Code
agent_extra_metrics.py
import requests
from agno.agent import Agent
from agno.media import Audio
from agno.models.openai import OpenAIChat, OpenAIResponses
from agno.utils.pprint import pprint_run_response
# Fetch the audio file and convert it to a base64 encoded string
url = "https://openaiassets.blob.core.windows.net/$web/API/docs/audio/alloy.wav"
response = requests.get(url)
response.raise_for_status()
wav_data = response.content
agent = Agent(
model=OpenAIChat(
id="gpt-4o-audio-preview",
modalities=["text", "audio"],
audio={"voice": "sage", "format": "wav"},
),
markdown=True,
)
run_response = agent.run(
"What's in this recording?",
audio=[Audio(content=wav_data, format="wav")],
)
pprint_run_response(run_response)
# Showing input audio and output audio tokens metrics
print(f"Input audio tokens: {run_response.metrics.audio_input_tokens}")
print(f"Output audio tokens: {run_response.metrics.audio_output_tokens}")
agent = Agent(
model=OpenAIResponses(id="gpt-5.2"),
markdown=True,
telemetry=False,
)
run_response = agent.run(
"Solve the trolley problem. Evaluate multiple ethical frameworks. Include an ASCII diagram of your solution.",
stream=False,
)
pprint_run_response(run_response)
# Showing reasoning tokens metrics
print(f"Reasoning tokens: {run_response.metrics.reasoning_tokens}")
agent = Agent(model=OpenAIResponses(id="gpt-5.2"), markdown=True, telemetry=False)
agent.run("Share a 2 sentence horror story" * 150)
run_response = agent.run("Share a 2 sentence horror story" * 150)
# Showing cached tokens metrics
print(f"Cached tokens: {run_response.metrics.cache_read_tokens}")
Usage
1
Create a Python file
Create
agent_extra_metrics.py with the code above.2
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
3
Install dependencies
uv pip install -U agno openai requests
4
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
5
Run Agent
python agent_extra_metrics.py