Wren Engine
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Wren Engine is a semantic engine designed for Model Context Protocol (MCP) clients and AI agents, enabling accurate and context-aware access to enterprise data.
Wren Engine Overview
What is Wren Engine?
Wren Engine is a semantic engine that powers Model Context Protocol (MCP) clients and AI agents. It aims to bridge the gap between natural language queries from AI and the complex, structured data within enterprises. By providing a semantic layer, Wren Engine allows AI to understand, retrieve, and process business data with precision, context, and governance.
How to use Wren Engine?
Wren Engine is designed to be embedded into any MCP client or AI agentic workflow. Developers can leverage its capabilities to connect AI agents to various enterprise data sources. The project provides modules like ibis-server (web server), wren-core (semantic core in Rust), wren-core-py (Python binding), and mcp-server (MCP server) for integration. Users can refer to the MCP Server README and various blog posts and tutorials for getting started and understanding concepts like Semantic SQL for AI Agents and Modeling Definition Language (MDL).
Key Features of Wren Engine
- Semantic Engine: Provides a semantic layer for AI agents to accurately understand and interact with business data.
- Broad Data Source Support: Connects to a wide range of enterprise data sources including BigQuery, Google Cloud Storage, MS SQL Server, MySQL, Oracle, PostgreSQL, Amazon S3, Snowflake, and Trino.
- Embeddable: Can be integrated into any MCP client or AI agentic workflow.
- Interoperable: Compatible with modern data stacks.
- Semantic-first: Enables AI to comprehend data models and business logic.
- Governance-ready: Respects roles, access controls, and definitions for secure data access.
- Open Standard: Built around the Model Context Protocol (MCP) to connect LLMs with tools, databases, and enterprise systems.
Use Cases of Wren Engine
- Enterprise AI Solutions: Powering AI agents to access and analyze structured enterprise data for BI dashboards, CRM updates, and compliance workflows.
- Context-aware AI: Enabling AI to understand and retrieve the right data with precision and context, addressing challenges like accurate semantic understanding, trusted calculations, and clarity on business terms.
- Secure Data Access: Ensuring user-based permissions and access control for AI interactions with sensitive enterprise data.
- Integration with LLMs: Facilitating complex workflows across enterprise data systems by interpreting intent, mapping to correct data, applying calculations, and ensuring security.
FAQ about Wren Engine
- What is the Model Context Protocol (MCP)? MCP is an open standard that connects Large Language Models (LLMs) with tools, databases, and enterprise systems, and Wren Engine is a key part of this ecosystem.
- What kind of data sources does Wren Engine support? It supports a wide array of databases and storage solutions including BigQuery, Google Cloud Storage, MS SQL Server, MySQL, Oracle, PostgreSQL, Amazon S3, Snowflake, and Trino.
- What is the current status of Wren Engine? The project is currently in beta, with active development and new versions released biweekly.
- How does Wren Engine help with enterprise data challenges? It addresses the need for accurate semantic understanding, trusted calculations, clarity on business terms, and user-based permissions and access control when AI interacts with enterprise data.
- Where can I find more information or get support? You can join their Discord server for feedback, or visit their GitHub Issues page for any problems.
Wren Engine's README
Wren Engine is the Semantic Engine for MCP Clients and AI Agents. Wren AI GenBI AI Agent is based on Wren Engine.
🔌 Supported Data Sources
- BigQuery
- Google Cloud Storage
- Local Files
- MS SQL Server
- Minio
- MySQL Server
- Oracle Server
- PostgreSQL Server
- Amazon S3
- Snowflake
- Trino
😫 Challenge Today
At the enterprise level, the stakes - and the complexity - are much higher. Businesses run on structured data stored in cloud warehouses, relational databases, and secure filesystems. From BI dashboards to CRM updates and compliance workflows, AI must not only execute commands but also understand and retrieve the right data, with precision and in context.
While many community and official MCP servers already support connections to major databases like PostgreSQL, MySQL, SQL Server, and more, there's a problem: raw access to data isn't enough.
Enterprises need:
- Accurate semantic understanding of their data models
- Trusted calculations and aggregations in reporting
- Clarity on business terms, like "active customer," "net revenue," or "churn rate"
- User-based permissions and access control
Natural language alone isn't enough to drive complex workflows across enterprise data systems. You need a layer that interprets intent, maps it to the correct data, applies calculations accurately, and ensures security.
🎯 Our Mission
Wren Engine is on a mission to power the future of MCP clients and AI agents through the Model Context Protocol (MCP) — a new open standard that connects LLMs with tools, databases, and enterprise systems.
As part of the MCP ecosystem, Wren Engine provides a semantic engine powered the next generation semantic layer that enables AI agents to access business data with accuracy, context, and governance.
By building the semantic layer directly into MCP clients, such as Claude, Cline, Cursor, etc. Wren Engine empowers AI Agents with precise business context and ensures accurate data interactions across diverse enterprise environments.
We believe the future of enterprise AI lies in context-aware, composable systems. That’s why Wren Engine is designed to be:
- 🔌 Embeddable into any MCP client or AI agentic workflow
- 🔄 Interoperable with modern data stacks (PostgreSQL, MySQL, Snowflake, etc.)
- 🧠 Semantic-first, enabling AI to “understand” your data model and business logic
- 🔐 Governance-ready, respecting roles, access controls, and definitions
With Wren Engine, you can scale AI adoption across teams — not just with better automation, but with better understanding.
Check our full article
🚀 Get Started with MCP
https://github.com/user-attachments/assets/dab9b50f-70d7-4eb3-8fc8-2ab55dc7d2ec
👉 Blog Post Tutorial: Powering AI-driven workflows with Wren Engine and Zapier via the Model Context Protocol (MCP)
🤔 Concepts
- Powering Semantic SQL for AI Agents with Apache DataFusion
- Quick start with Wren Engine
- What is semantics?
- What is Modeling Definition Language (MDL)?
- Benefits of Wren Engine with LLMs
🚧 Project Status
Wren Engine is currently in the beta version. The project team is actively working on progress and aiming to release new versions at least biweekly.
🛠️ Developer Guides
The project consists of 4 main modules:
- ibis-server: the Web server of Wren Engine powered by FastAPI and Ibis
- wren-core: the semantic core written in Rust powered by Apache DataFusion
- wren-core-py: the Python binding for wren-core
- mcp-server: the MCP server of Wren Engine powered by MCP Python SDK
⭐️ Community
- Welcome to our Discord server to give us feedback!
- If there is any issues, please visit Github Issues.
Wren Engine Reviews
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Wren Engine's Information
- Author
- Canner
- Category
- Data & Analytics
- Language
- Java
- License
- Apache License 2.0
- Stars
- 366
- Updated
- 3 months ago
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