mastering-langgraph
Build stateful AI agents and agentic workflows with LangGraph in Python. Covers tool-using agents with LLM-tool loops, branching workflows, conversation memory, human-in-the-loop oversight, and production monitoring. Use when - (1) building agents that use tools and loop until task complete, (2) creating multi-step workflows with conditional branches, (3) adding persistence/memory across turns with checkpointers, (4) implementing human approval with interrupt(), (5) debugging via time-travel or LangSmith. Covers StateGraph, nodes, edges, add_conditional_edges, MessagesState, thread_id, Command
npx skills add majiayu000/claude-skill-registry --skill langgraph-agent-skill --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LangGraph Development Guide Build stateful AI agents and workflows by defining graphs of nodes (steps) connected by edges (transitions). ## Contents - [Quick Start](#quick-start) - [Common Build Scenarios](#common-build-scenarios) - [Core Principles](#core-principles) - [Development Workflow](#development-workflow) - [Common Pitfalls](#common-pitfalls) - [Environment Setup](#environment-setup) - [Quick Verification](#quick-verification) - [API Essentials](#api-essentials) - [Next Steps](#next-steps) ## Quick Start Minimal chatbot with memory: ```python from langgraph.graph import StateGraph, START, END from langgraph.checkpoint.memory import InMemorySaver from langchain_openai import ChatOpenAI from langchain_core.messages import HumanMessage, AnyMessage from typing_extensions import TypedDict, Annotated import operator # 1. Define state class State(TypedDict): messages: Annotated[list[AnyMessage], operator.add] # Append mode # 2. Define node llm = ChatOpenAI(model="gpt-4") def chat(state: State) -> dict: response = llm.invoke(state["messages"]) return {"messages": [response]} # 3. Build graph graph = StateGraph(State) graph.add_node("chat", chat) graph.add_edge(START, "chat") gr
- Contents
- Quick Start
- Common Build Scenarios
- Simple Chatbot / Q&A
- Tool-Using Agent
- Structured Workflow
- Agent with Long-Term Memory
- Human-in-the-Loop
- Debugging / Production Monitoring
- Multi-Agent Systems
- Production Deployment
- New to LangGraph?
- Core Principles
- 1. Keep State Raw
Core pip install -U langgraph LLM providers (pick one or more) pip install langchain-openai pip install langchain-anthropic Production persistence pip install langgraph-checkpoint-postgres Observability pip install langsmith export OPENAI_API_KEY="sk-..."
What does the mastering-langgraph skill do?
Build stateful AI agents and agentic workflows with LangGraph in Python. Covers tool-using agents with LLM-tool loops, branching workflows, conversation memory, human-in-the-loop oversight, and production monitoring. Use when - (1) building agents that use tools and loop until task complete, (2) creating multi-step workflows with conditional branches, (3) adding persistence/memory across turns with checkpointers, (4) implementing human approval with interrupt(), (5) debugging via time-travel or LangSmith. Covers StateGraph, nodes, edges, add_conditional_edges, MessagesState, thread_id, Command
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill langgraph-agent-skill --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.