> ## 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.

# Mastra

> Use Mastra agents with Mem0 as the memory backend for cross-conversation storage and retrieval.

The [**Mastra**](https://mastra.ai/) integration demonstrates how to use Mastra's agent system with Mem0 as the memory backend through custom tools. This enables agents to remember and recall information across conversations.

## Overview

In this guide, we'll create a Mastra agent that:

1. Uses Mem0 to store information using a memory tool
2. Retrieves relevant memories using a search tool
3. Provides personalized responses based on past interactions
4. Maintains context across conversations and sessions

## Setup and Configuration

Install the required libraries:

```bash theme={null}
npm install @mastra/core @mastra/mem0 @ai-sdk/openai zod
```

Set up your environment variables:

<Note>Remember to get the Mem0 API key from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-mastra" rel="nofollow">Mem0 Platform</a>.</Note>

```bash theme={null}
MEM0_API_KEY=your-mem0-api-key
OPENAI_API_KEY=your-openai-api-key
```

## Initialize Mem0 Integration

Import required modules and set up the Mem0 integration:

```typescript theme={null}
import { Mem0Integration } from '@mastra/mem0';
import { createTool } from '@mastra/core/tools';
import { Agent } from '@mastra/core/agent';
import { openai } from '@ai-sdk/openai';
import { z } from 'zod';

// Initialize Mem0 integration
const mem0 = new Mem0Integration({
  config: {
    apiKey: process.env.MEM0_API_KEY || '',
    user_id: 'alice', // Unique user identifier
  },
});
```

## Create Memory Tools

Set up tools for memorizing and remembering information:

```typescript theme={null}
// Tool for remembering saved memories
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,
    };
  },
});

// Tool for saving new memories
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 Mastra Agent

Initialize an agent with memory tools and clear instructions:

```typescript theme={null}
// Create an agent with memory tools
const mem0Agent = new Agent({
  name: 'Mem0 Agent',
  instructions: `
    You are a helpful assistant that has the ability to memorize and remember facts using Mem0.
    Use the Mem0-memorize tool to save important information that might be useful later.
    Use the Mem0-remember tool to recall previously saved information when answering questions.
  `,
  model: openai('gpt-4.1-nano'),
  tools: { mem0RememberTool, mem0MemorizeTool },
});
```

## Key Features

1. **Tool-based Memory Control**: The agent decides when to save and retrieve information using specific tools
2. **Semantic Search**: Mem0 finds relevant memories based on semantic similarity, not just exact matches
3. **User-specific Memory Spaces**: Each user\_id maintains separate memory contexts
4. **Asynchronous Saving**: Memories are saved in the background to reduce response latency
5. **Cross-conversation Persistence**: Memories persist across different conversation threads
6. **Transparent Operations**: Memory operations are visible through tool usage

## Conclusion

By integrating Mastra with Mem0, you can build intelligent agents that learn and remember information across conversations. The tool-based approach provides transparency and control over memory operations, making it easy to create personalized and context-aware AI experiences.

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<Snippet file="star-on-github.mdx" />