Graphiti vs LlamaIndex

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	Graphiti RAG	LlamaIndex RAG
Tagline	Open-source temporal knowledge graph framework for building agent memory that updates in real time.	Data framework for connecting LLMs to your data.
Category	RAG	RAG
Pricing	Freemium· Open-source (Apache 2.0); managed Zep Cloud sold separately	Freemium· Free open-source; LlamaCloud paid
Model	Multi-model	BYO (Claude / GPT / open)
Editorial score	—	8.7 / 10
Use cases	agent-memorytemporal-knowledge-graphshybrid-retrievallong-running-agentscontext-engineering	RAGdata ingestionindexing
Pros	Real-time incremental graph updates without batch recomputation Temporal model tracks when facts were valid, not just current state Hybrid semantic + keyword + graph search out of the box Open source with an active commercial backer in Zep MCP server lets Claude Desktop and Cursor read agent memory directly	Focused on retrieval (not general agent stuff) Many ingestion connectors Strong production patterns LlamaCloud for managed ingestion
Cons	Requires running a graph database like Neo4j LLM calls during ingestion add cost vs plain vector RAG Steeper learning curve than drop-in RAG libraries	API surface is large Documentation can be hard to navigate
Website	help.getzep.com	www.llamaindex.ai

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