langgraph

# LangGraph LangGraph is a low-level orchestration framework and runtime for building, managing, and deploying long-running, stateful agents. It provides durable execution, streaming, human-in-the-loop interactions, and time-travel debugging. ## When to use Use LangGraph when you need to: - **Design custom agent workflows** with explicit graph-based control flow - **Add durable execution** so agents survive failures and restarts - **Implement human-in-the-loop** with interrupts and approval steps - **Build multi-agent systems** with state shared across agents - **Stream intermediate results** from long-running agent tasks - **Time-travel debug** by replaying agent execution from any checkpoint ## When NOT to use - For a simple tool-calling agent, use [LangChain](https://docs.langchain.com/oss/langchain/overview) agents instead—less boilerplate for common patterns - For a batteries-included agent with planning and subagents, use [Deep Agents](https://docs.langchain.com/oss/deepagents/overview) instead - LangGraph is the **orchestration layer**—use it when you need fine-grained control over agent behavior ## Install ```bash # Python pip install -U langgraph # JavaScript/TypeScript npm install @langchain/langgraph @langchain/core ``` ## Quick reference ### Graph API (recommended for most use cases) ```python from langgraph.graph import StateGraph, MessagesState, START, END def my_node(state: MessagesState): return {"messages": [{"role": "ai", "content": "hello world"}]} graph = StateGraph(MessagesState) graph.add_node(my_node) graph.add_edge(START, "my_node") graph.add_edge("my_node", END) graph = graph.compile() result = graph.invoke( {"messages": [{"role": "user", "content": "Hello!"}]} ) ``` ### Functional API (for simple pipelines) ```python from langgraph.func import entrypoint, task @task def step_one(input: str) -> str: return f"processed: {input}" @entrypoint() def pipeline(input: str) -> str: return step_one(input).result() ``` ### Add human-in-the-loop ```python from langgraph.types import interrupt def human_approval(state: MessagesState): answer = interrupt({"question": "Approve this action?"}) return {"messages": [{"role": "user", "content": answer}]} ``` ## Key concepts | Concept | Description | |---------|-------------| | `StateGraph` | Define nodes and edges that form your agent's control flow | | `MessagesState` | Built-in state schema for chat-based agents | | `compile()` | Compile a graph builder into an executable graph | | `interrupt()` | Pause execution and wait for human input | | Checkpointer | Persist state for durable execution and time-travel | | Graph API vs Functional API | Graph API for complex workflows; Functional API for linear pipelines | ## Key documentation - [Overview](https://docs.langchain.com/oss/langgraph/overview)—What LangGraph is and when to use it - [Quickstart](https://docs.langchain.com/oss/langgraph/quickstart)—Build your first graph - [Persistence](https://docs.langchain.com/oss/langgraph/persistence)—Add memory and durable execution - [Interrupts](https://docs.langchain.com/oss/langgraph/interrupts)—Human-in-the-loop patterns - [Streaming](https://docs.langchain.com/oss/langgraph/streaming)—Stream intermediate results - [Graph API](https://docs.langchain.com/oss/langgraph/graph-api)—Define nodes, edges, and state - [Deploy](https://docs.langchain.com/oss/langgraph/deploy)—Deploy to production with LangSmith ## API reference For SDK class and method details, use the [LangChain API Reference](https://reference.langchain.com) site: - Browse: `https://reference.langchain.com/python/langgraph` - MCP server: `https://reference.langchain.com/mcp` ## Related skills - **langchain**—Core building blocks for models, tools, and simple agents - **deep-agents**—High-level agent harness built on LangGraph - **langsmith**—Trace, evaluate, and deploy your LangGraph agents