Adversarial review of the new multipart keying turned up collisions where
two different provider requests computed the same replay key, which is the
dangerous failure for a replay harness: the second request silently gets the
first one's response instead of missing loudly.
- a part counts as an upload when it has a filename or declares its own
content type, and the declared content type joins the identity, so two
uploads of the same bytes under the same field no longer collapse
- the uploaded parts contribute a JSON list of [field, filename, type]
triples instead of a "field:filename" string, so a separator inside a
filename can no longer impersonate a field boundary
- repeated field names get a "name[n]" suffix with a literal "[" doubled
first, so a repeated field and a literally indexed one stay distinct
- a field value that is not UTF-8 is stored as a base64 sha256 digest;
base64 rather than hex because the canonicalizer rewrites 64-character
hex runs to <sha256> and folded every binary value onto one key
- a field whose name reads as a credential is stored as <secret>. This
stays key-preserving because the key is recomputed from the stored
request rather than saved beside it, so the live request carrying the
real value still matches its redacted fixture
- the uploaded byte length leaves the key. The canonicalizer absorbs
timestamp and id drift inside a file, and that drift moves the count,
so keeping it there made re-records miss
Also stops a lookalike parameter such as "xboundary=" from being read as
the multipart boundary, and gives the OpenAI batch backend model a single
constant instead of three copies of the literal.
BUNDLE_FORMAT_VERSION goes to 3 because all of this moves recorded keys.
A bundle recorded under the old rules now fails naming both versions
instead of missing on every call.
Chat completions, embeddings, the non-streaming /v1/messages tests, and the
OpenAI batch deployment now register through the provider edge, so
E2E_FIXTURE_MODE=record captures their provider calls and replay serves them
back offline. None of them was wired before, so record was a silent no-op over
these suites and replay quietly went live instead of using the bundle
Multipart uploads now key on their parsed parts: every ordinary form field,
plus the field name, filename, content digest, and length of each file part.
The boundary is envelope rather than content, so it stays out of the digest
instead of changing the key on every run. A body that does not parse as its
declared envelope still has the boundary normalized away before hashing, so
the fallback is at least stable, and it records a name that says why
Binary uploads hash byte for byte. Canonicalizing them first meant decoding
with errors="replace", which collapsed every invalid byte to one U+FFFD and
gave two different PDFs of the same length the same key
Bundles stay out of the repo: they hold verbatim provider response bodies and
expire seven days after recording. Publishing them for CI is LIT-5748, and
streaming fidelity is LIT-5742
Adds e2e coverage for batches terminal state and cost write-back, failure
paths, per-backend file content downloads, and two-gateway routing (LIT-5730).
* test(e2e): harden stage flakes for batches, UI, and MCP
Unique batch model names avoid load-balancing onto stale azure-batch
deployments that still pointed at the retired gpt-4.1-mini-batch, which
only the managed/unified path was hitting. Retry batch retrieve on 500
and /ui/api-keys navigation on ERR_ABORTED. Skip the MCP key-access suite
when the compose-only mcp-upstream is unreachable on stage k8s
* test(e2e): cover Datadog remote MCP via search_datadog_logs
Register the regional Datadog MCP endpoint with DD-API-KEY /
DD-APPLICATION-KEY static headers (CI-safe header auth; browser OAuth is
not headless-automatable). Seed a chat completion marked e2e-datadog-mcp-*,
assert the proxy shipped it, list tools, call search_datadog_logs for the
marker, and delete the server on teardown. Math-upstream key-access tests
only skip when that compose service is unreachable
* test(e2e): drop compose math MCP upstream; use Datadog only
Key-access denial and happy-path MCP e2e both register the real regional
Datadog remote MCP server with DD-API-KEY / DD-APPLICATION-KEY headers.
Remove the mcp-upstream compose service and FastMCP add/multiply fixture
* docs(e2e): require real Datadog MCP for all mcp suite tests
Document that tests/e2e/mcp must register via datadog_mcp helpers against
mcp.<site>/v1/mcp and must not introduce compose or fake MCP upstreams
* chore: restore mcp_e2e_upstream_server.py
Keep the FastMCP fixture file; e2e no longer wires it in compose, but the
module itself is not part of the Datadog-only cleanup
* fix(e2e): load tests/e2e/.env and fix datadog_reader importlib load
pytest on the host never inherited compose env_file keys, so DD_API_KEY
stayed empty. load_dotenv tests/e2e/.env in e2e_config. Register the
dynamically loaded datadog_reader module in sys.modules so dataclasses
do not crash under Python 3.12
* test(e2e/batches): harden azure/vertex unified lifecycle flakes
Put the provider deployment name in every JSONL body so Azure does not
depend on a perfect model rewrite. Retry create/retrieve/cancel on
transient statuses with backoff. Drop cancel assertions for azure and
vertex (registry only has a shared basic cell; create+retrieve prove
routing, cancel stays best-effort cleanup)
* test(e2e/ui): treat api-keys shell as success after SPA ERR_ABORTED
Post-login client redirects abort the first /ui/api-keys/ goto on stage.
Wait off /ui/login after cookie set, then accept the page once Create New
Key is visible even if goto raised ERR_ABORTED
* test(e2e): drop flaky key models dropdown Playwright suite
API management e2e already covers key generate/update persistence. The
UI Models-dropdown sentinel cases only added SPA ERR_ABORTED noise and
no unique product signal. Remove the suite and unused browser fixtures
* test(e2e): read datadog log delivery back from the real datadog api (#33604)
* test(e2e): read datadog log delivery back from the real datadog api
* test(e2e): compare datadog-read cost with math.isclose, not bit-equality
The response_cost now round-trips through DataDog's attribute indexing
pipeline, whose float serialization is not guaranteed to preserve the
exact bit pattern the proxy shipped. rel_tol=1e-9 (equal to 9 significant
digits) still fails on any real cost discrepancy while tolerating
representation drift. Addresses the Greptile P2 on this PR.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* test(e2e): widen the duplicate-settle window to 30s for real DataDog
Against the local sink one poll interval (5s) after the first hit was
enough to catch a same-call duplicate, because both events arrived in the
same flush batch. Against real DataDog, ingestion jitter can make one
call's two events searchable tens of seconds apart, so a 5s settle could
let the LIT-4447 duplicate slip past the exactly-one assertion. The reader
now keeps re-reading for DD_SETTLE_SECONDS (default 30s, env-overridable
via E2E_DD_SETTLE_SECONDS) after the first event appears, returning early
only when a duplicate is already visible - more waiting cannot clear it.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(e2e): point UI tests at dashboard service; register complexity router
Stage gateway 404s /ui; the Next.js dashboard is litellm-ui:3000. Drive
playwright against E2E_UI_BASE_URL and wait on login placeholders after
client render. Register complexity-smart-router via /model/new when the
proxy does not already list it so stage matches compose config
* docs(e2e): clarify E2E_UI_BASE_URL should be ALB when ingress splits UI
* docs(e2e): prefer single path-routing host for control plane and UI
CONTROL_PLANE and UI already default to PROXY_BASE_URL; clarify that
stage should set one ALB host rather than three endpoints
* fix(e2e): always capture complexity router model_id for teardown
Split /model/new from the data-plane wait so a propagation timeout still
deletes the control-plane registration (greptile orphan-model concern)
* fix(e2e): click exact Login button so SSO control is not matched
Playwright strict mode matched both Login and Login with SSO
* fix(router): score complexity by difficulty not request length
The LLM classifier prompt treated short wording as SIMPLE, so probes like
"Is P equal to NP?" stayed on the SIMPLE backend even though the classifier
ran. Judge intellectual difficulty so short hard questions route higher
* fix(e2e): open key edit via Key ID and wait for team models
Key Alias text is not the row open control on the virtual keys table;
KeyInfoView opens from the Key ID button in that row. Also wait for a
real team model in the edit Models dropdown so we do not race the async
availableModels fetch that only has All Team Models on first paint
* fix(e2e): keep settled DD events on empty search; bump mcp for OSV
Do not let a transient empty DataDog search wipe events already seen in
the settle window (Greptile P1). Make the logs-search from window
env-overridable via E2E_DD_SEARCH_FROM (Greptile P2). Prefer the mono
Key ID button when opening key edit. Bump mcp 1.26.0 -> 1.28.1 so OSV
clears the three high GHSA findings on the staging PR
* revert: drop mcp lock bump from e2e staging PR
OSV mcp upgrade is unrelated to the e2e fixes; leave the dep pin alone
---------
Co-authored-by: yucheng-berri <yucheng@berri.ai>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* 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
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.
* 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>
