diff --git a/README.md b/README.md index a8feb1a6f..6c838a3ea 100644 --- a/README.md +++ b/README.md @@ -39,13 +39,15 @@ AI agents can already call tools, browse docs, and write code. What they still s 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](https://a16z.com/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. +

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Wren Engine exists to solve that gap. -It gives AI agents a semantic layer they can reason over, so they can: +It gives AI agents a context layer they can reason over, so they can: - understand models instead of raw tables - use trusted metrics instead of inventing SQL @@ -57,7 +59,7 @@ This is the open source context engine for teams building the next generation of ## The Vision -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. +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. Moving beyond text-to-SQL and toward a living context layer that combines semantic meaning, system structure, governance, and human refinement. Wren Engine is our open source contribution to that future. @@ -78,14 +80,47 @@ Wren Engine turns business data into agent-usable context. At a high level: 1. You describe your business domain with Wren's semantic model and MDL. -2. Wren Engine analyzes intent, models, relationships, and access rules. -3. It plans and generates correct queries across your underlying data sources. +2. Wren Engine captures the context agents need: models, metrics, relationships, and access rules. +3. It analyzes intent and plans correct queries across your underlying data sources. 4. MCP clients and AI agents interact with that context through a clean interface. +5. 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?" +## Wren Engine vs. Other Data Tools + +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 helps an agent understand business meaning and produce the right query through a context layer + +| 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. + ## Built For Agent Builders Wren Engine is especially useful for the open source community building agent-native workflows in tools like: @@ -107,6 +142,8 @@ Use it to power experiences like: - 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. + ## Why Open Source We think agent infrastructure should be composable. @@ -121,6 +158,8 @@ Wren Engine is open source so the community can: - 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. + ## Architecture At A Glance ```text @@ -139,6 +178,8 @@ Core ideas: - `ibis-server` provides the execution and connector-facing API layer - `mcp-server` makes 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. + ## Repository Map This repository contains the core engine modules: @@ -171,10 +212,10 @@ Current open source support includes connectors such as: - MySQL - Oracle - PostgreSQL +- Redshift - SQL Server - Snowflake - Trino -- Redshift See the connector API docs in the project documentation for the latest connection schemas and capabilities. @@ -182,9 +223,11 @@ See the connector API docs in the project documentation for the latest connectio ### Use Wren through MCP -If you want to use Wren Engine from an AI agent or MCP-capable IDE, start here: +If you want to use Wren Engine from an Claude Code or MCP-capable IDE, start here: -- [MCP Server README](./mcp-server/README.md) +- [Quick start: Chat with jaffle_shop using Wren Engine + Claude Code](https://docs.getwren.ai/oss/engine/get_started/quickstart) +- [Quick start with Claude Desktop](https://docs.getwren.ai/oss/engine/get_started/quickstart_claude) +- [Understanding Wren AI project structure](https://docs.getwren.ai/oss/engine/get_started/structure) The MCP server includes: @@ -195,10 +238,10 @@ The MCP server includes: ### Learn the concepts -- [Quick start with Wren Engine](https://docs.getwren.ai/oss/engine/get_started/quickstart) -- [What is semantics?](https://docs.getwren.ai/oss/engine/concept/what_is_semantics) +- [What is context?](https://docs.getwren.ai/oss/engine/concept/what_is_context) - [What is Modeling Definition Language (MDL)?](https://docs.getwren.ai/oss/engine/concept/what_is_mdl) - [Benefits of Wren Engine with LLMs](https://docs.getwren.ai/oss/engine/concept/benefits_llm) +- [Your Data Agents Need Context](https://a16z.com/your-data-agents-need-context/) - [Powering Semantic SQL for AI Agents with Apache DataFusion](https://getwren.ai/post/powering-semantic-sql-for-ai-agents-with-apache-datafusion) ### Developer entry points @@ -213,7 +256,7 @@ The MCP server includes: Common workflows: ```bash -# Rust semantic engine +# Rust context engine cd wren-core cargo check --all-targets @@ -229,7 +272,7 @@ cd mcp-server ## Project Status -Wren Engine is actively evolving in the open. The current focus is to make the semantic layer, execution path, and MCP integration stronger for real-world agent workflows. +Wren Engine is actively evolving in the open. The current focus is to make the context 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. @@ -239,5 +282,6 @@ If you are building with agents today, this is a great time to get involved. - Open a [GitHub issue](https://github.com/Canner/wren-engine/issues) - Explore [Wren AI](https://github.com/Canner/WrenAI) to see the broader product vision - Read our mission piece: [Fueling the Next Wave of AI Agents](https://getwren.ai/post/fueling-the-next-wave-of-ai-agents-building-the-foundation-for-future-mcp-clients-and-enterprise-data-access) +- Read the market thesis from a16z: [Your Data Agents Need Context](https://a16z.com/your-data-agents-need-context/) Wren Engine is for builders who believe AI needs better context, not just better prompts.