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WrenAI

Open-source GenBI engine: agents write governed SQL and deploy shareable dashboards over 22+ data sources, grounded in a Git-friendly context layer (MDL semantics, definitions, memory).

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Curator's take

The strongest open answer to 'my agent writes confidently wrong SQL': business definitions, approved joins and past queries live in reviewable files, not prompts, and dry-plan validation catches errors before execution. Agent-driven by design (skills + CLI, works through Claude Code/Cursor). Skip for one-off charts from a CSV — the context layer is the point, and it takes real setup.

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README.md

Open-source GenBI: generative BI for AI agents.

Your agents generate, deploy, and govern dashboards from any database, grounded in a context layer they can actually trust.

Docs · Discord · Vision · Blog

📣 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 →

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.

Wren AI architecture

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, e

Continue your stack

What teams reach for next — and why each earns a place beside WrenAI. Ranked by curator confidence.

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ktx

Self-improving context layer for data agents — ingests dbt/Looker/wikis, maps your warehouse, builds a semantic layer with approved metrics, and serves Claude Code/Codex via CLI and MCP.

Why it fitsBoth are governed semantic layers that make agents trustworthy over company data: ktx curates warehouse metrics and serves read-only SQL context; Wren goes further into governed execution and agent-deployed dashboards.

scout

Company intelligence agent that navigates Slack, Drive, wiki and CRM live — no ingest/embed pipeline — and builds its own wiki + CRM as it learns your company.

Why it fitsTwo shapes of 'company context for agents': Scout navigates unstructured sources (Slack/Drive/wiki) building its own wiki; Wren governs the structured-data side with semantic models and SQL.