WrenAI

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### Open-source GenBI for AI agents

**Your agents generate governed SQL, deploy dashboards, and keep business definitions in Git, across 22+ data sources.**

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**[Get started in 3 commands ↓](#quickstart)** &nbsp;·&nbsp; **[Read the docs →](https://docs.getwren.ai)** &nbsp;·&nbsp; **[Join Discord →](https://discord.gg/5DvshJqG8Z)**

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## What WrenAI is

WrenAI is the open-source **generative BI (GenBI) engine**. It gives the AI agents you already use (Claude Code, Cursor, MCP clients, LangChain) a **governed semantic layer** and an **AI context layer**, so they turn business questions into correct SQL, ship the answer as a shareable dashboard, and stay inside your guardrails.

Schemas tell an agent where data lives. Wren tells it what the data *means*: approved metric definitions, enums, units, joins, worked examples, and the tribal knowledge buried in docs and chat threads. All of it lives as reviewable YAML and Markdown in a repo you own.

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  <source media="(prefers-color-scheme: dark)" srcset="./misc/wren-ai-architecture-dark.png">
  <img src="./misc/wren-ai-architecture.png" width="100%" alt="Wren AI generative BI architecture — semantic layer and AI context layer for AI agents">
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## Quickstart

Three commands, then your agent does the rest. Works with Claude Code, Cursor, Cline, Codex, and [50+ other agents](https://docs.getwren.ai/oss/get_started/quickstart).

**1. Install the CLI**

```bash
pip install wrenai
```

Add connector extras as you need them, for example `pip install "wrenai[postgres,memory]"`.

**2. Teach your agent about Wren**

```bash
npx skills add Canner/WrenAI
```

This installs a ~50-line discovery stub. Your agent fetches full workflow guides from the CLI on demand, so instructions always match the installed version.

**3. Open your agen