Mastering LlamaIndex: From Fundamentals to Building AI Apps/

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Building AI Agents in LlamaIndex: Single-Agent System

Building AI Agents in LlamaIndex: Single-Agent System

Learn how to build single-agent AI systems with LlamaIndex.

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An AI agent is an autonomous system that can perform tasks, retrieve information, and select the right tools to handle complex queries. Unlike basic chatbots that rely on static responses, AI agents think, decide, and act dynamically.

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LlamaIndex supports building intelligent agents as a core feature of its framework
LlamaIndex supports building intelligent agents as a core feature of its framework

Imagine managing customer support for an online store. A user asks, “Where’s my order?” Another inquires, “Can I return this item?” A third request, “Find me a laptop under $1,000.”

A simple chatbot might offer generic replies, but an AI agent can track shipments, process return requests, or search product catalogs—choosing the right tool for each task.

LlamaIndex provides a powerful framework for building AI agents that don’t just answer questions—they get things done. Let’s dive in!

We’ll start by building a simple single-agent AI system.

Building a simple agent in LlamaIndex

Let’s build a simple agent in LlamaIndex that acts as an intelligent customer support assistant. This agent will dynamically respond to queries like tracking orders, processing returns, and finding products—just like a real-world AI-powered assistant.

The core component to build an agent in LlamaIndex is FunctionAgent. We need it to build a simple single-agent system for our example.

FunctionAgent

A FunctionAgent is a part of LlamaIndex’s agent framework. It acts as a controller that:

  1. Receives a user query or task

  2. Thinks about how to solve it using the available tools

  3. Calls the appropriate functions (wrapped using FunctionTool)

  4. Responds with the final result

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A LlamaIndex FunctionAgent acting as a controller that interprets user queries using an LLM, invokes the right tools, and returns intelligent responses
A LlamaIndex FunctionAgent acting as a controller that interprets user queries using an LLM, invokes the right tools, and returns intelligent responses

FunctionAgent leverages an LLM to reason about what to do, which tools to call, and in what order.

Implementation

To implement the agent, we begin by importing the necessary modules from LlamaIndex and standard Python libraries. Each import serves a specific purpose in constructing and powering the agent:

from llama_index.llms.groq import Groq
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.core.tools import FunctionTool
import random
Importing required libraries

  • An LLM (large language model) is essential for implementing an ...