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LICENSE
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Apache License
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Version 2.0, January 2004
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This repository ships a template dataset package.
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For the full Apache-2.0 text, see: https://www.apache.org/licenses/LICENSE-2.0
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README.md
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---
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pretty_name: OpenToolTrace-X (Platinum)
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language:
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- en
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license: apache-2.0
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task_categories:
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- text-generation
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- question-answering
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- reinforcement-learning
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tags:
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- agents
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- tool-use
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- trajectories
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- verification
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- code
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- bash
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- git
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size_categories:
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- n<1K # sample pack; replace after scaling
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dataset_info:
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creator: "Within US AI"
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contact: "Within US AI"
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created: "2025-12-30T16:53:41Z"
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schema: "See Features section below"
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---
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# OpenToolTrace-X (Platinum)
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**Developer/Publisher:** Within US AI
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**Version:** 0.1.0 (sample pack)
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**Created:** 2025-12-30T16:53:41Z
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## What this dataset is
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`OpenToolTrace-X` is a **replayable, verifiable** corpus of tool-using agent trajectories.
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Each record contains:
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- A user goal (`prompt`) and constraints
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- An `initial_state` describing the starting environment/repo snapshot
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- A `trajectory` (tool calls + observations)
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- A `final_state` (artifacts/diff/output)
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- `verification` (tests, checksums, exit codes) to make outcomes **machine-checkable**
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## Features / schema (JSONL)
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- `task_id` (string)
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- `domain` (string; e.g., `python`, `bash`, `git`, `data`)
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- `difficulty` (int; 1–5)
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- `prompt` (string)
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- `constraints` (string)
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- `initial_state` (object)
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- `trajectory` (list of objects)
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- `final_state` (object)
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- `verification` (object)
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- `tags` (list of strings)
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- `created_utc` (string; ISO 8601)
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- `license_note` (string)
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### Trajectory step format
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Each step is a dict:
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- `tool` (e.g., `bash`, `python`, `git`)
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- `action` (command / code / args)
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- `observation` (stdout / structured output)
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- `exit_code` (int)
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- `stderr` (string, optional)
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- `artifacts_written` (list of strings, optional)
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## Data splits
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- `data/train.jsonl`
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- `data/validation.jsonl`
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- `data/test.jsonl`
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## Replay harness (scaffold)
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See `replay_harness/` for a safe, non-executing replay viewer.
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Integrate your own sandbox executor for real replays.
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## How to load
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```python
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from datasets import load_dataset
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ds = load_dataset("json", data_files={
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"train": "data/train.jsonl",
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"validation": "data/validation.jsonl",
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"test": "data/test.jsonl",
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})
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print(ds["train"][0])
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```
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