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

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

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

Add a memory → A-MEM extracts keywords, context, and tags via LLM
Find neighbors → Searches for semantically similar existing memories
Evolve → Decides whether to link, strengthen connections, or update related memories
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.

MCP Tools

A-MEM exposes 6 tools to your coding agent:

Tool	Description
`add_memory_note`	Store new knowledge (async, returns immediately)
`search_memories`	Semantic search across all memories
`search_memories_agentic`	Search + follow graph connections
`read_memory_note`	Get full details of a specific memory
`update_memory_note`	Modify existing memory
`delete_memory_note`	Remove a memory

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):

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

Environment Variables

Variable	Description	Default
`LLM_BACKEND`	`openai`, `ollama`, `sglang`, `openrouter`	`openai`
`LLM_MODEL`	Model name	`gpt-4o-mini`
`OPENAI_API_KEY`	OpenAI API key	—
`EMBEDDING_MODEL`	Sentence transformer model	`all-MiniLM-L6-v2`
`CHROMA_DB_PATH`	Storage directory	`./chroma_db`
`EVO_THRESHOLD`	Evolution trigger threshold	`100`

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 hook
a-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")

# Search
results = memory.search("database patterns", k=5)

# Read full details
note = 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

a-mem-mcp

Documentation