Background Execution Metrics

"""
Background Execution Metrics
=============================

Demonstrates that metrics are fully tracked for background runs.

When an agent runs in the background, the run completes asynchronously
and is stored in the database. Once complete, the run output includes
the same metrics as a synchronous run: token counts, model details,
duration, and time-to-first-token.
"""

import asyncio

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIChat
from agno.run.base import RunStatus
from agno.tools.yfinance import YFinanceTools
from rich.pretty import pprint

# ---------------------------------------------------------------------------
# Config
# ---------------------------------------------------------------------------
db = PostgresDb(
    db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
    session_table="bg_metrics_sessions",
)

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
agent = Agent(
    name="BackgroundMetricsAgent",
    model=OpenAIChat(id="gpt-4o-mini"),
    tools=[YFinanceTools(enable_stock_price=True)],
    db=db,
)


# ---------------------------------------------------------------------------
# Run in background and inspect metrics
# ---------------------------------------------------------------------------
async def main():
    # Start a background run
    run_output = await agent.arun(
        "What is the stock price of AAPL?",
        background=True,
    )

    print(f"Run ID: {run_output.run_id}")
    print(f"Status: {run_output.status}")

    # Poll for completion
    result = None
    for i in range(30):
        await asyncio.sleep(1)
        result = await agent.aget_run_output(
            run_id=run_output.run_id,
            session_id=run_output.session_id,
        )
        if result and result.status in (RunStatus.completed, RunStatus.error):
            print(f"Completed after {i + 1}s")
            break

    if result is None or result.status != RunStatus.completed:
        print("Run did not complete in time")
        return

    # ----- Run metrics -----
    print("\n" + "=" * 50)
    print("RUN METRICS")
    print("=" * 50)
    pprint(result.metrics)

    # ----- Model details breakdown -----
    print("\n" + "=" * 50)
    print("MODEL DETAILS")
    print("=" * 50)
    if result.metrics and result.metrics.details:
        for model_type, model_metrics_list in result.metrics.details.items():
            print(f"\n{model_type}:")
            for model_metric in model_metrics_list:
                pprint(model_metric)

    # ----- Session metrics -----
    print("\n" + "=" * 50)
    print("SESSION METRICS")
    print("=" * 50)
    session_metrics = agent.get_session_metrics()
    if session_metrics:
        pprint(session_metrics)


if __name__ == "__main__":
    asyncio.run(main())