LangGraph training
Stateful agent workflows with explicit control of each step.
Stateful agent workflows with explicit control of each step. Work on a bounded use case of your own. We agree level, tooling and programme with your team beforehand; product access and available features determine the exercises.
What you learn
- Design nodes, edges and a clear state schema for an agent workflow.
- Implement conditional routes, tool calls and structured results.
- Resume execution and add human approval for sensitive steps.
- Test a workflow with fixed scenarios and analyse traces.
Who it's for
- 01Python developers, Agentic Engineers and architects
Programme
Graphs & state
Design nodes, edges and a clear state schema for an agent workflow.
Routing & tools
Implement conditional routes, tool calls and structured results.
Checkpoints & interrupts
Resume execution and add human approval for sensitive steps.
Observability & tests
Test a workflow with fixed scenarios and analyse traces.
What you take away
What you take away
- A stateful agent graph with a human approval step.
- An overview of risks, prerequisites and next steps.
Prerequisites
- Python and basic experience with LLM tools and API integrations.
Frequently asked questions
Is the training tailored to our team?+
Yes. We discuss your experience, environment and learning goals during a no-obligation intake, then provide a programme and written quote.
How long is this training?+
Duration depends on the level and exercises. It is agreed in the programme beforehand; there is no fixed duration for this topic.
Where does the training take place?+
In-company, at our Utrecht office or remotely, as agreed with your team.
Schedule this training
Discuss your learning goals during a no-obligation intake. Afterwards, you receive a tailored programme and written quote.
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