The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
WrenAI
GenBI (Generative BI) for AI agents, an open-source, governed text-to-SQL through an open context layer that turns natural-language questions into trusted dashboards, charts,…
Install
$
npx skills add Canner/WrenAIClaude Code
claude mcp add wrenai -- npx skills add Canner/WrenAICursor / Claude Desktop (mcp.json)
{
"mcpServers": {
"wrenai": {
"command": "npx",
"args": [
"skills",
"add",
"Canner/WrenAI"
]
}
}
}Install command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →Open-source GenBI for AI agents
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 →
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
pip install wrenai
Add connector extras as you need them, for example pip install "wrenai[postgres,memory]".
2. Teach your agent about Wren
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 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?
pip install wrenai -i https://pypi.tuna.tsinghua.edu.cn/simple
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
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