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--- |
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license: mit |
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task_categories: |
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- question-answering |
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- text-classification |
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tags: |
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- conversational |
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- question-answering |
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- agentic |
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- multi-agent |
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- tool-calling |
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- function-calling |
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- routing |
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- agent-routing |
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- agentic workflows |
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- multi-agent coordination |
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--- |
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[](https://webxos.netlify.app) |
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[](https://github.com/webxos/webxos) |
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[](https://huggingface.co/webxos) |
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[](https://x.com/webxos) |
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# OPENCHEF!: AGENTIC DATASET |
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This dataset contains simulated multi-agent sessions generated by OPENCHEF!: AGENTIC, |
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designed for training AI agents in tool use and routing decisions. You can download the app from |
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the .zip folder in this repo. |
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The OPENCLAW Agentic Dataset contains 770 full multi-agent interaction traces generated |
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by OPENCHEF!:AGENTIC v2.0 — a small-scale simulated agent orchestration system built for |
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realistic tool-calling, routing, and error-recovery training. Each session simulates a user |
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prompt being handled by a team of specialized agents (Router, Email, Calendar, Code, Web, User |
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Proxy, System, etc.) using ReAct-style reasoning, explicit tool calls, and dynamic routing with |
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a deliberate 15% routing-error rate to force models to learn recovery patterns. Sessions average |
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8–15 steps, show 92.9% final success rate, and include rich metadata such as tool-use counts, |
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routing decisions, timestamps, and generator version. The dataset is already split into train |
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(539 ≈70%), validation (127 ≈16.5%), and test (104 ≈13.5%). |
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**Intended use** |
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Fine-tuning and evaluation of agentic LLMs on multi-agent coordination, function/tool calling, |
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routing logic, error detection & recovery, ReAct-style reasoning, and realistic agent simulation. |
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Especially valuable for studying how models handle routing mistakes, tool failures, and context |
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management across specialized agents. |
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## Key Stats |
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- **Total sessions**: 770 |
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- **Splits**: train (539 ≈70%), validation (127 ≈16.5%), test (104 ≈13.5%) |
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- **Success rate**: 92.9% |
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- **Average steps**: varies (typically 8–15 per session) |
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- **License**: MIT |
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### Dataset Summary |
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- **Total Sessions**: 770 |
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- **Train**: 539 (70.0%) |
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- **Validation**: 127 (16.5%) |
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- **Test**: 104 (13.5%) |
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- **Success Rate**: 92.9% |
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- **Generated**: 2026-01-30T10:54:18.423Z |
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### Supported Tasks |
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- Tool calling with function-calling format |
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- Multi-agent routing decisions |
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- Error recovery from routing mistakes |
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- Agent coordinationExample Tasks: |
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- Calendar: create/check events, conflict detection |
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- Email: search, read, forward/send |
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- Code: write/debug Node.js, TypeScript, Python scripts |
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- Web: search, summarize pages |
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- File/system: basic operations |
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### Data Fields |
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Each example contains: |
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- `id`: Unique session identifier |
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- `split`: Dataset split (train/validation/test) |
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- `task_description`: User instruction for the session |
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- `agents_involved`: List of agent types in the session |
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- `full_trace`: Complete session trace with thoughts, actions, and observations |
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- `final_success`: Whether the session completed successfully |
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- `steps_count`: Number of turns in the session |
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- `tool_use_count`: Number of tool calls made |
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- `routing_decisions`: Number of routing decisions made |
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- `metadata`: Generation metadata |
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### Data Splits |
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The dataset is split into train (70%), validation (15%), and test (15%) sets. |
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### For Training Agentic Models |
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This dataset is optimized for training models on: |
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- Tool calling behavior |
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- Routing decisions between specialized agents |
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- Error recovery from incorrect routing |
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- Multi-agent coordination |
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## Generation Details |
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- **Generator**: OPENCHEF!: AGENTIC v2.0 |
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- **Error Rate**: 15% intentional routing errors |
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- **Agent Types**: Router, Email Agent, Calendar Agent, Code Agent, Web Agent, User Proxy, System |
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- **Tool Types**: Email search/send, Calendar management, Code execution, Web search, File operations |
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- **Format**: Function-calling with ReAct-style traces |
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## Citation |
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If you use this dataset, please cite: |
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``` |
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@software{openclaw_agentic_dataset, |
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title = {openclaw_agentic_dataset}, |
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author = {webXOS}, |
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year = {2026}, |
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url = {webxos.netlify.app} |
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} |
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``` |
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## License |
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MIT License |
