LangChain integration guide

import { z } from "zod";
import { tool } from "@langchain/core/tools";
import { HumanMessage } from "@langchain/core/messages";
import { ChatOpenAI } from "@langchain/openai";

async function main() {
  const trendsKey = process.env.TRENDSAGI_API_KEY;
  const openaiKey = process.env.OPENAI_API_KEY;
  if (!trendsKey || !openaiKey) throw new Error("Set TRENDSAGI_API_KEY and OPENAI_API_KEY first");

  const llm = new ChatOpenAI({ model: "gpt-4o-mini", apiKey: openaiKey });
  const apiBaseUrl = process.env.API_BASE_URL ?? "https://api.trendsagi.com";

  const getSignals = tool(
    async ({ category }) => {
      const url = new URL("/api/trends", apiBaseUrl);
      url.search = new URLSearchParams({
        category,
        limit: "5",
        sort_by: "velocity",
      }).toString();

      const response = await fetch(url, {
        headers: { Authorization: `Bearer ${trendsKey}` },
      });
      if (!response.ok) throw new Error(`Request failed: ${response.status}`);

      const data = await response.json();
      return (data.trends ?? []).map((signal) => ({ name: signal.name, velocity: signal.average_velocity }));
    },
    {
      name: "get_signals",
      description: "Fetch high-velocity TrendsAGI signals for a category",
      schema: z.object({ category: z.string() }),
    }
  );

  const signals = await getSignals.invoke({ category: "Technology" });
  const result = await llm.invoke([
    new HumanMessage("Summarize these signals for an analyst: " + JSON.stringify(signals)),
  ]);

  console.log(result.content);
}

main().catch(console.error);