headroom/tests/test_cost_tracker_counterfactual.py
JD Davis 55efb1c77d
fix(proxy): keep OpenAI tool observations mutable in cache mode (#1884)
## Description

Diagnoses and fixes the low-savings OpenAI-compatible cache-mode path
reported in #1696.

OpenAI-compatible tool-calling clients can end a turn with `role:
"tool"` (or legacy `role: "function"`) rather than `role: "user"`. The
OpenAI chat handler's cache-mode freeze boundary treated those tails as
non-mutable, and because `HeadroomProxy` resolves
`_strict_previous_turn_frozen_count` from the Anthropic mixin first, the
OpenAI-specific helper was not used in production. That froze the entire
conversation before `ContentRouter` ran, leaving no live tool
observation to compress and producing near-pass-through savings on long
coding sessions.

This PR keeps final OpenAI tool/function observations mutable in cache
mode, explicitly calls the OpenAI helper to avoid the mixin-name
collision, and clamps negative token-savings artifacts at the
metrics/cost aggregation boundary so stats cannot under-report actual
forwarded savings.

Closes #1696

## Type of Change

- [x] Bug fix (non-breaking change that fixes an issue)
- [ ] New feature (non-breaking change that adds functionality)
- [ ] Breaking change (fix or feature that would cause existing
functionality to change)
- [ ] Documentation update
- [ ] Performance improvement
- [ ] Code refactoring (no functional changes)

## Changes Made

- Treat final OpenAI `user`, `tool`, and `function` messages as the
mutable cache-mode live zone.
- Route OpenAI cache-boundary calls through
`OpenAIHandlerMixin._strict_previous_turn_frozen_count` explicitly so
the Anthropic mixin method cannot shadow it in `HeadroomProxy`'s MRO.
- Preserve cache-mode live-tail boundaries even when compression-cache
state would otherwise freeze the whole request.
- Clamp negative `tokens_saved` artifacts in `CostTracker.record_tokens`
and `PrometheusMetrics.record_request`.
- Add regression coverage for OpenAI final `tool`/`function` tails,
over-frozen tracker state, and non-negative savings aggregation.

## Testing

- [ ] Unit tests pass (`pytest`)
- [x] Linting passes (`ruff check .`)
- [x] Type checking passes (`mypy headroom`)
- [x] New tests added for new functionality
- [x] Manual testing performed

### Test Output

```text
$ maturin build --profile ci --out dist --interpreter python
Built wheel for abi3 Python >= 3.10 to dist\headroom_ai-0.29.0-cp310-abi3-win_amd64.whl

$ python -m pytest tests\test_proxy_handler_helpers.py tests\test_proxy_openai_cache_stability.py tests\test_observability_metrics.py tests\test_cost_tracker_counterfactual.py
49 passed in 10.27s

$ python -m ruff check .
All checks passed!

$ python -m mypy headroom
Success: no issues found in 407 source files

$ python -m pytest
53 failed, 7703 passed, 488 skipped, 5893 warnings, 131 errors in 595.18s (0:09:55)
```

Full-suite note: the full local `pytest` run was attempted on
Windows/Python 3.13 after building `headroom._core`. It did not complete
green due to broad pre-existing/local-environment failures outside this
change area, dominated by SQLite/memory persistence permission/path
errors plus unrelated adapter/cache/tool tests. The focused regression
suite for this PR passes, and repo-level lint/type gates pass.

## Real Behavior Proof

- Environment: Windows, Python 3.13.13, Rust/Cargo available, local
`headroom._core` wheel built with `maturin build --profile ci`.
- Exact command / steps: ran the OpenAI cache-stability tests with final
`role: "tool"` and `role: "function"` chat tails.
- Observed result:
`test_openai_cache_mode_keeps_final_tool_observation_mutable[tool]` and
`[function]` pass, proving the pipeline receives `frozen_message_count
== 2` for a 3-message request instead of freezing all 3 messages.
- Not tested: live Lemonade/KiloCode upstream session; no local Lemonade
Server was available.

## Review Readiness

- [x] I have performed a self-review
- [x] This PR is ready for human review

## Checklist

- [x] My code follows the project's style guidelines
- [x] I have performed a self-review of my code
- [x] I have commented my code, particularly in hard-to-understand areas
- [ ] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [x] I have added tests that prove my fix is effective or that my
feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] I have updated the CHANGELOG.md if applicable

## Screenshots (if applicable)

N/A

## Additional Notes

Docs and CHANGELOG are N/A for this narrow proxy bug fix. The broad
local `pytest` checkbox is intentionally left unchecked because the full
suite had unrelated local-environment failures; see the test output
above. Focused regression tests, `ruff check .`, and `mypy headroom` are
green.
2026-07-09 07:51:01 -07:00

167 lines
5 KiB
Python

"""Tests for CostTracker savings calculation.
Savings are computed at model list price: saved_tokens * input_cost_per_token.
This is simple, monotonic, and transparent.
"""
from __future__ import annotations
from tests._dotenv import (
autouse_apply_env,
importorskip_no_env_leak,
load_env_overrides,
)
_env_overrides = load_env_overrides()
apply_dotenv = autouse_apply_env(_env_overrides)
importorskip_no_env_leak("litellm")
def test_savings_at_list_price():
"""savings_usd = tokens_saved * model list input price."""
from headroom.proxy.server import CostTracker
ct = CostTracker()
model = "claude-sonnet-4-20250514"
ct.record_tokens(
model,
tokens_saved=100_000,
tokens_sent=50_000,
cache_read_tokens=900_000,
cache_write_tokens=0,
uncached_tokens=50_000,
)
stats = ct.stats()
# Savings should be 100k tokens * list input price (NOT affected by cache mix)
import litellm
from headroom.pricing.litellm_pricing import resolve_litellm_model
resolved = resolve_litellm_model(model)
info = litellm.model_cost.get(resolved, {})
list_price = info.get("input_cost_per_token", 0)
expected = 100_000 * list_price
assert stats["total_tokens_saved"] == 100_000
assert abs(stats["savings_usd"] - expected) < 0.001
def test_savings_monotonic():
"""Adding more saved tokens always increases savings_usd."""
from headroom.proxy.server import CostTracker
ct = CostTracker()
model = "claude-sonnet-4-20250514"
ct.record_tokens(model, tokens_saved=10_000, tokens_sent=5_000)
stats1 = ct.stats()
ct.record_tokens(model, tokens_saved=10_000, tokens_sent=5_000)
stats2 = ct.stats()
assert stats2["savings_usd"] >= stats1["savings_usd"]
assert stats2["total_tokens_saved"] == 20_000
def test_savings_zero_when_no_tokens_saved():
"""No tokens saved → savings_usd is 0."""
from headroom.proxy.server import CostTracker
ct = CostTracker()
model = "claude-sonnet-4-20250514"
ct.record_tokens(model, tokens_saved=0, tokens_sent=5_000)
stats = ct.stats()
assert stats["savings_usd"] == 0
assert stats["total_tokens_saved"] == 0
def test_negative_token_savings_are_clamped_to_zero():
"""Estimator artifacts must not reduce cumulative savings below reality."""
from headroom.proxy.server import CostTracker
ct = CostTracker()
ct.record_tokens("openai-compatible", tokens_saved=-500, tokens_sent=5_000)
stats = ct.stats()
assert stats["total_tokens_saved"] == 0
assert stats["per_model"]["openai-compatible"]["tokens_saved"] == 0
def test_multi_model_savings():
"""Savings across multiple models use each model's own list price."""
from headroom.proxy.server import CostTracker
ct = CostTracker()
ct.record_tokens("claude-sonnet-4-20250514", tokens_saved=50_000, tokens_sent=10_000)
ct.record_tokens("claude-haiku-4-5-20251001", tokens_saved=50_000, tokens_sent=10_000)
stats = ct.stats()
# Haiku is cheaper than Sonnet, so same tokens saved → different $
assert stats["total_tokens_saved"] == 100_000
assert stats["savings_usd"] > 0
# Verify per-model breakdown exists
assert len(stats["per_model"]) == 2
def test_no_cost_without_headroom_field():
"""cost_without_headroom_usd should NOT be in stats (removed to avoid confusion)."""
from headroom.proxy.server import CostTracker
ct = CostTracker()
ct.record_tokens("claude-sonnet-4-20250514", tokens_saved=10_000, tokens_sent=5_000)
stats = ct.stats()
assert "cost_without_headroom_usd" not in stats
def test_budget_enforced_after_recording_costs():
"""record_tokens must populate cost history so check_budget enforces the limit.
Regression test: _costs was never written, so check_budget always
returned (True, budget_limit) and budgets were silently unenforced.
"""
from headroom.proxy.server import CostTracker
ct = CostTracker(budget_limit_usd=0.0001, budget_period="daily")
allowed, remaining = ct.check_budget()
assert allowed # nothing spent yet
# ~$1.50+ of Sonnet input at list price — far over the budget
ct.record_tokens(
"claude-sonnet-4-20250514",
tokens_saved=0,
tokens_sent=500_000,
uncached_tokens=500_000,
output_tokens=10_000,
)
assert ct.get_period_cost() > 0
allowed, remaining = ct.check_budget()
assert not allowed
assert remaining == 0
def test_budget_input_cost_counted_without_usage_breakdown():
"""When the call site has no API usage breakdown (cache/uncached all 0),
tokens_sent must be used as the input count — input cost must not be
silently dropped from the budget."""
from headroom.proxy.server import CostTracker
ct = CostTracker(budget_limit_usd=100.0)
ct.record_tokens(
"claude-sonnet-4-20250514",
tokens_saved=0,
tokens_sent=500_000,
)
# 500k input tokens at Sonnet list price is ~$1.50 — must be > output-only
assert ct.get_period_cost() > 0.5