> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-update-deprecated-models.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Team Metrics

> Team run and session metrics for token usage and performance.

When you run a team in Agno, the response you get (**TeamRunOutput**) includes detailed metrics about the run. These metrics help you understand resource usage (like **token usage** and **time**), performance, and other aspects of the model and tool calls across both the team leader and team members.

Metrics are available at multiple levels:

* **Per-message**: Each message (assistant, tool, etc.) has its own metrics.
* **Per-member run**: Each team member run has its own metrics. You can make member runs available on the `TeamRunOutput` by setting `store_member_responses=True`,
* **Team-level**: The `TeamRunOutput` aggregates metrics across all team leader and team member messages.
* **Session-level**: Aggregated metrics across all runs in the session, for both the team leader and all team members.

## Example Usage

Suppose you have a team that performs some tasks and you want to analyze the metrics after running it. Here's how you can access and print the metrics:

```python theme={null}
from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.team import Team
from agno.tools.yfinance import YFinanceTools
from agno.utils.pprint import pprint_run_response
from rich.pretty import pprint

# Create team members
stock_agent = Agent(
    name="Stock Agent",
    model=OpenAIResponses(id="gpt-5.2"),
    role="Get stock prices and financial data.",
    tools=[YFinanceTools()],
)

# Create the team
team = Team(
    name="Finance Team",
    model=OpenAIResponses(id="gpt-5.2"),
    members=[stock_agent],
    markdown=True,
    store_member_responses=True,
)

# Run the team
run_response = team.run(
    "What is the stock price of NVDA?"
)
pprint_run_response(run_response, markdown=True)

# Print team leader message metrics
print("---" * 5, "Team Leader Message Metrics", "---" * 5)
if run_response.messages:
    for message in run_response.messages:
        if message.role == "assistant":
            if message.content:
                print(f"Message: {message.content}")
            elif message.tool_calls:
                print(f"Tool calls: {message.tool_calls}")
            print("---" * 5, "Metrics", "---" * 5)
            pprint(message.metrics)
            print("---" * 20)

# Print aggregated team leader metrics
print("---" * 5, "Aggregated Metrics of Team", "---" * 5)
pprint(run_response.metrics)

# Print team leader session metrics
print("---" * 5, "Session Metrics", "---" * 5)
pprint(team.get_session_metrics().to_dict())

# Print team member message metrics
print("---" * 5, "Team Member Message Metrics", "---" * 5)
if run_response.member_responses:
    for member_response in run_response.member_responses:
        if member_response.messages:
            for message in member_response.messages:
                if message.role == "assistant":
                    if message.content:
                        print(f"Member Message: {message.content}")
                    elif message.tool_calls:
                        print(f"Member Tool calls: {message.tool_calls}")
                    print("---" * 5, "Member Metrics", "---" * 5)
                    pprint(message.metrics)
                    print("---" * 20)
```

You'll see the outputs with following information:

* `input_tokens`: The number of tokens sent to the model.
* `output_tokens`: The number of tokens received from the model.
* `total_tokens`: The sum of `input_tokens` and `output_tokens`.
* `audio_input_tokens`: The number of tokens sent to the model for audio input.
* `audio_output_tokens`: The number of tokens received from the model for audio output.
* `audio_total_tokens`: The sum of `audio_input_tokens` and `audio_output_tokens`.
* `cache_read_tokens`: The number of tokens read from the cache.
* `cache_write_tokens`: The number of tokens written to the cache.
* `reasoning_tokens`: The number of tokens used for reasoning.
* `duration`: The duration of the run in seconds.
* `time_to_first_token`: The time taken until the first token was generated.
* `provider_metrics`: Any provider-specific metrics.

## Developer Resources

* View the [TeamRunOutput schema](/reference/teams/team-response)
* View the [Metrics schema](/reference/agents/metrics)
* View [Cookbook](https://github.com/agno-agi/agno/tree/main/cookbook/04_teams/metrics/01_team_metrics.py)