Agentic AI with LangChain and LangGraph

IBM develops LangGraph through stateful agents, iteration and conditional logic. Reflection and collaborative workflows connect with agentic retrieval applications. It suits Python developers with LangChain foundations who want to understand how an agent’s control flow can be made explicit rather than rely on a single unconstrained loop.

What you will learn

• Explore state, memory and conditional workflows.
• Investigate reflection and self-improvement patterns.
• Build collaborative and retrieval-enhanced agent flows.

Who this suits

Python programming, basic LangChain understanding and familiarity with core AI concepts 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

Draw a small graph with a validation branch and specify what happens when a tool returns unusable information rather than assuming a successful response.

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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