remembrallmcp

Persistent knowledge memory and code intelligence for AI agents. Rust core, Postgres + pgvector, MCP protocol.

The problem: AI coding agents are stateless. Every session starts from zero - no memory of past decisions, no understanding of how the codebase fits together, no way to know what breaks when you change something.

The solution: RemembrallMCP gives agents two things most memory tools don't:

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

2. Code Dependency Graph - A live map of your codebase built with tree-sitter. Functions, classes, imports, and call relationships across 8 languages. Ask "what breaks if I change this?" and get an answer in milliseconds - before the agent touches anything.

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

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 | |---|---|---| | "What calls UserService?" | Agent greps, reads 8-15 files, sp