Manages a temporal knowledge graph to provide persistent, context-aware memory across multiple AI coding sessions.
Graphiti is a sophisticated memory system for Claude that utilizes a temporal knowledge graph backed by FalkorDB to store and retrieve project-specific insights. By proactively capturing architectural decisions, infrastructure topology, and 'failure-to-success' patterns during debugging, it ensures that lessons learned in one session are instantly available in the next. This skill effectively eliminates the 'cold start' problem by allowing the AI to recall previous solutions, user preferences, and complex system relationships without manual re-explanation, making it an essential tool for long-term project maintenance and complex problem-solving.
Key Features
01Persistent temporal knowledge graph for long-term context retention
02Semantic search across nodes, facts, and entity relationships
030 GitHub stars
04Infrastructure and service topology mapping for discovery and recall
05Structured session summaries and project-specific group organization
06Automated 'failure-to-success' learning capture for complex debugging
Use Cases
01Onboarding to a complex project by retrieving existing architectural patterns and service dependencies
02Recalling the root cause and fix for a non-obvious build error encountered in a previous session
03Maintaining consistent coding preferences and team conventions across different workspace environments
