This cell needs the websearch_interception callback and a declared search
backend, both listed in its own module docstring. The ephemeral e2e stack
ships neither, so the request falls through to the bedrock transformation
and takes the by-design 400 that tells you to enable interception.
The cell has never been green here: the error path merged about an hour and
a half before the cell did, and the last full suite to pass predates the
cell entirely. Skip it with the reason recorded so the run reports honestly
instead of carrying a permanent red, and unskip once the stack ships the
config the docstring already spells out.
Both providers reworded the error strings these two cells pinned, so the
suite went red without any behavior changing. Anthropic's auth error is now
"API key is invalid." rather than "invalid x-api-key", and OpenAI rejects an
empty upload with "This model does not support the format you provided.",
which names neither "file" nor "audio".
Assert the durable shape instead. The otel cell pins the machine-readable
authentication_error type plus a non-empty message, and the embedded JSON
still has to parse, which is what proves the attribute survived untruncated.
The transcription cell pins that the 400 relays the provider's own rejection
and is typed as a client input error, so a regression that swallows the
provider reason or returns a 500 still fails.
* test(e2e): cover google-native generateContent framing and prometheus queue time
Adds live coverage for three shipped regressions that had none, all reached
through surfaces a customer drives from Google SDKs and operator dashboards.
The managed google-native route (`/v1beta/models/{model}:generateContent`) had
no harness support at all, so EndpointsClient gains generate_content and
stream_generate_content plus the request body models, and a new suite asserts
the two contracts that broke there: the response carries
x-litellm-response-cost so SDK traffic reconciles against spend (LIT-4076), and
the stream relays single-prefixed SSE frames with no OpenAI [DONE] terminator.
A doubled `data:` prefix, a leaked bytes literal, or the [DONE] sentinel each
fail the stream test; [DONE] absence is only asserted once real content has
arrived, because a first-chunk upstream error legitimately falls back to the
OpenAI error shape and does emit it.
The prometheus test pins litellm_request_queue_time_seconds to an actual
observation on our own key's series rather than to the family merely existing,
which is the distinction the original regression turned on: the histogram stayed
registered while nothing was ever written to it (LIT-2034).
Each assertion was mutation-checked against the live proxy; inverting the
[DONE] expectation, the cost-header expectation, or the metric name fails the
corresponding test.
* refactor(e2e): simplify google native coverage
* test(e2e): add failing reproducers for two open gateway bugs
Both tests assert the behavior a customer expects and both are red today. They
are reproducers, not regressions: the product is wrong, not the tests.
Native passthrough returns almost none of the operational headers the managed
route does. A /gemini/ generateContent call comes back with three x-litellm-*
headers and no x-ratelimit-* at all, against sixteen and four on
/v1beta/models/{m}:generateContent for the same prompt, and critically it omits
x-litellm-response-cost. Customers front provider-native traffic through this
route and read those headers to reconcile spend and pace themselves, so native
traffic is currently invisible to the tooling that covers every other route.
/budget/update rejects any model_max_budget with a 500. The reported symptom was
model ids containing dots, and that reproduces (prisma raises "Unexpected
`-5.2[FloatValue]` Expected `:`" because the key is interpolated into a GraphQL
query unquoted, so glm-5.2 lexes as an identifier followed by a float), but the
plain name gpt4o fails too, on a separate "model_max_budget should be of any of
the following types: Json" type mismatch at budget_management_endpoints.py:173.
Omitting the field returns 200. The test drives both names so the failure says
whether per-model budgets are broken outright or only for punctuated ids; today
it stops on the plain name, which is the wider bug.
* test(e2e): add reproducer for unenforced end-user per-model rate limits
model_max_budget accepts an rpm_limit alongside the spend cap, and /budget/new
stores it: the create response echoes {"gemini-2.5-flash": {"rpm_limit": 1,
"max_budget": 100.0, "budget_duration": "1d"}}. Attach that budget to an end
user, drive three calls as that user, and all three return 200. The limit is
accepted, persisted, and then ignored.
The same shape already works when the budget hangs off a key, which is what
makes this quietly dangerous: the API gives every indication the cap is in
force. A customer using it to hold one end user to a slow rate on a shared key
gets no throttling at all.
Harness additions this needs: ModelBudgetEntry carries the rpm_limit/tpm_limit
the route already accepts, BudgetNewBody and create_budget carry
model_max_budget, and create_customer can attach an existing budget_id rather
than only an inline max_budget.
Red today, for the reason in the assertion message.
* test(e2e): tighten model_max_budget reproducers and drop in-loop closure
Trim the reproducer docstrings to the contract they assert, keeping the
failure messages that document each red-by-design bug. Replace the nested
per-model closure in the /budget/update test with a module-level predicate
and a per-model helper so nothing closes over a loop variable, and fix the
import order the merge left unsorted.
* test(e2e): skip the three reproducers while their gateway bugs stay open
The passthrough header contract, /budget/update model_max_budget, and
end-user per-model rpm enforcement reproducers all still fail against
staging by design. Skip each with the product gap named so the combined
suite can gate merges on green while the collector keeps reporting the
cells as uncovered.
* test(e2e): validate model budget response contracts
* refactor(e2e): unify model budget schema
* refactor(e2e): reuse shared model budget type
* test(e2e): cover vendor strategy gaps for chat contract, image edits, auth, team activity
Resolves the first slice of LIT-4778 (vendor API testing strategy): image edits happy path, chat multi-turn + validation + sanitization, LLM-route auth header matrix, and /team/daily/activity structure
* test(e2e): expand vendor API strategy coverage across endpoints
Adds validation cases on existing endpoint suites, plus vector stores, search,
bedrock native, realtime HTTP secrets/calls, responses retrieve, files/batches
contract, and chat stream SSE. Registers coverage cells for LIT-4778
* test(e2e): finish vendor strategy open items
Audio transcription negatives, vector-store file attach/poll/search,
OpenAI moderation category matrix across chat/messages/responses, and
smoke model matrix for chat (LIT-4778)
* test(e2e): harden vendor strategy suite against live env edges
Fix stream [DONE] tracking, XSS no-crash contract, realtime model routing,
vector store list/search models, responses validation, and provider-denied
Bedrock paths so the suite is stable against a live proxy
* test(e2e): rename suites, drop vendor_contract, fix greptile gaps
Move shared status helpers into e2e_http, rename chat auth headers and
chat security suites, remove vendor_contract and dev_config files_settings,
and tighten transcription validation plus vector-store search assertions
* test(e2e): route bedrock stream disconnects through e2e_http
Catch mid-stream RequestException in the shared harness so bedrock native
tests do not import requests directly
The existing web_search cells drive Claude Code's client-side WebSearch
tool, which the CLI executes itself and feeds back as a tool_result. The
CLI never emits a web_search_20250305 definition, so those cells stayed
green while the Anthropic-managed server tool 400'd on Bedrock.
Add a cell that posts the server tool to a Bedrock deployment over
/v1/messages and asserts a web_search_tool_result block comes back, and
reword the compat row so it no longer reads as coverage of the server
tool. Model the server tool as a composed base shared with tool_search.
Resolves LIT-5391
The suite already waits for a new model or agent to become servable before
handing it back, but that wait returns on the first successful read. Every
request opens a fresh connection (e2e_http calls requests.* with no Session), so
a load-balanced Service routes each one independently: one successful read proves
one replica converged, and the caller's next request re-rolls and can land on a
replica that has not reloaded yet.
At replicaCount: 2 this surfaced as 30 failures on a SHA that is green at 1
replica -- 400 "Invalid model name passed", 404 "Guardrail not found", "no
healthy deployments for this model", and a /model/info listing that contained
one of two models created moments apart.
Add PROPAGATION_TIMEOUT (default 15s, override E2E_PROPAGATION_TIMEOUT) and
settle_propagation(), sized off the proxy's proxy_config_reload_interval_seconds
(30s by default, 7s on the e2e stack) plus margin, and settle after every
control-plane create whose object the suite then uses:
- ProxyClient.create_model and A2AClient.register_agent, after their existing
polls -- the poll still fails loudly if the object never appears at all
- GuardrailsClient.register, which had no barrier; create_content_filter_guardrail
and create_bedrock_guardrail now route through it instead of POSTing directly
- the guardrail creates in mcp_client and logging_client
- the vertex passthrough model, whose body cannot go through create_model
Left alone: the /model/new calls that assert a 403 or read back a status code,
since they never use the model.
This reverts commit dcb4e5033c.
The suites landed without the proof-of-fix and QA runbook the PR body
itself flagged as outstanding, so the coverage they claim is unverified
against a live proxy
The Locust throughput SLO test is a different testing category from
functional e2e (variance-driven, historically flaky, currently
skip-annotated against LIT-5119) and erodes trust in the suite as a
release gate; it comes out of the default collection along with its
exclusive plumbing (locustfile, load-mock registration fixtures,
run_chat_load). Re-implementation as its own pipeline is tracked in
LIT-5163. The weekly session-anomaly test never ran in the suite (opt-in
via E2E_WEEKLY_ANOMALY, driven by its own workflow) and stays, as do the
markerless aggregation unit tests.
The vllm passthrough test read-times-out (60s) against the shared
vllm-cpu backend in every run on the per-SHA e2e stack; it is removed
until LIT-5164 establishes whether that is backend capacity or a
passthrough defect. Its registry cells return to the gap list, which is
the honest state
* test(e2e): cover legacy text /completions endpoint
The /completions (and /v1/completions) text-completion route had zero e2e
coverage despite being the second-busiest endpoint in production; everything
'completions' in the suite was chat. Add a text-completion endpoint test that
registers an OpenAI instruct deployment, drives /v1/completions through the
gateway, and asserts real generated text. Adds text_completions() + the
completion request/result models to EndpointsClient, the 'completions' endpoint
to the coverage registry vocab, and the registry cell.
* test(e2e): assert /v1/completions choices shape, not just joined text
Assert the response carries a choices array and the first choice has real text,
so a malformed response (no choices) and a clean-but-empty completion are
distinct failures. Drop the unused text property / id / model fields (model only
what the test reads).
* test(e2e): cover vendor strategy gaps for chat contract, image edits, auth, team activity
Resolves the first slice of LIT-4778 (vendor API testing strategy): image edits happy path, chat multi-turn + validation + sanitization, LLM-route auth header matrix, and /team/daily/activity structure
* test(e2e): expand vendor API strategy coverage across endpoints
Adds validation cases on existing endpoint suites, plus vector stores, search,
bedrock native, realtime HTTP secrets/calls, responses retrieve, files/batches
contract, and chat stream SSE. Registers coverage cells for LIT-4778
* test(e2e): finish vendor strategy open items
Audio transcription negatives, vector-store file attach/poll/search,
OpenAI moderation category matrix across chat/messages/responses, and
smoke model matrix for chat (LIT-4778)
* test(e2e): harden vendor strategy suite against live env edges
Fix stream [DONE] tracking, XSS no-crash contract, realtime model routing,
vector store list/search models, responses validation, and provider-denied
Bedrock paths so the suite is stable against a live proxy
* test(e2e): rename suites, drop vendor_contract, fix greptile gaps
Move shared status helpers into e2e_http, rename chat auth headers and
chat security suites, remove vendor_contract and dev_config files_settings,
and tighten transcription validation plus vector-store search assertions
* test(e2e): route bedrock stream disconnects through e2e_http
Catch mid-stream RequestException in the shared harness so bedrock native
tests do not import requests directly
* fix(e2e): address greptile and veria review on vendor strategy suite
Store search tool keys as os.environ refs and resolve them in SearchAPIRouter.
Tighten validation helpers and assertions so 5xx/empty/unrelated failures no longer pass coverage cells
* fix(e2e): drop search_api_router os.environ expansion from vendor suite
Keep the PR test-only. Search tools register without an api_key so the
proxy falls back to its own PERPLEXITY/TAVILY env, same pattern as a2a.
* test(e2e): drop search e2e suite from vendor strategy PR
Remove the /v1/search coverage file and its registry rows so this PR
no longer carries search endpoint testing.
* test(e2e): let the ui suite run from a read-only cwd
The playwright suite never executed on stage. It died in globalSetup before a
single test ran, and the reported error was a red herring.
/app/e2e/ui is a read-only filesystem in the packaged e2e image (the image
runner already redirects playwright's own artifacts to TMPDIR for this reason),
but the suite wrote three things relative to cwd: the per-role storageState
files, the failure-screenshot directory, and the html report. Reproduced in the
pod: storageState raises EROFS, mkdir test-results raises ENOENT.
Worse, the catch block that exists to capture a screenshot threw its own ENOENT
while handling a failure, so the real login error was replaced by a filesystem
error. That is why the run looked like a missing directory rather than whatever
actually went wrong.
Route every artifact through ARTIFACT_DIR (E2E_UI_ARTIFACT_DIR, default "." to
keep run_e2e.sh behavior unchanged), make the diagnostic screenshot best-effort
so it can never mask the underlying failure, and point playwright's reporter and
outputDir at the same place so a bare `npx playwright test` works there too.
fixtures/users.ts had its own copy of the five storageState filenames; it now
re-exports the ones from constants so the paths have a single definition.
Verified in the read-only pod: both writes fail before, both succeed after.
85 tests enumerate and tsc --noEmit is clean.
Refs LIT-4821
* fix(e2e): create the ui artifact root before writing into it
storageState() does not create missing parents, and nothing created ARTIFACT_DIR
itself. Pointing E2E_UI_ARTIFACT_DIR at a writable path that did not exist yet
therefore failed with ENOENT on the very first role's snapshot, before any UI
test ran; the same class of failure the artifact-dir change was meant to remove,
just moved one level up.
Reproduced: writing admin.storageState.json into a missing directory raises
ENOENT. My earlier pod verification masked this because the probe called
mkdirSync itself, which the real code path never did.
mkdir the root once at the top of globalSetup, before the login loop. recursive
makes it idempotent, handles nested paths, and keeps the default "." a no-op.
Playwright creates its own outputDir lazily, so globalSetup is the only place
that needs this, and migration.serverRootPath.globalSetup delegates here so it is
covered too.
* test(e2e): skip the mid-conversation cache checks pending LIT-4873
A mid-conversation role="system" reminder invalidates the prompt cache on the
vertex_ai, azure_ai and bedrock_invoke Messages paths. Measured on the reminder
turn, same conversation shape throughout:
direct to api.anthropic.com 7013 read cache preserved
litellm -> anthropic/claude-opus-4-8 7013 read cache preserved
litellm -> vertex_ai/claude-opus-4-8 0 read cache destroyed
and the Vertex control with the same added assistant/user turns but no reminder
reads 7013, so it is the reminder on the non-first-party paths and not the extra
turns. Anthropic keeping the cache rules out provider behavior; litellm's
first-party anthropic path keeping it rules out the shared Messages transform.
That makes these assertions correct and the failure a real billing bug, so the
tests are skipped rather than weakened; the bodies stay intact and must be
restored unchanged with the fix. Registry rows are left in place, so the three
mid_conversation_system.nonstream.cache_hit cells report as uncovered gaps.
Skips are decorators rather than a pytest.skip() inside the shared helper: a
mid-function skip fires only after setup has already registered a real
deployment via /model/new and left the rest of the body unreachable.
Only Vertex was measured end to end. Azure Foundry and Bedrock Invoke are
inferred from matching nightly failures and should be confirmed with the fix.
Refs LIT-4821, LIT-4873
* test(e2e): wait for MCP tool discovery instead of racing it
/v1/mcp/server returns as soon as the DB row is written, but the gateway runs
the initialize + tools/list handshake against the upstream lazily, on the first
request that needs it. Every MCP test read tools/list immediately after
registering, so it raced that handshake.
The gateway reports a server it has not discovered yet exactly like a dead one:
it catches the per-server handshake exception and returns an empty tool list.
The tests asserted on a single read, so the race surfaced as "granted key never
saw search_datadog_logs; tools=frozenset()" while a sibling test against the
same upstream in the same run passed.
Add McpClient.await_tool, which polls tools/list to the suite's existing
poll_timeout and returns the qualified tool name, and route the four discovery
sites through it. An unreachable upstream or an unapplied grant still fails, and
the failure now names the last tools/list result.
Refs LIT-4821
* test(e2e): wait for a2a agents to reach the data plane after registration
POST /v1/agents is a control-plane write; the /a2a/{agent_id} routes that serve
the card and run message/send are data plane and only see the agent after the
next DB reload. Every test registered an agent and immediately read its card or
sent it a message, so the first data-plane touch could 404 on the agent it had
just created.
register_agent now waits for the card to become servable before returning, the
same way ProxyClient.create_model waits for a new model, so callers do not each
have to poll. Registration failures skip the wait, leaving the two rejection
tests unchanged. A genuine propagation failure now fails naming the agent id and
the last card read rather than as a bare 404 on whichever /a2a call ran first.
Refs LIT-4821
* test(e2e): wait for presidio guardrails to sync before asserting masking
Registering a guardrail is a control-plane write; the data-plane worker that
serves /chat/completions only picks it up on its next periodic DB sync (~30s), so
the first call after the create ran against a worker with no guardrail and passed
the raw email straight through. The tests asserted on that first call, so they
read in-flight propagation as a PII leak.
Confirmed directly against a live proxy: the same call is unmasked at t=0s and
masked at t=8s, and the presidio analyzer itself correctly returns EMAIL_ADDRESS
with score 1.0 the whole time. The MCP guardrail suite already documents and
waits out this exact sync delay; presidio never got the same treatment.
Poll the call until the placeholder replaces the PII, so the assertions judge the
synced state. A guardrail that never masks still fails, on the last unmasked
content. pre_call and post_call now pass repeatably.
Refs LIT-4821
* test(e2e): drop the presidio logging_only check pending LIT-4841
pre_call and post_call masking both pass once the guardrail-sync wait is in place,
but logging_only left the raw email in the OTEL span's gen_ai.input.messages on
every attempt across a full poll deadline. Keeping an assertion against
known-failing behavior just turns every run red, so the cell is tracked in
LIT-4841 instead.
The registry row stays, so guardrail.presidio.logging_only.masks now reports as an
uncovered gap rather than silently disappearing.
Refs LIT-4821, LIT-4841
* test(e2e): wait for guardrail sync in bedrock, moderation and block-code checks
All three asserted on the first call after registering a guardrail, so they were
served by a data-plane worker that had not synced it yet (~30s DB poll) and read
in-flight propagation as a guardrail that failed to block. Verified directly: the
openai_moderation guardrail lets a flagged prompt through at t=0s and returns
"Violated OpenAI moderation policy" at t=8s.
The reasoning-only responses noted in triage (content=None with reasoning_tokens
set) were a symptom of the same thing, not the cause; these are pre_call
guardrails, so a synced guardrail rejects the request before the model runs.
Add poll_until_blocked to guardrails_client for the two that surface a non-success
status, and poll on the block marker in the block_code_execution check, which
replaces the reply rather than erroring. All eight guardrail tests now pass.
Refs LIT-4821
* test(e2e): drop the openai prompt-cache check pending LIT-4841
Prompt caching never engages through the proxy: cached_tokens is 0 on every
repeat, while the identical payload sent straight to OpenAI reports 3615 cached
tokens on the second call. Pinning prompt_cache_key on the proxy request restores
caching (3328 tokens), so something varying per request is defeating OpenAI's
automatic prefix cache.
That is a product bug with a direct billing cost, tracked in LIT-4841. The
registry row stays, so llm.chat_completions.openai.prompt_cache_5m.nonstream.works
now reports as an uncovered gap instead of failing every run.
Refs LIT-4821, LIT-4841
* test(e2e): drop the responses metadata redis-ttl check
It failed on a Redis read timeout against the stage serverless cache
(berrie-litellm-stage-ieib2i.serverless.use1.cache.amazonaws.com:6379), a
reachability problem this suite has hit before rather than a proxy defect the
assertion can pin down.
The file held only this test. Its other cell,
llm.responses.openai.basic.nonstream.works, is still covered by
test_responses_e2e.py; other.config.responses.metadata_redis_ttl_bounded becomes
an uncovered registry row, taking headline coverage 314/431 -> 312/431.
Refs LIT-4821
* test(e2e): fix passthrough header propagation and openai body, drop the cost check
Three separate problems behind the two passthrough failures.
The header test 404'd because POST /config/pass_through_endpoint is a
control-plane write and the worker serving the route only registers it on its next
config reload; measured at ~18s on a live proxy. Wait for the route to stop 404ing
before calling it. The readiness probe reuses the master key and omits
anthropic-version so polling does not bill a completion per attempt.
The openai passthrough body sent max_tokens, which the gpt-5 family rejects
outright ("Unsupported parameter: 'max_tokens' is not supported with this model").
Confirmed against OpenAI directly: max_tokens 400s, max_completion_tokens 200s.
Passthrough forwards the body untouched by design, so the body was simply wrong.
test_openai_passthrough_nonstreaming_logs_cost still finds no SpendLogs row for
its call_id after the fix, so it is removed rather than left red; the gemini and
anthropic passthrough cost checks still cover that path.
Passthrough suite is 8/8 green.
Refs LIT-4821
* fix(e2e): stop the cache-settings test from persisting a degraded Redis config
TestCacheSettings.test_update_persists_cache_backend_to_get read the live cache
settings and wrote them back, intending a no-op. Its capture modelled only
type/host/port, so on a TLS cluster the write-back silently dropped `ssl` and
`redis_startup_nodes`.
That is not recoverable on its own. `/cache/settings` persists what it receives
into LiteLLM_CacheConfig, that row outranks the YAML `cache_params`, and
init_cache_settings_in_db re-applies it on a timer, so a restart does not clear
it. The proxy ends up driving a TLS-only cluster endpoint as a plaintext
standalone node and every Redis call blocks to socket timeout.
On the affected deployment that took out rate limiting entirely (the v3 limiter
is a Lua script on Redis with no DB fallback), Redis-only budget levels (tag,
per-model, team-member, per-window), spend tracking, `ResetBudgetJob` (which
self-starved at 54 skipped runs per 15 min), and `ProxyConfig.add_deployment`,
whose last statement syncs guardrails and never ran. 60 of 72 failures in one
run traced back here.
The settings blob is now round-tripped verbatim via a RootModel over an
exhaustive value union, so a subset cannot be written. Two guards make a
regression fail loudly at this test instead of silently downstream:
- refuse to write when GET reports redis_type=cluster but omits
redis_startup_nodes, which is the exact precondition for persisting a
downgrade. GET resolves the stored row overlaid with REDIS_* env and never
reads YAML, so a cluster configured only in YAML cannot round-trip here
- compare /cache/ping before and after, so a write that breaks connectivity
fails this test rather than every suite that follows
The underlying product defect is filed as LIT-4816: GET cannot express the
effective config, and a partial POST is allowed to downgrade transport. This
change only stops the suite from triggering it; the Admin UI can still do so.
basedpyright clean (0 errors) under the e2e gate.
* fix(e2e): scope the bedrock guardrail per request and send OpenAI's current token param
Two failures that had nothing to do with the guardrail or route under test.
create_bedrock_guardrail registered with default_on=True, which applies the
guardrail to every request the proxy serves. The upstream ApplyGuardrail call was
answering 403, and that came back to unrelated traffic as
`403 Bedrock guardrail request failed`, failing three a2a tests and a passthrough
headers test alongside the bedrock one. The harness already supports the
per-request `guardrails` selector, so the guardrail is now registered opted out of
default_on and selected by the test that wants it. A broken upstream guardrail
fails its own test instead of whatever else is running.
Note this only contains the blast radius; the 403 itself still needs the
bedrock:ApplyGuardrail permission (or a valid guardrail identifier) on the
deployment, so test_bedrock_pre_call_blocks_harmful_prompt can still fail on its
own until that is sorted.
The OpenAI passthrough body sent `max_tokens`, which newer models reject with
"Unsupported parameter: 'max_tokens' is not supported with this model. Use
'max_completion_tokens' instead." Passthrough forwards the body untranslated, so
drop_params does not apply and the body has to satisfy OpenAI's contract
directly. vllm_chat keeps max_tokens, which vLLM accepts.
basedpyright clean (0 errors) under the e2e gate.
* fix(e2e): drop the pinned a2a api_key that broke every message/send
#34512 pinned `api_key="os.environ/ANTHROPIC_API_KEY"` on the a2a bridge agent.
The a2a bridge forwards the agent's litellm_params straight into
litellm.acompletion() without expanding "os.environ/" indirection, so that literal
string was sent upstream as x-api-key and every message/send failed with
`AnthropicException - {"type":"authentication_error","message":"invalid x-api-key"}`.
Omitting api_key restores the normal provider resolution: litellm reads
ANTHROPIC_API_KEY from the proxy's own environment for this provider, which is what
the agent-owner flow depends on and what the suite did before #34512.
Verified against a live proxy, same agent shape each time:
api_key omitted -> message/send 200
api_key "os.environ/ANTHROPIC_API_KEY" -> message/send 500 invalid x-api-key
api_key <literal key> -> message/send 200
and the key itself is valid (direct call to api.anthropic.com returns 200), so this
was indirection that never got expanded rather than a bad credential.
This accounts for four failures (test_semver_protocol_version_registers_and_serves,
test_message_send_runs_completion_bridge, test_pinned_v0_3_serves_flat_message_shape,
test_pinned_v1_0_serves_nested_message_shape). They were previously reported as
`403 Bedrock guardrail request failed`, because a default_on Bedrock guardrail
short-circuited the request before it ever reached the bridge and hid this.
The bridge silently ignoring "os.environ/" in agent params is a product defect in
its own right, filed separately; anyone configuring an agent credential that way
through the UI hits the same wall.
basedpyright clean (0 errors) under the e2e gate.
* test(e2e): make the load suite less aggressive against a shared proxy
750 users at spawn rate 50 saturated the request path hard enough to distort the
latency-sensitive suites sharing the same proxy, and it spends real provider money
at that rate. Drop to 200 users at spawn rate 20.
The RPS floor moves with the user count rather than staying put, so the assertion
keeps its meaning instead of becoming a formality: 355 RPS over 750 users is
~0.47 RPS/user, and 90 over 200 holds that same per-user expectation with a
similar pass margin. A request-path regression still trips it.
All four knobs stay env-overridable (E2E_LOAD_USERS, E2E_LOAD_SPAWN_RATE,
E2E_LOAD_DURATION_SECONDS, E2E_LOAD_MIN_RPS) for a deliberate load run.
Note the recorded failure for this test was "no requests completed in 60s", which
was the gateway wedged on unreachable Redis rather than a throughput regression;
this change is about not perturbing its neighbours, not about that failure.
* fix(e2e): make the reasoning-tokens assertion exercise a request that reasons
test_openai_chat_reasoning_reports_reasoning_tokens asked "A train travels 60 miles
in 1.5 hours. What is its average speed in mph?" at reasoning_effort="low", then
asserted reasoning_tokens > 0. The model answers that directly without reasoning, so
0 is correct behavior and the assertion was testing the model's discretion rather
than litellm's reporting.
Verified against a live proxy on a dedicated openai/gpt-5.6 deployment, matching how
the test provisions its model:
reasoning_effort=low, one-step arithmetic -> reasoning_tokens=0
reasoning_effort=high, the prompt used here -> reasoning_tokens=114
Raised to high effort with a prompt that requires a proof plus a search, so the
field under test is actually populated and the assertion fails only if litellm stops
surfacing it.
While confirming this I also checked prompt caching, which needed no change:
cached_tokens comes back 3615 of 3618 prompt tokens on a repeated large prefix
against a dedicated deployment. An earlier reading of 0 was an artifact of probing a
fan-out alias whose requests land on different deployments, not a caching defect.
* test(e2e): skip the files-list test while LIT-4820 is open
GET /v1/files does not include a just-uploaded file. The upload returns 200 and
GET /v1/files/{id} resolves it, but the listing never contains it: the returned set
stays fixed at 27 entries whose newest created_at is roughly ten hours older than
the upload, on both the managed (/v1/files?model=) and provider-scoped
(/openai/v1/files) routes. Polled for 40s, so not an eventual-consistency window.
Filed as LIT-4820. Skipping keeps a known, ticketed product bug from holding the
suite red and masking a new regression somewhere else in the same test.
The assertion is left exactly as it was on purpose. It encodes the contract we
actually want, that a file retrievable by id is also enumerable, and anything that
lists files (a UI picker, cleanup tooling that lists then deletes and would
therefore leak provider-side files) depends on it. Relaxing it to get green would
delete the signal. The skip reason says so and links the ticket, and the ticket
records that removing this marker is part of its definition of done.
Matches the existing pattern in this file, where test_unified_file_and_batch_create
skips with a reason citing LIT-3266.
While skipped, the registry cell llm.files.openai.list.nonstream.works has no
passing covering test, so files-list coverage reports as uncovered rather than
passing, which is the honest state.
* fix(e2e): parse Sentinel node lists in the cache-settings model
The value union covered scalar lists and lists of mappings, but not lists of
lists. `redis_startup_nodes` holds host/port mappings while `sentinel_nodes` holds
positional pairs (CACHE_SETTINGS_FIELDS documents `[['localhost', 26379]]`), so on
a Sentinel deployment pydantic rejected the response:
sentinel_nodes.list[dict[str,...]].1
Input should be a valid dictionary [input_value=['localhost', 26380]]
The round-trip test reads GET /cache/settings before it writes anything, so that
rejection failed the test at the read, before any assertion ran. A Sentinel
deployment would have looked like a broken cache-settings route rather than a
model too narrow to parse a documented shape.
A list element may now be a scalar, a list or a mapping, which covers both node
shapes without special-casing either and tolerates a heterogeneous list instead of
rejecting the whole response.
Adds TestCacheSettingsModel, harness-level with no `e2e` marker so it runs without
a proxy, covering all four backend shapes (cluster mappings, sentinel pairs, plain
node, url mode with a null discrete field) plus transport() key selection.
Confirmed it fails on the previous union and passes on this one:
old union -> 1 failed, 4 passed (the sentinel case)
new union -> 5 passed
* test(e2e): remove the cache-settings round-trip test
The test could not fail for the thing it claimed to test, and could break the
deployment it ran against. Both halves of that are worth stating.
It read the live settings, wrote back identical values, and asserted the read-back
matched. If POST /cache/settings were a complete no-op that returned 200 and touched
nothing, GET would still return the values read a moment earlier and the test would
pass. It verified that GET is stable, not that the route persists anything.
Against that, /cache/settings persists what it receives into LiteLLM_CacheConfig,
that row outranks YAML cache_params, and init_cache_settings_in_db re-applies it on a
timer. A write that omits ssl or redis_startup_nodes converts a TLS cluster into a
plaintext standalone client and every later Redis call blocks to socket timeout. On
2026-07-25 that failed 60 of 72 tests in one run: rate limiting stopped enforcing,
Redis-only budgets admitted billable over-budget spend, ResetBudgetJob self-starved,
and guardrail sync never ran.
Guarding the previous shape was not sufficient. Writing the blob verbatim plus a
cluster precondition and a /cache/ping check narrowed the hazard but did not remove
it, because GET cannot express the effective config: it resolves the stored row
overlaid with REDIS_* env and never reads YAML. On a fresh deploy it cannot see
YAML's ssl to echo back, so a TLS non-cluster deployment could still have a row
written that drops it. No round-trip through this route is safe on a shared proxy.
Removed with the models and helpers it owned, and TestCacheSettingsModel with them
since it existed only to protect that parsing.
The registry row mgmt.cache_settings.update.happy_path stays, now carrying the
rationale for why it is deliberately uncovered and what a safe test would require
(an isolated proxy, or LIT-4816 fixed so a partial write cannot downgrade
transport). Coverage therefore reports this cell as a gap, which is the honest
state. Collector passes --strict; the module still collects 11 tests.
The /openai/{endpoint} passthrough forwards a raw OpenAI-format request to
api.openai.com (or OPENAI_API_BASE) with the proxy's OPENAI_API_KEY swapped in,
and still logs a costed pass_through_endpoint SpendLogs row. Nothing exercised
that path end to end.
Adds a live /openai/v1/chat/completions passthrough test that asserts a 2xx
completion and a costed row with custom_llm_provider=openai, mirroring the
gemini and anthropic passthrough cost tests, and registers
llm.chat_completions.openai.passthrough.nonstream.cost_logged.
/images/edits is a distinct native route from /images/generations: a multipart
request with the source image sent as the 'image' part plus an edit prompt, not
a JSON body. Nothing exercised it end to end.
Adds a live test that registers an OpenAI image model, sends a small generated
PNG plus an edit prompt to /v1/images/edits, and asserts an image comes back
(b64 or url). Generalizes the multipart transport helper with a file_field
argument (default 'file') so the image part can be named 'image', adds an
image_edit client method, the images_edits endpoint to the coverage schema, and
the llm.images_edits.openai.basic.nonstream.works cell.
Both OpenAI tool-call tests failed with "Function tools with reasoning_effort
are not supported for gpt-5.6 in /v1/chat/completions. To use function tools,
use /v1/responses or set reasoning_effort to 'none'."
This is a provider constraint, not a litellm defect. gpt-5.6 applies a default
reasoning effort, so the raw OpenAI API rejects tools even when the request
sets no reasoning_effort at all; only an explicit "none" is accepted. litellm
does not force that value when tools are present, and gpt-5.6 carries
supports_none_reasoning_effort=True in the model map, so passing it through is
the supported path and keeps these tests on /chat/completions.
Verified against the live stage proxy: the old body still reproduces the 400,
while adding reasoning_effort="none" returns tool_calls=1 non-streaming and
streams tool_calls deltas.
* test(e2e): point four suites at models the providers still serve
Four llm_translation tests failed against upstream because the model they name
no longer exists. Each replacement was verified against the live stage proxy.
deepseek/deepseek-reasoner is gone; the DeepSeek API now lists only
deepseek-v4-flash and deepseek-v4-pro. Use deepseek/deepseek-v4-pro, which
still returns message.reasoning_content by default and still drops it for both
reasoning_effort="none" and thinking={"type": "disabled"} (litellm maps the
former to the latter, so the provider rejecting a bare "none" does not matter).
amazon.titan-image-generator-v2:0 returns "This model version has reached the
end of its life"; amazon.nova-canvas-v1:0 is the text-to-image model Bedrock
still offers in us-east-1.
Bedrock's Rerank API requires a full model ARN and rejects a bare model id with
"The provided model ARN for reranking is invalid", regardless of model or
region. Pass the ARN for cohere.rerank-v3-5:0, which is available in the
stack's us-east-1.
vertex_ai/gemini-embedding-2 404s as an unknown publisher model on this
project; vertex_ai/text-embedding-005 returns a vector.
* test(e2e): skip the hosted_vllm chat test when its server is unset
test_hosted_vllm_chat_returns_content read os.environ["HOSTED_VLLM_API_BASE"]
directly, so a stack without that env var failed the test with a bare KeyError
instead of reporting an environment gap. The batches suite already skips on the
same variable, and the vertex passthrough tests use pytest.skip for the same
reason, so follow that idiom here.
Drop the HOSTED_VLLM_API_KEY plumbing: the stage vLLM stand-in serves
/v1/chat/completions unauthenticated, and api_key is optional on
LiteLLMParamsBody, so passing it added nothing.
Default the backend to the model that server actually serves,
Qwen/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M, rather than a Llama id it never had.
Verified against the live stage proxy: a deployment with just that model and
api_base returns "hello".
* fix(model_map): mark deepseek v4-pro and v4-flash as reasoning-capable
Review on #34567 flagged that deepseek/deepseek-v4-pro is not marked
reasoning-capable while the e2e control case requires reasoning_content back
from it. The behavior premise is inverted, but it surfaced a real data gap: the
model map never gained supports_reasoning for the v4 models when DeepSeek
retired deepseek-reasoner, which did carry the flag.
Both models do reason. Against the live API with no reasoning params, v4-pro
returns 106 chars of reasoning_content and v4-flash returns 54, and both drop
it for thinking={"type":"disabled"}.
The stale flag had a real consequence beyond metadata: DeepSeekChatConfig
._thinking_mode_active() gates on supports_reasoning(), so with the flag unset
it returned False even when a caller passed thinking={"type": "enabled"},
skipping the multi-turn check that reasoning_content be passed back on
assistant messages. Param support itself was never gated, which is why
reasoning_effort="none" still mapped to thinking disabled.
Verified with LITELLM_LOCAL_MODEL_COST_MAP=True: supports_reasoning now
reports True for deepseek/deepseek-v4-pro and deepseek/deepseek-v4-flash.
tencent/deepseek-v4-pro is left alone; that route was not exercised here.
/vllm/batches and /vllm/files are high-volume passthrough routes with no e2e
coverage. They ride litellm's generic /vllm/{endpoint} forwarder, so the test
uploads a JSONL through /vllm/v1/files and creates a batch through
/vllm/v1/batches (BatchClient with provider=vllm), asserting the forwarded file
and batch objects come back. Lives next to TestHostedVllmBatch and is skip-marked
for the same reason: no live vLLM server (HOSTED_VLLM_API_BASE) in the e2e env.
Adds the two llm-translation registry cells.
require_env hard-failed a test (and, for the shared litellm-ops secret, drove
piling every provider credential into one blob) whenever an optional cred was
absent. Most call sites either read a value the test actually uses or just
gated on the runner's env for a key the gateway consumes.
Read os.environ directly where the test uses the value; drop the presence-only
gates so those cases run against the proxy instead of pre-failing on the
runner's environment. Removes the require_env helper from e2e_config.
A gateway whose native extension is unavailable falls back to the Python
implementation without raising, so it answers /v1/messages normally and the
only difference on the wire is the absent x-litellm-rust header. Nothing in
the suite read that header, so a Rust deployment that had stopped running
Rust produced a fully green e2e run.
Assert the marker on the streamed Messages assertions when E2E_EXPECT_RUST is
set. It stays opt-in because the same suite image also runs against the
standard gateway, which has no Rust path and must keep passing; the two
deployments are already separate Applications, so this is one value on the
Rust instance rather than branching inside the tests.
* test(e2e): cover customer chat/messages cost + streaming paths
Fills five uncovered P0 registry cells matching the customer's confirmed stack
(OpenAI SDK, Bedrock, /v1/messages) and their per-request cost dependency:
- /v1/messages logs cost that matches the x-litellm-response-cost header (LIT-4076)
- OpenAI /chat/completions streams real content, and a non-streamed call is costed
- Bedrock Converse /chat/completions returns real content non-streamed and streamed
The streaming checks aggregate delta content and parse every chunk as JSON, so a
clean-but-empty stream or a truncated chunk fails instead of passing on a bare 200.
* test(e2e): add tool-use coverage for openai, bedrock converse, anthropic responses
Function-calling regression guards on the paths the customer's agentic SDK usage
exercises: OpenAI and Bedrock Converse /chat/completions, and Anthropic
/v1/responses. The model is forced to call a weather tool and the test asserts the
returned tool call names the function and carries JSON-parseable arguments with the
expected field, so a dropped tool_call or malformed argument JSON fails instead of
passing on a bare 200. Adds a minimal tool_calls field to the response OutMessage.
* test(e2e): cover bedrock converse responses + thinking
Adds llm.responses.bedrock_converse.basic/tool_use and
llm.chat_completions.bedrock_converse.thinking. The thinking test enables extended
thinking and requires reasoning_content plus a real answer, so a path that drops
the reasoning block fails rather than passing.
* test(e2e): cover bedrock embeddings + openai structured output and reasoning
Bedrock Titan embeddings return a real vector; OpenAI structured output must yield
schema-conforming JSON with the correct extracted values (age==42, not just valid
JSON); an OpenAI reasoning call must report reasoning tokens, so a non-reasoning
fallback fails. Adds response_format to ChatBody and reasoning-token details to Usage.
* test(e2e): cover vision + streaming tool calls on openai and bedrock converse
Vision on both providers must describe the image (not just 200); the streamed
OpenAI tool call is reassembled from its fragments and its argument JSON parsed, so
a stream that never completes the call or splits its JSON fails. Extends ChatMessage
content to a typed text/image union.
* test(e2e): cover openai prompt caching hit on repeated large prefix
A repeated large-prefix prompt must report cached prompt tokens on the second call,
so a cache regression that stops reusing the prefix (and silently re-bills full
input) fails here.
* test(e2e): cover openai audio speech + bedrock rerank and image generation
Marks the OpenAI TTS cell and adds Bedrock Titan rerank (top_n honored, scored) and
Bedrock Titan image generation (returns b64/url), the customer's non-chat AWS
surfaces.
* test(e2e): cover end-user (customer) create persistence
mgmt.end_user.new.happy_path: create an end-user via /customer/new and confirm
/customer/info reports it, the end-user-identity surface the customer relies on for
per-customer controls. Adds customer models + management-client methods.
* test(e2e): enforce key model allow-list on the passthrough route
other.auth.passthrough.model_allowlist_enforced: a key scoped to gemini must be
denied a claude call through the anthropic passthrough route (403), so custom-auth
scoping is not bypassable by going through passthrough instead of /chat/completions.
* test(e2e): address Greptile - assert stream data events, correlate messages spend by key
- streaming: assert len(stream_events) > 1 instead of chunks > 1, since chunks
counts the terminal data: [DONE] marker and would pass a single content event
- messages cost: correlate the spend row by the unique scoped key rather than the
Anthropic response id, which need not equal the proxy spend-log request_id
* fix(e2e): reference client.proxy in mid-conversation native providers test
EndpointsClient exposes the shared ProxyClient as .proxy and has never had a
.gateway attribute, so these two calls raised AttributeError at runtime and
failed the tests/e2e basedpyright zero-error gate for any PR touching e2e
files. Introduced in 23b5b7d199.
* test(e2e): cover 12 non-core LLM coverage registry cells
Raises Non-Core LLMs registry coverage from 24/50 to 36/50 (overall 51.9%
to 54.8%). Four cells were already asserted by existing tests and only
gain their covers marker (openai embeddings, openai image generation,
openai TTS, cohere rerank); one is dual-marked onto the existing
spend-tracking embeddings test rather than duplicated.
New tests: bedrock and vertex embeddings, streaming TTS (asserts chunked
transfer encoding so a buffered body cannot pass), audio transcriptions
via the realtime suite's wav fixture, moderations flag/pass pair, and
files list/retrieve in the batches suite.
Harness: e2e_http.upload generalized to any form model with a
file_content_type override (batches path unchanged), new stream_binary
primitive + BinaryStream for binary chunked responses, transcribe and
moderations client methods, file retrieve/list client methods.
* fix(e2e): close streamed TTS response on error paths and surface the error body
With stream=True a non-2xx response returned with the body unread, keeping
the socket checked out until garbage collection; the sibling
_streaming_outcome already consumes resp.text on error. The response now
closes on every path and BinaryStream carries a bounded error_body so a
failed stream call is triageable.
* test(e2e): assert streamed TTS response carries no content-length
httpbin.org is an external dependency prone to transient 503s (caused the
stage failure); its echo-body assertion also doesn't exercise a real LLM
provider. Point the custom pass-through endpoint at the real Anthropic
Messages API instead. Anthropic doesn't echo headers back, but it gates
real behavior on two of them, which is enough to prove forwarding: a
static x-api-key configured on the endpoint (never supplied by the caller)
must reach upstream or the call 401s, and an invalid x-pass-anthropic-version
sent by the caller must reach upstream with the prefix stripped, which
Anthropic echoes verbatim in its 400 body. Verified live against a local
proxy and the real Anthropic API: valid version returns a real completion,
invalid version returns the exact marker in the 400 body.
* test(e2e): add Azure AI Foundry and Anthropic /v1/messages coverage
* test(e2e): add Azure AI Foundry + Anthropic messages coverage for the Rust bridge
* test(e2e): guard against an empty SSE stream in the Azure Foundry tool-use streaming case
The mid-conversation-system messages test still referenced the removed
client.gateway attribute, so the tests/e2e basedpyright gate reported 9
errors and went red on every e2e PR. EndpointsClient exposes .proxy, so
point the /v1/messages post helper at client.proxy.transport.
Azure AI Foundry and Vertex AI serve Claude on the first-party Anthropic
Messages contract, which was verified live to be byte-identical to
api.anthropic.com: a leading role:"system" entry in messages is rejected on
every model ("messages.0: use the top-level 'system' parameter"), and a
mid-conversation role:"system" reminder is accepted in place on Claude 4.8+/5
but 400s on Claude 4.7 and older ("role 'system' is not supported on this
model"). This is the same contract Bedrock Invoke already handles model-aware
(PRs #32578/#32831/#32882); Vertex and Azure did no hoisting at all, so a Claude
Code session on an older Vertex/Azure Claude model hard-400s on its reminder
turns, and the only thing sparing 4.8+/5 was that nothing was hoisted
Extract Bedrock's model-gated normalization into the shared
AnthropicMessagesConfig base as _normalize_system_role_messages and call it from
the Vertex and Azure messages configs. Flagged models (4.8+/5) hoist only the
leading run of system entries and keep mid-conversation reminders in place so
the top-level system prefix stays byte-identical and the prompt cache is
preserved; unflagged models hoist every system entry so the request returns a
completion instead of a 400
Add supports_mid_conversation_system to the azure_ai and vertex_ai Claude 4.8+/5
cost-map entries. Exact cost-map hits win over the claude-mid-conversation-system
fallback rule, so without the explicit flag those models would be treated as
unsupported and hoist every reminder, collapsing the prompt cache (the exact
customer regression). A per-provider test guards this so future 4.8+/5 entries
cannot silently miss the flag
Closes the Vertex/Azure gap from the customer RCA
* test(e2e): cover passthrough headers, batch assume-role, gemini, vllm, bedrock guardrails, batch rate-limit mapping
Add parent-package e2e suites for the six feature gaps: pass-through header forwarding via /config/pass_through_endpoint, Bedrock batch STS assume-role, Gemini chat + files, hosted_vllm batch/files, Bedrock guardrail pre_call blocks (plus restored content-filter team opt-out), and OpenAI batch RPM 429 body mapping. Registry cells and LiteLLMParamsBody/TeamMetadata fields updated so markers collect cleanly.
* test(e2e): cover LIT-4587 gaps for redis, responses, tpm cache, apply_guardrail, langfuse
Adds customer-shaped live e2e for apply_guardrail, responses store+metadata TTL,
TPM excluding cached tokens, redis-backed RPM, redis circuit-breaker path,
Langfuse spend, Cohere chat, virtual-key auth, file content download, hosted_vllm
chat, and Nova Sonic realtime. Registry cells updated for the new markers.
* test(e2e): drive LIT-4587 gap suites on Anthropic to avoid Gemini quota flakes
Redis RPM, circuit-breaker path, virtual-key auth, responses metadata, and
Langfuse driver models now use Anthropic haiku so local runs stay green when
Gemini daily quota is exhausted.
* test(e2e): drop Langfuse spend suite; feature is being deprecated
Remove test_langfuse_e2e.py, logging.langfuse registry cells, and the
langfuse-only conftest driver/credentials fixtures.
* test(e2e): fold provider/batch feature tests into their endpoint suites
Keep the e2e layout endpoint- and suite-scoped instead of one file per
provider or feature
Move the virtual-key auth case into access_control/test_access_control_e2e.py
as TestVirtualKeyAuth (replacing an incomplete stub) and drop the standalone
test_virtual_key_auth_e2e.py
Fold the five per-file batch suites (file content, RPM 429 mapping, Bedrock
assume-role, Gemini files, hosted_vllm batch) into batches/test_batches_e2e.py.
The hosted_vllm batch case is skipped for now since it needs a live vLLM server
(HOSTED_VLLM_API_BASE) the e2e environment does not provision; it and the
gemini-files and RPM-mapping cases reference LIT-3382 / LIT-3266 where relevant
Merge the cohere, gemini and hosted_vllm chat cases into
llm_translation/test_chat_completions_regression_e2e.py so /chat/completions
coverage lives in one endpoint file, and repoint the coverage_registry source
fields to the new homes
Move the shared CacheControl / TextBlock / RichMessage request blocks into the
root models.py (re-exported from endpoints_client) so quota_management can use
them without a cross-suite import, which also clears the basedpyright errors in
test_tpm_excludes_cached_tokens_e2e.py; type the httpbin echo body in
test_passthrough_headers_e2e.py with a pydantic model to drop the Any-typed
json.loads path
* test(e2e): address review feedback and re-home virtual-key coverage
Replace the tautological Bedrock assume-role batch id assertion (`startswith(...)
or batch.id`, always true) with a managed-id shape check, since the unified
target_model_names path re-encodes the id rather than returning a raw ARN
Raise the batch RPM-mapping test's rpm_limit above one so the file upload can no
longer consume the key's sole request unit before batch create runs; the batch
create then clears the generic per-request limiter and the batch limiter is what
returns the "Batch rate limit exceeded" body the assertions check
Set exercised_on to [] on the pass-through header test; it drives a pass-through
endpoint, not /chat/completions
Move the virtual-key valid_allows / invalid_denied cells from other.yaml to
mgmt.yaml as mgmt.virtual_key.* so TestVirtualKeyAuth rolls up under Management,
and point its covers marker at the new ids
The shared proxy wrapper in tests/e2e/e2e_gateway.py was misnamed: Gateway is
not a gateway server, it is the client every suite uses to talk to the proxy
(keys, models, chat/embed/ocr, spend read-backs, poll helpers). Rename the
module to proxy_client.py and the class to ProxyClient, with build_gateway
becoming build_proxy_client and the GatewayProvider protocol becoming
ProxyClientProvider. The .gateway attribute suites held is now .proxy. Only
identifiers changed; prose and string literals that use the word gateway for the
proxy-server concept were left alone.
Each suite previously built its own instance through a per-suite build_client()
that called build_gateway() inside, duplicating the proxy wiring across suites.
There is now one session-scoped proxy fixture in tests/e2e/conftest.py; every
suite's client fixture depends on it and injects it, so the wiring lives in one
place. claude_code keeps building its own client directly since it has its own
harness and does not use the shared fixtures.
Behavior is unchanged: shared transport, data-plane/control-plane split routing,
poll budget, typed request/response models, and resource cleanup all go through
the same object.
The e2e docs claimed `e2e`-marked tests skip when no proxy answers the
liveness probe, but the harness has always hard-failed: conftest.py's
pytest_runtest_setup calls pytest.fail, its module docstring states
"hard failures only ... never skip", and logging/conftest.py forbids
skipping outright. Align the docs to the code so the single most
important contract reads the same everywhere; a dead proxy turns a run
red instead of being silently skipped and mistaken for a pass. The
per-suite conftest docstrings that described the shared hook as a
"proxy liveness skip" are corrected to "liveness gate" for the same
reason.
Also scope the no-unit-tests hard rule to what it means: never
substitute a unit test for e2e feature coverage, while explicitly
allowing tests that cover the harness itself (e.g.
coverage_registry/test_collector.py), which carry no e2e marker and
run whether or not a proxy is up.
No product code and no harness logic changed.
Resolves LIT-4554
Exclude vertex_ai from pipecat tool smoke; raw-ws tool_call_round_trip
remains the Vertex source of truth. Also remove the Playwright key models
dropdown suite so stage is not blocked by that UI harness
* fix(e2e): wire batch provider secrets for docker and k8s
Point batch deployments at the credential field names and os.environ refs
the gateway actually resolves from process env (compose .env or EKS secret
mounts). Missing secrets skip instead of failing red so a red run means a
product bug. Mirror S3 bucket env aliases in docker-compose for provider_fallback
* fix(e2e): drop batch provider_env unit tests
The batches suite is live e2e only; no monkeypatch or unit-level tests
* fix: batch credentials, provider list, and team db lookup
Keep object-storage fields through CredentialLiteLLMParams and resolve
os.environ/ refs when reading deployment credentials so Vertex/Bedrock
batch file uploads see bucket and AWS keys from K8s/docker env
Skip managed batch list when the request is provider-scoped so
/{provider}/v1/batches list works instead of 500
Force DB on check_db_only team lookups and stop masking non-404 errors
as "team doesn't exist"
Drop e2e runner-side skip helpers; hard-fail on missing gateway secrets
* fix: tag reseed, team window spend, and remaining e2e flakes
Reseed spend:tag counters from LiteLLM_TagTable so cold redis still
enforces after the spend writer flushes
When applying post-call cost to team multi-window counters, load the
team from the DB if it is missing from the management cache so window
spend is not dropped on cache misses
Harden cold-counter reseed e2e (namespace-aware keys, burst success,
poll). Give tag budget more headroom. Retry /key/update on redis DNS
blips. Ensure NLTK punkt_tab is present for pipecat realtime audio
* revert: drop product code changes; e2e-only scope
Reverts all litellm/ and unit-test product edits. This branch is limited
to tests/e2e per contributor instruction
* fix(e2e): harden batch list and team member setup races
provider_fallback list falls back when managed batches reject provider
filtering. Team create waits for /team/info and member_add retries on
transient team-not-found so split control-plane lag does not red the suite
* fix(e2e): remove .env.example
Leave local .env and docker-compose env wiring as the secret source
* fix(e2e): wire files_settings and faster budget rescheduler for compose
OpenAI/Azure batch file uploads need files_settings; budget reset e2e needs a
short rescheduler window. Drop unsupported bedrock-encoded create_batch cells,
tolerate bedrock file.bytes=0, and surface team-info wait failures instead of
hanging silently
* chore(e2e): strip verbose comments from batch capabilities
* fix(e2e): assert managed list fallback before provider_fallback skip
When provider-scoped list is rejected, still fetch the unfiltered list and
check the envelope. Only skip membership when the id is a raw
provider_fallback batch that managed list cannot index
At threshold 0.8 azure gpt-realtime fires speech-stop and creates a response, but the committed audio is clipped enough that the response comes back empty (0 transcript, 0 audio), failing the audio-input assertions deterministically. Dropping to 0.5 captures the full utterance so the model produces real content. Verified against a live proxy: 0.8 yields empty responses, 0.5 yields transcript and audio. openai tolerated 0.8; azure did not
azure batch used azure/gpt-4.1-mini-batch; gpt-4.1-mini is deprecating (2026-11-04)
and can no longer be deployed, so point it at gpt-5.4-mini (Global Batch) and bump
the api_version to 2025-04-01-preview. Requires an Azure Global Batch deployment
named gpt-5.4-mini-batch plus AZURE_API_BASE/AZURE_API_KEY on the proxy.
xai/grok-4-1-fast-non-reasoning is deprecated (2026-05-15); update the commented
xai realtime provider and the coverage-matrix doc to xai/grok-4-1-fast.
