{"data":{"slug":"getzep-graphiti","name":"graphiti","tagline":"Build Real-Time Knowledge Graphs for AI Agents","github_url":"https://github.com/getzep/graphiti","owner":"getzep","repo":"graphiti","owner_avatar_url":"https://avatars.githubusercontent.com/u/132832125?v=4","primary_language":"Python","stars":30018,"forks":3042,"topics":["agents","graph","llms","rag"],"archived":false,"github_pushed_at":"2026-08-17T23:49:16+00:00","maintenance_label":"Very active","stars_delta_30d":1143,"url":"https://www.graphcanon.com/tools/getzep-graphiti","markdown_url":"https://www.graphcanon.com/tools/getzep-graphiti.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/getzep-graphiti","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=getzep-graphiti","description":"Build Real-Time Knowledge Graphs for AI Agents","homepage_url":"https://help.getzep.com/graphiti","license":"Apache-2.0","open_issues":488,"watchers":165,"ai_summary":"Graphiti is a Python-based toolkit designed for building real-time knowledge graphs utilized by AI agents.","readme_excerpt":"## Installation\n\nRequirements:\n\n- Python 3.10 or higher\n- Neo4j 5.26 / FalkorDB 1.1.2 / Amazon Neptune Database Cluster or Neptune Analytics Graph + Amazon OpenSearch\n  Serverless collection (serves as the full text search backend) / Kuzu 0.11.2 (**deprecated**, see below)\n- OpenAI API key (Graphiti defaults to OpenAI for LLM inference and embedding)\n\n> [!IMPORTANT]\n> Graphiti works best with LLM services that support Structured Output (such as OpenAI, Anthropic, and Gemini).\n> Using other services may result in incorrect output schemas and ingestion failures. This is particularly\n> problematic when using smaller models.\n\nOptional:\n\n- Google Gemini, Anthropic, or Groq API key (for alternative LLM providers)\n\n> [!TIP]\n> The simplest way to install Neo4j is via [Neo4j Desktop](https://neo4j.com/download/). It provides a user-friendly\n> interface to manage Neo4j instances and databases.\n> Alternatively, you can use FalkorDB on-premises via Docker and instantly start with the quickstart example:\n> ```\n> docker run -p 6379:6379 -p 3000:3000 -it --rm falkordb/falkordb:latest\n> ```\n\n```bash\npip install graphiti-core\n```\n\nor\n\n```bash\nuv add graphiti-core\n```\n\n---\n\n### You can also install optional LLM providers as extras:\n\n```bash\n\n---\n\n# Install with Anthropic support\npip install graphiti-core[anthropic]\n\n---\n\n# Install with Groq support\npip install graphiti-core[groq]\n\n---\n\n# Install with Google Gemini support\npip install graphiti-core[google-genai]\n\n---\n\n# Install with multiple providers\npip install graphiti-core[anthropic,groq,google-genai]\n\n---\n\n# Install with FalkorDB and LLM providers\npip install graphiti-core[falkordb,anthropic,google-genai]\n\n---\n\n# Install with Amazon Neptune\npip install graphiti-core[neptune]\n```\n\n---\n\n## Quick Start\n\n> [!IMPORTANT]\n> Graphiti defaults to using OpenAI for LLM inference and embedding. Ensure that an `OPENAI_API_KEY` is set in your\n> environment.\n> Support for Anthropic, Gemini, and Groq is available, too. Other LLM providers — both hosted OpenAI-compatible APIs\n> (DeepSeek, Together, OpenRouter, …) and local servers (Ollama, vLLM, llama.cpp, LM Studio) — may be used via their\n> OpenAI-compatible endpoints; see\n> [Using Graphiti with OpenAI-compatible providers and local LLMs](#using-graphiti-with-openai-compatible-providers-and-local-llms).\n\nFor a complete working example, see the [Quickstart Example](examples/quickstart/README.md) in the examples directory.\nThe quickstart demonstrates:\n\n1. Connecting to a Neo4j, Amazon Neptune, FalkorDB, or Kuzu database\n2. Initializing Graphiti indices and constraints\n3. Adding episodes to the graph (both text and structured JSON)\n4. Searching for relationships (edges) using hybrid search\n5. Reranking search results using graph distance\n6. Searching for nodes using predefined search recipes\n\nThe example is fully documented with clear explanations of each functionality and includes a comprehensive README with\nsetup instructions and next steps.\n\n---\n\n### Running with Docker Compose\n\nYou can use Docker Compose to quickly start the required services:\n\n- **Neo4j Docker:**\n\n  ```bash\n  docker compose up\n  ```\n\n  This will start the Neo4j Docker service and related components.\n\n- **FalkorDB Docker:**\n\n  ```bash\n  docker compose --profile falkordb up\n  ```\n\n  This will start the FalkorDB Docker service and related components.\n\n---\n\n### Quick Start\n\n```python\nfrom openai import AsyncOpenAI\nfrom graphiti_core import Graphiti\nfrom graphiti_core.llm_client.azure_openai_client import AzureOpenAILLMClient\nfrom graphiti_core.llm_client.config import LLMConfig\nfrom graphiti_core.embedder.azure_openai import AzureOpenAIEmbedderClient","github_created_at":"2024-08-08T22:08:30+00:00","created_at":"2026-07-07T17:36:19.891294+00:00","updated_at":"2026-08-18T00:02:29.094164+00:00","categories":[{"slug":"ai-agents","name":"AI Agents","url":"https://www.graphcanon.com/categories/ai-agents","markdown_url":"https://www.graphcanon.com/categories/ai-agents.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/ai-agents"},{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"}],"tags":[{"slug":"agents","name":"agents"},{"slug":"amazon-neptune","name":"amazon-neptune"},{"slug":"falkordb","name":"falkordb"},{"slug":"graph","name":"graph"},{"slug":"llms","name":"llms"},{"slug":"neo4j","name":"neo4j"},{"slug":"openserverlesscollection","name":"openserverlesscollection"},{"slug":"rag","name":"rag"}],"trust":{"provenance":{"is_fork":false,"github_id":840056306,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-18T00:02:28.309Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":3,"days_since_push":0,"last_release_at":"2026-07-27T20:57:23Z","stars_delta_30d":1143,"open_issues_delta_30d":45},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:07:13.343Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-18T00:02:28.765Z"},"deploy":{"source":"dockerfile:Dockerfile","self_host":true,"observed_at":"2026-08-18T00:02:28.765Z","managed_saas":false},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-08-18T00:02:28.765Z"},"has_docker":{"value":true,"source":"dockerfile:Dockerfile","observed_at":"2026-08-18T00:02:28.765Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-18T00:02:28.765Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["If you require seamless integration with LLM services that support structured output like OpenAI, Anthropic, or Google Gemini.","When working on a project that necessitates real-time knowledge graphs for AI agents using Python-based frameworks."],"when_not_to_use":["Consider alternative tools if your project primarily uses non-compliant LLM providers without structured output support.","If you prefer not to use Python or do not need the capability to build real-time knowledge graphs for AI applications."],"source":"enrich:decision_facts","observed_at":"2026-07-11T13:12:09.969Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Graphiti is a Python-based toolkit for building real-time knowledge graphs utilized by AI agents, supporting Neo4j, FalkorDB, Amazon Neptune, and Kuzu as database options along with OpenAI, Anthropic, Groq, and Gemini LМ"}]}}