How to Use the Mem0 MCP in Mastra AI

Q: Can Mastra AI agents use Mem0 for decision-making?

They can. By calling searchmemories, the agent retrieves past user data to decide which branch of the workflow to execute next.

Q: Does Mem0 support human-in-the-loop with Mastra AI?

You can wrap deletememory with requireToolApproval. This ensures a human verifies the deletion before it happens.

Connect Mem0 to Mastra AI workflows. Build autonomous agents that remember past user interactions and execute complex logic.

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Connect Mem0 MCP to Mastra AI

Create your Vinkius account to connect Mem0 to Mastra AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Mem0 with Mastra AI

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Stateful agents for Mastra AI

Mastra AI workflows often require memory to make branching decisions. The `add_memory` tool allows your agent to record facts about a user during a workflow execution. These memories act as a shared state across different steps of your pipeline. You no longer have to pass static variables through every single agent action.

Targeted memory retrieval

The `search_memories` tool provides the exact context your agent needs for a specific workflow step. It finds relevant past facts, even if the user hasn't mentioned them in the current session. This prevents your agents from asking the same questions twice. They recall preferences automatically, saving time and reducing redundant API calls.

Workflow-driven memory cleanup

You can trigger `delete_memory` based on the outcome of a Mastra AI workflow. If a process fails or a user session ends, your agent can prune the memory automatically. This keeps your database lean and relevant. It ensures the agent only operates on fresh, accurate information.

Setup guide

Set up Mem0 MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Mem0 tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "mem0-mcp-client",
  servers: {
    "mem0-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Mem0 Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Mem0 tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Mem0 transactions"
);
console.log(result.text);

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Common questions about Mem0 MCP in Mastra AI

Define the server in your MCP client configuration within Mastra. Then, spread the tools into your agent's tool list to make them available for your workflows.

They can. By calling `search_memories`, the agent retrieves past user data to decide which branch of the workflow to execute next.

You can wrap `delete_memory` with `requireToolApproval`. This ensures a human verifies the deletion before it happens.

The server handles your memory storage as a private, isolated set of facts. Only the user ID you provide will ever be associated with the stored content.

The search is limited by the query you send. It returns the most relevant memories, which you can then parse using your agent's logic.

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