Graphiti Knowledge Graph
Transforms conversations and structured data into a persistent Neo4j knowledge graph with temporal metadata, extracting entities and relationships from episodes while supporting dynamic information evolution and custom entity types for requirements, preferences, and procedures.
About
Built by michabbb as a working implementation of Graphiti's knowledge graph functionality, this server processes episodes (text, messages, or JSON) into nodes (entities) and facts (relationships) with temporal metadata stored in Neo4j. It supports dynamic information that evolves over time and includes custom entity extraction for requirements, preferences, and procedures. The implementation features Docker deployment, authentication via nonce tokens, background processing queues to prevent race conditions, making it valuable for applications that need persistent, queryable memory across conversations and complex data relationships.
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