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, and SQL across 20+ data sources, such as BigQuery, Snowflake, PostgreSQL, ClickHouse, Amazon Redshift, Databricks and more.
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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, and SQL across 20+ data s
Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.
Does WrenAI need an API key?
No required environment variable is listed on this record.
What transport does WrenAI use?
WrenAI runs locally over stdio.
When was WrenAI last checked?
Last checked 2026-11-12. 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.. Business meaning lives in Git. Metric definitions, enums, approved joins, and proven examples are versioned files. Review them in a pull request. Diff them. Tak. Works through the agents you already run. Claude Code, Cursor, Cline, Codex, MCP clients, LangC
From the publisher
Documentation
Open-source GenBI for AI agents
Your agents generate governed SQL, deploy dashboards, and keep business definitions in Git, across 22+ data sources.
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 : 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.
Key features
01. 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.
02. Business meaning lives in Git. Metric definitions, enums, approved joins, and proven examples are versioned files. Review them in a pull request. Diff them. Tak
03. 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.
04. Sits on your existing stack. Warehouse, transformation pipelines, existing semantic models. 22+ sources on one Apache DataFusion engine.
05. Open by default. Core engine, SDK, and skills under Apache-2.0. It runs without us.
means
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.
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
Generates & deploys dashboards
❌
✅ (manual, in-tool)
❌
✅ agent-driven
Works through your agents (Claude Code, Cursor, MCP…)
✅
❌
❌
✅
Open, reviewable, Git-friendly context
❌
❌
partial
✅
Governed execution across 22+ sources
❌
per-connector
✅ (definitions only)
✅
Wren is for you if…
You want AI agents to produce trustworthy BI, answers and dashboards, not just plausible SQL.
Your business logic (definitions, enums, units, approved joins) lives outside the database and your agents keep getting it wrong.
You want a context layer that is open, reviewable, and version-controlled, usable by every agent and every person, not gated behind one vendor's UI.
Skip Wren if you only need a one-off chart from a single CSV, or you're happy letting an agent guess at SQL with no governance.
Take it to your team with git push
Everything Wren writes on your laptop is plain YAML and Markdown in a repo you own. Git Sync turns that same repo into a governed, team-wide GenBI deployment in Wren Cloud or a self-hosted installation, including air-gapped. Nothing to export, nothing to re-model.
No new CLI to learn. Bind a directory once with wren cloud create or wren cloud link. After that, git push and git pull are the entire interface.
Metrics reviewed like code. A change to net_revenue shows up as a readable diff in a pull request on GitHub, GitLab, Bitbucket, or your own remote. Run CI on it. Promote staging to production like application code.
You keep the repo, always. Clone it, back it up, feed it to another tool, or leave with it, any time.
No reusable credential on disk. Every push authenticates with a fresh token that expires in 600 seconds. The durable key stays in ~/.wren/cloud.yml (mode 0600) and is never handed to git.
$ git push
To cloud.getwren.ai/acme/wren-analytics.git
9f2c1a4..b71e0d3 main -> main
✔ deploy queued · model queryable in Wren Cloud
The engine in this repo (MDL semantic layer, governed text-to-SQL, MCP server, CLI, 22+ connectors) is Apache-2.0, free forever, and self-hostable. The following are commercial, delivered as Wren AI Cloud or self-hosted Enterprise Plus:
Row- and column-level security and access control with users and groups
GenBI UI, dashboards, embedded and APIs
Scenario AI harnesses: GenBI Apps, Agentic Mode, AI-assisted context preparation
Advanced security and audit, support and SLAs, plus cloud, VPC, and air-gapped deployment
Wren is a governed semantic layer, expressed in the Modeling Definition Language (MDL): a Git-friendly definition of what your data means, not just where it lives. Every answer and dashboard is planned against it.
Models, columns, relationships, views: the shape of your data, decoupled from any one warehouse.
Cubes and metrics: approved, reusable definitions so "revenue" means the same thing everywhere.
Context beyond the schema: enums, units, approved joins, and definitions in instructions.md and queries.yml.
What's included
Engine: Apache DataFusion based. BigQuery, Snowflake, PostgreSQL, ClickHouse, Amazon Redshift, Databricks, DuckDB, and more. Connect a database →
GenBI dashboards: agent-built, browser-side apps powered by wren-core-wasm, deployable to Vercel or Cloudflare Pages
Knowledge and memory: version-controlled instructions.md and queries.yml, plus a local LanceDB memory index with hybrid retrieval
Agent SDKs: wren-langchain (LangChain / LangGraph), wren-pydantic, and a reference Python integration for other stacks. SDK overview →
wren skills get onboarding # workflow guide: set up project + first query
wren skills get enrich-context # workflow guide: add business context
wren skills get genbi # workflow guide: build & deploy a dashboard
wren query --sql '...' # query through the MDL semantic layer
wren ask "<question>" --guided # wrap a question for a weaker agent
wren ask "<question>" --direct # wrap a question for a stronger agent
Business intelligence produced by AI agents. Instead of a person building charts by hand, an agent generates governed SQL, deploys a dashboard, and shares it, grounded in an AI context layer so the output is trustworthy rather than merely plausible. Wren AI is the open-source GenBI engine.
Yes, and governed: questions become SQL planned against your semantic layer (MDL) and dry-plan validated before execution. Wren then goes further, deploying dashboards and managing the context that keeps answers correct.
Yes. Wren is a governed semantic layer expressed in MDL (models, metrics, relationships), paired with an AI context layer (memory, examples, unstructured knowledge) and a governed execution engine that runs those definitions across 22+ sources.
The reviewable, version-controlled knowledge agents need but schemas don't provide: business semantics, approved definitions, examples, memory, and governance. Read the vision: The missing context layer for AI agents over business data.
On 2026-05-07 Wren Engine merged into this repo under core/, and the previous Canner/wren-engine repo was archived. The earlier chat-first BI product is now Wren GenBI Classic, preserved on the legacy/v1 branch (tag v1-final) with no new features or security fixes. For a maintained, hosted version of that experience, see Wren AI Commercial. Read the announcement →