Datus-ai/Datus-agent
The Future of Data Engineering — A CLI SQL client for the modern data stack, enabling AI-native context engineering for data. observed · 2026-08-28
Health v2 · maintenance only
84/100
- Activity 99
- Release rhythm 96
- Longevity 30
Flags: no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 12
- age_days: 425
- days_rel: 31
- days_push: 7
- n_releases_24m: 16
Adoption not part of the score
1613 stars · 239 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
Datus is an open-source (Apache-2.0) AI data engineering agent delivered primarily as a Python CLI, with a VS Code/Cursor extension and optional cloud studio. It connects to data warehouses, catalogs, semantic layers, and BI tools, building an evolving context engine of schemas, metrics, and validated SQL to plan, generate, validate, and deploy data work.
Use cases
- generate validated SQL from natural language against my warehouse
- build and maintain semantic models and metrics with AI
- connect Snowflake and query it with an AI agent from the terminal
- create an evolving context engine for my data stack
- generate data pipelines and ETL with an LLM agent
- self-host an AI SQL agent with my own model
- extract and reuse knowledge from SQL feedback and benchmarks
When to choose
- you want an AI agent that plans, executes, and validates SQL end to end rather than a one-shot NL2SQL copilot
- you need a self-hosted, Apache-2.0 agent that works with your own warehouse and LLM
- you want persistent, evolvable context (schemas, metrics, validated SQL) shared across data tasks
- you prefer a CLI-first workflow with editor and MCP integrations
When to avoid
- you need a simple GUI-only SQL client without AI features
- you require enterprise governance like SSO, RBAC, and audit logs on the free tier
- you want a fully mature, long-proven product — the project is young and fast-moving
- your team cannot cover its own LLM and warehouse compute costs
Facets
cli-tool · maturity active
agent-framework llm-inference rag database search-engine cli chatbot mcp etl large-language-models databases analytics developer-tools python cli cross-platform self-hosted sql-agent context-engine nl2sql semantic-layer data-warehouse metrics vscode-extension data-engineering ai-agents
7 sources
- readme: https://github.com/Datus-ai/Datus-agent · fetched 2026-08-28 · bf0b990acf9b
- homepage: https://datus.ai/ · fetched 2026-08-29 · 9884a40414f7
- site_page: https://docs.datus.ai · fetched 2026-08-29 · 36c6c3c2e4f9
- registry_pypi: https://pypi.org/pypi/datus-agent/json · fetched 2026-08-29 · dcf5c60a6b94
- site_page: https://datus.ai/integrations · fetched 2026-08-29 · 48d005558380
- site_page: https://datus.ai/pricing · fetched 2026-08-29 · e24922d4033a
- site_page: https://datus.ai/faq · fetched 2026-08-29 · 15d802f81182
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| Datus-ai/Datus-agent | main | 84 |
For agents
markdown · JSON · MCP: product_card(name="Datus-ai/Datus-agent")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem