Best MCP memory servers compared
The best MCP memory server depends on the record: compare Basic Memory, Hjarni, Mem0, Notion, local options, and vtriv for context and task records.
The best MCP memory server depends on what you need a fresh AI session to recover. For a local Markdown knowledge base, start with Basic Memory. For hosted notes that Claude and eligible ChatGPT plans can both write to, Hjarni is a clean fit. For memory inside software you are building, Mem0 is built for that job. If your team already works in Notion, its MCP connection and external agents may be enough. If a connected client needs durable task records beside its context, vtriv has a more specific answer.
There is no universal winner. “Memory server” covers a note library, a graph, a managed workspace, and a record of ongoing work. A useful comparison starts with the record, not a feature tally.
The short answer
| If you need | Start with | Why |
|---|---|---|
| Local files, semantic search, and structured notes | Basic Memory | It is open source, file-first, and can run on your machine. |
| Hosted Markdown notes over OAuth MCP | Hjarni | It exposes direct read, write, and search tools; ChatGPT writes depend on the plan. |
| Memory inside an agent product you are building | Mem0 | Its model is scoped memory operations for users, agents, apps, and runs. |
| A local memory service with a dashboard | OpenMemory | It is a local-first Mem0 project for shared MCP memory across tools. |
| A broad team workspace you already use | Notion | Its MCP connection reaches pages and its External Agents work with task boards. |
| A small graph to study or adapt | MCP Memory reference server | It is a compact local reference implementation. |
| Context, durable lessons, and task records | vtriv | Its projects and task threads sit beside the memory record. |
The rows overlap on purpose. You can use a task schema in a notes product and you can keep notes in a project workspace. The question is what the product makes easy every day.
Basic Memory for local Markdown with more structure
Basic Memory is the closest substitute for a person who wants readable, portable memory across AI tools. Its core is plain Markdown plus full-text, semantic, and knowledge-graph indexing. The local edition is open source and keeps the graph on your own machine; the cloud edition adds private sync across devices and clients.
It has moved beyond a simple note folder. Basic Memory now supports structured search through frontmatter, custom schemas that include Task, prebuilt agent setups for tools such as Claude and Codex, and reusable memory packs. That makes it a strong fit for someone who wants a local or synced knowledge graph with more structure than free-form files.
Choose it when the center of gravity is the knowledge base itself. You want files you can edit, query by meaning or fields, and carry between tools. A task schema may be enough for a personal workflow. Before assuming it is or is not enough for an agent fleet, try the handoff you need: assignment, status changes, dependencies, a discussion thread, and the next agent finding the result without a pasted summary.
Basic Memory has a hosted plan, but local-first control remains a real reason to choose it. Its documentation is clear about the split, and worth reading if keeping the files on your own machine matters.
Hjarni for hosted notes without local setup
Hjarni is a hosted Markdown knowledge base with an MCP server built in. Its OAuth connection exposes tools to read, write, and search notes, which Claude and other compatible clients can use. In ChatGPT, custom-MCP write/modify access is currently a Business and Enterprise/Edu beta; Pro developer mode is read/fetch-only. It also offers folders, tags, version history, attachments, and Markdown or Obsidian ZIP import and export.
This is the straightforward answer when you want a shared note base that follows you across devices but do not want to run a local service or manage sync. It is also a good fit for a small team that wants people on different AI clients to work from the same notes.
Hjarni is candid about its boundary: it is a knowledge-management product, not project management. That keeps the choice simple. Pick it for hosted notes and MCP access. Pick a workspace with first-class work management when project task records are central.
Mem0 and OpenMemory for memory operations
Mem0’s hosted MCP server is no longer only an SDK story. It gives MCP clients memory operations to add, search, list, update, and delete memories scoped around users, agents, applications, and runs. The server is hosted by Mem0, and the memories live in the Mem0 account rather than on your computer.
That model is right when you are building an agent product and need to decide what it stores for each user or run. It can also work for someone who wants a hosted memory service across supported clients. The important distinction is that you are managing memory objects and retrieval, not maintaining a body of Markdown documents and work records.
Mem0 also ships OpenMemory, a local-first MCP server with a dashboard for browsing and managing memories. It gives you a private local store that several compatible tools can use. The tradeoff is operation: the project runs locally and its setup includes its own services and storage. That is a good trade for people who want local control and are comfortable owning the machine that keeps the memory available.
If you are choosing between Mem0 and a personal context workspace, ask who owns the surrounding workflow. If your application owns it, Mem0 is the natural starting point. If you are the person carrying context between existing tools, the note and workspace options deserve a closer look. Mem0 alternatives for shared AI memory goes deeper on that line.
Notion for teams already living in Notion
Notion MCP connects Claude, ChatGPT, Cursor, and other compatible AI apps to a Notion workspace. The connection exposes tools to read and write pages; in ChatGPT, custom-MCP write/modify access is currently a Business and Enterprise/Edu beta, while Pro developer mode is read/fetch-only. Notion also has External Agents, including guides for bringing Claude and Cursor agents onto a team task board.
That matters because a team does not need a new memory product just because MCP arrived. If its projects, permissions, databases, and working notes already live in Notion, using the workspace the team knows may be the best choice. The product is broad, and that breadth is useful when the human workspace comes first.
vtriv is a better fit when the primary object is a portable context record for agents: plain Markdown, reusable skills, project context, and task records an external runner can query. Notion is a better fit when its full team workspace is already the source of truth.
The MCP reference server for a small local graph
The Model Context Protocol project publishes a Memory reference server. It is a compact knowledge graph with entities, observations, relations, and tools to create, search, open, and delete them. The project presents it as a reference server and shows it running locally through npx.
Use it to learn the graph-shaped approach, test a narrow workflow, or adapt a minimal local server. It is deliberately small. You decide where it runs, where its data persists, how another machine reaches it, and how it is backed up. That control can be exactly right for an experiment. It is more work when you want the same record waiting in a browser, a coding tool, and an externally scheduled runner without running the service yourself.
vtriv for agents that need context and task records
vtriv is for the case where memory is only half the problem. It keeps a readable record of a profile, reusable skills, Markdown knowledge, and projects. Those projects carry tasks with assignment, status, dependencies, checklists, and comments. Connected assistants read and write the same record over one hosted MCP connection. An external client or runner decides when to query those records, select work, execute it, retry it, and write its result back.
The difference appears when a runner starts a new session. It can read the project background, query task records by assignment or status, select work under its own policy, and write the result and any durable lesson back to the same record. A later runner can see both the context and the handoff without rebuilding it from a chat transcript. Persistent memory for long-running AI agents explains that workflow in detail.
Choose vtriv when the question is not only “What should the agent remember?” but also “Which work records should its runner consider next, and where will a later session find the answer?” It is free to start, so the practical test is small: add a short profile, one project, one task, and one rule the task should leave behind. How to give Claude persistent memory across sessions walks through the connection.
Run the handoff test
Before moving your whole history, try one real piece of work. Create a context record. Give an assistant a task. Switch clients or start a fresh session. Then ask for the decision, the current status, and the next action.
If the answer is there, the memory is helping. If you have to explain the work again, the record is not carrying enough of the job. That result tells you more than a comparison table can.
If external runners use separate sessions and the record needs to include the work itself, start with vtriv.