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Provides persistent graph memory for LLMs with auto-linking and layered recall.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mem-graph" yet — see the docs or source repo.
Store the following user preferences, task history, and project context in mem-graph, and create entity relationships for future automatic recall: the user prefers concise replies; is building a recruiting assistant; and mainly uses Python, FastAPI, and PostgreSQL.
Returns stored memory nodes, relationship links, and a structured summary for future retrieval and recall.
Import these product research notes into mem-graph, use wikilinks and BM25 to automatically connect topics, competitors, and user pain points, and highlight highly related nodes.
Generates an interlinked knowledge graph with auto-linking results and highlighted key nodes.
Analyze activation across the current memory layers in mem-graph, and using spreading activation and synaptic decay, recommend which information should be reinforced, retained, or faded.
Outputs memory-layer analysis and recommendations to improve recall quality.
Provide persistent graph memory, semantic search, and traversal for AI agents.
Build a semantic graph from project files for search, knowledge, and task management.
Persistent knowledge-graph memory for MCP with semantic search and version tracking.
Provide persistent graph-memory storage and retrieval for LLM agents via MCP.
Build and query persistent knowledge graphs so coding agents remember across sessions.
Give AI assistants persistent knowledge graph memory across sessions and workflows.