Introduction to LangGraph

Building reliable tool-calling agents is a significant milestone, but a single agent that can invoke tools is only one piece of a larger puzzle. When a real-world workflow requires a researcher to gather information, a critic to evaluate quality, and a summarizer to produce a final output, the question shifts from “can each agent act correctly?” to “how do these agents coordinate across multiple steps?” This coordination problem, where control needs to loop back, branch conditionally, or fan out across agents, is exactly what LangGraph was built to solve. LangGraph is an open-source framework from the LangChain ecosystem that models multi-agent workflows as directed graphs with built-in state management. This lesson covers the four core concepts that make this possible: nodes, edges, cycles, and state.

Why linear chains fall short

Sequential chains in LangChain work well when every step feeds neatly into the next. Step A produces output, step B consumes it, step C finishes the job. But consider what happens when a Critic agent rejects the Researcher’s output. In a linear pipeline, there is no mechanism to route execution back to the Researcher for revision. The chain simply moves forward or fails.

Real agent workflows are inherently graph-shaped. They require conditional branching, where different agents are activated based on output quality. They require cycles, where execution loops back to retry or refine until a condition is met. And they require shared mutable state, where each agent reads from and writes to a common context that accumulates information over time.

Note: The AgentExecutor in LangChain supports tool-calling loops for a single agent, but it does not natively orchestrate multiple distinct agents with conditional routing between them.

LangGraph makes these patterns native to the framework rather than ad-hoc workarounds bolted onto linear chains. The following diagram illustrates the ...