* fix(proxy): keep the personal budget on a proxy-admin user object A token carrying the proxy_admin role (JWT admin scope, SSO) replaced the fetched LiteLLM_UserTable row with a bare admin object, so max_budget never reached common_checks and admins spent past their personal budget on /chat/completions. Keep the row and force only user_role. Co-authored-by: L4XB <lukas.buck@e-mail.de> Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(e2e): budget denial for a proxy-admin JWT is the 422 ExceededBudget response Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(proxy): carry only the personal budget onto a forced proxy-admin user object Keeping the whole DB row also carried its models list, so a proxy admin whose row had no-default-models or a restricted list got 403 on personal requests. Build the admin object as before and copy max_budget and user_email across. The built-in admin identity (the master key and the UI username/password login) keeps ignoring its row's budget. * test(proxy): pin the contrast case in the admin model-list and admin-name budget tests Each test now also shows the same cached row refusing a non-exempt token, so the pass case cannot go green on a row the gate never read. * test(e2e): drive the personal-budget case with a proxy_admin user row and a plain JWT The Buildkite stack keeps the default admin_allowed_routes, so an admin-scope JWT gets 403 on /chat/completions before any budget check and the case could never pass there. A plain token whose sub resolves to a proxy_admin row goes through the same forced-admin rebuild. Drop the stage-mirror admin_allowed_routes edit, which the deployed stack never read. * ci: rerun e2e after an unrelated Gemini streaming spend-row timeout --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: L4XB <lukas.buck@e-mail.de> Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com> |
||
|---|---|---|
| .. | ||
| __init__.py | ||
| collector.py | ||
| guardrail.yaml | ||
| llm_claude_code_compat.yaml | ||
| llm_conversational.yaml | ||
| llm_nonconversational.yaml | ||
| logging.yaml | ||
| management_cases.py | ||
| mcp.yaml | ||
| mgmt.yaml | ||
| other.yaml | ||
| quota_management.yaml | ||
| README.md | ||
| registry.py | ||
| reliability.yaml | ||
| schema.py | ||
e2e coverage registry
This directory is the denominator for e2e test coverage: the set of behaviors we
want covered, one row per behavior, checked into the repo so coverage is a number we
can track instead of a guess. It implements the plan in the "E2E Coverage Tracking"
note; the naming grammar lives in tests/e2e/AGENTS.md.
The model
A cell is one customer-noticeable behavior a single e2e test can assert pass/fail
on, for example llm.chat_completions.bedrock_converse.tool_use.stream.works. Cells are
grouped module > feature > test, with LLM cells split into Core LLMs and
Non-Core LLMs for dashboarding. Each cell carries a tier (P0/P1/P2), a source, and a
fail_before_fix flag.
The rows live in per-prefix YAML files (llm_*.yaml, mgmt.yaml, mcp.yaml,
reliability.yaml, quota_management.yaml, logging.yaml, guardrail.yaml,
other.yaml) and validate against
the discriminated union in schema.py, so an LLM row cannot carry a guardrail field and
vice versa. llm rows with subject_endpoint of chat_completions, messages, or
responses roll up to Core LLMs; all other LLM endpoints roll up to Non-Core LLMs.
LLM endpoint, route, and capability values are typed in schema.py, so new taxonomy
values require an explicit schema change. logging and guardrail are two id-prefixes
that roll up into the single Logging & Guardrails dashboard module.
A test declares what it covers with a marker:
@pytest.mark.covers("llm.chat_completions.openai.tool_use.stream.works")
def test_openai_streaming_tool_calls(self) -> None:
...
The number
collector.py diffs the registry against those markers and reports coverage per module.
It is static: a collect-only pass reads the markers, so it runs no test and needs no live
proxy. Whether a covered cell currently passes or fails is a separate, live concern.
A skipped test asserts nothing, so its markers do not count. A cell is covered only when
at least one test pytest would actually run declares it; a cell claimed by both a live
test and a skipped one stays covered. Skip state comes from pytest's own evaluator, so
skip and skipif resolve exactly as they do in the e2e run, which also means a
skipif on an absent credential makes that cell uncovered in the environments where the
test cannot run. Cells left uncovered this way are listed under the headline (and counted
by litellm_e2e_coverage_skipped_markers) so an unskipped-pending gap is visible rather
than inflating the number. The one skip the collector cannot see is pytest.skip()
called from inside a test body, since it does not exist until the test runs.
cd tests/e2e && PYTHONPATH=. python -m coverage_registry.collector
Use --format loki after the e2e pytest run in the same Kubernetes job/pod to print
structured stdout lines for Loki:
cd tests/e2e && PYTHONPATH=. python -m coverage_registry.collector --format loki --strict
This emits exactly one COVERAGE_TOTAL line and one COVERAGE_MODULE line per module
in MODULE_ORDER, in that order. Loki uses log-safe module= labels from
LOKI_MODULE_LABELS (core_llms, management_ui, etc.) so existing JSON and
Prometheus consumers keep their human-readable module names unchanged.
The headline is overall coverage. The collector also lists markers that point at ids not in the registry, so a typo or an unenumerated behavior surfaces instead of being silently dropped.
Use strict mode in CI once existing draft markers are reconciled:
cd tests/e2e && PYTHONPATH=. python -m coverage_registry.collector --strict
Strict mode exits non-zero on @pytest.mark.covers(...) ids that are not checked into
the registry. Add --fail-on-collection-errors when the job should also fail on pytest
collection errors.
Provider x feature matrix: customer-run Bedrock combinations
The provider and feature combinations customers actually run get explicit cells, expanded
here as incidents surface new ones. The current Bedrock set, seeded from a customer's
production shape (regional us.anthropic.* inference-profile ids over both chat routes,
provider response headers for AWS-side correlation, and the Test Connection probe for a
responses-mode Bedrock Mantle deployment):
| Cell | Feature | Covering test |
|---|---|---|
llm.chat_completions.bedrock_converse.basic.nonstream.works |
regional us. id, Converse |
llm_translation/test_chat_completions_regression_e2e.py |
llm.chat_completions.bedrock_converse.basic.stream.works |
regional us. id, Converse stream |
llm_translation/test_chat_completions_regression_e2e.py |
llm.chat_completions.bedrock_invoke.basic.nonstream.works |
regional us. id, Invoke |
llm_translation/test_bedrock_provider_matrix_e2e.py |
llm.chat_completions.bedrock_invoke.basic.stream.works |
regional us. id, Invoke stream |
llm_translation/test_bedrock_provider_matrix_e2e.py |
llm.chat_completions.bedrock_converse.response_headers.nonstream.works |
llm_provider-* headers |
llm_translation/test_bedrock_provider_matrix_e2e.py |
llm.chat_completions.bedrock_converse.response_headers.stream.works |
llm_provider-* headers, stream |
llm_translation/test_bedrock_provider_matrix_e2e.py |
mgmt.model.test_connection.happy_path |
Test Connection, Bedrock Mantle | management/test_model_test_connection_e2e.py |
Status: this is a draft for review
The cells were enumerated from the codebase and the tiers are a first proposal. Known things to settle before treating the set as final:
- tiers are proposed, not signed off; 125 P0 is a lot to prove fail-before-fix, so P0 may want tightening
- a few cells need a support check or a prune (for example
llm.embeddings.anthropic.*andreliability.perf.throughput.under_slo) - auth is covered in two places (
other.auth.*and the mgmt authz assertions); the boundary needs a decision, and the auth cluster may deserve promotion to its own module - the P2 "niche" cells each stand in for a large tail of integrations/providers by design, so the denominator is deliberately P0-weighted rather than a full inventory