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Text-to-Query Agents with MongoDB and LangGraph

IntermediateSkill Level
4.8+
31 reviews
Updated 03/2026
Discover how to talk to your data using text-to-query AI agents with MongoDB and LangGraph.
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PythonArtificial Intelligence2 hr5 videos13 Exercises1,050 XPStatement of Accomplishment

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Course Description

Have you ever wished that your could chat with your database? This is now a possibility with LLMs and AI agents. In this course, you'll learn how to interact and chat with a MongoDB database instance, using LangGraph for agent orchestration. You'll learn about the challenges and value of text-to-query workflows, build your own to answer data-related questions about a movies dataset, and even implement memory checkpointing to pick up your conversations where they left off!

Prerequisites

Introduction to Functions in Python
1

Text-to-Query Agents: Applications and Architecture

Learn how to build text-to-query agents so you can have conversations with your data!
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2

Automating Text-to-Query Workflows with Agents

Make your text-to-query workflow agentic! Building and orchestrating AI agents with LangGraph will allow you to create smart and dynamic text-to-query workflows.
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3

Adding Memory to the Agent

Integrate a short-term memory into your text-to-query agent so it doesn't have to re-run queries to combine insights.
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Text-to-Query Agents with MongoDB and LangGraph
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*4.8
from 31 reviews
84%
16%
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0%
  • Kim Chuan
    2 weeks ago

  • Joaquim
    2 weeks ago

  • Joaquín
    2 weeks ago

    The hands on exercises need definitions from previous exercises. I must to copy the cells from previous exercises and paste them into the current exercise, otherwise the hands on module fails.

  • Hassan
    3 weeks ago

  • Michael
    6 weeks ago

  • Andrew
    2 months ago

Kim Chuan

Joaquim

"The hands on exercises need definitions from previous exercises. I must to copy the cells from previous exercises and paste them into the current exercise, otherwise the hands on module fails."

Joaquín

FAQs

What will I be able to do after completing this course on text-to-query agents?

You will be able to build an AI agent that converts natural language questions into MongoDB queries, retrieves data, and maintains conversation context through memory checkpointing.

What technologies are used in this course?

You will use Python, LangGraph for agent orchestration, and MongoDB as the database. The course covers how these tools work together for text-to-query workflows.

What dataset is used for the hands-on exercises?

You will work with a movies dataset stored in MongoDB, building agents that answer data-related questions about the collection through natural language.

What is memory checkpointing and why is it useful?

Memory checkpointing lets your agent remember previous queries and results so it can combine insights across a conversation without re-running earlier queries.

Do I need prior experience with MongoDB or LangGraph?

No prior MongoDB or LangGraph experience is required. You need Intermediate Python and familiarity with writing functions in Python to get started.

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