Adds authority-weighted memory storage to Claude with conflict detection and typed relationship graphs. Built on sqlite-vec and sentence-transformers for local semantic search without API calls. Stores memories with high/medium/low priority tiers that affect retrieval ranking, so newer explicit instructions outrank older inferred ones even when semantically similar. Exposes tools for store_memory with auto-resolve, retrieve_memories ranked by similarity times authority score, and graph traversal via typed edges like supersedes and contradicts. Includes a migration script for importing from mcp-memory-service or markdown-based systems. Reach for this when you need memory that understands some facts should override others, not just which embedding is closest.
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".
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| Stale memories persist silently | Supersession tracking: old memories marked status=superseded with typed edges |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 |
pip install mcp-memory-graph
Or run directly:
git clone https://github.com/RetroRobAI/mcp-memory-graph
cd mcp-memory-graph
pip install -r requirements.txt
python server.py
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"
}
}
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.
python -m mcp_memory_graph.migrate
The script will:
mcp-memory-service SQLite databaseYour existing memory service is never modified — the script only reads from it.
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 |
| 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) |
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.
supersedes — this memory replaces anotherrelates_to — connected but not conflictingcontradicts — explicitly conflicting, unresolvedreferenced_by — another memory cites this oneMIT
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