A Mem Mcp

A-MEM: Self-evolving memory for coding agents

[PyPI version] [PyPI downloads] [MCP Registry]

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 firstpip 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 read_memory_note 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 latersearch_memories(query="authentication flow", k=5)
# Deep search with connectionssearch_memories_agentic(query="security", k=5)

Advanced Configuration

JSON Config

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

{  "mcpServers": {    "a-mem": {      "command": "a-mem-mcp",      "env": {        "LLM_BACKEND": "openai",        "LLM_MODEL": "gpt-4o-mini",        "OPENAI_API_KEY": "sk-..."      }    }  }}

Environment Variables

Memory Scope

• Project-specific (default): Each project gets isolated memory in ./chroma_db
• Global: Share across projects by setting CHROMA_DB_PATH=~/.local/share/a-mem/chroma_db

Alternative Backends

Ollama (local, free)

claude mcp add a-mem -s user -- a-mem-mcp \  -e LLM_BACKEND=ollama \  -e LLM_MODEL=llama2

OpenRouter (100+ models)

claude mcp add a-mem -s user -- a-mem-mcp \  -e LLM_BACKEND=openrouter \  -e LLM_MODEL=anthropic/claude-3.5-sonnet \  -e OPENROUTER_API_KEY=sk-or-...

Hook Management (Claude Code)

The session-start hook reminds Claude to use memory tools. It installs automatically with Claude Code, but you can manage it manually:

a-mem-install-hook     # Install/reinstall hooka-mem-uninstall-hook   # Remove hook completely

Python API

Use A-MEM directly in Python (works with any agent or application):

from agentic_memory.memory_system import AgenticMemorySystem
memory = AgenticMemorySystem(    llm_backend="openai",    llm_model="gpt-4o-mini")
# Add (auto-generates keywords, tags, context)memory_id = memory.add_note("FastAPI app uses dependency injection for DB sessions")
# Searchresults = memory.search("database patterns", k=5)
# Read full detailsnote = memory.read(memory_id)print(note.keywords, note.tags, note.links)

Research

A-MEM implements concepts from the paper:

A-MEM: Agentic Memory for LLM Agents Xu et al., 2025 arXiv:2502.12110