README - Databend — ContextQMD

<h1 align="center">Databend</h1> <h3 align="center">Enterprise Data Warehouse for AI Agents</h3> <p align="center">Large-scale analytics, vector search, full-text search — with flexible agent orchestration and secure Python UDF sandboxes. Built for enterprise AI workloads.</p> <div align="center">

<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>

💡 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
Analytics, vector search, full-text search, auto schema evolution, transactions.	🤖 Agent-Ready
Sandbox UDF + SQL orchestration. Build and run agents on your enterprise data.
🏢 Enterprise Scale
Elastic compute, cloud native. S3/Azure/GCS.	🌿 Branching
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:

bash

pip install databend

python

import databend
ctx = databend.SessionContext()
ctx.sql("SELECT 'Hello, Databend!'").show()

3. Docker

Run the full warehouse locally:

bash

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

sql

-- 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>

<a href="https://databend.com">🌐 Website</a> • <a href="https://x.com/DatabendLabs">🐦 Twitter</a>

</div>