docs/cookbooks/integrations/mastra-agent.mdx
In this example you'll learn how to use Mem0 to add long-term memory capabilities to Mastra's agent via tool-use. This memory integration can work alongside Mastra's agent memory features.
You can find the complete example code in the Mastra repository.
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.
Install the Integration Package
To install the Mem0 integration, run:
npm install @mastra/mem0
Add the Integration to Your Project
Create a new file for your integrations and import the integration:
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.
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
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
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:
@mastra/mem0 integrationquestion as an input and returns the memory as a string