iflow-mcp_memory

# 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`).