> 📣 **2026-05-07** — Wren Engine has merged into this repo under [`core/`](./core). The previous `Canner/wren-engine` repo is archived. The previous WrenAI GenBI app is preserved on the [`legacy/v1`](https://github.com/Canner/WrenAI/tree/legacy/v1) branch (tag `v1-final`). [Read the announcement →](https://github.com/Canner/WrenAI/discussions/2205)
---
## Why WrenAI?
AI agents fail on business data not because they can't write SQL — they fail because they don't know what your warehouse means. Overlapping tables, inconsistent naming, metric definitions scattered across dashboards and SQL files: an LLM with raw database access guesses just as badly as a new hire on day one.
WrenAI is the open context layer that fills that gap. You model your business in **[MDL](./core/wren-mdl/)** (Modeling Definition Language) — entities, relationships, calculations, governed access patterns — and any agent (Claude, Cursor, ChatGPT, internal copilots, customer-facing apps) queries through the same layer your analysts already use.
A Rust engine powered by [Apache DataFusion](https://datafusion.apache.org/) translates the modeled SQL and runs it against 20+ data sources (PostgreSQL, BigQuery, Snowflake, Spark, etc.). Use it as a Python SDK, a CLI, a WASM module in the browser, or as building blocks for agent skills.
## Quick start
The fastest path is to let an AI coding agent (Claude Code, Cursor, Aider, etc.) drive the install:
```bash
# Install WrenAI skills into your AI agent
npx skills add Canner/WrenAI --skill '*'
```
Start a new agent session and ask:
> Use the `wren-onboarding` skill to install and set up Wren AI Core.
The `wren-onboarding` skill walks the agent through environment checks, package install, project scaffolding, the first data source connection, and a first query.
Full CLI guide and manual install steps: [`core/wren/README.md`](./core/wren/README.md). Installable extras for each connector are listed there.
## Supported Data Sources
Wren Engine is built to work across modern data stacks, including warehouses, databases, and file-based sources.
Current open source support includes connectors such as:
- Amazon S3
- Apache Spark
- Apache Doris
- Athena
- BigQuery
- ClickHouse
- Databricks
- DuckDB
- Google Cloud Storage
- Local files
- MinIO
- MySQL
- Oracle
- PostgreSQL
- Redshift
- SQL Server
- Snowflake
- Trino
See the connector API docs in the project documentation for the latest connection schemas and capabilities.
## Repository map
| Path | What's there |
|---|---|
| [`core/`](./core) | Rust engine + Python/WASM bindings + CLI. The context layer's core machinery. |
| [`core/wren-core/`](./core/wren-core) | Rust semantic engine (Cargo workspace). |
| [`core/wren-core-base/`](./core/wren-core-base) | Manifest types (`Model`, `Column`, `Cube`, `Relationship`, `View`). |
| [`core/wren-core-py/`](./core/wren-core-py) | PyO3 bindings (PyPI: `wren-core`). |
| [`core/wren-core-wasm/`](./core/wren-core-wasm) | WebAssembly build for in-browser semantic SQL (npm: `wren-core-wasm`). |
| [`core/wren/`](./core/wren) | Python SDK + `wren` CLI (PyPI: `wren-engine`). |
| [`core/wren-mdl/`](./core/wren-mdl) | MDL JSON schema. |
| [`skills/`](./skills) | CLI-based agent skills (`wren-generate-mdl`, `wren-usage`, `wren-dlt-connector`, `wren-onboarding`). |
| [`sdk/`](./sdk) | Framework integrations. [`sdk/wren-langchain/`](./sdk/wren-langchain) (PyPI: `wren-langchain`) is shipped; CrewAI / Pydantic-AI / Goose / LlamaIndex / Mastra are _coming soon_. |
| [`examples/`](./examples) | End-to-end example projects — _coming soon_. |
| [`docs/core/`](./docs/core) | Module documentation. |
## Community
- **Discussions**: [github.com/Canner/WrenAI/discussions](https://github.com/Canner/WrenAI/discussions)
- **Issues**: [github.com/Canner/WrenAI/issues](https://github.com/Canner/WrenAI/issues)
- **Discord**: [discord.gg/canner](https://discord.gg/canner)
- **Docs site**: [docs.getwren.ai](https://docs.getwren.ai)
## License
WrenAI is multi-licensed:
- **`core/**`, `sdk/**`, `skills/**`, `examples/**`, root-level files** — [Apache License 2.0](LICENSE-APACHE-2.0)
- **`docs/**`** — [Creative Commons Attribution 4.0 International (CC BY 4.0)](LICENSE-CC-BY-4.0)
Future modules may be introduced under [GNU Affero General Public License v3.0](LICENSE-AGPL-3.0); the full text is committed here pre-emptively. See [LICENSE](LICENSE) for the authoritative path-to-license map.
Published packages declare their effective license in their package manifest (`Cargo.toml`, `pyproject.toml`, `package.json`).
## Our Contributors
