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The Mastra 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:
- Uses Mem0 to store information using a memory tool
- Retrieves relevant memories using a search tool
- Provides personalized responses based on past interactions
- Maintains context across conversations and sessions
Setup and Configuration
Install the required libraries:
npm install @mastra/core @mastra/mem0 @ai-sdk/openai zod
Set up your environment variables:
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:
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
},
});
Set up tools for memorizing and remembering information:
// 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:
// 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-4o'),
tools: { mem0RememberTool, mem0MemorizeTool },
});
Key Features
- Tool-based Memory Control: The agent decides when to save and retrieve information using specific tools
- Semantic Search: Mem0 finds relevant memories based on semantic similarity, not just exact matches
- User-specific Memory Spaces: Each user_id maintains separate memory contexts
- Asynchronous Saving: Memories are saved in the background to reduce response latency
- Cross-conversation Persistence: Memories persist across different conversation threads
- 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.
Help
- For more details on Mastra, visit the Mastra documentation.
- For Mem0 documentation, refer to the Mem0 Platform.
- If you need further assistance, please feel free to reach out to us through the following methods:
