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README.md

@@ -1,109 +1,249 @@
 ---
-title: FastAPI NVIDIA A10G
-emoji: 🚀
+title: ORYNXML Complete AI Platform
+emoji: 🤖
 colorFrom: blue
-colorTo: green
+colorTo: indigo
 sdk: docker
-pinned: false
+app_port: 7860
+pinned: true
 license: apache-2.0
+short_description: Complete AI Platform with 211 models across 8 categories
 suggested_hardware: a10g-large
 suggested_storage: large
+tags:
+- AI
+- Authentication
+- Multi-Modal
+- HuggingFace
+- OpenManus
+- Qwen
+- DeepSeek
+- TTS
+- STT
+- Face-Swap
+- Avatar
+- Arabic
+- English
+- Cloudflare
 ---
 
-# FastAPI Service with NVIDIA A10G Large GPU
+<p align="center">
+  <img src="assets/logo.jpg" width="200"/>
+</p>
 
-High-performance FastAPI service running on NVIDIA A10G Large GPU (24GB VRAM).
+English | [中文](README_zh.md) | [한국어](README_ko.md) | [日本語](README_ja.md)
 
-## 🚀 Features
+[![GitHub stars](https://img.shields.io/github/stars/FoundationAgents/OpenManus?style=social)](https://github.com/FoundationAgents/OpenManus/stargazers)
+&ensp;
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) &ensp;
+[![Discord Follow](https://dcbadge.vercel.app/api/server/DYn29wFk9z?style=flat)](https://discord.gg/DYn29wFk9z)
+[![Demo](https://img.shields.io/badge/Demo-Hugging%20Face-yellow)](https://huggingface.co/spaces/lyh-917/OpenManusDemo)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15186407.svg)](https://doi.org/10.5281/zenodo.15186407)
 
-- **FastAPI Framework**: Modern, fast web framework for building APIs
-- **Uvicorn Server**: Lightning-fast ASGI server with uvicorn[standard]
-- **GPU Acceleration**: NVIDIA A10G Large (24GB VRAM, 24 vCPU, 96GB RAM)
-- **Docker SDK**: Containerized deployment for reliability
-- **PyTorch Support**: Full CUDA support for ML workloads
-- **Auto-scaling**: Optimized for high-performance workloads
+# 👋 OpenManus - Complete AI Platform
 
-## 📊 Hardware Specs
+🤖 **200+ AI Models + Mobile Authentication + Cloudflare Services**
 
-- **GPU**: NVIDIA A10G Large (24GB VRAM)
-- **CPU**: 24 vCPUs
-- **RAM**: 96GB
-- **Storage**: Large (100GB)
-- **Cost**: ~$3.15/hour
+Manus is incredible, but OpenManus can achieve any idea without an *Invite Code* 🛫!
 
-## 🔗 API Endpoints
+## 🌟 Environment Variables
 
-### Root
+Set these in your HuggingFace Space settings for full functionality:
+
+```bash
+# Required for full Cloudflare integration
+CLOUDFLARE_API_TOKEN=your_cloudflare_token
+CLOUDFLARE_ACCOUNT_ID=your_account_id
+CLOUDFLARE_D1_DATABASE_ID=your_d1_database_id
+CLOUDFLARE_R2_BUCKET_NAME=your_r2_bucket
+CLOUDFLARE_KV_NAMESPACE_ID=your_kv_namespace
+
+# Enhanced AI model access
+HF_TOKEN=your_huggingface_token
+OPENAI_API_KEY=your_openai_key
+ANTHROPIC_API_KEY=your_anthropic_key
+
+# Application configuration
+ENVIRONMENT=production
+LOG_LEVEL=INFO
+SECRET_KEY=your_secret_key
 ```
-GET /
+
+Our team members [@Xinbin Liang](https://github.com/mannaandpoem) and [@Jinyu Xiang](https://github.com/XiangJinyu) (core authors), along with [@Zhaoyang Yu](https://github.com/MoshiQAQ), [@Jiayi Zhang](https://github.com/didiforgithub), and [@Sirui Hong](https://github.com/stellaHSR), we are from [@MetaGPT](https://github.com/geekan/MetaGPT). The prototype is launched within 3 hours and we are keeping building!
+
+It's a simple implementation, so we welcome any suggestions, contributions, and feedback!
+
+Enjoy your own agent with OpenManus!
+
+We're also excited to introduce [OpenManus-RL](https://github.com/OpenManus/OpenManus-RL), an open-source project dedicated to reinforcement learning (RL)- based (such as GRPO) tuning methods for LLM agents, developed collaboratively by researchers from UIUC and OpenManus.
+
+## Project Demo
+
+<video src="https://private-user-images.githubusercontent.com/61239030/420168772-6dcfd0d2-9142-45d9-b74e-d10aa75073c6.mp4?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.UuHQCgWYkh0OQq9qsUWqGsUbhG3i9jcZDAMeHjLt5T4" data-canonical-src="https://private-user-images.githubusercontent.com/61239030/420168772-6dcfd0d2-9142-45d9-b74e-d10aa75073c6.mp4?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.UuHQCgWYkh0OQq9qsUWqGsUbhG3i9jcZDAMeHjLt5T4" controls="controls" muted="muted" class="d-block rounded-bottom-2 border-top width-fit" style="max-height:640px; min-height: 200px"></video>
+
+## Installation
+
+We provide two installation methods. Method 2 (using uv) is recommended for faster installation and better dependency management.
+
+### Method 1: Using conda
+
+1. Create a new conda environment:
+
+```bash
+conda create -n open_manus python=3.12
+conda activate open_manus
 ```
-Returns API information and available endpoints.
 
-### Health Check
+2. Clone the repository:
+
+```bash
+git clone https://github.com/FoundationAgents/OpenManus.git
+cd OpenManus
 ```
-GET /health
+
+3. Install dependencies:
+
+```bash
+pip install -r requirements.txt
 ```
-Returns service health status and GPU availability.
 
-### GPU Information
+### Method 2: Using uv (Recommended)
+
+1. Install uv (A fast Python package installer and resolver):
+
+```bash
+curl -LsSf https://astral.sh/uv/install.sh | sh
 ```
-GET /gpu-info
+
+2. Clone the repository:
+
+```bash
+git clone https://github.com/FoundationAgents/OpenManus.git
+cd OpenManus
 ```
-Returns detailed GPU specifications and memory information.
 
-### Process Text
+3. Create a new virtual environment and activate it:
+
+```bash
+uv venv --python 3.12
+source .venv/bin/activate  # On Unix/macOS
+# Or on Windows:
+# .venv\Scripts\activate
 ```
-POST /process
-Content-Type: application/json
 
-{
-  "text": "Your text here",
-  "max_length": 100
-}
+4. Install dependencies:
+
+```bash
+uv pip install -r requirements.txt
 ```
-Example text processing endpoint.
 
-## 🛠️ API Documentation
+### Browser Automation Tool (Optional)
+```bash
+playwright install
+```
 
-Interactive API documentation available at:
-- Swagger UI: `/docs`
-- ReDoc: `/redoc`
+## Configuration
 
-## 🔧 Configuration
+OpenManus requires configuration for the LLM APIs it uses. Follow these steps to set up your configuration:
 
-The service runs on port 7860 (HuggingFace Spaces default) with:
-- Single worker process for GPU efficiency
-- CORS enabled for cross-origin requests
-- Automatic GPU detection and utilization
+1. Create a `config.toml` file in the `config` directory (you can copy from the example):
 
-## 📦 Dependencies
+```bash
+cp config/config.example.toml config/config.toml
+```
 
-- FastAPI 0.104.1
-- Uvicorn[standard] 0.24.0
-- PyTorch 2.1.0 (CUDA support)
-- Pydantic 2.5.0
+2. Edit `config/config.toml` to add your API keys and customize settings:
+
+```toml
+# Global LLM configuration
+[llm]
+model = "gpt-4o"
+base_url = "https://api.openai.com/v1"
+api_key = "sk-..."  # Replace with your actual API key
+max_tokens = 4096
+temperature = 0.0
+
+# Optional configuration for specific LLM models
+[llm.vision]
+model = "gpt-4o"
+base_url = "https://api.openai.com/v1"
+api_key = "sk-..."  # Replace with your actual API key
+```
 
-## 🚀 Usage
+## Quick Start
 
-```python
-import requests
+One line for run OpenManus:
 
-# Health check
-response = requests.get("https://huggingface.co/spaces/Speedofmastery/yyuujhu")
-print(response.json())
+```bash
+python main.py
+```
 
-# GPU info
-gpu_info = requests.get("https://huggingface.co/spaces/Speedofmastery/yyuujhu/gpu-info")
-print(gpu_info.json())
+Then input your idea via terminal!
 
-# Process text
-result = requests.post(
-    "https://huggingface.co/spaces/Speedofmastery/yyuujhu/process",
-    json={"text": "Hello World", "max_length": 100}
-)
-print(result.json())
+For MCP tool version, you can run:
+```bash
+python run_mcp.py
 ```
 
-## 📝 License
+For unstable multi-agent version, you also can run:
+
+```bash
+python run_flow.py
+```
+
+### Custom Adding Multiple Agents
+
+Currently, besides the general OpenManus Agent, we have also integrated the DataAnalysis Agent, which is suitable for data analysis and data visualization tasks. You can add this agent to `run_flow` in `config.toml`.
+
+```toml
+# Optional configuration for run-flow
+[runflow]
+use_data_analysis_agent = true     # Disabled by default, change to true to activate
+```
+In addition, you need to install the relevant dependencies to ensure the agent runs properly: [Detailed Installation Guide](app/tool/chart_visualization/README.md##Installation)
+
+## How to contribute
+
+We welcome any friendly suggestions and helpful contributions! Just create issues or submit pull requests.
+
+Or contact @mannaandpoem via 📧email: mannaandpoem@gmail.com
+
+**Note**: Before submitting a pull request, please use the pre-commit tool to check your changes. Run `pre-commit run --all-files` to execute the checks.
+
+## Community Group
+Join our networking group on Feishu and share your experience with other developers!
 
-Apache 2.0
+<div align="center" style="display: flex; gap: 20px;">
+    <img src="assets/community_group.jpg" alt="OpenManus 交流群" width="300" />
+</div>
+
+## Star History
+
+[![Star History Chart](https://api.star-history.com/svg?repos=FoundationAgents/OpenManus&type=Date)](https://star-history.com/#FoundationAgents/OpenManus&Date)
+
+## Sponsors
+Thanks to [PPIO](https://ppinfra.com/user/register?invited_by=OCPKCN&utm_source=github_openmanus&utm_medium=github_readme&utm_campaign=link) for computing source support.
+> PPIO: The most affordable and easily-integrated MaaS and GPU cloud solution.
+
+
+## Acknowledgement
+
+Thanks to [anthropic-computer-use](https://github.com/anthropics/anthropic-quickstarts/tree/main/computer-use-demo), [browser-use](https://github.com/browser-use/browser-use) and [crawl4ai](https://github.com/unclecode/crawl4ai) for providing basic support for this project!
+
+Additionally, we are grateful to [AAAJ](https://github.com/metauto-ai/agent-as-a-judge), [MetaGPT](https://github.com/geekan/MetaGPT), [OpenHands](https://github.com/All-Hands-AI/OpenHands) and [SWE-agent](https://github.com/SWE-agent/SWE-agent).
+
+We also thank stepfun(阶跃星辰) for supporting our Hugging Face demo space.
+
+OpenManus is built by contributors from MetaGPT. Huge thanks to this agent community!
+
+## Cite
+```bibtex
+@misc{openmanus2025,
+  author = {Xinbin Liang and Jinyu Xiang and Zhaoyang Yu and Jiayi Zhang and Sirui Hong and Sheng Fan and Xiao Tang},
+  title = {OpenManus: An open-source framework for building general AI agents},
+  year = {2025},
+  publisher = {Zenodo},
+  doi = {10.5281/zenodo.15186407},
+  url = {https://doi.org/10.5281/zenodo.15186407},
+}
+```