Build temporal context graphs for agent memory from evolving facts with Graphiti

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.

Prerequisites

Python environment, Graphiti library, backing graph/database components per Graphiti docs, agent application that can ingest episodes and query memory.

Installation

Use the upstream install or setup path that matches your environment:

• docker run -p 6379:6379 -p 3000:3000 -it --rm falkordb/falkordb:latest
• pip install graphiti-core
• uv add graphiti-core
• pip install graphiti-core[falkordb]

Requirements and caveats from upstream:

• | Retrieval & performance | Pre-configured, production-ready retrieval with sub-200ms performance at scale | Custom implementation required; performance depends on your setup |
• | Developer tools | Dashboard with graph visualization, debug logs, API logs; SDKs for Python, TypeScript, and Go | Build your own tools |
• Python 3.10 or higher

Basic usage or getting-started notes:

• Neo4j 5.26 / FalkorDB 1.1.2 / Kuzu 0.11.2 / Amazon Neptune Database Cluster or Neptune Analytics Graph + Amazon

• OpenSearch Serverless collection (serves as the full text search backend)

• OpenAI API key (Graphiti defaults to OpenAI for LLM inference and embedding)

• Source: https://github.com/getzep/graphiti

• Extracted from upstream docs: https://raw.githubusercontent.com/getzep/graphiti/HEAD/README.md

Documentation

• https://help.getzep.com/graphiti

Source

• Agent Skill Exchange