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roboticforce/remembrallmcp

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为AI编码代理提供完整的代码库知识。一个字段感知码图(函数、类、方法、字段、引用)加上持久内存。Rust、Postgres + pgvector、MCP。

概览

为AI编码代理提供完整的代码库知识。一个字段感知代码图和持久内存,基于Rust、Postgres + pgvector,并通过MCP暴露。 AI编码代理每次会话都能看到几页内容。他们会从零开始重新推导出代码库的组合——没有调用什么的地图,无法知道变化时哪里出了问题,也没有记忆过去会话中做出的决策。 RemembrallMCP 为代理提供了整个代码库——一个跨 9 种语言的字段感知依赖图(函数、类、方法及其间的引用),以及会话间存续的持久内存。 - 用树置构建的代码库实时映射。函数、类、方法和数据字段,以及跨9种语言的调用、导入、定义、继承和字段-引用关系。问“如果我改了这个会坏什么?”——精确到单个结构字段——并在代理触碰任何东西之前,毫秒内得到答案。

概述(AI 翻译)

为AI编码代理提供完整的代码库知识。一个字段感知代码图和持久内存,基于Rust、Postgres + pgvector,并通过MCP暴露。

问题所在: AI编码代理每次会话只看到几页内容。他们从零开始读取、阅读并重新推导出代码库如何拼接——没有调用什么的地图,无法知道变化时哪里出了问题,也没有记忆过去会话中的决策。

解决方案: RemembrallMCP 为代理提供了完整的代码库——一个跨 9 种语言的字段感知依赖图(函数、类、方法、字段及其间的引用),以及会话间存续的持久内存。

1.字段感知代码图——用树置图构建的代码库实时映射。函数、类、方法和数据字段,以及跨9种语言的调用、导入、定义、继承和字段引用关系。问“如果我改变这个会坏什么?”——只剩一个结构体字段——你可以在毫秒内得到答案,甚至在代理触碰任何东西之前。

2.持续记忆——在会话间得以保存的决策、模式和组织知识。

本页由 AI 翻译,技术细节请以原文为准。

README

以下为原文 README 节选,完整内容请查看原文。

RemembrallMCP

Whole-codebase knowledge for AI coding agents. A field-aware code graph plus persistent memory, built on Rust, Postgres + pgvector, and exposed over MCP.

The problem: AI coding agents see a few pages out of the book each session. They grep, read, and re-derive how the codebase fits together from scratch - no map of what calls what, no way to know what breaks when something changes, and no memory of decisions made in past sessions.

The solution: RemembrallMCP gives the agent the whole codebase - a field-aware dependency graph (functions, classes, methods, fields, and the references between them) across 9 languages, plus persistent memory that survives between sessions.

1. Field-Aware Code Graph - A live map of your codebase built with tree-sitter. Functions, classes, methods, and data fields, plus call, import, defines, inherits, and field-reference relationships across 9 languages. Ask “what breaks if I change this?” - down to a single struct field - and get an answer in milliseconds, before the agent touches anything.

2. Persistent Memory - Decisions, patterns, and organizational knowledge that survive between sessions. Hybrid semantic + full-text search finds relevant context instantly.

remembrall_recall("authentication middleware patterns")
-> 3 relevant memories from past sessions

remembrall_index("/path/to/project", "myapp")
-> Builds dependency graph: 847 symbols, 1,203 relationships

remembrall_impact("AuthMiddleware", direction="upstream")
-> 12 files depend on AuthMiddleware (with confidence scores)

remembrall_impact("amount", direction="upstream")
-> methods that read self.amount, across the whole codebase

remembrall_store("Switched from JWT to session tokens because...")
-> Decision stored for future sessions

Why the code graph matters

Without RemembrallMCP, agents explore your codebase from scratch every session. Claude Code spawns Explore agents, Codex reads dozens of files, Cursor greps through directories - all burning tokens and time just to understand what calls what. A single “find all callers of this function” task can cost thousands of tokens across multiple tool calls.

With RemembrallMCP, that same query is a single remembrall_impact call that returns in <1ms with zero exploration tokens. The dependency graph is already built and waiting.

Without RemembrallMCP With RemembrallMCP
在 GitHub 查看完整 README

安装

This server does not publish a one-line install command.

Open the repository installation guide 新手?先看 mcpskill.org 教程 →

配置

{ "mcpServers": { "remembrall": { "command": "remembrall" } } }