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 Montréal with the support of experienced trainers.
Onsite live training can also be held at customer premises in Montréal 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.
NobleProg — Your Local Training Provider
Montreal - René Lévesque
1250 Boulevard René Lévesque Ouest, Montréal, Canada, H3B 4W8
Situé au centre-ville de Montréal, l'immeuble possède des stations de métro et des autoroutes à proximité, vous offrant un transport rapide et facile à travers la ville et au-delà.
Montreal - University Street
2001 Blvd. Robert Bourassa, Montreal, canada, H3A 2A6
Le Centre de la rue University occupe un immeuble de bureaux au cœur du quartier des affaires de Montréal. Il est niché parmi les nombreux gratte-ciels du centre-ville de Montréal et bénéficie d'une connexion directe au plus grand complexe souterrain du monde.
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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