Wren

**Your agents generate governed SQL, deploy dashboards, and keep business definitions in Git, across 22+ data sources.**
**Get started in 3 commands ↓**  ·  **Read the docs →**  ·  **Join Discord →**
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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.
Quickstart
Three commands, then your agent does the rest. Works with Claude Code, Cursor, Cline, Codex, and 50+ other agents.
**1. Install the CLI**
Add connector extras as you need them, for example pip install "wrenai[postgres,memory]".
**2. Teach your agent about Wren**
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 agent in a project folder and ask**
> "Use Wren to set up my Postgres database."
The agent checks your environment, creates a connection profile, scaffolds the project, and runs a first query.
**No database handy?** Say "Use Wren with the bundled jaffle_shop sample" and run the same flow against a real sample warehouse.
Slow pip install from mainland China?
If HuggingFace model downloads time out, run export HF_ENDPOINT=https://hf-mirror.com before using the CLI.
Then keep going
Once you're connected, these three prompts cover the whole GenBI loop:
| Beat | Ask your agent | What happens |
|---|---|---|
| **Know** | "Enrich my Wren project with the business context in raw/." | Runs wren skills get enrich-context. Writes definitions, examples, and memory as reviewable files. |
| **Generate** | "Who are our top 10 customers by sales this quarter?" | Recalls MDL context and past queries, writes governed SQL, executes via wren query. |
| **Deploy** | "Turn that into a dashboard I can filter and share, deployed to Vercel." | Runs wren skills get genbi. Builds a browser-side app and returns a live URL on your Vercel or Cloudflare Pages account. |
**Follow the full quickstart →**  ·  Build & deploy a GenBI app →
Why agent builders pick WrenAI
- **Answers *and* dashboards, not just SQL.** Generate a governed answer, deploy it as a dashboard, share the URL. One agent-driven loop, end to end.
- **Correct instead of confidently wrong.** Schema-aware retrieval, MDL planning, dry-plan validation, row limits, value profiling, and structured errors with hints keep agent SQL inside guardrails.
- **Business meaning lives in Git.** Metric definitions, enums, approved joins, and proven examples are versioned files. Review them in a pull request. Diff them. Take them with you.
- **Works through the agents you already run.** Claude Code, Cursor, Cline, Codex, MCP clients, LangChain. Wren is a layer, not another chat UI to adopt.
- **Sits on your existing stack.** Warehouse, transformation pipelines, existing semantic models. 22+ sources on one Apache DataFusion engine.
- **Open by default.** Core engine, SDK, and skills under Apache-2.0. It runs without us.
How Wren compares
| | A raw LLM agent | A traditional BI tool | A bare semantic layer | **WrenAI** |
|---|:---:|:---:|:---:|:---:|
| Writes SQL for you | ✅ (often wrong) | ❌ | ❌ | ✅ governed |
| Knows your business definitions | ❌ | partial, in-tool | ✅ (schema only) | ✅ + non-schema knowledge |