> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mem0.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Persistent Mastra Agents

> Extend Mastra agents with persistent memories powered by Mem0.

In this example you'll learn how to use Mem0 to add long-term memory capabilities to [Mastra's agent](https://mastra.ai/) via tool-use. This memory integration can work alongside Mastra's [agent memory features](https://mastra.ai/docs/agents/01-agent-memory).

You can find the complete example code in the [Mastra repository](https://github.com/mastra-ai/mastra/tree/main/examples/memory-with-mem0).

## Overview

This guide will show you how to integrate Mem0 with Mastra to add long-term memory capabilities to your agents. We'll create tools that allow agents to save and retrieve memories using Mem0's API.

## Installation

**Install the Integration Package**

To install the Mem0 integration, run:

```bash theme={null}
npm install @mastra/mem0
```

**Add the Integration to Your Project**

Create a new file for your integrations and import the integration:

```typescript integrations/index.ts theme={null}
import { Mem0Integration } from "@mastra/mem0";

export const mem0 = new Mem0Integration({
  config: {
    apiKey: process.env.MEM0_API_KEY!,
    userId: "alice",
  },
});
```

**Use the Integration in Tools or Workflows**

You can now use the integration when defining tools for your agents or in workflows.

```typescript tools/index.ts theme={null}
import { createTool } from "@mastra/core";
import { z } from "zod";
import { mem0 } from "../integrations";

export const mem0RememberTool = createTool({
  id: "Mem0-remember",
  description:
    "Remember your agent memories that you've previously saved using the Mem0-memorize tool.",
  inputSchema: z.object({
    question: z
      .string()
      .describe("Question used to look up the answer in saved memories."),
  }),
  outputSchema: z.object({
    answer: z.string().describe("Remembered answer"),
  }),
  execute: async ({ context }) => {
    console.log(`Searching memory "${context.question}"`);
    const memory = await mem0.searchMemory(context.question);
    console.log(`\nFound memory "${memory}"\n`);

    return {
      answer: memory,
    };
  },
});

export const mem0MemorizeTool = createTool({
  id: "Mem0-memorize",
  description:
    "Save information to mem0 so you can remember it later using the Mem0-remember tool.",
  inputSchema: z.object({
    statement: z.string().describe("A statement to save into memory"),
  }),
  execute: async ({ context }) => {
    console.log(`\nCreating memory "${context.statement}"\n`);
    // to reduce latency memories can be saved async without blocking tool execution
    void mem0.createMemory(context.statement).then(() => {
      console.log(`\nMemory "${context.statement}" saved.\n`);
    });
    return { success: true };
  },
});
```

**Create a New Agent**

```typescript agents/index.ts theme={null}
import { openai } from '@ai-sdk/openai';
import { Agent } from '@mastra/core/agent';
import { mem0MemorizeTool, mem0RememberTool } from '../tools';

export const mem0Agent = new Agent({
  name: 'Mem0 Agent',
  instructions: `
    You are a helpful assistant that has the ability to memorize and remember facts using Mem0.
  `,
  model: openai('gpt-4.1-nano'),
  tools: { mem0RememberTool, mem0MemorizeTool },
});
```

**Run the Agent**

```typescript index.ts theme={null}
import { Mastra } from '@mastra/core/mastra';
import { createLogger } from '@mastra/core/logger';

import { mem0Agent } from './agents';

export const mastra = new Mastra({
  agents: { mem0Agent },
  logger: createLogger({
    name: 'Mastra',
    level: 'error',
  }),
});
```

In the example above:

* We import the `@mastra/mem0` integration
* We define two tools that use the Mem0 API client to create new memories and recall previously saved memories
* The tool accepts `question` as an input and returns the memory as a string

***

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