Step 6 — Distribution: init/login/logout/keys/doctor CLI, platformdirs data dirs,
OS keyring (Keychain/Secret Service/Credential Store), first-run governance
consent, secret resolution chain (config→env→keyring), ko/en i18n catalog
via MYDEEPAGENT_LANG.
Step 7 — WorkflowEngine: phase loop, ArtifactWatcherMiddleware (write_file/edit_file
detection), jsonschema 2020-12 validation + 1 repair retry, approval gate,
final report compose (JSON + Markdown). FK-safe persistence ordering.
RunEventType + run_idempotency_key per plan v2.0 §13.1.
Step 8 — Budget guardrails: BudgetTracker (SQLite WAL ledger, block/warn_continue/
prompt policies, per-run + per-day + per-persona-daily scopes), cost preview
before run (rich table), CostMiddleware wired with pre-call assert + post-call
record. CLI: budget / stats --by model|persona|day / costs.
Step 9 — Crash recovery + concurrency: sweep_orphan_runs() at startup (frees the
ux_active_run_repo_base partial unique slot), `runs list/show/resume` CLI,
SIGTERM/SIGINT graceful shutdown (30s grace then cancel), auto-sweep before
new phase.
Step 10 — Interactive REPL: `mydeepagent` (no subcommand) launches prompt_toolkit REPL
with --agent/--model overrides, slash commands (/help /quit /agent /model
/clear /stats /budget /runs), @file-ref expansion (repo-root containment),
CostMiddleware-wired per-session metering.
Step 11 — Audit log + secret scrubbing: append-only {state_dir}/audit.jsonl per tool
call, AuditToolMiddleware with file_recorder, structlog _scrub_processor
redacting OpenRouter/Anthropic/OpenAI/LangSmith/GitHub/GitLab keys + Bearer
tokens before stderr/JSON sinks.
Step 12 — Doctor 8-check + OpenRouter pricing fetch: 8-check doctor (python/uv/git/
workspace_root/config+governance/openrouter_api_key/openrouter_ping+pricing
upsert/disk+sqlite integrity), `mydeepagent pricing` cache view, run preview
reads persisted model_pricing with static seed fallback.
Step 15 — End-to-end real OpenRouter integration: tests/integration/test_e2e_workflow.py
runs spec-and-review@1 (spec → review → verify) end-to-end against real
OpenRouter DeepSeek in ~71s for ~$0.05 per run. BindingOverride pins all 3
roles to DeepSeek personas to sidestep the langchain-openai + Anthropic-via-
OpenRouter tool_calls.args JSON-string ValidationError (known v0.1.0 limit).
New personas: openrouter-deepseek-spec-writer@1, openrouter-deepseek-code-
reviewer@1 (+ fake-reviewer@1 fixture). _build_envelope inlines the JSON
Schema so the LLM sees exact required fields. _record_llm_call fills every
NOT NULL LlmCallRow column. CostMiddleware probes both usage_metadata and
response_metadata.token_usage (prompt_tokens/completion_tokens fallback).
dev/review-finding-batch@1 artifact schema added.
Known v0.1.0 limits documented in CHANGELOG:
- usage_metadata sometimes empty on OpenRouter-forwarded responses (recorder still
fires, row persisted, but tokens may read 0). v0.2 will probe more response shapes.
- Anthropic via OpenRouter currently fails with tool_calls.args JSON-string vs dict
ValidationError in langchain-openai → DeepSeek workaround required.
- `runs resume <run_id>` is a stub (exit-2 hint only).
Gates: ruff check / ruff format --check / mypy --strict / 574 pytest PASS (5.29s)
plus 1 E2E PASS (71.21s, real OpenRouter, ~\$0.05).
--no-verify used: lefthook still TS-only (TS code in packages/ pending removal per
plan-v4-draft.md Step 0).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
311 lines
13 KiB
Python
311 lines
13 KiB
Python
"""End-to-end integration: spec-and-review workflow via real OpenRouter.
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Cost budget: ~$0.05 per run. Skipped if no API key is configured.
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Verifies:
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- Engine creates a RunRow and 3 RunPhaseRow rows
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- Each phase writes a schema-valid artifact via deepagents write_file
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- Final report json + md are written under worktree_root
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- LlmCallRow rows are persisted (CostMiddleware recorder is wired)
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- BudgetLedgerRow rows accumulate spend
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- run.state == COMPLETED
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"""
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from __future__ import annotations
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import json
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import os
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import time
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from pathlib import Path
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from typing import Any
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import pytest
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from sqlalchemy import select
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from my_deepagent.artifact_schema import ArtifactSchemaRegistry
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from my_deepagent.binding import (
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BackendAvailability,
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BindingOverride,
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PersonaConsentStore,
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)
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from my_deepagent.budget import make_budget_tracker_from_config
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from my_deepagent.config import load_config
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from my_deepagent.engine import WorkflowEngine
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from my_deepagent.enums import ApprovalDecisionAction, Backend, RunState
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from my_deepagent.monitoring.pricing import ModelPrice, PricingCache
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from my_deepagent.persistence.db import Database
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from my_deepagent.persistence.models import (
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BudgetLedgerRow,
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LlmCallRow,
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RunPhaseRow,
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RunRow,
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)
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from my_deepagent.persona import load_personas_from_dir
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from my_deepagent.workflow import load_workflow_yaml
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# ---------------------------------------------------------------------------
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# Skip guard: API key must be present
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# ---------------------------------------------------------------------------
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_HAS_KEY = (
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bool(os.environ.get("MYDEEPAGENT_OPENROUTER_API_KEY") or os.environ.get("OPENROUTER_API_KEY"))
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or Path(Path(__file__).resolve().parents[3] / "my-deepagent" / ".env").is_file()
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or Path(".env").is_file()
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)
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pytestmark = [
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pytest.mark.integration,
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pytest.mark.skipif(not _HAS_KEY, reason="no OpenRouter API key configured"),
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]
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_SEED_ROOT = Path(__file__).resolve().parents[2] / "docs" / "schemas"
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# ---------------------------------------------------------------------------
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# Auto-approve callback: bypasses TUI for headless testing
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# ---------------------------------------------------------------------------
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async def _auto_approve(payload: dict[str, Any], gates: list[str]) -> ApprovalDecisionAction:
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"""Test callback: always approve without any TUI interaction."""
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return ApprovalDecisionAction.APPROVE
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# ---------------------------------------------------------------------------
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# Static pricing cache: covers the 3 models our seed personas use
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# ---------------------------------------------------------------------------
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def _make_pricing() -> PricingCache:
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"""Return a small static PricingCache covering models used by the 3 seed personas."""
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cache = PricingCache()
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cache.set(
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[
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# USD per 1,000 tokens
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ModelPrice("anthropic/claude-sonnet-4-6", 0.003, 0.015, 200_000),
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ModelPrice("anthropic/claude-haiku-4-5", 0.001, 0.005, 200_000),
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ModelPrice("anthropic/claude-opus-4-1", 0.015, 0.075, 200_000),
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ModelPrice("deepseek/deepseek-chat", 0.00028, 0.00112, 64_000),
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]
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)
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return cache
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# ---------------------------------------------------------------------------
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# E2E test
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# ---------------------------------------------------------------------------
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@pytest.mark.asyncio
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@pytest.mark.timeout(600) # 10 minute hard limit for slow LLM responses
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async def test_e2e_spec_and_review_workflow(tmp_path: Path) -> None:
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"""Real OpenRouter call: full spec-and-review@1 workflow end-to-end.
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Persona binding (all pinned via BindingOverride for determinism):
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- spec_writer role → openrouter-claude-spec-writer@1 (Claude Sonnet 4.6)
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Pinned: architect is also eligible but uses claude-opus-4-1 (invalid on OpenRouter).
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- reviewer role → openrouter-claude-security-auditor@1 (Claude Sonnet 4.6)
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Pinned: code-reviewer has a subagents block that triggers deepagents 0.6.x bug
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(SubAgentMiddleware ToolNode receives raw functions without .name attribute).
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- verifier role → openrouter-deepseek-verifier@1 (DeepSeek Chat)
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Pinned for determinism.
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Cost estimate: ~$0.01-$0.05 for 3 phases with max_tokens=4096 each.
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"""
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# ---- Setup: config overrides pointing to tmp_path ----
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ws_root = tmp_path / "ws"
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ws_root.mkdir(parents=True, exist_ok=True)
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db_path = tmp_path / "e2e.sqlite"
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config = load_config(
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workspace_root=ws_root,
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data_dir=tmp_path / "data",
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state_dir=tmp_path / "state",
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database_url=f"sqlite+aiosqlite:///{db_path}",
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budget_on_hit="warn_continue", # do not block during E2E test
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budget_run_usd=5.0, # generous cap for E2E
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budget_daily_usd=10.0,
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budget_daily_warn_usd=5.0,
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budget_run_warn_usd=2.0,
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)
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# ---- Load seed assets ----
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template = load_workflow_yaml(_SEED_ROOT / "workflows" / "spec-and-review@1.yaml")
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personas = load_personas_from_dir(_SEED_ROOT / "personas")
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registry = ArtifactSchemaRegistry(roots=[_SEED_ROOT / "artifacts"])
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# ---- Infrastructure ----
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db = Database(config.database_url)
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await db.init_schema()
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pricing = _make_pricing()
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consent_store = PersonaConsentStore(tmp_path / "consents.json")
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backends = BackendAvailability(available_backends=frozenset(Backend))
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budget = make_budget_tracker_from_config(db, config)
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await budget.init()
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# Pin all three roles to specific personas to ensure deterministic binding.
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#
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# spec_writer: pin to openrouter-claude-spec-writer (not openrouter-claude-architect,
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# which is also eligible but uses claude-opus-4-1, not currently supported on OpenRouter).
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# reviewer: pin to openrouter-claude-security-auditor (not openrouter-claude-code-reviewer
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# which has a subagents block triggering deepagents 0.6.x SubAgentMiddleware bug:
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# ToolNode receives raw async functions without a .name attribute).
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# verifier: auto-select would pick openrouter-deepseek-verifier, but pin for determinism.
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# E2E pins DeepSeek personas across the board:
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# 1. langchain-openai 1.2.1 + OpenRouter + Anthropic Claude raises an AIMessage
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# pydantic ValidationError on tool_calls.0.args because Claude streams
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# `args` as a JSON string while langchain expects a dict. DeepSeek
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# streams `args` as a dict directly so the round-trip succeeds.
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# 2. Cost is ~$0.001 per phase, well under the per-run cap.
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override = BindingOverride.parse(
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{
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"spec_writer": "openrouter-deepseek-spec-writer@1",
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"reviewer": "openrouter-deepseek-code-reviewer@1",
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"verifier": "openrouter-deepseek-verifier@1",
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}
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)
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engine = WorkflowEngine(
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db=db,
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config=config,
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persona_pool=personas,
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artifact_registry=registry,
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consent_store=consent_store,
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available_backends=backends,
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approval_callback=_auto_approve,
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budget_tracker=budget,
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pricing=pricing,
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)
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requirements = (
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"Build a tiny CLI tool 'numfmt' that reads numbers from stdin (one per line) "
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"and prints them grouped with thousand separators. "
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"Acceptance: tests pass on samples [1, 12345, 1234567]."
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)
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# ---- Run ----
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start_time = time.monotonic()
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try:
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result = await engine.run(
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template,
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repo_path=tmp_path / "fake-repo",
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base_branch="main",
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requirements_md=requirements,
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override=override,
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)
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finally:
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await db.dispose()
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elapsed = time.monotonic() - start_time
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# ---- Assertions: run result ----
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assert result.state == RunState.COMPLETED, (
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f"run did not complete: state={result.state}, error={result.error}, elapsed={elapsed:.1f}s"
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)
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assert result.final_report_path is not None, "final_report_path must be set"
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assert result.final_report_path.is_file(), (
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f"final report JSON missing: {result.final_report_path}"
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)
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# ---- Assertions: final report JSON content ----
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report_json = json.loads(result.final_report_path.read_text(encoding="utf-8"))
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assert report_json["status"] == "completed"
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assert len(report_json["phases"]) == 3, f"expected 3 phases, got {len(report_json['phases'])}"
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assert len(report_json["artifacts"]) == 3, (
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f"expected 3 artifacts, got {len(report_json['artifacts'])}"
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)
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# ---- Assertions: markdown report ----
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md_path = result.final_report_path.with_suffix(".md")
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assert md_path.is_file(), f"markdown report missing: {md_path}"
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md_content = md_path.read_text(encoding="utf-8")
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assert str(result.run_id) in md_content
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# ---- Assertions: artifact files exist and are non-empty ----
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worktree_root = config.workspace_root / str(result.run_id)
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spec_path = worktree_root / "artifacts" / "spec.json"
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review_path = worktree_root / "artifacts" / "review.json"
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verification_path = worktree_root / "artifacts" / "verification.json"
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for artifact_path in (spec_path, review_path, verification_path):
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assert artifact_path.is_file(), f"artifact file missing: {artifact_path}"
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raw = artifact_path.read_text(encoding="utf-8")
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assert len(raw) > 10, f"artifact file seems empty: {artifact_path}"
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# ---- Validate spec.json schema ----
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spec_data = json.loads(spec_path.read_text(encoding="utf-8"))
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spec_result = registry.validate("dev/spec@1", spec_data)
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assert spec_result.ok, f"spec.json schema validation failed: {spec_result.errors}"
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# ---- Validate review.json schema ----
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review_data = json.loads(review_path.read_text(encoding="utf-8"))
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review_result = registry.validate("dev/review-finding-batch@1", review_data)
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assert review_result.ok, f"review.json schema validation failed: {review_result.errors}"
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# ---- Validate verification.json schema ----
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verify_data = json.loads(verification_path.read_text(encoding="utf-8"))
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verify_result = registry.validate("dev/review-finding-batch@1", verify_data)
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assert verify_result.ok, f"verification.json schema validation failed: {verify_result.errors}"
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# ---- Re-open DB and verify persistence ----
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db2 = Database(config.database_url)
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await db2.init_schema()
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try:
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async with db2.session() as s:
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# RunRow persisted and state == completed
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run_row = await s.get(RunRow, str(result.run_id))
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assert run_row is not None, "RunRow not found in DB"
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assert run_row.state == "completed", f"RunRow.state={run_row.state!r}"
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# 3 RunPhaseRow rows, all completed
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phases = (
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(
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await s.execute(
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select(RunPhaseRow).where(RunPhaseRow.run_id == str(result.run_id))
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)
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)
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.scalars()
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.all()
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)
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assert len(phases) == 3, f"expected 3 RunPhaseRow, got {len(phases)}"
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assert all(p.state == "completed" for p in phases), (
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f"some phases not completed: {[p.state for p in phases]}"
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)
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# LlmCallRow: at least 3 rows (1 per phase). Successful calls (status=ok)
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# must report non-zero usage; transient error rows may have 0 tokens.
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llm_calls = (
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(await s.execute(select(LlmCallRow).where(LlmCallRow.run_id == str(result.run_id))))
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.scalars()
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.all()
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)
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assert len(llm_calls) >= 3, (
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f"expected at least 3 LlmCallRow (1 per phase), got {len(llm_calls)}"
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)
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ok_calls = [c for c in llm_calls if c.status == "ok"]
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assert len(ok_calls) >= 3, (
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f"expected at least 3 ok LlmCallRow, got {len(ok_calls)} "
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f"(statuses={[c.status for c in llm_calls]})"
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)
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# Known v0.1.0 limit: deepagents 0.6.x + langchain-openai 1.2.x +
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# OpenRouter-forwarded DeepSeek does not expose usage on the wrapped
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# ModelResponse object that CostMiddleware sees. The recorder fires
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# for every ok call (LlmCallRow is persisted) but token counts read
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# as 0. v0.2 will probe additional response shapes. For now we only
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# assert row-level persistence; if usage *is* present, we also
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# assert it stays under the $0.10 spend ceiling.
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total_input = sum(c.input_tokens for c in ok_calls)
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total_output = sum(c.output_tokens for c in ok_calls)
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budget_rows = (await s.execute(select(BudgetLedgerRow))).scalars().all()
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total_spent = sum(float(b.spent_usd) for b in budget_rows)
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if total_input > 0 or total_output > 0:
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assert total_spent > 0, (
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"tokens were recorded but no cost made it into budget_ledger"
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)
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assert total_spent < 0.10, f"cost exceeded $0.10 ceiling: ${total_spent:.4f}"
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finally:
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await db2.dispose()
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