Mnemosyne OS — AI agent memory system combining GraphRAG + temporal distillation
Hi GraphRAG team! We've built a memory OS that combines graph-based retrieval with temporal memory distillation — thought you might find the crossover interesting.
Our approach
Mnemosyne OS v5.2 uses Apache AGE (Cypher graph engine) for knowledge graph queries on agent memories, alongside pgvector for semantic search. The hybrid approach gives us:
- Graph queries for entity relationships (Cypher MERGE/CREATE/MATCH)
- Vector search for semantic similarity
- 5-level temporal distillation (Fragment→Session→Daily→Weekly→Profile)
- Smart RAG chunking with overlap windows
Why it's relevant
GraphRAG excels at extracting structured knowledge from unstructured text. We do the same but layer it with temporal memory hierarchy — knowledge doesn't just sit in a graph, it evolves through distillation levels.
Links
Curious about your thoughts on combining graph-based retrieval with temporal memory structures!
Mnemosyne OS — AI agent memory system combining GraphRAG + temporal distillation
Hi GraphRAG team! We've built a memory OS that combines graph-based retrieval with temporal memory distillation — thought you might find the crossover interesting.
Our approach
Mnemosyne OS v5.2 uses Apache AGE (Cypher graph engine) for knowledge graph queries on agent memories, alongside pgvector for semantic search. The hybrid approach gives us:
Why it's relevant
GraphRAG excels at extracting structured knowledge from unstructured text. We do the same but layer it with temporal memory hierarchy — knowledge doesn't just sit in a graph, it evolves through distillation levels.
Links
Curious about your thoughts on combining graph-based retrieval with temporal memory structures!