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MongoDB Storage

The MongoDB storage implementation provides a scalable storage solution using MongoDB databases with support for both document storage and vector operations.

Installation​

npm install @mastra/mongodb@latest

Usage​

Ensure you have a MongoDB Atlas Local (via Docker) or MongoDB Atlas Cloud instance with Atlas Search enabled. MongoDB 7.0+ is recommended.

import { MongoDBStore } from "@mastra/mongodb";

const storage = new MongoDBStore({
  url: process.env.MONGODB_URL,
  dbName: process.env.MONGODB_DATABASE,
});

Parameters​

url:

string
MongoDB connection string (e.g., mongodb+srv://user:password@cluster.mongodb.net)

dbName:

string
The name of the database you want the storage to use.

options?:

MongoClientOptions
MongoDB client options for advanced configuration (SSL, connection pooling, etc.). See advanced configuration [here](https://www.mongodb.com/docs/drivers/node/current/connect/connection-options/).

Constructor Examples​

You can instantiate MongoDBStore in the following ways:

import { MongoDBStore } from "@mastra/mongodb";

// Basic connection without custom options
const store1 = new MongoDBStore({
  url: "mongodb+srv://user:password@cluster.mongodb.net",
  dbName: "mastra_storage",
});

// Using connection string with options
const store2 = new MongoDBStore({
  url: "mongodb+srv://user:password@cluster.mongodb.net",
  dbName: "mastra_storage",
  options: {
    retryWrites: true,
    maxPoolSize: 10,
    serverSelectionTimeoutMS: 5000,
    socketTimeoutMS: 45000,
  },
});

Additional Notes​

Collection Management​

The storage implementation handles collection creation and management automatically. It creates the following collections:

  • mastra_workflow_snapshot: Stores workflow state and execution data
  • mastra_evals: Stores evaluation results and metadata
  • mastra_threads: Stores conversation threads
  • mastra_messages: Stores individual messages
  • mastra_traces: Stores telemetry and tracing data
  • mastra_scorers: Stores scoring and evaluation data
  • mastra_resources: Stores resource working memory data

Vector Search Capabilities​

MongoDB storage includes built-in vector search capabilities for AI applications:

Vector Index Creation​

import { MongoDBVector } from "@mastra/mongodb";

const vectorStore = new MongoDBVector({
  url: process.env.MONGODB_URL,
  dbName: process.env.MONGODB_DATABASE,
});

// Create a vector index for embeddings
await vectorStore.createIndex({
  indexName: "document_embeddings",
  dimension: 1536,
});

Vector Operations​

// Store vectors with metadata
await vectorStore.upsert({
  indexName: "document_embeddings",
  vectors: [
    {
      id: "doc-1",
      values: [0.1, 0.2, 0.3, ...], // 1536-dimensional vector
      metadata: {
        title: "Document Title",
        category: "technical",
        source: "api-docs",
      },
    },
  ],
});

// Similarity search
const results = await vectorStore.query({
  indexName: "document_embeddings",
  vector: queryEmbedding,
  topK: 5,
  filter: {
    category: "technical",
  },
});