litellm/tests/e2e/coverage_registry
Emerson Gomes ecb8ef5ca8
feat(realtime)!: support latest OpenAI audio models (#35600)
* feat(realtime): support latest OpenAI audio models

* fix(transcription): type SDK stream duration metadata explicitly

* fix(realtime): preserve transport settings and translation duration

* refactor(realtime): reuse SDK client initialization

* fix(audio): bill finalized streams and authorize transcription models

* refactor(audio): keep transcription pricing selection together

* fix(realtime): reconcile model metadata and test contracts

* fix(realtime): preserve provider duration and model capabilities

* fix(realtime): keep transcription validation compatible with cost map guard

* fix(realtime): address transcription and billing review findings

* fix(realtime): preserve input usage and provider routing

* fix(realtime): bill retained output and route Azure aliases

* fix(realtime): reject malformed provider durations

* fix(realtime): preserve batch cache rates and Azure v1 routes

* fix(transcription): reject nonfinite provider durations

* refactor(realtime): align annotations with current lint rules

* fix(realtime): preserve translation deployment pricing and input usage

* test(transcription): verify synchronous stream finalization

* fix(realtime): retain missing translation input usage at close

* fix(transcription): reject streams that bypass output guardrails

* fix(realtime): reconcile audio protocols billing and current CI

* fix(realtime): trust confirmed formats and authorize default models

* fix(realtime): preserve absent models and reject unsupported guardrails

* fix(realtime): reject translation policy pipelines

* chore(ci): rerun transient infrastructure timing failure

* fix(realtime): reconcile audio protocols with current upstream

* fix(audio): validate provider responses at SDK boundaries

* fix(realtime): preserve minimum OpenAI SDK compatibility

* fix(realtime): preserve deferred Live session authorization

* fix(realtime): preserve upstream authentication and transcription behavior

* fix(audio): scope provider duration precedence to configured models

* fix(typing): avoid Final declarations inside transcription cost loop

* refactor(audio): reuse transcription mapping and duration resolution

* fix(realtime): enforce model budgets and preserve translation routing

* fix(audio): log streamed transcripts whose done event reports detected languages

* fix(audio): pass stream, languages and keywords through unchecked for non-OpenAI transcription providers

Providers without a streaming transcription handler got UnsupportedParamsError
for stream=True, languages and keywords, where the merge base forwarded them as
provider-specific params. Only openai and azure validate them now.

* fix(audio): bill streamed gpt-transcribe from the provider's reported duration

The stream path computed cost after logging had replaced the transcription's
duration usage, so it fell back to the measured file length while the
unstreamed call billed the provider's seconds.

* fix(transcription): log streamed transcription usage from the provider response

The stream logging path built the spend payload from the response after
its duration usage was transformed away, so whisper-1 with stream=true and
streamed gpt-transcribe logged an empty usage object. Bill and log from the
provider's TranscriptionResponse in both the sync and async paths.

* fix(realtime): keep main's routing, protocol and transport behavior around the new translation routes

- Send OpenAI realtime and translation client_secrets and calls through litellm's httpx client again, so ssl_verify and custom CA settings apply and upstream errors keep main's shape
- Use the input transcription model as the session model only for transcription sessions
- Drop the new /openai/v1 translation aliases and record each lazy feature's own routes, so the /openai pass-through and the MCP routes resolve as on main
- Let a configured Azure realtime_protocol win for every model, with GA as the default only when none is set
- Return x-litellm headers on streamed transcriptions and mark the request done
- Refuse translation sessions before connecting upstream when realtime guardrails apply or the provider is not OpenAI or Azure
- Accept json and text for gpt-transcribe, and drop other formats under drop_params instead of failing
- Bill translation sessions on output audio seconds only, at the single published rate
- Document the 201 SDP answer on the translation calls routes and the transcription 200 response

* fix(realtime): restore main behavior for guardrails, Azure transcription, and realtime clients

Streamed transcription runs unstreamed when a deployment post_call guardrail applies, so the
guardrail still sees the transcript. Azure transcription maps v1, latest and preview to the
default dated version on the deployments route for every model. The SDK client secret keeps
session.model ahead of model, realtime WebSockets connect to api_base again, realtime metadata
keeps only guardrails, and managed agents need an explicit transcription model. Reverts the two
unrelated streamed Responses error codes and the duplicate cached_tokens_details field, and the
gpt-transcribe format check only reads the request provider's cost map entry.

* fix(realtime): keep main's transcription, client secret and route behavior where the PR changed it

Transcription reaches every provider as on main again, sends stream only when asked, forwards language and
languages together, keeps form key order, bills provider seconds only when above zero, honors base_model for the
new flags, keeps main's Azure api_version on the proxy path, and streams carry chat's headers, an error event on a
mid-stream failure, and a spend row when the stream ends early. Translation cost reads both per-second rates from
one source and spend rows carry the billed seconds.

Realtime transcription WebSockets keep model in the upstream URL outside OpenAI's hosts, client secrets forward
explicit nulls, accept conversation and delay, and answer 400 on a malformed body, the /openai translation paths
are back to main, translation refuses only for guardrails realtime sessions run, nested aliases resolve and later
session.update models are authorized, translation client secrets run per-model auth, beta clients bridged to GA
get beta-shaped session events, and transcription sessions send the deployment model in both shapes.

* fix(realtime): address review findings on transcription billing, streaming and realtime auth

Per-second pricing replaces token billing only when the priced entry sets no token rates, translation rates fall back per key to the cost map, transcription cache keys include languages and keywords, a stream request a post_call guardrail forces unstreamed comes back as redacted SSE, Azure beta clients are bridged only for GA-only deployments, nested transcription models on the realtime WebSocket are checked against the key, and translation spend carries key and team metadata

* fix(azure): pick the GA realtime protocol for deployments whose model_info.base_model is GA-only

* fix(realtime): preserve prior merge resolutions after rebase

* fix(realtime): enforce translation policies and normalize Azure transcription URLs

* fix(cookbook): stop translation once output idles after the upload ends

* fix(realtime): bill translation on input audio and drop pre-read stream cost headers

Translation spend now uses the input audio seconds the session sent, priced
through input_cost_per_second, instead of the translated output duration.
A streamed transcription no longer returns cost headers before the stream is
read, since the cost is only known after the last event. Also removes an
unused realtime guardrail helper.

* fix(realtime): drop top-level session nulls, bill dropped transcription streams, and bridge beta response fields

Client secret, translation and transcription session bodies drop top-level nulls again, as main did, while nested nulls such as turn_detection still pass through. Transcription client secrets send the deployment model as session.model. A streamed transcription the client drops still runs its success callbacks, and the stream ends with data: [DONE] like OpenAI's. The beta to GA bridge maps max_response_output_tokens, speed and noise reduction, remaps response.create, and drops temperature under drop_params. The Entra token for realtime HTTP routes is fetched off the event loop

* test(e2e): cover realtime translation, live transcription, client secrets, and streamed transcription

* fix(realtime): close review gaps in translation billing, transcription streams, and Azure GA

Bill translation sessions from the deployment's own per-second rates, add per-second prices for
Azure gpt-4o transcribe models, keep stream-scoped guardrails seeing the caller's stream flag, map
mid-stream transcription errors to LiteLLM exceptions with a [DONE] frame, map GA responses back to
the beta shape for bridged beta clients, honor the minting key's guardrail opt-out on translation
calls, restore the Azure transcription session URL, and flag GA-only Azure realtime models

* fix(realtime): bill a translation rate the deployment omits from the session model

* test(e2e): check translated speech is not English instead of exact French wording

* fix(realtime): keep main's behavior for non-bridged backends, stream error frames, gateway URLs and Azure transcribe prices

Remap beta events to GA only for bridged beta clients and translation sessions, bridge beta clients on GA-pinned Azure deployments, log error-only upstream sessions as failures, flag dated Azure GA realtime models, drop the unpublished Azure per-second transcribe rates, log a mid-stream transcription failure once, keep main's SSE error frame outside transcription, drop unsupported transcription params under drop_params, keep the caller's Azure auth headers, keep a gateway /v1 path, let key opt-outs release string-callback guardrails on translation, and keep the cookbook waiting while a long upload is still sending

* fix(realtime): keep the proxy's realtime session gate off translation sessions

A key that opts out of a legacy callback guardrail now gets a working translation session, since the transcription-guardrail gate no longer sends a realtime session.update the translation upstream rejects

* fix(realtime): keep websocket model limits, bill measured transcription length, route transcription sessions on the allowed model

A WebSocket opened without a query model now authorizes as an empty model, transcription cost prefers the measured upload length over the provider's reported seconds, transcription sessions overwrite nested models with the authorized top-level model again, and a non-boolean transcription stream value answers 400 or is dropped under drop_params.

* fix(ci): copy the rust bridge extension into the openapi docs gate's base worktree

* fix(model-catalog): accept the realtime translation endpoints in the cost map

* fix(cookbook): close the translation session and read until session.closed

* fix(realtime): treat an empty transcription stream as unset and pass the cost-map guard

An empty `stream` form value runs the transcription unstreamed again, as
OpenAI does, instead of answering 400. The four translate cost-map entries
drop the translation routes from supported_endpoints until the main
branch's catalog parser accepts them, and the nested transcription model
lookup uses the router registry entry point.

* test(proxy): drop the mid-stream error frame pin that main's error shape change replaced

* fix(transcription): bill uploaded seconds when a stream fails mid-transcript

* fix(proxy): bill uploaded seconds when a streamed transcription breaks mid-stream

* fix(audio): keep transcription's positional arguments and redact streamed error details

- Move the new languages, keywords and stream options to the end of transcription's parameters, so a prompt or format passed by position keeps its meaning
- Redact internal hostnames, paths and tracebacks from a streamed transcription's mid-stream error event, as chat streams already do
- Move the CA-bundle check for the realtime HTTP routes, which serves real TLS on loopback, from the unit suite to the SDK integration suite

* fix(audio): stream the guarded transcript to SDK callers that asked for a stream

* fix(logging): keep a streamed transcript out of callbacks that turned off message logging

A streamed transcription's assembled response reached success callbacks with its text intact under turn_off_message_logging or a callback's message_logging=False, because the shared streaming redactor had no transcription branch. It now redacts the text, as the unstreamed call already does

* fix(proxy): stream a transcription the deployment streamed when the client sent no stream field

---------

Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
2026-11-03 07:27:52 +00:00
..
__init__.py chore: consolidate CLAUDE.md into AGENTS.md 2026-09-19 02:30:35 +00:00
collector.py fix(e2e-changed): keep the gate off suites the stack cannot run 2026-09-05 21:03:50 -07:00
guardrail.yaml test: move Sail, Weave and Presidio e2e tests to integration tests (#49533) 2026-10-31 18:31:01 -07:00
llm_claude_code_compat.yaml test(e2e): replay a real tool-search assistant turn back to Bedrock Invoke (#36856) 2026-08-17 11:59:26 -07:00
llm_conversational.yaml test: move Sail, Weave and Presidio e2e tests to integration tests (#49533) 2026-10-31 18:31:01 -07:00
llm_nonconversational.yaml feat(realtime)!: support latest OpenAI audio models (#35600) 2026-11-03 07:27:52 +00:00
logging.yaml fix(langfuse): log Responses API token usage instead of zeros (#42212) 2026-10-10 16:42:59 -07:00
management_cases.py test: enforce isolated actors and stop OIDC process groups 2026-09-12 13:49:49 -07:00
mcp.yaml refactor(mcp): drop the experimental label from the MCP server and client paths (#48513) 2026-10-27 17:47:50 +00:00
mgmt.yaml test(e2e): repair fallback management test for config-owned fallbacks (#47023) 2026-10-17 01:27:39 +00:00
other.yaml test(a2a): replace the third-party agent e2e test with an integration test (#49541) 2026-10-31 18:59:04 -07:00
quota_management.yaml refactor(anthropic): rename llms/anthropic/pass_through to llms/anthropic/messages (#48510) 2026-10-27 10:08:25 -07:00
README.md chore: consolidate CLAUDE.md into AGENTS.md 2026-09-19 02:30:35 +00:00
registry.py ci: gate tests/e2e on zero basedpyright errors in pre-commit and lint CI 2026-07-11 10:25:22 -07:00
reliability.yaml test(e2e): assert the sibling-replica cooldown through the router (#44706) 2026-10-06 02:12:57 +00:00
schema.py feat(realtime)!: support latest OpenAI audio models (#35600) 2026-11-03 07:27:52 +00:00

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.* and reliability.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