MCP Memory Graph

mcp-memory-graph

<!-- mcp-name: io.github.RetroRobAI/mcp-memory-graph -->

A context-aware memory MCP server for Claude Code and any MCP-compatible AI agent.

Goes beyond basic vector search by adding authority weighting, conflict detection, and typed relationship edges between memories — so your agent always retrieves the right answer when sources disagree.

Inspired by the context engine architecture described in Unblocked's "How a Context Engine Actually Works".

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Why this exists

Standard memory MCP servers store and retrieve memories by semantic similarity. That works until you have conflicting memories — an old instruction saying one thing and a new one saying another. Without authority weighting, the agent retrieves whichever is semantically closer to the query, not whichever is more trustworthy.

mcp-memory-graph solves this with three mechanisms:

| Problem | Solution | |---|---| | All memories treated equally | Priority tiers: high / medium / low → authority scores 1.0 / 0.6 / 0.3 | | Stale memories persist silently | Supersession tracking: old memories marked status=superseded with typed edges | | Duplicates accumulate over time | Conflict detection before every store; auto-resolve by authority |

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Installation

pip install mcp-memory-graph

Or run directly: ``bash git clone https://github.com/RetroRobAI/mcp-memory-graph cd mcp-memory-graph pip install -r requirements.txt python server.py ``

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Claude Code setup

Add to ~/.claude.json under mcpServers:

"mcp-memory-graph": {
  "type": "stdio",
  "command": "mcp-memory-graph",
  "env": {
    "MEMORY_GRAPH_DB_PATH": "/path/to/memories.db"
  }
}

Or with the raw script:

"mcp-memory-graph": {
  "type": "stdio",
  "command": "python",
  "args": ["/path/to/mcp-memory-graph/server.py"],
  "env": {
    "MEMORY_GRAPH_DB_PATH": "/path/to/memories.db"
  }
}

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Migrating from an existing memory service

If you have an existing memory service (mcp-memory-service, Mem0, or a markdown-based memory system), you can import your memories into mcp-memory-graph using the included migration script.

Migration is manual and opt-in — it never runs automatically. Nothing is written until you explicitly confirm.

Run the migration script

python -m mcp_memory_graph.migrate

The script will:

1. Auto-detect any existing mcp-memory-service SQLite database
2. Ask if you have a markdown memory directory to import
3. Show you how many memories it found
4. Present three choices:

• [1] Migrate — import everything into mcp-memory-graph
• [2] Run in parallel — start mcp-memory-graph fresh, keep your old service running
• [3] Skip — do nothing

1. Ask for a final confirmation before writing anything

Your existing memory service is never modified — the script only reads from it.

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Configuration

All settings via environment variables:

| Variable | Default | Description | |---|---|---| | MEMORY_GRAPH_DB_PATH | ~/.mcp-memory-graph/memories.db | SQLite database path | | MEMORY_GRAPH_MODEL | all-MiniLM-L6-v2 | sentence-transformers model | | MEMORY_GRAPH_DIM | 384 | Embedding dimensions | | MEMORY_GRAPH_CONFLICT_THRESHOLD | 0.85 | Cosine similarity above which memories are flagged as conflicting | | MEMORY_GRAPH_DEFAULT_RESULTS | 10 | Default retrieval limit |

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Tools

| Tool | Description | |---|---| | store_memory | Store with conflict detection and optional auto-resolve | | retrieve_memories | Semantic search ranked by similarity × authority | | check_conflicts | Preview conflicts before storing | | update_memory | Update content/priority with supersession tracking | | delete_memory | Soft delete (preserves history) | | add_memory_edge | Manually add typed relationship | | get_related_memories | Traverse relationship graph for a memory | | list_memories | List with filters (status, type, priority) |

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Priority system

priority="high"    # authority_score=1.0  — explicit instructions, confirmed preferences
priority="medium"  # authority_score=0.6  — inferred preferences, reference data
priority="low"     # authority_score=0.3  — session summaries, historical context

Retrieval ranking: weighted_score = 1 - (distance / (authority_score + 0.001) / 10)

A high-authority memory will rank above a semantically closer low-authority one when their similarity scores are within ~3x of each other.

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Edge types

• supersedes — this memory replaces another
• relates_to — connected but not conflicting
• contradicts — explicitly conflicting, unresolved
• referenced_by — another memory cites this one

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Stack

• sqlite-vec — vector similarity search
• sentence-transformers — local embeddings, no API key needed
• FastMCP — MCP server framework

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License

MIT