MongoDB and partners have developed specific product integrations to help you leverage MongoDB in your AI-powered applications and AI agents.
This page highlights notable AI integrations that MongoDB and partners have developed. You can use with popular AI providers and LLMs through their standard connection methods and APIs. For a complete list of integrations and partner services, see Explore MongoDB Partner Ecosystem.
Frameworks
You can use the following open-source frameworks to store custom data in your MongoDB clusters and implement features such as RAG with MongoDB Vector Search.
Framework | Description | Documentation |
|---|---|---|
Framework for building AI applications by using "chains," LangChain-specific components that can be combined together for various use cases. The LangChain MongoDB integration provides several components for RAG. | ||
Brings LangChain capabilities to the Go ecosystem. | ||
Brings LangChain capabilities to Java. | ||
Framework that provides several tools for connecting custom data sources to LLMs and building RAG applications. | ||
Framework from Microsoft that combines various AI services with your applications for use cases including RAG. | ||
Python framework for building custom applications with LLMs, embedding models, vector search, and more for use cases such as RAG. | ||
Applies Spring design principles to AI applications for use cases including RAG. |
Agent Frameworks
You can use the following open-source frameworks to build AI agents and multi-agent applications that use MongoDB to implement features such as agentic RAG and agent memory.
Framework | Description | Documentation |
|---|---|---|
Specialized framework within the LangChain ecosystem for building AI agents and complex multi-agent workflows, with support for persistence, streaming, and memory. | ||
Python framework for building autonomous AI agents with specialized roles and multi-agent applications with "crews" that can complete complex tasks by delegating work amongst themselves. |
Platforms
You can also integrate with the following enterprise platforms to build generative AI applications. These platforms provide pre-trained models and other tools to help you build AI applications and agents in production.
Platform | Description | Documentation |
|---|---|---|
Fully-managed platform for building generative AI applications. Integrate MongoDB as a knowledge base to store custom data in MongoDB Atlas, implement RAG, and deploy agents. | ||
Platform from Google Cloud for building and deploying AI applications and agents. Includes tools and pre-trained models from Google that you can use with MongoDB Atlas for RAG and other use cases such as natural language querying. |
Tools
You can integrate with the following AI tools.
Tool | Description | Documentation |
|---|---|---|
Model Context Protocol (MCP) is an open standard for how LLMs connect to and interact with external resources and services. Use our official MCP Server implementation to interact with your MongoDB data and clusters from your agentic AI tools. | ||
No-code workflow automation tool that enables you to build agentic workflows through interactive nodes in a visual canvas. Supports multiple MongoDB nodes, including nodes for RAG and memory for your AI agents. |
Community-Maintained Integrations
The community maintains several integrations with MongoDB. These are integrations that are contributed to on an open-source basis but are not managed directly by MongoDB. The following table highlights some of these integrations.
Integration | Description | Documentation |
|---|---|---|
Open-source TypeScript framework that provides components for building AI agents, including workflows, RAG, and evals. Use MongoDB for vector storage and retrieval, RAG, and memory. |
For questions or issues with these integrations, refer to the documentation and resources provided by the respective framework maintainers.