@Joseperko1982
Basic information
Customized Graphiti MCP server for brainstorming knowledge graphs with specialized entity types for ideas, themes, stakeholders, constraints, and creative collaboration
No standard config provided
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Graphiti is a framework for building real-time, temporally-aware knowledge graphs for AI agents. Its MCP server allows AI assistants to manage episodes, entities, and relationships through the Model Context Protocol, integrating dynamic user interactions and business data.
Install graphiti‑core via pip or poetry, set up a Neo4j 5.26+ database and an OpenAI API key, then use the Python API to add episodes and search the graph. The MCP server can be deployed with Docker for integration with MCP‑compatible clients like Claude or Cursor.
Graphiti focuses on dynamic, incrementally updated data with hybrid retrieval and explicit bi‑temporal tracking, while GraphRAG is designed for static document summarization using LLM‑driven community analysis.
Python 3.10+, Neo4j 5.26+, and an OpenAI API key for LLM inference and embeddings. Optional providers (Anthropic, Groq, Gemini) are supported via extra installs.
All graph data, embeddings, and metadata are stored in a Neo4j database. The server requires a running Neo4j instance.
Graphiti supports semantic embeddings, BM25 keyword search, and graph traversal, combined into a hybrid retrieval pipeline. It can also rerank results by graph distance.
Yes. Graphiti uses temporal edge invalidation to handle changes and contradictions, maintaining a complete history of when facts were known and when they changed.
This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable,
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