How to Create an Agent With Langchain, Llama Index and CrewAI

Introduction: Why Building AI Agents is the Future of Intelligent Applications

Artificial intelligence is transforming the way we interact with technology, making it smarter, faster, and more responsive. One of the most exciting advancements in AI today is the development of AI agents — intelligent software entities capable of understanding natural language, retrieving information, performing tasks, and making decisions autonomously.

Whether you're a software developer, data scientist, or AI enthusiast, mastering how to build AI agents can unlock powerful new possibilities. Imagine creating your own chatbot that understands complex questions, a research assistant that pulls answers from large document collections, or an automation tool that seamlessly integrates with APIs and business workflows. This is not just the future; it's the present, with tools and frameworks designed to make AI agent development accessible to everyone.

In this comprehensive guide, we'll walk you through how to create AI agents using three of the most popular and powerful frameworks available in 2025: LangChain, Llama Index (formerly GPT Index), and CrewAI. Each tool offers unique strengths and use cases:

• LangChain excels in orchestrating complex multi-tool pipelines involving language models, memory, and external APIs.
• Llama Index specializes in building semantic search engines and retrieval-augmented generation (RAG) by indexing your documents for fast and relevant AI responses.
• CrewAI provides a modular, task-based approach perfect for quick prototyping and collaborative AI workflows.

Throughout this tutorial, you will not only get clear explanations of each framework's purpose and architecture but also hands-on code examples so you can start building your own AI agents right away.

By the end of this post, you'll understand:

• The core concepts behind AI agents and why they matter
• How to set up and code your first agent with LangChain, Llama Index, and CrewAI
• The key differences between these frameworks to help you choose the best fit for your project
• Answers to common questions about building, deploying, and scaling AI agents

Whether you want to build a smart chatbot, a knowledge retrieval system, or a custom automation assistant, this guide has you covered.

Ready to jump in? Let's start with LangChain — one of the most versatile frameworks for creating intelligent AI agents.

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1. Building an Agent with LangChain

LangChain is a powerful Python library designed to help developers build AI applications that combine Large Language Models (LLMs) with tools, external data, and memory. It allows you to orchestrate multiple components to create intelligent agents capable of complex reasoning.

Why Use LangChain?

• Easily combine language models with external APIs and tools
• Supports memory and context persistence
• Flexible chains and agent types for custom workflows
• Large and active community with rich documentation

LangChain Agent Example with Code

python
from langchain.llms import OpenAI  
from langchain.agents import initialize_agent, Tool  
from langchain.agents.agent_types import AgentType

# Simple calculator tool for LangChain agent  
def calculator_tool(input_text: str) -> str:  
    try:  
        return str(eval(input_text))  
    except Exception:  
        return "Sorry, I can't calculate that."

tools = [Tool(name="Calculator", func=calculator_tool, description="Performs math calculations")]

llm = OpenAI(temperature=0)

agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)

response = agent.run("What is 12 * 8?")  
print(response)

This example creates a LangChain agent with a calculator tool. The agent can answer math questions by evaluating the input.

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2. Building an Agent with Llama Index (GPT Index)

Llama Index focuses on building semantic indexes over document collections, enabling retrieval-augmented generation (RAG). This makes it ideal for knowledge-based agents that answer questions by searching your own data.

Why Use Llama Index?

• Easy to create vector indexes from your documents
• Enables AI to provide context-aware answers
• Perfect for knowledge bases, FAQs, and enterprise data
• Supports multiple backends and LLMs

Llama Index Agent Example with Code

python
from llama_index import GPTSimpleVectorIndex, SimpleDirectoryReader

# Load documents from a local directory  
documents = SimpleDirectoryReader('data/').load_data()
# Create a vector index for semantic search  
index = GPTSimpleVectorIndex(documents)
# Query the index with a question  
query = "What is the capital of Italy?"  
response = index.query(query)
print(response.response)

This example loads documents from a directory, creates a vector index, and queries it to get a relevant answer.

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3. Building an Agent with CrewAI

CrewAI is an emerging, modular AI agent framework focused on simplicity and task-based workflows. It's designed for rapid prototyping and collaboration with flexible agent definitions.

Why Use CrewAI?

• Modular tasks make building agents easy and flexible
• Lightweight and beginner-friendly
• Suitable for quick prototyping and small-to-medium applications
• Supports collaborative agent workflows

CrewAI Agent Example with Code

python
from crewai import Agent, Task

# Define a simple echo task  
def echo_task(input_text):  
    return f"Echo: {input_text}"

# Create an agent with the echo task  
agent = Agent(name="EchoAgent", tasks={"echo": Task(echo_task)})

# Run the agent with input  
result = agent.run("echo", "Hello CrewAI!")  
print(result)

This simple example creates an agent that echoes back whatever input it receives.

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Differences Between LangChain, Llama Index and CrewAI

Choosing the right framework depends on your project's goals and requirements. Here's a quick comparison:

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Frequently Asked Questions (FAQ)

Q1: Which AI agent framework should I choose?

• Use LangChain for complex pipelines with multiple tools and APIs.
• Use Llama Index if you want fast semantic search over your documents.
• Use CrewAI for modular, lightweight agents ideal for rapid prototyping.

Q2: Do I need API keys for these tools?

• Usually yes, especially for LangChain and Llama Index when connecting to OpenAI or other LLM providers.

Q3: Can these tools be combined?

• Absolutely. For example, you can use Llama Index to build a knowledge base and LangChain to manage conversation flow around it.

Q4: Are these frameworks suitable for production?

• LangChain and Llama Index are widely used in production environments; CrewAI is newer but promising.

Q5: How can I extend agents with custom tools?

• All three frameworks allow you to add custom tools, APIs, or tasks to extend functionality.

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Start Building Your AI Agent Today

With LangChain, Llama Index, and CrewAI, building intelligent AI agents is more accessible than ever. Experiment with the code examples above, explore the frameworks' capabilities, and create custom AI assistants that fit your unique needs.

If you want personalized help building advanced AI agents or combining these tools, just reach out — I'm here to help!