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README.md

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+# ToolGym
+
+**ToolGym** is an **open-world tool-using environment** for *scalable agent testing and data curation*.
+
+> Large tool pools • long-horizon workflows • wild constraints • unreliable tool states  
+> 面向真实世界工具生态的可扩展评测与数据引擎
+
+---
+
+## Quick links
+
+- 🏆 **Leaderboard**: **(add link here)** — e.g., `/spaces/ToolGym/leaderboard`
+- 📦 **Dataset(s)**: `/datasets/ToolGym/ToolGym`
+- 💬 **Discussions**: `/datasets/ToolGym/ToolGym/discussions`
+- 📄 **Paper / Technical report**: **(add link here)**
+- 💻 **Code**: **(add link here)**
+
+---
+
+## Key stats
+
+- **5,571** validated tools (unified in **MCP format**)  
+- **204** real-world apps covered, from **276** MCP servers  
+- Long-horizon tasks with **wild, realistic constraints** (avg. **28.5** tool-use rounds per task)  
+- A **State Controller** that injects realistic failures & drift (timeouts, rate limits, transient unavailability, etc.)  
+- An evaluation protocol that scores **quality, robustness, constraint following, and planning**  
+- **1,170** tool-use trajectories curated for instruction tuning / training
+
+(Stats and design details are summarized from our paper draft.) :contentReference[oaicite:0]{index=0}
+
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-title: README
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+
+## What is ToolGym?
+
+ToolGym is designed to close the gap between “clean” function-calling benchmarks and **messy real-world tool ecosystems**. It supports both:
+
+- **Benchmarking**: stress-test agents on long, multi-tool workflows under constraints and failures
+- **Data curation**: automatically collect high-quality trajectories for training tool-using agents
+
 ---
 
-Edit this `README.md` markdown file to author your organization card.
+## Core components
+
+### 1) Tool universe (MCP)
+
+We curate and validate a large library of production-like tools, then standardize them under a unified **Model Context Protocol (MCP)** interface so agents can call tools consistently across apps and servers.
+
+### 2) Tool retrieval index
+
+Because open-world tool selection is the real challenge, ToolGym includes a retrieval layer so agents can search tools using natural language queries and load relevant tools on demand.
+
+### 3) Task creation engine
+
+ToolGym can synthesize **long-horizon, multi-tool workflows** that look like real user requests:
+- multi-step dependencies
+- cross-app orchestration
+- dense constraints (format, ordering, trade-offs, verification requirements, etc.)
+
+### 4) State Controller (robustness testing)
+
+To go beyond “happy-path” evaluation, ToolGym introduces a controllable middleware that can inject:
+- tool-level failures (timeouts, temporary unavailability)
+- state-level drift (corrupted/delayed results, expired sessions)
+- constraint changes mid-execution (updated preferences, shifting deadlines)
+
+### 5) Evaluation protocol
+
+ToolGym evaluates agents on multiple axes, including:
+- **Answer quality** (completeness, grounding)
+- **Robustness** (schema compliance, recovery, flexibility)
+- **Constraint following** (format + other constraints)
+- **Planning** (goal decomposition, progress tracking, efficiency)
+
+---
+
+## Leaderboard
+
+We maintain a public leaderboard for ToolGym.  
+➡️ **Leaderboard link**: **(add link here)**
+
+If you use our leaderboard results, please cite the corresponding paper/technical report (link above).
+
+---
+
+## License
+
+- This organization and its public repos are released under the **MIT** license unless otherwise specified in each repo.
+
+---
+
+## Contributing
+
+Community contributions are welcome:
+- Open a discussion: `/datasets/ToolGym/ToolGym/discussions`
+- Submit PRs to the relevant repo (dataset / code / leaderboard Space)
+
+---
+
+## Contact
+
+For questions, collaborations, or leaderboard submissions, please open an issue/discussion or contact the maintainers via the links above.