{"id":"bad5d98f-3f5f-4e82-a2e0-c60abbb5a70a","shortId":"UGY5Uu","kind":"skill","title":"Build temporal context graphs for agent memory from evolving facts with Graphiti","tagline":"Use Graphiti when an agent needs long-term memory that tracks what changed, when it changed, and which source episode produced each fact, instead of storing flat chunks or chat history alone.","description":"# Build temporal context graphs for agent memory from evolving facts with Graphiti\n\nUse Graphiti when an agent needs long-term memory that tracks what changed, when it changed, and which source episode produced each fact, instead of storing flat chunks or chat history alone.\n\n## Prerequisites\n\nPython environment, Graphiti library, backing graph/database components per Graphiti docs, agent application that can ingest episodes and query memory.\n\n## Installation\n\nUse the upstream install or setup path that matches your environment:\n- docker run -p 6379:6379 -p 3000:3000 -it --rm falkordb/falkordb:latest\n- pip install graphiti-core\n- uv add graphiti-core\n- pip install graphiti-core[falkordb]\n\nRequirements and caveats from upstream:\n- | **Retrieval & performance** | Pre-configured, production-ready retrieval with sub-200ms performance at scale | Custom implementation required; performance depends on your setup |\n- | **Developer tools** | Dashboard with graph visualization, debug logs, API logs; SDKs for Python, TypeScript, and Go | Build your own tools |\n- Python 3.10 or higher\n\nBasic usage or getting-started notes:\n- Neo4j 5.26 / FalkorDB 1.1.2 / Kuzu 0.11.2 / Amazon Neptune Database Cluster or Neptune Analytics Graph + Amazon\n- OpenSearch Serverless collection (serves as the full text search backend)\n- OpenAI API key (Graphiti defaults to OpenAI for LLM inference and embedding)\n\n- Source: https://github.com/getzep/graphiti\n- Extracted from upstream docs: https://raw.githubusercontent.com/getzep/graphiti/HEAD/README.md\n\n## Documentation\n\n- 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