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Server APIs & Backends

This guide covers adding LlamaIndex.TS agents to traditional server environments where you have full Node.js runtime access.

LlamaIndex.TS works seamlessly with:

  • Node.js (v18+)
  • Bun (v1.0+)
  • Deno (v1.30+)
import express from 'express';
import { agent } from '@llamaindex/workflow';
import { tool } from 'llamaindex';
import { openai } from '@llamaindex/openai';
import { z } from 'zod';
const app = express();
app.use(express.json());
// Initialize agent once at startup
let myAgent: any;
async function initializeAgent() {
// Create tools for the agent
const sumTool = tool({
name: "sum",
description: "Adds two numbers",
parameters: z.object({
a: z.number(),
b: z.number(),
}),
execute: ({ a, b }) => a + b,
});
const multiplyTool = tool({
name: "multiply",
description: "Multiplies two numbers",
parameters: z.object({
a: z.number(),
b: z.number(),
}),
execute: ({ a, b }) => a * b,
});
// Create the agent
myAgent = agent({
tools: [sumTool, multiplyTool],
llm: openai({ model: "gpt-4o-mini" }),
});
}
app.post('/api/chat', async (req, res) => {
try {
const { message } = req.body;
const result = await myAgent.run(message);
res.json({ response: result.data });
} catch (error) {
res.status(500).json({ error: 'Chat failed' });
}
});
// Initialize and start server
initializeAgent().then(() => {
app.listen(3000, () => {
console.log('Server running on port 3000');
});
});
import Fastify from 'fastify';
import { agent } from '@llamaindex/workflow';
import { tool } from 'llamaindex';
import { openai } from '@llamaindex/openai';
import { z } from 'zod';
const fastify = Fastify();
let myAgent: any;
async function initializeAgent() {
const sumTool = tool({
name: "sum",
description: "Adds two numbers",
parameters: z.object({
a: z.number(),
b: z.number(),
}),
execute: ({ a, b }) => a + b,
});
myAgent = agent({
tools: [sumTool],
llm: openai({ model: "gpt-4o-mini" }),
});
}
fastify.post('/api/chat', async (request, reply) => {
try {
const { message } = request.body as { message: string };
const result = await myAgent.run(message);
return { response: result.data };
} catch (error) {
reply.status(500).send({ error: 'Chat failed' });
}
});
const start = async () => {
await initializeAgent();
await fastify.listen({ port: 3000 });
console.log('Server running on port 3000');
};
start();
import { Hono } from "hono";
import { agent } from "@llamaindex/workflow";
import { tool } from "llamaindex";
import { openai } from "@llamaindex/openai";
import { z } from "zod";
type Bindings = {
OPENAI_API_KEY: string;
};
const app = new Hono<{ Bindings: Bindings }>();
app.post("/api/chat", async (c) => {
const { setEnvs } = await import("@llamaindex/env");
setEnvs(c.env);
const { message } = await c.req.json();
const greetTool = tool({
name: "greet",
description: "Greets a user",
parameters: z.object({
name: z.string(),
}),
execute: ({ name }) => `Hello, ${name}!`,
});
const myAgent = agent({
tools: [greetTool],
llm: openai({ model: "gpt-4o-mini" }),
});
try {
const result = await myAgent.run(message);
return c.json({ response: result.data });
} catch (error) {
return c.json({ error: error.message }, 500);
}
});
export default app;

For real-time agent responses:

import { agentStreamEvent } from "@llamaindex/workflow";
app.post('/api/chat-stream', async (req, res) => {
const { message } = req.body;
res.writeHead(200, {
'Content-Type': 'text/plain',
'Transfer-Encoding': 'chunked',
});
try {
const events = myAgent.runStream(message);
for await (const event of events) {
if (agentStreamEvent.include(event)) {
res.write(event.data.delta);
}
}
res.end();
} catch (error) {
res.write('Error: ' + error.message);
res.end();
}
});