Two answers to the same team problem
Both products exist because assistant memory is personal. Claude Code, ChatGPT, and Cursor each remember things for one person, so an engineering team's context stays trapped in individual sessions. Shared team memory is one of the most-requested features on the Claude Code issue tracker, and both Hjarni and Context Cloud answer it the same structural way: put the memory outside the assistant, behind an MCP server every teammate connects to.
From there the two products diverge on what memory is. Context Cloud stores typed knowledge chunks: each is a typed entry (a decision, finding, convention, or piece of state) with an author, which drives hybrid retrieval and automatic deduplication of conflicting or stale context. Hjarni stores Markdown notes in folders: documents with titles, tags, wiki-links, and a revision history, organized the way a team wiki is.
About the "only MCP memory server with shared workspaces" line
Context Cloud markets itself as the only MCP memory server with shared workspaces, RBAC, attribution, and cross-tool support. Read that as a description of the chunk-store category, not the landscape. Every item on that list ships in Hjarni today: team workspaces as shared folders, per-folder roles, per-edit attribution that names the person or the specific assistant, and one hosted MCP server that Claude, ChatGPT, Claude Code, Cursor, and other clients connect to. The category is young and claims move fast on every side, ours included. The practical takeaway is simply that shared team memory over MCP is a choice between shapes, not a single vendor.
Chunks are for agents. Notes are for agents and the people who manage them.
Memory your team can read is memory your team can trust
The deep difference is what happens when a human needs to look at the memory. A chunk store is built for agent recall: the dashboard shows you typed entries, hybrid retrieval does the search, and automatic deduplication manages contradictions. That is a real engineering feature set, and Context Cloud has a web dashboard to browse and curate it. But the unit is still a chunk. When a new hire asks where the deploy runbook is, or a lead wants to review what the agents have been writing all sprint, a folder of documents reads more like the wiki they expect.
A Hjarni note is a document. The deploy runbook is a page a person opens, reads, and edits, and the same page is what every teammate's assistant cites. Every edit is a version attributed to a person or a named assistant, with a diff, a one-click revert, and a recoverable Trash behind it. Your team memory doubles as your team documentation, so keeping it honest is a reading job, not an audit job.
Search follows the same philosophy. Context Cloud's hybrid search, vector matching fused with keyword ranking, can match by meaning across thousands of chunks, which is genuinely useful. Hjarni's search is plain-text over Markdown: less clever, but transparent. It is the same index behind the app, the REST API, and the MCP search tool, it works the same in any language, and what you can see in the app is exactly what your AI can find.
When Context Cloud is the better fit
If your team's memory is meant to be consumed mostly by agents inside coding tools, and you want typed entries, hybrid recall, and automatic deduplication of stale context, Context Cloud is built for exactly that. Teams standardized on Claude, Cursor, Codex, and Windsurf who think of memory as infrastructure rather than documentation will feel at home. It is free today, so trying it costs nothing; check its export options before you commit a team's knowledge to any store, theirs or ours.
When teams choose Hjarni instead
Teams pick Hjarni when the memory should serve people too, not only agents. It is a full knowledge base you write in directly: a web editor, iOS and Android apps, and a REST API alongside the MCP server, all EU-hosted. The notes your agents read are the wiki your humans read. Folder instructions tell every assistant how to behave in each space. And the exit is always open: notes are plain Markdown, exportable as a ZIP, so a roadmap change on anyone's side cannot trap your team's knowledge. Sharing is asynchronous, like a wiki, not live co-editing, and that is the grain decisions and runbooks actually want.