A memory layer vs a notes app
mem0 calls itself a universal memory layer. It is plumbing: an SDK and API that extracts facts from an agent's conversations, stores them, and retrieves them later so the next session remembers what happened. The OpenMemory MCP server exposes that memory to clients like Claude Desktop, Cursor, and VS Code with read and write tools. The author of the content is your code, not a person.
Hjarni is a different product. You write notes in the app. They are plain Markdown, organized in folders, tagged, and linked. The built-in MCP server lets ChatGPT, Claude, and other clients search, read, and update them. The author is you.
Who writes, and who reads
With mem0, the agent writes and the agent reads. You wire it into an application, and it accumulates memories automatically as conversations happen. The OpenMemory dashboard lets you browse what was stored, but it is a window onto agent-written memory, not a place a person sits down to write.
With Hjarni, a human writes and any connected AI reads. You decide what goes in. The note you write is the one your assistant sees next, with no extraction step in between.
mem0 is the memory your agents build for themselves. Hjarni is the knowledge a person writes down on purpose.
The extraction step is where the trust lives
mem0's promise is trustworthy memory, and the hard part of that promise is the LLM extraction stage that turns a conversation into stored facts. The case is not that this never works. The trouble is that the stage sits between what was said and what gets saved, so it can both keep low-signal noise and quietly skip a real fact. In a public 32-day production audit, a team found that roughly 98 percent of about 10,000 accumulated entries were junk, with only a few dozen worth keeping, and concluded the extraction prompt, not the model, was the bottleneck. When you cannot fully see why one detail was kept and another dropped, the memory is harder to trust.
Hjarni does not have that stage. A person or an agent writes a note in Markdown, and that exact text is what gets stored and what your assistant reads back. This is not a claim that a write can never fail. It is that there is no re-interpretation layer deciding what your words really meant before saving them, so what gets written is what you wrote, sitting in a note you can open, name, and revert.
When mem0 is the better fit
If you are a developer building an agent or application and you need a programmatic memory backend your code writes to, mem0 is built for exactly that. The core is open-source and Apache-2.0, you can self-host it, and there is a managed cloud with a free tier (reportedly around 10,000 memories and 1,000 retrievals; check their current pricing). That is the right tool when memory is a feature of software you are shipping.
When people choose Hjarni instead
The switch happens when the job is "I want to write notes my AI can read", not "I am building an app that needs to remember". Hjarni is the knowledge base and the MCP server in one hosted product. There is no SDK to wire in, no extraction pipeline, no vector store to run. You write a note, your AI reads it.
Folder-level AI instructions also matter here. Tell Claude to keep one folder formal and skip another when answering. mem0 has no folder notion because its unit is an extracted memory, not a document a person organizes.