Wren AI - Open-Source GenBI Agent

Ask your database anything in plain English. Wren AI generates accurate SQL, charts, and BI insights — backed by a semantic layer that keeps LLM outputs grounded and trustworthy.

😍 Demos

https://github.com/user-attachments/assets/f9c1cb34-5a95-4580-8890-ec9644da4160

▶️ Watch the full GenBI walkthrough — end-to-end from question to chart

💡 Why a Semantic Layer?

Feeding raw DDL to an LLM gets you SQL that looks right but means the wrong thing — "revenue" joins the wrong tables, "active user" uses the wrong filter. Wren AI's semantic layer (MDL) encodes your business definitions once, then every generated query is grounded in that shared understanding. The LLM doesn't guess what your metrics mean; the semantic layer tells it.

🤖 Features

	What you get	Why it matters
Talk to Your Data	Ask in any language → precise SQL & answers	Slash the SQL learning curve
GenBI Insights	AI-written summaries, charts & reports	Decision-ready context in one click
Semantic Layer	MDL models encode schema, metrics, joins	Keeps LLM outputs accurate & governed
Embed via API	Generate queries & charts inside your apps (API Docs)	Build custom agents, SaaS features, chatbots (Streamlit Live Demo)

🤩 Learn more about GenBI

🔌 Data Sources

Cloud Warehouses	Databases	Query Engines
BigQuery	PostgreSQL	Trino
Snowflake	MySQL	Athena (Trino)
Redshift	Microsoft SQL Server	DuckDB
Databricks	ClickHouse
	Oracle

Don't see yours? Vote for it — community votes drive our connector roadmap.

🧠 LLM Models

Wren AI works with any LLM provider you're already using:

Cloud APIs	Platform Services	Self-hosted
OpenAI	Azure OpenAI	Ollama
Anthropic	Google AI Studio (Gemini)
DeepSeek	Vertex AI (Gemini + Anthropic)
Groq	AWS Bedrock
	Databricks

[!TIP]
For best results, use a frontier model (GPT-4o, Claude Sonnet, Gemini Pro). Wren AI works with smaller and local models too — accuracy scales with model capability. See configuration examples for setup guides.

🚀 Getting Started

Three ways to get started — pick what fits:

Option	Best for	Link
Self-hosted (Docker)	Full control, local data	Installation guide
Wren AI Cloud	Try it without setup	getwren.ai

Compare OSS vs. Cloud plans. Full documentation at docs.getwren.ai.

🏗️ Architecture

wrenai-architecture

User questions flow from the Next.js UI → Apollo GraphQL → AI Service (RAG + LLM) → Wren Engine (semantic query execution) → your database. The semantic layer (MDL) sits at the center, making sure the LLM's SQL reflects your actual business definitions.

👉 Deep dive into the design

🧑‍💻 For Developers

WrenAI is a full-stack AI system with interesting problems at every layer — semantic modeling, RAG retrieval, LLM-driven SQL generation, and query execution across heterogeneous data sources. Here's what the stack actually looks like under the hood:

Layer	What it does
wren-ui	Next.js + Apollo GraphQL — semantic modeling UI and the BFF that wires everything together
wren-ai-service	Python/FastAPI pipeline — intent classification, vector retrieval from Qdrant, LLM prompting, and SQL correction loops
wren-engine	Rust + Apache DataFusion — the query execution core that resolves MDL semantics (metrics, joins, access controls) before SQL reaches the database

wren-engine is a separate open-source project and the part of the stack closest to the metal. It's where MDL definitions get translated into actual query plans across 15+ data sources. If you work with Rust, DataFusion, or database connectors, it's worth a look — the codebase is approachable and there are real unsolved problems around query planning, semantic resolution, and MCP (Model Context Protocol) agent integration.

Some areas where contributions tend to have the most impact across both repos:

Data source connectors — wren-engine supports 15+ sources; new connectors are always useful
MCP integration — wren-engine exposes an MCP server; agent-native workflows are still early and evolving
SQL generation quality — prompt engineering, correction loop heuristics, and eval harnesses in wren-ai-service
Semantic layer tooling — MDL schema inference, validation, and developer ergonomics in wren-ui

🛠️ Contribution

Read Contribution Guidelines for setup & PR guidelines.
Open an issue for bugs, feature requests, or discussion.
If Wren AI is useful to you, a ⭐ goes a long way — it helps more people find the project.

⭐️ Community

Join 1.7k+ developers in our Discord for real-time help and roadmap previews.
Visit GitHub Issues for bugs and feature requests.
Explore our public roadmap to see what's coming next.
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🎉 Our Contributors

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