MongoDB Agent

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.

Integrations Used (6)

Lmchatopenai

Workflow Structure (6 nodes)

100%

Langchain.chat Trigger

When chat message received

Langchain.agent

AI Agent - Movie Recommendation

Langchain.lm Chat Open Ai

OpenAI Chat Model

Langchain.memory Buffer Window

Window Buffer Memory

Mongo Db Tool

MongoDBAggregate

Langchain.tool Workflow

insertFavorite

Node Types:

default

action

trigger

Requires credentials

Prerequisites

Required Credentials (2)

Mongo Db

Used by: MongoDBAggregate

Docs

OpenAI

Used by: OpenAI Chat Model

Docs

Complexity

Moderate

Some experience helpful. Multiple integrations or logic.

Setup time: ~25 min

Score: 22/100

When to Use This Workflow

Real-time Response

React instantly when events happen - new orders, form submissions, or API calls.

Zero delay between trigger and action for time-sensitive workflows.

Time Savings

Replace repetitive manual tasks with reliable automation.

Reclaim hours every week for higher-value work.

Error Reduction

Eliminate human error from routine processes with consistent automation.

Improve accuracy and reliability across your workflows.

workflow.json

{
  "id": "22PddLUgcjSJbT1w",
  "meta": {
    "instanceId": "fa7d5e2425ec76075df7100dbafffed91cc6f71f12fe92614bf78af63c54a61d",
    "templateCredsSetupCompleted": true
  },
  "name": "MongoDB Agent",
  "tags": [],
  "nodes": [
    {
      "id": "d8c07efe-eca0-48cb-80e6-ea8117073c5f",
      "name": "OpenAI Chat Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        1300,
        560
      ],
      "parameters": {
        "options": {}
      },
...