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
| Attribute | Cognee | memU |
|---|---|---|
| Category | Memory | Memory |
| Pricing | FREEMIUM | FREEMIUM |
| License | Open core | Open core |
| Deployment | Hybrid | Hybrid |
| Platforms (differs) | API | API, Web |
| Model support (differs) | BYO key / model | Model-agnostic |
| Vendor (differs) | Cognee | NevaMind AI |
| Capabilities (differs) |
|
|
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
When to pick which
Across the signals we compare, Cognee and memU differ on platforms:
- Platforms
- API · API, Web
Their capability lists differ in recorded depth — compare the full lists above before deciding.