MongoDB AI agent - intelligent movie recommendations
Needs changesNeeds changes. This template uses 1 thing(s) that n8n 3.0 removes: AI Agent v1 (old agent modes). 1 breaking item(s) found. Static check, last run 2026-10-19.
What to replace before upgrading
| Removed in 3.0 | Replace with |
|---|---|
| AI Agent v1 (old agent modes) AI Agent - Movie Recommendation (v1.7) | Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent. |
Nodes used (6)
- OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi - Mongo Db Tool
n8n-nodes-base.mongoDbTool - Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow - Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - Call n8n Workflow Tool
@n8n/n8n-nodes-langchain.toolWorkflow - AI Agent
@n8n/n8n-nodes-langchain.agent
What it does
Who is this for? This workflow is designed for: - Database administrators and developers working with MongoDB - Content managers handling movie databases - Organizations looking to implement AI-powered search and recommendation systems - Developers interested in combining LangChain, OpenAI, and MongoDB capabilities What problem does this workflow solve? Traditional database queries can be complex and require specific MongoDB syntax knowledge. This workflow addresses: - The complexity of writing MongoDB aggregation pipelines - The need for natural language interaction with movie databases - The challenge of maintaining user preferences and favorites - The gap between AI language models and database operations What this workflow does This workflow creates an intelligent agent that: 1. Accepts natural language queries about movies 2. Translates user requests into MongoDB aggregation pipelines 3. Queries a movie database containing detailed information including: - Plot summaries - Genre classifications - Cast and director information - Runtime and release dates - Ratings and awards 4. Provides contextual responses using OpenAI's language model 5. Allows users to save favorite movies to the database 6. Maintains conversation context using a window buffer memory Setup 1. Required Credentials: - OpenAI API credentials - MongoDB connection details 2. Node Configuration: - Configure the MongoDB connection in the MongoDBAggregate node - Set up the OpenAI Chat Model with your API key - Ensure the webhook trigger is properly configured for receiving chat messages 3. Database Requirements: - A MongoDB collection named "movies" with the specified document structure - Proper indexes for efficient querying - Appropriate user permissions for read/write operations How to customize this workflow 1. Modify the Document Structure: - Update the tool description in the MongoDBAggregate node to match your collection schema - Adjust the aggregation pipeline templates for your specific use case 2. Enhance the AI Agent: - Customize the prompt in the "AI Agent - Movie Recommendation" node - Modify the window buffer memory size based on your context needs - Add additional tools for more functionality 3. Extend Functionality: - Add more MongoDB operations beyond aggregation - Implement additional workflows for different types of queries - Create custom error handling and validation - Add user authentication and rate limiting 4. Integration Options: - Connect to external APIs for additional movie data - Add webhook endpoints for different platforms - Implement caching mechanisms for frequent queries - Add data transformation nodes for specific output formats This workflow serves as a foundation that can be adapted to various use cases beyond movie recommendations, such as e-commerce product search, content management systems, or any scenario requiring intelligent database interaction.