Agent Extra Metrics

This example demonstrates how to collect special token metrics including audio, cached, and reasoning tokens. It shows different types of advanced metrics available when working with various OpenAI models.

Code

agent_extra_metrics.py

"""Show special token metrics like audio, cached and reasoning tokens"""

import requests
from agno.agent import Agent
from agno.media import Audio
from agno.models.openai import OpenAIChat
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-5-mini-audio-preview",
        modalities=["text", "audio"],
        audio={"voice": "sage", "format": "wav"},
    ),
    markdown=True,
)
run_response = agent.run(
    "What's in these recording?",
    audio=[Audio(content=wav_data, format="wav")],
)
pprint_run_response(run_response)
# Showing input audio, output audio and total 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}")
print(f"Audio tokens: {run_response.metrics.audio_total_tokens}")

agent = Agent(
    model=OpenAIChat(id="gpt-5-mini"),
    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=OpenAIChat(id="gpt-5-mini"), 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 virtual environment

   Open the Terminal and create a python virtual environment.

   Mac

   python3 -m venv .venv
   source .venv/bin/activate
   
2. Install libraries

   pip install -U agno openai requests
   
3. Export your OpenAI API key

   Mac/Linux

     export OPENAI_API_KEY="your_openai_api_key_here"
   
4. Create a Python file

   Create a Python file and add the above code.

   touch agent_extra_metrics.py
   
5. Run Agent

   Mac

   python agent_extra_metrics.py
   
6. Find All Cookbooks

   Explore all the available cookbooks in the Agno repository. Click the link below to view the code on GitHub:

   Agno Cookbooks on GitHub