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Zen Agentic Dataset
8.47 Billion Tokens of real-world agentic AI programming, blockchain development, and cutting-edge infrastructure code.
Dataset Overview
A comprehensive training dataset combining Claude Code interactions with full git history from 1,400+ repositories spanning 15 years of professional development.
| Metric | Value |
|---|---|
| Total Tokens | 8.47 billion |
| Training Samples | 3.35 million |
| Validation Samples | 100,000 |
| Total Size | ~27 GB |
| Repositories | 1,452 |
| Time Span | 15 years (2010-2025) |
Data Composition
| Component | Tokens | Percentage |
|---|---|---|
| Claude Code Debug Sessions | 2.42B | 29% |
| Claude Conversations | 1.14B | 13% |
| Claude Interactions | 0.86B | 10% |
| Git History | 4.03B | 48% |
Domain Coverage
Agentic AI & LLM Infrastructure
- Model Context Protocol (MCP) - 260+ tool implementations
- Multi-agent orchestration - Claude, GPT-4, Gemini integrations
- Agent frameworks - Planning, memory, tool use, reflection
Web3 & Blockchain
- Smart contracts - Solidity, Vyper (ERC20, ERC721, DeFi)
- Consensus engines - Snow family, BFT, DAG-based protocols
- Cross-chain bridges and DeFi protocols
Cryptography & Security
- Post-quantum cryptography implementations
- Threshold cryptography and MPC
- Zero-knowledge proofs experimentation
Modern Development
- Full-stack TypeScript - Next.js 14+, React 18+
- Systems programming - Rust, Go, Python, C/C++
- DevOps - Docker, Kubernetes, CI/CD
Licensing & Access
This dataset is available for research and commercial licensing.
For Developers & Researchers
We award grants to individuals and teams who want to train models on this dataset, particularly those building:
- Models for specific blockchain ecosystems
- Open-source AI tools using OpenAI-compatible protocols
- Research advancing agentic AI capabilities
To Request Access
Contact: z@hanzo.ai
Please include:
- Intended use case (training, research, evaluation)
- Organization/affiliation
- Target ecosystem (if applicable)
- Licensing requirements
Supported Organizations
Dataset mirrors are maintained by:
- Hanzo AI - AI infrastructure platform
- Lux Network - AI compute settlement layer
- Zen LM - Open model research
- Zoo Labs - Decentralized AI research
Models Trained on This Dataset
| Model | Size | Architecture | Status |
|---|---|---|---|
| Zen Coder 4B | 4B | Qwen3 | Trained |
| Zen Coder 24B | 24B | Devstral Small 2 | Trained |
| Zen Coder 123B | 123B | Devstral 2 | Training |
| Zen Coder Max | 358B | GLM-4.7 (MoE) | Planned |
| Zen Coder Ultra | 1T | Kimi K2 (MoE) | Planned |
Training Framework
Use Zen Trainer for fine-tuning:
from zen_trainer import ZenTrainer
trainer = ZenTrainer(
model_key="qwen3-4b",
dataset_path="hanzoai/zen-agentic-dataset-private", # Requires access
output_dir="./output/my-model",
)
trainer.train()
Related Projects
- Zen Trainer - Training framework
- Hanzo MCP - Model Context Protocol (260+ tools)
- Hanzo AI - AI infrastructure platform
- Lux Network - AI compute settlement layer
- Zoo Labs - Decentralized AI research
Citation
@dataset{zen_agentic_dataset,
author = {Kelling, Zach},
title = {Zen Agentic Dataset: 8.47B Tokens of Agentic AI Programming},
year = {2025},
publisher = {Zoo Labs Foundation},
url = {https://huggingface.co/datasets/hanzoai/zen-agentic-dataset}
}
Maintainer: z@hanzo.ai License: Commercial - Contact for licensing terms
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