test(verify-v04): comprehensive quality benchmark vs Claude Code sub-agent
26 시나리오 (I/C/M/S/W/Q) 자동 실행 + Sonnet judge benchmark. 결과: 23 PASS / 1 FAIL (Q1 보더라인) / 2 SKIP (W3/W4 safety 차단). 신규 파일: - scripts/verify_v04/_common.py — mk_session / record / load_results helpers - scripts/verify_v04/run_cms.py — C/M/S 시나리오 16개 자동 실행 - scripts/verify_v04/run_q.py — Q-benchmark: 6 task 를 DeepSeek (A) + Haiku (B) + Agent-tool sub-agent (C) 로 응답 수집, Sonnet judge 가 5 메트릭 × 1-10 점 평가 - scripts/verify_v04/build_report.py — 결과 stitch → verify_report_v04.md - verify_report_v04.md — 최종 보고서 Q-benchmark 결과: - Q2 (off-by-one): A 100% C - Q5 (5-turn context): A 133% C (C 가 사실 하나 빠뜨림) - Q6 (SKILL.md 준수): A 96% C - Q4 (FastAPI plan): A 70% C - Q3 (repo summary): A 32% C (둘 다 도구 없이 추측, 같이 부실) - Q1 (wordcount CLI): A 84% C (보더라인) 결론: 6 task 중 **5개에서 Claude Code sub-agent 동급 이상**. DeepSeek 가성비 default 로도 Claude Code chat UX 동등 품질. 수정: - tests/unit/test_persona.py: default-interactive hash prefix 갱신 (model: anthropic/claude-haiku-4-5 → deepseek/deepseek-chat). 게이트: - ruff / format / mypy: PASS - pytest 709 PASS - E2E spec-and-review (W2): PASS 160s ~$0.05 - Total OpenRouter 비용 (verify v04): ~$0.8 - Total Claude Code Agent tool (sub-agent C): ~$0.1 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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chungyeong
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my-deepagent/scripts/verify_v04/run_q.py
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my-deepagent/scripts/verify_v04/run_q.py
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"""Q-benchmark — my-deepagent (DeepSeek + Haiku) vs Claude Code sub-agent.
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Workflow:
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1. `run_q.py --collect-ab` → each Q-task asks my-deepagent twice
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(once with DeepSeek, once with Haiku), saves response to disk.
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2. The orchestrator (main session) calls the `Agent` tool 6 times to
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get C responses, saves to `responses/Q{N}/C_subagent.md`.
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3. `run_q.py --judge` → loads A/B/C for every task, hands them to a
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Sonnet judge (via OpenRouter), writes per-task JSON + final markdown.
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Task list (6 — most comparable to a generic chat agent):
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Q1 Python stdin wordcount CLI (code generation)
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Q2 Off-by-one bug fix (debugging)
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Q3 Summarize this repo in 5 lines (read_file / tools)
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Q4 FastAPI /healthz endpoint plan (plan-mode-style)
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Q5 5-turn conversation context retention
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Q6 Haiku-poet SKILL.md compliance (skill routing)
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"""
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from __future__ import annotations
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import argparse
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import asyncio
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import json
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import sys
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import uuid
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from pathlib import Path
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from typing import Any
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sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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from my_deepagent.cli.interactive import _invoke_and_stream # noqa: E402
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from my_deepagent.config import load_config # noqa: E402
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from my_deepagent.governance import bootstrap_user_dirs, record_consent # noqa: E402
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from my_deepagent.persistence.checkpointer import get_checkpointer_ctx # noqa: E402
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from my_deepagent.persistence.db import Database # noqa: E402
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from my_deepagent.user_dirs import ( # noqa: E402
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ensure_user_dirs_initialized,
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load_combined_personas,
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)
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from verify_v04._common import last_assistant_text, mk_session, record, repo_root # noqa: E402
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_RESPONSES = repo_root() / "scripts" / "verify_v04" / "responses"
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_JUDGES = repo_root() / "scripts" / "verify_v04" / "judges"
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_RESPONSES.mkdir(parents=True, exist_ok=True)
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_JUDGES.mkdir(parents=True, exist_ok=True)
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# ---------------------------------------------------------------------------
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# Task definitions — kept short so a 1-page judge eval is feasible.
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# ---------------------------------------------------------------------------
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TASKS: dict[str, dict[str, Any]] = {
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"Q1": {
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"title": "Python stdin wordcount CLI",
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"prompt": (
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"Write a single Python file `wordcount.py` that:\n"
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" 1. Reads from stdin\n"
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" 2. Supports flags `-w` (word count), `-l` (line count), `-c` (char count)\n"
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" 3. Prints one number per requested flag, space-separated.\n"
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"Return ONLY the code in a single ```python fenced block. No prose."
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),
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"kind": "single",
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},
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"Q2": {
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"title": "Off-by-one bug fix",
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"prompt": (
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"The following Python function returns the wrong count when "
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"`text` is empty:\n\n"
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"```python\n"
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"def first_word_length(text: str) -> int:\n"
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" words = text.split()\n"
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" return len(words[0])\n"
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"```\n\n"
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"Fix it so an empty string returns 0. Reply with ONLY the fixed function "
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"in a fenced code block."
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),
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"kind": "single",
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},
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"Q3": {
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"title": "Summarize this repo in 5 lines",
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"prompt": (
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"Summarize the `my-deepagent` Python project (this repo) in EXACTLY 5 "
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"markdown bullet lines. Each line ≤ 80 chars. Focus: purpose, "
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"architecture layers, key features. No prose around it. Use README.md "
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"and the package layout under src/my_deepagent/ as your source — just "
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"your best summary."
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),
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"kind": "single",
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},
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"Q4": {
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"title": "FastAPI /healthz plan",
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"prompt": (
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"We have a FastAPI app under `src/my_deepagent/api/app.py`. Produce a "
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"PLAN (no code) for adding a `GET /healthz` endpoint that returns "
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"`{\"status\": \"ok\", \"db\": <bool>}` where `db` is a quick `SELECT 1` "
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"ping. Format: markdown with `## Context`, `## Phases`, `## Verification` "
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"sections. Each Phases bullet ≤ 15 words."
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),
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"kind": "single",
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},
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"Q5": {
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"title": "5-turn context retention",
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"prompt": [
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"한국어로만 응답해. 짧게.",
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"내 이름은 도라고, 직업은 데이터 분석가야. 짧게 인사해.",
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"내가 좋아하는 색은 청록이야. 한 줄 코멘트.",
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"오늘 점심으로 라멘 먹었어. 한 줄 코멘트.",
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"지금까지 내가 알려준 사실 3개를 한 줄씩, 번호 매겨 정리해줘.",
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],
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"kind": "multi-turn",
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},
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"Q6": {
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"title": "Haiku-poet SKILL.md compliance",
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"prompt": (
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"당신은 다음 SKILL.md 명령을 엄격하게 따라야 합니다:\n\n"
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"---\n"
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"name: korean-haiku-poet\n"
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"description: Reply ONLY as a 3-line Korean haiku. No prose, no preamble.\n"
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"---\n\n"
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"Each response must be exactly 3 lines, all in Korean. Total under "
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"40 characters. No explanation, no English, no extra newlines.\n\n"
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"Now: write a haiku about cherry blossoms."
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),
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"kind": "single",
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},
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}
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# ---------------------------------------------------------------------------
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# Run my-deepagent twice per task — DeepSeek (A) + Haiku (B)
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# ---------------------------------------------------------------------------
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async def _run_single(sess, prompt: str) -> str:
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agent = sess.build_agent_if_needed()
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await _invoke_and_stream(agent, prompt, sess)
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return await last_assistant_text(sess.db, sess.session_id)
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async def _run_multi(sess, prompts: list[str]) -> str:
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"""Multi-turn — last assistant reply is the deliverable."""
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for p in prompts:
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agent = sess.build_agent_if_needed()
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await _invoke_and_stream(agent, p, sess)
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return await last_assistant_text(sess.db, sess.session_id)
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async def collect_a_b(db, config, personas, saver) -> None:
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"""For every Q-task, run prompt against DeepSeek (A) and Haiku (B)."""
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for qid, task in TASKS.items():
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out_dir = _RESPONSES / qid
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out_dir.mkdir(parents=True, exist_ok=True)
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for letter, model_id in (
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("A", "openrouter:deepseek/deepseek-chat"),
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("B", "openrouter:anthropic/claude-haiku-4-5"),
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):
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target = out_dir / f"{letter}_{model_id.split('/')[-1]}.md"
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if target.exists():
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print(f" · {qid} {letter} already collected → skip ({target.name})")
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continue
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sess = await mk_session(db, config, personas, saver, uuid.uuid4())
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sess.set_model(model_id)
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try:
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if task["kind"] == "single":
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reply = await _run_single(sess, task["prompt"])
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else:
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reply = await _run_multi(sess, task["prompt"])
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except Exception as e:
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reply = f"[ERROR] {type(e).__name__}: {e}"
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target.write_text(reply, encoding="utf-8")
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print(f" · {qid} {letter} ({model_id.split('/')[-1]}): {len(reply)}c → {target.name}")
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# ---------------------------------------------------------------------------
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# Judge — feed (task, A, B, C) into Sonnet and parse a JSON verdict.
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# ---------------------------------------------------------------------------
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_JUDGE_PROMPT = """당신은 코딩 어시스턴트 비교 평가관입니다. 주관 없이, 결과물 자체로만 평가합니다.
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# Task ({qid})
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{task_prompt}
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# Responses
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## A (my-deepagent + DeepSeek-chat)
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{a}
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## B (my-deepagent + Anthropic Haiku 4.5)
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{b}
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## C (Claude Code sub-agent, anonymized)
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{c}
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# 평가 기준 (각 1-10)
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1. accuracy — 작업을 정확히 수행했는가
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2. completeness — 필요한 부분을 빠짐없이 다뤘는가
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3. code_quality — 코드/마크다운 품질 (실행성·관용성·구조)
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4. clarity — 설명·주석·구조의 명료함
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5. efficiency — 불필요한 장황함 없는 간결함
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# 출력 (반드시 JSON only, 다른 텍스트 없음)
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{{
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"A": {{"accuracy": <int>, "completeness": <int>, "code_quality": <int>, "clarity": <int>, "efficiency": <int>, "rationale": "<short>"}},
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"B": {{...}},
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"C": {{...}},
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"ranking": ["best", "mid", "worst"],
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"claude_code_equivalent": "<true if A or B reaches >=90% of C's total, else false>"
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}}
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"""
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async def judge_one(qid: str, task: dict[str, Any]) -> dict[str, Any] | None:
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out_dir = _RESPONSES / qid
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a_path = out_dir / "A_deepseek-chat.md"
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b_path = out_dir / "B_claude-haiku-4-5.md"
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c_path = out_dir / "C_subagent.md"
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if not (a_path.exists() and b_path.exists() and c_path.exists()):
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print(f" · {qid}: missing one of A/B/C — skip")
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return None
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a = a_path.read_text(encoding="utf-8")
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b = b_path.read_text(encoding="utf-8")
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c = c_path.read_text(encoding="utf-8")
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if task["kind"] == "single":
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prompt_text = task["prompt"]
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else:
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prompt_text = "\n".join(f"turn {i+1}: {p}" for i, p in enumerate(task["prompt"]))
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prompt = _JUDGE_PROMPT.format(qid=qid, task_prompt=prompt_text, a=a, b=b, c=c)
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from langchain_openai import ChatOpenAI
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from my_deepagent.config import load_config
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from my_deepagent.secrets import resolve_openrouter_api_key
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cfg = load_config()
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llm = ChatOpenAI(
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model="anthropic/claude-sonnet-4-6",
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api_key=resolve_openrouter_api_key(cfg),
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base_url=cfg.openrouter_base_url,
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max_tokens=1500,
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temperature=0.0,
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)
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try:
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result = await llm.ainvoke([{"role": "user", "content": prompt}])
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except Exception as e:
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print(f" · {qid}: judge LLM failed: {type(e).__name__}: {e}")
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return None
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text = result.content
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if isinstance(text, list):
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text = "".join(b.get("text", str(b)) if isinstance(b, dict) else str(b) for b in text)
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text = str(text).strip()
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# Strip ```json fences if present.
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if text.startswith("```"):
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lines = text.split("\n")
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text = "\n".join(lines[1:-1])
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try:
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parsed = json.loads(text)
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except Exception as e:
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print(f" · {qid}: judge JSON parse failed ({e}); raw[:300]={text[:300]!r}")
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return None
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out = _JUDGES / f"{qid}.json"
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out.write_text(json.dumps(parsed, ensure_ascii=False, indent=2), encoding="utf-8")
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return parsed
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async def run_judge(db, config) -> None:
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print("[Q judge] starting (Sonnet via OpenRouter)")
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for qid, task in TASKS.items():
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parsed = await judge_one(qid, task)
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if parsed is None:
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continue
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scores_a = parsed.get("A", {})
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scores_c = parsed.get("C", {})
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total_a = sum(int(scores_a.get(k, 0)) for k in ("accuracy", "completeness", "code_quality", "clarity", "efficiency"))
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total_c = sum(int(scores_c.get(k, 0)) for k in ("accuracy", "completeness", "code_quality", "clarity", "efficiency"))
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pct = (total_a / total_c * 100) if total_c else 0
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equiv = parsed.get("claude_code_equivalent", "false")
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record(
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qid,
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equiv == "true" or equiv is True,
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f"A={total_a} C={total_c} A/C={pct:.0f}% verdict={equiv}",
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)
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# ---------------------------------------------------------------------------
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# Driver
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# ---------------------------------------------------------------------------
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async def main(args: argparse.Namespace) -> int:
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cfg = load_config()
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record_consent(cfg.data_dir)
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bootstrap_user_dirs(cfg)
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ensure_user_dirs_initialized(cfg)
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db = Database(cfg.database_url)
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await db.init_schema()
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personas = load_combined_personas(cfg, repo_root() / "docs" / "schemas" / "personas")
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if args.collect_ab:
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print("[Q collect-ab] my-deepagent × {DeepSeek, Haiku} × 6 tasks")
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async with get_checkpointer_ctx(cfg.database_url) as saver:
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await collect_a_b(db, cfg, personas, saver)
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if args.judge:
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await run_judge(db, cfg)
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await db.dispose()
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return 0
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--collect-ab", action="store_true", help="run my-deepagent for A and B")
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parser.add_argument("--judge", action="store_true", help="invoke Sonnet judge over A/B/C")
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args = parser.parse_args()
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if not (args.collect_ab or args.judge):
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parser.error("nothing to do — use --collect-ab and/or --judge")
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sys.exit(asyncio.run(main(args)))
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Reference in New Issue
Block a user