{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/3"}],"enrichment":{"capability":"Graphiti builds and queries temporal context graphs for AI agents, tracking how facts change over time with full provenance to source data, supporting both semantic and keyword retrieval alongside graph traversal.","skillfed_tags":["temporal-graphs","agent-memory","knowledge-graph"],"use_cases":["Build AI agent memory systems that track how user preferences, relationships, and facts change over time with full audit trails.","Query historical state of entities at any point in time without recomputing the entire graph.","Combine semantic search with keyword matching and graph traversal to retrieve precise context for agent decision-making.","Integrate continuously arriving structured and unstructured data into a coherent, queryable graph without batch reprocessing.","Define custom entity and relationship types for domain-specific applications using Pydantic models."],"what_it_does":"Graphiti is a temporal graph framework that builds evolving context graphs for AI agents by continuously integrating user interactions, structured and unstructured data, and external information. Unlike static knowledge graphs or traditional RAG, it tracks when facts become true and when they are superseded, maintaining full temporal history and provenance to source episodes. Each entity, relationship, and fact has a validity window, allowing queries across time, meaning, and relationships.\n\nThe framework supports incremental updates without full recomputation, hybrid retrieval combining semantic embeddings with keyword search and graph traversal, and custom entity and relationship types defined via Pydantic models. It requires a pluggable graph database backend (Neo4j, FalkorDB, or Amazon Neptune) and integrates with OpenAI for embeddings and PostHog for telemetry. The design prioritizes real-time interaction and precise historical queries over batch processing, making it suitable for applications where context must evolve with every interaction.","worth_installing":"Yes. Graphiti is actively maintained, has no known vulnerabilities, installs with low friction, and solves a specific problem\u2014temporal context graphs for evolving agent memory\u2014that traditional RAG and static knowledge graphs do not address well. The Apache-2.0 license is permissive. The main condition is that you must provision and manage a graph database backend (Neo4j, FalkorDB, or Neptune) separately; it is not a standalone solution."},"id":"graphiti-core","links":{"html":"https://skillfed.io/packages/graphiti-core","md":"https://skillfed.io/packages/graphiti-core.md","pypi":"https://pypi.org/project/graphiti-core/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-27","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"graphiti-core","python_support":"supports_current","summary":"A temporal graph building library"},"popularity":{"monthly_downloads":1647586,"position":3688,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.29.3"}