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Persistent knowledge memory layer for AI agents. Rust, Postgres + pgvector, MCP protocol.

20 starsRust

RemembrallMCP – code dependency graph for agents and memory

Name: RemembrallMCP – code dependency graph for agents and memory
Availability: InStock
Author: cdnsteve

by cdnsteve·Apr 4, 2026·3 points·0 comments

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AI Analysis

●●SolidBig BrainSolve My Problem

Tree-sitter dependency graph saves 5,000-20,000 tokens per agent query vs exploration.

Strengths

•Wire-protocol parsing with tree-sitter means zero code changes to existing apps.
•Hybrid semantic + full-text search in Postgres with pgvector for fast recall.
•Impact analysis queries return in under 10ms regardless of codebase size.

Weaknesses

•Chat-with-codebase category already has Cursor, Continue, and Sourcegraph Cody.
•Requires Docker container setup and Postgres instance to run locally.

Post Description

After watching Claude Code and Codex keep running Explore in my codebase over and over, I started to think that there must be a better way. RemembrallMCP is my attempt to fix this.

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.

Run the whole thing inside a docker container for getting started easily. Claude uses MCP to leverage it. Written in Rust.