langgraph_skill

Overview

This skill provides expert guidance for designing stateful, multi-actor AI agent workflows using LangGraph. It focuses on graphs, nodes, edges, state management, checkpointing, streaming, and common agent architectures like ReAct, Plan-and-Execute, and multi-agent supervisors. The guidance emphasizes practical patterns, persistence, and human-in-the-loop control for reliable, production-ready agents.

How this skill works

The skill explains how to define a typed state, add nodes that return partial state updates, and connect nodes with edges or conditional edges to create cyclic, stateful workflows. It covers compiling graphs into runnable apps with optional checkpointers for persistence, streaming node outputs and model tokens, and composing subgraphs or prebuilt agent components such as ReAct or ToolNode. It also shows patterns for parallel branches, dynamic tool routing, and interrupt points for human approval.

When to use it

• Building multi-step agents that must maintain conversation or workflow state across steps.
• Coordinating multiple specialized agents (researcher, coder, planner/executor) within one workflow.
• Implementing persistent conversations or long-running workflows with checkpointing.
• Needing real-time UX with token and node-level streaming.
• Adding human-in-the-loop interrupts before sensitive or irreversible actions.

Best practices

• Design minimal, explicit state shapes and use reducers (e.g., add_messages) for accumulators.
• Have node functions return partial updates instead of replacing full state.
• Always enable a checkpointer for production to preserve continuity across invocations.
• Use conditional edges for dynamic routing and build fallback edges for error handling.
• Test nodes independently and validate subgraphs before composing larger workflows.

Example use cases

• A ReAct agent that invokes search and calculator tools and loops until a final answer is produced.
• Plan-and-Execute pipeline where a planner emits a step list and an executor runs each step sequentially.
• Multi-agent supervisor that routes tasks between a researcher and a coder based on a routing chain.
• Long-running conversational assistant that persists dialogue using a SQLite checkpointer.
• Human-in-the-loop approval flow that interrupts before a sensitive action and resumes after approval.