Agentic AI Mastery: From N8N to DSPy to LangGraph

Mike Taylor and Rhys Fisher use the same agent projects to compare three development approaches: n8n, DSPy and LangGraph. That repeated-problem structure helps a learner see which responsibilities belong to the workflow design and which depend on the chosen framework. This is a comparative engineering course rather than a purely no-code n8n introduction.

What you will learn

• Recognize shared components of an agentic system.
• Compare visual workflows with Python-based agent implementations.
• Follow tool use and orchestration in practical agent projects.

Who this suits

Python coding is a stated requirement. The course suits developers, or technically confident n8n users, who want to understand alternatives before committing a project to one framework.

Before you enroll

Agent libraries and APIs change quickly. Check the sample repository and package versions, and plan time to adapt an older example instead of assuming every installation command will still work unchanged.

Udemy offers paid course access; current prices, discounts and subscription inclusion vary. A completion certificate is a record of study, not a professional license. Hosting, AI APIs and connected services can add costs beyond enrollment.

Put it into practice

Implement one small research task in two of the approaches, using fixed sample documents. Compare the amount of setup, the visibility of failures and the ease of reproducing the same result.

Source and review

This overview is based on the provider's published information, checked on 12 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.

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