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import openai |
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import os |
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from openai import OpenAI |
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API_KEY = os.getenv("OPENAI_API_KEY") |
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client = OpenAI(api_key=API_KEY) |
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import gradio as gr |
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def gpt41(users_input): |
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response = client.responses.create( |
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model="gpt-4.1-mini", |
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input=f''' |
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You name is AI Academy, the purpose of this chatbot is to support the user |
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On their learning journey to create AI Agents. Diego have created a youtube video under 'AI Academy' youtube Channel, |
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Explaining how to create AI Agents, for beginners that dont know how to code. The proocess shows them how to download |
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an IDE then start to code. README: |
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'Intro |
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This repository is a basic guide on how to build AI Agents using pyhton and OpenAI Agents SDK |
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Requirements |
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OpenAI API Key (This has a cost attached to it, you will need a credit card, the good news is that is very cheap, model: GPT 4.1 mini. Input price: $0.40 / 1M tokens (like 'words') and $1.60 for 1M output) Get your key here -> https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://platform.openai.com/api-keys&ved=2ahUKEwi3m7vSg--NAxUzTTABHY5LJRcQFnoECCkQAQ&usg=AOvVaw1YhcGDWJXhiKSfmL59Pnfn |
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Beginner friendly step by step |
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-Download Cursor, VS Code or Windsurf to your computer (Google it) -Open your IDE (Cursor, Windsurf, VSCode) and then click on Open Folder (create a folder for this project) -Go to the top left and chose Terminal -> New Terminal |
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In the Terminal: copy and paste this + enter: |
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git clone https://github.com/diegocp01/openai_agents.git |
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Make sure your folder name on the left is the same as the terminal. (if not, take a screenshoot and ask chatgpt. Is a quick cd command) |
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-Create a new file inside of the openai-sdk-agent folder, called '.env' -Inside of the .env file copy this: |
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# Copy and paste your openai api key below instead of the 'sk-12....' |
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OPENAI_API_KEY=sk-12232432 |
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# The LLM to use find more llm names here |
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#https://platform.openai.com/docs/models |
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MODEL_CHOICE=gpt-4.1-nano |
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-In the Terminal: Install the required dependencies: Create a virtual enviroment like this -> |
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python -m venv .venv |
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If your IDE asks you to create a python ENV click YES |
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-Then in the terminal paste this (This activates the enviroment): |
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source .venv/bin/activate |
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-Now you will install the openai agents sdk and other frameworks needed -(The frameworks are listed in the requirements file) |
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pip install -r requirements.txt |
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Then paste this in the terminal (with your openai key from .env file) |
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export OPENAI_API_KEY=sk-122 |
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Files |
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v1_basic_agent.py - Basic Agent |
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v2_structured_output.py - Agent with organized outputs |
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v3_tool_calls.py - Agent with access to tools |
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v4_handoffs.py - Orchestrator Agents with Specialized agents |
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Running the Agents |
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Basic Agent (v1) |
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Run the basic agent example: |
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python v1_basic_agent.py |
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Structured Output Agent (v2) |
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Run the Agent with organized outputs: |
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python v2_structured_output.py |
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Tool Calls Agent (v3) |
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Run the tool calls travel agent example: |
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python v3_tool_calls.py |
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Now we will give our agent some TOOLS! (This is when it gets fun!) Recipe Agent |
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Handoffs Agent (v4) |
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Run the Orchestrator Agents with Specialized agents |
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python v4_handoffs.py |
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Run the interactive user interface app |
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streamlit run app.py' |
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Here is the code for the basic structure: |
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v1_basic_agent.py ' |
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# ============================================================================== |
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# This Agent is only a regular talk to ChatGPT, you send a prompt and get a response |
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# ============================================================================== |
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# --- Imports --- |
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from agents import Agent, Runner |
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from dotenv import load_dotenv |
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# --- Load environment variables --- |
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load_dotenv() |
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# --- Agent --- |
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agent = Agent( |
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name="Assistant", |
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instructions="You are a helpful assistant", # This is the system prompt that tells the AI how to behave, change it if you want |
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model="gpt-4.1-nano" # Change the model here |
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) |
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# --- Main -- |
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# The main function runs the AI agent synchronously with a predefined prompt, |
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# asking it to write a haiku about recursion, and then prints the generated response. |
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def main(): |
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result = Runner.run_sync(agent, """ |
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Say: Hello my name is ChatGPT, this is a test. Now add something you will |
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like to tell the user. Your whole output is max 30 words. |
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""") # This is the PROMPT |
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print(result.final_output) |
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# --- Run --- |
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# This ensures that the main() function only runs when this script is executed directly, |
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# not when it is imported as a module in another file. |
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if __name__ == "__main__": |
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main()' |
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If the user asks for code for the other files you ask the user to provide it. |
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The user's question: {users_input} |
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''', |
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max_output_tokens=700 |
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) |
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return(response.output_text) |
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demo = gr.Interface( |
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fn=gpt41, |
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inputs="text", |
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outputs="text", |
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title="🧠 AI Academy Support Assistant", |
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description="Welcome to your AI Agent learning! 🚀\n" |
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"**Disclaimer**: This chatbot is stateless — it does NOT remember past messages. " |
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"Ask one question at a time." |
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) |
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demo.launch() |
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