Cognee vs Supermemory
A side-by-side comparison of Cognee and Supermemory, two Memory tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Cognee | Supermemory |
|---|---|---|
| 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 | Supermemory |
| 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
Supermemory
MIT-licensed memory engine you self-host or call as a managed API — one recall endpoint across any model.
- MIT-licensed, self-host or managed
- Single recall API across any model
- Connectors: Drive, Gmail, Notion
- Ships MCP server and SDKs
- Younger project, evolving API
- Smaller track record than peers
- Self-hosting needs infra work
When to pick which
Both cover Agent memory, RAG pipeline, and Vector search.
Pick Cognee if you need Knowledge graph and Embeddings.
- Knowledge graph (secondary capability)
- Embeddings (secondary capability)
Pick Supermemory if you need MCP server and Document parsing (structured).
- MCP server (secondary capability)
- Document parsing (structured) (secondary capability)
They also differ on:
- Platforms
- API · API, Web