Cognee vs Memvid
A side-by-side comparison of Cognee and Memvid, two Memory tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Cognee | Memvid |
|---|---|---|
| Category | Memory | Memory |
| Pricing | FREEMIUM | FREEMIUM |
| License | Open core | Open core |
| Deployment | Hybrid | Hybrid |
| Platforms | API | API |
| Model support (differs) | BYO key / model | Model-agnostic |
| Vendor (differs) | Cognee | Memvid, Inc. |
| 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
Memvid
No database or infra to run — memory ships as one portable, offline .mv2 file with sub-5ms retrieval, unlike server-based memory layers.
- No vector DB or RAG pipeline to run
- Works fully offline
- Model-agnostic and multimodal
- Apache-2.0 licensed
- Append-only, versioned storage
- Video-frame approach is novel and debated
- Smaller ecosystem than mem0-style stacks
- Young commercial offering (2026)
- Best for embed/edge, not multi-writer servers
When to pick which
Both cover Agent memory, RAG pipeline, Embeddings, and Vector search.
Pick Cognee if you need Knowledge graph.
- Knowledge graph (secondary capability)
Pick Memvid if you need MCP server.
- MCP server (secondary capability)