Building a Multi-Agent Pipeline

Production agentic systems rarely rely on a single LLM call. Content generation pipelines, automated code review, and report writing workflows all share a common pattern: multiple specialized agents collaborate through shared state, each contributing a distinct capability. The previous lesson introduced LangGraph’s core abstractions (nodes, edges, conditional routing, cycles, and TypedDict state) at a conceptual level. This lesson puts every one of those abstractions into practice by building a three-agent pipeline where a Researcher generates content, a Critic evaluates quality and requests revisions, and a Summarizer produces a final condensed output.

Think of it like an editorial workflow at a newspaper. A journalist drafts an article, an editor reviews it and sends it back with feedback, and once approved, a copy editor condenses it into a brief. No single person does everything, but they all work from the same shared document.

The implementation follows four milestones: defining the state schema, implementing each agent node, wiring the graph with conditional routing and cycles, and adding memory plus Runnable composability. The pipeline pattern built here will later benefit from the observability hooks covered in the next lesson on LangChain Callbacks.

Defining the shared state schema

The first implementation step is defining the TypedDict that every node will read from and write to. Each field in this schema serves a specific purpose in the pipeline.

• Topic: Holds the user’s original research query and remains unchanged throughout execution.

• research_content: Stores the Researcher’s latest output, overwritten on each revision pass.

• Critique: Contains the Critic’s feedback text, which the Researcher reads when revising.

• revision_needed: A boolean flag that drives the conditional routing decision at the Critic node.

• Summary: Stores the Summarizer’s final condensed output, populated only at the ...