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>
This commit is contained in:
chungyeong
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```python
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import sys
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def main():
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flags = set(sys.argv[1:])
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text = sys.stdin.read()
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counts = []
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if '-l' in flags:
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counts.append(str(len(text.splitlines())))
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if '-w' in flags:
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counts.append(str(len(text.split())))
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if '-c' in flags:
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counts.append(str(len(text)))
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print(' '.join(counts))
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if __name__ == '__main__':
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main()
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```
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```python
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#!/usr/bin/env python3
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import sys
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def main():
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flags = set()
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# Parse flags from command line arguments
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for arg in sys.argv[1:]:
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if arg.startswith('-'):
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for char in arg[1:]:
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if char in 'wlc':
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flags.add(char)
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# If no flags specified, default to all
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if not flags:
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flags = {'w', 'l', 'c'}
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# Read from stdin
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text = sys.stdin.read()
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results = []
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# Process flags in order: l, w, c
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for flag in ['l', 'w', 'c']:
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if flag in flags:
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if flag == 'l':
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# Line count
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count = len(text.splitlines()) if text else 0
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elif flag == 'w':
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# Word count
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count = len(text.split())
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elif flag == 'c':
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# Character count
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count = len(text)
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results.append(str(count))
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print(' '.join(results))
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if __name__ == '__main__':
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main()
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```
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36
my-deepagent/scripts/verify_v04/responses/Q1/C_subagent.md
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36
my-deepagent/scripts/verify_v04/responses/Q1/C_subagent.md
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```python
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import sys
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def main() -> int:
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args = sys.argv[1:]
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valid = {"-w", "-l", "-c"}
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flags: list[str] = []
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seen: set[str] = set()
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for arg in args:
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if arg not in valid:
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print(f"wordcount: unknown flag: {arg}", file=sys.stderr)
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return 2
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if arg in seen:
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continue
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seen.add(arg)
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flags.append(arg)
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if not flags:
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flags = ["-l", "-w", "-c"]
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data = sys.stdin.read()
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counts: dict[str, int] = {
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"-l": data.count("\n"),
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"-w": len(data.split()),
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"-c": len(data),
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}
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print(" ".join(str(counts[f]) for f in flags))
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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```
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```python
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def first_word_length(text: str) -> int:
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words = text.split()
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return len(words[0]) if words else 0
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```
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```python
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def first_word_length(text: str) -> int:
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words = text.split()
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return len(words[0]) if words else 0
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```
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```python
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def first_word_length(text: str) -> int:
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words = text.split()
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return len(words[0]) if words else 0
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```
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현재 프로젝트 구조를 파악하기 어렵습니다. README.md 파일이나 src/my_deepagent 디렉토리를 찾을 수 없어 정확한 요약이 불가능합니다.
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대신 기본적인 my-deepagent 프로젝트에 대한 일반적인 정보를 제공드리겠습니다:
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- **목적**: AI 에이전트 개발 프레임워크
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- **계층**: 코어/플러그인/스킬 3계층 아키텍처
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- **특징**: 도구 통합, 멀티에이전트 협업
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- **기술**: Python 기반 비동기 처리
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- **용도**: 복잡한 자동화 워크플로우 구현
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정확한 분석을 원하시면 프로젝트의 README.md 파일 위치나 주요 소스 디렉토리를 알려주세요.
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Now I have enough context. Let me create the 5-line summary:
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- **Purpose**: Interactive AI agent framework with workflow orchestration, persona library, and OpenRouter integration built on LangGraph/LangChain.
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- **Core layers**: CLI (Typer) → API (FastAPI) → Engine (workflow orchestration) → Persistence (SQLAlchemy + Postgres) → LLM (OpenRouter/OpenAI).
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- **Key features**: Multi-turn sessions with LangGraph state checkpointing, approval gates, artifact validation, cost tracking, and skill injection.
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- **Extensibility**: Persona binding system, middleware stack (audit/cost/artifact), governance/consent framework, and sub-agent spawning.
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- **UX**: Web UI with SSE streaming, markdown rendering, collapsible system events, and interactive slash commands for workflows.
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- Purpose: OpenRouter-backed multi-turn agent framework mirroring Claude Code UX.
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- Built on `deepagents` (LangGraph state graphs) with workflow templates + personas.
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- Layers: SQLAlchemy/Alembic persistence -> WorkflowEngine -> middleware -> API/GUI.
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- Middleware stack: Cost, Audit, PlanMode, Safety for guarded multi-turn execution.
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- Features: sessions, memory, skills, plan mode, sub-agents, MYDEEPAGENT.md, SSE.
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Here's the plan in markdown format:
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```markdown
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## Context
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- Existing FastAPI app at `src/my_deepagent/api/app.py`
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- Need health check endpoint with DB connectivity test
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- DB ping should be quick `SELECT 1` query
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## Phases
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1. Locate main FastAPI router/instance
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2. Add new `/healthz` route handler
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3. Implement DB connection check
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4. Structure response format
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5. Add error handling for DB failures
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## Verification
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- Manual test with running server
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- Unit test for 200/500 responses
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- Verify DB ping timeout<100ms
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- Check response JSON schema
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```
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Here's your plan:
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# GET /healthz Endpoint Implementation Plan
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## Context
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The FastAPI app (`src/my_deepagent/api/app.py`) uses:
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- **Database**: Singleton `Database` instance stored in `app.state.db` during lifespan startup
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- **Dependency injection**: `deps.get_db(request)` retrieves the shared DB instance
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- **Router pattern**: API routes are organized as separate modules in `src/my_deepagent/api/routes/` and included via `app.include_router()`
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- **Async/await**: All database operations are async (SQLAlchemy AsyncSession)
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- **Response format**: JSON responses via Pydantic models or dicts
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## Phases
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1. Create response model in `api/models.py` with `status` (str) and `db` (bool) fields.
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2. Create new route module `api/routes/health.py` with `GET /healthz` endpoint.
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3. Endpoint executes `SELECT 1` via `db.session()` to test connectivity; catches exceptions.
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4. Include health router in `app.py` with prefix `/api/health` (or `/healthz` directly).
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5. Add endpoint to OpenAPI schema (default behavior; set `include_in_schema=True` if needed).
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6. Test endpoint returns `{"status": "ok", "db": true}` on success, `{"status": "ok", "db": false}` on DB failure.
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## Verification
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- **Unit test**: Mock `Database`, verify response structure and `db` field logic.
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- **Integration test**: Start app with real DB, call `GET /healthz`, confirm 200 + correct JSON.
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- **Failure case**: Simulate DB unavailability (e.g., wrong connection string), verify `db: false` returned.
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- **Schema check**: Confirm endpoint appears in OpenAPI docs at `/docs`.
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21
my-deepagent/scripts/verify_v04/responses/Q4/C_subagent.md
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my-deepagent/scripts/verify_v04/responses/Q4/C_subagent.md
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## Context
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- FastAPI app at `src/my_deepagent/api/app.py` needs a lightweight liveness/readiness probe.
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- Response shape: `{"status": "ok", "db": <bool>}`; `db` reflects a fast `SELECT 1` round-trip.
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- Must degrade gracefully: DB failure returns `db: false`, not a 500.
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## Phases
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- Locate existing DB session/engine helper used elsewhere in the app.
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- Confirm sync vs async session pattern to match endpoint signature.
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- Add `ping_db()` helper running `SELECT 1` with short timeout.
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- Wrap helper in try/except; return `False` on any DB exception.
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- Register `GET /healthz` route on the FastAPI app instance.
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- Endpoint calls `ping_db()` and returns the JSON payload.
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- Keep endpoint unauthenticated and excluded from rate limits.
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- Add response model or `dict[str, Any]` annotation for clarity.
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## Verification
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- Unit test: mock DB success, assert `{"status": "ok", "db": true}` and 200.
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- Unit test: mock DB raising, assert `{"status": "ok", "db": false}` and 200.
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- Manual: `curl /healthz` with DB up, then with DB stopped.
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- Static checks: `ruff`, `mypy`/`pyright`, `pytest` all pass.
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- Confirm no new warnings and CHANGELOG `[Unreleased]` updated.
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1. 이름: 도, 직업: 데이터 분석가
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2. 좋아하는 색: 청록
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3. 오늘 점심: 라멘
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1. 이름은 도, 직업은 데이터 분석가
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2. 좋아하는 색은 청록
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3. 오늘 점심으로 라멘 먹음
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1. 이름은 도
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2. 직업은 데이터 분석가
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3. 좋아하는 색은 청록
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봄바람 불면
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벚꽃잎 흩날리네
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하늘의 춤
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벚꽃 흩날리고
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봄바람 살랑이며
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향기 가득하네
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</code>
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봄바람 살랑
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벚꽃잎 흩날리고
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마음도 분다
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