The open context engine for AI agents
Wren Engine is the open foundation behind Wren AI: a semantic, governed, agent-ready context layer for business data.
AI agents can already call tools, browse docs, and write code. What they still struggle with is business context.
Enterprise data is not just rows in a warehouse. It is definitions, metrics, relationships, permissions, lineage, and intent. An agent that can connect to PostgreSQL or Snowflake still does not know what "net revenue", "active customer", or "pipeline coverage" actually mean in your company.
This is not just our thesis. In Your Data Agents Need Context, a16z argues that data agents break down when they only have connectivity and SQL generation, but lack business definitions, source-of-truth context, and the operational knowledge that explains how a company actually runs.
Wren Engine exists to solve that gap.
It gives AI agents a semantic engine they can reason over, so they can:
- understand models instead of raw tables
- use trusted metrics instead of inventing SQL
- follow relationships instead of guessing joins
- respect governance instead of bypassing it
- turn natural language into accurate, explainable data access
This is the open source context engine for teams building the next generation of agent experiences.
We believe the future of AI is not tool calling alone. It is context-rich systems where agents can reason, retrieve, plan, and act on top of a shared understanding of business reality.
Wren Engine is our open source contribution to that future.
It is the semantic and execution foundation beneath Wren AI, and it is designed to be useful well beyond a single product:
- embedded in MCP servers and agent workflows
- connected to modern warehouses, databases, and file systems
- expressive enough to model business meaning through MDL
- robust enough to support governed enterprise use cases
- open enough for the community to extend, integrate, and build on
If Wren AI is the full vision, Wren Engine is the open core that makes that vision interoperable.
Wren Engine turns business data into agent-usable context.
At a high level:
- You describe your business domain with Wren's semantic model and MDL.
- Wren Engine captures the context agents need: models, metrics, relationships, and access rules.
- It analyzes intent and plans correct queries across your underlying data sources.
- MCP clients and AI agents interact with that context through a clean interface.
- Teams keep refining the model as business logic and systems evolve.
This is the practical open source path from text-to-SQL toward context-aware data agents.
That means your agent is no longer asking, "Which raw table should I query?"
It is asking, "Which business concept, metric, or governed slice of context do I need to answer this task correctly?"
People often compare Wren Engine to catalog services like DataHub, raw database MCP servers, BI semantic tools, or text-to-SQL agents.
The simple difference is:
- those tools usually help an agent find data or generate SQL
- Wren Engine helps an agent understand business meaning and produce the right query through a semantic engine
| Tool type | What it gives the agent | What Wren Engine adds |
|---|---|---|
| Data catalog services | Tables, columns, lineage, owners, descriptions | Business models, metrics, relationships, and governed query planning |
| Raw database or schema access | Direct access to schemas and SQL execution | A business layer above raw tables so the agent does not have to guess intent |
| BI or semantic tools | Curated metrics and entities for analytics workflows | An open context layer designed for MCP and agent workflows |
| Text-to-SQL agents | Fast SQL generation from natural language | Better accuracy by grounding generation in explicit business definitions |
Many teams will want both:
- a catalog to inventory and document the data estate
- Wren Engine to turn that data into agent-ready context
Why that matters:
- more accurate answers because joins and metrics are defined instead of guessed
- more consistent answers because every agent uses the same business definitions
- safer data access because governance can be carried into query planning
- less prompt engineering because the context lives in the engine, not in the prompt
Without Wren, an agent may know where the data is but still not know how to answer the question correctly.
Wren Engine is especially useful for the open source community building agent-native workflows in tools like:
- OpenClaw
- Cloud Code
- VS Code
- Claude Desktop
- Cline
- Cursor
If your environment can speak MCP, call HTTP APIs, or embed a semantic service, Wren Engine can become the context layer behind your agent.
Use it to power experiences like:
- natural-language analytics with trusted business definitions
- AI copilots that can answer questions across governed enterprise data
- agents that generate dashboards, reports, and workflow decisions
- code assistants that need real business context, not just schema dumps
- internal AI tools that should be grounded in semantic models instead of ad hoc SQL
This is especially important in developer-facing agent environments, where the assistant may understand your codebase but still lacks the business context required to answer data questions correctly.
We think agent infrastructure should be composable.
The world does not need one more closed box that only works in one UI, one cloud, or one workflow. It needs shared infrastructure that developers can inspect, extend, self-host, and integrate anywhere.
Wren Engine is open source so the community can:
- run it locally or in their own stack
- connect it to their preferred MCP client or IDE
- contribute connectors, optimizations, and semantic capabilities
- build opinionated agent products on a transparent foundation
- help define what a real context layer for AI should look like
We want that context layer to be inspectable, composable, and community-owned, not trapped inside a single proprietary interface.
User / Agent
-> MCP Client or App (OpenClaw, Cloud Code, VS Code, Claude Desktop, Cline, Cursor, etc.)
-> Wren MCP Server or HTTP API
-> Wren Engine semantic engine
-> Query planning and optimization
-> Your warehouse, database, or file-backed data source
Core ideas:
MDLcaptures business meaning, not just physical schemawren-coreperforms semantic analysis and query planning in Rustibis-serverprovides the execution and connector-facing API layermcp-servermakes Wren easy to use from MCP-compatible agents
That last point matters: context only helps agents when it is available at runtime. Wren is built to expose that layer over MCP and APIs.
This repository contains the core engine modules:
| Module | What it does |
|---|---|
wren-core |
Rust context engine powered by Apache DataFusion for MDL analysis, planning, and optimization |
wren-core-base |
Shared manifest and modeling types |
wren-core-py |
PyO3 bindings that expose the engine to Python |
ibis-server |
FastAPI server for query execution, validation, metadata, and connectors |
mcp-server |
MCP server for AI agents and MCP-compatible clients |
Supporting modules include wren-core-legacy, example, mock-web-server, and benchmarking utilities.
Wren Engine is built to work across modern data stacks, including warehouses, databases, and file-based sources.
Current open source support includes connectors such as:
- Amazon S3
- Apache Spark
- Athena
- BigQuery
- Databricks
- DuckDB
- Google Cloud Storage
- Local files
- MinIO
- MySQL
- Oracle
- PostgreSQL
- Redshift
- SQL Server
- Snowflake
- Trino
See the connector API docs in the project documentation for the latest connection schemas and capabilities.
If you want to use Wren Engine from an Claude Code or MCP-capable IDE, start here:
- Quick start: Chat with jaffle_shop using Wren Engine + Claude Code
- Quick start with Claude Desktop
- Understanding Wren AI project structure
The MCP server includes:
- a local Web UI for connection and MDL setup
- read-only mode for safer agent usage
- manifest deployment and validation tools
- metadata tools for remote schema discovery
- What is context?
- What is Modeling Definition Language (MDL)?
- Benefits of Wren Engine with LLMs
- Your Data Agents Need Context
- Powering Semantic SQL for AI Agents with Apache DataFusion
Common workflows:
# Rust context engine
cd wren-core
cargo check --all-targets
# Python + connector server
cd ibis-server
just install
just dev
# MCP server
cd mcp-server
# see module README for uv-based setupWren Engine is actively evolving in the open. The current focus is to make the semantic engine, execution path, and MCP integration stronger for real-world agent workflows.
If you are building with agents today, this is a great time to get involved.
- Join our Discord community
- Open a GitHub issue
- Explore Wren AI to see the broader product vision
- Read our mission piece: Fueling the Next Wave of AI Agents
- Read the market thesis from a16z: Your Data Agents Need Context
Wren Engine is for builders who believe AI needs better context, not just better prompts.