deploy(solution, strategy=DeployStrategy.MODAL)
deployed_program.stop()
```
For detailed integration steps, see the [Quickstart](https://docs.leeroo.com/docs/quickstart) and [Installation](https://docs.leeroo.com/docs/installation) guides.
## Examples
| Example | Description |
|---------|-------------|
| [**CUDA Optimization**](examples/cuda_optimization/README.md) | Optimize CUDA kernels for GPU performance |
| [**PyTorch Optimization**](examples/pytorch_optimization/README.md) | Optimize PyTorch operations for speedup |
| [**ML Model Development**](examples/ml_model_development/README.md) | Improve ML model accuracy on tabular data |
| [**Prompt Engineering**](examples/prompt_engineering/README.md) | Optimize prompts for better LLM performance |
| [**Agentic Scaffold**](examples/agentic_scaffold/README.md) | Optimize agentic AI workflows |
## Supported Benchmarks
| Benchmark | Description |
|-----------|-------------|
| [**MLE-Bench**](benchmarks/mle/README.md) | Kaggle ML competitions — tabular, image, text, audio problems |
| [**ALE-Bench**](benchmarks/ale/README.md) | AtCoder algorithmic optimization — C++ solution generation |
## 📚 Documentation & Support
- **Full Documentation**: [docs.leeroo.com](https://docs.leeroo.com)
- **Community**: [Discord](https://discord.gg/hqVbPNNEZM)
- **Website**: [leeroo.com](https://leeroo.com)
## Contributing
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details on how to get started.
## Citation
If you use Kapso in your research, please cite:
```bibtex
@misc{nadaf2026kapsoknowledgegroundedframeworkautonomous,
title={KAPSO: A Knowledge-grounded framework for Autonomous Program Synthesis and Optimization},
author={Alireza Nadafian and Alireza Mohammadshahi and Majid Yazdani},
year={2026},
eprint={2601.21526},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2601.21526},
}
```
| text/markdown | null | Leeroo Team <team@leeroo.com> | null | null | MIT | ai, ml, optimization, code-generation, llm, agents | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Topic :: Scientific/Engineering :: Artificial Intelligence"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"GitPython",
"aider-chat>=0.35.0",
"PyYAML",
"python-dotenv",
"neo4j",
"weaviate-client>=4.0.0",
"openai>=1.0.0",
"cryptography<46.0.0",
"neo4j; extra == \"mle\"",
"openai; extra == \"mle\"",
"pytest>=7.0.0; extra == \"dev\"",
"pytest-cov>=4.0.0; extra == \"dev\"",
"black>=23.0.0; extra == \"dev\"",
"flake8>=6.0.0; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://leeroo.com",
"Documentation, https://docs.leeroo.com",
"Repository, https://github.com/leeroo-ai/kapso",
"Issues, https://github.com/leeroo-ai/kapso/issues"
] | twine/6.2.0 CPython/3.10.19 | 2026-02-20T10:16:43.815895 | leeroo_kapso-0.1.5.tar.gz | 415,394 | e3/7f/65d1c801110e5db3bb4eb516538736eacd657cfa877ea2f3374d1f99e84e/leeroo_kapso-0.1.5.tar.gz | source | sdist | null | false | a5c9ed5579eef95972850c5c00d7858b | 6365ede11afa9dd8d54780edd1335902c165b8820befb4e96e8f77c8038a5fee | e37f65d1c801110e5db3bb4eb516538736eacd657cfa877ea2f3374d1f99e84e | null | [
"LICENSE"
] | 213 |
2.4 | akida | 2.19.1 | Akida Execution Engine | # Akida Execution Engine
The Akida Execution Engine is an interface to the Brainchip Akida Neural
Processor.
To allow the development of Akida models without an actual Akida hardware, it
includes a software backend that simulates the Akida Neural Processor.
| text/markdown | Matthieu Hernandez | hmatthieu@brainchip.com | null | null | Proprietary | null | [] | [] | https://doc.brainchipinc.com | null | >=3.10 | [] | [] | [] | [
"numpy"
] | [] | [] | [] | [] | twine/5.1.0 CPython/3.11.9 | 2026-02-20T10:16:42.515408 | akida-2.19.1-cp312-cp312-win_amd64.whl | 2,195,983 | 66/37/0d7b5e3ab4d786fd461f34d0236109d943b09988f0c9d70b57564d8b2b38/akida-2.19.1-cp312-cp312-win_amd64.whl | cp312 | bdist_wheel | null | false | 7ed57882863fb41ab1e923286b271c95 | ac8f22f70b1c254a2572c3394870d263ca6860a90ce57d1479027b51a53e77af | 66370d7b5e3ab4d786fd461f34d0236109d943b09988f0c9d70b57564d8b2b38 | null | [] | 663 |
2.4 | sysame | 0.0.22 | Transport Modelling Helper Package | # SysAME
Welcome to the **SysAME** project! This repository provides a comprehensive library for transport modeling and simulation support.
## Installation
1. pip:
```
pip install sysame
```
1. uv:
```
uv add sysame
```
# SysAME Custom License
Copyright (c) 2025 sysame.com
## Permission and Usage
This license applies to the SysAME software package, which consists of:
1. Open interface components (primarily Python APIs and related documentation)
2. Closed-source compiled components (primarily written in Rust and C++)
### You are free to:
- Use the software for any purpose, including commercial use
- Modify and distribute the open interface components
- Create and distribute derivative works that interface with the software
### Under the following conditions:
#### 1. Attribution Requirement
You must provide appropriate citation and acknowledgment when using SysAME in any academic, research, commercial, or other work. The citation should include:
- The name of the software (SysAME)
- The author/organisation
- The website or repository URL
- The version of the software used
A suggested citation format is:
```
Ishtaiwi, M.. (2026). SysAME [Software]. Version 0.0.22. Retrieved from sysame.com.
```
#### 2. Core Implementation Protection
The compiled Rust/C++ components of SysAME are provided in binary form only and are not open source. You are expressly prohibited from:
- Reverse engineering, decompiling, or disassembling the compiled components
- Attempting to extract, modify, or recreate the source code of the compiled components
- Distributing modified versions of the compiled components
#### 3. Redistribution
If you redistribute SysAME or works derived from it, you must include:
- This license text
- All attribution notices
- A clear notice that your work uses SysAME and is subject to this license
## Disclaimer of Warranty
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
## Enforcement
Violation of the terms of this license, particularly regarding the protection of compiled components or the attribution requirements, will constitute a breach of this license and may result in legal action.
## Contact
For licensing questions or permissions beyond the scope of this license, please contact help@sysame.com
| text/markdown | null | "M.I." <help@sysame.com> | null | null | null | null | [] | [] | null | null | >=3.12 | [] | [] | [] | [
"contextily>=1.6.2",
"dbfread>=2.0.7",
"lxml>=6.0.2",
"matplotlib>=3.10.3",
"mypy>=1.15.0",
"networkx>=3.4.2",
"numpy>=2.2.5",
"openmatrix>=0.3.5.0",
"pandas>=2.2.3",
"polars>=1.29.0",
"py7zr>=0.22.0",
"pyarrow>=20.0.0",
"pytest>=8.3.5",
"scipy>=1.15.3",
"seaborn>=0.13.2",
"shapely>=2.1.0"
] | [] | [] | [] | [] | uv/0.9.18 {"installer":{"name":"uv","version":"0.9.18","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":null,"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null} | 2026-02-20T10:16:34.833141 | sysame-0.0.22-py3-none-any.whl | 92,576 | 95/44/32059658da06fc0dfb89bcdbde69cf540be0be856cee40676b7fb131bc52/sysame-0.0.22-py3-none-any.whl | py3 | bdist_wheel | null | false | 6b1b097bf5b4cfadcb0c48dee5c7b8b2 | fbbfb0de65c2d2df77b373f882b000259b5082d4c7097e41e7ef9bb531fc377e | 954432059658da06fc0dfb89bcdbde69cf540be0be856cee40676b7fb131bc52 | null | [
"LICENSE.md"
] | 87 |
2.4 | filehash-tool | 2.2.7 | 本CLI工具用于计算、存储、验证磁盘文件的hash值，可检查文件是否被篡改。 | This tool only provides Simplified Chinese user interface.
### 简介
本CLI工具用于计算、存储、验证磁盘文件的hash值，可检查文件是否被篡改。
有以下功能：
- 将磁盘文件的**hash值**、**大小**、**路径**、**登记时间**、**数据库ID**保存到数据库的hash链中
- 验证数据库中的记录，检查记录的路径、hash值、大小
- 验证磁盘文件，将hash值、大小、路径与数据库中的记录对比
- 仅打印数据库中的记录
- 仅计算磁盘文件的hash值
建议配置环境变量使用。目前大多数下载网站提供`sha256`值，因此默认使用`sha256`算法。
### CLI命令
路径使用[glob](https://docs.python.org/3/library/glob.html)语法：`*` `?` `[]` `**`
在Linux/macOS下路径须加引号，使用`/`分隔符。
🟢添加文件信息到数据库：
```shell
filehash add 'E:\software\python-3.13.12-amd64.exe'
filehash a 'E:\software\*.exe'
filehash a 'E:\software\**\*.exe' # **遍历子目录
```
🟠验证数据库中的记录：
```shell
# 如果磁盘文件已被删除，会提示文件不存在，但不会中止。
filehash verify_record '*\python*.exe'
filehash vr '*\software\*.exe'
filehash vr '*.exe'
filehash vr '*' # 验证数据库中所有文件
```
🔵验证磁盘上的文件：
```shell
# 如果数据库中没有该hash值、大小的数据，程序会报错、中止。
# 此功能可Windows/Linux双系统时使用
filehash verify_file 'E:\software\python-3.13.12-amd64.exe'
filehash vf 'E:\software\**\*.exe'
```
🟤在终端运行filehash，打印完整的帮助信息：
```
PS E:\> filehash
usage: filehash [-h] [-m HASH_METH] [-n] [--db-dir DIR] [--backup-dir DIR] [--backup-size SIZE]
[CMD] [PATH]
文件hash校验。版本: 2.2.7
https://pypi.org/project/filehash-tool
positional arguments:
CMD 命令
PATH 路径，使用glob语法，*表示所有文件，dir/**/*.exe遍历子目录。
options:
-h, --help show this help message and exit
-m, --hash-meth HASH_METH
创建数据库时使用的hash算法，覆盖FILEHASH_HASH_METH环境变量。
-n, --no-space 打印hash时，不添加空格。
--db-dir DIR 数据库目录，覆盖FILEHASH_DB_DIR环境变量。
--backup-dir DIR 备份保存的数据库目录，覆盖FILEHASH_BACKUP_DIR环境变量。
--backup-size SIZE 备份保存的数据库数量，覆盖FILEHASH_BACKUP_SIZE环境变量。
可用命令:
add/a 登记文件到数据库
verify_record/vr 验证数据库中的记录
print_record/pr 打印数据库中的记录
print_existing_record/per 打印数据库中尚存在的记录
verify_file/vf 验证磁盘文件
print_file/pf 计算文件hash值 (不加载数据库)
保证存在的hash算法: blake2b, blake2s, md5, sha1, sha224, sha256, sha384,
sha3_224, sha3_256, sha3_384, sha3_512, sha512, shake_128, shake_256
其它可用的hash算法: md5-sha1, ripemd160, sha512_224, sha512_256, sm3
当前创建数据库使用的hash算法: sha256
```
| text/markdown | null | Ma Lin <malincns@163.com> | null | null | null | null | [
"Programming Language :: Python :: 3",
"Development Status :: 5 - Production/Stable",
"Topic :: System :: Archiving",
"Topic :: System :: Archiving :: Backup",
"Topic :: System :: Shells",
"Topic :: Utilities",
"Operating System :: Unix",
"Operating System :: POSIX",
"Operating System :: MacOS",
"Operating System :: Microsoft :: Windows"
] | [] | null | null | >=3.7 | [] | [] | [] | [
"colorama"
] | [] | [] | [] | [
"Repository, https://bitbucket.org/wjssz/filehash"
] | twine/6.2.0 CPython/3.13.12 | 2026-02-20T10:15:47.011611 | filehash_tool-2.2.7.tar.gz | 12,126 | 4a/44/9911659a052ff9c506d9a58a80144b4b748eaafff1488572fd94324af05e/filehash_tool-2.2.7.tar.gz | source | sdist | null | false | 81b00443dd5a796df319a6486a4a143a | acd14ad475faaa6fffe155607a8825e6e0e199923b4867f9f3d77c26007d1bd5 | 4a449911659a052ff9c506d9a58a80144b4b748eaafff1488572fd94324af05e | MIT | [] | 218 |
2.4 | protein-quest | 1.1.1 | Search/retrieve/filter proteins and protein structures | # protein-quest
[](https://www.bonvinlab.org/protein-quest/)
[](https://github.com/haddocking/protein-quest/actions/workflows/ci.yml)
[](https://www.research-software.nl/software/protein-quest)
[](https://bio.tools/protein-quest)
[](https://pypi.org/project/protein-quest/)
[](https://doi.org/10.5281/zenodo.16941288)
[](https://doi.org/10.5281/zenodo.17910832)
[](https://app.codacy.com/gh/haddocking/protein-quest/coverage?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_coverage)
[](https://fairsoftwarechecklist.net/v0.2?f=31&a=32113&i=32121&r=133)
[](https://fair-software.eu)
[](https://github.com/kucherenko/jscpd/)
Python package to search/retrieve/filter proteins and protein structures.
It uses
- [Uniprot Sparql endpoint](https://sparql.uniprot.org/) to search for proteins
and their measured or predicted 3D structures.
- [Uniprot taxonomy](https://www.uniprot.org/taxonomy?query=*) to search for
taxonomy.
- [QuickGO](https://www.ebi.ac.uk/QuickGO/api/index.html) to search for Gene
Ontology terms.
- [gemmi](https://project-gemmi.github.io/) to work with macromolecular models.
- [dask-distributed](https://docs.dask.org/en/latest/) to compute in parallel.
- [rocrate-action-recorder](https://rocrate-action-recorder.readthedocs.io/) for
provenance tracking.
The package is used by
- [protein-detective](https://github.com/haddocking/protein-detective)
An example workflow:
```mermaid
graph TB;
taxonomy[/Search taxon/] -. taxon_ids .-> searchuniprot[/Search UniprotKB/]
goterm[/Search GO term/] -. go_ids .-> searchuniprot[/Search UniprotKB/]
searchuniprot --> |uniprot_accessions|searchpdbe[/Search PDBe/]
searchuniprot --> |uniprot_accessions|searchaf[/Search Alphafold/]
searchuniprot -. uniprot_accessions .-> searchemdb[/Search EMDB/]
searchuniprot -. uniprot_accessions .-> searchuniprotdetails[/Search UniProt details/]
searchintactionpartners[/Search interaction partners/] -.-x |uniprot_accessions|searchuniprot
searchcomplexes[/Search complexes/]
searchpdbe -->|pdb_ids|fetchpdbe[Retrieve PDBe]
searchaf --> |uniprot_accessions|fetchad(Retrieve AlphaFold)
searchemdb -. emdb_ids .->fetchemdb[Retrieve EMDB]
fetchpdbe -->|mmcif_files| chainfilter{{Filter on chain of uniprot}}
chainfilter --> |mmcif_files| residuefilter{{Filter on chain length}}
fetchad -->|mmcif_files| confidencefilter{{Filter out low confidence}}
confidencefilter --> |mmcif_files| ssfilter{{Filter on secondary structure}}
residuefilter --> |mmcif_files| ssfilter
ssfilter -. mmcif_files .-> convert2cif([Convert to cif])
ssfilter -. mmcif_files .-> convert2uniprot_accessions([Convert to UniProt accessions])
classDef dashedBorder stroke-dasharray: 5 5;
goterm:::dashedBorder
taxonomy:::dashedBorder
searchemdb:::dashedBorder
fetchemdb:::dashedBorder
searchintactionpartners:::dashedBorder
searchcomplexes:::dashedBorder
searchuniprotdetails:::dashedBorder
convert2cif:::dashedBorder
convert2uniprot_accessions:::dashedBorder
```
(Dotted nodes and edges are side-quests.)
## Install
```shell
pip install protein-quest
```
Or to use the latest development version:
```shell
pip install git+https://github.com/haddocking/protein-quest.git
```
## Usage
The main entry point is the `protein-quest` command line tool which has multiple
subcommands to perform actions.
To use programmaticly, see the
[Jupyter notebooks](https://www.bonvinlab.org/protein-quest/notebooks) and
[API documentation](https://www.bonvinlab.org/protein-quest/autoapi/protein_quest/).
While downloading or copying files it uses a global cache (located at
`~/.cache/protein-quest`) and hardlinks to save disk space and improve speed.
This behavior can be customized with the `--no-cache`, `--cache-dir`, and
`--copy-method` command line arguments.
### Search Uniprot accessions
```shell
protein-quest search uniprot \
--taxon-id 9606 \
--reviewed \
--subcellular-location-uniprot "nucleus" \
--subcellular-location-go GO:0005634 \
--molecular-function-go GO:0003677 \
--limit 100 \
uniprot_accs.txt
```
([GO:0005634](https://www.ebi.ac.uk/QuickGO/term/GO:0005634) is "Nucleus" and
[GO:0003677](https://www.ebi.ac.uk/QuickGO/term/GO:0003677) is "DNA binding")
### Search for PDBe structures of uniprot accessions
```shell
protein-quest search pdbe uniprot_accs.txt pdbe.csv
```
`pdbe.csv` file is written containing the the PDB id and chain of each uniprot
accession.
### Search for Alphafold structures of uniprot accessions
```shell
protein-quest search alphafold uniprot_accs.txt alphafold.csv
```
### Search for EMDB structures of uniprot accessions
```shell
protein-quest search emdb uniprot_accs.txt emdbs.csv
```
### To retrieve PDB structure files
```shell
protein-quest retrieve pdbe pdbe.csv downloads-pdbe/
```
### To retrieve AlphaFold structure files
```shell
protein-quest retrieve alphafold alphafold.csv downloads-af/
```
For each entry downloads the cif file.
### To retrieve EMDB volume files
```shell
protein-quest retrieve emdb emdbs.csv downloads-emdb/
```
### To filter AlphaFold structures on confidence
Filter AlphaFoldDB structures based on confidence (pLDDT). Keeps entries with
requested number of residues which have a confidence score above the threshold.
Also writes pdb files with only those residues.
```shell
protein-quest filter confidence \
--confidence-threshold 50 \
--min-residues 100 \
--max-residues 1000 \
./downloads-af ./filtered
```
### To filter PDBe files on chain of uniprot accession
Make PDBe files smaller by only keeping first chain of found uniprot entry and
renaming to chain A.
```shell
protein-quest filter chain \
pdbe.csv \
./downloads-pdbe ./filtered-chains
```
### To filter PDBe files on nr of residues
```shell
protein-quest filter residue \
--min-residues 100 \
--max-residues 1000 \
./filtered-chains ./filtered
```
### To filter on secondary structure
To filter on structure being mostly alpha helices and have no beta sheets. See
the following
[notebook](https://www.bonvinlab.org/protein-detective/SSE_elements.html) to
determine the ratio of secondary structure elements.
```shell
protein-quest filter secondary-structure \
--ratio-min-helix-residues 0.5 \
--ratio-max-sheet-residues 0.0 \
--write-stats filtered-ss-stats.csv \
./filtered-chains ./filtered-ss
```
### Search Taxonomy
```shell
protein-quest search taxonomy "Homo sapiens" -
```
### Search Gene Ontology (GO)
You might not know what the identifier of a
[Gene Ontology](https://geneontology.org/) term is at
`protein-quest search uniprot`. You can use following command to search for a
Gene Ontology (GO) term.
```shell
protein-quest search go --limit 5 --aspect cellular_component apoptosome -
```
### Search for interaction partners
Use <https://www.ebi.ac.uk/complexportal> to find interaction partners of given
UniProt accession.
```shell
protein-quest search interaction-partners Q05471 interaction-partners-of-Q05471.txt
```
The `interaction-partners-of-Q05471.txt` file contains uniprot accessions (one
per line).
### Search for complexes
Given Uniprot accessions search for macromolecular complexes at
<https://www.ebi.ac.uk/complexportal> and return the complex entries and their
members.
```shell
echo Q05471 | protein-quest search complexes - complexes.csv
```
The `complexes.csv` looks like
```csv
query_protein,complex_id,complex_url,complex_title,members
Q05471,CPX-2122,https://www.ebi.ac.uk/complexportal/complex/CPX-2122,Swr1 chromatin remodelling complex,P31376;P35817;P38326;P53201;P53930;P60010;P80428;Q03388;Q03433;Q03940;Q05471;Q06707;Q12464;Q12509
```
### Search for UniProt details
To get details (like protein name, sequence length, organism) for a list of
UniProt accessions.
```shell
protein-quest search uniprot-details uniprot_accs.txt uniprot_details.csv
```
The `uniprot_details.csv` looks like:
```csv
uniprot_accession,uniprot_id,sequence_length,reviewed,protein_name,taxon_id,taxon_name
A0A087WUV0,ZN892_HUMAN,522,True,Zinc finger protein 892,9606,Homo sapiens
```
### Convert structure files to .cif format
Some tools (for example [powerfit](https://github.com/haddocking/powerfit)) only
work with `.cif` files and not `*.cif.gz` or `*.bcif` files.
```shell
protein-quest convert structures --format cif --output-dir ./filtered-cif ./filtered-ss
```
### Convert structure files to UniProt accessions
After running some filters you might want to know which UniProt accessions are
still present in the filtered structures.
```shell
protein-quest convert uniprot ./filtered-ss uniprot_accs.filtered.txt
```
## Provenance
You can use `protein-quest --prov ...` to store provenance information of your
CLI invocations in a
[Research Object crate](https://www.researchobject.org/ro-crate/) file called
ro-crate-metadata.json.
## Model Context Protocol (MCP) server
Protein quest can also help LLMs like Claude Sonnet 4 by providing a
[set of tools](https://modelcontextprotocol.io/docs/learn/server-concepts#tools-ai-actions)
for protein structures.
To run mcp server you have to install the `mcp` extra with:
```shell
pip install protein-quest[mcp]
```
The server can be started with:
```shell
protein-quest mcp
```
The mcp server contains an prompt template to search/retrieve/filter candidate
structures.
## Shell autocompletion
The `protein-quest` command line tool supports shell autocompletion using
[shtab](https://docs.iterative.ai/shtab).
Initialize for bash shell with:
```shell
mkdir -p ~/.local/share/bash-completion/completions
protein-quest --print-completion bash > ~/.local/share/bash-completion/completions/protein-quest
```
Initialize for zsh shell with:
```shell
mkdir -p ~/.local/share/zsh/site-functions
protein-quest --print-completion zsh > ~/.local/share/zsh/site-functions/_protein-quest
fpath=("$HOME/.local/share/zsh/site-functions" $fpath)
autoload -Uz compinit && compinit
```
## Contributing
For development information and contribution guidelines, please see
[CONTRIBUTING.md](CONTRIBUTING.md).
| text/markdown | null | null | null | null | null | alphafold, mmcif, pdb, protein, protein structure, uniprot | [
"Development Status :: 5 - Production/Stable",
"Environment :: Console",
"Framework :: AsyncIO",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: Apache Software License",
"Natural Language :: English",
"Operating System :: MacOS",
"Operating System :: POSIX",
"Operating System :: POSIX :: Linux",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14",
"Topic :: Scientific/Engineering :: Bio-Informatics",
"Topic :: Scientific/Engineering :: Chemistry",
"Typing :: Typed"
] | [] | null | null | >=3.13 | [] | [] | [] | [
"aiofiles>=24.1.0",
"aiohttp-retry>=2.9.1",
"aiohttp[speedups]>=3.11.18",
"attrs>=25.3.0",
"cattrs[orjson]>=24.1.3",
"dask>=2025.5.1",
"distributed>=2025.5.1",
"gemmi>=0.7.4",
"mmcif>=0.92.0",
"platformdirs>=4.3.8",
"psutil>=7.0.0",
"rich-argparse>=1.7.1",
"rich>=14.0.0",
"rocrate-action-recorder>=0.2.0",
"shtab>=1.7.2",
"sparqlwrapper>=2.0.0",
"tqdm>=4.67.1",
"yarl>=1.20.1",
"fastmcp>=2.11.3; extra == \"mcp\"",
"pydantic>=2.12.0; extra == \"mcp\""
] | [] | [] | [] | [
"Homepage, https://github.com/haddocking/protein-quest",
"Issues, https://github.com/haddocking/protein-quest/issues",
"Documentation, https://www.bonvinlab.org/protein-quest/",
"Source, https://github.com/haddocking/protein-quest"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:13:20.152742 | protein_quest-1.1.1.tar.gz | 59,249 | dc/d8/008a2ad0749dff1abcde655b65251e536451cb10714e574316f20ab76c59/protein_quest-1.1.1.tar.gz | source | sdist | null | false | d1b85c4dd1e85fdc06bffd6cb313800f | f189dc02a9b7a1e7b602f1d5734145c1db683a1353424a404cbff39a5bce808b | dcd8008a2ad0749dff1abcde655b65251e536451cb10714e574316f20ab76c59 | null | [
"LICENSE"
] | 206 |
2.4 | payaza | 0.1.0 | Unofficial Python SDK for the Payaza Africa API | # Payaza Python SDK
> Unofficial Python SDK for the [Payaza Africa](https://payaza.africa) API.
[](https://www.python.org)
[](LICENSE)
---
## Installation
```bash
pip install payaza
```
---
## Quick start
## Contributing
PRs are welcome! Please open an issue first to discuss what you'd like to change.
---
## ⚠️ Disclaimer
This is an **unofficial** SDK and is not affiliated with or endorsed by Payaza Africa Limited.
Endpoint paths and request/response shapes are based on the public documentation at
[docs.payaza.africa](https://docs.payaza.africa/developers/apis) — always cross-check
against the latest official docs and update accordingly.
---
## License
MIT © Shagbaor Agber
| text/markdown | null | Shagbaor Agber <dxtlive@gmail.com> | null | null | null | payaza, payments, africa, fintech, sdk | [
"Development Status :: 3 - Alpha",
"Intended Audience :: Developers",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Topic :: Software Development :: Libraries :: Python Modules"
] | [] | null | null | >=3.8 | [] | [] | [] | [
"requests>=2.28",
"pytest>=7; extra == \"dev\"",
"responses>=0.25; extra == \"dev\"",
"pytest-cov; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/0xAfterSnow/payaza-python",
"Documentation, https://docs.payaza.africa/developers/apis",
"Bug Tracker, https://github.com/0xAfterSnow/payaza-python/issues"
] | twine/6.2.0 CPython/3.13.9 | 2026-02-20T10:13:12.218042 | payaza-0.1.0.tar.gz | 3,637 | 8e/6f/65732e5cf82337244f246138661c44ba7657bdd641da7abc2acdf68023fa/payaza-0.1.0.tar.gz | source | sdist | null | false | ffd771f78ea644484226502c262328a2 | c56a5d3e49329df78d8a356006fd29836ab9671d7f144bb3f8feee803fc23e2c | 8e6f65732e5cf82337244f246138661c44ba7657bdd641da7abc2acdf68023fa | MIT | [
"LICENSE"
] | 232 |
2.4 | iredis | 1.16.0 | Terminal client for Redis with auto-completion and syntax highlighting. | <p align="center">
<img width="100" height="100" src="https://raw.githubusercontent.com/laixintao/iredis/master/docs/assets/logo.png" />
</p>
<h3 align="center">Interactive Redis: A Cli for Redis with AutoCompletion and Syntax Highlighting.</h3>
<p align="center">
<a href="https://github.com/laixintao/iredis/actions"><img src="https://github.com/laixintao/iredis/actions/workflows/test.yaml/badge.svg?branch=master" alt="Github Action"></a>
<a href="https://badge.fury.io/py/iredis"><img src="https://badge.fury.io/py/iredis.svg" alt="PyPI version"></a>
<img src="https://badgen.net/badge/python/3.10%20%7C%203.11%20%7C%203.12" alt="Python version">
<a href="https://pepy.tech/project/iredis"><img src="https://pepy.tech/badge/iredis" alt="Download stats"></a>
</p>
<p align="center">
<img src="./docs/assets/demo.svg" alt="demo">
</p>
IRedis is a terminal client for redis with auto-completion and syntax
highlighting. IRedis lets you type Redis commands smoothly, and displays results
in a user-friendly format.
IRedis is an alternative for redis-cli. In most cases, IRedis behaves exactly
the same as redis-cli. Besides, it is safer to use IRedis on production servers
than redis-cli: IRedis will prevent accidentally running dangerous commands,
like `KEYS *` (see
[Redis docs / Latency generated by slow commands](https://redis.io/topics/latency#latency-generated-by-slow-commands)).
## Features
- Advanced code completion. If you run command `KEYS` then run `DEL`, IRedis
will auto-complete your command based on `KEYS` result.
- Command validation. IRedis will validate command while you are typing, and
highlight errors. E.g. try `CLUSTER MEET IP PORT`, IRedis will validate IP and
PORT for you.
- Command highlighting, fully based on redis grammar. Any valid command in
IRedis shell is a valid redis command.
- Human-friendly result display.
- _pipeline_ feature, you can use your favorite shell tools to parse redis'
response, like `get json | jq .`.
- Support pager for long output.
- Support connection via URL, `iredis --url redis://example.com:6379/1`.
- Support cluster, IRedis will auto reissue command for `MOVED` response in
cluster mode.
- Store server configuration: `iredis -d prod-redis` (see [dsn](#using-dsn) for
more).
- `peek` command to check the key's type then automatically call
`get`/`lrange`/`sscan`, etc, depending on types. You don't need to call the
`type` command then type another command to get the value. `peek` will also
display the key's length and memory usage.
- <kbd>Ctrl</kbd> + <kbd>C</kbd> to cancel the current typed command, this won't
exit IRedis, exactly like bash behaviour. Use <kbd>Ctrl</kbd> + <kbd>D</kbd>
to send a EOF to exit IRedis.
- <kbd>Ctrl</kbd> + <kbd>R</kbd> to open **reverse-i-search** to search through
your command history.
- Auto suggestions. (Like [fish shell](http://fishshell.com/).)
- Support `--encode=utf-8`, to decode Redis' bytes responses.
- Command hint on bottom, include command syntax, supported redis version, and
time complexity.
- Official docs with built-in `HELP` command, try `HELP SET`!
- Written in pure Python, but IRedis was packaged into a single binary with
[PyOxidizer](https://github.com/indygreg/PyOxidizer), you can use cURL to
download and run, it just works, even you don't have a Python interpreter.
- You can change the cli prompt using `--prompt` option or set via `~/.iredisrc`
config file.
- Hide password for `AUTH` command.
- Says "Goodbye!" to you when you exit!
- For full features, please see: [iredis.xbin.io](https://www.iredis.xbin.io)
## Install
### Pip
Install via pip:
```
pip install iredis
```
[pipx](https://github.com/pipxproject/pipx) is recommended:
```
pipx install iredis
```
### Brew
For Mac users, you can install iredis via brew 🍻
```
brew install iredis
```
### Linux
You can also use your Linux package manager to install IRedis, like `apt` in
Ubuntu (Only available on Ubuntu 21.04+).
```shell
apt install iredis
```
[](https://repology.org/project/iredis/versions)
### Download Binary
Or you can download the executable binary with cURL(or wget), untar, then run.
It is especially useful when you don't have a python interpreter(E.g. the
[official Redis docker image](https://hub.docker.com/_/redis/) which doesn't
have Python installed.):
```
wget https://github.com/laixintao/iredis/releases/download/v1.15.2/iredis.tar.gz \
&& tar -xzf iredis.tar.gz \
&& ./iredis
```
(Check the [release page](https://github.com/laixintao/iredis/releases) if you
want to download an old version of IRedis.)
Please note that the single binary build only support until IRedis v1.15.2, all
versions before IRedis v1.15.2 has the single binary build that you can download
directly. After v1.15.2, as the PyOxidizer [is no longer maintained](
https://gregoryszorc.com/blog/2024/03/17/my-shifting-open-source-priorities/),
IRedis doesn't do the single binary build either.
## Usage
Once you install IRedis, you will know how to use it. Just remember, IRedis
supports similar options like redis-cli, like `-h` for redis-server's host and
`-p` for port.
```
$ iredis --help
Usage: iredis [OPTIONS] [CMD]...
IRedis: Interactive Redis
When no command is given, IRedis starts in interactive mode.
Examples:
- iredis
- iredis -d dsn
- iredis -h 127.0.0.1 -p 6379
- iredis -h 127.0.0.1 -p 6379 -a <password>
- iredis --url redis://localhost:7890/3
Type "help" in interactive mode for information on available commands and
settings.
Options:
-h TEXT Server hostname (default: 127.0.0.1).
-p TEXT Server port (default: 6379).
-s, --socket TEXT Server socket (overrides hostname and port).
-n INTEGER Database number.(overwrites dsn/url's db
number)
-u, --username TEXT User name used to auth, will be ignore for
redis version < 6.
-a, --password TEXT Password to use when connecting to the
server.
--url TEXT Use Redis URL to indicate connection(Can set
with env `IREDIS_URL`), Example: redis:/
/[[username]:[password]]@localhost:6379/0
rediss://[[username]:[password]]@localhost:6
379/0 unix://[[username]:[password]]@/pa
th/to/socket.sock?db=0
-d, --dsn TEXT Use DSN configured into the [alias_dsn]
section of iredisrc file. (Can set with env
`IREDIS_DSN`)
--newbie / --no-newbie Show command hints and useful helps.
--iredisrc TEXT Config file for iredis, default is
~/.iredisrc.
--decode TEXT decode response, default is No decode, which
will output all bytes literals.
--client_name TEXT Assign a name to the current connection.
--raw / --no-raw Use raw formatting for replies (default when
STDOUT is not a tty). However, you can use
--no-raw to force formatted output even when
STDOUT is not a tty.
--rainbow / --no-rainbow Display colorful prompt.
--shell / --no-shell Allow to run shell commands, default to
True.
--pager / --no-pager Using pager when output is too tall for your
window, default to True.
--verify-ssl [none|optional|required]
Set the TLS certificate verification
strategy
--prompt TEXT Prompt format (supported interpolations:
{client_name}, {db}, {host}, {path}, {port},
{username}, {client_addr}, {client_id}).
--version Show the version and exit.
--help Show this message and exit.
```
### Using DSN
IRedis support storing server configuration in config file. Here is a DSN
config:
```
[alias_dsn]
dev=redis://localhost:6379/4
staging=redis://username:password@staging-redis.example.com:6379/1
```
Put this in your `iredisrc` then connect via `iredis -d staging` or
`iredis -d dev`.
### Change The Default Prompt
You can change the prompt str, the default prompt is:
```shell
127.0.0.1:6379>
```
Which is rendered by `{host}:{port}[{db}]> `, you can change this via `--prompt`
option or change
[iredisrc](https://github.com/laixintao/iredis/blob/master/iredis/data/iredisrc)
config file. The prompwt string uses python string format engine, supported
interpolations:
- `{client_name}`
- `{db}`
- `{host}`
- `{path}`
- `{port}`
- `{username}`
- `{client_addr}`
- `{client_id}`
The `--prompt` utilize
[Python String format engine](https://docs.python.org/3/library/string.html#formatstrings),
so as long as it is a valid string formatter, it will work( anything that
`"<your prompt>".format(...)` accepts). For example, you can limit your Redis
server host name's length to 5 by setting `--prompt` to
`iredis --prompt '{host:.5s}'`.
### Configuration
IRedis supports config files. Command-line options will always take precedence
over config. Configuration resolution from highest to lowest precedence is:
- _Options from command line_
- `$PWD/.iredisrc`
- `~/.iredisrc` (this path can be changed with `iredis --iredisrc $YOUR_PATH`)
- `/etc/iredisrc`
- default config in IRedis package.
You can copy the _self-explained_ default config here:
https://raw.githubusercontent.com/laixintao/iredis/master/iredis/data/iredisrc
And then make your own changes.
(If you are using an old versions of IRedis, please use the config file below,
and change the version in URL):
https://raw.githubusercontent.com/laixintao/iredis/v1.0.4/iredis/data/iredisrc
### Keys
IRedis support unix/readline-style REPL keyboard shortcuts, which means keys
like <kbd>Ctrl</kbd> + <kbd>F</kbd> to forward work.
Also:
- <kbd>Ctrl</kbd> + <kbd>D</kbd> (i.e. EOF) to exit; you can also use the `exit`
command.
- <kbd>Ctrl</kbd> + <kbd>L</kbd> to clear screen; you can also use the `clear`
command.
- <kbd>Ctrl</kbd> + <kbd>X</kbd> <kbd>Ctrl</kbd> + <kbd>E</kbd> to open an
editor to edit command, or <kbd>V</kbd> in vi-mode.
## Development
### Release Strategy
IRedis is built and released by `GitHub Actions`. Whenever a tag is pushed to
the `master` branch, a new release is built and uploaded to pypi.org, it's very
convenient.
Thus, we release as often as possible, so that users can always enjoy the new
features and bugfixes quickly. Any bugfix or new feature will get at least a
patch release, whereas big features will get a minor release.
### Setup Environment
IRedis favors [poetry](https://github.com/sdispater/poetry) as package
management tool. To setup a develop environment on your computer:
First, install poetry (you can do it in a python's virtualenv):
```
pip install poetry
```
Then run (which is similar to `pip install -e .`):
```
poetry install
```
**Be careful running testcases locally, it may flush you db!!!**
### Code style
Code is formatted with [black](https://github.com/psf/black). After modifying code, run:
```bash
black .
```
We recommend installing [pre-commit](https://pre-commit.com/) so black and flake8 run automatically on each `git commit`:
```bash
poetry run pre-commit install
```
### Development Logs
This is a command-line tool, so we don't write logs to stdout.
You can `tail -f ~/.iredis.log` to see logs, the log is pretty clear, you can
see what actually happens from log files.
### Catch Up with Latest Redis-doc
IRedis use a git submodule to track current-up-to-date redis-doc version. To
catch up with latest:
1. Git pull in redis-doc
2. Copy doc files to `/data`: `cp -r redis-doc/commands* iredis/data`
3. Prettier
markdown`prettier --prose-wrap always iredis/data/commands/*.md --write`
4. Check the diff, update IRedis' code if needed.
## Related Projects
- [redis-tui](https://github.com/mylxsw/redis-tui)
If you like iredis, you may also like other cli tools by
[dbcli](https://www.dbcli.com/):
- [pgcli](https://www.pgcli.com) - Postgres Client with Auto-completion and
Syntax Highlighting
- [mycli](https://www.mycli.net) - MySQL/MariaDB/Percona Client with
Auto-completion and Syntax Highlighting
- [litecli](https://litecli.com) - SQLite Client with Auto-completion and Syntax
Highlighting
- [mssql-cli](https://github.com/dbcli/mssql-cli) - Microsoft SQL Server Client
with Auto-completion and Syntax Highlighting
- [athenacli](https://github.com/dbcli/athenacli) - AWS Athena Client with
Auto-completion and Syntax Highlighting
- [vcli](https://github.com/dbcli/vcli) - VerticaDB client
- [iredis](https://github.com/laixintao/iredis/) - Client for Redis with
AutoCompletion and Syntax Highlighting
IRedis is build on the top of
[prompt_toolkit](https://github.com/jonathanslenders/python-prompt-toolkit), a
Python library (by [Jonathan Slenders](https://twitter.com/jonathan_s)) for
building rich commandline applications.
| text/markdown | laixintao | laixintao1995@163.com | null | null | BSD-3-Clause | Redis, key-value store, Commandline tools, Redis Client | [
"Development Status :: 4 - Beta",
"Environment :: Console",
"Environment :: Console :: Curses",
"Environment :: MacOS X",
"Intended Audience :: Developers",
"License :: OSI Approved :: BSD License",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14",
"Topic :: Database"
] | [] | null | null | <4.0,>=3.10 | [] | [] | [] | [
"Pygments<3,>=2",
"click<9.0,>=8.0",
"configobj<6.0,>=5.0",
"mistune<4.0,>=3.0",
"packaging<25.0,>=24.2",
"prompt_toolkit<4,>=3",
"python-dateutil<3.0.0,>=2.8.2",
"redis<8.0.0,>=5.0.0"
] | [] | [] | [] | [
"Homepage, https://github.com/laixintao/iredis",
"Repository, https://github.com/laixintao/iredis"
] | poetry/2.3.2 CPython/3.12.12 Linux/6.11.0-1018-azure | 2026-02-20T10:12:54.184169 | iredis-1.16.0-py3-none-any.whl | 388,123 | cd/d2/4642adb5d0dbdc75e647c60217d9194e20f6a41f1de258de72af6b8447e9/iredis-1.16.0-py3-none-any.whl | py3 | bdist_wheel | null | false | 79bf21733c8b4b9fa966e1205ef9cf95 | 8b51086e5cae9e3d136a74cd9b35c5b75491dcb333d2dae476078094df4cc5d2 | cdd24642adb5d0dbdc75e647c60217d9194e20f6a41f1de258de72af6b8447e9 | null | [
"LICENSE"
] | 356 |
2.4 | dataenginex | 0.3.5 | DataEngineX - Core framework for data engineering projects | # dataenginex
`dataenginex` is the core DataEngineX framework package for building observable, production-ready data and API services.
It provides:
- FastAPI application primitives and API extensions
- Middleware for structured logging, metrics, and tracing
- Data quality and validation utilities
- Lakehouse and warehouse building blocks
- Reusable ML support modules for model-serving workflows
## Install
```bash
pip install dataenginex
```
## Package Scope
This package is the core library from the DEX monorepo.
`careerdex` and `weatherdex` are maintained in the same repository but are not part of this package release flow.
## Quick Usage
```python
from dataenginex import __version__
print(__version__)
```
## Source and Docs
- Repository: https://github.com/data-literate/DEX
- CI/CD guide: `docs/CI_CD.md`
- Release notes: `packages/dataenginex/src/dataenginex/RELEASE_NOTES.md`
| text/markdown | Jay | jayapal.myaka99@gmail.com | null | null | MIT | null | [
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14"
] | [] | null | null | >=3.11 | [] | [] | [] | [
"email-validator>=2.0.0",
"fastapi>=0.128.4",
"httpx>=0.28.0",
"loguru>=0.7.3",
"opentelemetry-api>=1.39.0",
"opentelemetry-exporter-otlp>=1.39.0",
"opentelemetry-instrumentation-fastapi>=0.60b1",
"opentelemetry-sdk>=1.39.0",
"prometheus-client>=0.24.0",
"python-dotenv>=1.2.0",
"python-json-logger>=4.0.0",
"pyyaml>=6.0.2",
"structlog>=25.5.0",
"uvicorn>=0.40.0"
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:12:22.001597 | dataenginex-0.3.5.tar.gz | 39,253 | 07/c0/4ccac001fe958b14d9820f3b960fc0089560000420553732d449b494642e/dataenginex-0.3.5.tar.gz | source | sdist | null | false | 5280d2ab9497e4889b1eed4d1c547f26 | bc9778839355637ca4970f95c75d8affdfa269b7a251509189f28a9029a0cbb0 | 07c04ccac001fe958b14d9820f3b960fc0089560000420553732d449b494642e | null | [] | 213 |
2.4 | uup-dump-api-py | 0.1.5 | Python Wrapper of UUP Dump API | # UUP Dump API - Python Module
A Python wrapper for the [UUP Dump API](https://uupdump.net/) with comprehensive logging and exception handling.
## Features
- ✅ Complete API coverage for all UUP Dump endpoints
- ✅ Comprehensive exception handling with custom error types
- ✅ Detailed logging at multiple levels (DEBUG, INFO, WARNING, ERROR)
- ✅ Automatic retry and timeout handling
- ✅ Type hints for better IDE support
- ✅ User-friendly error messages mapped from API error codes
## Quick Start
```python
from adapter import RestAdapter
from exceptions import UUPDumpAPIError
# Create API client
api = RestAdapter(timeout=10)
try:
# Search for Windows 11 updates
result = api.listid(search="Windows 11", sortByDate=True)
if 'response' in result:
builds = result['response']['builds']
print(f"Found {len(builds)} updates")
except UUPDumpAPIError as e:
print(f"Error: {e}")
```
## Logging
The module uses Python's built-in `logging` module for comprehensive logging.
### Basic Logging Setup
```python
import logging
from adapter import RestAdapter
# Create adapter with INFO level logging (default)
api = RestAdapter(log_level=logging.INFO)
# Or use DEBUG for detailed troubleshooting
api = RestAdapter(log_level=logging.DEBUG)
```
### Custom Logging Configuration
```python
import logging
# Configure logging manually
logging.basicConfig(
level=logging.DEBUG,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
filename='uup_api.log' # Log to file
)
api = RestAdapter()
```
### Logging Levels
- **DEBUG**: Detailed information including request parameters and response status
- **INFO**: General information about operations (default)
- **WARNING**: Warning messages for potentially problematic situations
- **ERROR**: Error messages when operations fail
### Example Log Output
```
2024-01-15 10:30:45 - adapter - INFO - Initialized UUP Dump API adapter (base_url=https://api.uupdump.net, timeout=10s)
2024-01-15 10:30:45 - adapter - INFO - Listing updates (search='Windows 11', sortByDate=True)
2024-01-15 10:30:45 - adapter - DEBUG - Making GET request to https://api.uupdump.net/listid.php with params: {'search': 'Windows 11', 'sortByDate': '1'}
2024-01-15 10:30:46 - adapter - DEBUG - Response status: 200
2024-01-15 10:30:46 - adapter - DEBUG - Request successful
```
## Exception Handling
The module provides custom exceptions for different error scenarios.
### Exception Hierarchy
```
UUPDumpAPIError (base exception)
├── UUPDumpHTTPError # HTTP request failures
├── UUPDumpTimeoutError # Request timeouts
├── UUPDumpConnectionError # Connection failures
├── UUPDumpValidationError # Invalid parameters
└── UUPDumpResponseError # API-level errors
```
### Exception Usage
```python
from adapter import RestAdapter
from exceptions import (
UUPDumpAPIError,
UUPDumpResponseError,
UUPDumpTimeoutError,
UUPDumpConnectionError
)
api = RestAdapter(timeout=5)
try:
result = api.fetchupd(arch="amd64", ring="Retail")
except UUPDumpResponseError as e:
# API returned an error (e.g., NO_UPDATE_FOUND, UNKNOWN_ARCH)
print(f"API Error: {e}")
print(f"Error Code: {e.error_code}")
except UUPDumpTimeoutError as e:
# Request timed out
print(f"Timeout: {e}")
except UUPDumpConnectionError as e:
# Could not connect to API
print(f"Connection Error: {e}")
except UUPDumpAPIError as e:
# Catch-all for any other errors
print(f"Unexpected Error: {e}")
```
### API Error Codes
The module automatically maps API error codes to human-readable messages:
| Error Code | Description |
|------------|-------------|
| `UNKNOWN_ARCH` | Invalid architecture specified |
| `UNKNOWN_RING` | Invalid ring/channel specified |
| `NO_UPDATE_FOUND` | No update matching criteria |
| `UNSUPPORTED_LANG` | Unsupported language |
| `NO_FILES` | No files available for update |
| `WU_REQUEST_FAILED` | Windows Update request failed |
| ... | See `exceptions.py` for complete list |
## API Methods
### `listid(search, sortByDate)`
List available updates.
```python
result = api.listid(search="Windows 11", sortByDate=True)
```
### `fetchupd(arch, ring, flight, build, ...)`
Fetch update information from Windows Update.
```python
result = api.fetchupd(
arch="amd64",
ring="Retail",
flight="Mainline",
build="22621",
sku=48
)
```
### `get_files(updateId, usePack, desiredEdition, ...)`
Get file list for an update.
```python
result = api.get_files(
updateId="your-uuid-here",
usePack="en-us",
desiredEdition="professional"
)
```
### `list_editions(lang, updateId)`
List available editions.
```python
result = api.list_editions(lang="en-us", updateId="your-uuid-here")
```
### `list_langs(updateId, returnInfo)`
List available languages.
```python
result = api.list_langs(updateId="your-uuid-here")
```
### `update_info(updateId, onlyInfo, ignoreFiles)`
Get detailed update information.
```python
result = api.update_info(updateId="your-uuid-here", ignoreFiles=True)
```
### `api_version()`
Get API version.
```python
result = api.api_version()
```
## Complete Example
```python
import logging
from adapter import RestAdapter
from exceptions import UUPDumpAPIError, UUPDumpResponseError
# Configure logging
logging.basicConfig(level=logging.INFO)
# Create API client
api = RestAdapter(timeout=10)
try:
# 1. Search for updates
print("Searching for updates...")
search_result = api.listid(search="23H2", sortByDate=True)
if 'response' in search_result:
builds = search_result['response']['builds']
update_id = list(builds.keys())[0]
# 2. Get update info
print(f"Getting info for {update_id}...")
info = api.update_info(updateId=update_id)
# 3. List languages
print("Listing languages...")
langs = api.list_langs(updateId=update_id)
# 4. List editions
print("Listing editions...")
editions = api.list_editions(lang="en-us", updateId=update_id)
print("Success!")
except UUPDumpResponseError as e:
print(f"API Error [{e.error_code}]: {e}")
except UUPDumpAPIError as e:
print(f"Error: {e}")
```
## Error Handling Best Practices
1. **Always catch specific exceptions first**, then fall back to the base exception:
```python
try:
result = api.fetchupd(...)
except UUPDumpResponseError as e:
# Handle API errors
except UUPDumpTimeoutError as e:
# Handle timeouts
except UUPDumpAPIError as e:
# Handle any other errors
```
2. **Check for error codes** in API responses:
```python
try:
result = api.fetchupd(arch="invalid")
except UUPDumpResponseError as e:
if e.error_code == "UNKNOWN_ARCH":
print("Please use: amd64, x86, arm64, or all")
```
3. **Use appropriate logging levels**:
- Development: `logging.DEBUG`
- Production: `logging.INFO` or `logging.WARNING`
4. **Set reasonable timeouts** based on your use case:
```python
# For interactive applications
api = RestAdapter(timeout=10)
# For background tasks
api = RestAdapter(timeout=30)
```
## License
MIT License
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
| text/markdown | null | null | null | null | MIT | null | [] | [] | null | null | >=3.10 | [] | [] | [] | [
"requests>=2.31.0"
] | [] | [] | [] | [
"Homepage, https://github.com/Cairnstew/uup-dump-api-py",
"Issues, https://github.com/Cairnstew/uup-dump-api-py/issues"
] | uv/0.10.2 {"installer":{"name":"uv","version":"0.10.2","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"NixOS","version":"25.11","id":"xantusia","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null} | 2026-02-20T10:12:09.243227 | uup_dump_api_py-0.1.5.tar.gz | 13,994 | 73/e2/20b8e790e1375bee6220db091fd36ff85d9580ad17920f2ac5ffed452ecc/uup_dump_api_py-0.1.5.tar.gz | source | sdist | null | false | 78c3b2eb536d6a62c8fb0864e8bcc6e4 | 15cd68e2f3a4929daa64e97e2dafef397c3643eed22c911aa587b1af4095ac5c | 73e220b8e790e1375bee6220db091fd36ff85d9580ad17920f2ac5ffed452ecc | null | [
"LICENSE"
] | 219 |
2.4 | bblocks-client | 0.0.2 | OGC Blocks client | # bblocks-client
A Python client library for working with [OGC Blocks](https://ogcincubator.github.io/bblocks-docs/) registers.
OGC Blocks are reusable specification components that support validation, semantic annotation via JSON-LD,
and federation across distributed registers.
## Installation
```bash
pip install bblocks_client
```
Optional extras for validation and semantic uplift:
```bash
# JSON Schema validation
pip install bblocks_client[jsonschema]
# SHACL validation and semantic uplift (RDF/JSON-LD)
pip install bblocks_client[rdf]
# All extras
pip install bblocks_client[all]
```
## Usage
### Loading a register
```python
from ogc.bblocks.register import load_register
register = load_register("https://example.org/bblocks/register.json")
# Look up a block by identifier
bblock = register.get_item_summary("my.org.bblock-id")
```
Imported registers (dependencies) are loaded automatically. Pass `load_dependencies=False` to skip this.
### Accessing block metadata
`get_item_summary()` returns a `BuildingBlockSummary` with lightweight metadata.
`get_item_full()` fetches the full `BuildingBlock`, including examples and semantic uplift configuration.
```python
bblock = register.get_item_full("my.org.bblock-id")
print(bblock.name)
print(bblock.status) # Status enum: stable, experimental, etc.
print(bblock.depends_on) # Set of dependency identifiers
print(bblock.ld_context) # URL to JSON-LD context
print(bblock.schema) # Dict of media-type -> schema URL
```
### JSON Schema validation
Requires `bblocks_client[jsonschema]`.
```python
from ogc.bblocks.validate import validate_json
result = validate_json(bblock, {"type": "Feature", ...})
if not result.valid:
print(result.exception)
# Or raise directly:
result.raise_for_invalid()
```
### SHACL validation
Requires `bblocks_client[rdf]`.
```python
from rdflib import Graph
from ogc.bblocks.validate import validate_shacl
graph = Graph().parse("data.ttl")
result = validate_shacl(bblock, graph)
print(result.report)
result.raise_for_invalid()
```
### Semantic uplift (JSON to RDF)
Requires `bblocks_client[rdf]`.
```python
from ogc.bblocks.semantic_uplift import uplift_json
rdf_graph = uplift_json(bblock, {"name": "Alice", ...})
print(rdf_graph.serialize(format="turtle"))
```
This applies the block's JSON-LD context to the input data, producing an RDF graph.
Pre- and post-processing steps (jq, SPARQL, SHACL rules) defined in the block are applied automatically.
## License
Apache 2.0
| text/markdown | null | Alejandro Villar <avillar@ogc.org> | null | null | null | ogc, ogc blocks, ogc rainbow | [
"Development Status :: 4 - Beta",
"Topic :: Scientific/Engineering",
"Topic :: Utilities",
"Topic :: Software Development :: Libraries"
] | [] | null | null | >=3.7 | [] | [] | [] | [
"dacite~=1.9.2",
"pyyaml~=6.0",
"jsonschema~=4.26.0; extra == \"jsonschema\"",
"rdflib~=7.0; extra == \"rdf\"",
"pyshacl~=0.28; extra == \"rdf\"",
"jq~=1.11.0; extra == \"rdf\"",
"bblocks_client[jsonschema]; extra == \"all\"",
"bblocks_client[rdf]; extra == \"all\""
] | [] | [] | [] | [
"Homepage, https://github.com/ogcincubator/bblocks-docs/",
"Documentation, https://github.com/ogcincubator/bblocks-docs/",
"Repository, https://github.com/ogcincubator/bblocks-client-python.git"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:11:49.228689 | bblocks_client-0.0.2.tar.gz | 14,992 | cf/8a/fc390a1315c750996f51c28fc6e4b035bdbcc32c3b42ab82f84b9ed59c33/bblocks_client-0.0.2.tar.gz | source | sdist | null | false | 3e11dffef4b81ba49a0a268ab6c46475 | 7f2a4e3c17cb549cb9d18ccb932496a598a471b72910bfbf8a638f4d1d4353ea | cf8afc390a1315c750996f51c28fc6e4b035bdbcc32c3b42ab82f84b9ed59c33 | Apache-2.0 | [
"LICENSE"
] | 222 |
2.4 | pyxll-jupyter | 0.7.1 | Adds Jupyter notebooks to Microsoft Excel using PyXLL. | # PyXLL-Jupyter
Integration for Jupyter notebooks and Microsoft Excel.
See the [Python Jupyter Notebooks in Excel](https://www.pyxll.com/blog/python-jupyter-notebooks-in-excel/) blog post for more details.
## Requirements
- PyXLL >= 5.1.0
- Jupyter >= 1.0.0
- notebook >= 6.0.0
- PySide2, or PySide6 for Python >= 3.10
- pywin32 >= 301, for `%xl_get`/`%xl_set` magic functions
### Optional
- jupyterlab >= 4.0.0
## Installation
To install this package use:
pip install pyxll-jupyter
Once installed a "Jupyter Notebook" button will be added to the PyXLL ribbon tab in Excel, so
long as you have PyXLL 5 or above already installed.
When using Jupyter in Excel the Python kernel runs inside the Excel process using PyXLL. You
can interact with Excel from code in the Jupyter notebook, and write Excel functions
using the PyXLL decorators @xl_menu and @xl_macro etc.
As the kernel runs in the existing Python interpreter in the Excel process it is not possible
to restart the kernel or to use other Python versions or other languages.
## Configuration
To configure, add any of the following settings to your pyxll.cfg file. You do not need
to set all of these, only the ones you wish to change::
[JUPYTER]
; Workbook settings
use_workbook_dir = 0
notebook_dir = C:\Path\To\Your\Documents
subcommand = notebook
; Browser settings
qt =
allow_cookies = 1
private_browser = 0
cache_path =
storage_path =
; Other settings
timeout = 60
disable_ribbon = 0
pause_on_focus_lost = 1
If *use_workbook_dir* is set and the current workbook is saved then Jupyter will open in the same folder
as the current workbook.
*notebook_dir* can be set to an existing folder that will be used as the root documents folder the Jupyter
opens in.
The *subcommand* option can be used to switch the Jupyter subcommand used to launch the Jupyter web server.
It can be set to either `notebook` for the default Jupyter notebook interface, or `lab` if using Jupyterlab
*(experimental)*.
*qt* can be used to switch which Qt implementation is used. Possible values are 'PySide6', 'PyQt6', 'PySide2',
and 'PyQt5'. By default, whichever Qt implementation is installed will be used.
*allow_cookies* will prevent the Qt browser from saving cookies if set to 0.
*private_browser* will prevent the Qt browser from using any previously stored data or saving any data from
the browser session.
*cache_path* can be set to an existing folder for the browser to save cached data. By default this will be
the Qt browser's default cached data path.
*storage_path* can be set to an existing folder for the browser to save persistent storage data. By default this will be
the Qt browser's default persistent storage path.
*timeout* is the maximum number of seconds of inactivity to wait for when starting the Jupyter server process. If you
are getting timeout errors then increasing this may help.
If *disable_ribbon* is set then the ribbon button to start Jupyter will not be shown, however Jupyter
may still be opened using the "OpenJupyterNotebook" macro.
If *pause_on_focus_lost* is set then the Jupyter kernel will be paused whenever no Jupyter tasks panes are
focused. If Jupyter is opened in a web browser this has no effect and the kernel will not be paused.
## Experimental JupyterLab Support
Jupyterlab can be used instead of the default Jupyter Notebook interface by specifying
`subcommand = lab` in the ``[JUPYTER]`` section of the pyxll.cfg file.
This requires Jupyterlab >= 4.0.0 to be installed. At the time of writing, version 4 of Jupyterlab is in
pre-release and can be installed using:
pip install --pre jupyterlab
### Qt
The pyxll-jupyter package uses the Qt [QWebEngineView](https://doc.qt.io/qt-5/qwebengineview.html) widget, and by
default will use the [PySide2](https://pypi.org/project/PySide2/) package for Python <= 3.9 or
the [PySide6](https://pypi.org/project/PySide6/) package for Python >= 3.10.
This can be changed to use [PyQt5](https://www.riverbankcomputing.com/software/pyqt/) by setting `qt = PyQt5` in
the `JUPYTER` section of the config. You will need to have both the `pyqt5` and `pyqtwebengine` packages installed
if using this option. Both can be installed using pip as follows:
pip install pyqt5 pyqtwebengine
## Magic Functions
The following magic functions are available in addition to the standard Jupyter magic functions:
```
%xl_get [-c CELL] [-t TYPE] [-x]
Get the current selection in Excel into Python.
optional arguments:
-c CELL, --cell CELL Address of cell to get value of.
-t TYPE, --type TYPE Datatype to convert the value to.
-x, --no-auto-resize Don't auto-resize the range.
```
```
%xl_set [-c CELL] [-t TYPE] [-f FORMATTER] [-x] value
Set a value to the current selection in Excel.
positional arguments:
value Value to set in Excel.
optional arguments:
-c CELL, --cell CELL Address of cell to get value of.
-t TYPE, --type TYPE Datatype to convert the value to.
-f FORMATTER, --formatter FORMATTER
PyXLL Formatter to use when setting the value.
-x, --no-auto-resize Don't auto-resize the range.
```
```
%xl_plot [-n NAME] [-c CELL] [-w WIDTH] [-h HEIGHT] figure
Plot a figure to Excel in the same way as pyxll.plot.
The figure is exported as an image and inserted into Excel as a Picture object.
If the --name argument is used and the picture already exists then it will not
be resized or moved.
positional arguments:
figure Figure to plot.
optional arguments:
-n NAME, --name NAME Name of the picture object in Excel to use.
-c CELL, --cell CELL Address of cell to use when creating the Picture in
Excel.
-w WIDTH, --width WIDTH
Width in points to use when creating the Picture in
Excel.
-h HEIGHT, --height HEIGHT
Height in points to use when creating the Picture in
Excel.
```
## Opening from VBA
You can open the Jupyter notebook from VBA using the ``OpenJupyterNotebook`` macro, called
via VBA's ``Run`` method. For example::
Run "OpenJupyterNotebook"
The macro takes two arguments, the initial path and a boolean to open in a browser rather
than in Excel task pane if True.
The initial path can either be a valid path, or an empty string.
For example, to open Jupyter in a browser with the default path you would run the macro as follows::
Run "OpenJupyterNotebook", "", True
For more information about installing and using PyXLL see https://www.pyxll.com.
Copyright (c) PyXLL Ltd
| text/markdown | null | Tony Roberts <tony@pyxll.com> | null | null | null | null | [
"Programming Language :: Python :: 3",
"Operating System :: Microsoft :: Windows"
] | [] | null | null | null | [] | [] | [] | [
"pyxll>=5.1.0",
"jupyter>=1.0.0",
"jupyter-client>=6.0.0",
"notebook>=6.0.0",
"packaging; python_version >= \"3.10\"",
"PySide2; python_version < \"3.10\"",
"PySide6!=6.4.2; python_version >= \"3.10\"",
"pywin32>=301"
] | [] | [] | [] | [
"Repository, https://github.com/pyxll/pyxll-jupyter",
"Issues, https://github.com/pyxll/pyxll-jupyter/issues"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:11:45.019802 | pyxll_jupyter-0.7.1.tar.gz | 46,267 | e3/1f/e313a8d7296a91c9d59c77e78717ec93af18a27538a5c1c43c15daa7db01/pyxll_jupyter-0.7.1.tar.gz | source | sdist | null | false | fce662877ec872be135c443292876012 | 2fff0157cbc6efb8c6c26a2b6b8390a9d03ddab2a3acb7afc26d551015475d66 | e31fe313a8d7296a91c9d59c77e78717ec93af18a27538a5c1c43c15daa7db01 | MIT | [
"LICENSE.md"
] | 220 |
2.4 | usecortex-ai | 0.5.8 | The official Python SDK for the Cortex AI platform. | # Cortex AI Python SDK - [usecortex.ai](https://www.usecortex.ai/)
The official Python SDK for the Cortex AI platform. Build powerful, context-aware AI applications in your Python applications.
**Cortex** is your plug-and-play memory infrastructure. It powers intelligent, context-aware retrieval for any AI app or agent. Whether you’re building a customer support bot, research copilot, or internal knowledge assistant.
[Learn more about the SDK from our docs](https://docs.usecortex.ai/)
## Core features
* **Dynamic retrieval and querying** that always retrieve the most relevant context
* **Built-in long-term memory** that evolves with every user interaction
* **Personalization hooks** for user preferences, intent, and history
* **Developer-first SDK** with the most flexible APIs and fine-grained controls
## Getting started
### Installation
```bash
pip install usecortex-ai
```
### Client setup
We provide both synchronous and asynchronous clients. Use **`AsyncCortexAI`** when working with async/await patterns, and **`CortexAI`** for traditional synchronous workflows. Client initialization does not trigger any network requests, so you can safely create as many client instances as needed. Both clients expose the exact same set of methods.
```python
import os
from usecortex_ai import CortexAI, AsyncCortexAI
api_key = os.environ["CORTEX_API_KEY"] # Set your Cortex API key in the environment variable CORTEX_API_KEY. Optional, but recommended.
# Sync client
client = CortexAI(token=api_key)
# Async client (for async/await usage)
async_client = AsyncCortexAI(token=api_key)
```
### Create a Tenant
You can consider a `tenant` as a single database that can have internal isolated collections called `sub-tenants`. [Know more about the concept of tenant here](https://docs.usecortex.ai/essentials/multi-tenant)
```python
def create_tenant():
return client.tenant.create(tenant_id="my-company")
```
### Ingest Your Data
When you index your data, you make it ready for retrieval from Cortex using natural language.
```python
# ingest in your knowledge base
with open("a.pdf", 'rb') as f1, open("b.pdf", 'rb') as f2:
files = [
("a.pdf", f1),
("b.pdf", f2)
]
upload_result = client.upload.knowledge(
tenant_id="tenant_123",
files=files,
file_metadata=[
{
"id": "doc_a",
"tenant_metadata": {"dept": "sales"},
"document_metadata": {"author": "Alice"}
},
{
"id": "doc_b",
"tenant_metadata": {"dept": "marketing"},
"document_metadata": {"author": "Bob"}
}
]
))
# Ingest user memories
from cortex import CortexClient
client = CortexClient(api_key="your_api_key")
# Simple text memory
result = client.user_memory.add(
memories=[
{
"text": "User prefers detailed explanations and dark mode",
"infer": True,
"user_name": "John"
}
],
tenant_id="tenant-01",
sub_tenant_id="",
upsert=True
)
# Markdown content
markdown_result = client.user_memory.add(
memories=[
{
"text": "# Meeting Notes\n\n## Key Points\n- Budget approved",
"is_markdown": True,
"infer": False,
"title": "Meeting Notes"
}
],
tenant_id="tenant-01",
sub_tenant_id="",
upsert=True
)
# User-assistant pairs with inference
conversation_result = client.user_memory.add(
memories=[
{
"user_assistant_pairs": [
{"user": "What are my preferences?", "assistant": "You prefer dark mode."},
{"user": "How do I like reports?", "assistant": "Weekly summaries with charts."}
],
"infer": True,
"user_name": "John",
"custom_instructions": "Extract user preferences"
}
],
tenant_id="tenant-01",
sub_tenant_id="",
upsert=True
)
```
**For a more detailed explanation** of document upload, including supported file formats, processing pipeline, metadata handling, and advanced configuration options, refer to the [Ingest Knowledge endpoint](https://docs.usecortex.ai/api-reference/endpoint/add-knowledge-memories).
### Search
```python
# Semantic Recall
results = client.recall.full_recall(
query="Which mode does user prefer",
tenant_id="tenant_1234",
sub_tenant_id="sub_tenant_4567",
alpha=0.8,
recency_bias=0
)
# Get ingested data (memories + knowledge base)
all_sources = client.data.list_data(
tenant_id="tenant_1234",
sub_tenant_id="sub_tenant_4567"
)
```
**For a more detailed explanation** of search and retrieval, including query parameters, scoring mechanisms, result structure, and advanced search features, refer to the [Search endpoint documentation](https://docs.usecortex.ai/api-reference/endpoint/search).
## SDK Method Structure & Type Safety
Our SDKs follow a predictable pattern that mirrors the API structure while providing full type safety.
> **Method Mapping** : `client.<group>.<function_name>` mirrors `api.usecortex.ai/<group>/<function_name>`
>
> For example: `client.upload.upload_text()` corresponds to `POST /upload/upload_text`
The SDKs provide exact type parity with the API specification:
- **Request Parameters** : Every field documented in the API reference (required, optional, types, validation rules) is reflected in the SDK method signatures
- **Response Objects** : Return types match the exact JSON schema documented for each endpoint
- **Error Types** : Exception structures mirror the error response formats from the API
- **Nested Objects** : Complex nested parameters and responses maintain their full structure and typing
> This means you can rely on your IDE’s autocomplete and type checking. If a parameter is optional in the API docs, it’s optional in the SDK. If a response contains a specific field, your IDE will know about it. Our SDKs are built in such a way that your IDE will automatically provide **autocompletion, type-checking, inline documentation with examples, and compile time validation** for each and every method.
>
> Just hit **Cmd+Space/Ctrl+Space!**
## Links
- **Homepage:** [usecortex.ai](https://www.usecortex.ai/)
- **Documentation:** [docs.usecortex.ai](https://docs.usecortex.ai/)
## Our docs
Please refer to our [API reference](https://docs.usecortex.ai/api-reference/introduction) for detailed explanations of every API endpoint, parameter options, and advanced use cases.
## Support
If you have any questions or need help, please reach out to our support team at [founders@usecortex.ai](mailto:founders@usecortex.ai).
| text/markdown | null | Soham Ratnaparkhi <soham@usecortex.ai> | null | null | Copyright (c) 2024 Cortex AI
All Rights Reserved.
PROPRIETARY AND CONFIDENTIAL
This software is the proprietary and confidential property of Cortex AI ("the Company").
Permission is hereby granted to users to install and use this software as part of the Cortex AI service, subject to the terms and conditions of the service agreement entered into with the Company.
You may not, without the express written permission of the Company:
1. Copy, modify, or create derivative works of the software.
2. Distribute, sell, rent, lease, sublicense, or otherwise transfer the software to any third party.
3. Reverse engineer, decompile, or disassemble the software, except and only to the extent that such activity is expressly permitted by applicable law notwithstanding this limitation.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. | cortex, ai, sdk, api, generative ai, rag | [
"Development Status :: 5 - Production/Stable",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Intended Audience :: Developers",
"Topic :: Software Development :: Libraries :: Python Modules",
"Typing :: Typed"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"httpx>=0.24",
"pydantic<3,>=1.10"
] | [] | [] | [] | [
"Homepage, https://www.usecortex.ai/",
"Documentation, https://docs.usecortex.ai/"
] | twine/6.2.0 CPython/3.11.14 | 2026-02-20T10:11:08.171961 | usecortex_ai-0.5.8.tar.gz | 64,696 | 9d/8d/3c888c705924463323ad14fb5355df04cf8bb8c19e49284f34406aef4f5d/usecortex_ai-0.5.8.tar.gz | source | sdist | null | false | c51cbecaa24d7fdd8ad1b5aba71d2032 | 6f5cbff40f23b7b4a1542a48f0522735fd87ac211573479babf691c8a66f8de3 | 9d8d3c888c705924463323ad14fb5355df04cf8bb8c19e49284f34406aef4f5d | null | [
"LICENSE"
] | 212 |
2.4 | ccs-digitalmarketplace-utils | 79.14.0 | Common utils for Digital Marketplace apps. | Digital Marketplace utils
=========================
[](https://badge.fury.io/py/ccs-digitalmarketplace-utils)
## What's in here?
* Digital Marketplace API clients
* Formatting utilities for Digital Marketplace
* Digital Marketplace logging for Flask using JSON Logging
* Utility functions/libraries for Amazon S3, Mailchimp, Notify, Cloudwatch
* Helper code for Flask configuration
* A formed version of Flask Feature Flags
## Logging from applications
When logging from applications you should write your message as a [format
string](https://docs.python.org/2/library/string.html#format-string-syntax) and pass any required
arguments to the log method in the `extra` named argument. This allows our logging to use them as
separate fields in our JSON logs making it much easier to search and aggregate on them.
```python
logger.info("the user {user_id} did the thing '{thing}'", extra={
'user_id': user_id, 'thing': thing
})
```
Note that apart from not getting the benefit, passing the formatted message can be dangerous. User
generated content may be passed, unescaped to the `.format` method.
## Versioning
Releases of this project follow [semantic versioning](http://semver.org/), ie
> Given a version number MAJOR.MINOR.PATCH, increment the:
>
> - MAJOR version when you make incompatible API changes,
> - MINOR version when you add functionality in a backwards-compatible manner, and
> - PATCH version when you make backwards-compatible bug fixes.
To make a new version:
- update the version in the `dmutils/__init__.py` file
- if you are making a major change, also update the change log;
When the pull request is merged a GitHub Action will tag the new version.
## Pre-commit hooks
This project has a [pre-commit hook][pre-commit hook] to do some general file checks and check the `pyproject.toml`.
Follow the [Quick start][pre-commit quick start] to see how to set this up in your local checkout of this project.
## Licence
Unless stated otherwise, the codebase is released under [the MIT License][mit].
This covers both the codebase and any sample code in the documentation.
The documentation is [© Crown copyright][copyright] and available under the terms
of the [Open Government 3.0][ogl] licence.
[mit]: LICENCE
[copyright]: http://www.nationalarchives.gov.uk/information-management/re-using-public-sector-information/uk-government-licensing-framework/crown-copyright/
[ogl]: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
[pre-commit hook]: https://pre-commit.com/
[pre-commit quick start]: https://pre-commit.com/#quick-start
| text/markdown | GDS Developers, CCS Developers | null | null | null | null | null | [] | [] | null | null | <3.15,>=3.11 | [] | [] | [] | [
"Flask-WTF>=1.2.1",
"Flask<3.2,>=3.0",
"Flask-gzip>=0.2",
"Flask-Login>=0.6.3",
"Flask-Session<0.9.0,>=0.6.0",
"boto3<2,>=1.7.83",
"contextlib2>=21.6.0",
"cryptography>=41.0.4",
"ccs-digitalmarketplace-apiclient>=37.4.1",
"mailchimp3==3.0.21",
"requests<3,>=2.22.0",
"redis>=5.0.1",
"filetype<2,>=1.2.0",
"notifications-python-client<11.0.0,>=8.1.0",
"odfpy>=1.4.1",
"python-json-logger<5.0.0,>=4.0.0",
"pytz",
"unicodecsv>=0.14.1",
"urllib3<3",
"werkzeug<3.2,>=3.0",
"workdays>=1.4",
"ruff==0.15.1; extra == \"dev\"",
"freezegun==1.5.5; extra == \"dev\"",
"hypothesis==6.151.5; extra == \"dev\"",
"moto==5.1.21; extra == \"dev\"",
"mypy==1.19.1; extra == \"dev\"",
"pytest==9.0.2; extra == \"dev\"",
"pytest-cov==7.0.0; extra == \"dev\"",
"pytest-datadir==1.8.0; extra == \"dev\"",
"requests-mock==1.12.1; extra == \"dev\"",
"testfixtures==10.0.0; extra == \"dev\"",
"ccs-digitalmarketplace-test-utils==7.7.0; extra == \"dev\"",
"types-python-dateutil; extra == \"dev\"",
"types-pytz; extra == \"dev\"",
"types-redis; extra == \"dev\"",
"types-requests; extra == \"dev\"",
"pre-commit==4.5.1; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/Crown-Commercial-Service/ccs-digitalmarketplace-utils",
"Repository, https://github.com/Crown-Commercial-Service/ccs-digitalmarketplace-utils.git",
"Issues, https://github.com/Crown-Commercial-Service/ccs-digitalmarketplace-utils/issues",
"Changelog, https://github.com/Crown-Commercial-Service/ccs-digitalmarketplace-utils/CHANGELOG.md"
] | twine/6.2.0 CPython/3.11.14 | 2026-02-20T10:10:50.959058 | ccs_digitalmarketplace_utils-79.14.0.tar.gz | 84,648 | fe/c2/3370e7ffa64ae5e7ecde3b8f43d95df0749b3026c27962e52d667758e947/ccs_digitalmarketplace_utils-79.14.0.tar.gz | source | sdist | null | false | af2793f536d73db11beceb4908347da9 | 70d35d6739069cc68d81247891af556fb8f9b84678d3d325355250a925309cc9 | fec23370e7ffa64ae5e7ecde3b8f43d95df0749b3026c27962e52d667758e947 | null | [
"LICENCE"
] | 211 |
2.4 | mycli | 1.55.0 | CLI for MySQL Database. With auto-completion and syntax highlighting. | # mycli
[](https://github.com/dbcli/mycli/actions?query=workflow%3Amycli)
A command line client for MySQL that can do auto-completion and syntax highlighting.
Homepage: [http://mycli.net](http://mycli.net)
Documentation: [http://mycli.net/docs](http://mycli.net/docs)
Postgres Equivalent: [http://pgcli.com](http://pgcli.com)
Quick Start
-----------
If you already know how to install Python packages, then you can install it via `pip`:
You might need sudo on Linux.
```bash
pip install -U 'mycli[all]'
```
or
```bash
brew update && brew install mycli # Only on macOS
```
or
```bash
sudo apt-get install mycli # Only on Debian or Ubuntu
```
### Usage
See
```bash
mycli --help
```
Features
--------
`mycli` is written using [prompt_toolkit](https://github.com/jonathanslenders/python-prompt-toolkit/).
* Auto-completion as you type for SQL keywords as well as tables, views and
columns in the database.
* Fuzzy history search using [fzf](https://github.com/junegunn/fzf).
* Syntax highlighting using Pygments.
* Smart-completion (enabled by default) will suggest context-sensitive completion.
- `SELECT * FROM <tab>` will only show table names.
- `SELECT * FROM users WHERE <tab>` will only show column names.
* Support for multiline queries.
* Favorite queries with optional positional parameters. Save a query using
`\fs <alias> <query>` and execute it with `\f <alias>`.
* Timing of sql statements and table rendering.
* Log every query and its results to a file (disabled by default).
* Pretty print tabular data (with colors!).
* Support for SSL connections
* Shell-style trailing redirects with `$>`, `$>>` and `$|` operators.
* Support for querying LLMs with context derived from your schema.
* Support for storing passwords in the system keyring.
Mycli creates a config file `~/.myclirc` on first run; you can use the
options in that file to configure the above features, and more.
Some features are only exposed as [key bindings](doc/key_bindings.rst).
Contributions:
--------------
If you're interested in contributing to this project, first of all I would like
to extend my heartfelt gratitude. I've written a small doc to describe how to
get this running in a development setup.
https://github.com/dbcli/mycli/blob/main/CONTRIBUTING.md
## Additional Install Instructions:
These are some alternative ways to install mycli that are not managed by our team but provided by OS package maintainers. These packages could be slightly out of date and take time to release the latest version.
### Arch, Manjaro
You can install the mycli package available in the AUR:
```
yay -S mycli
```
### Debian, Ubuntu
On Debian, Ubuntu distributions, you can easily install the mycli package using apt:
```
sudo apt-get install mycli
```
### Fedora
Fedora has a package available for mycli, install it using dnf:
```
sudo dnf install mycli
```
### Windows
#### Option 1: Native Windows
Install the `less` pager, for example by `scoop install less`.
Follow the instructions on this blogpost: http://web.archive.org/web/20221006045208/https://www.codewall.co.uk/installing-using-mycli-on-windows/
**Mycli is not tested on Windows**, but the libraries used in the app are Windows-compatible.
This means it should work without any modifications, but isn't supported.
PRs to add native Windows testing to Mycli CI would be welcome!
#### Option 2: WSL
Everything should work as expected in WSL. This is a good option for using
Mycli on Windows.
### Thanks:
This project was funded through kickstarter. My thanks to the [backers](http://mycli.net/sponsors) who supported the project.
A special thanks to [Jonathan Slenders](https://twitter.com/jonathan_s) for
creating [Python Prompt Toolkit](http://github.com/jonathanslenders/python-prompt-toolkit),
which is quite literally the backbone library, that made this app possible.
Jonathan has also provided valuable feedback and support during the development
of this app.
[Click](http://click.pocoo.org/) is used for command line option parsing
and printing error messages.
Thanks to [PyMysql](https://github.com/PyMySQL/PyMySQL) for a pure python adapter to MySQL database.
### Compatibility
Mycli is tested on macOS and Linux, and requires Python 3.10 or better.
To connect to MySQL versions earlier than 5.5, you may need to set the following in `~/.myclirc`:
```
# character set for connections without --charset being set at the CLI
default_character_set = utf8
```
or set `--charset=utf8` when invoking MyCLI.
### Configuration and Usage
For more information on using and configuring mycli, [check out our documentation](http://mycli.net/docs).
Common topics include:
- [Configuring mycli](http://mycli.net/config)
- [Using/Disabling the pager](http://mycli.net/pager)
- [Syntax colors](http://mycli.net/syntax)
| text/markdown | null | Mycli Core Team <mycli-dev@googlegroups.com> | null | null | null | null | [] | [] | null | null | >=3.10 | [] | [] | [] | [
"click~=8.3.1",
"cryptography~=46.0.5",
"Pygments~=2.19.2",
"prompt_toolkit<4.0.0,>=3.0.6",
"PyMySQL~=1.1.2",
"sqlparse<0.6.0,>=0.3.0",
"sqlglot[rs]==27.*",
"configobj~=5.0.9",
"cli_helpers[styles]~=2.10.1",
"pyperclip~=1.11.0",
"pycryptodomex~=3.23.0",
"pyfzf~=0.3.1",
"rapidfuzz~=3.14.3",
"keyring~=25.7.0",
"paramiko~=3.5.1; extra == \"ssh\"",
"sshtunnel~=0.4.0; extra == \"ssh\"",
"llm~=0.28.0; extra == \"llm\"",
"setuptools==82.*; extra == \"llm\"",
"pip==26.*; extra == \"llm\"",
"mycli[ssh]; extra == \"all\"",
"mycli[llm]; extra == \"all\"",
"behave~=1.3.3; extra == \"dev\"",
"coverage~=7.13.4; extra == \"dev\"",
"mypy~=1.19.1; extra == \"dev\"",
"pexpect~=4.9.0; extra == \"dev\"",
"pytest~=9.0.2; extra == \"dev\"",
"pytest-cov~=7.0.0; extra == \"dev\"",
"tox~=4.35.0; extra == \"dev\"",
"pdbpp~=0.11.7; extra == \"dev\"",
"paramiko~=3.5.1; extra == \"dev\"",
"sshtunnel~=0.4.0; extra == \"dev\"",
"llm~=0.28.0; extra == \"dev\"",
"setuptools==82.*; extra == \"dev\"",
"pip==26.*; extra == \"dev\"",
"ruff~=0.15.0; extra == \"dev\""
] | [] | [] | [] | [
"homepage, http://mycli.net"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:09:43.342285 | mycli-1.55.0.tar.gz | 342,165 | 28/1c/6f423310b346703ac0e711035c619fc8233375201f2f4865c87cdd0633ec/mycli-1.55.0.tar.gz | source | sdist | null | false | b971e8cdbfc7a3597e6c19de222c6f61 | 6d295cfc0817a7f95a43d91b5fc3b04a23fccdd7d970b67d51ad62d75af38cd3 | 281c6f423310b346703ac0e711035c619fc8233375201f2f4865c87cdd0633ec | BSD-3-Clause | [
"LICENSE.txt",
"AUTHORS.rst"
] | 801 |
2.4 | jsonschema-path | 0.4.1 | JSONSchema Spec with object-oriented paths | ***************
JSONSchema Path
***************
.. image:: https://img.shields.io/pypi/v/jsonschema-path.svg
:target: https://pypi.python.org/pypi/jsonschema-path
.. image:: https://travis-ci.org/p1c2u/jsonschema-path.svg?branch=master
:target: https://travis-ci.org/p1c2u/jsonschema-path
.. image:: https://img.shields.io/codecov/c/github/p1c2u/jsonschema-path/master.svg?style=flat
:target: https://codecov.io/github/p1c2u/jsonschema-path?branch=master
.. image:: https://img.shields.io/pypi/pyversions/jsonschema-path.svg
:target: https://pypi.python.org/pypi/jsonschema-path
.. image:: https://img.shields.io/pypi/format/jsonschema-path.svg
:target: https://pypi.python.org/pypi/jsonschema-path
.. image:: https://img.shields.io/pypi/status/jsonschema-path.svg
:target: https://pypi.python.org/pypi/jsonschema-path
About
#####
Object-oriented JSONSchema
Key features
############
* Traverse schema like paths
* Access schema on demand with separate dereferencing accessor layer
Installation
############
.. code-block:: console
pip install jsonschema-path
Alternatively you can download the code and install from the repository:
.. code-block:: console
pip install -e git+https://github.com/p1c2u/jsonschema-path.git#egg=jsonschema_path
Usage
#####
.. code-block:: python
>>> from jsonschema_path import SchemaPath
>>> d = {
... "properties": {
... "info": {
... "$ref": "#/$defs/Info",
... },
... },
... "$defs": {
... "Info": {
... "properties": {
... "title": {
... "$ref": "http://example.com",
... },
... "version": {
... "type": "string",
... "default": "1.0",
... },
... },
... },
... },
... }
>>> path = SchemaPath.from_dict(d)
>>> # Stat keys
>>> "properties" in path
True
>>> # Concatenate paths with /
>>> info_path = path / "properties" / "info"
>>> # Stat keys with implicit dereferencing
>>> "properties" in info_path
True
>>> # Concatenate paths with implicit dereferencing
>>> version_path = info_path / "properties" / "version"
>>> # Open content with implicit dereferencing
>>> with version_path.open() as contents:
... print(contents)
{'type': 'string', 'default': '1.0'}
Benchmarks
##########
Benchmarks mirror the lightweight (dependency-free) JSON output format used in
`pathable`.
Run locally with Poetry:
.. code-block:: console
poetry run python -m tests.benchmarks.bench_parse --output reports/bench-parse.json
poetry run python -m tests.benchmarks.bench_lookup --output reports/bench-lookup.json
For a quick smoke run:
.. code-block:: console
poetry run python -m tests.benchmarks.bench_parse --output reports/bench-parse.quick.json --quick
poetry run python -m tests.benchmarks.bench_lookup --output reports/bench-lookup.quick.json --quick
You can also control repeats/warmup via env vars:
.. code-block:: console
export JSONSCHEMA_PATH_BENCH_REPEATS=5
export JSONSCHEMA_PATH_BENCH_WARMUP=1
Compare two results:
.. code-block:: console
poetry run python -m tests.benchmarks.compare_results \
--baseline reports/bench-lookup-master.json \
--candidate reports/bench-lookup.json \
--tolerance 0.20
Related projects
################
* `openapi-core <https://github.com/p1c2u/openapi-core>`__
Python library that adds client-side and server-side support for the OpenAPI.
* `openapi-spec-validator <https://github.com/p1c2u/openapi-spec-validator>`__
Python library that validates OpenAPI Specs against the OpenAPI 2.0 (aka Swagger) and OpenAPI 3.0 specification
* `openapi-schema-validator <https://github.com/p1c2u/openapi-schema-validator>`__
Python library that validates schema against the OpenAPI Schema Specification v3.0.
License
#######
Copyright (c) 2017-2025, Artur Maciag, All rights reserved. Apache-2.0
| text/x-rst | Artur Maciag | maciag.artur@gmail.com | null | null | Apache-2.0 | jsonschema, swagger, spec | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"License :: OSI Approved :: Apache Software License",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14",
"Topic :: Software Development :: Libraries",
"Topic :: Software Development :: Libraries :: Python Modules"
] | [] | null | null | <4.0.0,>=3.10 | [] | [] | [] | [
"PyYAML>=5.1",
"pathable<0.6.0,>=0.5.0",
"referencing<0.38.0",
"requests<3.0.0,>=2.31.0; extra == \"requests\""
] | [] | [] | [] | [
"Repository, https://github.com/p1c2u/jsonschema-path"
] | twine/6.1.0 CPython/3.13.7 | 2026-02-20T10:09:41.611056 | jsonschema_path-0.4.1.tar.gz | 13,450 | a8/8d/4b2e648cf643d19e1f76260d9cb002d242e38b4298d6da110bd3c3d8d0d2/jsonschema_path-0.4.1.tar.gz | source | sdist | null | false | b256fb2abe76a59f7f74186a0388e6bd | ffca3bd37f66364ae3afeaa2804d6078a9ab3b9359ade4dd9923aabbbd475e71 | a88d4b2e648cf643d19e1f76260d9cb002d242e38b4298d6da110bd3c3d8d0d2 | null | [
"LICENSE"
] | 1,255,135 |
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