Dev Tools
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Context optimization for coding agents: sandbox tool output, keep sessions lean, remember more
★ 25.7k starsElastic License 2.0
View guide →GenBI for AI agents - governed text-to-SQL and dashboards through an open context layer
WrenAI is an open-source dev tools project by Canner (Canner/WrenAI on GitHub). It has 17,823 GitHub stars and 2,040 forks, is released under the NOASSERTION license, and was last updated on GitHub on 8 October 2026.
Decide which tables and business definitions belong in the semantic (MDL) layer.
Install the wrenai Python package, or add the agent skills with npx.
Connect your warehouse or database and author the context with the included skills.
Pose a natural-language question and inspect the governed SQL and chart it produces.
Questions are answered through a curated semantic layer, so generated SQL respects defined models and rules.
Works across BigQuery, Snowflake, PostgreSQL, ClickHouse, Redshift, Databricks, DuckDB and more.
Produces dashboards and charts, including browser-side apps powered by the WASM build of the engine.
Keeps version-controlled instructions and saved queries, plus a local LanceDB memory index with hybrid retrieval.
Integrates with LangChain/LangGraph and Pydantic AI, with agent skills for context authoring.
Bind a directory once, then git push deploys to Wren Cloud - git is the interface.
Get the source code from GitHub and enter the project directory.
git clone https://github.com/Canner/WrenAI.git
cd WrenAIThe Python SDK and CLI install from PyPI.
pip install wrenaiGive your coding agent the WrenAI skills for context authoring.
npx skills add Canner/WrenAIIt is maintained by Canner (the Wren AI team) on GitHub.
The open-source core is free - the README's License section states Apache 2.0, while Wren Cloud and some enterprise features are commercial under the project's open-core model.
You need credentials for your own data warehouse/database and an LLM for question answering; WrenAI itself does not sell query tokens.
Compared with raw LLM text-to-SQL, WrenAI's semantic layer and governed execution primitives (dry-plan validation, row limits, structured errors) are designed to make agent-generated SQL trustworthy.
Run pip install wrenai, connect a database, author a small context, and ask your first question.
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