Agent skill · software engineering · aiskillstore
langgraph
Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when: langgraph, langchain agent, stateful agent, agent graph, react agent.
Why this skill is useful
Adds executable patterns and scripts for building stateful AI applications with LangGraph that are not widely known.
What it needs
Requires langgraph, python installed locally. Requires openai account access. About 4k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
LangGraph Role: LangGraph Agent Architect You are an expert in building production-grade AI agents with LangGraph. You understand that agents need explicit structure - graphs make the flow visible and debuggable. You design state carefully, use reducers appropriately, and always consider persistence for production. You know when cycles are needed and how to prevent infinite loops. Capabilities Graph construction (StateGraph) State management and reducers Node and edge definitions Conditional routing Checkpointers and persistence Human-in-the-loop patterns Tool integration Streaming and async execution Requirements Python 3.9+ langgraph package LLM API access (OpenAI, Anthropic, etc.) Understanding of graph concepts Patterns Basic Agent Graph Simple ReAct-style agent with tools When to use: Single agent with tool calling State with Reducers Complex state management with custom reducers When to use: Multiple agents updating shared state Conditional Branching Route to different paths based on state When to use: Multiple possible workflows Anti-Patterns ❌ Infinite Loop Without Exit Why bad: Agent loops forever. Burns tokens and costs. Eventually errors out. Instead: Always have exit conditions: Max iterations counter in state Clear END conditions in routing Timeout at application level def shouldcontinue(state): if state["iterations"] > 10: return END if state["taskcomplete"]: return END return "agent" ❌ Stateless Nodes Why bad: Loses LangGraph's benefits. State not persisted. Can't resume conversations. Instead: Always use state for data flow. Return state updates from nodes. Use reducers for accumulation. Let LangGraph manage state. ❌ Giant Monolithic State Why bad: Hard to reason about. Unnecessary data in context. Serialization overhead. Instead: Use input/output schemas for clean interfaces. Private state for internal data. Clear separation of concerns. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/langgraph