MongoDB Agent
Configured with Agent, Chat Trigger, OpenAI Chat Model, Memory Buffer Window, and additional nodes. Review the connections and setup before adapting this template.
Node categories in this workflow
- AI & models
- Data store
- Trigger
- Subworkflow
Icons describe general node roles, inferred from node types. Inspect the parameters for the configured action.
Drag to pan; use + and − to zoom. Reconstructed from the exported node positions; nothing runs here. Dashed lines represent non-main connections. Full node names and notes appear below.
How it works
The exported workflow contains the following configured nodes. Their connections are shown above; inspect the JSON for the complete logic, expressions, and external actions.
| Node | Category | Type | Operation |
|---|---|---|---|
| OpenAI Chat Model | AI & models | OpenAI Chat Model | See node parameters |
| MongoDBAggregate | Data store | Mongo Db Tool | aggregate |
| Window Buffer Memory | Data store | Memory Buffer Window | See node parameters |
| When chat message received | Trigger | Chat Trigger | See node parameters |
| insertFavorite | Subworkflow | Tool Workflow | See node parameters |
| AI Agent - Movie Recommendation | AI & models | Agent | See node parameters |
Setup
- Download the JSON and import it into an isolated n8n instance. Leave the workflow inactive.
- Connect your own credentials through n8n’s credential store. Replace
REPLACE_WITH_*values with your resource identifiers. - Inspect API destinations, Code nodes, database queries, file paths, and outbound actions.
- Run with synthetic data and harmless destinations. Enable triggers only after checking the actual outputs.
Requirements
An n8n instance with the listed node types and compatible versions. Service-connected workflows may require accounts, credentials, or paid APIs.
Credential types declared in the source
mongoDbopenAiApi
Workflow notes
AI Agent powered by OpenAI and MongoDB
AI Agent powered by OpenAI and MongoDB
This flow is designed to work as an AI autonomous agent that can get chat messages, query data from MongoDB using the aggregation framework.
Following by augmenting the results from the sample movies collection and allowing storing my favorite movies back to the database using an "insert" flow.
Process
Process
The message is being processed by the "Chat Model" and the correct tool is used according to the message.
MongoDB Agent
MongoDB Agent
Purpose and structure
This folder contains the MongoDB Agent workflow. Its executable nodes use agent, chatTrigger, lmChatOpenAi, memoryBufferWindow, mongoDbTool, toolWorkflow. The table and connection map below are derived from the JSON, not an inferred guarantee of behavior.
Setup
Import inactive. Configure your own credentials and replace REPLACE_WITH_* values. Review external destinations and Code nodes before running.
Entry points
When chat message received
Verification
Sanitized source candidate; not runtime-tested. This overview is generated from node metadata. Read the folder README for the connection map, credential types, and license.
Attribution
https://github.com/Zie619/n8n-workflows/blob/94007c1445d9258a7da116646b79473e7c7c3282/workflows/Mongodbtool/1555_Mongodbtool_Stickynote_Automation_Triggered.json MIT; preserve source notices.