Two suites broke because they stood in for production objects with stand-ins
that no longer answer the same way.
The redaction test faked a ResponsesAPIResponse and then reassigned
builtins.isinstance so the fake would pass the type check. Redaction now gates
on a tuple of accepted types, and the patched isinstance only recognised the
bare class, so the fake fell through to the generic branch and the assertions
ran against a plain dict. Building a real ResponsesAPIResponse drops the
builtins patch entirely and exercises the same type gate production takes.
The batch rate limiter tests constructed _PROXY_BatchRateLimiter with
parallel_request_limiter=None even though the parameter is not optional. That
stayed harmless until the output-token estimate started reading the limiter,
which turned it into an AttributeError. Inject the limiter the proxy injects,
sharing one InternalUsageCache the way _add_proxy_hooks does.
Three groups, all verified by running the suite rather than by inspection.
18 files whose every test function carries an unconditional @pytest.mark.skip,
39 test functions in total. They are collected on every CI run and always skip,
so they advertise coverage the suite does not have. Reasons on the marks include
"AWS Suspended Account", "lakera deprecated their v1 endpoint" and "moved to
using 'otel' for logging"; 26 of the marks predate 2025.
30 test functions with a byte-identical body and identical decorators to a
sibling in the same file and class, differing only in name. Deleting one of each
pair removes no coverage. Four further candidates were excluded because they
override an inherited test, where deleting the override un-shadows the base
class implementation instead of removing a duplicate.
9 test functions that a later definition of the same name shadows, so Python
never binds them and pytest cannot collect them.
One file that is a demo script rather than a test; its own docstring says to run
it with python.
Verification: collecting the 26 edited files gives 2,492 node IDs before and
2,462 after. The 30 duplicate deletions account for exactly 30 removals, the 9
shadowed deletions account for 0 (confirming at runtime that they were never
collectable), nothing unexplained disappeared, and nothing new appeared. No
other test or module imports any deleted symbol.
Completed-batch cost tracking parsed the whole output file into a list of
dicts, pretty-printed it into debug strings even with debug logging off, and
walked the list three times (cost, usage, models), so a large batch output
could pin a worker's memory. The output is now folded line by line into small
per-line stats records via _aggregate_batch_cost_usage_models, the eager
json.dumps debug calls are gone, and the raw-vertex path computes cost and
usage in one call instead of two. _get_batch_output_file_content_as_dictionary
becomes _fetch_batch_output_file_content (returns bytes); the superseded
three-pass helpers are deleted and their tests migrated
OpenAI is winding down self-serve fine-tuning and the org can no longer
create fine-tuning jobs (403 training_not_available; the CI key surfaces
it as a 500 server_error), so test_create_fine_tune_jobs_async fails on
every batches_testing run since 2026-07-11 and reruns never clear it.
The request contract stays covered by the mocked create/list/cancel/
retrieve tests in the same file, and the deleted test's unique
standard_logging_object assertions now run inside
test_mock_openai_create_fine_tune_job.
* tests: add e2e tests for spend, budgets and llms
* style: make chained comparison of status_code clearer
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* remove e2e_tests folder
* test: add spend tracking tests
* fix: p0 issues, added types and shared functions for each test suite
* style: carry clearer status_code comparison into renamed e2e dir
* refactor: migrate to gateway client
* fix: add new tests, split gateway
* test(e2e): add live batches suite across providers and routing scenarios
* test(batches): cover real cost tracking on completed batch retrieve
* test(e2e): assert managed vs raw file and batch id shapes per routing scenario
* test(e2e): assert full response shape of each batches and files endpoint
* test(e2e): only accept transitional statuses for a freshly created batch
* test(prompt-factory): make test_convert_url deterministic with a data URL
picsum.photos is down (HTTP 522), so test_convert_url failed on every
run. Swap the live external image for an inline data: URL and assert the
round-trip through convert_url_to_base64 genuinely.
A data URL is already inline base64 image data, so convert_url_to_base64
now short-circuits it instead of attempting an impossible HTTP fetch;
add a regression for that branch in the mapped image_handling test
* fix: pass through async image data urls
* fix(image-handling): short-circuit data URLs in async path too
Bugbot flagged that convert_url_to_base64 returns data: base64 URLs
unchanged but async_convert_url_to_base64 still tried to fetch them,
so async OCR flows (Bedrock, Azure) would reject inline images the sync
path accepts. Add the same guard to the async function and a regression
test that asserts the async path returns the data URL without touching
the HTTP client
* Fix: openai batches lifecycle
* Fix: add e2e azure openai tests
* Fix e2e for vertex ai
* Add all models for testing
* test(managed-files): assert idempotent upsert in store_unified_file_id
store_unified_file_id switched from create to upsert to avoid
UniqueViolationError when re-storing the same unified_file_id (e.g.
batch output files stored before metadata is available). Update the
unit test to assert the upsert call and its create payload instead of
the removed create call.
* test(batches): reconcile vertex_ai native batch-id comment with fallback guard
* fix(test-config): keep rust-ocr models in model_list by moving files_settings after it
* fix(test-config): move batch models after OCR block to keep merge with internal_staging clean
* fix(batches): use '24hrs' completion window and allow managed-files listing with provider filter
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* style: ruff format transformation.py and endpoints.py
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(e2e/batches): set Azure raw_model to gpt-4.1-mini-batch to match deployed model
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(vertex-ai/batches): correct completion_window to 24h per Literal type definition
* test(vertex-ai/batches): align completion_window assertion to 24h
* fix: update managed file metadata on upsert
---------
Co-authored-by: mubashir1osmani <mubashir.osmani777@gmail.com>
Co-authored-by: Mateo Wang <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(vertex_ai/files): upload batch files in a single media request to fix 499s on large uploads
PR #31036 switched the vertex batch file upload from a single GCS media
upload to a chunked resumable session. The resumable path sends the body as
many sequential PUTs, each waiting a full round-trip to GCS before the next,
so a multi-GB upload accumulates hundreds of round-trips and overruns the
client/load-balancer request timeout, surfacing as 499s (client closed
connection) on files as small as 500MB. This was a regression from the
last-known-good commit, where the upload completed as one continuous request.
Revert the batch upload to a single uploadType=media request, but stage the
transformed payload to a temp file first so peak memory stays bounded (the
goal of the resumable rewrite) without the per-chunk round-trips. The temp
file is closed deterministically (TemporaryFile unlinks on close), not left
to the GC. The now-unused resumable chunked-upload plumbing is removed.
Also swap the per-row transform's stdlib json for orjson (parse + serialize),
which is ~4x faster on this hot path; the streaming body now emits compact
orjson bytes.
The request stays synchronous, so the returned file object is real and
POST /v1/batches keeps working immediately against the uploaded object.
Tests: single media request carries the whole payload with a real
Content-Length (no chunked transfer-encoding); failed upload raises; the
staged temp file is closed deterministically; byte-for-byte transform parity.
* test(vertex_ai/files): mock single media upload POST instead of removed resumable method
test_avertex_batch_prediction patched BaseLLMHTTPHandler._aresumable_chunked_upload, which was removed when the batch jsonl upload moved from a chunked resumable GCS session to a single uploadType=media request. Patch the raw httpx.AsyncClient.post that _astage_and_upload_media issues so the real staging, upload and response transform run while the GCS object response is mocked, and assert the media URL and Content-Type.
* fix(vertex_ai/files): forward request timeout to media upload, drop orjson, sort imports
Forward the per-request timeout through _stage_and_upload_media /
_astage_and_upload_media to the GCS POST. Every other upload branch forwards
it; the new media path was dropping it, so a caller-provided timeout was
silently ignored (the files path passes 600s by default, but a custom
request_timeout would not have reached this upload). Regression test asserts
the resolved timeout reaches the request (mutation-verified).
Revert the orjson swap in the batch transform: importing orjson at module load
in this core-path file broke `import litellm` on environments without orjson
(the Windows import test). Back to stdlib json; the upload leg dominates large
uploads anyway, so the transform-side win was marginal.
Fix import ordering in llm_http_handler.py (I001) introduced by the new imports.
* fix(vertex_ai/files): stream batch upload to GCS instead of staging to a temp file
Addresses a disk-exhaustion concern: staging the full transformed batch body to
a local temp file before the GCS request meant an authenticated user could fill
the proxy's temp volume with large concurrent uploads (on top of Starlette's
input spool).
GCS's simple/media upload accepts chunked transfer-encoding, so stream the
transform straight to the single media request instead. Each block is produced
on a worker thread (the transform never runs on the event loop) and sent
chunked, so the body is neither buffered in memory nor written to disk, and the
upload is still one continuous request (no per-chunk round-trips, no 499). Drops
the temp-file staging, the tempfile/IO imports, and Content-Length computation.
Regression test asserts the upload streams (chunked transfer-encoding, no
Content-Length) and creates no temp file; mutation-verified that reintroducing
staging fails it.
* fix(vertex/files): stream OpenAI->Vertex batch JSONL uploads to fix OOM on large files
Large (1GB+) batch JSONL uploads to Vertex AI / GCS caused OOM or killed the worker
because the request body was buffered and multiplied 2-3x in size. The create-file
path is now streaming end-to-end: transform_create_file_request returns a
ResumableChunkedUploadConfig carrying a lazy _OpenAIToVertexBatchUploadStream, and the
HTTP handler opens a GCS resumable session and PUTs the body in bounded 8 MiB chunks
(Content-Range, 308 between chunks) so the transformed payload is never held in full.
The proxy /v1/files endpoint streams from Starlette's spooled upload handle instead of
reading the whole body, and batch rate limiting counts tokens and models in a single
streaming pass.
Only gcs_bucket_name is supported for the GCS target; the legacy bucket_name key is
intentionally not read.
Also removes the unreachable VertexAIFilesHandler create path and everything only it
kept alive (VertexAIJsonlFilesTransformation, _stream_openai_jsonl_to_vertex, the legacy
transform helpers), plus the orphaned batch_utils helpers the streaming rewrite replaced.
* fix(batches): return original JSONL on unparseable row to avoid silent batch truncation
The streaming rewrite of replace_model_in_jsonl accumulated physical lines and
skipped a row on JSONDecodeError to support multi-line objects, but a genuinely
malformed or truncated row never completes: it poisons the buffer, swallows every
following row, and the function still returned the partial rewrite (the rows before
the bad one, already model-rewritten) as if the batch were complete. That turned the
pre-rewrite behavior of returning the original file unchanged (so the provider rejects
the bad batch loudly) into a silent partial submission.
Restore the original-content fallback: when an unparseable remainder is left after the
loop, return the original file_content (rewinding a consumed seekable source) instead of
the truncated output. The multi-line happy path is unchanged.
* test(batches): mock resumable GCS upload in vertex batch prediction test
The vertex batch file-create path now streams to a GCS resumable session via
_aresumable_chunked_upload (httpx send) instead of AsyncHTTPHandler.post, so the
existing test's post mock no longer intercepted the upload and a real request hit
GCS (401). Mock _aresumable_chunked_upload to return the GCS object response; the
resumable protocol itself is covered in test_vertex_ai_files_streaming.py.
* fix(batches): resilient per-row token accounting; no hard-block on count failure
The batch input-file pass iterated a generator whose json.loads raised on a
malformed line; the outer except caught it and stopped the loop, so any body.model
on rows after a bad line was never collected and the model allowlist check ran
against a partial set. It also hard-blocked the batch with a 400 whenever token
counting raised, a backwards-incompatible change from the prior swallow-and-proceed
behavior that breaks legitimate rows the token counter cannot measure (e.g. some
multimodal content).
Iterate the JSONL line-by-line and account each row independently. A malformed line
is skipped (its request cannot run upstream anyway) and a row the counter cannot
measure falls back to a conservative size-based estimate. The loop never aborts, so
the allowlist check always sees every parseable model, and the token total is never
zeroed, so a crafted uncountable row still cannot evade the TPM limit, without
hard-rejecting a legitimate batch.
* perf(vertex/files): unblock async upload; drop empty finalize; widen batch MIME types
Three review follow-ups on the resumable batch upload:
- _aresumable_chunked_upload pulled chunks from a synchronous generator that runs
the per-row transform inline on the event loop thread, blocking other requests
between PUTs on large uploads. Each chunk is now produced via asyncio.to_thread.
- _iter_resumable_chunks no longer yields a trailing empty chunk, so an exactly
chunk-aligned upload finalizes on its last data chunk instead of an extra
zero-byte PUT; a 0-byte stream still finalizes via the caller's empty request.
- valid_content_type now accepts the MIME types clients label .jsonl batch uploads
with (text/plain, application/json, ndjson, ...), so such a batch file no longer
silently bypasses the streaming path into the buffered media upload.
* fix(vertex/files): keep legacy bucket_name as GCS bucket fallback
The rename to gcs_bucket_name dropped the legacy bucket_name key entirely, so an SDK caller passing bucket_name to a Vertex AI file create/retrieve/content call with GCS_BUCKET_NAME unset got ValueError("GCS bucket_name is required") where it previously resolved the bucket. _get_configured_bucket_name now reads gcs_bucket_name, then bucket_name, then the env var, and bucket_name is restored to OPTIONAL_KWARGS_KEYS so it survives get_litellm_params on the retrieve and content paths. gcs_bucket_name keeps precedence when both are present
* style: sort imports in llm_http_handler to satisfy I001 budget
---------
Co-authored-by: Yuneng Jiang <yuneng@berri.ai>
* fix(model_prices): correct regional processing uplift assignment
gpt-4.1, gpt-4o, gpt-5, and their variants were incorrectly carrying
the 10% EU/US regional processing uplift multiplier. Per OpenAI's
pricing docs, the uplift applies only to models released on or after
2026-03-05 (gpt-5.4 series and gpt-5.5 series).
Removes the uplift from: gpt-4.1, gpt-4.1-mini, gpt-4.1-nano,
gpt-4o, gpt-4o-2024-08-06, gpt-4o-2024-11-20, gpt-4o-mini, gpt-5,
gpt-5-pro, gpt-5-mini, gpt-5-nano.
Adds the uplift to: gpt-5.4, gpt-5.4-mini, gpt-5.4-nano, gpt-5.4-pro,
gpt-5.5, gpt-5.5-pro.
* fix(model_prices): apply same regional uplift correction to backup file
* fix(model_prices): add regional uplift to date-versioned gpt-5.4/5.5 siblings
* test(model_prices): update data residency tests to use gpt-5.4 as the uplift model
The tests were using gpt-5 which no longer carries the regional processing
uplift after correcting which models have it. Switch to gpt-5.4 (released
2026-03-05, the cutoff date) and add a regression parametrize covering
all pre-cutoff models to pin that they stay uplift-free.
* test(batches): use gpt-5.4 for data residency uplift assertion
batch_cost_calculator's data residency uplift test still pinned gpt-5,
which no longer carries the regional processing uplift after this change.
Switch it to gpt-5.4 (the canonical post-cutoff uplift model), matching
the llm_cost_calc test update.
---------
Co-authored-by: mgalbato <37748295+mgalbato@users.noreply.github.com>
* test: stabilize batch VCR coverage
* test: replay bedrock batch s3 uploads
* test: stop batch tests leaking live uploads
* test: keep bedrock batch workflow off live s3
* test: mock bedrock batch workflow network
* test: accept realtime guardrail refusal wording
* test: update gemini thought signature model
* test: quiet logging worker atexit flush
* test: address Greptile review on batch VCR fixes
Handle content= bodies in the bedrock batch post stub so payload
extraction does not raise a TypeError when a request omits json and
data. Restore litellm list state faithfully by preserving None instead
of coercing it to an empty list, so callbacks that start as None are not
turned into [] after a test. Set logging.raiseExceptions inside the try
block in the atexit flush so the finally always restores the previous
value.
* test: scope atexit logging suppression to the drain loop
Wrap only the queue drain loop in LoggingWorker._flush_on_exit with the
logging.raiseExceptions toggle so the process-wide global is suppressed for
the smallest possible window, keeping other threads' logging error reporting
intact outside the loop.
* test: cover atexit flush error-swallow branch in LoggingWorker
The _flush_on_exit drain loop was wrapped in a try/finally to scope the
logging.raiseExceptions toggle, which reindented the existing edge-case
branches into the diff and dropped patch coverage below target. Add a
regression test that enqueues a coroutine which raises during the atexit
flush and asserts the failure is swallowed while later queued events are
still drained, exercising the silent-failure path directly.
This reverts the Bedrock CI account migration (#28728). The original account
(888602223428) was put under an AWS security restriction after a leaked key
and has since been reactivated, while the replacement account (941277531214)
lacks access to several models the suites exercise (legacy Bedrock Claude 3
models, Cohere, Nova Canvas image gen, Bedrock batch inference, and flagship
Opus). Pointing CI back at the reactivated account restores that coverage.
This is the exact inverse of #28728: all hardcoded 941277531214 references go
back to 888602223428 (provisioned/imported-model ARNs, AgentCore runtime ARNs
and their suffixes, batch execution role ARN, and the example proxy config),
the S3 buckets revert to litellm-proxy and load-testing-oct, the guardrail IDs
revert to wf0hkdb5x07f and ff6ujrregl1q, the SageMaker endpoint and Knowledge
Base revert to their original ids, and the live-call tests go back to the
legacy model strings. The grid_spec fail_reason workaround for the unentitled
Opus cells is dropped while keeping the unrelated bedrock_effort_ceiling field
added after the migration.
The CircleCI AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY env vars still point at
941277531214 and must be set to the reactivated account's fresh credentials
separately via the CircleCI API; AWS_REGION_NAME stays us-west-2.
* feat(openai): apply regional-processing cost uplift for EU/US data residency
OpenAI charges a 10% uplift on the latest GPT models when requests are
served from a regionalized hostname (eu./us.api.openai.com). Infer the
region from `api_base`, expose it on `kwargs["litellm_params"]["data_residency"]`,
and multiply the computed cost by a per-model
`regional_processing_uplift_multiplier_<region>` field.
https://claude.ai/code/session_012ebH44s7ohYxjoix5CXzTW
* test: allow regional_processing_uplift_multiplier_{eu,us} in model_prices schema
* fix(cost): tighten data_residency inference and restore model_cost in tests
- Only infer OpenAI data_residency when custom_llm_provider == "openai";
drop the implicit None fallback so non-OpenAI callers can't accidentally
pick up a regional tag from a stray OpenAI hostname.
- _local_model_cost_map fixture now snapshots and restores
litellm.model_cost and LITELLM_LOCAL_MODEL_COST_MAP so tests don't leak
state across the session.
* refactor(openai): move data_residency helper under llms/openai
* fix: thread data_residency through realtime stream cost calculation
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* fix(cost): thread data_residency through batch_cost_calculator
Apply the OpenAI regional-processing uplift multiplier to retrieve_batch
cost paths so Batch API requests served via eu./us.api.openai.com are
priced at the same uplifted token rates as completions/transcriptions.
* refactor(openai): encapsulate provider check inside infer_openai_data_residency
Move the custom_llm_provider == "openai" guard from get_litellm_params
into the helper itself so the core utility no longer carries
provider-specific dispatch logic. Callers pass through the provider
unconditionally; the helper returns None for any non-OpenAI provider.
* fix(responses): thread data_residency through Responses logging params
The Responses API paths build their logging litellm_params dict after
provider resolution but did not include data_residency, so cost calc
saw None even when the effective api_base was a regional OpenAI host.
---------
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* chore(tests): migrate Bedrock CI from AWS account 888602223428 to 941277531214
The original account (888602223428) was put under a security restriction by
AWS after a root access key leaked in a PR comment. While that account works
its way through the AWS Support unlock process, Bedrock-touching CI tests have
been migrated to a fresh account (941277531214).
Changes:
- Replace 26 hardcoded references to 888602223428 with 941277531214 across
8 files (provisioned-model ARNs, imported-model ARNs, AgentCore runtime
ARNs, batch execution role ARN, and example proxy config).
- The provisioned-model and imported-model ARNs are referenced only from
mocked unit tests — no AWS resources to recreate.
- The batch execution IAM role has been recreated in the new account with
the same name and equivalent permissions.
- The two AgentCore runtimes (hosted_agent_r9jvp-3ySZuRHjLC,
hosted_agent_13sf6-cALnp38iZD) are being recreated in the new account
under the same names — see tools/agentcore-deploy/ in a follow-up.
CircleCI env vars AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY / AWS_REGION_NAME
were updated separately via the CircleCI API to point at the new account.
Smoke-tested locally against the new account:
aws bedrock-runtime converse --region us-west-2 \
--model-id us.anthropic.claude-sonnet-4-5-20250929-v1:0 \
--messages '[{"role":"user","content":[{"text":"ping"}]}]'
→ 200, model returned 'pong'
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* chore(tests): refresh AgentCore ARN suffixes to match newly-deployed runtimes
The first migration commit replaced just the account ID, but AgentCore
auto-assigns a random 10-char suffix to every runtime on creation — we
can't reuse the original suffixes (`3ySZuRHjLC`, `cALnp38iZD`) in the
new account. Updated the AgentCore-runtime ARNs in the three files that
reference real runtime IDs (not the mock-based unit-test ARNs).
Deployed runtimes:
arn:aws:bedrock-agentcore:us-west-2:941277531214:runtime/hosted_agent_r9jvp-Rq79QFC2fp
arn:aws:bedrock-agentcore:us-west-2:941277531214:runtime/hosted_agent_13sf6-4046UzHSwy
Both runtimes are status=READY and pass a smoke invoke:
$ aws bedrock-agentcore invoke-agent-runtime --agent-runtime-arn ... --payload '{"prompt":"ping"}'
→ 200, {"result": "echo: ping"}
The agent is a minimal echo (see /tmp/agentcore_deploy/agent.py for the
deploy artifacts). Tests that only verify the SDK wiring will pass; if any
test asserts on agent output content, swap the echo for the real agent.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* chore(tests): point Bedrock batch tests at new-account S3 bucket
The account migration (888602223428 -> 941277531214) was a flat
account-ID swap, which only rewrites ARNs that embed the account
number. S3 bucket names carry no account ID, so the live Bedrock
batch tests still uploaded to `litellm-proxy` — a bucket that lives
in the old account. S3 names are globally unique, and the old account
still holds that name, so it can't be recreated in the new account.
Rename to `litellm-proxy-941277531214` (account-ID suffix guarantees
global uniqueness). The bucket must be created in 941277531214 and the
batch execution role granted s3:GetObject/PutObject/ListBucket on it
before this job is run in CI.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* chore(tests): point live S3 logging test at new-account bucket
Same account-ID-free blind spot as the batch bucket: `load-testing-oct`
lives in the old account and its name can't be reused globally. The
`logging_testing` CI job is wired into the workflow and runs
test_basic_s3_logging, which uploads to this bucket with the CI env
creds, then lists and deletes objects — a live dependency.
Rename to `load-testing-oct-941277531214`. The bucket must exist in the
new account with the CI IAM principal granted
s3:PutObject/GetObject/ListBucket/DeleteObject before this job runs.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* chore(tests): repoint Bedrock guardrail IDs to new-account guardrails
The migration left guardrail IDs untouched (no account ID in them), so
all live guardrail tests failed with "guardrail identifier or version
does not exist" against 941277531214. Recreated both guardrails in the
new account and updated the hardcoded IDs:
- wf0hkdb5x07f -> zgkmukebruil (PII mask: PHONE + CREDIT_DEBIT_CARD,
with explicit inputAction=ANONYMIZE so masking applies to INPUT,
which is the source litellm's moderation hook sends)
- ff6ujrregl1q -> 4w3d1di3snt5 (blocks "coffee"; blocked message set
to the exact string the tests assert on)
Updated test_bedrock_guardrails.py, otel_test_config.yaml, and the
guardrailConfig in test_bedrock_completion.py. Verified locally: the 5
previously-failing guardrail tests now pass.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* test(bedrock): migrate legacy models to current inference profiles
The new CI account (941277531214) cannot invoke legacy Bedrock models
(AWS gates them: "marked by provider as Legacy... not actively using in
the last 30 days"). Migrated the live-call tests:
- anthropic.claude-3-sonnet-20240229 -> us.anthropic.claude-sonnet-4-5-20250929-v1:0
- anthropic.claude-3-haiku-20240307 -> us.anthropic.claude-haiku-4-5-20251001-v1:0
Current Claude models on Bedrock require the us. inference-profile prefix
(bare on-demand ids are rejected).
cohere.command-r-plus has no working replacement (all Cohere is legacy-
gated in the new account): swapped to claude-haiku-4-5 in provider-
agnostic param lists. amazon.titan-image-generator skipped (no working
replacement). Mocked/transformation/cost tests that reference the legacy
strings are intentionally left unchanged. Verified live against the new
account.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* test(bedrock): repoint SageMaker + Knowledge Base to new-account resources
These referenced account-scoped resources by hardcoded id that only
existed in the old account, so the migration's account-ID swap missed
them. Recreated in 941277531214 and repointed:
- SageMaker endpoint jumpstart-dft-hf-textgeneration1-mp-20240815-185614
-> litellm-ci-textgen (gpt2 on a TGI container, ml.g5.xlarge)
- Bedrock Knowledge Base T37J8R4WTM -> LCYXFBR2TU (OpenSearch Serverless
vector store + titan-embed-text-v2, seeded with a LiteLLM doc)
Verified live: test_sagemaker.py (12 passed) and
test_bedrock_knowledgebase_hook.py (12 passed).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* test(reasoning_effort_grid): skip bedrock claude-opus-4-7 cells (not entitled on 941277531214)
claude-opus-4-7 is listed in the new Bedrock CI account's foundation
models but invoke is denied (AccessDeniedException: "not available for
this account"). Bedrock access to the flagship Opus requires an AWS
Sales request, not the self-serve model-access toggle, so it can't be
enabled inline with the rest of the account migration.
Add an optional `skip_reason` to ModelEntry and set it on the
bedrock-claude-opus-4-7 entry; the grid test honors it via pytest.skip.
Cell count (231) and route coverage are unchanged, so the structural
asserts still pass. Restore coverage by deleting the one skip_reason
line once access is granted.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* test(bedrock): swap/skip legacy-gated models unavailable on new CI account
The migrated AWS account (941277531214) cannot access several models that
the old account could, so the remaining red CI jobs were hitting real
Bedrock "Access denied / Legacy" and "account not authorized" errors:
- image_gen: skip both Nova Canvas test classes (amazon.nova-canvas-v1:0 is
legacy-gated), matching the existing titan skip.
- batches: skip test_async_file_and_batch (Bedrock batch inference is not
authorized on the new account; requires an AWS support case).
- litellm_overhead: swap legacy claude-3-5-haiku for the active
us.anthropic.claude-haiku-4-5 inference profile.
- test_completion_claude_3_function_call: swap legacy claude-3-sonnet for the
active us.anthropic.claude-sonnet-4-5 inference profile.
https://claude.ai/code/session_01Y7zgHYu9GX29YRwV4yiWAa
* test(bedrock): fix remaining e2e legacy-model + batch failures on new CI account
- e2e_openai_endpoints: skip test_bedrock_batches_api (Bedrock batch inference
is not authorized on account 941277531214) and migrate the missed
s3_bucket_name in oai_misc_config.yaml to litellm-proxy-941277531214.
- build_and_test: swap legacy bedrock claude-3-sonnet for the active
us.anthropic.claude-sonnet-4-5 inference profile in the proxy structured
output e2e test.
https://claude.ai/code/session_01Y7zgHYu9GX29YRwV4yiWAa
* test(bedrock): make opus-4-7 + batch cells fail loudly and mock image-gen (#28791)
Replace the silent skips added for the new CI account with noisier behavior:
- reasoning-effort grid: opus-4-7 cells now fail (when AWS creds are present)
instead of skipping, so the missing entitlement stays visible in CI; they
still skip when AWS creds are absent (local dev)
- Bedrock batch inference tests: drop the skip so they run and fail until
batch access is granted
- Titan + Nova Canvas image-gen tests: mock the Bedrock HTTP call so the
transform + cost-tracking path stays under test without live model access
https://claude.ai/code/session_01MT7SWDnXUjv6e6EPG7BDjT
Co-authored-by: Claude <noreply@anthropic.com>
* test(bedrock): use pytest.xfail for known-failing opus-4-7 cells
Replace pytest.fail with pytest.xfail when a model has a fail_reason,
so known-broken cells stay visible as XFAIL without keeping CI red.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
---------
Co-authored-by: Mateo <mateo@Mateos-MacBook-Pro.local>
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* test: modernize models used in CircleCI e2e test suites
Replaces obsolete models (gpt-4o, gpt-4o-mini, gpt-3.5-turbo,
claude-3-5-sonnet-20240620, claude-sonnet-4-20250514) with current
equivalents across the e2e_openai_endpoints and
proxy_e2e_anthropic_messages_tests CircleCI jobs.
- gpt-4o -> gpt-5.5 (responses API e2e tests)
- gpt-4o-mini -> gpt-5-mini (websocket responses, oai_misc_config)
- gpt-4o-mini-2024-07-18 -> gpt-4.1-mini-2025-04-14 (fine-tuning,
still actively fine-tunable)
- gpt-4 / gpt-3.5-turbo target_model_names example -> gpt-5.5 /
gpt-5-mini
- bedrock claude-3-5-sonnet-20240620 batch entry -> haiku-4-5-20251001
(also aligning oai_misc_config model_name with what
test_bedrock_batches_api.py actually requests)
- bedrock claude-sonnet-4-20250514 (deprecated, retires 2026-06-15)
-> claude-sonnet-4-5-20250929
* test: point bedrock-claude-sonnet-4 alias at Sonnet 4.6, not 4.5
Greptile/Cursor flagged that after the previous commit, the
bedrock-claude-sonnet-4 alias collided with bedrock-claude-sonnet-4.5
(both pointed to claude-sonnet-4-5-20250929). Rename to
bedrock-claude-sonnet-4.6 and point it at the Sonnet 4.6 Bedrock ID
(us.anthropic.claude-sonnet-4-6, already in the litellm model
registry) so the alias name matches the underlying model version.
* test: modernize models across remaining CI-mounted configs & tests
Expands the modernization sweep to all CircleCI-mounted proxy configs
and to test directories where the model literal is a fixture/route key
(not the test's subject).
Config changes:
- proxy_server_config.yaml: bump gpt-3.5-turbo / gpt-3.5-turbo-1106 /
gpt-4o / gemini-1.5-flash / dall-e-3 underlying models; rename
gpt-3.5-turbo-end-user-test alias to gpt-5-mini-end-user-test; bump
text-embedding-ada-002 underlying to text-embedding-3-small. User-
facing aliases (gpt-3.5-turbo, gpt-4, text-embedding-ada-002, etc.)
preserved for backward compatibility with tests.
- simple_config.yaml, otel_test_config.yaml, spend_tracking_config.yaml:
bump gpt-3.5-turbo underlying to gpt-5-mini.
- pass_through_config.yaml: claude-3-5-sonnet / claude-3-7-sonnet /
claude-3-haiku entries replaced with claude-sonnet-4-5 / claude-
haiku-4-5 / claude-opus-4-7.
- oai_misc_config.yaml: align alias name with the gpt-5-mini rename.
Test changes (proactive: claude-sonnet-4-20250514 / claude-opus-4-
20250514 retire 2026-06-15):
- tests/llm_translation/test_anthropic_completion.py: bump 3 references
+ paired Vertex AI ID to claude-sonnet-4-5.
- tests/llm_translation/test_optional_params.py: bump 2 references.
- tests/pass_through_unit_tests/test_anthropic_messages_passthrough.py
and test_bedrock_anthropic_messages_test.py: bump router fixtures
using the deprecated model IDs.
- tests/pass_through_unit_tests/base_anthropic_messages_tool_search_test.py:
modernize docstring examples.
- tests/test_end_users.py: update references to renamed alias.
* test: modernize placeholder model literals in router_unit_tests
Mass replace_all on fixture/placeholder model literals across the
router_unit_tests/ suite (model name is a routing key / label, not the
test subject). Sub-agent sweep so far — additional commits will follow
for logging_callback_tests/, enterprise/, top-level tests/test_*.py,
and other CI-mounted dirs.
Mappings applied:
- gpt-3.5-turbo -> gpt-5-mini
- gpt-4 (bare) -> gpt-5.5
- gpt-4o (bare) -> gpt-5
- text-embedding-ada-002 -> text-embedding-3-small
- claude-3-sonnet-20240229 / claude-3-opus-20240229 /
claude-3-haiku-20240307 / claude-3-5-sonnet-20240620 ->
claude-sonnet-4-5-20250929 / claude-opus-4-7 /
claude-haiku-4-5-20251001 as appropriate
Explicitly preserved:
- gpt-4o-mini-* variants (transcribe, tts, etc.) where they're current
- gpt-4-turbo / gpt-4-vision-preview / gpt-4-0613 (subject literals)
- JSONL batch body literals
- Mock LLM response model fields (must match upstream)
- Fake/mock identifiers
* test: modernize placeholder model literals across remaining CI suites
Sub-agent sweep across logging_callback_tests/, guardrails_tests/,
enterprise/, pass_through_unit_tests/, otel_tests/,
llm_responses_api_testing/, batches_tests/, spend_tracking_tests/,
litellm_utils_tests/, unified_google_tests/, and a few top-level
tests/test_*.py files where the model literal is a fixture or
placeholder (router model_list, mock standard logging payload, mock
callback data) rather than the test's subject.
Mappings applied (see scope notes below):
- gpt-3.5-turbo -> gpt-5-mini
- gpt-4 (bare) -> gpt-5.5
- gpt-4o (bare) -> gpt-5.5 (corrected from initial gpt-5 — bare gpt-5
is not a valid OpenAI alias; only gpt-5.5 / gpt-5.4 / gpt-5.2-codex
/ gpt-5-mini exist)
- gpt-4o-mini (bare) -> gpt-5-mini
- text-embedding-ada-002 -> text-embedding-3-small
- claude-3-sonnet-20240229 -> claude-sonnet-4-5-20250929
- claude-3-opus-20240229 -> claude-opus-4-7
- claude-3-haiku-20240307 -> claude-haiku-4-5-20251001
- claude-3-5-sonnet-20240620/20241022 -> claude-sonnet-4-5-20250929
- claude-3-7-sonnet-20250219 -> claude-sonnet-4-6
- gemini-1.5-flash -> gemini-2.5-flash
- gemini-1.5-pro -> gemini-2.5-pro
Explicitly preserved (not modernized):
- llm_translation/ tests where model is the SUBJECT (provider-specific
translation/transformation logic). Only the deprecated 20250514
references were already bumped in a prior commit.
- Cost-calc / tokenizer subject tests in test_utils.py (skip-ranges
documented by the sub-agent).
- Bedrock model IDs in test_health_check.py path-stripping tests.
- JSONL batch request bodies and mock LLM response bodies (must match
upstream literal).
- Langfuse expected-request-body JSON fixtures (cost values are exact-
match-asserted; changing the model would shift response_cost).
- gpt-3.5-turbo-instruct (text-completion endpoint; no modern OpenAI
equivalent).
- Top-level tests calling the proxy through user-facing aliases
(gpt-3.5-turbo, gpt-4, text-embedding-ada-002, dall-e-3) — aliases
in proxy_server_config.yaml stay; only the underlying model was
bumped.
- tests/test_gpt5_azure_temperature_support.py (the test's whole point
is model-name handling).
- Fake / mock / openai/fake identifiers.
Notable side fixes:
- test_spend_accuracy_tests.py: UPSTREAM_MODEL now matches what
spend_tracking_config.yaml's proxy actually routes to (gpt-5-mini),
resolving a latent inconsistency.
- proxy_server_config.yaml: bare `gpt-5` alias renamed to `gpt-5.5`
(bare gpt-5 is not a valid OpenAI alias).
- test_batches_logging_unit_tests.py: explicit_models list entries
kept distinct (gpt-5-mini + gpt-5.5) after bulk rename.
* test: fix CI failures from model modernization sweep
CI surfaced 4 categories of regression from the bulk modernization:
1. Azure deployment names are customer-specific. Reverted:
- tests/litellm_utils_tests/test_health_check.py: azure/text-
embedding-3-small -> azure/text-embedding-ada-002 (the CI Azure
account does not have a text-embedding-3-small deployment).
- tests/logging_callback_tests/test_custom_callback_router.py:
same revert for two router fixtures driving aembedding.
2. gpt-5 family does not accept temperature != 1. Tests that pass a
custom temperature swapped from gpt-5-mini to gpt-4.1-mini (modern
non-reasoning OpenAI mini that still accepts temperature/logprobs):
- tests/logging_callback_tests/test_datadog.py
- tests/logging_callback_tests/test_langsmith_unit_test.py
- tests/logging_callback_tests/test_otel_logging.py
3. proxy_server_config.yaml's gpt-3.5-turbo-large alias was routing to
gpt-5.5 (a reasoning model that rejects logprobs). The proxy test
tests/test_openai_endpoints.py::test_chat_completion_streaming
exercises logprobs/top_logprobs through that alias. Bumped the
underlying model to gpt-4.1 (non-reasoning, still modern).
4. tests/logging_callback_tests/test_gcs_pub_sub.py asserts against a
pinned JSON fixture (gcs_pub_sub_body/spend_logs_payload.json) with
hardcoded model="gpt-4o" and a model-specific spend value. Reverted
the litellm.acompletion calls in the test to model="gpt-4o" so the
fixture's exact-match assertions still hold.
5. tests/pass_through_unit_tests/test_anthropic_messages_passthrough.py:
anthropic.messages.create routing to openai/gpt-5-mini returned an
empty content[0] with max_tokens=100 (reasoning-token consumption).
Swapped to openai/gpt-4.1-mini.
* test: fix Assistants API model + 2 cursor[bot] review nits
1. pass_through_unit_tests/test_custom_logger_passthrough.py: gpt-5.5
isn't accepted by the /v1/assistants endpoint
("unsupported_model"). Switch to gpt-4.1-mini (modern, Assistants-
API-supported, non-reasoning).
2. example_config_yaml/pass_through_config.yaml: the previous sweep
bumped the claude-3-7-sonnet alias to claude-opus-4-7, which is a
tier change (Sonnet -> Opus). Map to claude-sonnet-4-6 to keep the
Sonnet tier intact. (Cursor bugbot review.)
3. example_config_yaml/simple_config.yaml: model_name was left as
gpt-3.5-turbo while the underlying was bumped to gpt-5-mini, which
muddles the "simple" example. Make both sides gpt-5-mini so the
most basic example is a straight 1:1 mapping again. (Cursor bugbot
review.)
* fix: revert gpt-4/gpt-3.5-turbo alias underlying to non-reasoning models
tests/test_openai_endpoints.py::test_completion calls the proxy alias
"gpt-4" with temperature=0, and other tests call gpt-3.5-turbo with
custom temperature / logprobs / the legacy /v1/completions endpoint.
The earlier modernization mapped both aliases to gpt-5.5 / gpt-5-mini,
which are reasoning models that reject temperature != 1 and don't
expose /v1/completions. Map the aliases to gpt-4.1 / gpt-4.1-mini
(modern non-reasoning OpenAI models) instead — keeps user-facing
aliases preserved while picking a current underlying that still
supports the parameters/endpoints the tests exercise.
* fix(vertex-ai): fix zero cost/usage on completed Vertex AI batch jobs
Vertex batch jobs recorded 0 spend and 0 tokens after PR #25627 added
automatic transformation of GCS predictions.jsonl to OpenAI format.
Two bugs fixed:
1. batch_utils.py: the Vertex-specific cost/usage reader
(calculate_vertex_ai_batch_cost_and_usage) was always invoked and
reads raw usageMetadata fields that no longer exist in the
OpenAI-shaped output. Now the reader is only used when
disable_vertex_batch_output_transformation=True; otherwise the
generic path handles the already-transformed OpenAI-shaped content.
2. cost_calculator.py: batch_cost_calculator skipped the global
litellm.get_model_info() lookup when a model_info dict was passed
in, even when that dict had no pricing fields (e.g. deployment
metadata with only id/db_model). It now falls back to the global
pricing table when the provided model_info has no pricing data.
Co-authored-by: Cursor <cursoragent@cursor.com>
* Update litellm/cost_calculator.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* fix(cost-calculator): use not-any guard for pricing fallback in batch_cost_calculator
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(cost-calculator): treat explicit zero batch pricing as set in model_info
The fallback to litellm.get_model_info() used truthy checks on pricing
fields, so 0.0 was treated as missing and replaced by global rates.
Use `is not None` like elsewhere in cost calculation. Add regression test.
Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com>
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com>
* fix(tests): drop deprecated OpenAI-Beta realtime header
OpenAI deprecated the 'OpenAI-Beta: realtime=v1' header; the live
service now returns code 4000 invalid_beta with
"Unknown beta requested: 'realtime'.". Two integration tests in
tests/llm_translation/realtime/test_realtime_guardrails_openai.py
hardcoded the header and started failing across all PRs.
Library code is unaffected: the OpenAI realtime handler only
forwards 'OpenAI-Beta: realtime=v1' upstream when the proxy *client*
sends it (litellm/llms/openai/realtime/handler.py). Default proxy
behavior uses the GA protocol.
Connect to OpenAI without the deprecated header, and accept the GA
event name 'response.output_audio_transcript.delta' alongside the
beta-protocol name 'response.audio_transcript.delta' for the
transcript-delta assertion.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* fix(logging): post_call tolerates non-JSON-serializable values
post_call() did json.dumps(original_response) without default=str, so
any provider passing a dict containing datetime/Decimal/etc. would
raise TypeError. Bedrock batch retrieval hits this with
get_model_invocation_job() responses that include datetime fields
(submitTime, lastModifiedTime, endTime), failing
tests/batches_tests/test_bedrock_files_and_batches.py::test_async_file_and_batch
across all PRs.
Pass default=str so non-serializable values fall back to str().
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* fix(tests): mock boto3 in bedrock retrieve batch test
The test patched AsyncHTTPHandler.get, but the bedrock retrieve
handler uses boto3.client('bedrock').get_model_invocation_job
directly, so the real AWS call was being made on every run, failing
with AccessDeniedException because the hardcoded test ARN belongs to
a different AWS account.
- Mock boto3.client and BedrockBatchesConfig.get_credentials so the
test never touches AWS.
- Use status=Completed in the mock response so output_file_id is
populated (the handler intentionally leaves it None for
non-completed jobs).
- Assert the predicted per-job output object URI (matches what the
handler actually returns) instead of the bare output prefix.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* docs(tests): include GA event name in guardrail-block test docstring
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
Strip VCR wiring from the batches test conftest. Drops:
- import of `_vcr_conftest_common` helpers
- the `vcr_config` fixture, `pytest_recording_configure`,
`_vcr_outcome_gate`, `pytest_runtest_makereport`
- the `apply_vcr_auto_marker_to_items` call in
`pytest_collection_modifyitems`
- `VerboseReporterState` / its `pytest_configure` /
`pytest_runtest_logreport` hooks (purely VCR-verdict plumbing)
Why: every test in this directory creates ephemeral OpenAI / Bedrock /
vLLM resources whose IDs change per run (file-XXX, batch-XXX,
ft-XXX, ...). VCR's path/query/body matchers don't match across runs,
so `record_mode="new_episodes"` was silently passing through to the
live API and recording many new cassette entries every run. Cassette
bloat without replay benefit.
Behaviour after this change is identical to running the directory
without `CASSETTE_REDIS_URL` set: tests that have keys hit live APIs,
tests that don't continue to skip via their existing skipif markers.
Conftest now keeps only path setup and the session-scoped `event_loop`
fixture.
OpenAI announced gpt-3.5-turbo-0125 (and fine-tuning of gpt-3.5-turbo
in general) for shutdown on 2026-10-23, with the announcement landing
2026-04-22. The hard-fail date is ~5 months out, but timing fits the
recent uptick in this test flaking and OpenAI may already be running
the deprecated model's pipeline with deprioritized infra.
Bump to gpt-4o-mini-2024-07-18 — currently supported for fine-tuning,
no announced shutdown. Updates the live test plus the mocked test for
consistency. Belt-and-suspenders with the existing propagation-retry
helper.
Previous fix polled `litellm.afile_retrieve` for `status == "processed"`
before calling the fine-tuning endpoint. That doesn't actually solve
the race:
- OpenAI's `FileObject.status` field is deprecated per the SDK type and
not authoritative — it can read "processed" before the file is usable.
- The retrieve and fine-tuning endpoints don't share a consistency
model, so retrieve succeeding tells you nothing about FT visibility.
Replace with a retry around the actual `acreate_fine_tuning_job` call
that catches the OpenAI 400 `'file-... does not exist'` and backs off
exponentially (1s → cap 8s, 12 attempts, ~70s total budget). The
operation succeeding is the only reliable signal that propagation
finished.
OpenAI file uploads are eventually consistent — a freshly uploaded file
may briefly 404 from `retrieve` and is rejected by the fine-tuning
endpoint with `'file-... does not exist'` until processing finishes.
The async fine-tuning test called `acreate_fine_tuning_job` immediately
after `acreate_file` and flaked on this race.
Add a polling helper that waits up to ~30s for `status=processed` (and
short-circuits on `error`), called between upload and FT job creation.
Mirrors the same propagation lag covered by the `await asyncio.sleep(1)`
in the sister batches test, but more robust against longer delays.
* test: add 24hr Redis-backed VCR cache to additional test suites
Extracts the existing llm_translation VCR plumbing into a reusable helper
(tests/_vcr_conftest_common.py) and wires it into the conftest.py files
of the test directories listed in LIT-2787:
audio_tests, batches_tests, guardrails_tests, image_gen_tests,
litellm_utils_tests, local_testing, logging_callback_tests,
pass_through_unit_tests, router_unit_tests, unified_google_tests
The same helper is also adopted by the pre-existing llm_translation and
llm_responses_api_testing conftests to remove the copy-pasted VCR setup.
Each consuming conftest:
- registers the Redis persister via pytest_recording_configure
- auto-marks collected tests with pytest.mark.vcr (skipping respx-using
files where applicable, since respx and vcrpy both patch httpx)
- gates cassette writes on test success via _vcr_outcome_gate
The cache is opt-in via CASSETTE_REDIS_URL; when unset, VCR is disabled
and tests hit live providers as before. LITELLM_VCR_DISABLE=1 still
forces a bypass for ad-hoc local runs.
Test directories that run LiteLLM proxy in Docker (build_and_test,
proxy_logging_guardrails_model_info_tests, proxy_store_model_in_db_tests)
are intentionally not included: VCR.py patches the in-process httpx
transport and cannot intercept calls made from inside a Docker container.
The installing_litellm_on_python* jobs make no LLM calls and don't
benefit from caching.
https://linear.app/litellm-ai/issue/LIT-2787/add-24hr-caching-to-additional-test-suites
* test(vcr): add safe-body matcher to handle JSONL and binary request bodies
vcrpy's stock body matcher inspects Content-Type and unconditionally
runs json.loads on application/json bodies. JSON Lines payloads (used
by the Bedrock batch S3 PUT and other upload paths) crash that with
json.JSONDecodeError: Extra data, before the matcher can return
'not a match'.
This was the root cause of the batches_testing CI job failing on
test_async_create_file once VCR auto-marking was applied to the
batches_tests directory.
Add a conservative byte-equality body matcher and use it in place of
'body' in the shared match_on tuple. The matcher is strictly more
conservative than vcrpy's default — the only thing it gives up is
'different JSON key order is treated as the same body', which doesn't
apply to deterministic litellm-built request payloads. It can never
produce a false positive that the default would have rejected, so
there is no cross-contamination risk.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* test(vcr): exclude tests that VCR replay actively breaks
A few tests are incompatible with cassette replay and were failing on
the latest CI run after VCR auto-marking was extended to local_testing
and logging_callback_tests:
- test_amazing_s3_logs.py (logging_callback_tests): the test asserts on
a per-run response_id that should round-trip through a real S3
PUT/LIST. vcrpy's boto3 stub intercepts the PUT and the LIST replays
stale keys, so the freshly-generated id is never found.
- test_async_embedding_azure (logging_callback_tests) and
test_amazing_sync_embedding (local_testing): the failure branches
deliberately pass api_key='my-bad-key' to assert that the failure
callback fires. We scrub auth headers from cassettes (so the bad-key
request matches the prior good-key request), and vcrpy replays the
recorded 200 — the failure callback never fires.
- test_assistants.py (local_testing): the OpenAI Assistants polling
APIs mint fresh thread/run IDs every recording session and then poll
until status=='completed'. Replays of those polled GETs can never
match a freshly-generated run id, so every CI run effectively
re-records and the suite blows past the 15m no_output_timeout.
Skip these from VCR auto-marking so they continue to hit live providers
as they did before this change. The remaining tests in each directory
still get cached.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* test(vcr): expand skip lists for second batch of incompatible tests
Followup to the previous commit. After re-running CI on the rebuilt
branch, three more tests surfaced as VCR-replay-incompatible:
- litellm_utils_testing :: test_get_valid_models_from_dynamic_api_key
Calls GET /v1/models with api_key='123' to assert the result is empty.
We scrub auth headers, so the bad-key request matches the prior
good-key cassette and replays the recorded model list.
- litellm_utils_testing :: test_litellm_overhead.py
Measures litellm_overhead_time_ms as a percentage of total wall-clock
time. With cached responses the upstream 'network' time collapses to
microseconds, blowing past the 40%% threshold the test asserts on.
Skip the whole file (every parametrization is at risk).
- local_testing_part1 :: test_async_custom_handler_completion and
test_async_custom_handler_embedding
Same bad-key failure-callback pattern as the already-skipped
test_amazing_sync_embedding.
- litellm_router_testing :: test_router_caching.py
Asserts on litellm's own router-level response cache by comparing
response1.id to response2.id across repeat upstream calls (test
bypasses litellm cache via ttl=0 and expects upstream to return a
*new* id). With VCR replay both upstream calls return the same
cassette body, so the ids are identical. Skip the whole file.
- logging_callback_tests :: test_async_chat_azure (preemptive)
Same shape as already-skipped test_async_embedding_azure; was masked
by upstream OpenAI rate-limit failures on baseline.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* test(vcr): use item.path and tighten matcher docstring
- Replace pytest's deprecated item.fspath with item.path in
apply_vcr_auto_marker_to_items so we don't emit deprecation
warnings under pytest 8.
- Clarify _safe_body_matcher docstring to reflect actual behavior
(direct == first, then UTF-8 bytes comparison, no repr fallback).
Addresses Greptile review feedback on PR #27159.
* test(vcr): swallow all RedisError on cassette save/load
Cassette persistence is strictly best-effort: any Redis-side failure
(connection blip, timeout, OutOfMemoryError when the maxmemory cap is
hit, READONLY replicas, etc.) should degrade to 'test passed but
cassette not cached' rather than fail the test on teardown.
Previously the persister only caught ConnectionError and TimeoutError,
so OutOfMemoryError — which Redis Cloud raises when the cassette cache
hits its memory cap and there are no evictable keys — propagated out of
vcrpy's autouse fixture and ERRORed otherwise-passing tests on
teardown. This caused the litellm_utils_testing CircleCI job to fail on
the latest commit's run, even though the underlying test was a unit
test that used mock_response and produced no real upstream traffic
(the cassette was dirtied by a background langfuse callback). The
rerun only succeeded because Redis evictions happened to free enough
room before the SET — i.e. it was timing-dependent flakiness.
Catch redis.exceptions.RedisError (the common base of all server- and
client-side Redis exceptions) on both save and load, and parametrize
the regression tests across ConnectionError, TimeoutError, and
OutOfMemoryError to pin the new behavior.
* test(vcr): surface cassette-cache failures with warnings + session banner
When the persister silently swallows a Redis OOM (or any RedisError) on
save/load there is otherwise no visible signal that the cache is
degraded — tests pass, the cassette just isn't persisted, and the next
session still hits the same Redis at the same near-cap memory.
Add three layers of observability so that failure mode is loud:
1. Per-process health counters ("save_failures", "load_failures", and
the last error string for each), exposed via cassette_cache_health()
and reset via reset_cassette_cache_health(). The persister
increments these in addition to logging.
2. VCRCassetteCacheWarning (UserWarning subclass) emitted via
warnings.warn() inside the persister's except block. Pytest's
built-in warnings summary at session end automatically lists every
such warning, so the failure is visible in CI logs without any
conftest-level wiring.
3. Session-end banner via emit_cassette_cache_session_banner() and a
stderr-fallback atexit handler registered from
register_persister_if_enabled(). Two states:
- red "VCR CASSETTE CACHE DEGRADED" when save_failures or
load_failures > 0
- yellow "VCR CASSETTE CACHE NEAR CAPACITY" (no failures, but
used_memory >= 85% of maxmemory) so the next session knows
the Redis is approaching OOM before any SET actually fails
Capacity comes from a best-effort INFO memory probe
(cassette_cache_capacity_snapshot) that returns None on any failure or
when maxmemory is uncapped. The atexit handler skips xdist workers so
only the controller emits.
Tests: parametrize the existing save/load swallow-error tests across
ConnectionError/TimeoutError/OutOfMemoryError, add direct tests for
the health counters and warning emission, and a new
test_vcr_conftest_common_banner.py covering banner output for every
state (silent/red/yellow/disabled/xdist-worker).
* test(vcr): bucket cassettes by API key fingerprint, drop bad-key skips
Tests that deliberately call an LLM API with a bad key (e.g. to assert
that the failure callback fires, or that check_valid_key returns False)
were being silently served the prior good-key cassette: we scrub the
real Authorization / x-api-key header from the cassette before storing
it, so a follow-up bad-key call is byte-identical to the good-key call
under the existing match_on tuple.
Add a 'key_fingerprint' custom matcher that distinguishes requests by
the SHA-256 of their API-key headers. The fingerprint is stamped into
a synthetic 'x-litellm-key-fp' header by a new before_record_request
hook, which then strips the real auth headers (we have to do the
scrubbing here instead of via vcrpy's filter_headers knob, because
filter_headers runs *first* and would erase the value we want to hash).
Bad-key requests now get a different cassette bucket than good-key
requests, so vcrpy will not replay a recorded 200 in place of the
expected 401. The fingerprint is a one-way hash of the secret, so
cassettes never contain the key.
This permanently removes the 'bad-key' category of skips:
- tests/local_testing: dropped ::test_amazing_sync_embedding,
::test_async_custom_handler_completion,
::test_async_custom_handler_embedding
- tests/logging_callback_tests: dropped ::test_async_chat_azure,
::test_async_embedding_azure
- tests/litellm_utils_tests: dropped
::test_get_valid_models_from_dynamic_api_key
Coverage: 7 new unit tests in tests/test_litellm/test_vcr_safe_body_matcher.py
covering header stripping, fingerprint determinism, no-auth bucketing,
good-vs-bad key discrimination, x-api-key (Anthropic/Azure) discrimination,
and idempotence under replay.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* test(vcr): drop redundant comments and docstrings
Trim narration of code that is already self-evident from function and
variable names. Keep the two genuinely non-obvious bits:
- ordering constraint between filter_headers and before_record_request,
which would invite a maintainer to re-introduce the bug if removed
- the per-directory _VCR_INCOMPATIBLE_FILES rationale, since 'why
exactly is this skipped' is not knowable from the test name alone
Also drop the 40-line commented-out drop-in conftest snippet at the
bottom of _vcr_conftest_common.py — the consuming conftests are the
canonical reference.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* test(vcr): make _before_record_request idempotent
vcrpy invokes before_record_request more than once per request:
can_play_response_for calls it, then __contains__ /
_responses (reached via play_response) call it again on the
result. The second invocation sees a request whose auth headers we
already stripped, so a naive recompute yields "no-key" and
overwrites the real fingerprint stored in the header.
This makes can_play_response_for and play_response disagree on
matchability — the former says "yes, we have a stored response for
this" (matching no-key to no-key) and the latter throws
UnhandledHTTPRequestError because it computes a fresh real
fingerprint that doesn't match the stored no-key.
In CI this manifested as ~30 failing tests across guardrails_testing,
audio_testing, batches_testing, image_gen_testing, llm_responses_api,
litellm_router_unit_testing, etc. Skip the recompute when the header
is already set, so re-applying the hook is a no-op.
Adds a regression test that fires the hook twice on the same dict and
asserts the fingerprint stays put.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* test(vcr): drop more redundant docstrings and headers
* test(vcr): enable 24hr cache for ocr_tests and search_tests
These two directories were the only non-dockerized test suites in the
build_and_test workflow that make live LLM/provider API calls but were
not VCR-enabled by this PR. Together they account for 96 tests:
- tests/ocr_tests/ (31): Mistral OCR, Azure AI OCR, Azure Document
Intelligence, Vertex AI OCR. Pure-unit tests inside the same files
(e.g. TestAzureDocumentIntelligencePagesParam) make no HTTP calls
and become benign VCR NOOPs.
- tests/search_tests/ (65): Brave, DataForSEO, DuckDuckGo, Exa,
Firecrawl, Google PSE, Linkup, Parallel.ai, Perplexity, SearchAPI,
Searxng, Serper, Tavily.
Both directories use the canonical minimal conftest pattern from
tests/audio_tests/conftest.py with no skip lists. None of the test
files use respx, none assert on per-call upstream non-determinism
(no response1.id != response2.id, no overhead-as-fraction-of-total,
no live polling), so the default match_on tuple should cache cleanly.
If a flake surfaces during the first cassette-recording CI run, we
can add a targeted skip the same way we did for the other dirs.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
---------
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
The redirect-following added to async_safe_get checks response.is_redirect
on every hop. Two vertex batch tests stub AsyncHTTPHandler.get with a bare
MagicMock, whose default-truthy is_redirect made the redirect path fire,
then crashed in httpx.URL().join() because headers.get('location') was
also a MagicMock instead of a string. Set is_redirect=False explicitly so
the mocked response models a non-redirect terminal response.
Also tighten _extract_redirect_url to raise SSRFError on non-string
Location values (defense-in-depth — a real httpx Response always returns
str|None, but this avoids a confusing TypeError if anything else ever
slips through).
This is an unrelated CI fix piggybacked on the SCIM PR to unblock the
batches test suite.
After dedaf74a5e, _async_retrieve_batch wraps the GET in async_safe_get,
which inspects response.is_redirect. test_avertex_batch_prediction's
MagicMock response left is_redirect unset, so it auto-generated a truthy
mock, sent the redirect-follow loop into _extract_redirect_url, and
httpx.URL().join(<MagicMock>) raised TypeError. Set is_redirect=False
so the response is treated as terminal.
- Add cancel/retrieve overrides in AzureOpenAIFineTuningAPI to normalize responses
- Expand _AZURE_STATUS_MAP to handle all known Azure statuses
- Add "pending" to OpenAIFileObject.status allowed values
- Fix async test mock to return awaitable LiteLLMFineTuningJob
- Add test_openai_file_object_accepts_pending_status
Made-with: Cursor
- Move trainingType injection to AzureOpenAIFineTuningAPI handler
- Guard normalization with is_azure flag to only apply to Azure responses
- Override acreate_fine_tuning_job in Azure handler to use is_azure=True
- Update test to directly test _ensure_training_type method
- Add test for OpenAI unchanged behavior
Made-with: Cursor
- Default trainingType=1 for Azure when omitted to avoid misleading "base model does not support fine-tuning" error
- Normalize Azure FineTuningJob responses (pending→queued, null fields→defaults) to match OpenAI schema
- Add pending status support to OpenAIFileObject for Azure file uploads
- Add test coverage for trainingType default and response normalization
Made-with: Cursor
- test_avertex_batch_prediction: Add google.auth.default mock and env vars
so the test doesn't depend on real GCP credentials (was already a unit
test with mocked HTTP, just missing auth mock)
- test_async_create_batch[openai]: Add DNS pre-check that skips gracefully
when api.openai.com is unreachable instead of failing after 4 retries
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
batch_cost_calculator only checked the global cost map, ignoring
deployment-level custom pricing (input_cost_per_token_batches etc.).
Add optional model_info param through the batch cost chain and pass
it from CheckBatchCost.
Remove orphaned test files that are not referenced in any tests or code:
- flux2_test_image.png
- test_generic_guardrail_config.yaml
- test_image_edit.png (root only, tests/image_gen_tests/ copy preserved)
- document.txt
- batch_small.jsonl (root and tests/batches_tests/)
