Graphiti vs Pathway

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

	Graphiti RAG	Pathway RAG
Tagline	Open-source temporal knowledge graph framework for building agent memory that updates in real time.	Live data framework for production RAG and streaming ETL pipelines in Python.
Category	RAG	RAG
Pricing	Freemium· Basic: $10 · Pro: $30 · Enterprise: $100	Freemium· Community free (BSL 1.1, 8GB/4 cores); Scale and Enterprise tiers with license key
Model	Multi-model	Multi-model
Editorial score	7.2 / 10	7.3 / 10
Use cases	agent-memorytemporal-knowledge-graphshybrid-retrievallong-running-agentscontext-engineering	live-ragstreaming-etldocument-indexingmultimodal-raganomaly-detection
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	Genuinely live indexing - documents update without rebuild jobs Self-hosted under BSL 1.1, no data leaves your infra Rich connector library (Kafka, S3, SharePoint, Postgres, Delta Lake) Same pipeline handles batch and streaming 20+ production-ready templates including multimodal and adaptive RAG
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	Steeper learning curve than prompt-chain frameworks BSL is not OSI-approved - commercial restrictions apply at scale Smaller community than LangChain/LlamaIndex Pricing for Scale/Enterprise tiers not transparent
Website	help.getzep.com	pathway.com

Pick Graphiti if

✅ 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

Pick Pathway if

✅ Genuinely live indexing - documents update without rebuild jobs
✅ Self-hosted under BSL 1.1, no data leaves your infra
✅ Rich connector library (Kafka, S3, SharePoint, Postgres, Delta Lake)
✅ Same pipeline handles batch and streaming

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