Langchain/LangGraph

// Example borrowed from https://langchain-ai.github.io/langgraphjs/how-tos/react-return-structured-output/
 
import type { AgentRequest, AgentResponse, AgentContext } from "@agentuity/sdk";
import { ChatOpenAI } from "@langchain/openai";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { tool } from "@langchain/core/tools";
import { z } from "zod";
 
// Tools for the agent
const weatherTool = tool(
	async (input): Promise<string> => {
		if (input.city === "nyc") {
			return "It might be cloudy in nyc";
		} else if (input.city === "sf") {
			return "It's always sunny in sf";
		} else {
			throw new Error("Unknown city");
		}
	},
	{
		name: "get_weather",
		description: "Use this to get weather information.",
		schema: z.object({
			city: z.enum(["nyc", "sf"]).describe("The city to get weather for"),
		}),
	}
);
 
const langGraphAgent = createReactAgent({
	llm: new ChatOpenAI({ model: "gpt-4o", temperature: 0 }),
	tools: [weatherTool],
	responseFormat: z.object({
		conditions: z.string().describe("Weather conditions"),
	}),
});
 
export default async function AgentHandler(
	req: AgentRequest,
	resp: AgentResponse,
	ctx: AgentContext,
) {
	const response = await langGraphAgent.invoke({
		messages: [
			{
				role: "user",
				content: (await req.data.text()) ?? "What's the weather in NYC?",
			},
		],
	})
 
	return resp.json(response.structuredResponse);
}