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README.md

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+# OPENCHEF!: AGENTIC v2.0 - Multi-Agent Routing Dataset
+
+## Dataset Description
+
+This dataset contains simulated multi-agent sessions generated by OPENCHEF!: AGENTIC v2.0, designed for training AI agents in tool use and routing decisions.
+
+### Dataset Summary
+- **Total Sessions**: 770
+- **Train**: 539 (70.0%)
+- **Validation**: 127 (16.5%)
+- **Test**: 104 (13.5%)
+- **Success Rate**: 92.9%
+- **Generated**: 2026-01-30T10:54:18.423Z
+
+### Supported Tasks
+- Tool calling with function-calling format
+- Multi-agent routing decisions
+- Error recovery from routing mistakes
+- Agent coordination
+
+### Data Fields
+
+Each example contains:
+- `id`: Unique session identifier
+- `split`: Dataset split (train/validation/test)
+- `task_description`: User instruction for the session
+- `agents_involved`: List of agent types in the session
+- `full_trace`: Complete session trace with thoughts, actions, and observations
+- `final_success`: Whether the session completed successfully
+- `steps_count`: Number of turns in the session
+- `tool_use_count`: Number of tool calls made
+- `routing_decisions`: Number of routing decisions made
+- `metadata`: Generation metadata
+
+### Data Splits
+
+The dataset is split into train (70%), validation (15%), and test (15%) sets.
+
+## Usage
+
+### For Hugging Face Datasets
+```python
+from datasets import load_dataset
+
+# Load directly from JSONL files
+dataset = load_dataset('json', data_files={
+    'train': 'data/train.jsonl',
+    'validation': 'data/validation.jsonl',
+    'test': 'data/test.jsonl'
+})
+```
+
+### For Training Agentic Models
+This dataset is optimized for training models on:
+- Tool calling behavior
+- Routing decisions between specialized agents
+- Error recovery from incorrect routing
+- Multi-agent coordination
+
+## Generation Details
+
+- **Generator**: OPENCHEF!: AGENTIC v2.0
+- **Error Rate**: 15% intentional routing errors
+- **Agent Types**: Router, Email Agent, Calendar Agent, Code Agent, Web Agent, User Proxy, System
+- **Tool Types**: Email search/send, Calendar management, Code execution, Web search, File operations
+- **Format**: Function-calling with ReAct-style traces
+
+## Citation
+
+If you use this dataset, please cite:
+```
+@software{openchef2024,
+  title = {OPENCHEF!: AGENTIC v2.0 - Multi-Agent Routing Dataset Generator},
+  author = {OPENCLAW.ai Team},
+  year = {2024},
+  url = {https://github.com/openclaw/openchef}
+}
+```
+
+## License
+
+MIT License

dataset_card.json

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+{
+  "description": "Multi-agent routing and tool use dataset for training AI agents",
+  "citation": "OPENCHEF!: AGENTIC v2.0 - Multi-Agent Routing Dataset",
+  "homepage": "https://github.com/openclaw/openchef",
+  "license": "mit",
+  "features": {
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+}