Cognee vs Memori
A side-by-side comparison of Cognee and Memori, two Memory tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Cognee | Memori |
|---|---|---|
| Category | Memory | Memory |
| Pricing | FREEMIUM | FREEMIUM |
| License | Open core | Open core |
| Deployment | Hybrid | Hybrid |
| Platforms | API | API |
| Model support | BYO key / model | BYO key / model |
| Vendor (differs) | Cognee | MemoriLabs |
| 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
Memori
Stores agent memory in ordinary SQL (Postgres/MySQL/SQLite) with one line of code — no separate vector database to run or pay for.
- Persistent memory in standard SQL databases
- LLM-agnostic; bring any provider key
- No vendor lock-in; data stays in your DB
- Dual-mode working + long-term recall
- Younger than Mem0/Zep, smaller ecosystem
- SQL-first design may not suit graph use cases
- Self-hosting means you run the database
When to pick which
We found no distinguishing difference between Cognee and Memori on the signals we track — pricing, license, platforms, and model support. Their capability lists differ only in recorded depth — compare them above. Pick on team fit.