Files
dev-puppeteer/my-deepagent/tests/unit/test_logging.py
chungyeong 733c9be0bd feat(my-deepagent): v0.1.0 Step 6~15 — REPL/Budget/Recovery/Audit/Pricing + real OpenRouter E2E
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>
2026-05-16 16:32:46 +09:00

122 lines
3.6 KiB
Python

"""Unit tests for src/my_deepagent/logging.py — secret scrubbing."""
from __future__ import annotations
from typing import Any
from my_deepagent.logging import _scrub_processor, scrub, scrub_value
_REDACTED = "[REDACTED]"
# ---------------------------------------------------------------------------
# scrub — individual patterns
# ---------------------------------------------------------------------------
def test_scrub_openrouter_key() -> None:
secret = "sk-or-v1-abc1234567890123456789xyz"
assert scrub(secret) == _REDACTED
def test_scrub_anthropic_key() -> None:
secret = "sk-ant-api03-abcdef1234567890abcdef1234567890xyz"
assert scrub(secret) == _REDACTED
def test_scrub_openai_project_key() -> None:
secret = "sk-proj-abcdefghijklmnopqrstuvwxyz12345"
assert scrub(secret) == _REDACTED
def test_scrub_openai_general_key() -> None:
# must be 30+ chars after "sk-"
secret = "sk-abcdefghijklmnopqrstuvwxyz1234567890"
assert scrub(secret) == _REDACTED
def test_scrub_github_pat() -> None:
secret = "ghp_abcdefghijklmnopqrstuvwxyz1234567890"
assert scrub(secret) == _REDACTED
def test_scrub_bearer_token() -> None:
text = "Bearer eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9.payload"
result = scrub(text)
assert _REDACTED in result
def test_scrub_plain_text_unchanged() -> None:
text = "normal log message with no secrets here"
assert scrub(text) == text
def test_scrub_partial_match_in_larger_string() -> None:
text = f"calling API with key=sk-ant-api03-{'x' * 30}"
result = scrub(text)
assert _REDACTED in result
assert "calling API with key=" in result
# ---------------------------------------------------------------------------
# scrub_value — recursive
# ---------------------------------------------------------------------------
def test_scrub_value_dict_scrubs_string_values() -> None:
secret = f"sk-or-v1-{'a' * 25}"
data: dict[str, Any] = {"key": secret, "n": 42}
result = scrub_value(data)
assert result["key"] == _REDACTED
assert result["n"] == 42
def test_scrub_value_list_scrubs_all_strings() -> None:
secret_ant = f"sk-ant-api03-{'b' * 30}"
secret_ghp = f"ghp_{'c' * 35}"
data: list[Any] = [1, secret_ant, {"k": secret_ghp}]
result = scrub_value(data)
assert result[0] == 1
assert result[1] == _REDACTED
assert result[2]["k"] == _REDACTED
def test_scrub_value_non_string_passes_through() -> None:
assert scrub_value(42) == 42
assert scrub_value(3.14) == 3.14
assert scrub_value(None) is None
assert scrub_value(True) is True
def test_scrub_value_tuple_scrubs_strings() -> None:
secret = f"sk-or-v1-{'d' * 22}"
result = scrub_value((secret, "safe"))
assert isinstance(result, tuple)
assert result[0] == _REDACTED
assert result[1] == "safe"
# ---------------------------------------------------------------------------
# _scrub_processor
# ---------------------------------------------------------------------------
def test_scrub_processor_scrubs_event_dict_values() -> None:
secret = f"sk-ant-api03-{'e' * 30}"
event_dict: dict[str, Any] = {
"event": "calling model",
"api_key": secret,
"model": "claude-3",
}
result = _scrub_processor(None, "info", event_dict)
assert result["api_key"] == _REDACTED
assert result["event"] == "calling model"
assert result["model"] == "claude-3"
def test_scrub_processor_returns_dict() -> None:
event_dict: dict[str, Any] = {"event": "no secrets here", "count": 5}
result = _scrub_processor(None, "debug", event_dict)
assert isinstance(result, dict)
assert result["count"] == 5