LangGraph brings structure to the chaos of AI agents, turning loosely connected models into orchestrated systems that can reason, act, and adapt.
Instead of treating prompts as isolated commands, LangGraph training courses show how to design flows where agents collaborate, remember, and make decisions across evolving tasks.
Participants work in live, instructor-led sessions that emphasize interactive, hands-on practice — whether online through an interactive remote desktop or onsite in Enschede TU with the support of experienced trainers.
Onsite live training can also be held at customer premises in Enschede TU or at NobleProg corporate training centers, giving teams the space to experiment with agentic AI in a focused environment.
Also known as the LangChain Graph Framework, LangGraph is quickly becoming the backbone of multi-agent applications, offering developers and innovators a pathway to build AI systems that behave less like tools and more like collaborators.
Capitool 10 is gelegen tegenover de Universiteit Twente en op slechts vier minuten lopen van het treinstation Enschede Kennispark. Het pand ligt halverwege Enschede en Hengelo en is omgeven door bedrijven die verbonden zijn aan de universiteit; een gewilde omgeving voor start-ups en groeiende bedrijven. In de buurt van het pand vindt u bovendien de snelwegen naar Enschede, Hengelo en Amsterdam.
LangGraph is a framework for composing graph-structured LLM workflows that support branching, tool use, memory, and controllable execution.
This instructor-led, live training (online or onsite) is aimed at intermediate-level engineers and product teams who wish to combine LangGraph’s graph logic with LLM agent loops to build dynamic, context-aware applications such as customer support agents, decision trees, and information retrieval systems.
By the end of this training, participants will be able to:
Design graph-based workflows that coordinate LLM agents, tools, and memory.
Implement conditional routing, retries, and fallbacks for robust execution.
Integrate retrieval, APIs, and structured outputs into agent loops.
Evaluate, monitor, and harden agent behavior for reliability and safety.
Format of the Course
Interactive lecture and facilitated discussion.
Guided labs and code walkthroughs in a sandbox environment.
Scenario-based design exercises and peer reviews.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
LangGraph is a framework for building graph-structured LLM applications that support planning, branching, tool use, memory, and controllable execution.
This instructor-led, live training (online or onsite) is aimed at beginner-level developers, prompt engineers, and data practitioners who wish to design and build reliable, multi-step LLM workflows using LangGraph.
By the end of this training, participants will be able to:
Explain core LangGraph concepts (nodes, edges, state) and when to use them.
Build prompt chains that branch, call tools, and maintain memory.
Integrate retrieval and external APIs into graph workflows.
Test, debug, and evaluate LangGraph apps for reliability and safety.
Format of the Course
Interactive lecture and facilitated discussion.
Guided labs and code walkthroughs in a sandbox environment.
Scenario-based exercises on design, testing, and evaluation.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
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