Data Agent Ready Warehouse : One for Analytics, Search, AI, Python Sandbox. — rebuilt from scratch. Unified architecture on your S3.
9,434
stars
34,383
commits
Rust
primary language
Sep 7, 2026
updated
Large-scale analytics, vector search, full-text search — with flexible agent orchestration and secure Python UDF sandboxes. Built for enterprise AI workloads.
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. |
Start for free on Databend Cloud — Production-ready in 60 seconds.
Ideal for development and testing. Requires Python 3.12 or 3.13 and databend-driver 0.34.0 or later:
pip install "databend-driver[local]>=0.34.0"
from databend_driver import connect
conn = connect("databend+local:///./local-state")
print(conn.query_row("SELECT 'Hello, Databend!'").values())
Run the full warehouse locally:
docker run -p 8000:8000 datafuselabs/databend
Databend's Sandbox UDF enables flexible agent orchestration with a three-layer architecture:
-- 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;
Contributors are immortalized in the system.contributors table 🏆
Apache 2.0 + Elastic 2.0 | Licensing FAQ
(top 30 of 222)
Rust
95.0%
Python
2.5%
Shell
2.1%
Data Agent Ready Warehouse : One for Analytics, Search, AI, Python Sandbox. — rebuilt from scratch. Unified architecture on your S3.
9,434
stars
34,383
commits
Rust
primary language
Sep 7, 2026
updated
Large-scale analytics, vector search, full-text search — with flexible agent orchestration and secure Python UDF sandboxes. Built for enterprise AI workloads.
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. |
Start for free on Databend Cloud — Production-ready in 60 seconds.
Ideal for development and testing. Requires Python 3.12 or 3.13 and databend-driver 0.34.0 or later:
pip install "databend-driver[local]>=0.34.0"
from databend_driver import connect
conn = connect("databend+local:///./local-state")
print(conn.query_row("SELECT 'Hello, Databend!'").values())
Run the full warehouse locally:
docker run -p 8000:8000 datafuselabs/databend
Databend's Sandbox UDF enables flexible agent orchestration with a three-layer architecture:
-- 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;
Contributors are immortalized in the system.contributors table 🏆
Apache 2.0 + Elastic 2.0 | Licensing FAQ
(top 30 of 222)
Rust
95.0%
Python
2.5%
Shell
2.1%