Running Your First OpenAI Agent

Introduction & Lesson Overview

Welcome to the first lesson of our course, Getting Started with OpenAI Agents in Python . In this lesson, you will take your very first steps with the OpenAI Agents SDK . This course is designed for learners who want to build intelligent, task-oriented agents using Python. You do not need any prior experience with the SDK, and we will guide you through each concept with clear explanations and practical examples. By the end of this lesson, you will know what an OpenAI agent is, how it differs from a simple chat-based language model, and how to create and run your own agent using the OpenAI Agents SDK. This foundational knowledge will prepare you for more advanced topics and hands-on exercises in the rest of the course.

What is an Agent?

An agent, in this context, is more than just a chatbot. While traditional chat LLMs like the first versions of ChatGPT simply respond to prompts with text, agents are designed to perform specific tasks, follow detailed instructions, and even use tools or delegate work to other agents. For example, a chat LLM might answer, “What’s the weather like in Paris?” with a general response based on its training data. An agent, on the other hand, could be set up as a travel assistant, given instructions to always provide up-to-date information, and even use external APIs to fetch the latest weather. Agents are configurable, can be given roles, and can be equipped with tools and safety checks, making them much more powerful and reliable for real-world applications.

Overview of the OpenAI Agents SDK

The OpenAI Agents SDK is a Python library designed to help you easily build, manage, and run AI agents powered by large language models. With this SDK, you can create agents that do much more than just chat—they can reason, use tools, interact with other agents, and perform complex, multi-step tasks in a structured way. The SDK's simple and flexible design makes it easy to prototype and deploy agentic applications quickly. For more information, you can visit the official OpenAI Agents SDK Documentation . Throughout this course, you'll be working in the CodeSignal environment where everything is already set up for you—no installation or API key configuration needed. You can focus entirely on learning and experimenting with the code.

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Running the Agent Synchronously

Once you have defined your agent, you can run it using the Runner class. The OpenAI Agents SDK provides multiple ways to run your agent: synchronously, asynchronously, or even with streaming responses. In this first lesson, we'll focus on the synchronous approach using the run_sync method, which lets you interact with the agent in a blocking way, meaning your code will wait for the agent to finish before moving on.

The run_sync method requires two main parameters:

starting_agent: the Agent instance you want to run.
input: the initial input or prompt for the agent, typically a string representing the user's message.

Here is how you can run the agent with a user prompt:

Python

from agents import Runner

# Run the agent synchronously with an input prompt
result = Runner.run_sync(
    starting_agent=agent,
    input="What's your top recommendation for adventure seekers?"
)

from agents import Runner

# Run the agent synchronously with an input prompt
result = Runner.run_sync(
    starting_agent=agent,
    input="What's your top recommendation for adventure seekers?"
)

In this example, we ask the agent to recommend a destination for adventure seekers. The method takes the agent and the input prompt, runs the agent loop, and returns the result when the agent is done.

When you run an agent, the Runner executes what's known as the "agent loop":

The Runner calls the language model for the current agent with the provided input
The language model produces its output
If the language model returns a final_output, the loop ends and returns the result
If the language model delegates to another agent (does a handoff), the Runner updates the current agent and input, then re-runs the loop
If the language model needs to use tools, the Runner executes those tool calls, appends the results, and re-runs the loop
This process continues until a final output is reached or the maximum number of turns is exceeded

This loop structure is what gives agents their power to handle complex, multi-step tasks that might require different capabilities or external information.

Extracting and Interpreting the Final Output

After running the agent, you will get a result object. The most important part of this object is the final_output attribute, which contains the agent’s final answer.

To see the output, you can print it like this:

Python

# Print the agent's final output
print(result.final_output)

# Print the agent's final output
print(result.final_output)

If you run the full example, the output might look like this:

text

Absolutely—there’s a world of excitement out there! For adventure seekers, one of my **top recommendations** is **New Zealand**.

### Why New Zealand?
- **Epic Landscapes:** From glaciers and fjords to volcanoes and rainforests, the scenery is unbeatable.
- **Thrill Activities:** Try bungee jumping off the Kawarau Bridge, skydiving with views of lakes and peaks, or ziplining through lush forests.
- **World-Class Hiking:** Walk the famous Tongariro Alpine Crossing or explore the Milford Track’s dramatic beauty.
- **Water Sports:** Go white-water rafting, jet boating, or surfing on wild coastlines.
- **Lord of the Rings Vibes:** Explore filming locations for your very own Middle-earth adventure.

### Additional Hotspots for Adventure:
- **Patagonia, Chile & Argentina:** Trekking, ice climbing, and horseback riding through jaw-dropping wilderness.
- **Costa Rica:** Ziplining, surfing, canyoning, and wildlife reserves in a compact, friendly country.
- **Iceland:** Glacier hikes, ice cave tours, and volcano trekking under the midnight sun or northern lights.
- **South Africa:** Shark cage diving, bungee jumping off Bloukrans Bridge, and world-class safari.

**Travel Tip:** Always check local safety guidelines and consider booking tours with reputable, eco-friendly companies for safe and sustainable adventures.

Want more details about any of these spots or help planning your trip? Just let me know!

Absolutely—there’s a world of excitement out there! For adventure seekers, one of my **top recommendations** is **New Zealand**.

### Why New Zealand?
- **Epic Landscapes:** From glaciers and fjords to volcanoes and rainforests, the scenery is unbeatable.
- **Thrill Activities:** Try bungee jumping off the Kawarau Bridge, skydiving with views of lakes and peaks, or ziplining through lush forests.
- **World-Class Hiking:** Walk the famous Tongariro Alpine Crossing or explore the Milford Track’s dramatic beauty.
- **Water Sports:** Go white-water rafting, jet boating, or surfing on wild coastlines.
- **Lord of the Rings Vibes:** Explore filming locations for your very own Middle-earth adventure.

### Additional Hotspots for Adventure:
- **Patagonia, Chile & Argentina:** Trekking, ice climbing, and horseback riding through jaw-dropping wilderness.
- **Costa Rica:** Ziplining, surfing, canyoning, and wildlife reserves in a compact, friendly country.
- **Iceland:** Glacier hikes, ice cave tours, and volcano trekking under the midnight sun or northern lights.
- **South Africa:** Shark cage diving, bungee jumping off Bloukrans Bridge, and world-class safari.

**Travel Tip:** Always check local safety guidelines and consider booking tours with reputable, eco-friendly companies for safe and sustainable adventures.

Want more details about any of these spots or help planning your trip? Just let me know!

This output shows how the agent follows its instructions, providing an enthusiastic and tailored recommendation. In real-world applications, you can use this output to power chatbots, virtual assistants, or any system that needs intelligent, role-based responses.

Introduction & Lesson Overview

What is an Agent?

Overview of the OpenAI Agents SDK

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Local Setup Steps

Understanding the Agent Loop

Creating Your First Agent

Specifying a Language Model

Tune Model Parameters

Running the Agent Synchronously

Extracting and Interpreting the Final Output

Lesson Summary & Next Steps