expeditedprojects/hutchdb

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哈奇DB

一个用于AI代理的数据库。不需要模式、SQL或结构。 一个结构化的工作区，Claude Code、Codex、Cursor、Hermes、Claw、Grokbot 及其他 MCP 客户端可以一起阅读和更新。开源。

     

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什么是HutchDB？

HutchDB Core 是一个无头、单用户 MCP 服务器，为你运行的每个代理提供相同的持久结构化数据。连接 Claude Code、Codex、Cursor、VS Code 或其他 MCP 客户端，它们可以存储、查询和更新跨会话共享的记录集合。

集合使用模式可选的 Postgres JSONB，支持全文搜索、可选验证和视图定义。没有仪表盘、登录界面，也没有 OAuth 仪式**——只有一个 MCP 端点和一个你控制的 Postgres 数据库。

这个仓库就是开源软件引擎。如果你希望人员和代理在同一个可视化工作空间中工作，HutchDB Cloud 增加了网页视图、发布页面、社交登录和组织共享功能。请参见下文 Core 与 Cloud。

你能用它做什么？

代理工作数据的默认归宿是一个标记文件：一个TODO.md，一个笔记目录，一个充满散文的内存文件。

HutchDB

A database for AI agents. No schema, SQL, or structure required. A structured workspace that Claude Code, Codex, Cursor, Hermes, Claw, Grokbot, and other MCP clients can read and update together. Open source.

     

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What is HutchDB?

HutchDB Core is a headless, single-user MCP server that gives every agent you run the same durable, structured data. Connect Claude Code, Codex, Cursor, VS Code, or another MCP client and they can store, query, and update shared collections of records across sessions.

Collections use schema-optional Postgres JSONB with full-text search, optional validation, and view definitions. There is no dashboard, no login screen, and no OAuth ceremony — just an MCP endpoint and a Postgres database you control.

This repo is the OSS engine. If you want people and agents working from the same visual workspace, HutchDB Cloud adds web views, published pages, social login, and organization sharing. See Core vs Cloud below.

What can you use it for?

The default home for agent working data is a markdown file: a TODO.md, a notes directory, a memory file full of prose. That works for one agent taking notes to itself. It breaks down the moment you want to filter (“open bugs above severity 2”), aggregate (“pipeline value by stage”), or have two agents update the same list without overwriting each other.

HutchDB holds the same information as real records: individually addressable, queryable, and safe for multiple agents to read and update at once. That makes it fit anywhere an agent produces or uses structured data that needs to outlast one chat. Some patterns people run on it:

Research that accumulates. Customer interviews, competitor teardowns, scraped listings, literature reviews. Each session adds records to a collection; any later session can query the whole set.

“Save these five competitor pricing pages to competitor-pricing, then tell me who changed since last month.”