What Is the Best Memory MCP for Multi-Agent Systems?

TL;DR

The best memory MCP for a multi-agent system depends on what the agents share. If they need the same company facts, use Sentra: one remote MCP server that gives every agent the same current, permission-checked memory built from the company's systems. If they share working notes they write themselves, use a writable shared scope such as Zep or Supermemory.

What a multi-agent system needs from memory

A single agent needs to remember its own task. Several agents working together need more, and most memory MCP servers were designed for the single-agent case. Four things separate the two.

• A shared scope. If each agent or user gets a private store by default, agents cannot see what the others learned.
• One version of each fact. When agents write freely to a shared store, they can write conflicting values. Something has to decide which one holds.
• Permissions per caller. A planning agent acting for a manager and a support agent acting for a contractor should not read the same things.
• No dependence on one host. The agents may run in Claude Code, Cursor, an orchestration framework and a custom service at once. A remote server over HTTP reaches all of them.

The main memory MCP servers, compared for multi-agent use

The details come from each project's documentation. Mem0 runs a hosted MCP server with 11 tools, including add_memory, search_memories and update_memory (docs.mem0.ai). Zep's Context MCP Server gives each signed-in user their own memory in a project graph, with writes on by default. Supermemory's MCP server scopes memory to spaces. Graphiti's MCP server isolates each group_id in its own graph, and its README calls it experimental with no authentication or authorization. Cognee is an Apache 2.0 memory engine with an MCP server (github.com). Letta is often listed here too, but it is an agent platform that acts as an MCP client; its agents share memory through memory blocks (docs.letta.com). All fifteen servers we reviewed are in the best MCP memory servers.

Why Sentra fits when agents must agree on company facts

Sentra is the company's memory, served to agents over MCP and REST. It reads what happens across the company's connected tools, works out what is true now and who owns what, and keeps it as one memory every permitted person and agent shares. It knows when each fact became true and when it stopped, so an agent never restates a superseded decision as current.

For a multi-agent system, three properties matter. Every agent connects to the same remote server at https://api.sentra.app/mcp/, so there is no per-agent store to keep in sync. An OAuth connection signs in as the person, so a coding agent, a support agent and an analyst asking the same question each get the answer their own access allows, and a failed permission check counts as no access. And the memory is read-oriented by design: ingestion happens through connected sources and not through a public write endpoint, so one agent cannot overwrite what another relies on.

In Claude Code the setup is one command:

claude mcp add --transport http sentra https://api.sentra.app/mcp/

Clients that read a static config file, such as Cursor, point at the same URL with a key generated in Settings. Sentra is SOC 2 Type II and ISO 27001 certified.

When something else fits better

Sentra does not store what your agents write from code. If your agents need a shared scratchpad, such as a plan one agent drafts and another revises, use a writable server: Zep with a shared graph, Supermemory with a shared space, or Letta's shared memory blocks if you build on Letta. If you want to run everything locally and own the graph, Graphiti or Cognee is the closer fit, with access control added by you. If each agent only needs to remember its own user, Mem0 is the simplest option.

The two designs work together. An agent can keep its working notes in a per-agent or shared writable server and read company memory from Sentra, so it remembers its own task and knows what the company knows. How that fits with an orchestrator is covered in agent orchestration vs shared memory, and the Claude Code case is in how to give Claude Code subagents shared context.

How to choose

1. Write down what the agents must share: company facts, or notes they produce themselves.

2. Check the default scope. Many servers start private per user and need a setting before agents share anything.

3. Ask what happens on a conflicting write. If nothing reconciles it, your agents will disagree.

4. Test permissions with two identities and confirm each agent sees only what its user may see.

To connect your agents to one company memory, book a demo.

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