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 graphget_knowledge_graph_size()
: Returns the current size category of the graph ("small", "medium", or "large")add_entities(entities)
: Adds new entities to the memoryadd_relations(relations)
: Creates relationships between entitiesadd_observations(entity_name, observations)
: Adds observations to existing entitiesdelete_entities(entity_names)
: Removes entities from memorydelete_relations(relations)
: Removes relationshipssearch_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:
- Load its memory at the start of conversations
- Reference relevant information during interactions
- 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