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AI Agents with LangChain & Autonomous Workflows · Lezione

Costruire il primo agente semplice

Segua una guida passo passo per configurare l'ambiente e costruire un agente AI di base usando LangChain.

Costruire il primo agente semplice è una lezione AI Agents with LangChain & Autonomous Workflows gratuita su CoddyKit. Questa è la lezione 3 di 4. Puoi leggere la lezione completa qui gratuitamente — poi esercitati direttamente nel browser con un editor di codice integrato e un tutor IA disponibile 24/7. Fa parte del percorso di apprendimento AI Agents with LangChain & Autonomous Workflows, e i tuoi progressi si sincronizzano tra il web e l'app CoddyKit. Il corso AI Agents with LangChain & Autonomous Workflows include 4 lezioni in totale.

Parti di questa lezione non sono ancora state tradotte e vengono mostrate in inglese.

Build Your First Agent!

Time to build your first AI agent with LangChain — connecting an LLM to basic tools so it can answer your queries intelligently.

Set Up Your Python Environment

First, set up Python 3.9+ and a virtual environment to isolate dependencies. Create one with venv, then activate it before you install anything.

Install LangChain & Dependencies

Now install LangChain and the OpenAI integration. Run the pip command inside your activated virtual environment.

Connect to an LLM

Your agent needs a brain. Initialize ChatOpenAI with your API key stored as the OPENAI_API_KEY environment variable — never hardcode it.

import os
from langchain_openai import ChatOpenAI

def main():
    # In a real setup, ensure OPENAI_API_KEY is set
    # For demonstration, we'll assume it's available
    # os.environ["OPENAI_API_KEY"] = "sk-..." # DON'T hardcode!

    try:
        llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
        print("LLM initialized successfully!")
        # You can test it:
        # response = llm.invoke("Hello, LLM!")
        # print(response.content)
    except Exception as e:
        print(f"Error initializing LLM: {e}")
        print("Please ensure OPENAI_API_KEY is set.")

if __name__ == "__main__":
    main()

Agents Need Tools

An agent is an LLM plus tools — functions or APIs that let it act on the world: search, run code, calculate. Here we give ours a calculator.

import os
from langchain_openai import ChatOpenAI
from langchain.tools import Tool
from langchain_community.utilities import LLMMathChain

def main():
    # Ensure OPENAI_API_KEY is set
    llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

    # Create a basic math tool
    llm_math_chain = LLMMathChain.from_llm(llm)
    math_tool = Tool.from_function(
        func=llm_math_chain.run,
        name="Calculator",
        description="Useful for when you need to answer questions about math."
    )
    tools = [math_tool]
    print("Basic math tool created and ready!")

if __name__ == "__main__":
    main()

Choosing Your Agent Type

The agent type defines how it reasons. We'll use create_react_agent — ReAct means Reason and Act: it plans, observes results, then refines.

Combining Components

Now combine the pieces: create_react_agent takes your LLM, tools, and prompt to build the logic, then AgentExecutor wraps it to make it runnable.

import os
from langchain_openai import ChatOpenAI
from langchain.tools import Tool
from langchain_community.utilities import LLMMathChain
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import ChatPromptTemplate

def main():
    llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
    llm_math_chain = LLMMathChain.from_llm(llm)
    math_tool = Tool.from_function(
        func=llm_math_chain.run,
        name="Calculator",
        description="Useful for when you need to answer questions about math."
    )
    tools = [math_tool]

    # Define the prompt for the agent
    prompt = ChatPromptTemplate.from_messages([
        ("system", "You are a helpful AI assistant. Use tools when necessary."),
        ("human", "{input}"),
        ("placeholder", "{agent_scratchpad}")
    ])

    # Create the agent
    agent = create_react_agent(llm, tools, prompt)

    # Create the agent executor
    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
    print("Agent assembled and ready for action!")

if __name__ == "__main__":
    main()

Asking Your Agent a Question

With the agent assembled, give it a task via invoke. Ask a math question and watch it reach for its calculator tool to answer.

import os
from langchain_openai import ChatOpenAI
from langchain.tools import Tool
from langchain_community.utilities import LLMMathChain
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import ChatPromptTemplate

def main():
    llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
    llm_math_chain = LLMMathChain.from_llm(llm)
    math_tool = Tool.from_function(
        func=llm_math_chain.run,
        name="Calculator",
        description="Useful for when you need to answer questions about math."
    )
    tools = [math_tool]

    prompt = ChatPromptTemplate.from_messages([
        ("system", "You are a helpful AI assistant. Use tools when necessary."),
        ("human", "{input}"),
        ("placeholder", "{agent_scratchpad}")
    ])

    agent = create_react_agent(llm, tools, prompt)
    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

    print("Asking the agent: 'What is 123 multiplied by 456?'")
    response = agent_executor.invoke({"input": "What is 123 multiplied by 456?"})
    print("\nAgent's final answer:")
    print(response["output"])

if __name__ == "__main__":
    main()

Understanding the Agent's Response

With verbose=True you see the agent's thought process: Thought, Action, Action Input, Observation, then Final Answer. That transparency is key to debugging.

Your Complete First Agent!

Here's your complete first agent. Set your API key, run it, and try changing the input question to see it reason through different problems.

import os
from langchain_openai import ChatOpenAI
from langchain.tools import Tool
from langchain_community.utilities import LLMMathChain
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import ChatPromptTemplate

def main():
    # 1. Set up your LLM
    # Ensure OPENAI_API_KEY is set as an environment variable
    # os.environ["OPENAI_API_KEY"] = "YOUR_KEY_HERE" # DO NOT hardcode!
    try:
        llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
    except Exception as e:
        print(f"Error: {e}. Please ensure OPENAI_API_KEY is set.")
        return

    # 2. Define your tools
    llm_math_chain = LLMMathChain.from_llm(llm)
    math_tool = Tool.from_function(
        func=llm_math_chain.run,
        name="Calculator",
        description="Useful for when you need to answer questions about math."
    )
    tools = [math_tool]

    # 3. Define the agent's prompt
    prompt = ChatPromptTemplate.from_messages([
        ("system", "You are a helpful AI assistant. Use tools when necessary."),
        ("human", "{input}"),
        ("placeholder", "{agent_scratchpad}")
    ])

    # 4. Create the agent
    agent = create_react_agent(llm, tools, prompt)

    # 5. Create the AgentExecutor
    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

    # 6. Run the agent
    print("\n--- Running the Agent ---")
    question = "What is the square root of 144 plus 25?"
    print(f"Agent input: '{question}'")
    response = agent_executor.invoke({"input": question})
    print("\n--- Agent's Final Answer ---")
    print(response["output"])

if __name__ == "__main__":
    main()

Agent Components Check

You've seen how to build a basic agent. Which of the following are essential components when constructing a LangChain agent using create_react_agent?

Recap: Your First Agent!

Recap: you built a real agent — set up the environment, connected an LLM, added a tool, assembled it with create_react_agent and AgentExecutor, and watched it think.

Domande Frequenti

La lezione «Costruire il primo agente semplice» è gratuita?

Sì — il testo completo di «Costruire il primo agente semplice» è gratuito qui sul web. Per esercitarvi in modo interattivo (un editor di codice integrato e un tutor IA 24/7) e sbloccare il resto del corso AI Agents with LangChain & Autonomous Workflows, passa a CoddyKit PRO. Il corso AI Agents with LangChain & Autonomous Workflows include 4 lezioni in totale.

Cosa imparerò in «Costruire il primo agente semplice»?

Segua una guida passo passo per configurare l'ambiente e costruire un agente AI di base usando LangChain. Eserciti AI Agents with LangChain & Autonomous Workflows con codice pratico che esegui direttamente nel browser, e un tutor IA 24/7 risponde alle tue domande mentre lavori sulla lezione.

Ho bisogno di esperienza per iniziare AI Agents with LangChain & Autonomous Workflows?

Non è richiesta alcuna esperienza precedente. AI Agents with LangChain & Autonomous Workflows su CoddyKit è strutturato per principianti e studenti avanzati, quindi puoi iniziare da qui o dall'inizio e procedere al tuo ritmo. Questa è la lezione 3 di 4.

Quanto tempo richiede la lezione «Costruire il primo agente semplice»?

La maggior parte delle lezioni CoddyKit richiede circa 5–10 minuti. Ogni lezione è breve e interattiva, quindi fai progressi costanti e riprendi esattamente da dove hai lasciato su web e app.

Posso scrivere ed eseguire codice in questa lezione AI Agents with LangChain & Autonomous Workflows?

Sì. Ogni lezione AI Agents with LangChain & Autonomous Workflows include un editor di codice integrato, quindi scrivi ed esegui codice reale direttamente nel tuo browser e ricevi feedback istantaneo dall'IA — nessuna configurazione locale necessaria.

Tutte le lezioni di questo corso

  1. Comprendere gli agenti AI e gli LLM
  2. Spiegazione dei componenti fondamentali di LangChain
  3. Costruire il primo agente semplice
  4. Aggiungere memoria e stato della conversazione agli agenti
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