long-term-memory 0.4.2

Long-term memory system for AI assistants — persistent, searchable, self-organizing memory powered by semantic search, knowledge graphs, and reinforcement learning

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
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
• Forgetting pipeline — Decay-based aging with consolidation, pinning, and immutable protection
• Sleep cycle — Periodic maintenance: 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.

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

---

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: 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

---

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 (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

---

Configuration

All settings via environment variables:

Variable	Default	Description
AIMEMORY_DB_PATH	./memory_db	ChromaDB persistence directory
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)

---

Architecture

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

---

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.