Re-migrates the e2e tests that main extended through endpoints_client
since this branch was opened (Azure Foundry, mid-conversation system
messages, Bedrock web search, google native streaming) onto the
provider SDK clients, so no endpoints_client reference remains
The e2e harness exists to prove product features end to end against a live
proxy. The prior Hard Rule carved out an exception for "tests that cover the
harness itself" and pointed at coverage_registry/test_collector.py, which in
practice invited unit tests of harness helpers to be staged alongside e2e
work. That is the wrong tool: harness logic that is worth locking down does
not need a mock-driven unit test living under tests/e2e.
Drop the carve-out. The Hard Rule now reads that no unit tests of any kind
belong under tests/e2e, and the passing mention of unmarked harness coverage
in the transport section is removed so the doc no longer contradicts itself.
coverage_registry/test_collector.py still exists on disk and is left in place
for now; whether to relocate or remove it is a separate decision.
create-release.yml is back to calling the chaos test through no mechanism at
all; it never called it. Also drops the E2E_REDIS_CHAOS opt-in gate itself:
the redis_chaos marker still exists for -m selection and is still excluded
from the per-PR selector by path (tests/e2e/(ui|claude_code|load)/), but the
test no longer needs an env var to run once its file is targeted.
Co-Authored-By: Claude Code <noreply@anthropic.com>
Now that create-release.yml gates stable and RC releases on this test directly,
the weekly schedule is redundant: every release gets a run against its own
commit instead of whatever happened to be on the default branch that Saturday.
workflow_dispatch stays for manual runs.
Co-Authored-By: Claude Code <noreply@anthropic.com>
Local runs measured 3.5 KB per request, so 10 KB keeps close to 3x headroom
while tightening from the earlier 12 KB.
Co-Authored-By: Claude Code <noreply@anthropic.com>
Local runs measured p50 0.19s, p90 0.23s, p99 0.69s, so 2s/3s/5s left several
times that as slack. 1s/2s/3s keeps a comfortable margin while catching a
smaller regression than the looser ceilings would have.
Co-Authored-By: Claude Code <noreply@anthropic.com>
Three local runs measured 1.33x-1.36x, so 2x is the tightest bound the data
supports and still catches a regression far smaller than 4x would. Noted in the
comment that this is the ceiling to loosen first if a weekly run trips it,
since core count shifts how much of baseline CPU is fixed per-request work.
Co-Authored-By: Claude Code <noreply@anthropic.com>
RSS moved 0.91x-1.40x across three identical local runs, so it stays loose at
2x rather than the arbitrary 1.5x carried over from the pre-padding-payload
calibration. CPU per request held steady at 1.33x-1.36x across the same runs,
so 4x replaces the looser 6x it inherited from stale numbers.
Co-Authored-By: Claude Code <noreply@anthropic.com>
A ratio against the healthy phase cannot bound either metric. Once the Redis
circuit breaker opens, a request skips Redis instead of waiting on its socket
timeout, so the chaos phase can measure cheaper than the baseline it is compared
against: local runs came in at 0.61x baseline p90 while a log-bytes ratio read
724x. Splitting Budget into RatioBudget and AbsoluteBudget lets RSS and CPU keep
the ratio they need, since both are machine-shaped, while latency and log volume
get the wall-clock ceiling a user actually cares about.
Co-Authored-By: Claude Code <noreply@anthropic.com>
Pad the locust payload to tens of KB so per-request bookkeeping cost scales with
body size instead of hiding behind a 40-byte prompt. Turn on
use_redis_transaction_buffer in the chaos config and JSON_LOGS in the workflow so
the spend buffer, pod lock, and JSON-encoded breaker tracebacks are all part of
the measured chaos cost. Add a log-bytes-per-request budget alongside latency,
RSS, and CPU, reading the proxy's log file size at each phase split; its ceiling
is uncalibrated since no chaos run has measured it yet.
Co-Authored-By: Claude Code <noreply@anthropic.com>
The Anthropic Messages route reaches the same Redis touchpoints and cost-tracking
callback through its own request path, so a failure-path regression there would not
surface from chat completions alone. Each simulated user now picks one endpoint round
robin and stays on it, and the per-endpoint split is asserted and reported so a run
that silently drove only one route fails instead of passing.
Co-Authored-By: Claude Code <noreply@anthropic.com>
