Long-Term Memory

Persistent, self-organizing memory for AI assistants.

Drop-in MCP server that gives Claude (and any MCP client) long-term memory — powered by semantic search, knowledge graphs, and reinforcement learning.

Note: This package was previously published as mcp-memory-server. That package is deprecated — please use long-term-memory going forward.

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Why Long-Term Memory?

Current AI memory tools have two critical problems:

Problem	How we solve it
Manual retrieval — you must ask "do you remember X?"	auto_search runs every turn, injecting relevant memories automatically
Missed memories — AI decides what to save, so experiences/stories get lost	Every turn is auto-logged; sleep cycle extracts what the AI missed
Token waste — entire memory dump inserted into context	Multi-resolution composer selects top-K memories within a token budget

Key Features

• RL-powered policy — Contextual bandit decides when to save, skip, or retrieve (not just keyword matching)
• Semantic search — ChromaDB + multilingual sentence-transformer embeddings (intfloat/multilingual-e5-small)
• Knowledge graph — Entity-relation graph (NetworkX) for multi-hop reasoning
• GraphRAG hybrid retrieval — Vector similarity + graph traversal, fused and re-ranked by an RL re-ranker
• Auto-linking — New memories automatically link to similar existing ones (similarity ≥ 0.92)
• Multi-resolution text — Full text → summary → entity triples, composed within token budget
• Automatic conversation logging — All turns recorded to SQLite; high-value turns instantly extracted to ChromaDB
• Sentence-level splitting — Multi-sentence turns split into individual memories with independent categories
• Sleep cycle memory extraction — Batch-processes missed memories from conversation logs using progressive RL extraction
• Auto category classification — memory_save auto-classifies content category from patterns
• Forgetting pipeline — Decay-based aging with consolidation, pinning, and immutable protection
• Sleep cycle — Periodic maintenance: extraction, dedup, compress, forget, checkpoint
• Live graph — Real-time WebSocket visualization of the memory graph
• Multilingual — Korean and English pattern support out of the box

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Quick Start (2 minutes)

1. Install

pip install long-term-memory

Or with uv:

uv pip install long-term-memory

Optional extras

pip install long-term-memory[ko]     # Korean NLP support
pip install long-term-memory[live]   # Real-time graph visualization
pip install long-term-memory[viz]    # Static graph visualization

2. Setup client instructions

# For OpenClaw
aimemory-setup openclaw

# For Claude Code
aimemory-setup claude

This injects memory usage instructions into your client's configuration files (SOUL.md/TOOLS.md for OpenClaw, CLAUDE.md for Claude Code). Re-run anytime to update.

Custom database path

By default, memories are stored in ./memory_db (resolved to an absolute path at install time). To use a custom location:

# OpenClaw — sets the DB path in the extension and mcporter config
aimemory-setup openclaw --db-path /path/to/my/memory_db

# Claude Code
aimemory-setup claude --db-path /path/to/my/memory_db

# Shell script (OpenClaw)
bash scripts/install_openclaw.sh --db-path /path/to/my/memory_db

You can also set the AIMEMORY_DB_PATH environment variable, which all components respect:

export AIMEMORY_DB_PATH=/path/to/my/memory_db
aimemory-setup openclaw   # picks up the env var automatically

All components (MCP server, live viewer, OpenClaw extension) will use the same absolute path, ensuring data consistency.

3. Connect to OpenClaw

mcporter config add aimemory --command aimemory-mcp --scope home

4. Connect to Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "aimemory": {
      "command": "aimemory-mcp"
    }
  }
}

That's it. Claude now has persistent memory across all conversations.

With live graph visualization

{
  "mcpServers": {
    "aimemory": {
      "command": "aimemory-mcp",
      "args": ["--with-live"]
    }
  }
}

Then open http://127.0.0.1:8765 to see the live memory graph.

Advanced: custom data path or uv project mode

{
  "mcpServers": {
    "aimemory": {
      "command": "uv",
      "args": ["run", "--project", "/path/to/long-term-memory", "aimemory-mcp", "--with-live"],
      "env": {
        "AIMEMORY_DB_PATH": "/path/to/memory_db"
      }
    }
  }
}

5. Connect to Claude Code

claude mcp add aimemory -- aimemory-mcp

Or with live graph:

claude mcp add aimemory -- aimemory-mcp --with-live

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Live Graph Visualization

Real-time WebSocket-based memory graph that updates as memories are saved, searched, or deleted.

# Option 1: auto-start with MCP server
aimemory-mcp --with-live

# Option 2: standalone server
aimemory-live --port 8765

# Option 3: standalone with custom DB path
aimemory-live --db-path /path/to/memory_db

# Option 4: via environment variable
AIMEMORY_LIVE=1 aimemory-mcp

Open http://127.0.0.1:8765 in a browser. Requires the [live] extra (pip install long-term-memory[live]). Features:

• Force-directed graph layout with category-based coloring
• New nodes glow green on save, blue on search
• Event log sidebar with hover-to-highlight (hover a log entry to highlight related nodes)
• Persistent event history across browser refreshes
• Cross-process events — MCP server pushes events to the live graph via WebSocket

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MCP Tools (13)

Tool	Description
auto_search	Auto-retrieve relevant memories at turn start (multi-resolution context)
memory_save	Save a new memory with keywords, category, and relations
memory_search	Semantic similarity search
memory_update	Update content or keywords of an existing memory
memory_delete	Delete a memory (respects immutability)
memory_get_related	BFS graph traversal for related memories
memory_pin / memory_unpin	Protect memories from forgetting
memory_stats	Total count and category breakdown
memory_visualize	Generate interactive graph HTML
sleep_cycle_run	Trigger maintenance (extraction + consolidation + forgetting + checkpoint)
policy_status	RL policy state (epsilon, action distribution, updates)
policy_decide	Ask the RL policy for a SAVE/SKIP/RETRIEVE decision with reasoning

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Configuration

All settings via environment variables:

Variable	Default	Description
AIMEMORY_DB_PATH	./memory_db	ChromaDB persistence directory (use absolute path to ensure all components share the same DB)
AIMEMORY_LANGUAGE	ko	Language for pattern matching (ko / en)
AIMEMORY_EMBEDDING_MODEL	intfloat/multilingual-e5-small	Sentence-transformer model
AIMEMORY_LOG_LEVEL	INFO	Logging level
AIMEMORY_ENHANCED_POLICY	0	Enable 778d enhanced RL policy (1 to enable)
AIMEMORY_GRAPH_RAG	0	Enable GraphRAG hybrid retrieval (1 to enable)
AIMEMORY_LIVE_HOST	127.0.0.1	Live graph server host (for event push)
AIMEMORY_LIVE_PORT	8765	Live graph server port (for event push)

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Architecture

┌─────────────────────────────────────────────────┐
│                   MCP Client                     │
│     (Claude Desktop / Claude Code / OpenClaw)    │
└────────────────────┬────────────────────────────┘
                     │ stdio (JSON-RPC)
┌────────────────────▼────────────────────────────┐
│              FastMCP Server (13 tools)           │
├──────────────────────────────────────────────────┤
│              MemoryBridge (orchestrator)          │
├──────────┬──────────┬──────────┬─────────────────┤
│ RL Policy│ Retrieval│ Storage  │ Maintenance      │
│          │          │          │                  │
│ Rule-    │ ChromaDB │ Graph    │ Sleep Cycle      │
│ Based +  │ vector + │ Memory   │ (extraction,     │
│ MLP      │ Knowledge│ Store    │  consolidation,  │
│ Bandit   │ Graph    │          │  forgetting,     │
│          │ (GraphRAG)│         │  checkpoints)    │
│ Re-ranker│          │ SQLite   │                  │
│ (11d MLP)│          │ Conv Log │ Extraction RL    │
└──────────┴──────────┴──────────┴─────────────────┘
         ↕ WebSocket (cross-process)
┌──────────────────────────────────────────────────┐
│          Live Graph Server (aimemory-live)        │
│     vis.js force-directed graph + event log      │
└──────────────────────────────────────────────────┘

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Development

# Clone and install dev dependencies
git clone https://github.com/ihwooMil/long-term-memory.git
cd long-term-memory
uv sync --extra dev

# Run tests (611+ tests)
uv run pytest tests/ -q

# Lint & format
uv run ruff check src/ tests/
uv run ruff format src/ tests/

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Migrating from mcp-memory-server

pip uninstall mcp-memory-server
pip install long-term-memory

No code changes needed — the Python import name (aimemory) and CLI commands (aimemory-mcp, aimemory-viz, aimemory-live) remain the same.

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License

MIT — see LICENSE for details.