Graphiti FAQs

Question 1

How does Graphiti differ from traditional RAG?

Accepted Answer

Unlike traditional Retrieval-Augmented Generation (RAG), Graphiti continuously integrates user interactions, structured/unstructured data, and external information into a queryable graph, supporting incremental updates and historical queries without recomputation.

Question 2

What is Graphiti?

Accepted Answer

Graphiti is a framework for building and querying temporally-aware knowledge graphs, designed for AI agents operating in dynamic environments. It enables real-time data integration and precise historical queries.

Question 3

What are some use cases for Graphiti?

Accepted Answer

Graphiti is ideal for applications requiring dynamic data management, state-based reasoning for agents, and complex queries over evolving data. Example use cases include powering AI agent memory, integrating user interactions, and building context-aware AI applications.

Question 4

What are the key features of Graphiti?

Accepted Answer

Graphiti features real-time incremental updates, a bi-temporal data model, hybrid retrieval (semantic, keyword, graph-based), custom entity definitions with Pydantic models, and a scalable architecture for large datasets.

Question 5

What are the tech requirements for running Graphiti?

Accepted Answer

You will need Python 3.10 or higher, Neo4j 5.26 or higher, and an OpenAI-compatible API key for LLM inference and embedding. Optionally, you can use Google Gemini, Anthropic, or Groq API keys.

Graphiti

Graphiti

主な機能

ユースケース

主な機能

ユースケース