Agent-as-Judge Eval Metrics

"""
Agent-as-Judge Eval Metrics
============================

Demonstrates that eval model metrics are accumulated back into the
original agent's run_output when AgentAsJudgeEval is used as a post_hook.

After the agent runs, the evaluator agent makes its own model call.
Those eval tokens show up under "eval_model" in run_output.metrics.details.
"""

from agno.agent import Agent
from agno.eval.agent_as_judge import AgentAsJudgeEval
from agno.models.openai import OpenAIChat
from rich.pretty import pprint

# ---------------------------------------------------------------------------
# Create eval as a post-hook
# ---------------------------------------------------------------------------
eval_hook = AgentAsJudgeEval(
    name="Quality Check",
    model=OpenAIChat(id="gpt-5.6-luna"),
    criteria="Response should be accurate, clear, and concise",
    scoring_strategy="binary",
)

agent = Agent(
    model=OpenAIChat(id="gpt-5.6-luna"),
    instructions="Answer questions concisely.",
    post_hooks=[eval_hook],
)

# ---------------------------------------------------------------------------
# Run
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    result = agent.run("What is the capital of France?")

    # The run metrics now include both agent model + eval model tokens
    if result.metrics:
        print("Total tokens (agent + eval):", result.metrics.total_tokens)

        if result.metrics.details:
            # Agent's own model call
            if "model" in result.metrics.details:
                agent_tokens = sum(
                    metric.total_tokens for metric in result.metrics.details["model"]
                )
                print("Agent model tokens:", agent_tokens)

            # Eval model call (accumulated from evaluator agent)
            if "eval_model" in result.metrics.details:
                eval_tokens = sum(
                    metric.total_tokens
                    for metric in result.metrics.details["eval_model"]
                )
                print("Eval model tokens:", eval_tokens)
                for metric in result.metrics.details["eval_model"]:
                    print(f"  Evaluator: {metric.id} ({metric.provider})")

            print("\nFull metrics details:")
            pprint(result.metrics.to_dict())