LangChain: AI Engineering with LangChain & LangGraph

Eden Marco’s course approaches LangChain through application development, combining model interaction with retrieval and agent workflows. The current curriculum also includes LangGraph and MCP. Its value for an experienced programmer is seeing how the components fit together while retaining enough understanding to inspect the underlying code.

What you will learn

• Connect models with application tools
• Build retrieval pipelines and vector-store integrations
• Work with structured model outputs
• Follow agent workflows across LangChain and LangGraph

Who this suits

The provider explicitly expects software engineering foundations. Python, Git, package management, and debugging should already be familiar. A language-model introduction can help if model requests and context windows are new.

Before you enroll

LangChain APIs change quickly. Use the dependency versions associated with the lessons and consult current documentation when adapting an example. Paid model APIs or hosted vector stores can introduce additional costs, even when a demonstration starts with a trial.

Paid course access follows Udemy’s current purchase or subscription terms. Check regional pricing, plan inclusion, and completion-certificate conditions on the provider page.

Put it into practice

Create a question-answering application over a short document set. Inspect retrieved passages and compare the framework pipeline with a direct model request.

Source and review

This overview is based on the provider's published information, checked on 11 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Udemy.

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