Tavily research integration

process.env.TAVILY_API_KEY = "YOUR_API_KEY";

process.env.LANGSMITH_TRACING = "true";
process.env.LANGSMITH_API_KEY = "your-api-key";

import { TavilyResearch } from "@langchain/tavily";

const tool = new TavilyResearch({
  // model: "mini",
  // citationFormat: "apa",
  // stream: false,
});

await tool.invoke({
  input: "What are the latest developments in AI?",
});

// This is usually generated by a model, but we'll create a tool call directly for demo purposes.
const modelGeneratedToolCall = {
  args: {
    input: "What are the latest developments in AI?",
  },
  id: "1",
  name: tool.name,
  type: "tool_call",
};

await tool.invoke(modelGeneratedToolCall);

import { ChatOpenAI } from "@langchain/openai";
import { TavilyResearch } from "@langchain/tavily";
import { createAgent } from "langchain";

const llm = new ChatOpenAI({
  model: "gpt-5.5",
});

const tavilyResearchTool = new TavilyResearch({
  model: "mini",
});

const agent = createAgent({
  model: llm,
  tools: [tavilyResearchTool],
});

const userInput =
  "Research the latest developments in AI agents and summarize key trends.";

const stream = await agent.streamEvents(
  { messages: [{ role: "user", content: userInput }] },
  { version: "v3" },
);

await Promise.all([
  (async () => {
    for await (const message of stream.messages) {
      for await (const token of message.text) {
        process.stdout.write(token);
      }
    }
  })(),
  (async () => {
    for await (const call of stream.toolCalls) {
      console.dir({ name: call.name, input: call.input }, { depth: null });
      await call.output;
    }
  })(),
]);

await stream.output;