Inspecting and Understanding Agent Result Properties

Introduction And Lesson Overview

Welcome back! In the previous lesson, you learned how to define and run your first OpenAI agent using the Agents SDK . You also saw how to extract the agent’s final output from the result object. In this lesson, we will take a closer look at the result object that is returned after running an agent. Understanding the structure and properties of this object is key to building more advanced applications, debugging your agent’s behavior, and making the most of the SDK’s features. By the end of this lesson, you will know how to inspect and interpret the different attributes of the result object, including the final output, the original input, the last agent that ran, new items generated during the run, and the raw responses from the language model. You will also see how these properties can help you understand what happened during the agent’s run and how to use this information in your own projects.

The Structure Of The Result Object

When you run an agent synchronously (using Runner.run_sync) or asynchronously (using Runner.run), you receive a RunResult object. If you run an agent in streamed mode (using Runner.run_streamed), you receive a RunResultStreaming object instead. Both types of result objects provide detailed information about the agent’s execution. Some of the most relevant properties include: final_output : The final output produced by the last agent that ran. Its type can vary depending on the agent’s configuration—it may be a string or a more complex object if the agent specifies an output_type . input : The original input or prompt provided to the agent at the start of the run. last_agent : The agent instance that produced the final output. This is especially useful in workflows involving multiple agents or handoffs. new_items : A list of items generated during the run, such as messages, tool calls, or handoffs. These items provide a step-by-step record of the agent’s reasoning and actions. raw_responses : The raw outputs from the language model for each step in the agent loop, including generated text, tool calls, and finish reasons. By inspecting these properties, you gain a comprehensive view of the agent’s execution, including the input, output and intermediate steps. This structure is designed to support both simple and advanced use cases, from basic logging to complex multi-agent workflows.

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Python

from agents import Agent, Runner, ModelSettings

# Define the agent
agent = Agent(
    name="Travel Genie",
    instructions=(
        "You are Travel Genie, a friendly and knowledgeable travel assistant. "
        "Recommend exciting destinations and offer helpful travel tips."
    ),
    model="gpt-5.6-terra",
    model_settings=ModelSettings(reasoning={"effort": "none"}),
)

from agents import Agent, Runner, ModelSettings

# Define the agent
agent = Agent(
    name="Travel Genie",
    instructions=(
        "You are Travel Genie, a friendly and knowledgeable travel assistant. "
        "Recommend exciting destinations and offer helpful travel tips."
    ),
    model="gpt-5.6-terra",
    model_settings=ModelSettings(reasoning={"effort": "none"}),
)

Python

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

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

Python

# Print the agent's final output
print("Final Output:", result.final_output)

# Print the agent's final output
print("Final Output:", result.final_output)

text

Final Output: Absolutely! For adventure seekers, **New Zealand** is a top pick. This country is a playground for thrill-seekers, offering everything from **bungee jumping in Queenstown** (the "adventure capital of the world"), **skydiving over Lake Taupo**, **white-water rafting in Rotorua**, to **hiking the Tongariro Alpine Crossing**.

**Why it's great for adventure lovers:**
- **Variety:** Mountains, forests, glaciers, beaches—all packed into a compact country.
- **World-class operators:** Safety is prioritized, and there are plenty of reputable guides for every activity.
- **Unique experiences:** Try the original commercial bungee jump, conquer icy crevasses on the Franz Josef Glacier, or go black-water rafting in the Waitomo Glowworm Caves.

**Travel Tips:**
- **Best time to visit:** December to March (summer), but winter brings amazing skiing and snowboarding.
- **Getting around:** Consider renting a camper van for maximum flexibility.
- **Book ahead:** Popular activities like glacier hikes and skydives can sell out, so reserve early.

Other awesome contenders:
- **Costa Rica** for jungle zip lines, volcano hikes, and surfing.
- **South Africa** for shark cage diving, safaris, and mountain biking.
- **Iceland** for glacier trekking, ice cave exploration, and volcanic landscapes.

Let me know what kind of adventure excites you most, and I can tailor recommendations just for you!

Final Output: Absolutely! For adventure seekers, **New Zealand** is a top pick. This country is a playground for thrill-seekers, offering everything from **bungee jumping in Queenstown** (the "adventure capital of the world"), **skydiving over Lake Taupo**, **white-water rafting in Rotorua**, to **hiking the Tongariro Alpine Crossing**.

**Why it's great for adventure lovers:**
- **Variety:** Mountains, forests, glaciers, beaches—all packed into a compact country.
- **World-class operators:** Safety is prioritized, and there are plenty of reputable guides for every activity.
- **Unique experiences:** Try the original commercial bungee jump, conquer icy crevasses on the Franz Josef Glacier, or go black-water rafting in the Waitomo Glowworm Caves.

**Travel Tips:**
- **Best time to visit:** December to March (summer), but winter brings amazing skiing and snowboarding.
- **Getting around:** Consider renting a camper van for maximum flexibility.
- **Book ahead:** Popular activities like glacier hikes and skydives can sell out, so reserve early.

Other awesome contenders:
- **Costa Rica** for jungle zip lines, volcano hikes, and surfing.
- **South Africa** for shark cage diving, safaris, and mountain biking.
- **Iceland** for glacier trekking, ice cave exploration, and volcanic landscapes.

Let me know what kind of adventure excites you most, and I can tailor recommendations just for you!

Python

# Print the original input provided to the agent
print("Original Input:", result.input)

# Print the original input provided to the agent
print("Original Input:", result.input)

text

Original Input: What's your top recommendation for adventure seekers?

Original Input: What's your top recommendation for adventure seekers?

Python

# Print the last agent that ran
print("Last Agent:", result.last_agent.name)

# Print the last agent that ran
print("Last Agent:", result.last_agent.name)

text

Last Agent: Travel Genie

Last Agent: Travel Genie

Python

# Print new items generated during the run
print("New Items:")
for item in result.new_items:
    print(f"  - {item}")

# Print new items generated during the run
print("New Items:")
for item in result.new_items:
    print(f"  - {item}")

text

New Items:
  - MessageOutputItem(agent=Agent(name='Travel Genie', ...), raw_item=ResponseOutputMessage(...), type='message_output_item')

New Items:
  - MessageOutputItem(agent=Agent(name='Travel Genie', ...), raw_item=ResponseOutputMessage(...), type='message_output_item')

Python

# Print raw responses from the LLM
print("Raw Responses:")
for response in result.raw_responses:
    print(f"  - {response}")

# Print raw responses from the LLM
print("Raw Responses:")
for response in result.raw_responses:
    print(f"  - {response}")

text

Raw Responses:
  - ModelResponse(output=[ResponseOutputMessage(id='msg_682306258fac8198afb05543a94d7a14063efd0493fd7c51', content=[ResponseOutputText(annotations=[], text='Absolutely! For adventure seekers, **New Zealand** is a top pick. ...', type='output_text')], role='assistant', status='completed', type='message')], usage=Usage(requests=1, input_tokens=40, output_tokens=301, total_tokens=341), response_id='resp_68230624da2881988611e4c883ce653a063efd0493fd7c51')

Raw Responses:
  - ModelResponse(output=[ResponseOutputMessage(id='msg_682306258fac8198afb05543a94d7a14063efd0493fd7c51', content=[ResponseOutputText(annotations=[], text='Absolutely! For adventure seekers, **New Zealand** is a top pick. ...', type='output_text')], role='assistant', status='completed', type='message')], usage=Usage(requests=1, input_tokens=40, output_tokens=301, total_tokens=341), response_id='resp_68230624da2881988611e4c883ce653a063efd0493fd7c51')

Python

from agents import Agent, Runner, ModelSettings

# Define the agent
agent = Agent(
    name="Travel Genie",
    instructions=(
        "You are Travel Genie, a friendly and knowledgeable travel assistant. "
        "Recommend exciting destinations and offer helpful travel tips."
    ),
    model="gpt-5.6-terra",
    model_settings=ModelSettings(reasoning={"effort": "none"}),
)

Python

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

Python

# Print the agent's final output
print("Final Output:", result.final_output)

text

Final Output: Absolutely! For adventure seekers, **New Zealand** is a top pick. This country is a playground for thrill-seekers, offering everything from **bungee jumping in Queenstown** (the "adventure capital of the world"), **skydiving over Lake Taupo**, **white-water rafting in Rotorua**, to **hiking the Tongariro Alpine Crossing**.

**Why it's great for adventure lovers:**
- **Variety:** Mountains, forests, glaciers, beaches—all packed into a compact country.
- **World-class operators:** Safety is prioritized, and there are plenty of reputable guides for every activity.
- **Unique experiences:** Try the original commercial bungee jump, conquer icy crevasses on the Franz Josef Glacier, or go black-water rafting in the Waitomo Glowworm Caves.

**Travel Tips:**
- **Best time to visit:** December to March (summer), but winter brings amazing skiing and snowboarding.
- **Getting around:** Consider renting a camper van for maximum flexibility.
- **Book ahead:** Popular activities like glacier hikes and skydives can sell out, so reserve early.

Other awesome contenders:
- **Costa Rica** for jungle zip lines, volcano hikes, and surfing.
- **South Africa** for shark cage diving, safaris, and mountain biking.
- **Iceland** for glacier trekking, ice cave exploration, and volcanic landscapes.

Let me know what kind of adventure excites you most, and I can tailor recommendations just for you!

Python

# Print the original input provided to the agent
print("Original Input:", result.input)

text

Original Input: What's your top recommendation for adventure seekers?

Python

# Print the last agent that ran
print("Last Agent:", result.last_agent.name)

text

Last Agent: Travel Genie

Python

# Print new items generated during the run
print("New Items:")
for item in result.new_items:
    print(f"  - {item}")

text

New Items:
  - MessageOutputItem(agent=Agent(name='Travel Genie', ...), raw_item=ResponseOutputMessage(...), type='message_output_item')

Python

# Print raw responses from the LLM
print("Raw Responses:")
for response in result.raw_responses:
    print(f"  - {response}")

text

Raw Responses:
  - ModelResponse(output=[ResponseOutputMessage(id='msg_682306258fac8198afb05543a94d7a14063efd0493fd7c51', content=[ResponseOutputText(annotations=[], text='Absolutely! For adventure seekers, **New Zealand** is a top pick. ...', type='output_text')], role='assistant', status='completed', type='message')], usage=Usage(requests=1, input_tokens=40, output_tokens=301, total_tokens=341), response_id='resp_68230624da2881988611e4c883ce653a063efd0493fd7c51')

Introduction And Lesson Overview

The Structure Of The Result Object

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Creating An Agent For Our Examples

Inspecting The Final Output Property

Inspecting the Original Input Property

Inspecting the Last Agent Property

Inspecting the New Items Property

Analyzing Raw Responses

Lesson Summary And Next Steps