Basic Memory vs Cognee
A side-by-side comparison of Basic Memory and Cognee, two Memory tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
Basic Memory
MemoryPersistent, local knowledge graph for AI tools — stored as plain Markdown.
View Basic MemoryAt a glance
| Attribute | Basic Memory | Cognee |
|---|---|---|
| Category | Memory | Memory |
| Pricing | FREEMIUM | FREEMIUM |
| License | Open core | Open core |
| Deployment | Hybrid | Hybrid |
| Platforms (differs) | CLI, macOS, Windows, Linux | API |
| Model support (differs) | Model-agnostic | BYO key / model |
| Vendor (differs) | Basic Machines | Cognee |
| Capabilities (differs) |
|
|
The honest brief
Basic Memory
Unlike cloud memory stores, your graph stays in editable local Markdown you can open in Obsidian — portable and inspectable, not locked in a DB.
- Plain-Markdown, local-first storage
- Obsidian-compatible knowledge graph
- Works across Claude, Cursor, VS Code via MCP
- Open-source, no vendor lock-in
- AGPL-3.0 may not suit every team
- Cross-device sync is a paid cloud add-on
- Setup needs a local CLI install
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
When to pick which
Both cover Agent memory and Knowledge graph.
Pick Basic Memory if you need MCP server and Note-taking / PKM.
- MCP server (primary capability)
- Note-taking / PKM (secondary capability)
Pick Cognee if you need RAG pipeline, Embeddings, and Vector search.
- RAG pipeline (secondary capability)
- Embeddings (secondary capability)
- Vector search (secondary capability)
They also differ on:
- Platforms
- CLI, macOS, Windows, Linux · API