--- title: "WrenAI" type: "tool" slug: "canner-wrenai" canonical_url: "https://www.graphcanon.com/tools/canner-wrenai" github_url: "https://github.com/Canner/WrenAI" homepage_url: "https://docs.getwren.ai/oss/introduction" stars: 15752 forks: 1801 primary_language: "Python" license: "Other" categories: ["data-retrieval", "ai-agents"] tags: ["llm", "genbi", "charts", "duckdb", "openai", "anthropic", "bigquery", "agent"] updated_at: "2026-07-07T18:29:20.185561+00:00" --- # WrenAI > GenBI: Generative BI for AI agents Open-source, governed text-to-SQL system that transforms natural-language questions into trusted dashboards and SQL queries across multiple data sources. ## Facts - Repository: https://github.com/Canner/WrenAI - Homepage: https://docs.getwren.ai/oss/introduction - Stars: 15,752 · Forks: 1,801 · Open issues: 335 · Watchers: 95 - Primary language: Python - License: Other - Last pushed: 2026-07-07T02:43:29+00:00 ## Categories - [Data & Retrieval](/categories/data-retrieval.md) - [AI Agents](/categories/ai-agents.md) ## Tags llm, genbi, charts, duckdb, openai, anthropic, bigquery, agent ## Related tools - [ECC](/tools/affaan-m-ecc.md) - The agent harness performance optimization system (★ 226,962) - [hermes-agent](/tools/nousresearch-hermes-agent.md) - The self-improving AI agent built by Nous Research (★ 210,880) - [AutoGPT](/tools/significant-gravitas-autogpt.md) - AutoGPT: Build, Deploy, and Run AI Agents (★ 185,417) - [ollama](/tools/ollama-ollama.md) - Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. (★ 175,659) - [transformers](/tools/huggingface-transformers.md) - 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models (★ 162,347) - [langflow](/tools/langflow-ai-langflow.md) - Langflow is a powerful platform for building and deploying AI-powered agents and workflows. (★ 151,298) - [dify](/tools/langgenius-dify.md) - Production-ready platform for agentic workflow development (★ 148,070) - [firecrawl](/tools/firecrawl-firecrawl.md) - The API to search, scrape, and interact with the web at scale. (★ 147,117) ## README (excerpt) ```text
WrenAI ### 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](https://docs.getwren.ai) · [Discord](https://discord.gg/5DvshJqG8Z) · [Vision](https://www.getwren.ai/post/the-missing-context-layer-for-ai-agents-over-business-data) · [Blog](https://www.getwren.ai/blog) Canner/WrenAI | Trendshift
> 📣 **2026-05-07**: Wren Engine has merged into this repo under [`core/`](./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`](https://github.com/Canner/WrenAI/tree/legacy/v1) branch (tag `v1-final`) and is now **Wren GenBI Classic**; see [A note on the "GenBI" name](#a-note-on-the-genbi-name) below. [Read the announcement →](https://github.com/Canner/WrenAI/discussions/2205) --- ## 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`](https://docs.getwren.ai/oss/sdk/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, ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/canner-wrenai`](/api/graphcanon/tools/canner-wrenai) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_