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MCP Server

Memory MCP

A knowledge-graph-based memory system for AI agents that enables persistent information storage between conversations.

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8/23/2025
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Please check the documentation below.

README Documentation

Memory MCP

A knowledge-graph-based memory system for AI agents that enables persistent information storage between conversations.

Features

  • Persistent memory storage using a knowledge graph structure
  • Entity-relation model for organizing information
  • Tools for adding, searching, and retrieving memories

Tools

The system provides the following MCP tools:

  • load_knowledge_graph(): Retrieves the entire knowledge graph
  • get_knowledge_graph_size(): Returns the current size category of the graph ("small", "medium", or "large")
  • add_entities(entities): Adds new entities to the memory
  • add_relations(relations): Creates relationships between entities
  • add_observations(entity_name, observations): Adds observations to existing entities
  • delete_entities(entity_names): Removes entities from memory
  • delete_relations(relations): Removes relationships
  • search_nodes(query, search_mode): Searches for entities and relations matching a query. Supports three search modes:
    • "exact_phrase": Matches the entire query as a substring
    • "any_token": Matches if any word in the query matches (default)
    • "all_tokens": Matches if all words in the query match
  • open_nodes(names): Retrieves specific entities and their relationships between them

Usage

Run the agent with:

uv run memory_agent.py

The agent will automatically:

  1. Load its memory at the start of conversations
  2. Reference relevant information during interactions
  3. Update its memory with new information when the conversation ends

Exit a conversation by typing q.

Configuration

Set the memory storage location with the MEMORY_FILE_PATH environment variable (defaults to memory.json).

Quick Actions

Key Features

Model Context Protocol
Secure Communication
Real-time Updates
Open Source