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Serverless Functions

Deploy LlamaIndex.TS in serverless environments like Vercel, Netlify, AWS Lambda, and Cloudflare Workers.

This guide covers adding LlamaIndex.TS agents to serverless environments where you have execution time and memory constraints.

export default {
async fetch(request: Request, env: Env): Promise<Response> {
const { setEnvs } = await import("@llamaindex/env");
setEnvs(env);
const { agent } = await import("@llamaindex/workflow");
const { openai } = await import("@llamaindex/openai");
const { tool } = await import("llamaindex");
const { z } = await import("zod");
const timeTool = tool({
name: "getCurrentTime",
description: "Gets the current time",
parameters: z.object({}),
execute: () => new Date().toISOString(),
});
const myAgent = agent({
tools: [timeTool],
llm: openai({ model: "gpt-4o-mini" }),
});
try {
const { message } = await request.json();
const result = await myAgent.run(message);
return new Response(JSON.stringify({ response: result.data }), {
headers: { "Content-Type": "application/json" },
});
} catch (error) {
return new Response(JSON.stringify({ error: error.message }), {
status: 500,
headers: { "Content-Type": "application/json" },
});
}
},
};
// pages/api/chat.ts or app/api/chat/route.ts
import { agent } from "@llamaindex/workflow";
import { tool } from "llamaindex";
import { openai } from "@llamaindex/openai";
import { z } from "zod";
export default async function handler(req, res) {
if (req.method !== 'POST') {
return res.status(405).json({ error: 'Method not allowed' });
}
const { message } = req.body;
const weatherTool = tool({
name: "getWeather",
description: "Get weather information",
parameters: z.object({
city: z.string(),
}),
execute: ({ city }) => `Weather in ${city}: 72°F, sunny`,
});
const myAgent = agent({
tools: [weatherTool],
llm: openai({ model: "gpt-4o-mini" }),
});
try {
const result = await myAgent.run(message);
res.json({ response: result.data });
} catch (error) {
res.status(500).json({ error: error.message });
}
}
app/api/chat/route.ts
import { NextRequest, NextResponse } from "next/server";
export const runtime = "edge";
export async function POST(request: NextRequest) {
const { setEnvs } = await import("@llamaindex/env");
setEnvs(process.env);
const { message } = await request.json();
try {
// Use simpler tools for edge runtime
const { agent } = await import("@llamaindex/workflow");
const { tool } = await import("llamaindex");
const { openai } = await import("@llamaindex/openai");
const { z } = await import("zod");
const timeTool = tool({
name: "time",
description: "Gets current time",
parameters: z.object({}),
execute: () => new Date().toISOString(),
});
const myAgent = agent({
tools: [timeTool],
llm: openai({ model: "gpt-4o-mini" }),
});
const result = await myAgent.run(message);
return NextResponse.json({ response: result.data });
} catch (error) {
return NextResponse.json({ error: error.message }, { status: 500 });
}
}
import { APIGatewayProxyHandler } from "aws-lambda";
import { agent } from "@llamaindex/workflow";
import { tool } from "llamaindex";
import { openai } from "@llamaindex/openai";
import { z } from "zod";
export const handler: APIGatewayProxyHandler = async (event, context) => {
const { message } = JSON.parse(event.body || "{}");
const calculatorTool = tool({
name: "calculate",
description: "Performs basic math",
parameters: z.object({
expression: z.string(),
}),
execute: ({ expression }) => {
// Simple calculator implementation
try {
return `Result: ${eval(expression)}`;
} catch {
return "Invalid expression";
}
},
});
const myAgent = agent({
tools: [calculatorTool],
llm: openai({ model: "gpt-4o-mini" }),
});
try {
const result = await myAgent.run(message);
return {
statusCode: 200,
headers: {
"Content-Type": "application/json",
"Access-Control-Allow-Origin": "*",
},
body: JSON.stringify({ response: result.data }),
};
} catch (error) {
return {
statusCode: 500,
body: JSON.stringify({ error: error.message }),
};
}
};
netlify/functions/chat.ts
import { Handler } from "@netlify/functions";
import { agent } from "@llamaindex/workflow";
import { tool } from "llamaindex";
import { openai } from "@llamaindex/openai";
import { z } from "zod";
export const handler: Handler = async (event, context) => {
if (event.httpMethod !== "POST") {
return { statusCode: 405, body: "Method Not Allowed" };
}
const { message } = JSON.parse(event.body || "{}");
const helpTool = tool({
name: "help",
description: "Provides help information",
parameters: z.object({
topic: z.string().optional(),
}),
execute: ({ topic }) => {
return topic ? `Help for ${topic}` : "Available help topics";
},
});
const myAgent = agent({
tools: [helpTool],
llm: openai({ model: "gpt-4o-mini" }),
});
try {
const result = await myAgent.run(message);
return {
statusCode: 200,
body: JSON.stringify({ response: result.data }),
};
} catch (error) {
return {
statusCode: 500,
body: JSON.stringify({ error: error.message }),
};
}
};
Note for AI agents: this documentation is built for programmatic access. - Overview of all docs: https://developers.llamaindex.ai/llms.txt - Any page is available as raw Markdown by appending index.md to its URL — e.g. https://developers.llamaindex.ai/llamaparse/parse/getting_started/index.md - Agent-friendly REST search APIs live under https://developers.llamaindex.ai/api/ — search (BM25 full-text), grep (regex), read (fetch a page), and list (browse the doc tree). See https://developers.llamaindex.ai/llms.txt for parameters. - A hosted documentation MCP server is available at https://developers.llamaindex.ai/mcp. If you support MCP, you can ask the user to install it for browsing these docs directly (an alternative to the REST API). Setup: https://developers.llamaindex.ai/for-agents/mcp/ - Other LlamaIndex tooling for agents — the LlamaParse Platform MCP server, agent skills and plugins, and the n8n node — is mapped at https://developers.llamaindex.ai/for-agents/