You can integrate MongoDB with n8n to build automations and agentic workflows using their no-code visual interface. This page provides an overview of the integration and describes the different types of nodes that you can use in your workflows.
Get Started with n8n
To install n8n, refer to the n8n documentation or run the following command using npm to quickly get started:
npx n8n
To learn how to build a basic AI agent using n8n and MongoDB, see Build an AI Agent with MongoDB and n8n.
Important
All MongoDB n8n nodes require you to configure your MongoDB credentials in n8n. To learn more, see MongoDB Credentials.
MongoDB Node
The MongoDB node allows you to automate work in MongoDB and integrate MongoDB with other nodes in your n8n workflows.
Usage
Use the MongoDB node in any of your custom n8n workflows.
Operations
The MongoDB node supports the following operations:
Category | Operation | Description |
|---|---|---|
Document Operations | Aggregate Documents | Perform aggregation operations to process and transform data using MongoDB aggregation pipelines. |
Find Documents | Query and retrieve documents from your MongoDB collections with flexible filtering options. | |
Insert Documents | Add new documents to your MongoDB collections. | |
Update Documents | Modify existing documents in your collections. | |
Delete Documents | Remove documents from your collections. | |
Find and Replace Documents | Search for documents and replace them with new content. | |
Find and Update Documents | Search for documents and update specific fields. | |
Search Index Operations | Create Search Indexes | Create new search and vector search indexes on your collections. |
List Search Indexes | Retrieve information about existing search indexes. | |
Update Search Indexes | Modify existing search index configurations. | |
Drop Search Indexes | Remove search indexes that are no longer needed. |
Tip
To learn more, see n8n MongoDB node documentation
MongoDB Atlas Vector Store Node
The MongoDB Atlas Vector Store node enables you to use MongoDB Vector Search in your agentic workflows.
Note
Before you can start using this node, configure the MongoDB Vector Search Index.
Usage
Use the MongoDB Vector Store node in the following workflow patterns:
Connect directly to an AI agent as a tool to perform agentic RAG.
AI Agent (tools connector) → MongoDB Vector Store
For a tutorial, see Build an AI Agent with MongoDB and n8n.
To learn more about AI agents in n8n, see AI agent node.
Use the MongoDB Atlas Vector Store as a regular node to insert or retrieve documents in your custom workflows:
Trigger → MongoDB Vector Store (Insert/Get) → Next Node
To learn more, see Nodes.
Use the node as a retriever in a question-answering chain:
Question and Answer Chain → Vector Store Retriever → MongoDB Vector Store
To learn more about Q&A in n8n, see Question and Answer Chain node.
Use the node as a question-answering tool for an AI agent:
AI Agent → Vector Store Question Answer Tool → MongoDB Vector Store
To learn more about the question-answering tool in n8n, see Vector Store Question Answer Tool node.
Operation Modes
The MongoDB Vector Store node supports the following operation modes. The retrieve document mode is only available in certain workflow patterns.
Operation Mode | Description |
|---|---|
Get Many | Retrieve multiple documents using similarity search based on a prompt. Returns documents with similarity scores. |
Insert Documents | Add new documents with vector embeddings to your collection. |
Retrieve Documents (As Vector Store for Chain/Tool) | Only available when you use the node as a retriever or a tool. Must be connected to a retriever node or root node. |
Retrieve Documents (As Tool for AI Agent) | Only available when you use the node as a tool for an AI agent. The agent uses this vector store when the name and description are relevant to the prompt. |
Parameters
Category | Setting | Operation Mode | Description |
|---|---|---|---|
Common Parameters | MongoDB Collection | All | Name of the MongoDB collection to use. |
Vector Index Name | All | Name of the Vector Search index in your MongoDB collection. | |
Embedding Field | All | Field name in your documents that contains the vector embeddings. | |
Metadata Field | All | Field name in your documents that contains the text metadata. | |
Mode-Specific Parameters | Name | Retrieve Documents (As Tool for AI Agent) | Name of the vector store tool for the AI agent. |
Description | Retrieve Documents (As Tool for AI Agent) | Explanation for the LLM about what this tool does. | |
Limit | Retrieve Documents (As Tool for AI Agent) | Number of results to retrieve from the vector store. | |
Additional Options | Metadata Filter | Get Many, Retrieve Documents (As Tool for AI Agent), Retrieve Documents (As Vector Store for Chain/Tool) | Filter results based on metadata criteria. |
Rerank Results | Get Many, Retrieve Documents (As Tool for AI Agent), Retrieve Documents (As Vector Store for Chain/Tool) | Enable result reranking (requires connecting a reranker node). |
Tip
To learn more, see n8n MongoDB Vector Store node documentation
MongoDB Chat Memory Node
The MongoDB Chat Memory node allows you to use MongoDB as a memory store for storing chat history in your AI workflows. This enables persistent conversation context across workflow executions.
Usage
You must use the MongoDB Chat Memory node as a sub-node by adding it to the Memory section of an AI agent node. For a tutorial, see Build an AI Agent with MongoDB and n8n.
Note
If you add multiple MongoDB Chat Memory nodes to your workflow, all nodes access the same memory instance by default. For separate memory instances, use different session IDs in each memory node.
Parameters
Parameter | Description |
|---|---|
Session ID | Method for determining how the session key is identified. You can define the session key through a connected trigger, or you can define the key manually. |
Session Key | Unique identifier for the chat session. |
Collection Name | Name of the collection to store chat history. MongoDB creates the
collection if it doesn't exist. Defaults to |
Database Name | Name of the database to store chat history. If not provided, n8n uses the database from credentials. |
Context Window Length | Number of previous interactions to consider for context. |
Tip
To learn more, see n8n MongoDB Chat Memory node documentation
Additional n8n Resources
To learn more about n8n, use the following resources: