Mem0 alternatives for shared AI memory
Compare Mem0's hosted MCP server and OpenMemory with Zep, Letta, and vtriv for shared AI memory, readable context, and durable task records.
Mem0 is a real option for people who want memory across AI tools. Its hosted MCP server connects compatible clients to one Mem0 account, while OpenMemory provides a local-first MCP server and dashboard.
The useful dividing line is the record you want to keep. Mem0 manages scoped memory objects that an assistant can add, search, list, update, and delete. vtriv keeps readable Markdown documents, reusable skills, project context, and structured task threads. Zep and Letta solve related problems for people building agents or adopting a stateful agent platform.
All of these products can help a fresh session recover context. They differ in what you can see, organize, and hand to the next session.
Memory objects and working context
A memory service stores facts or passages as objects. In Mem0, those objects can be scoped to users, agents, applications, and runs. An assistant retrieves the relevant memories when it needs them. That model works for a personal preference shared across compatible clients and for customer memory inside an application.
A context workspace keeps authored material and the work around it as named records. A profile, a reusable instruction, a project note, and a task discussion each have a distinct job. The source remains something a person can open and revise as a whole.
Both models provide persistence. The choice is whether memory objects are enough, or whether the documents and work structure also need to survive between sessions.
Mem0 offers hosted and local MCP memory
Mem0’s hosted MCP server runs at https://mcp.mem0.ai/mcp. Compatible clients can sign in through an OAuth browser flow or send a Mem0 API key as a bearer token. Its tools add, search, list, retrieve, update, and delete memories. The memories live in the connected Mem0 account.
Once each client is configured and signed in, Claude Code, Codex, Cursor, and the other clients listed in Mem0’s guide can use that account. No application integration is required. Mem0 still provides its API and SDKs for developers who want the same memory model inside a support bot, research assistant, or other product.
OpenMemory is the local route. It runs an MCP server and dashboard on your machine, keeps its memory store there, and lets compatible clients add, search, list, and delete memories. The dashboard provides a place to browse those memories and control client access.
Hosted Mem0 fits someone who wants account-backed memory across clients without running the service. OpenMemory fits someone who wants the store on a local machine and is comfortable running the server and dashboard. In both cases, the primary record is a collection of memory objects.
Zep is for agents that need history and time
Zep is application infrastructure with a more specific strength. It ingests conversations and other business data, models how facts change over time, and returns prepared context to an agent. Its docs describe a temporal Context Graph and APIs in Python, TypeScript, and Go. That is a strong fit for an agent that needs to know that a customer’s address changed last month, a ticket was resolved yesterday, or a policy replaced an older policy.
The question for Zep is usually: what will my app feed into this, and what does the agent need back? Its own memory guide recommends adding chat history as the conversation happens. That makes sense in an app where every turn passes through your service.
For a single person moving among AI tools, that plumbing is the job. If you are already building an agent platform, Zep may save you a great deal of work. If you want a persistent record for the tools on your laptop today, a direct MCP service or context workspace is a closer fit.
Letta is an agent platform, with one useful exception
Letta is broader than a memory API. Its platform is for building stateful agents, with persistent memory, tools, and a server that owns the agent’s state. That makes it attractive when the agent itself is your product or your main working environment. The API, SDKs, and agent runtime are the point.
Letta also offers Letta Code, a personal coding and desktop agent. If you want to adopt one capable agent and customize its memory and skills, that is a real option. It is closer to choosing a new home for your work than adding a shared memory layer underneath the assistants you already use.
Letta fits when you want Letta’s agent. Zep fits when your application needs temporal context. Mem0 and OpenMemory cover the simpler case where compatible clients need to share memory directly.
How to choose a shared context tool
The first decision is the unit you expect to correct. A short preference, fact, or remembered passage fits a memory object. A policy, project brief, or set of standing instructions may be easier to maintain as a document with a name and readable source.
The next decision is where the service should live. Hosted Mem0 keeps the memories in an account available to configured clients. OpenMemory keeps them on the machine running its services. A local Markdown folder is simpler still if every client can reach the same machine. Notion or Obsidian may already be enough when they hold the material and the clients have reliable access to it.
The last question is whether the system must carry the work itself. Memory can preserve the facts around a task. A project record can also expose its assignment, status, next action, and discussion. The best MCP memory servers compares these shapes, including local files, hosted notes, memory objects, and agent workspaces.
Where vtriv fits
vtriv fits when the source and the work need to remain readable. It keeps plain Markdown organized around a profile, reusable skills, a knowledge tree, and projects. Project tasks carry assignment, status, dependencies, checklists, and comment threads. You can edit the record in the app, and connected assistants can read and write it over one hosted MCP connection.
The difference appears at the start of a session. Your profile supplies the facts and preferences that apply everywhere. A relevant project brings its own notes and open work. A skill holds the instructions for a recurring kind of task. The assistant gets an explicit record rather than a set of retrieved memories alone.
Tasks stay beside that context. An external runner can query their stored state, select work under its own policy, and write results into the same thread a later session will read. The long-running agent workflow explains how task state, progress, and durable lessons fit together.
A useful trial is small: a short profile and the few documents you dread re-explaining. What’s worth keeping for your AI is a filter for that first pass. If the frustration began with Claude Code files scattered across repositories, CLAUDE.md at scale covers what should stay local and what should travel with you.
The goal is modest: open a new client and have it begin from the work you already did. Get started with vtriv.