Cognee vs memU
A side-by-side comparison of Cognee and memU, two Memory tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
The honest brief
Cognee
Builds an LLM-derived knowledge graph alongside embeddings, so recall follows relationships, not just vector similarity.
- Self-hostable Python SDK
- Recall follows concept relationships
- Bring your own LLM/embedding provider
- Newer, smaller ecosystem
- Cognify pipeline adds LLM cost
- Self-host setup overhead
memU
Stores memories as a hierarchical file system rather than only vector embeddings, cutting context tokens roughly 10x for always-on agents.
- Built for proactive, always-on agents
- Semantic recall via vector indexing
- Self-host or hosted cloud API
- Multimodal memory ingestion
- Younger project, evolving API
- Smaller ecosystem than vector DBs
- Hosted pricing details limited