Memory with MongoDB
This example demonstrates how to use Mastra’s memory system with MongoDB as the storage backend.
Prerequisites
This example uses the openai model and requires a MongoDB database. Make sure to add the following to your .env file:
OPENAI_API_KEY=<your-api-key>
MONGODB_URI=<your-connection-string>
MONGODB_DB_NAME=<your-db-name>And install the following package:
npm install @mastra/mongodbAdding memory to an agent
To add MongoDB memory to an agent use the Memory class and create a new storage key using MongoDBStore. The configuration supports both local and remote MongoDB instances.
import { Memory } from "@mastra/memory";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";
import { MongoDBStore } from "@mastra/mongodb";
export const mongodbAgent = new Agent({
name: "mongodb-agent",
instructions: "You are an AI agent with the ability to automatically recall memories from previous interactions.",
model: openai("gpt-4o"),
memory: new Memory({
storage: new MongoDBStore({
url: process.env.MONGODB_URI!,
dbName: process.env.MONGODB_DB_NAME!
}),
options: {
threads: {
generateTitle: true
}
}
})
});Vector embeddings with MongoDB
Embeddings are numeric vectors used by memory’s semanticRecall to retrieve related messages by meaning (not keywords).
Note: You must use a deployment hosted on MongoDB Atlas to successfully use the MongoDB Vector database.
This setup uses FastEmbed, a local embedding model, to generate vector embeddings.
To use this, install @mastra/fastembed:
npm install @mastra/fastembedAdd the following to your agent:
import { Memory } from "@mastra/memory";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";
import { MongoDBStore, MongoDBVector } from "@mastra/mongodb";
import { fastembed } from "@mastra/fastembed";
export const mongodbAgent = new Agent({
name: "mongodb-agent",
instructions: "You are an AI agent with the ability to automatically recall memories from previous interactions.",
model: openai("gpt-4o"),
memory: new Memory({
storage: new MongoDBStore({
url: process.env.MONGODB_URI!,
dbName: process.env.MONGODB_DB_NAME!
}),
vector: new MongoDBVector({
uri: process.env.MONGODB_URI!,
dbName: process.env.MONGODB_DB_NAME!
}),
embedder: fastembed,
options: {
lastMessages: 10,
semanticRecall: {
topK: 3,
messageRange: 2
},
threads: {
generateTitle: true // generates descriptive thread titles automatically
}
}
})
});Usage example
Use memoryOptions to scope recall for this request. Set lastMessages: 5 to limit recency-based recall, and use semanticRecall to fetch the topK: 3 most relevant messages, including messageRange: 2 neighboring messages for context around each match.
import "dotenv/config";
import { mastra } from "./mastra";
const threadId = "123";
const resourceId = "user-456";
const agent = mastra.getAgent("mongodbAgent");
const message = await agent.stream("My name is Mastra", {
memory: {
thread: threadId,
resource: resourceId
}
});
await message.textStream.pipeTo(new WritableStream());
const stream = await agent.stream("What's my name?", {
memory: {
thread: threadId,
resource: resourceId
},
memoryOptions: {
lastMessages: 5,
semanticRecall: {
topK: 3,
messageRange: 2
}
}
});
for await (const chunk of stream.textStream) {
process.stdout.write(chunk);
}