langgraph-agents

# LangGraph Agents ## When to Use This Skill | Use this skill when... | Use a sibling skill instead when... | |---|---| | Building stateful agents as graphs of nodes/edges with checkpointing | Writing simple LCEL chains without state — use `langchain-development` | | Adding human-in-the-loop approval, streaming, or time-travel debugging | Doing basic tool binding without a graph — use `langchain-development` | | Composing multi-agent systems as subgraphs | Needing hierarchical planning + file-system context — use `deep-agents` | | Wiring graphs into an initialised project | Scaffolding a brand-new project — use `langchain-init` (`/langchain:init`) | ## Core Expertise LangGraph is a low-level orchestration framework for stateful agents: - Graph-based workflow definition (nodes and edges) - Durable execution with checkpointing - Human-in-the-loop interactions - Short-term and long-term memory - Streaming and time-travel debugging - LangSmith observability integration ## Installation ```bash # Core LangGraph package npm install @langchain/langgraph # Required dependencies npm install @langchain/core npm install @langchain/openai # or your preferred model provider # Optional: Checkpointing backends npm install @langchain/langgraph-checkpoint-sqlite ``` ## Graph Fundamentals ### State Definition ```typescript import { Annotation, StateGraph } from "@langchain/langgraph"; // Define state schema using Annotation const StateAnnotation = Annotation.Root({ messages: Annotation<BaseMessage[]>({ reducer: (prev, next) => [...prev, ...next], default: () => [], }), currentStep: Annotation<string>({ reducer: (_, next) => next, default: () => "start", }), }); type State = typeof StateAnnotation.State; ``` ### Basic Graph ```typescript import { StateGraph, START, END } from "@langchain/langgraph"; const graph = new StateGraph(StateAnnotation) .addNode("agent", agentNode) .addNode("tools", toolsNode) .addEdge(START, "agent") .addConditionalEdges("agent", routeAgent) .addEdge("tools", "agent") .compile(); ``` ### Nodes ```typescript // Nodes are async functions that receive and return state async function agentNode(state: State): Promise<Partial<State>> { const response = await model.invoke(state.messages); return { messages: [response], }; } async function toolsNode(state: State): Promise<Partial<State>> { const lastMessage = state.messages[state.messages.length - 1]; const toolCalls = lastMessage.tool_calls || []; const results = await Promise.all( toolCalls.map(tc => tools[tc.name].invoke(tc.args)) ); return { messages: results.map((r, i) => new ToolMessage({ content: r, tool_call_id: toolCalls[i].id }) ), }; } ``` ### Conditional Edges ```typescript function routeAgent(state: State): string { const lastMessage = state.messages[state.messages.length - 1]; if (lastMessage.tool_calls?.length) { return "tools"; } return END; } // Add conditional routing graph.addConditionalEdges("agent", routeAgent, { tools: "tools", [END]: END, }); ``` ## Prebuilt Agents ### ReAct Agent ```typescript import { createReactAgent } from "@langchain/langgraph/prebuilt"; import { ChatOpenAI } from "@langchain/openai"; const model = new ChatOpenAI({ model: "gpt-4o" }); const agent = createReactAgent({ llm: model, tools: [searchTool, calculatorTool], }); // Run the agent const result = await agent.invoke({ messages: [{ role: "user", content: "What's the weather in NYC?" }], }); ``` ### With System Prompt ```typescript const agent = createReactAgent({ llm: model, tools: [searchTool], stateModifier: "You are a helpful research assistant.", }); ``` ## Agentic Optimizations | Context | Pattern | |---------|---------| | Quick iteration | Use `MemorySaver` for development | | Production | Use `SqliteSaver` or external DB | | Debug state | `graph.getState(config)` | | Time travel | `graph.getStateHistory(config)` | | Trace execution | Enable `LANGCHAIN_TRACING_V2` | | Reduce tokens | Stream updates, not full state | | Human approval | `interruptBefore: ["dangerous_node"]` | ## Quick Reference ### Core Imports | Import | Package | |--------|---------| | `StateGraph` | `@langchain/langgraph` | | `Annotation` | `@langchain/langgraph` | | `START, END` | `@langchain/langgraph` | | `MemorySaver` | `@langchain/langgraph` | | `createReactAgent` | `@langchain/langgraph/prebuilt` | ### Graph Methods | Method | Description | |--------|-------------| | `.addNode(id, fn)` | Add a node | | `.addEdge(from, to)` | Add unconditional edge | | `.addConditionalEdges(from, fn)` | Add conditional routing | | `.compile()` | Build executable graph | | `.invoke(input, config)` | Run to completion | | `.stream(input, config)` | Stream execution | | `.getState(config)` | Get current state | | `.updateState(config, update)` | Modify state | ### Stream Modes | Mode | Output | |------|--------| | `"values"` | Full state after each step | | `"updates"` | Only changed values | | `"messages"` | Message chunks for streaming UI | | `"debug"` | Detailed execution info | ### Config Options | Option | Description | |--------|-------------| | `thread_id` | Conversation/session ID | | `checkpoint_id` | Specific checkpoint to resume | | `recursion_limit` | Max graph iterations (default: 25) | For checkpointing backends, human-in-the-loop interrupts, streaming modes, subgraph composition, long-term memory, and composite graph patterns, see [REFERENCE.md](REFERENCE.md).