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goldmedal merged 4 commits into from
Mar 17, 2026
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chore: update README with a16z context-layer framing #1453

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a5cbe49
Update README with a16z context-layer framing
chilijung Mar 17, 2026
e479f61
update link
chilijung Mar 17, 2026
d06e136
docs: clarify Wren Engine positioning against adjacent tools
chilijung Mar 17, 2026
1f275fc
Update README with simpler Wren comparison and quickstart links
chilijung Mar 17, 2026
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60 changes: 53 additions & 7 deletions README.md
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Expand Up @@ -39,6 +39,8 @@ 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.

<p align="center">
<img width="920" height="638" alt="without_wren_engine" src="https://github.com/user-attachments/assets/3295dde5-ce41-4e56-a8ad-daff6a0c3459" />
</p>
Expand All @@ -59,6 +61,8 @@ This is the open source context engine for teams building the next generation of

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.

The a16z post captures this shift well: the market is 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.

It is the semantic and execution foundation beneath Wren AI, and it is designed to be useful well beyond a single product:
Expand All @@ -78,14 +82,47 @@ 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.
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⚠️ Potential issue | 🟡 Minor

Use hyphenated compound adjective at Line 90.

“open source” should be hyphenated here because it modifies “path.”

✏️ Proposed copy edit 
-This is the practical open source path from text-to-SQL toward context-aware data agents.
+This is the practical open-source path from text-to-SQL toward context-aware data agents.
📝 Committable suggestion

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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

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This is the practical open source path from text-to-SQL toward context-aware data agents.
This is the practical open-source path from text-to-SQL toward context-aware data agents.
🧰 Tools
🪛 LanguageTool

[grammar] ~90-~90: Use a hyphen to join words.
Context: ...tems evolve. This is the practical open source path from text-to-SQL toward cont...

(QB_NEW_EN_HYPHEN)

🤖 Prompt for AI Agents 
Verify each finding against the current code and only fix it if needed.

In `@README.md` at line 90, The phrase "open source path" in the README sentence
"This is the practical open source path from text-to-SQL toward context-aware
data agents." should use a hyphenated compound adjective; update it to
"open-source path" wherever that exact sentence or the fragment "open source
path" appears in README.md so the adjective correctly modifies "path."


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 semantic 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 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:
Expand All @@ -107,6 +144,8 @@ 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.
Expand All @@ -121,6 +160,8 @@ 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
Expand All @@ -139,6 +180,8 @@ 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:
Expand Down Expand Up @@ -171,20 +214,22 @@ 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.

## Get Started

### 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:

Expand All @@ -195,10 +240,10 @@ 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
Expand Down Expand Up @@ -239,5 +284,6 @@ 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.
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