Project Scoping

Every lesson up to this point has taught one pattern at a time. We learned routing without memory. We learned retries without tools. We learned human-approval gates without checkpointing. This was deliberate; keeping each lesson focused meant each concept had room to breathe and be understood on its own.

The next four lessons are different. They form a single guided build in which every pattern we have covered is applied to one coherent system. There is no new LangGraph syntax to learn in this chapter. What is new is the experience of designing and building a workflow that is large enough to be worth shipping, specific enough to serve a real need, and structured well enough to extend when requirements change.

The system we are building is a research assistant. It accepts a research question, checks whether the question is specific enough to act on, plans a set of targeted searches, executes those searches across multiple knowledge sources, synthesizes the findings into a structured response, and delivers the result with source attribution and a confidence rating.

What the research assistant does

Before writing state schemas or node functions, it helps to understand the full user experience the system is designed to deliver.

A user submits a research question such as “What is the pricing for the Pro plan and what are the API rate limits?” The assistant checks whether the question is clear and answerable. If it is vague, for example, “Tell me about the plans,” the assistant responds with a focused clarification question rather than producing a low-quality answer. When the question is clear, the assistant breaks it into two or three specific search queries, runs each query against the available knowledge sources, ...