Build a LangGraph Workflow with Explicit State and Checkpoints

A practical approach

Step 1

Define the state schema first: the fields the workflow needs, such as the user's request, retrieved documents, a draft and an approval status.

Step 2

Write each node as a focused function that reads the state and returns only the fields it updates.

Step 3

Connect nodes with edges, and use conditional edges for decisions such as 'needs more research' or 'ready for review'.

Step 4

Add a checkpointer so state is saved per thread, allowing resumption and inspection.

Step 5

Insert a human-approval step before any action with real consequences, pausing the graph until a reviewer approves.

Worked example

A course-outline assistant gathers requirements, drafts modules, checks them against a syllabus template and sends the outline to a reviewer. Built as one long prompt loop, it sometimes skipped the check and could not resume after a timeout. Rebuilt as a graph with four nodes, a conditional edge back to drafting when the check fails and a checkpoint before review, each run can be inspected step by step and resumed exactly where it stopped.

A mistake to avoid

Do not put everything into a single 'messages' list and let the model infer the workflow's progress from it. Explicit state fields make decisions testable and prevent the workflow from losing track of what has been done.

Try it yourself

Sketch a three-step workflow you would like to automate as nodes and edges. Write its state schema with each field's type and which node updates it, and mark where a human should approve before continuing.

Your implementation checklist

• The workflow has an explicit state schema
• Nodes are focused and return only their updates
• Decisions use conditional edges based on state
• A checkpointer saves state for resumption
• Consequential actions pause for human approval