A Mem MCP server

A-MEM: Self-evolving memory for coding agents

mcp-name: io.github.DiaaAj/a-mem-mcp

A-MEM is a self-evolving memory system for coding agents. Unlike simple vector stores, A-MEM automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships. Memories don't just get stored—they evolve and connect over time.

Currently tested with Claude Code. Support for other MCP-compatible agents is planned.

Quick Start

Install

pip install a-mem

Add to Claude Code

claude mcp add a-mem -s user -- a-mem-mcp \
  -e LLM_BACKEND=openai \
  -e LLM_MODEL=gpt-4o-mini \
  -e OPENAI_API_KEY=sk-...

That's it! A session-start hook installs automatically to remind Claude to use memory.

Note: Memory is stored per-project in ./chroma_db. For global memory across all projects, see Memory Scope.

Uninstall

a-mem-uninstall-hook   # Remove hooks first
pip uninstall a-mem

How It Works

t=0              t=1                t=2

                 ◉───◉             ◉───◉
 ◉               │                 ╱ │ ╲
                 ◉                ◉──┼──◉
                                     │
                                     ◉

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━▶
            self-evolving memory

1. Add a memory → A-MEM extracts keywords, context, and tags via LLM
2. Find neighbors → Searches for semantically similar existing memories
3. Evolve → Decides whether to link, strengthen connections, or update related memories
4. Store → Persists to ChromaDB with full metadata and relationships

The result: a knowledge graph that grows smarter over time, not just bigger.

Features

Self-Evolving Memory Memories aren't static. When you add new knowledge, A-MEM automatically finds related memories and strengthens connections, updates context, and evolves tags.

Semantic + Structural Search Combines vector similarity with graph traversal. Find memories by meaning, then explore their connections.

Peek and Drill Start with breadth-first search to capture relevant memories via lightweight metadata (id, context, keywords, tags). Then drill depth-first into specific memories with readmemorynote for full content. This minimizes token usage while maximizing recall.

MCP Tools

A-MEM exposes 8 tools to your coding agent:

Example Usage

# The agent calls these automatically, but here's what happens:

# Store a memory (returns task_id immediately)
add_memory_note(content="Auth uses JWT in httpOnly cookies, validated by AuthMiddleware")

# Search later
search_memories(query="authentication flow", k=5)

# Deep search with connections
search_memories_agentic(query="security", k=5)

Advanced Configuration

JSON Config

For more control, edit ~/.claude/settings.json (global) or .claude/settings.local.json (project):

Shortened. The full README is on GitHub.