📖 The AI Tool Bible

Cognee vs LlamaIndex

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

 
Cognee
RAG
LlamaIndex
RAG
TaglineOpen-source graph-memory layer that gives AI agents persistent, queryable context across sessions.Data framework for connecting LLMs to your data.
CategoryRAGRAG
PricingFreemium· Hobby free (1M tokens/mo); Growth $5/workspace/mo + token usage; Enterprise customFreemium· Free open-source; LlamaCloud paid
ModelMulti-model (Claude, OpenAI, others)BYO (Claude / GPT / open)
Editorial score—8.7 / 10
Use cases
agent-memoryknowledge-graphsragmulti-agent-systemssecond-braincontext-retrieval
RAGdata ingestionindexing
Pros
  • Open source and self-hostable with a sizable GitHub community
  • Graph-based memory beats flat vector RAG for entity-heavy domains
  • MCP server makes it easy to plug into Claude Desktop and agent frameworks
  • Generous free tier (1M tokens/month) for experimentation
  • Adapters for warehouses, docs, chats, and APIs out of the box
  • Focused on retrieval (not general agent stuff)
  • Many ingestion connectors
  • Strong production patterns
  • LlamaCloud for managed ingestion
Cons
  • Graph memory adds operational complexity vs. a plain vector store
  • Still a young product; ontologies and governance features are evolving
  • Token-based pricing on top of LLM costs can compound at scale
  • API surface is large
  • Documentation can be hard to navigate
Websitewww.cognee.aiwww.llamaindex.ai
Pick Cognee if
  • ✅ Open source and self-hostable with a sizable GitHub community
  • ✅ Graph-based memory beats flat vector RAG for entity-heavy domains
  • ✅ MCP server makes it easy to plug into Claude Desktop and agent frameworks
  • ✅ Generous free tier (1M tokens/month) for experimentation
Pick LlamaIndex if
  • ✅ Focused on retrieval (not general agent stuff)
  • ✅ Many ingestion connectors
  • ✅ Strong production patterns
  • ✅ LlamaCloud for managed ingestion
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