Part 1 — Build a Support Workflow in Python Before Adding LangGraph
Start with a small working program, learn what a workflow means, and identify the problems a graph will help us organize.
Guided reading · 8 parts
Eight project-based articles for developers who know basic Python. Build routing, retrieval, persistent human approval, retries, an optional local LLM, and a tested final application. Every part includes a downloadable code ZIP.
Start with Part 1Start with a small working program, learn what a workflow means, and identify the problems a graph will help us organize.
Build and run a StateGraph, then trace how nodes return changes to a shared dictionary-shaped state.
Give technical questions and human-only requests different paths, and keep a readable log of the nodes that ran.
Find relevant local notes, attach source references to a reply, and stop a failed revision loop after a deliberate limit.
Store a review checkpoint in SQLite, end the process, and continue the same ticket with an explicit approval or rejection.
Recover from a simulated transient lookup failure, show the nodes that complete, and prevent a repeated queue operation from creating duplicate rows.
Replace one drafting function with an optional Ollama adapter, validate its JSON response, and keep source checks and human review around generated text.
Run the assembled application across separate commands, inspect the local outbox, and evaluate a fixed set of tickets before publishing the repository.