Agentic AI with LangGraph, CrewAI, AutoGen and BeeAI

IBM compares agentic frameworks through the workflows and design patterns they support. LangGraph provides one approach to explicit control flow, while CrewAI, BeeAI and AG2 broaden the comparison. It suits learners with existing agent-development foundations who want to choose a framework around a concrete requirement rather than assume each abstraction solves the same problem.

What you will learn

• Compare agentic framework design patterns.
• Implement workflow patterns with LangGraph.
• Explore CrewAI, BeeAI and AG2 alternatives.

Who this suits

Working Python, LangChain and LangGraph knowledge and an understanding of agentic systems are recommended.

Before you enroll

Model APIs, search tools and hosting may cost extra. Check framework and model versions before reproducing recorded code. Use fictional data and restrict tool permissions while learning. Inspect state transitions, failures and retry behavior rather than judge an agent from one successful answer. Generated output and autonomous actions need validation; a project demonstration does not establish production reliability.

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Put it into practice

Describe one fictional agent task and compare two framework approaches, identifying the control, observability and failure-handling requirements behind the choice.

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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