mcp-memory-graph
About
Persistent memory for AI agents using a semantic knowledge graph. Store, retrieve, and connect memories with semantic search — so your AI remembers context across sessions.
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Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
mcp-memory-graphCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"mcp-memory-graph": {
"server": {
"command": "uvx",
"args": [
"mcp-memory-graph"
]
}
}
}
}McpServers
{
"server": {
"command": "uvx",
"args": [
"mcp-memory-graph"
]
}
}Transport
"stdio"Package
"mcp-memory-graph"Registry
"pypi"A context-aware memory MCP server for Claude Code and any MCP-compatible AI agent.
Goes beyond basic vector search by addingauthority weighting,conflict detection, andtyped relationship edgesbetween memories — so your agent always retrieves the right answer when sources disagree.
Inspired by the context engine architecture described inUnblocked'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-graphsolves this with three mechanisms:
git clone https://github.com/RetroRobAI/mcp-memory-graph cd mcp-memory-graph pip install -r requirements.txt python server.py
Add to~/.claude.jsonundermcpServers:
"mcp-memory-graph": { "type": "stdio", "command": "mcp-memory-graph", "env": { "MEMORY_GRAPH_DB_PATH": "/path/to/memories.db" } }
"mcp-memory-graph": { "type": "stdio", "command": "python", "args": ["/path/to/mcp-memory-graph/server.py"], "env": { "MEMORY_GRAPH_DB_PATH": "/path/to/memories.db" } }
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.
- Auto-detect any existingmcp-memory-serviceSQLite database
- Ask if you have a markdown memory directory to import
- Show you how many memories it found
- 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
Your existing memory service is never modified — the script only reads from it.
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 another
- relates_to— connected but not conflicting
- contradicts— explicitly conflicting, unresolved
- referenced_by— another memory cites this one
- sqlite-vec— vector similarity search
- sentence-transformers— local embeddings, no API key needed
- FastMCP— MCP server framework
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