LangGraph.js - Quickstart¶

// Optional, add tracing in LangSmith
// process.env.LANGCHAIN_API_KEY = "ls__...";
// process.env.LANGCHAIN_CALLBACKS_BACKGROUND = "true";
// process.env.LANGCHAIN_TRACING_V2 = "true";
// process.env.LANGCHAIN_PROJECT = "Quickstart: LangGraphJS";

// agent.mts

// IMPORTANT - Add your API keys here. Be careful not to publish them.
process.env.OPENAI_API_KEY = "sk-...";
process.env.TAVILY_API_KEY = "tvly-...";

import { TavilySearchResults } from "@langchain/community/tools/tavily_search";
import { ChatOpenAI } from "@langchain/openai";
import { MemorySaver } from "@langchain/langgraph";
import { HumanMessage } from "@langchain/core/messages";
import { createReactAgent } from "@langchain/langgraph/prebuilt";

// Define the tools for the agent to use
const agentTools = [new TavilySearchResults({ maxResults: 3 })];
const agentModel = new ChatOpenAI({ temperature: 0 });

// Initialize memory to persist state between graph runs
const agentCheckpointer = new MemorySaver();
const agent = createReactAgent({
  llm: agentModel,
  tools: agentTools,
  checkpointSaver: agentCheckpointer,
});

// Now it's time to use!
const agentFinalState = await agent.invoke(
  { messages: [new HumanMessage("what is the current weather in sf")] },
  { configurable: { thread_id: "42" } },
);

console.log(
  agentFinalState.messages[agentFinalState.messages.length - 1].content,
);

const agentNextState = await agent.invoke(
  { messages: [new HumanMessage("what about ny")] },
  { configurable: { thread_id: "42" } },
);

console.log(
  agentNextState.messages[agentNextState.messages.length - 1].content,
);

// Note: tslab only works inside a jupyter notebook. Don't worry about running this code yourself!
import * as tslab from "tslab";

const graph = agent.getGraph();
const image = await graph.drawMermaidPng();
const arrayBuffer = await image.arrayBuffer();

await tslab.display.png(new Uint8Array(arrayBuffer));

// agent.mts

// IMPORTANT - Add your API keys here. Be careful not to publish them.
process.env.OPENAI_API_KEY = "sk-...";
process.env.TAVILY_API_KEY = "tvly-...";

import { TavilySearchResults } from "@langchain/community/tools/tavily_search";
import { ChatOpenAI } from "@langchain/openai";
import { HumanMessage, AIMessage } from "@langchain/core/messages";
import { ToolNode } from "@langchain/langgraph/prebuilt";
import { StateGraph, MessagesAnnotation } from "@langchain/langgraph";

// Define the tools for the agent to use
const tools = [new TavilySearchResults({ maxResults: 3 })];
const toolNode = new ToolNode(tools);

// Create a model and give it access to the tools
const model = new ChatOpenAI({
  model: "gpt-4o-mini",
  temperature: 0,
}).bindTools(tools);

// Define the function that determines whether to continue or not
function shouldContinue({ messages }: typeof MessagesAnnotation.State) {
  const lastMessage = messages[messages.length - 1] as AIMessage;

  // If the LLM makes a tool call, then we route to the "tools" node
  if (lastMessage.tool_calls?.length) {
    return "tools";
  }
  // Otherwise, we stop (reply to the user) using the special "__end__" node
  return "__end__";
}

// Define the function that calls the model
async function callModel(state: typeof MessagesAnnotation.State) {
  const response = await model.invoke(state.messages);

  // We return a list, because this will get added to the existing list
  return { messages: [response] };
}

// Define a new graph
const workflow = new StateGraph(MessagesAnnotation)
  .addNode("agent", callModel)
  .addEdge("__start__", "agent") // __start__ is a special name for the entrypoint
  .addNode("tools", toolNode)
  .addEdge("tools", "agent")
  .addConditionalEdges("agent", shouldContinue);

// Finally, we compile it into a LangChain Runnable.
const app = workflow.compile();

// Use the agent
const finalState = await app.invoke({
  messages: [new HumanMessage("what is the weather in sf")],
});
console.log(finalState.messages[finalState.messages.length - 1].content);

const nextState = await app.invoke({
  // Including the messages from the previous run gives the LLM context.
  // This way it knows we're asking about the weather in NY
  messages: [...finalState.messages, new HumanMessage("what about ny")],
});
console.log(nextState.messages[nextState.messages.length - 1].content);