Pyyan / Compare / Cognee vs LlamaIndex vs LangChain & LangGraph
Cognee vs LlamaIndex vs LangChain & LangGraph
3 of 5RAG & Knowledge Graphs · verified 13 Aug 2026
| Specification | Cognee | LlamaIndex | LangChain & LangGraph |
|---|---|---|---|
| Summary | Graph and vector together, with a pipeline that improves itself. | The strongest option for document-centric retrieval. | Orchestration, with retrieval as one piece. |
| Kind | Framework | Framework | Framework |
| Graph | Yes, core | Yes, property graph index | Via integrations |
| Language | Python | Python, TypeScript | Python, TypeScript |
| Licence | Apache 2.0 | MIT | MIT |
| GitHub stars | ~7k | ~45k | ~120k |
| Category | RAG & Knowledge Graphs | RAG & Knowledge Graphs | RAG & Knowledge Graphs |
| Official | Cognee ↗ | LlamaIndex ↗ | LangChain ↗ |
Highlighted rows are where these differ.
Cognee
- Many retrieval modes over one memory layer
- Re-weights the graph from feedback
Best for deep knowledge retrieval.
LlamaIndex
- Deep document handling and many indexing strategies
- The common advice is LlamaIndex for retrieval, LangGraph for orchestration
Best for PDFs, knowledge bases, structured data.
LangChain & LangGraph
- LangGraph adds durable state and checkpointing
- Widely used, and widely argued about
Best for agents that retrieve as one step among many.