Ag2 Eval Comparison

Evaluation — comparing builds (variants & pairwise)

When to use

• Rank N models / prompts / configs on a leaderboard → run_variants
• Decide which of two is better, head-to-head → run_pairwise with pairwise_judge (LLM) or human_pairwise (people)

For running and grading a single agent (scorers, CI, persistence), use ag2-evaluation.

Install

pip install "ag2[openai,tracing]"

Required. Run this install before delivering the code. If you cannot run commands, state the exact pip install command.

Leaderboard — run_variants

Variants is a frozen dataclass holding a mapping of named Agent instances plus an axis label naming what you varied. Build each agent with the one thing that differs (config, prompt, tools, middleware, …), hold the rest fixed, score each, rank:

from ag2 import Agent
from ag2.config import OpenAIConfig, GeminiConfig
from ag2.eval import Variants, run_variants
from ag2.eval.scorers import agent_judge

board = await run_variants(
    suite,
    variants=Variants(
        {
            "gpt-4o": Agent("a", prompt="Answer helpfully.", config=OpenAIConfig("gpt-4o")),
            "flash":  Agent("a", prompt="Answer helpfully.", config=GeminiConfig("gemini-3-flash-preview")),
        },
        axis="config",                  # label for what was varied (used in summary)
    ),
    scorers=[agent_judge(OpenAIConfig("gpt-4o"), criterion="Helpful and accurate.", key="quality")],
    store_dir="runs",
    repeats=5,                          # optional: N runs per variant for stability
)
print(board.summary("quality"))         # ranked leaderboard
board.best("quality")                   # winning variant name (None if tied)
board.leaderboard("quality")            # list[LeaderboardRow] — variant, score, n, rank
board.results["gpt-4o"]                 # each variant's full RunResult

Vary whatever you like across the agents — set axis to label it (e.g. "config", "prompt", "tools"). Tied scores share a rank; a 3-way tie usually means the eval isn't discriminating — make it harder, or score quality with a judge.

Head-to-head (LLM) — run_pairwise + pairwise_judge

A comparator picks a winner PER task. pairwise_judge shows the pair in BOTH orders and counts a win only if it's consistent — else a tie (cancels position bias):

from ag2.eval import run_pairwise
from ag2.eval.scorers import pairwise_judge

result = await run_pairwise(
    suite, variant_a=agent_v1, variant_b=agent_v2,
    comparators=[pairwise_judge(OpenAIConfig("gpt-4o"), criterion="more helpful answer", key="quality")],
    store_dir="runs",
)
wr = result.win_rate("quality")         # B's win-rate
print(wr.rate, wr.ci, wr.wins, wr.losses, wr.ties)   # ties count 0.5; ci is a Wilson 95% interval
print(result.flips("quality"))          # int — count of cases where the two orders disagreed

variant_a / variant_b are Agent instances; comparators= is a plural iterable. result.agreement("quality", "human") returns an Agreement (.rate, .cohen_kappa, …) between two comparator keys. Use a judge model different from the variants.

Head-to-head (human) — human_pairwise

Same unit, decided by a person. The pair is blinded and order-randomized; the default prints it and reads 1 / 2 / tie. Pass your own async ask(task, response_1, response_2) to collect a vote from a UI (returns "1", "2", or "tie"):

from ag2.eval.scorers import human_pairwise

async def ask(task, response_1, response_2) -> str:
    return await my_ui.compare(task.inputs["input"], response_1, response_2)   # "1" / "2" / "tie"

result = await run_pairwise(suite, variant_a=agent_v1, variant_b=agent_v2,
                            comparators=[human_pairwise(key="quality", ask=ask)], store_dir="runs")

At scale, export a blinded manifest, label it in any tool, import it. evaluate_pairwise is the grade-only twin of run_pairwise (pairs two existing trace sources by task_id):

from ag2.eval import evaluate_pairwise, DirectoryTraceSource
from ag2.eval.scorers import export_pairwise_cases, human_labels

a, b = DirectoryTraceSource("runs/champion"), DirectoryTraceSource("runs/challenger")
await export_pairwise_cases(a, b, criteria=["more helpful"], out="labels.jsonl", suite=suite)   # blinded JSONL
# a person adds  "preferred": "1" | "2" | "tie"  per line, then:
result = await evaluate_pairwise(a, b, suite=suite, store_dir="runs",
                                 comparators=[human_labels("labels.jsonl", criterion="more helpful", key="helpful")])

The manifest hides which model is which; its first_variant field de-blinds it for human_labels.

Common pitfalls

• Judge == a variant's model — self-preference bias; use a different judge model.
• Bare win-rate on few pairs — report wr.ci (Wilson); a small n straddles 50%.
• Passing factories, not agents — run_variants (variants=Variants({name: Agent(...)})) and run_pairwise (variant_a=/variant_b=) take Agent instances, not build callables. Vary the model per task with model_config= (a dict[task_id, ModelConfig]) rather than rebuilding the agent. Keep pairwise_judge's default swap (don't set swap=False) for unbiased verdicts.

Going deeper

• website/docs/user-guide/evaluation/ — variants (the Variants mapping + axis), pairwise (comparators, win-rate, blinded labeling)
• ag2-evaluation — single-agent run/grade, scorers, CI, persistence