## Quick Start
### Install
```bash
pip install a-mem
```
### Add to Claude Code
```bash
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](#memory-scope).
### Uninstall
```bash
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 `read_memory_note` for full content. This minimizes token usage while maximizing recall.
## MCP Tools
A-MEM exposes 8 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 |
| `search_memories_by_time` | Search within a time range |
| `read_memory_note` | Get full details (supports bulk reads) |
| `update_memory_note` | Modify existing memory |
| `delete_memory_note` | Remove a memory |
| `check_task_status` | Check async task completion |
### Example Usage
```python
# 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):
```json
{
"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)**
```bash
claude mcp add a-mem -s user -- a-mem-mcp \
-e LLM_BACKEND=ollama \
-e LLM_MODEL=llama2
```
**OpenRouter (100+ models)**
```bash
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:
```bash
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):
```python
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](https://arxiv.org/pdf/2502.12110)