WrenAI

📣 2026-05-07: Wren Engine has merged into this repo under core/. The previous Canner/wren-engine repo is archived. The previous WrenAI GenBI app (the Docker-based chat-first BI product) is preserved on the legacy/v1 branch (tag v1-final) and is now Wren GenBI Classic; see A note on the "GenBI" name below. Read the announcement →

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What WrenAI is

WrenAI is the open-source GenBI engine: it lets AI agents generate, deploy, and govern business intelligence, from a SQL answer to a shareable dashboard, across 22+ data sources.

What makes the output trustworthy is the layer underneath: an open context layer that gives agents what schemas don't. That means business semantics, approved definitions, examples, memory, and governance, plus the unstructured company knowledge that lives in your docs, wikis, and chat threads. Generative BI is only as good as the context it stands on, and Wren is that context, made reviewable and reusable by every agent you already run.

GenBI in three beats: Generate · Deploy · Know

• Generate. Your agent turns a business question into governed SQL and charts. Schema-aware retrieval, MDL planning, dry-plan validation, and structured errors keep it correct instead of confidently wrong.
• Deploy. Turn any answer into a shareable, browser-side dashboard powered by wren-core-wasm and ship it to your own Vercel or Cloudflare Pages account with one command.
• Know. The knowledge that makes all of this correct lives in versionable, evidence-linked files: semantic models (MDL), company definitions (instructions.md), and a memory of what worked. Reviewable. Git-friendly. Never locked inside someone else's UI.

Why agent builders pick WrenAI

• Generative BI, end to end. Not just text-to-SQL. Generate the answer, deploy the dashboard, share the URL, all driven by the agents you already use.
• Knowledge management built in. Business meaning, approved definitions, and proven examples are captured as reviewable, version-controlled context, not buried in prompts.
• Open by default. Open-sourced core, SDK, and skills under the Apache-2.0 license.
• Correctness as primitives. Rich schema retrieval, dry-plan validation, structured errors with hints, value profiling, eval runner. The agent orchestrates; the trace lives in its reasoning.
• Sits on top of your existing stack. Warehouse, transformation pipelines, your existing semantic layer. Not another tool to maintain.

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,