databendlabs / databend
Data Agent Ready Warehouse : One for Analytics, Search, AI, Python Sandbox. — rebuilt from scratch. Unified architecture on your S3.
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Repository Summary (README)
Preview<a href="https://databend.com/">☁️ Try Cloud</a> • <a href="#-quick-start">🚀 Quick Start</a> • <a href="https://docs.databend.com/">📖 Documentation</a> • <a href="https://link.databend.com/join-slack">💬 Slack</a>
<br><br>
<a href="https://github.com/databendlabs/databend/actions/workflows/release.yml"> <img src="https://img.shields.io/github/actions/workflow/status/datafuselabs/databend/release.yml?branch=main" alt="CI Status" /> </a> <img src="https://img.shields.io/badge/Platform-Linux%2C%20macOS%2C%20ARM-green.svg?style=flat" alt="Platform" /> </div> <br> <img src="https://github.com/user-attachments/assets/4c288d5c-9365-44f7-8cde-b2c7ebe15622" alt="databend" width="100%" />💡 Why Databend?
Databend is an open-source enterprise data warehouse built in Rust.
Core capabilities: Analytics, vector search, full-text search, auto schema evolution — unified in one engine.
Agent-ready: Sandbox UDFs for agent logic, SQL for orchestration, transactions for reliability, branching for safe experimentation on production data.
| 📊 Core Engine<br>Analytics, vector search, full-text search, auto schema evolution, transactions. | 🤖 Agent-Ready<br>Sandbox UDF + SQL orchestration. Build and run agents on your enterprise data. |
| 🏢 Enterprise Scale<br>Elastic compute, cloud native. S3/Azure/GCS. | 🌿 Branching<br>Git-like data versioning. Agents safely operate on production snapshots. |
⚡ Quick Start
1. Cloud (Recommended)
Start for free on Databend Cloud — Production-ready in 60 seconds.
2. Local (Python)
Ideal for development and testing:
pip install databend
import databend
ctx = databend.SessionContext()
ctx.sql("SELECT 'Hello, Databend!'").show()
3. Docker
Run the full warehouse locally:
docker run -p 8000:8000 datafuselabs/databend
🤖 Agent-Ready Architecture
Databend's Sandbox UDF enables flexible agent orchestration with a three-layer architecture:
- Control Plane: Resource scheduling, permission validation, sandbox lifecycle management
- Execution Plane (Databend): SQL orchestration, issues requests via Arrow Flight
- Compute Plane (Sandbox Workers): Isolated sandboxes running your agent logic
-- Define your agent logic
CREATE FUNCTION my_agent(input STRING) RETURNS STRING
LANGUAGE python HANDLER = 'run'
AS $$
def run(input):
# Your agent logic: LLM calls, tool use, reasoning...
return response
$$;
-- Orchestrate agents with SQL
SELECT my_agent(question) FROM tasks;
🚀 Use Cases
- AI Agents: Sandbox UDF + SQL orchestration + branching for safe operations
- Analytics & BI: Large-scale SQL analytics — Learn more
- Search & RAG: Vector + full-text search — Learn more
🤝 Community & Support
Contributors are immortalized in the system.contributors table 🏆
📄 License
Apache 2.0 + Elastic 2.0 | Licensing FAQ
<div align="center"> <strong>Enterprise warehouse, agent ready</strong><br> <a href="https://databend.com">🌐 Website</a> • <a href="https://x.com/DatabendLabs">🐦 Twitter</a> </div>