LangGraph for Workflows
Master stateful, graph-based agent workflows with cycles, branching, and human-in-the-loop patterns
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0 / 5 completedBuilding Blocks of a Graph
A LangGraph workflow consists of nodes (functions that process state) and edges (connections that define execution flow). Nodes can be LLM calls, tool executions, or custom logic. Edges can be conditional, enabling dynamic routing.
Interactive: Explore Graph Nodes
START
Agent
Tool
END
📐 Core Components
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Nodes
Functions that process the current state. Can call LLMs, execute tools, or run custom logic.
graph.add_node("agent", agent_function)➡️
Edges
Connections between nodes. Can be fixed (always go to next node) or conditional (route based on state).
graph.add_edge("agent", "tool")🌀
Conditional Edges
Dynamic routing based on state. Example: route to "tool" if agent wants tool, else go to "end".
graph.add_conditional_edges("agent", route_function)💡 The Power of Cycles
Unlike LangChain's linear chains, LangGraph allows cycles—edges that loop back to earlier nodes. This enables:
Retry Logic
If tool fails, loop back to agent to try different approach
Iterative Refinement
Generate output, evaluate quality, loop back to improve until satisfactory