RemembrallMCP MCP server

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.

The savings scale with codebase size. On a small project, an agent can grep and read its way through. On a 500-file monorepo, that exploration becomes the bottleneck - agents hit context limits, spawn multiple sub-agents, or miss cross-module dependencies entirely. RemembrallMCP's graph queries stay under 10ms regardless of project size because the structure is pre-indexed in Postgres, not discovered at runtime.

Shortened. The full README is on GitHub.