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Author SHA1 Message Date
Yassin Kortam
27c1dfbdc7
fix(otel): accept UPPER_SNAKE_CASE OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT in v2 (#30562) 
V1 read this env var case-insensitively, so SPAN_AND_EVENT enabled content
capture. The v2 config compared the value against its lower_snake_case
canonical constants without normalizing, so an operator carrying the
SPAN_AND_EVENT spelling forward silently left capture off and no
gen_ai.input/output.messages reached the span. Normalize the value to lower
case at the config boundary so both spellings work.
 2026-06-16 14:18:42 -07:00
Yassin Kortam
f444539ea9
fix(otel): export v2 gen_ai client metrics to the configured meter provider (#30549) 
* fix(otel): export v2 gen_ai client metrics to the configured meter provider

The V2 OpenTelemetry integration recorded the six gen_ai.client.* histograms
into a MeterProvider it built locally in _init_metrics and never published. The
recording code ran fine; the metrics simply landed in a provider disconnected
from the global pipeline, so an operator's configured readers/exporters (and the
server-metric instrumentation bound to the global meter provider) never saw them.

Resolve the meter provider the OTel-idiomatic way instead: reuse the operator's
globally configured MeterProvider when one is set so its readers receive the
GenAI histograms, build and register one as the global only when none is set so
V2 owns metrics export (mirroring how V2 owns trace export), and keep the
injected meter_provider as an explicit override for DI and tests.

* refactor(otel): hoist meter imports and harden global resolution

Move the opentelemetry metrics and sdk MeterProvider imports to module top
instead of importing inside resolve_meter_provider/build_meter_provider; the
SDK is already a top-level dependency for tracing, so the per-call imports
added nothing.

resolve_meter_provider now reuses an explicit NoOpMeterProvider as well as a
real SDK provider, so an operator opt-out is honored, and the built provider
is always the one returned so its reader thread is never orphaned.

Drive the regression test through the public metrics.get_meter_provider via
monkeypatch rather than writing opentelemetry's private _METER_PROVIDER slot,
and add focused tests for the injected and no-op resolution branches.

* fix(otel): type resolve_meter_provider as the api MeterProvider base

mypy flagged the return as incompatible because honoring an explicit
NoOpMeterProvider returns a value of the opentelemetry api MeterProvider base
rather than the sdk subclass. Annotate the resolver in terms of the api base and
keep the sdk class for construction and the reuse isinstance check.
 2026-06-16 12:15:26 -07:00
Yassin Kortam
b8b0d458af
fix(otel): stamp gen_ai.input/output.messages on v2 spans (#30548) 
The canonical GenAI mapper's _LLM_CALL_ATTRS table had no extractors for
gen_ai.input.messages or gen_ai.output.messages, so V2 LLM spans never carried
prompt or completion content even when capture was enabled via
OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=span_and_event. The request
and response bodies were already captured onto LLMCallSpanData.messages_in and
choices_out, but the mapper never read them.

Add the two extractors, serializing messages_in and output_messages(d) through
serialize_messages so the keys are omitted when content capture is off and the
spans stay sparse.

Resolves LIT-3788
 2026-06-16 12:14:35 -07:00
Sameer Kankute
816fca939f
chore(oss): litellm oss staging 150626 (#30463) 
* fix(pricing): add GitHub Copilot MAI Code Flash pricing (#30415)

* fix(pricing): add GitHub Copilot MAI Code Flash pricing

Add GitHub Copilot pricing entries for MAI-Code-1-Flash and the internal Copilot CLI model name so cost calculation can price input, cached input, and output tokens.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* test(pricing): cover GitHub Copilot MAI Code Flash pricing

Add regression coverage for both GitHub Copilot MAI-Code-1-Flash model names, including cached input pricing, chat endpoint metadata, and cost_per_token arithmetic.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* fix(router/proxy): propagate completed_response through FallbackResponsesStreamWrapper for streaming /v1/responses container ownership (#30210) (#30213)

* fix(router/proxy): propagate completed_response through FallbackResponsesStreamWrapper for streaming /v1/responses container ownership (#30210)

#28990 added ownership recording for streaming /v1/responses via
_wrap_responses_stream_for_container_ownership, which reads
`getattr(stream_response, 'completed_response', None)` to extract the
ResponsesAPIResponse. The unit test bypassed the Router, so it never
exercised the production wrapping path.

Through the Router (every proxy deployment), the stream is wrapped by
FallbackResponsesStreamWrapper (router.py:2527). Its __init__ set
`self.completed_response = None` and __anext__ only forwarded chunks
— the inner source iterator's terminal event never bubbled up to the
attribute the ownership hook reads, so the hook silently recorded
nothing and every follow-up /v1/containers/<id>/files call returned
403 for non-admin keys.

This commit:

- router.py: pre-resolves the responses-API terminal event tuple
  (response.completed / .incomplete / .failed) once per
  _aresponses_streaming_iterator call, and has the wrapper's __anext__
  sniff each forwarded chunk's .type. First terminal event hit gets
  stored on the wrapper's completed_response. Iterator-agnostic — works
  for source_iterator AND any future wrapper.

- common_request_processing.py: when _extract_completed_responses_response
  returns None we now warn instead of silently skipping. Reporter on
  #30210 lost a day to this exact silent skip; the warning surfaces
  future regressions of the same shape directly in operator logs.

Fixes #30210

* fix(router): type-ignore wrapper getattr-defaults; broaden ownership-skip warning

CI lint (mypy) flagged the three pre-existing getattr(..., None) assignments
in FallbackResponsesStreamWrapper.__init__:

  router.py:2564 self.response = getattr(source_iterator, 'response', None)
  router.py:2565 self.model    = getattr(source_iterator, 'model', None)
  router.py:2566 self.logging_obj = getattr(..., None)

Those lines also exist on litellm_internal_staging and pass mypy there.
Adding the typed terminal-event tuple above the class made the function
body more narrowable, which surfaced the pre-existing mismatch — base
class declares non-Optional types but the bridge path
(LiteLLMCompletionStreamingIterator) legitimately omits these. Keep
the None fallback and silence with type: ignore[assignment].

Greptile 4/5 note: the ownership-skip warning hard-named code_interpreter
which misleads operators when a non-code_interpreter stream aborts.
Generalize to 'any tool container (e.g. code_interpreter)'.

* fix(register_model): drop synthesized zero costs to preserve sparse entries (#30198) (#30201)

* fix(register_model): drop synthesized zero costs to preserve sparse entries (#30198)

get_model_info synthesizes input_cost_per_token / output_cost_per_token = 0
when they are absent from the raw entry (the price-unknown and free cases
share the same representation). register_model then merges that result back
into litellm.model_cost, which flips a sparse entry from 'no cost keys'
(priced via model name) to 'cost keys = 0' (free).

That defeats _is_cost_explicitly_configured (#24949) on re-registration:
_is_model_cost_zero returns True, common_checks skips every tag / key /
team / user / org budget check for the group, and over-budget traffic
keeps returning 200. Spend keeps recording because cost calc still resolves
by model name, so the symptom is silent and only triggers on the second
register_model pass (router rebuild, /model/update, config sync).

Mirror the existing litellm_provider-None guard one block above and pop
the cost fields from the synthesized result when they are absent from the
raw entry and not in the caller's value. Caller-provided zeros (genuinely
free models, BYOK overrides) are preserved.

Fixes #30198

* fix(register_model): switch _raw_entry to is-None checks + drop dead test assertion

Greptile #30201 review notes:
- the `or`-chain in the raw-entry lookup treated an empty dict (a key
  with no fields) as falsy and fell through to the second arm — replace
  with explicit `is None` checks so a present-but-empty entry is still
  taken at face value.
- the first assertion in `test_router_double_init_keeps_db_model_entry_sparse`
  used `in (None, 0)` which passes under the bug condition (cost = 0
  matches the tuple); the strong follow-up assertion already covers
  every shape, so drop the dead branch.

* fix(bedrock mantle): use unique function-call id for responses->chat tool calls (#30426)

* fix(bedrock mantle): use unique function-call id for responses->chat tool calls

...

* fix(bedrock mantle): scope unique tool-call id fallback to degenerate call_id

The previous revision preferred the Responses item id for every tool call, which broke providers (and existing tests) where call_id is a unique, canonical correlation key. Restrict the fallback to the degenerate index-based call_id that Bedrock Mantle returns (call_0, call_1, ... resetting per response) and keep call_id otherwise. Revert the change to the OUTPUT_ITEM_DONE streaming handler, whose tool_call_chunk is never emitted (dead code, per review). Extend the regression tests to assert a normal call_id is preserved.

* fix(router): preserve azure_ad_token through CredentialLiteLLMParams for /v1/files + batches (#30235) (#30241)

* fix(router): preserve azure_ad_token through CredentialLiteLLMParams for /v1/files + batches (#30235)

Router.get_deployment_credentials_with_provider re-validates a
deployment's litellm_params through CredentialLiteLLMParams before
handing them to file/batch/passthrough callers:

    return CredentialLiteLLMParams(
        **deployment.litellm_params.model_dump(exclude_none=True)
    ).model_dump(exclude_none=True)

Any field NOT declared on CredentialLiteLLMParams gets silently dropped
on the way through. azure_ad_token was undeclared, so Azure deployments
using OAuth/M2M (azure_ad_token instead of a static api_key) silently
lost their token at the files endpoint and the proxy returned:

    Missing credentials. Please pass one of api_key, azure_ad_token,
    azure_ad_token_provider, ...

Declare azure_ad_token on CredentialLiteLLMParams alongside api_key /
api_base / api_version so it rides through the round-trip. Static-key
deployments stay unaffected (Optional, default None, dropped by
exclude_none=True). Provider-callable (azure_ad_token_provider) is a
separate concern and out of scope here.

Fixes #30235

* fix(ui-types): regenerate schema.d.ts for new azure_ad_token field

CI's 'Verify schema.d.ts matches the proxy OpenAPI spec' check
auto-detected the new field and emitted the exact diff to apply.
Two schemas had `aws_secret_access_key` from CredentialLiteLLMParams,
both get the new azure_ad_token marker next to it.

* fix(proxy): org_admin with own user_id now sees all org teams on /v2/team/list (#30247)

When the UI sends the callers own user_id (as it does for non-Admin
global roles), _enforce_list_team_v2_access now nulls it out for org
admins so _build_team_list_where_conditions scopes by organization_id
only -- matching the legacy /team/list behavior and the documented intent.

Fixes #30215

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>

* test(vertex_ai): multi-region regression coverage for cachedContents host (#29571) (#29707)

litellm_internal_staging already routes the cachedContents URL through
get_vertex_base_url, fixing the multi-region 404 reported in #29571 —
but carries no test coverage for the actual regression scenario (eu/us
must resolve to the REP host aiplatform.{geo}.rep.googleapis.com).

Add TestContextCachingMultiRegionUrls: parametrized eu/us REP-host
assertions (including absence of the old broken {geo}-aiplatform host),
plus regional (us-central1) and global no-regression checks.

* fix(proxy): close upstream LLM stream when client disconnects mid-stream (#30245)

* fix(proxy): close upstream LLM stream when client disconnects mid-stream

When a streaming client disconnects, Starlette abandons the response
body iterator without calling aclose(), so the proxy's connection to
the upstream backend stays open until garbage collection, which may
never come. The backend (e.g. vLLM) keeps generating into a dead pipe:
small responses drain invisibly into TCP buffers while large ones block
the backend on a full send buffer indefinitely (observed via lsof as an
ESTABLISHED proxy->backend connection minutes after the client left)

create_response now returns a StreamingResponse subclass that closes
both its body iterator and the wrapped upstream-facing generator in a
shielded finally. The upstream generator is closed directly rather than
through a cascade because aclose() on a never-started generator skips
its body, which would make the cascade a no-op when the client
disconnects before the first chunk is sent.
async_streaming_data_generator also gains the same shielded
finally-aclose that async_data_generator in proxy_server.py already
had, covering the Anthropic and Google SSE paths

With this, killing a streaming client causes the backend to observe the
abort within about a second and free its slot, while completed streams
are unaffected. No flag is needed, unlike the non-streaming opt-in
cancel in #30223: this only releases resources after the client is
already gone and does not change any response a client can observe

Fixes #30244

* fix(proxy): close upstream even when body iterator aclose raises BaseException

Addresses the Greptile finding on #30245: the cleanup loop caught only
Exception while the generator-level cleanup catches BaseException, so a
CancelledError or GeneratorExit escaping body_iterator.aclose() would
skip closing the upstream generator. Both sites now use the same scope
and a regression test pins that the upstream is closed even when the
body iterator explodes with a BaseException

* fix(llms): expose aclose on BaseModelResponseIterator so stream close reaches the provider connection

The response-level close added for #30244 only worked for SDK-based
providers (e.g. openai), whose streams expose aclose all the way down.
Providers served by base_llm_http_handler (hosted_vllm and most modern
transformation-based providers) wrap a bare response.aiter_lines()
generator in BaseModelResponseIterator, which had no aclose or close at
all, and nothing retained the httpx response object; so
CustomStreamWrapper.aclose() silently did nothing and the upstream
connection stayed open. Verified with a vLLM-style mock: with
hosted_vllm/ the backend streamed all 100 chunks to completion after
the client disconnected, while openai/ aborted at chunk 6

BaseModelResponseIterator now carries an optional http_response and an
aclose() that closes it; make_async_call_stream_helper attaches the
response after building the iterator. With this, hosted_vllm aborts the
backend within ~1.6s of the client dropping, and completed streams are
unaffected

---------

Co-authored-by: kursad <kursad.lacin@brado.net>

* feat(anthropic): surface compaction usage iterations data (#27065)

* feat(anthropic): surface compaction usage iterations data

* style: apply black formatting to fix lint checks

* fix(usage): correct calculate usage with cached tokens when use ChatCompletionUsageBlock (#30422)

* fix(usage): correct calculate usage with cached tokens when use ChatCompletionUsageBlock

* fix(usage): optimize test imports

* feat: add fastCRW search provider (#30434)

* feat(provider): add LibertAI as a JSON-configured OpenAI-compatible provider (#30203)

* feat(provider): add LibertAI as a JSON-configured OpenAI-compatible provider

* libertai: update served endpoints backup + add mode/matrix tests

Addresses review feedback:
- Add libertai to litellm/provider_endpoints_support_backup.json, the file
  actually served by GET /public/supported_endpoints (the root
  provider_endpoints_support.json already had it).
- Add tests asserting bge-m3 normalizes to mode='embedding' and that the
  served matrix lists libertai. embeddings stays false: the JSON-configured
  provider path only wires chat routing (OpenAILike embedding handler is
  reached only for literal openai_like/llamafile/lm_studio), matching the
  llamagate precedent; bge-m3 remains in the cost map for metadata.

---------

Co-authored-by: Moshe Malawach <moshemalawach@users.noreply.github.com>

* feat(provider): add ModelScope as an OpenAI-compatible provider (#28460)

* add ModelScope API support

* add modelscope api support

* update modelscope model list

* add image-genetation support

* update test and multimodal

* fix: address PR review feedback for modelscope provider

* update README

* fix(customer_endpoints): restrict /customer/daily/activity to admin-only (#28849)

* fix(customer_endpoints): restrict /customer/daily/activity to admin-only

* fix(customer_endpoints): check role before prisma_client guard

* fix(custom_guardrail): key disable_global_guardrails takes precedence over team guardrail list (#28563)

* fix(fallbacks): preserve fallback model in SDK fallback responses (#28260)

* fix(fallbacks): preserve fallback model in response when using SDK-level fallbacks

* fix(fallbacks): gate x-litellm-* passthrough to trusted callers only

The previous patch unconditionally let `x-litellm-*` keys bypass the
`llm_provider-` prefix in `process_response_headers`. That function is
also called on raw upstream-provider response headers (e.g. from
`llm_http_handler.py`), so a malicious provider could return
`x-litellm-attempted-fallbacks` and spoof a LiteLLM-internal marker,
bypassing the proxy model-override guard.

Add a `preserve_litellm_internal_headers` flag (default False). Only
`response_metadata.py`, which re-processes the already-built
`_hidden_params["additional_headers"]` dict (LiteLLM-owned), passes
True. Raw provider header callsites keep the default False, so upstream
`x-litellm-*` still gets the `llm_provider-` prefix.

Adds a regression test for the spoofing case and renames the existing
preserve test to make the trusted-path semantics explicit.

* fix(fallbacks): ignore preserve_litellm_internal_headers for raw httpx.Headers inputs

* style(core_helpers): apply black formatting

* fix(lint): remove banned typing.List/Dict/Any imports and suppress PLR0913 on interface overrides

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix(lint): apply black formatting to modelscope chat transformation

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix(lint): replace noqa with proper fixes — use **kwargs and Awaitable instead of Any/List

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix(lint): remove unused AllMessageValues import

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* revert: restore base_model_iterator.py to original PR state

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix(lint): restore full method signatures for MyPy compatibility; bump PLR0913 budget for new provider files

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix(lint): use @override to suppress PLR0913 on inherited signatures instead of bumping budget

The overrides keep their full base-class signatures for MyPy compatibility, but those signatures carry more than five parameters, which tripped PLR0913 on each subclass redeclaration. Since the arity is dictated by the base class and cannot be reduced, decorate the overrides with typing_extensions.override; ruff treats that as the intended signal that the parameter count is not under the author's control and skips PLR0913. This restores the PLR0913 baseline to 1813.

* fix(lint): add @override to modelscope image generation overrides

Apply the same typing_extensions.override treatment to the image generation config so its inherited-signature overrides do not count against PLR0913.

---------

Co-authored-by: Joel Tony <github@jaytau.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Co-authored-by: hcl <chenglunhu@gmail.com>
Co-authored-by: ztko <96878659+koztkozt@users.noreply.github.com>
Co-authored-by: Nahrin <nahrin@nahrinoda.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Humphrey <a739376838@gmail.com>
Co-authored-by: kursadlacin <kursadlacin@gmail.com>
Co-authored-by: kursad <kursad.lacin@brado.net>
Co-authored-by: Dushyant Acharya <dushyantacharya873@gmail.com>
Co-authored-by: Yuriy <yuriy.shuyskiy@gmail.com>
Co-authored-by: Recep S <22618852+us@users.noreply.github.com>
Co-authored-by: Moshe Malawach <moshe.malawach@protonmail.com>
Co-authored-by: Moshe Malawach <moshemalawach@users.noreply.github.com>
Co-authored-by: Rongkun Yan <2493404415@qq.com>
Co-authored-by: Varshith <kvarshithgowda@gmail.com>
Co-authored-by: Mateo Wang <277851410+mateo-berri@users.noreply.github.com>
 2026-06-16 12:06:41 -07:00
Yassin Kortam
4faeabc254
fix(guardrails): run pre_call hook once for model-level guardrails (#30543) 
* fix(guardrails): run pre_call hook once for model-level guardrails

A CustomGuardrail attached to a deployment via litellm_params.guardrails
gets its async_pre_call_hook invoked twice per request: once by the proxy
pre-call loop and again by async_pre_call_deployment_hook after the router
spreads the model-level guardrails into the top-level request kwargs.

Record in request metadata that the proxy pre-call loop already ran a given
guardrail, and have the deployment hook skip it when the marker is present.
Direct-SDK usage never runs the proxy loop, so the deployment hook stays the
sole invocation there and still fires exactly once.

The marker key is stripped from untrusted caller metadata so a request body
cannot suppress a model-only guardrail by pre-seeding it.

* fix(guardrails): mark pre_call dedup on the post-hook request data

Record the exactly-once marker after async_pre_call_hook runs, on the data
object that flows downstream, rather than before it. A guardrail whose hook
returns a brand-new request dict (instead of mutating or spreading the one it
received) would otherwise discard the marker, letting the deployment hook
re-run the guardrail a second time.
 2026-06-16 11:17:03 -07:00
Shivam Rawat
fc9d789d24
fix(integrations): cap Anthropic cache_control injection at 4 blocks (#30480) 
* fix(integrations): cap Anthropic cache_control injection at 4 blocks

Respect Anthropic's 4 cache_control breakpoint limit by counting client-supplied blocks, skipping messages that already carry cache_control, and stopping further auto-injection once the limit is reached.

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(integrations): reserve cache slot for tool_config and short-circuit cap

Address review feedback on the cache_control cap: break out of the injection loop before resolving target indices once the limit is reached, and reserve one of the four breakpoint slots when a tool_config injection point is present so the cachePoint appended by the Bedrock transform does not push the total past Anthropic's limit.

Co-authored-by: Cursor <cursoragent@cursor.com>

---------

Co-authored-by: Cursor <cursoragent@cursor.com>
 2026-06-15 20:50:38 -07:00
Yassin Kortam
45d5153c12
feat(otel-v2): emit the 6 gen_ai.client.* metrics at parity with v1 (#30326) 
* fix(otel): cap metric attribute cardinality with include/exclude lists

OTEL metrics stamped every per-request hidden_params and metadata.* field
onto each gen_ai.client.* sample, so near-unique values created one metric
time series per request and backends like Splunk Observability Cloud throttled
and dropped the data.

Add an attributes block under callback_settings.otel with mutually-exclusive
include_list (allowlist) and exclude_list (denylist), validated against the
known attribute names at startup and applied once to the metric attributes in
_record_metrics. Spans are untouched, and with no config every attribute is
still emitted so existing setups are unaffected.

Resolves LIT-3600

* fix(otel): resolve metric attribute filter from callback_settings

The proxy usually constructs the OpenTelemetry logger without forwarding the
attributes kwarg, while the filter lives under
litellm.callback_settings["otel"]["attributes"]. __init__ only read the kwarg,
so the recording instance kept config.attributes=None and shipped metrics at
full cardinality even when the filter was configured; a live proxy run exposed
this. Fall back to the global at init for the base otel logger, and add a
regression test that drives the real success hook through the callback_settings
path (the unit tests passed before because they injected the config directly).

* fix(otel): reject gen_ai.token.type from metric attribute filter lists

gen_ai.token.type was a member of VALID_METRIC_ATTRIBUTE_NAMES, so an
operator could list it in include_list or exclude_list and pass startup
validation. The attribute is injected into the input/output token series
after _filter_metric_attributes runs, so the filter never sees it and the
request silently has no effect.

Reject it loudly from either list instead, matching the contract that a
non-actionable attribute name fails fast rather than falling through to a
no-op. It stays a structural discriminator on the token-usage histogram.

* fix(otel): resolve metric attribute filter lazily at record time

The proxy constructs the OpenTelemetry logger before it populates
litellm.callback_settings["otel"]["attributes"], so resolving the filter at
__init__ left config.attributes None and shipped metrics at full cardinality. A
live proxy run confirmed the leak. Resolve the filter on the first metric record
instead, when callback_settings is populated, while still validating an explicit
config eagerly so a bad SDK config fails at startup. The regression test now
constructs the logger before populating callback_settings to mirror that
ordering, so it fails if the filter is resolved too early.

* fix(otel): don't cache invalid filter on lazy callback_settings path

On the lazy callback_settings resolution path, _ensure_metric_attribute_filter
wrote self.config.attributes before validating it. When validation then failed,
_metric_attr_filter_resolved stayed False while config.attributes held the bad
filter, so the next record skipped the callback_settings re-read and re-raised
the stale error indefinitely; fixing the misconfiguration required a restart.

Drop the premature write and resolve from the local value. A subsequent record
now re-reads callback_settings, so a corrected config takes effect without a
restart. The write was dead on the success path anyway, since the resolved
frozensets are what the filter reads.

* feat(otel-v2): emit the 6 gen_ai.client.* metrics at parity with v1

The v2 OpenTelemetry integration was a span engine: it declared two metric
histograms but never created a meter or recorded anything. Bring it to parity
with v1 so a v2-default deployment gets bounded metrics.

Adds the 4 missing metric names, all 6 histograms, a meter-provider builder that
mirrors v1's exporter selection, and a GenAIMetricRecorder that records token
usage (split input/output), cost, operation duration, TTFT (streaming), TPOT,
and response duration on the success hook. Gated on config.enable_metrics so the
default is unchanged.

The attribute cardinality filter is reused from v1 by import (no duplication of
the valid-name set or validation) and resolved lazily from
callback_settings.otel.attributes, matching v1. A misconfigured filter raises
out of the recorder; the logger surfaces it once at ERROR and records nothing,
rather than silently disabling metrics, and a corrected config recovers without
a restart.

* test(otel-v2): drop duplicate misconfig logger test (covered in test_otel_v2_logger)
 2026-06-15 16:12:49 -07:00
Yassin Kortam
3b84150137
fix(otel): record full error message on standard exception event in otel v2 (#30380) 
The v2 span engine only stamped error.type and stuffed the message into the
span status description; it never recorded the standard OTel exception event.
Backends that dynamic-map unknown string fields (e.g. Elasticsearch) index the
message as a keyword capped at ignore_above:1024, truncating it. Emit the full
message under the recognized exception.message semconv field via a span event so
it is mapped as full text instead.

Co-authored-by: Claude <noreply@anthropic.com>
 2026-06-13 18:42:43 +00:00
Yassin Kortam
f49707bc66
fix(otel): cap metric attribute cardinality with include/exclude lists (#30257) 
* fix(otel): cap metric attribute cardinality with include/exclude lists

OTEL metrics stamped every per-request hidden_params and metadata.* field
onto each gen_ai.client.* sample, so near-unique values created one metric
time series per request and backends like Splunk Observability Cloud throttled
and dropped the data.

Add an attributes block under callback_settings.otel with mutually-exclusive
include_list (allowlist) and exclude_list (denylist), validated against the
known attribute names at startup and applied once to the metric attributes in
_record_metrics. Spans are untouched, and with no config every attribute is
still emitted so existing setups are unaffected.

Resolves LIT-3600

* fix(otel): resolve metric attribute filter from callback_settings

The proxy usually constructs the OpenTelemetry logger without forwarding the
attributes kwarg, while the filter lives under
litellm.callback_settings["otel"]["attributes"]. __init__ only read the kwarg,
so the recording instance kept config.attributes=None and shipped metrics at
full cardinality even when the filter was configured; a live proxy run exposed
this. Fall back to the global at init for the base otel logger, and add a
regression test that drives the real success hook through the callback_settings
path (the unit tests passed before because they injected the config directly).

* fix(otel): reject gen_ai.token.type from metric attribute filter lists

gen_ai.token.type was a member of VALID_METRIC_ATTRIBUTE_NAMES, so an
operator could list it in include_list or exclude_list and pass startup
validation. The attribute is injected into the input/output token series
after _filter_metric_attributes runs, so the filter never sees it and the
request silently has no effect.

Reject it loudly from either list instead, matching the contract that a
non-actionable attribute name fails fast rather than falling through to a
no-op. It stays a structural discriminator on the token-usage histogram.

* fix(otel): resolve metric attribute filter lazily at record time

The proxy constructs the OpenTelemetry logger before it populates
litellm.callback_settings["otel"]["attributes"], so resolving the filter at
__init__ left config.attributes None and shipped metrics at full cardinality. A
live proxy run confirmed the leak. Resolve the filter on the first metric record
instead, when callback_settings is populated, while still validating an explicit
config eagerly so a bad SDK config fails at startup. The regression test now
constructs the logger before populating callback_settings to mirror that
ordering, so it fails if the filter is resolved too early.

* fix(otel): don't cache invalid filter on lazy callback_settings path

On the lazy callback_settings resolution path, _ensure_metric_attribute_filter
wrote self.config.attributes before validating it. When validation then failed,
_metric_attr_filter_resolved stayed False while config.attributes held the bad
filter, so the next record skipped the callback_settings re-read and re-raised
the stale error indefinitely; fixing the misconfiguration required a restart.

Drop the premature write and resolve from the local value. A subsequent record
now re-reads callback_settings, so a corrected config takes effect without a
restart. The write was dead on the success path anyway, since the resolved
frozensets are what the filter reads.
 2026-06-12 17:29:46 -07:00
Sameer Kankute
079c136742
chore(oss): litellm oss staging 120626 (#30292) 
* feat(bedrock): add bedrock mantle gemma 4 models (#30264)

* feat(bedrock): add bedrock mantle gemma 4 models

* test(bedrock): harden mantle local cost fixture

* feat(responses): enable the responses API for the Tensormesh provider (#30209)

* feat(responses): enable the responses API for the Tensormesh provider

* Update litellm/llms/openai_like/providers.json

Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>

---------

Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>

* fix(langfuse_otel): mark LLM spans as generations (#30250)

* fix(bedrock): stop stream_chunk_size leaking into invoke request bodies (#30240)

stream_chunk_size is a LiteLLM-internal knob for re-chunking the HTTP
response stream. The invoke transformations splat optional_params into the
provider request body without dropping it, and Bedrock rejects unknown
fields, so any bedrock/invoke request that sets the parameter fails with
ValidationException: stream_chunk_size: Extra inputs are not permitted.
Drop it in the invoke dispatcher (covers cohere, titan, mistral, meta,
ai21) and in the Claude messages-format request builder (the route used
for bedrock/invoke Anthropic models)

* fix(bedrock): stop buffering streamed tool-call argument deltas (#30231)

* fix(bedrock): stop buffering streamed tool-call argument deltas

Two issues made Bedrock tool-use streaming arrive as a single end-of-stream
burst through LiteLLM while plain text streamed fine.

First, the anthropic-beta allowlist mapped fine-grained-tool-streaming-2025-05-14
to null for bedrock and bedrock_converse, so the header was silently stripped.
Without that beta, Anthropic models on Bedrock buffer tool input server-side and
emit all toolUse.input deltas at once (verified against converse-stream and
invoke-with-response-stream directly). Bedrock accepts the beta via
additionalModelRequestFields.anthropic_beta, so it is now forwarded.

Second, the streaming reads re-chunked the AWS event stream with
iter_bytes(chunk_size=1024). httpx's ByteChunker only releases full 1024-byte
blocks, so the small early events (messageStart, contentBlockStart, first
deltas) sat in the buffer until enough bytes accumulated, pushing
time-to-first-byte from ~1.4s to ~8.5s on buffered tool-use streams. The
default is now no re-chunking; an explicit stream_chunk_size is still honored.

* test(bedrock): cover explicit stream_chunk_size on sync invoke path

* test(bedrock): cover stream_chunk_size plumbing through converse completion

* test(bedrock): cover stream_chunk_size default in legacy BedrockLLM streaming

* test(bedrock): merge converse handler tests into existing mapped test file

pytest imports test modules by basename in non-package test dirs, so the new
tests/test_litellm/llms/bedrock/chat/test_converse_handler.py collided with
the pre-existing tests/test_litellm/llms/chat/test_converse_handler.py and
broke collection in CI. Move the new tests into the existing file

* feat(otel): emit v2 cost breakdown + stamp tracer scope version (#30156)

Read the StandardLoggingPayload cost_breakdown into a typed LLMCost on
LLMCallSpanData and emit each component under litellm.cost.* (absent
components omitted, so spans stay sparse). Stamp litellm.__version__ as
the instrumentation scope version so every v2 span carries a
deterministic scope.version.

Tests under tests/test_litellm/integrations/otel/.

* fix(proxy): cancel in-flight upstream LLM request on client disconnect (opt-in) (#30223)

* fix(proxy): cancel in-flight upstream LLM request on client disconnect (opt-in)

On the non-streaming path, base_process_llm_request awaited the LLM call
with no disconnect monitoring; when the HTTP client went away the
upstream request kept running until completion or request_timeout (6000s
default), holding a backend slot (e.g. a vLLM GPU slot) for output
nobody would read

Add an opt-in general_settings.cancel_on_disconnect flag, default off,
so the default code path is unchanged. When enabled, a receive-based
watcher task observes http.disconnect and cancels the asyncio.gather
driving the upstream call. The resulting CancelledError is converted to
HTTPException 499 only when the disconnect event is set, so
server-initiated cancellations still propagate as-is. The 499 then flows
through _handle_llm_api_exception like any other failure, meaning
post_call_failure_hook still releases max_parallel_requests slots and
fires spend and alerting callbacks; it is logged at info level instead
of a full traceback

Also removes the dead check_request_disconnection helper in
proxy_server.py (zero call sites) along with its behavior-pin tests

Builds on the receive-based design from #25776

Addresses #13774. Re-fixes #22805 (regressed after the #14295 revert)

Co-authored-by: CreateRandom <18438707+CreateRandom@users.noreply.github.com>

* fix(proxy): scope 499 quiet logging to disconnects and harden watcher

Address the two P2 findings from the Greptile review on #30223. The
info-level logging in _log_llm_api_exception now applies only to the
disconnect-specific HTTPException (status 499 plus the shared
_CLIENT_DISCONNECT_DETAIL message), so any other 499 raised by hooks or
guardrails keeps its full traceback. The disconnect watcher now catches
exceptions from request.receive() (e.g. a transport reset) and logs a
warning instead of dying silently, making the degradation to no-op
visible; a test pins that the LLM call is not cancelled in that case

---------

Co-authored-by: kursad <kursad.lacin@brado.net>
Co-authored-by: CreateRandom <18438707+CreateRandom@users.noreply.github.com>

* fix(bedrock): grant aws-external-anthropic:* in OIDC session policy for claude_platform (#30200) (#30205)

The inline STS session policy passed to assume_role_with_web_identity
acts as an IAM PERMISSION CEILING — effective permissions are the
intersection of the role's identity policies and this policy. Any
action not listed is silently denied even when the IAM role grants it.

#27678 added the bedrock/claude_platform/<model> route but its
service-side action namespace is aws-external-anthropic:*, not
bedrock:*. Without a matching statement here, every claude_platform
request via OIDC (GCP federation, EKS Pod Identity webhook, etc.) 403s
with 'no session policy allows the aws-external-anthropic:CreateInference
action' — even with a fully permissive identity policy.

Add a second ClaudePlatformLiteLLM statement covering CreateInference,
CreateBatchInference, CancelBatchInference, DeleteBatchInference,
CountTokens, Get*, List*. Keep aws:SecureTransport=true parity with the
bedrock statement.

Static creds + IRSA flow through different code paths and are not
affected.

Fixes #30200

* fix(proxy): set Retry-After header on RouterRateLimitError 429 responses (#30098)

* Set Retry-After header on RouterRateLimitError responses

When all deployments for a model are in cooldown, the proxy returns a
429 whose cooldown timing is only available by parsing the error
message string. RouterRateLimitError already carries cooldown_time, so
expose it as a standard retry-after header in
_handle_llm_api_exception. The value is rounded up so clients never
retry before the cooldown window ends.

Fixes #27823.

* Set Retry-After after response-headers hook so cooldown wins

The cooldown-derived retry-after was assigned before the
post_call_response_headers_hook merge, so a callback returning a
retry-after key (including a stale or empty value) silently clobbered
it. Move the RouterRateLimitError block after the callback merge so the
cooldown value is authoritative for this error type.

* fix(router): route aspeech through async_function_with_fallbacks (#30104)

* fix(router): route aspeech through async_function_with_fallbacks

Router.aspeech selected a deployment and awaited litellm.aspeech
directly, so TTS requests got no retry on failure and no failover to
backup deployments; the except block only fired an exception alert and
re-raised. Every other router endpoint (acompletion, aembedding,
atranscription, arerank) already delegates to
async_function_with_fallbacks

Mirror the atranscription pattern: move deployment selection and the
litellm.aspeech call into a private _aspeech method, then have the
public aspeech set kwargs["original_function"] = self._aspeech and
await self.async_function_with_fallbacks(**kwargs). _aspeech also picks
up the shared _get_async_openai_model_client helper and the same
total/success/fail call accounting the sibling endpoints use

Fixes #27778.

* fix(router): apply deployment kwargs and rpm semaphore in _aspeech

Bring _aspeech fully in line with _atranscription: call
_update_kwargs_with_deployment so deployment metadata, model_info,
timeout, and default litellm params flow into the request, and wrap
the litellm.aspeech call with the max_parallel_requests semaphore plus
async_routing_strategy_pre_call_checks so TTS respects rpm limits the
same way the other router endpoints do

Also add a unit test that exercises _aspeech directly and asserts the
deployment metadata reaches the underlying call

* fix(slack_alerting): stop false-positive hanging request alerts for requests below the alerting threshold (#30106)

* fix(slack_alerting): skip hanging request alerts below the threshold

The hanging request check alerted on any cached request whose
completion status was not yet recorded, with no minimum age check.
Since the background loop runs every alerting_threshold / 2 seconds,
any request that happened to be in flight at a check fired a
"hanging - Ns+ request time" alert even if it was only seconds old,
producing a steady stream of false positives.

Add a created_at timestamp to HangingRequestData, stamped when the
request enters the hanging request cache, and skip requests younger
than alerting_threshold without evicting them, so a later check can
still alert if they never complete. Extend the cache TTL from
threshold + 60s to 1.5x threshold + 60s; with the age check, entries
only become alertable after threshold seconds, and the check period
is threshold / 2, so the old TTL could evict a genuinely hanging
request before any check saw it cross the threshold.

Fixes #27855.

* fix(slack_alerting): alert once per hanging request

The min-age gate stops false positives for young in-flight requests, but
a genuinely hanging request still re-alerted on every checker tick within
the cache TTL. With the wider TTL (1.5x threshold + 60s) that is 1-2 extra
Slack notifications per stuck request at the default 600s threshold.

Flag a HangingRequestData entry as alerted once its alert fires and skip
flagged entries on later ticks, so each hang produces exactly one alert.
The cache reference is mutated in place, so the TTL is untouched and still
handles cleanup. Adds a regression test asserting one alert across multiple
ticks.

Fixes #27855.

* fix(health): treat all-proxy-models keys as unrestricted in /health (#30087)

* fix(health): treat all-proxy-models keys as unrestricted in /health

A key granted all model permissions stores the literal
"all-proxy-models" marker in its models list. The /health access
filter compared that marker against real model_names, so the model
list filtered down to nothing and the WebUI health check returned
healthy_count=0, unhealthy_count=0 with HTTP 503. Skip the filter
(both the live path and the background-cache model_id scoping) when
the marker is present, matching how auth_checks treats
SpecialModelNames.all_proxy_models.

Fixes #29744.

* fix(health): resolve all-team-models sentinel to the team allowlist

Same failure shape as the all-proxy-models case: a key carrying the
literal "all-team-models" entry matches no real model_name, so the
/health access filter would zero out the model list. Resolve the
sentinel to the key's team models when team_id is set, matching
get_key_models in model_checks.py. Without a team_id the sentinel
stays unresolved and matches nothing, denying rather than widening
access, mirroring _resolve_key_models_for_auth_check.

* feat(proxy): auto-enable drop_params for Claude Code requests (#30218)

* feat(proxy): auto-enable drop_params for Claude Code requests

Claude Code identifies itself with a claude-cli/<version> user agent and
sends Anthropic-specific params (top_k, thinking, etc.) on every request.
When the proxy routes those requests to a non-Anthropic provider, the
unsupported params fail the call unless drop_params is configured. Detect
the Claude Code user agent in add_litellm_data_to_request and default
drop_params to true for those requests, without overriding an explicit
drop_params value sent by the caller.

* feat(proxy): respect operator litellm_settings drop_params over Claude Code default

An explicit drop_params in the operator's litellm_settings (true or false)
now suppresses the Claude Code user agent default, so an operator who
deliberately configured drop_params: false keeps strict param validation
for Claude Code clients too. The auto-default only fills the gap when
neither the request body nor the config sets a value.

* fix(snowflake): migrate to native endpoints with auto-routing for Claude models (#29964)

* fix(snowflake): migrate to native Cortex REST API endpoints

Replaces the legacy /api/v2/cortex/inference:complete endpoint with the
native OpenAI-compatible /api/v2/cortex/v1/chat/completions endpoint,
fixing error 390142 (Incoming request does not contain a valid payload)
when using model: snowflake/<model> in LiteLLM proxy.

Changes:
- litellm/llms/snowflake/chat/transformation.py: route to native
  /cortex/v1/chat/completions, remove Snowflake-specific tool_spec
  payload transformation, remove content_list response handling,
  add stream to supported params
- litellm/llms/snowflake/anthropic/transformation.py (new):
  SnowflakeCortexAnthropicConfig routes Claude models to /cortex/v1/messages
  with anthropic-version header and Anthropic->OpenAI response transform
- tests: 29 unit tests covering URL routing, auth headers, payload
  format, and response parsing

* fix(snowflake): map max_tokens to max_completion_tokens for native endpoint

* fix: handle multi-turn tool conversations and OpenAI→Anthropic tool format conversion

- _extract_system_and_messages now preserves tool_calls from assistant messages
  and converts them to Anthropic tool_use content blocks
- tool role messages are converted to user role with tool_result content blocks
  (as required by Anthropic Messages API)
- Added _transform_tools_to_anthropic() to convert OpenAI tool format
  (type/function/parameters) to Anthropic format (name/input_schema)
- Added comprehensive tests for multi-turn tool conversations

Addresses review feedback on PR #29964

* test: add coverage for malformed JSON and non-string tool arguments

* fix(tests): update chat transformation tests for native OpenAI-compatible endpoint

* style: apply black formatting

* fix: resolve mypy type errors in anthropic transformation

* fix: correct mypy type: ignore error codes (attr-defined)

* fix: use max_tokens instead of max_completion_tokens for Snowflake endpoint compatibility

* refactor: merge Anthropic config into unified SnowflakeConfig with auto-routing

- Remove separate SnowflakeCortexAnthropicConfig and anthropic/ directory
- SnowflakeConfig now auto-routes based on model name:
  - Claude models → /messages endpoint (Anthropic format)
  - All others → /chat/completions endpoint (OpenAI format)
- No new provider needed (stays as SNOWFLAKE = 'snowflake')
- Tool message transformation for Claude: tool_calls → tool_use blocks,
  tool role → user with tool_result
- OpenAI → Anthropic tool format conversion (parameters → input_schema)
- Addresses Greptile feedback about unwired SnowflakeCortexAnthropicConfig

* fix: use max_completion_tokens for /chat/completions (Snowflake deprecated max_tokens on this endpoint)

* fix(tests): update assertions for Claude auto-routing to /messages endpoint

* fix(snowflake): add tool_choice conversion and preserve max_completion_tokens in Anthropic path

* fix(snowflake): use ChatCompletionMessageToolCall objects and strip model prefix on OpenAI path

* fix(snowflake): collect multiple system messages to prevent guardrail override

* chore: remove committed .pyc files and add __pycache__ to .gitignore

* fix: remove unused Union import

* fix: restore original .gitignore (accidentally replaced in earlier commit)

* feat(snowflake): add streaming response handler for both Anthropic and OpenAI SSE formats

* fix: remove unused AsyncIterator and Iterator imports

* fix: add missing total_tokens to ChatCompletionUsageBlock

* fix(snowflake): coalesce consecutive tool results into single user message for Anthropic

* fix(snowflake): handle message_start event for streaming input_tokens tracking

* fix: evict last deleted model in multi-instance deployments (#28608)

* fix: evict last deleted model in multi-instance deployments

_delete_deployment had an early return when db_models was empty,
preventing eviction of the last deleted model during reconciliation.

- Remove len(db_models)==0 early return from _delete_deployment
- Return None (not []) from _get_models_from_db on DB failure so
  callers can distinguish a transient failure from a genuinely empty DB
- Guard _update_llm_router against None to skip updates on DB failure

Fixes #28443

* test: remove dead MagicMock assignment in type_mismatch test

* fix: update test to pass [] not None to _update_llm_router

test_ProxyConfig__update_llm_router_bad_proxy_logging_raises was passing
None as new_models to get through to the proxy_logging_obj check, but
the None guard we added now returns early before reaching that path.
Pass [] instead so the test exercises the intended AttributeError case.

Signed-off-by: Rudra Dudhat <contact.rdudhat@gmail.com>

* chore: regenerate API types to sync schema.d.ts with proxy OpenAPI spec

Signed-off-by: Rudra Dudhat <contact.rdudhat@gmail.com>

---------

Signed-off-by: Rudra Dudhat <contact.rdudhat@gmail.com>

* fix: invalidate Redis spend counter on /key/reset_spend (#29694)

* fix: set Redis spend counter to reset_to value on /key/reset_spend

Previously, the Redis spend counter was always set to 0.0 after a reset,
even when reset_to was a non-zero value (partial reset). This caused
the budget to be under-enforced for up to 60 seconds until the counter
expired and fell through to the DB.

Now the counter is set to the actual reset_to value, so partial resets
are reflected correctly and budget enforcement is consistent.

* test: update reset_key_spend test to match direct cache set

The implementation now sets spend_counter_cache directly instead of
calling _invalidate_spend_counter. Update the test to verify the
in_memory_cache.set_cache call with the correct key, value, and ttl.

---------

Co-authored-by: michaelxer <michaelxer@users.noreply.github.com>

* fix: add scaleway models pricing (#27659)

* fix: Add embeddings support for Scaleway provider

* fix: resolve merge conflicts

* fix(main): clarify backend route handling for Swagger static assets (#30196)

* fix(main): clarify backend route handling for Swagger static assets

* fix(allowlist): add BACKEND_MOUNT_PATHS for Swagger static assets

* fix(voyage): route multimodal embeddings to correct endpoint (#30193)

* fix(voyage): route multimodal embeddings to correct endpoint

* test(voyage): cover multimodal embedding edge cases

* test(voyage): cover api key fallback

* fix(voyage): raise early on missing api key and malformed image url

* test(voyage): cover utils routing and helper

* fix(voyage): route supported openai params for multimodal models

* style: apply black formatting

* fix(ui): infer Azure API version from API base (#30204)

* fix(ui): infer Azure API version from API base

* fix(ui): address Azure API version feedback

* Update litellm/llms/snowflake/chat/transformation.py

Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>

* feat(datadog): add team-scoped Datadog callback support (#29947)

Enable teams to configure their own Datadog credentials via
POST /team/{team_id}/callback, following the same pattern as Langfuse.

* Merge pull request #29528 from aanchal22/litellm_byok-alias-merge

fix(proxy): atomic merge for team model aliases and team.models on BYOK create

* feat: add EmpirioLabs as an OpenAI-compatible provider (#30278)

Co-authored-by: Adam Dalloul <adam.d.developer@gmail.com>

* fix: resolve failing tests and lint in snowflake/team endpoints

- Black-format snowflake/chat/transformation.py to fix lint failure
- Update Anthropic config test to expect default max_tokens of 4096 (matches implementation)
- Add AsyncMock + execute_raw mock to team_model_add cache-refresh pin test
- Add model_dump mock and patch cache/logging in test_uses_atomic_array_append_with_dedup

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix(test): update test_db_error_new_model_check for new _delete_deployment logic

_delete_deployment no longer short-circuits on empty db_models — it now
treats [] as a valid empty-DB state and proceeds to check config models.
Mock get_config to return the two router deployments so they appear in
combined_id_list and are protected, which matches the real-world scenario
where a DB error occurs but the models are config-backed.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(proxy): register cancel_on_disconnect in ConfigGeneralSettings and config list (#30295)

* feat(proxy): register cancel_on_disconnect in ConfigGeneralSettings and config list

Follow-up to #30223 per maintainer review: documents the flag in
ConfigGeneralSettings with a short description and adds it to
allowed_args in get_config_list so the UI and /config/list expose it.
A test pins that /config/list returns the field with type Boolean,
which requires both registrations to be present

* chore(ui): regenerate schema.d.ts for cancel_on_disconnect

---------

Co-authored-by: kursad <kursad.lacin@brado.net>

* fix(datadog): never fall back to env DD_API_KEY for caller-supplied destinations

Team/key-scoped Datadog loggers could be pointed at an arbitrary dd_agent_host or
dd_site while omitting dd_api_key, causing the proxy's global DD_API_KEY to be sent
as the DD-API-KEY header to that destination. Gate the env-var fallback behind an
allow_env_credentials flag, set to False when the destination is caller-supplied,
mirroring the existing langfuse/langsmith pattern.

---------

Signed-off-by: Rudra Dudhat <contact.rdudhat@gmail.com>
Co-authored-by: Emerson Gomes <emerson.gomes@thalesgroup.com>
Co-authored-by: daitran-tensormesh <dai@tensormesh.ai>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Co-authored-by: Muspi Merol <me@promplate.dev>
Co-authored-by: fangkang <fangkangm@gmail.com>
Co-authored-by: Chris Hoogeboom <chris.hoogeboom@gmail.com>
Co-authored-by: kursadlacin <kursadlacin@gmail.com>
Co-authored-by: kursad <kursad.lacin@brado.net>
Co-authored-by: CreateRandom <18438707+CreateRandom@users.noreply.github.com>
Co-authored-by: hcl <chenglunhu@gmail.com>
Co-authored-by: Filippo Menghi <113345637+Cyberfilo@users.noreply.github.com>
Co-authored-by: Mateo Wang <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: sfc-gh-nashukla <navnit.shukla@snowflake.com>
Co-authored-by: Rudra Dudhat <contact.rdudhat@gmail.com>
Co-authored-by: Michael <52305679+michaelxer@users.noreply.github.com>
Co-authored-by: michaelxer <michaelxer@users.noreply.github.com>
Co-authored-by: Quentin Champenois <26109239+Quentinchampenois@users.noreply.github.com>
Co-authored-by: mauriceberentsen <mauriceberentsen@live.nl>
Co-authored-by: lost9999 <56498264+lost9999@users.noreply.github.com>
Co-authored-by: GaetanVDB07 <86427581+GaetanVDB07@users.noreply.github.com>
Co-authored-by: Aanchal Khandelwal <aan2210khandelwal@gmail.com>
Co-authored-by: Adam Dalloul <adam_dalloul@icloud.com>
Co-authored-by: Adam Dalloul <adam.d.developer@gmail.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
 2026-06-12 09:49:25 -07:00
Sameer Kankute
cfcdf8714a
feat: litellm oss 110626 (#30202) 
* Add gpt-realtime-whisper Realtime transcription support (OpenAI + Azure) (#29775)

* Add gpt-realtime-whisper Realtime transcription support (OpenAI + Azure)

Adds first-class support for the gpt-realtime-whisper streaming speech-to-text
model, which uses the Realtime transcription session API rather than the
file-based /audio/transcriptions path.

Model registration: registers gpt-realtime-whisper and azure/gpt-realtime-whisper
with audio-duration pricing (input_cost_per_second = 0.017/60, matching the
published $0.017/minute input audio rate).

REST endpoint: implements POST /v1/realtime/transcription_sessions (plus /realtime
and /openai/v1 aliases) to mint an ephemeral transcription session for the
WebRTC flow. Adds request/response types, OpenAI and Azure URL builders, a shared
base handler (refactored from the client_secrets handler), the
acreate_realtime_transcription_session SDK function, and route registration. The
proxy encrypts the ephemeral key returned under client_secret.value and records
the session type in the token so the follow-up /realtime/calls replays
type=transcription rather than type=realtime.

WebSocket: forwards intent=transcription through to the Azure handler (OpenAI
already received it) with URL-encoding, so gpt-realtime-whisper opens a
transcription session. Transcription-only sessions no longer trigger an
erroneous response.create.

Cost tracking: transcription sessions emit no response.done events; their usage
arrives on conversation.item.input_audio_transcription.completed as
{type: duration, seconds}. That usage is captured out-of-band (usage only, no
transcript duplication) and billed by input_cost_per_second, with a token-billed
fallback for token-priced transcription models.

Adds tests for pricing math, URL builders, request/response types, the proxy
route and SDK function, WebSocket intent forwarding, transcription-session
streaming behavior, and the /realtime/calls session-type replay.

* Address PR review: URL-encode all Azure WS query params; forward query_params through provider_config branch

* Address PR review: session_type validation, model auth fix, cost perf, billing fallback, detail/docs cleanup

* Improve test coverage: detection from backend, error paths, unknown usage type, resolved_model None

* Backport realtime transcription websocket fixes

* Enforce authorized realtime transcription model

* Enforce realtime transcription model access

* Enforce realtime resolved model scopes

* Enforce WebRTC transcription model scope

* Lazy evaluate debug log in pass-through endpoint (#30177)

* Pass through debug lazy logging

* fix(proxy): convert remaining eager pass-through debug logs to lazy formatting

* fix(parallel_ai): migrate search integration from v1beta to v1 endpoint (#30157)

* fix(parallel_ai): migrate search integration from v1beta to v1 endpoint

The Parallel Search API moved from /v1beta/search (processor: base/pro,
parallel-beta header) to /v1/search (mode: turbo/basic/advanced, no beta
header). Request fields moved too: max_results, source_policy, and excerpt
settings are now nested under advanced_settings, and source_policy uses
include_domains/exclude_domains. The v1 response returns publish_date per
result, which now maps to SearchResult.date instead of being hardcoded to
None. The legacy processor param is mapped to the equivalent mode so
existing callers keep working.

* fix(parallel_ai): default mode to basic and simplify param handling

The v1 API defaults to advanced mode when mode is omitted, while v1beta
defaulted to the base processor. Without an explicit default, callers who
pass no mode would be silently upgraded to a tier costing 2.25x more while
litellm's cost map reports the basic-tier price. Sending mode=basic
preserves the v1beta default and keeps cost tracking accurate.

Also replaces the handled_params set with pop-as-consumed param handling so
mapped params no longer need to be tracked in two places, and extends the
tests to pin the default mode, processor=base mapping, mode-over-processor
precedence, and top-level v1 param passthrough.

* fix(parallel_ai): avoid double /v1 when api_base is already versioned

A PARALLEL_AI_API_BASE like https://api.parallel.ai/v1 previously produced
.../v1/v1/search. Strip a trailing /v1 before appending the search path and
cover the api_base variants with a parametrized test.

---------

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>

* feat(focus): add Mavvrik destination for FOCUS export (#29935)

* fix: preserve responses streaming flag (#30189)

* fix: preserve responses streaming flag

* test: cover async responses streaming flag

* fix(spend/daily-activity): stable offset pagination via id tiebreaker (#30164) (#30167)

date alone is not a unique sort key for LiteLLM_DailyUserSpend or
LiteLLM_DailyTeamSpend (many rows per date: api_key x model x
model_group x provider x endpoint). Offset pagination over a
non-unique sort landed on arbitrary boundaries, so a client paging
through all results and summing per-page metrics (the Usage dashboard)
got non-deterministic totals - sometimes inflated, sometimes deflated,
different at different page_size values.

Adding the row's UUID id (present on both tables) as a secondary sort
gives every page a stable cursor. order=[{date desc}, {id asc}].

Fixes #30164

* fix(oci): inject a default maxTokens so omitted max_tokens doesn't truncate responses (#30018)

* fix(oci): inject default maxTokens so omitted max_tokens doesn't truncate

OCI GenAI applies a tiny server-side maxTokens default (~20 tokens) when the
request omits it, so any call that doesn't send max_tokens comes back cut off
mid-string with finishReason "length". MLflow judges never send max_tokens, so
their JSON responses arrived as unterminated strings and json.loads failed in
MLflow's gateway adapter.

When no maxTokens/maxCompletionTokens target is set, inject
DEFAULT_OCI_CHAT_MAX_TOKENS (env-overridable, defaults 4096), mirroring the
Anthropic config's default-max-tokens behaviour. An explicit max_tokens still
wins, and reasoning models still route to maxCompletionTokens. Used a fixed
default rather than the catalog max_output_tokens because the catalog value is
unreliable for some models (grok-4 reports max_output_tokens equal to its
context window, not a real output cap, which would risk 400s).

Adds TestOCIDefaultMaxTokens covering Cohere and generic injection, the
explicit-override case, and the reasoning maxCompletionTokens branch.

* test(oci): e2e regression that omitted max_tokens isn't truncated

Real-proxy integration test asserting a chat completion that omits max_tokens
completes with finish_reason "stop" instead of being cut off at OCI's ~20-token
server default. Fails before the maxTokens-default injection (finish_reason
"length", ~19 tokens), passes after.

* test(oci): update cohere default-params test for injected maxTokens

test_cohere_default_parameters asserted no maxTokens was injected, encoding the
old behaviour where OCI's ~20-token server default truncated responses. Now
that transform_request injects DEFAULT_OCI_CHAT_MAX_TOKENS, assert maxTokens
equals that default while the other params (topK/topP/frequencyPenalty) stay
pass-through with no hardcoded default.

* fix(oci): make DEFAULT_OCI_CHAT_MAX_TOKENS a plain constant

Drop the os.getenv override. The env knob was not requested and introducing a
new env var forced a cross-repo dependency on litellm-docs (test_env_keys.py
validates every referenced env var against the docs table there). A plain 4096
constant keeps the PR self-contained; callers who want a different limit pass
max_tokens explicitly per request.

* fix(oci): route all OpenAI commercial models to maxCompletionTokens

OCI serves OpenAI models (gpt-4.1, gpt-5.1 through 5.5, o-series) that
the litellm catalog doesn't track, so the supports_reasoning lookup
returned False for them and the provider sent maxTokens, which the
reasoning families reject with HTTP 400. With the injected default
maxTokens this broke every request to those models, not just ones with
an explicit max_tokens. Route the whole openai.* vendor prefix to
maxCompletionTokens since OpenAI accepts max_completion_tokens on every
chat model; the openai.gpt-oss-* open weights are served by OCI's own
stack and keep maxTokens. Verified live against gpt-5.2, gpt-5, gpt-4o,
gpt-4.1, gpt-oss-120b, llama-3.3, command-a and grok-3-mini

* test(oci): hoist transformation imports and drop unused ones

Makes the generic-chat test file ruff-clean: the per-test local imports
of OCIChatConfig/OCIVendors shadowed the module-level import (F811) and
left it unused (F401), and json plus three OCI type imports were never
referenced

* fix(oci): translate response_format json_schema to OCI's accepted shape (#29691)

* fix(oci): translate response_format json_schema to OCI's accepted shape

OCI GenAI rejected every json_schema response_format with HTTP 400
"Please pass in correct format of request", which broke structured-output
callers such as MLflow LLM judges (they always send a json_schema).

The provider forwarded OpenAI's raw json_schema body unchanged. For GENERIC
models OCI's ResponseJsonSchema accepts only name/description/schema/isStrict,
so OpenAI's `strict` key (and any other extra) 400s the request; the key must
be renamed to isStrict and the body whitelisted. For Cohere models there is no
JSON_SCHEMA type at all; the schema has to ride on JSON_OBJECT as
{"type": "JSON_OBJECT", "schema": ...}. Cohere type values must also be the
canonical uppercase TEXT/JSON_OBJECT.

_normalize_response_format now branches by vendor and emits the exact shape
each one accepts (verified live against OCI GenAI for Cohere, Meta, Gemini and
Grok). Drops the unused, incorrect Cohere response-format pydantic models.

Two existing tests asserted the broken behavior (lowercase type, raw
jsonSchema on Cohere); they are rewritten to assert the corrected shape, and
generic/Cohere json_schema regression tests are added.

* fix(oci): raise early on json_schema response_format with no body

A GENERIC model request with {"type": "json_schema"} and no json_schema
object fell through to the JSON_OBJECT branch and emitted a bodyless
{"type": "JSON_SCHEMA"}, which OCI rejects with an opaque HTTP 400. Raise a
descriptive 400 at translation time instead. Cohere is unaffected since it
always maps to JSON_OBJECT.

* test(oci): gateway integration test for response_format json_schema

Added to tests/integration/ (the real-network integration suite) reusing the
existing OCI proxy harness, not tests/llm_translation/ which is mock-only.

---------

Co-authored-by: Sameer Kankute <sameer@berri.ai>

* fix(oci): accept default n=1 on Cohere instead of hard-failing (#29705)

* fix(oci): accept default n=1 on Cohere instead of hard-failing

Cohere on OCI has no numGenerations field, so n was mapped to False and
map_openai_params raised "param `n` is not supported on OCI" whenever a client
sent n. But n=1 (and None) is the OpenAI default single-generation request,
which every OCI model produces anyway, so standard clients that always send
n=1 (such as the MLflow gateway) were rejected with a 500.

Drop n=1/None silently for Cohere; only n>1 is genuinely unsupported and still
raises (or drops under drop_params). Generic models are unaffected and keep
numGenerations, including n>1.

* docs(oci): explain why n is not advertised for Cohere despite tolerating n=1

* test(oci): gateway integration test for Cohere default n=1

Added to tests/integration/ (the real-network integration suite) reusing the
existing OCI proxy harness, not tests/llm_translation/ which is mock-only.

---------

Co-authored-by: Sameer Kankute <sameer@berri.ai>

* fix(oci): drop max_retries instead of hard-failing on OCI (#29727)

max_retries is a litellm-level control param (litellm applies retries itself),
not a generation param OCI accepts. The provider mapped it to False and raised
"param `max_retries` is not supported on OCI" whenever it was present. The
litellm proxy injects max_retries on every request, so any OCI call through the
proxy 500'd unless drop_params was set.

Drop max_retries silently in map_openai_params. Adds a unit test (Cohere and
generic) and a gateway integration test that a plain request succeeds through a
proxy without drop_params.

Co-authored-by: Sameer Kankute <sameer@berri.ai>

* fix(spend-logs): rehydrate metadata JSONB text on ui_view_spend_logs (#29682)

Fixes #29674.

`/spend/logs/ui` raw-SQL path returns the JSONB metadata column as a
string — prisma's query_raw skips the ORM-layer hydration. The UI reads
metadata.status / metadata.error_information as object fields, so
provider-failure rows look like successes.

Fix: json.loads the metadata field right after query_raw, fall back to
{} on malformed JSON.

3 existing error-code/error-message tests called json.loads on
response.data[0]["metadata"] — they were leaning on the bug. Updated
to read the dict directly. Plus 2 new regression tests (failure metadata
roundtrip + invalid-json fallback). Reverting the fix makes both new
tests fail with AssertionError: metadata should be dict, got <class 'str'>.

* fix(proxy): release max_parallel_requests slot when a stream is cancelled mid-flight (#27955) (#30020)

* fix(proxy): release max_parallel_requests slot when a stream is cancelled mid-flight (#27955)

* fix: refund max_parallel_requests on disconnect from outer streaming generators

The cancellation refund previously lived in async_post_call_streaming_iterator_hook,
but that hook is nested inside the outer streaming generators and a nested async
generator only receives GeneratorExit on garbage collection (non-deterministic).
With only the v3 limiter enabled, /chat/completions also bypasses the hook entirely
(needs_iterator_wrap() is false). Move the release into async_data_generator and
async_streaming_data_generator, the generators Starlette closes on client disconnect,
so the refund fires deterministically on every streaming route. Warn when no event
loop is running, and document the window TTL refresh on the decrement

* fix(mcp): propagate model into model_call_details for passthrough tool calls (#30122)

* fix(mcp): propagate model into model_call_details for passthrough tool calls

The @client decorator on call_mcp_tool creates the logging object via
function_setup without a model kwarg, so model_call_details["model"]
starts as None. execute_mcp_tool only set logging_obj.model as an
instance attribute, which the spend-log writer never reads (it reads
kwargs["model"] from model_call_details). MCP passthrough tools/call
rows therefore persisted with model="" while list_tools rows showed
"MCP: list_tools", degrading the Logs UI display and bucketing all MCP
tool spend under an empty model in DailyUserSpend.

Propagate the model into model_call_details alongside the existing
attribute assignment so the StandardLoggingPayload and SpendLogs writer
pick it up. Covers the /mcp passthrough, REST /mcp-rest/tools/call, and
orchestrated paths (the latter already passed model into function_setup,
so this is a no-op there).

* test(mcp): trim regression test docstring

* fix(mcp): surface upstream challenges for delegated OAuth (#30124)

* fix(mcp): surface upstream challenges for delegated OAuth

* docs(mcp): clarify delegated upstream auth comments

* perf(benchmarks): add CPU timing metrics to streaming benchmark (#29980)

* Add CPU timing metrics to streaming benchmark

* Fix spacing around timing sample dataclass

* fix(gemini): don't emit empty choices on metadata-only stream chunks (#29167)

web_search + reasoning makes Gemini stream mid-chunks that carry only
grounding/thought metadata — no content part, no finishReason.
_process_candidates skips content-less candidates and the existing
fallback only ran when finishReason was set, so choices stayed empty
and the downstream streaming handler raised IndexError on choices[0].
Emit an empty-delta choice for content-less chunks regardless of
finishReason.

Fixes #28884

* fix(key): allow /key/update to clear budget_limits with [] or null (#30085)

* Fix /key/update rejecting budget_limits clear requests with HTTP 400

Sending budget_limits: [] or null to /key/update returned HTTP 400, so
once a key had budget windows the last one could never be removed.

prepare_key_update_data only json.dumps'd budget_limits when the value
was truthy, so [] and None passed through raw to the Prisma Json?
column; jsonify_object only serializes dicts, and prisma-client-py has
no DbNull sentinel for Json? writes, so Prisma rejected both shapes.

Serialize the clear case explicitly as the JSON literal null, matching
how memory_endpoints encodes metadata for the same column type. Truthy
values keep the existing reset_at window initialization path.

Fixes #30067.

* Require admin access for budget_limits changes on /key/update

Clearing budget_limits via [] or null is a budget mutation, but
_validate_update_key_data only counted max_budget and spend as budget
changes before deciding whether to skip _check_key_admin_access. A
non-admin key owner or a team member with /key/update could therefore
remove a key's per-window spend caps without admin authorization.

Treat any explicit budget_limits value in the request (set, change, or
clear) as a budget change so it gates through the same admin check as
max_budget. model_fields_set is used because an explicit null is
indistinguishable from an omitted field by value alone.

* fix(proxy): persist guardrail info in spend logs for /v1/responses (#30092)

Pre-call guardrail blocks on /v1/responses wrote guardrail_information
as null in LiteLLM_SpendLogs because _handle_logging_proxy_only_error
splits request_data by LoggedLiteLLMParams keys and litellm_metadata,
where the Responses API stores request metadata including
standard_logging_guardrail_information, was not among them. It fell
into optional_params, so merge_litellm_metadata never saw it. Add
litellm_metadata to LoggedLiteLLMParams so it routes into
litellm_params the same way metadata does on the chat completions path

Fixes #28971.

* fix(proxy): handle non-standard SSE frames in Anthropic passthrough logging (#26000)

Some third-party Anthropic-compatible providers emit non-standard SSE
frames (OpenAI-style [DONE] sentinels, non-JSON keep-alive lines) in
streaming responses. These caused json.JSONDecodeError in
_build_complete_streaming_response, breaking the passthrough logging
pipeline so the request was never logged or billed.

Skip whole-line 'data: [DONE]' sentinels and catch JSONDecodeError per
event. Matching the full line (not a substring) keeps a valid chunk
whose text payload contains '[DONE]' from being dropped.

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Sameer Kankute <sameer@berri.ai>

* feat(newrelic): Add New Relic extension  (#26989)

* initial New Relic integration.

* Minor fixes for basic observability.

* Implemented basic support for the success path. Generates New Relic
custom events needed by the AI Monitorin interface.

* Supportability metric is sent on first request.

* Emit supportability metric every hour instead of once a day.

* Add the start/end times to the messages before sending them so that the
start time and end time reflect the correct time and both are not set
to 'now'.

* Make use of `turn_off_message_logging` configuration that is available
by default from CustomLogger.

* Enabling New Relic agent to be wired when docker container starts if an environment variable
is set.

* If we cannot find trace information, send the AI events without the
trace ID attached.

* Use a fake trace_id if we cannot find one.

* Implementing a configuration so that users can use litellm configuration
to disable sending LLM messages to New Relic. There is a second method
to do this via New Relic env var.

* Mised file.

* Cleaning up logic to turn off recording content via either the
LiteLLM configuration or an env var.

* Removing debugging.
Fixed logic / comments around how often to send supportability metric.

* Initial version of public doc for New Relic.

* Use a proper name for the doc file.

* Updating newrelic.md document.

* Updating LiteLLM documentation for New Relic extension.

* Moving New Relic imports into the methods to support unit tests.

* Adding unit tests for the New Relic extension.

* Updating linting and the unit tests that are not running in the CI environment.

* Address reviewer feedback on New Relic integration.

- Fix _record_error_metric to use app.record_custom_metric() instead of
  module-level newrelic.agent.record_custom_metric() so the call works
  outside of an active transaction context
- Remove unreachable except ImportError block in _get_trace_context
- Update stale "23 hours" comment to "27 hours" (matches 97200s threshold)
- Remove commented-out debug code from _process_success
- Fix docs typo: NEW_RELIC_CUSTOM_INSIGHTS_EVENTS_MAX_SAMPLES_STOREDA ->
  NEW_RELIC_CUSTOM_INSIGHTS_EVENTS_MAX_SAMPLES_STORED
- Update TestRecordErrorMetric to verify app.record_custom_metric call

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* Reformating for the linter.

* Addressing additional automated feedback.

- Removed a legacy comment about the New Relic header
- Reordered imports in one file
- Switched another file to use the import at the top of the file instead of inline when used
- Added unit tests for untested methods that were identified

* Addressing new feedback.

- Proper handling of time to floats. Created a util method and updated code to use it.
- added the missing guard to ensure the app is enabled

* Addressing feedback.

- When an error occurs, still check if the periodic supportability metric should be emitted
- Added a check to ensure the extension is ready in the error handler to match _process_success

* Updating the NR event timestamps to more accurately reflect when
the messages were generated.

* Addressing feedback for potential better practice.

* Addressing feedback on accessing default values. Added tests for most of
these cases.

* Adding a new catch exception block based on feedback.

* Addressing feedback about a potential issue around a timestamp for the
supportability metric.

* Addressing minor feedback on length of generated, fallback traceId.

* Addressing feedback.

- A few more cases were found where the dictionary access might not return the correct value.
- Handling cases where `traceparent` is not lower cased

* Addressed feedback where the newrelic options might not apply correctly.

* Addressing some feedback.

* Addressing feedback.

* Validating testing / formatting for our changes.

* Updating linting, adding tests, defining data type for UI.

* Configuration for the logging callback definition.

* Adding a newrelic image for the UI to use.

* Putting the New Relic callback in proper alphabetic order.

* Copying the logo to a committed output directory so it shows up in a locally
built container.

* Adding missing definition of new env vars that were causing a build failure.

* Addressing automated feedback from greptile.

* Adding a few more unit tests to increase the code coverage just a bit more.

* Additional unit tests to push coverage to almost 90%.

* Adding a custom newrelic docker image build process. This removes the need to add the newrelic agent
to the core litellm container or dependencies.

* Clarifying message when the New Relic agent is not installed and someone
is trying to use the newrelic extension. Either use the proper image
when using docker, or install the agent manually when running from source.

* Ensuring pip is available to install the New Relic agent.

* Updating the definition and handling of traceId (no spanId).
Clarifying behavior of env vars vs UI configuration for
the newrelic extension.

* Removing entries from the New Relic logger configuraiton UI as these
values must be set as part of running the image.

* Removing a stale doc file that has moved to the litellm-docs repo.
Cleanup of Dockerfile to remove a LABEL that was incorrect.

* Updating container image name to be the best guess for the new name.

* Addressing feedback from greptile.

- Added a comment around token_count=0
- Updated the boolean parser to allow a wider set of options which matches existing patterns in other parts of LiteLLM.

* Removing option for a separate New Relic container image. The agreement
is to handle this in the New Relic integration docs.

* Updating error message when New Relic agent is not available.

* Wiring in the test message from the LiteLLM callback UX.

* Missed saving one of the file conflicts.

* Fixed a lint error I introduced. Somehow, I dropped another string
and now added it back.

* Adding newrelic to the schema definition.

* Added an admin check on the call before sending test message
as mentioned by the AI code review.

* Updating to use should_redact_message_logging(kwargs) as part of the
logic to determine if message content should be sent to New Relic
or not. This still uses the `record_content` property as well, but
both have to be true in order for content to be included.

---------

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>

* Add Azure AI Foundry DeepSeek V3.1 and V4 Pro/Flash global pricing to cost map (#30134)

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(logging): translate Responses bridge result to ModelResponse for spend logs (#28985)

PR #29394 fixed the AnthropicResponse.model_validate crash for the streaming
anthropic_messages -> OpenAI Responses bridge by unwrapping terminal events
and returning the inner ResponsesAPIResponse. The spend_logs row lands and
usage/cost are correct, but the row's response field stores the Responses
API shape (output[...].content[...].text). The proxy UI Logs tab reads
response.choices[0].message via parseMessages in prettyMessagesUtils.ts
with no fallback for the Responses shape, so the OutputCard renders "No
response data available" for every cross-routed call. The same shape
mismatch affects every downstream consumer of spend_logs that assumes the
canonical chat-completion shape

This change keeps the unwrap from #29394 but routes the resulting
ResponsesAPIResponse (and the bare-response non-streaming path) through
LiteLLMResponsesTransformationHandler.transform_response, which is the
same conversion already used by the chat-completion Responses bridge.
Spend_logs now stores a ModelResponse with choices[0].message.content, so
the UI and other consumers see the assistant text. On a translation
failure (eg. empty output on an incomplete response) the handler falls
back to a minimal ModelResponse carrying model and usage so the row still
lands rather than being dropped as a Non-Blocking error

Also corrects a stale comment in the Responses adapter that implied the
call type was reclassified to acompletion; the code preserves
anthropic_messages and the success handler translates back to
ModelResponse for the row

Fixes #28595

* fix(anthropic-adapter): re-emit first delta on streaming content-block transitions (#30024)

* fix(anthropic-adapter): re-emit first delta on streaming content-block transitions

The `/v1/messages` -> `/v1/chat/completions` streaming adapter
(`AnthropicStreamWrapper`) silently dropped the first non-empty delta of
every content block that started via a *transition* (e.g. text -> tool_use ->
text, text -> thinking).

When an upstream chunk both triggers a new content block (its type differs
from the active block) and carries that block's first delta, the wrapper
emitted `content_block_stop` -> `content_block_start` and then only re-queued
the trigger chunk when it was an `input_json_delta` (bundled tool args). The
synthesized `content_block_start` always carries an empty body, so the first
`text_delta` / `thinking_delta` was lost — the client output started from the
second token (e.g. "Hi, how can I help you?" rendered as ", how can I help
you?", or text resuming after a tool call lost its first sentence). This is
especially visible with Claude Code-style clients that consume Anthropic
Messages streaming events strictly.

Fix: re-queue the trigger chunk's translated delta whenever it carries
non-empty content (text/thinking/signature/tool args), via a shared
`_trigger_delta_has_content` helper used by both the sync and async paths.
Empty trigger deltas are still suppressed so no spurious empty
`content_block_delta` is introduced.

Fixes #30014

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* test(anthropic-adapter): cover all _trigger_delta_has_content branches

Add a direct parametrized unit test for the re-emit predicate so every delta
type (text/input_json/thinking/signature), the empty-payload guards, and the
malformed/non-delta cases are exercised independently of upstream chunk
translation. Raises patch coverage for the new helper.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

---------

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>

* feat: add opt-in healthy_only filter to GET /v1/models (#30130)

* feat: add opt-in healthy_only filter to GET /v1/models

Adds an opt-in `healthy_only=true` query parameter to GET /v1/models and
GET /models that hides models whose backing deployments are all marked
unhealthy by background health checks.

- Add Router.async_get_fully_unhealthy_model_names(), mirroring the
  semantics of get_fully_blocked_model_names(): a model is hidden only
  when every backing deployment is unhealthy and the health state is
  not stale (fail open otherwise).
- Reuses the existing DeploymentHealthCache populated by
  _run_background_health_check(), so no new health state is introduced.
- No-op when allowed_fails_policy is set, mirroring
  _async_filter_health_check_unhealthy_deployments semantics.
- team_public_model_name aliases are aggregated alongside model_name.
- Hiding is presentation-only; default behavior is unchanged.

Fixes #30128

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* docs: address Greptile review notes

- Note team-alias asymmetry vs get_fully_blocked_model_names
- Debug-log when healthy_only is set but no health state is available

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

---------

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>

* Dedupe team soft budget alerts by team_id instead of token (#30097)

_team_soft_budget_check sends type="soft_budget" alerts with
event_group=TEAM, but SoftBudgetAlert.get_id always returned the
request token. The alert cache key was therefore scoped per virtual
key, so every active key in a team over its soft budget fired its own
alert within budget_alert_ttl. Branch on event_group so team-level
alerts dedupe by team_id, matching TeamBudgetAlert, while key and
project level alerts keep per-token dedupe.

Fixes #27398.

* feat(bedrock guardrails): support contextual grounding qualifiers (request-side) (#30057)

* test: add failing tests for Bedrock contextual grounding (request-side)

Drive the request-side of Bedrock contextual grounding: callers tag message
content blocks as grounding_source/query, the post_call hook assembles an
ApplyGuardrail(OUTPUT) call carrying source + query + response(guard_content),
and the bedrock converse transform must render the tags as prompt text instead
of silently dropping them. Non-grounding payloads must stay byte-identical.

* feat(bedrock guardrails): support contextual grounding qualifiers

Bedrock contextual grounding scores a model response against a reference
source and the user query, expressed via a per-content-block `qualifiers`
array on ApplyGuardrail. The guardrail hook previously sent plain text only,
so grounding could not be driven through it even though the response-side
contextualGroundingPolicy parsing already existed.

Callers now tag message content blocks `{"type":"grounding_source"}` /
`{"type":"query"}` (mirroring the existing `guarded_text` marker). On the
generate path the bedrock converse transform renders them as plain text; at
post_call the hook harvests them from the request and assembles one
ApplyGuardrail(OUTPUT) call carrying grounding_source + query + the response
(as guard_content). Requests without these tags produce a byte-identical
payload, so existing behaviour is unchanged.

* Feat(guardrail): Adding support for custom Ovalix guardrail (#21887)

* Feat(guardrail): Adding support for custom Ovalix guardrail

* Internal CR comments fixes

* greptileai comments fixes

* fix conflict

* fixes

* fix sha256

* clarify Ovalix actor-id hash is for normalization, not PII protection

* fix(github_copilot): normalize per-event item_id in /responses streaming (#30072)

GitHub Copilot's native /v1/responses stream assigns a different item_id to
every event of a single output item (output_item.added, the part.added /
delta / done events, and output_item.done). Spec-strict clients like the
Vercel AI SDK key streaming parts by item_id and abort with
"reasoning part <id> not found" / "text part <id> not found" when a delta
references an unregistered id.

Override transform_streaming_response in GithubCopilotResponsesAPIConfig to
anchor every event of an output item to the id from its output_item.added.
Copilot accepts that id paired with the final encrypted_content on the next
turn, so multi-turn replay is unaffected.

Fixes #30071

* feat: add /model/block and /model/unblock endpoints (#30125)

* feat: add /model/block and /model/unblock endpoints

Add dedicated proxy-admin POST /model/block and /model/unblock endpoints
over the existing blocked flag on LiteLLM_ProxyModelTable, mirroring the
/key/block and /key/unblock pattern. Calling a model whose deployments are
all blocked now returns a clear 403 "Model is blocked" instead of a generic
no-deployment error, including direct-dispatch route types (e.g. eval) via a
pre-route guard. Includes audit-log entries for block/unblock and unit tests.

Closes #29742

Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com>

* chore: regenerate dashboard API types for model block/unblock endpoints

Regenerate ui/litellm-dashboard/src/lib/http/schema.d.ts from the proxy
OpenAPI spec (npm run gen:api) so it includes the new endpoints.

Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com>

* fix: widen router block-helper param type and add direct unit tests

Type the _are_all_deployments_blocked deployments parameter to match its
callers (DeploymentTypedDict) so mypy passes, and add
tests/test_litellm/test_router_block_helpers.py with direct unit tests for
the three block helper methods so router_code_coverage recognizes them.

Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com>

* fix: restore type-ignore on messages arg after black reflow

Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com>

* refactor: raise model-block 403 in proxy layer, not SDK Router

Keep the SDK Router's documented behavior for blocked deployments (filtered ->
"no healthy deployment") and move the 403 PermissionDeniedError into the proxy
layer (route_llm_request), where model blocking is an admin concept. This avoids
a backwards-incompatible 403 for SDK users who set blocked=True on their own
deployments, per maintainer review.

Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com>

---------

Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com>
Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com>
Co-authored-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>

* fix: add week unit support to get_next_standardized_reset_time (#30100)

* fix: add week unit support to get_next_standardized_reset_time

The function handled d/h/m/s/mo units but silently fell through to
the default next-midnight branch for the w (week) unit. This was
inconsistent: _extract_from_regex already accepted w in its character
class, and duration_in_seconds already returned value * 604800 for it.

Add the missing elif unit == 'w' branch that delegates to
_handle_day_reset with value * 7, which reuses the existing Monday-
alignment logic for 1w and the generic N-day-from-midnight path for
larger multiples.

Add test_week_based_resets covering 1w from a Wednesday (expects next
Monday) and 2w from a Monday (expects 14 days forward at midnight).

Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com>

* test: exercise relative week semantics with non-Monday base dates + add docstring

Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com>

---------

Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com>
Co-authored-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com>

* fix: black formatting and remove undocumented MAVVRIK_FOCUS_FREQUENCY env var

* fix: black formatting with correct version and sync schema.d.ts for healthy_only param

* fix: resolve mypy errors and add transcription_sessions to JSON schema endpoint enum

* fix: restore MAVVRIK_FOCUS_FREQUENCY guard and exclude it from docs key scan

* fix: address Greptile P2 comments - move constant, use UTC datetime, skip redundant team lookup

* revert: restore original team lookup logic in can_key_call_resolved_model

---------

Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com>
Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com>
Co-authored-by: Emerson Gomes <emerson.gomes@thalesgroup.com>
Co-authored-by: nina-hu <nina.huuu@gmail.com>
Co-authored-by: Sahith Jagarlamudi <104647530+s-jag@users.noreply.github.com>
Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Praveen Ghuge <95286176+pghuge-cloudwiz@users.noreply.github.com>
Co-authored-by: alex107ivanov <30668368+alex107ivanov@users.noreply.github.com>
Co-authored-by: hcl <chenglunhu@gmail.com>
Co-authored-by: Fede Kamelhar <federico.kamelhar@oracle.com>
Co-authored-by: Armaan Sandhu <74664101+Ar-maan05@users.noreply.github.com>
Co-authored-by: Teo Xian Zhong Augustine <35527068+auggie246@users.noreply.github.com>
Co-authored-by: King Star <mcxin.y@gmail.com>
Co-authored-by: Saksham Maggo <122939011+SakshamMaggo@users.noreply.github.com>
Co-authored-by: Filippo Menghi <113345637+Cyberfilo@users.noreply.github.com>
Co-authored-by: Kelvin <leikaiwei@outlook.com>
Co-authored-by: Josh Bonczkowski <josh.bonczkowski@gmail.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: M. Dennis Turp <mdturp@pm.me>
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Piotr Minkina <piotrminkina@users.noreply.github.com>
Co-authored-by: Martín Alcalá Rubí <martin@tryolabs.com>
Co-authored-by: T. Kobayashi <13004314+nix-tkobayashi@users.noreply.github.com>
Co-authored-by: João Costa <13508071+jpv-costa@users.noreply.github.com>
Co-authored-by: Shalom <shalom@ovalix.io>
Co-authored-by: codgician <15964984+codgician@users.noreply.github.com>
Co-authored-by: FugoP <kim@pomsora.com>
Co-authored-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
 2026-06-11 22:30:26 -07:00
yuneng-jiang
7899463c6a
fix(callbacks): forward callback_settings to callback initializers and guard consumers against non-dict values (#30161) 
* fix(datadog): pass callback_specific_params so DatadogCostManagementLogger receives cost_tag_keys (#29590)

* fix(datadog): pass callback_specific_params so DatadogCostManagementLogger receives cost_tag_keys

* test(proxy): regression test that load_config forwards callback_specific_params

* fix(proxy): guard lakera_prompt_injection callback_specific_params against non-dict

Addresses review feedback: forwarding callback_settings as callback_specific_params
(so DatadogCostManagementLogger receives cost_tag_keys) exposed the
lakera_prompt_injection branch, which did lakeraAI_Moderation(**callback_specific_params
["lakera_prompt_injection"]) with no type guard. A config like
`callback_settings: {lakera_prompt_injection: "any-string"}` then hit `**"any-string"`
-> TypeError: argument after ** must be a mapping, not str.

Guard the lakera branch with isinstance(dict), matching the existing presidio and
datadog_cost_management branches (non-dict values fall back to {}). Add a regression
test asserting initialize_callbacks_on_proxy ignores a non-dict value instead of crashing.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* test: inject fake lakera_ai module to avoid importing the real one

CI fix for the lakera regression test: it stubbed litellm.proxy.proxy_server with
a SimpleNamespace and then monkeypatch.setattr'd the real lakera_ai module, which
forces importing it — and lakera_ai does `from litellm.proxy.proxy_server import
LiteLLM_TeamTable`, absent on the stub -> ImportError under proxy-infra tests.

Inject a fake lakera_ai module into sys.modules instead, so the callbacks branch's
`from ...lakera_ai import lakeraAI_Moderation` resolves to the stub without loading
the real module. The guard under test (isinstance(dict) in the lakera branch) is
unchanged.

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(callbacks): guard compression/websearch interceptors against non-dict callback_settings (#30153)

#29590 forwards the full callback_settings dict into initialize_callbacks_on_proxy, which activates the compression_interception and websearch_interception consumers. Their initialize_from_proxy_config read the callback_settings subkey without an isinstance(dict) guard, so a non-dict value such as `compression_interception: true` reached from_config_yaml(...).get(...) and aborted proxy startup with AttributeError. #29590 added that guard for lakera_prompt_injection but not for these two

Mirror the isinstance(dict) guard already used by the lakera, presidio, and datadog branches so a non-dict value is ignored and the callback initializes with defaults. A parametrized test feeds every callback_settings consumer a non-dict value through initialize_callbacks_on_proxy to catch a future consumer that forgets the guard

* fix(callbacks): normalize non-dict callback_specific_params to empty dict

A blank callback_settings: key in YAML loads as None, and
config.get('callback_settings', {}) returns None because dict.get only
falls back to the default when the key is absent. Forwarding that value
verbatim to initialize_callbacks_on_proxy made the first
'<name>' in callback_specific_params membership test raise
TypeError: argument of type 'NoneType' is not iterable, aborting proxy
startup. Same failure for any non-dict root such as callback_settings: true.

Normalize the value at the function boundary so both callsites (and any
future ones) initialize callbacks with their defaults instead of crashing.

---------

Co-authored-by: Hedi Daoud <150018939+hdaoud23@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
 2026-06-10 15:22:00 -07:00
milan-berri
411bd3da5b
feat(vantage): include organization metadata in FOCUS Tags export (#28184) 
* feat(vantage): include organization metadata in FOCUS Tags export

Join LiteLLM_OrganizationTable when building Vantage/FOCUS export rows so
organization_id and organization_alias appear in Tags for org-level filtering.

Co-authored-by: Cursor <cursoragent@cursor.com>

* test(focus): include api_requests in organization Tags tests

FocusTransformer now requires api_requests after staging merge; add the
column to test fixtures so integrations CI can run the Tags assertions.

Co-authored-by: Cursor <cursoragent@cursor.com>

---------

Co-authored-by: Cursor <cursoragent@cursor.com>
 2026-06-09 02:59:21 +03:00
Sameer Kankute
dfb68a23de
feat(galileo): add health check support for UI callback test (#29908) 
* feat(galileo): add health check support for UI callback test

Register galileo in /health/services so the proxy UI callback connection test works.

Co-authored-by: Cursor <cursoragent@cursor.com>

* feat(galileo): verify API key via /current_user health check

Call Galileo's current_user endpoint so the UI callback test validates credentials against the provider.

Co-authored-by: Cursor <cursoragent@cursor.com>

* chore(ui): regenerate schema.d.ts for galileo health service

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(galileo): return IntegrationHealthCheckStatus from async_health_check

Fixes mypy assignment error in health_services_endpoint where response was
narrowed to IntegrationHealthCheckStatus from earlier branches.

Co-authored-by: Cursor <cursoragent@cursor.com>

* Fix Galileo logging to match Langfuse across all endpoint types.

Stop skipping ingest when output is empty and log embeddings with a placeholder so embedding, speech, and other non-text responses are recorded like Langfuse.

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(galileo): remove unreachable health-check guard and None output sentinel

The use_v2_api flag is derived from bool(api_key), so the inner
GALILEO_API_KEY check inside the v2 branch could never run; collapse the
credential validation into the username/password path with a combined
message. _serialize_galileo_output now returns an empty string for None,
so _get_galileo_input_output_content always yields a str and the
post-call None coalescing guard is no longer needed.

* test(galileo): cover async_health_check failure paths and empty model response

Add regression tests for the Galileo health check unhealthy branches
(missing project id, missing base url, missing credentials, auth
failure, and request exception) and for logging a model response with
no choices, which now queues an empty output instead of being skipped.

---------

Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
 2026-06-08 13:57:03 -07:00
Sameer Kankute
32c88ca74f
Litellm oss staging 080626 (#29932) 
* feat(bedrock_mantle): add SigV4/IAM auth to Responses API route (fixes #29665) (#29788)

* feat(responses): add default no-op sign_request to BaseResponsesAPIConfig

* feat(responses): call sign_request after body is final, send signed bytes when signed

* feat(bedrock_mantle): add SigV4 sign_request via composed BaseAWSLLM (bearer path)

* test(bedrock_mantle): cover SigV4 access-key, AssumeRole, body bytes, region/auth consistency

* feat(bedrock_mantle): defer auth to sign_request; validate_environment no longer requires bearer

* docs(bedrock_mantle): document SigV4 + Bearer auth on Responses route

* test(responses): cover fake-stream signing order and mantle bearer arg/env precedence

* fix(bedrock_mantle): wrap all botocore credential errors with both-paths guidance

* fix(bedrock_mantle): catch specific credential errors, not all BotoCoreError, so STS transport failures are not masked

* fix(bedrock_mantle): sign the compact Responses route too, not just create

* fix(github-copilot): route per-model on /v1/responses based on model info (#29747)

* feat(focus): add GCS destination for FOCUS export (#29751)

* test: add failing tests for FocusGCSDestination

* feat: add FocusGCSDestination reusing GCSBucketBase auth

* feat: register FocusGCSDestination in factory; export from __init__

* fix(focus): preserve GCS_PATH_SERVICE_ACCOUNT when service_account_json not in config

* style: apply Black formatting to gcs_destination and tests

* style: apply Black formatting to factory.py

* fix(bedrock): omit empty additionalModelRequestFields and system from Converse API payload (#29565)

Amazon Nova Pro (and other strict Bedrock models) return 400 Malformed input
request when additionalModelRequestFields: {} or system: [] are present in the
payload. Both fields are optional in CommonRequestObject (total=False) and must
be omitted rather than sent as empty structures.

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix(proxy): recognize *.cognitiveservices.azure.com as OpenAI-compatible in pass-through cost tracking (#29730)

* fix(proxy): recognize *.cognitiveservices.azure.com as OpenAI-compatible

Azure OpenAI resources created via the newer "Azure AI Foundry" /
Cognitive Services pathway live on `*.cognitiveservices.azure.com`
subdomains, not the older `openai.azure.com`. Both are valid Azure
OpenAI surfaces in production today.

The OpenAI pass-through cost-tracking handler hard-codes only the older
hostname in five places (four `is_openai_*_route` methods on
OpenAIPassthroughLoggingHandler, plus is_openai_route on
PassThroughEndpointLogging). As a result, calls from newer Azure
deployments are silently classified as "not an OpenAI route", the
dispatch into the cost-tracking handler is skipped, and tokens/cost
never get extracted into LiteLLM_SpendLogs — the row gets written with
prompt_tokens=0, completion_tokens=0, spend=0, model='unknown'.

Reproduced 2026-06-04 against a real Azure OpenAI deployment on
`*.cognitiveservices.azure.com` proxied through LiteLLM v1.88.0.

Fix: factor the hostname check into a single helper
`_is_openai_compatible_host` listing all three recognized surfaces
(api.openai.com, openai.azure.com, cognitiveservices.azure.com), and
have all five call sites delegate to it. Purely additive — never
weakens recognition for the originally-supported hostnames.

Adds a test
`test_is_openai_route_recognizes_cognitiveservices_azure_com` that
exercises all four `is_openai_*_route` static methods against
`*.cognitiveservices.azure.com` URLs (positive cases per route + a
small cross-route negative to confirm route-specific path matching
still works on the new hostname).

Out of scope for this PR (separate followup):
  - `openai_passthrough_handler` calls chat/completions
    `transform_response` on Responses API payloads (`output:` not
    `choices:`), which throws inside the dispatch and drops the
    SpendLogs row entirely. Recognized + tracked separately.

* ci: trigger fresh run

Empty commit to re-run checks. The previous auth-and-jwt failure was
a transient HuggingFace Hub 429 rate-limit hitting tokenizer downloads
in tests/proxy_unit_tests/test_custom_tokenizer_bug.py — unrelated to
this PR's scope (hostname recognition in pass-through cost tracking).
No code change.

---------

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>

* fix(responses): preserve forced-function tool_choice name in Responses to Chat transform (#29812)

The Responses API forces a specific function with a top-level name
({"type": "function", "name": "X"}), but _transform_tool_choice only handled the
nested Chat Completions shape and fell through to returning "required" for the flat
form, silently dropping the function name and degrading a forced function call to
force-any-tool. Map the flat Responses shape to the nested Chat shape, keeping the
"required" fallback when no name is present.

* Preserve x-anthropic-billing-header system blocks for first-party Anthropic (#29584)

* Preserve x-anthropic-billing-header system blocks for first-party Anthropic

PR #20951 strips system blocks beginning with "x-anthropic-billing-header:" for
every Anthropic target. That block is how the first-party Anthropic API recognizes
Claude Code subscription (OAuth) traffic, so dropping it makes requests that carry
only that block, such as the auto-mode tool-safety classifier, fail with a
misleading 429 rate_limit_error; normal turns still work because they also carry
the "You are Claude Code" identity block.

Gate the strip behind should_strip_billing_metadata(), defaulting to False on the
first-party AnthropicConfig and AnthropicMessagesConfig so the block is kept, and
overridden to True on the providers that reach these transforms and reject the
block (Bedrock platform, Vertex, Azure for the chat path; Minimax, Azure, DeepSeek
for the messages path). Behavior for those providers is unchanged.

* Strip billing header on Bedrock invoke and Vertex messages pass-through

Two more subclasses reach the gated strip but inherited keep-by-default.
AmazonAnthropicClaudeConfig (Bedrock invoke) calls AnthropicConfig.transform_request,
which calls translate_system_message, and VertexAIPartnerModelsAnthropicMessagesConfig
(Vertex messages pass-through) calls super().transform_anthropic_messages_request.
Override should_strip_billing_metadata() to True on both.

Add a parametrized test asserting the flag for every first-party base (False) and
provider subclass (True), covering all overrides, plus a translate_system_message
regression test for the Bedrock invoke path.

* fix(cache): log hashed cache keys (#29890)

* fix(ui): save routing groups as list (#29889)

* Revert "fix(ui): save routing groups as list (#29889)" (#29928)

This reverts commit 9b1f78ffa7.

* feat(parasail): add Parasail as a JSON-configured OpenAI-compatible provider (#29842)

* feat(parasail): add Parasail as a JSON-configured OpenAI-compatible provider

Registers parasail in the openai_like JSON provider loader with both
/v1/chat/completions and /v1/responses support. Parasail's Responses API
rejects store:true and any request that omits store, so the loader gains a
force_store_false special_handling flag; the parasail entry sets it and
the generated Responses config overrides store=false on every call. This
keeps callers from hitting "State storage not supported" and matches what
Parasail's docs require.

Adds the PARASAIL enum value, listing under openai_compatible_providers,
provider documentation at docs/my-website/docs/providers/parasail.md, and
a focused unit test file under tests/test_litellm/llms/parasail/ that
covers JSON registration, chat URL construction, Responses URL
construction with PARASAIL_API_BASE override, and the force_store_false
regression in both the caller-sent-store=true and caller-omitted cases.

* fix(parasail): register in provider_endpoints_support, drop in-repo docs

Greptile review feedback. The provider doc belongs in the litellm-docs
repo, not this one's docs/my-website tree; removing it here. Adds the
parasail entry to provider_endpoints_support.json so the
check_provider_folders_documented.py CI check passes (chat_completions
and responses true; others false).

* fix: normalize Anthropic passthrough server tool usage (#29827)

* test(anthropic): cover server_tool_use dict cost tracking

* fix: normalize Anthropic server tool usage

(cherry picked from commit 982f726bed)

* fix: keep server tool usage subscriptable

(cherry picked from commit 70280b9b27)

---------

Co-authored-by: Genmin <joey@joeyroth.com>

* fix(proxy): fix typo generic_role_mappoings -> generic_role_mappings in ui_sso.py (#29753)

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>

* feat(proxy): add disable_budget_reservation general setting (#27639) (#29493)

* feat(proxy): add disable_budget_reservation general setting (#27639)

* feat(proxy): register disable_budget_reservation in ConfigGeneralSettings (#27639)

* docs(proxy): document disable_budget_reservation concurrency tradeoff (#27639)

* ci: re-trigger flaky docker build (prisma generate ECONNRESET)

* fix(proxy): warn and document budget enforcement tradeoff when disable_budget_reservation is set (#27639)

* feat(gemini_tts): adding support to Gemini TTS languageCode parameters (#29623)

* Adding support to Gemini TTS Language Code parameters

* Mapping Gemini TTS languageCode param in Docstring

* Use snake_case for language_code input keyMapping Gemini TTS languageCode param in Docstring

* Restoring files modified under enterprise/litellm_enterprise due to lint/formatting checks

---------

Co-authored-by: João Garrido <joaogarrido@google.com>

* feat(guardrails): capture user and model metadata in CrowdStrike AIDR (#29517)

* fix(proxy): require OpenAI path segment for shared Azure Cognitive Services domains

Address Greptile review: the `*.cognitiveservices.azure.com` /
`*.openai.azure.com` domains are shared by every Azure Cognitive Service
(Speech, Vision, Language, ...), so a hostname-only substring match
misclassified non-OpenAI Azure traffic as OpenAI routes.

- Replace the substring host test with suffix matching (rejects look-alike
  domains like cognitiveservices.azure.com.attacker.example).
- Add `_is_openai_compatible_url` that requires an OpenAI-style path marker
  (`/openai/` or `/v1/`) on the shared Azure domains, and use it in
  PassThroughEndpointLogging.is_openai_route (previously hostname-only).
- Add negative tests for Azure Speech/Vision paths and look-alike domains.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* fix: support Responses input in Redis semantic cache (#29581)

* fix: support responses input in redis semantic cache

* test: cover redis semantic prompt extraction

* test: handle blank redis semantic text fallbacks

* chore: remove async cache dead statement

* test: cover redis semantic cache miss paths

* fix: filter sensitive cache lookup kwargs

* chore: rerun ci after huggingface rate limit

* chore(ui): regenerate dashboard API types (npm run gen:api)

Sync src/lib/http/schema.d.ts with the proxy OpenAPI spec: adds the
disable_budget_reservation general-settings field and picks up the
RateLimitError docstring reindent. Fixes the gen:api CI drift check.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* test(bedrock): assert empty additionalModelRequestFields is omitted

The Converse transformer now drops an empty additionalModelRequestFields
block instead of sending it as `{}`. Update test_bedrock_top_k_param so
models without top_k support (llama3) assert the key is absent rather than
equal to an empty dict.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

---------

Co-authored-by: Kent <72616338+kingdoooo@users.noreply.github.com>
Co-authored-by: codgician <15964984+codgician@users.noreply.github.com>
Co-authored-by: Praveen Ghuge <95286176+pghuge-cloudwiz@users.noreply.github.com>
Co-authored-by: Roi <roytev@gmail.com>
Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: Liam Scott <liam@uilliam.com>
Co-authored-by: abhay23-AI <abhaytrivedi22@gmail.com>
Co-authored-by: Ceder Dens <cederdens@gmail.com>
Co-authored-by: 冯基魁 <56265583+fengjikui@users.noreply.github.com>
Co-authored-by: Kai Huang <kaihuang724@gmail.com>
Co-authored-by: rinto <54238243+ririnto@users.noreply.github.com>
Co-authored-by: Genmin <joey@joeyroth.com>
Co-authored-by: Arnav Bhilwariya <arnavbhilwariya0408@gmail.com>
Co-authored-by: Armaan Sandhu <74664101+Ar-maan05@users.noreply.github.com>
Co-authored-by: João Garrido <48538534+johngarrido@users.noreply.github.com>
Co-authored-by: João Garrido <joaogarrido@google.com>
Co-authored-by: Kenan Yildirim <kenan@kenany.me>
Co-authored-by: Dávid Balatoni <balcsida@gmail.com>
 2026-06-08 13:49:52 -07:00
Mateo Wang
13924fa1d6
feat: standardize rate limit errors with category, rate_limit_type, model, and llm_provider fields (#27687) 
* feat(exceptions): add RateLimitErrorCategory + headers/detail fields on RateLimitError

LiteLLM previously surfaced rate-limit conditions through several unrelated
error classes (RateLimitError, FastAPI HTTPException(429), BaseLLMException).
This commit adds the data model needed to consolidate them under a single
class:

* RateLimitErrorCategory enum exposing four categorical values
  (vendor_rate_limit, vendor_batch_rate_limit, litellm_rate_limit,
  litellm_batch_rate_limit) so callers can switch on the rate-limit source.
* New optional fields on RateLimitError:
  - category (defaults to vendor_rate_limit, preserving today's behavior for
    every existing call site in exception_mapping_utils);
  - headers (preserves retry-after / rate_limit_type / reset_at across the
    proxy boundary instead of dropping them on the floor);
  - detail (mirrors FastAPI HTTPException.detail so the same instance can be
    serialized through both paths).

litellm.RateLimitErrorCategory is re-exported at the package root to match
the existing exception-export pattern.

LIT-2968

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* feat(proxy): add ProxyRateLimitError unifying RateLimitError + HTTPException

Adds a single proxy-side error class that subclasses BOTH
litellm.exceptions.RateLimitError AND fastapi.HTTPException via cooperative
multiple inheritance.

Why both bases:
* Subclassing RateLimitError lets user code catch every rate-limit source
  with one 'except RateLimitError' and switch on the new .category field.
* Subclassing HTTPException keeps every existing FastAPI plumbing path (the
  isinstance(e, HTTPException) branches in proxy_server.py route handlers,
  FastAPI's own dispatcher, and tests asserting pytest.raises(HTTPException))
  working without modification, and preserves retry-after / rate_limit_type /
  reset_at headers on the wire.

The class declaration order is (HTTPException, RateLimitError) so the MRO
puts HTTPException's no-super-call __init__ ahead of openai's cooperative
__init__ chain — preventing openai.APIError.super().__init__(message) from
landing in HTTPException.__init__(status_code=message).

LIT-2968

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* refactor(proxy/hooks): raise ProxyRateLimitError from budget + iteration limiters

Replaces three bare HTTPException(status_code=429, ...) call sites with
ProxyRateLimitError, which is both a RateLimitError (catchable by category)
and an HTTPException (preserves existing FastAPI serialization). Drops the
now-unused HTTPException import in the iteration / per-session limiters.

LIT-2968

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* refactor(proxy/hooks): raise ProxyRateLimitError from parallel-request limiters

Replaces HTTPException(status_code=429, ...) call sites in the v1 and v3
parallel-request limiters (key/team/user/model/customer rate limits) with
ProxyRateLimitError. Updates the raise_rate_limit_error helper's return type
annotation accordingly.

LIT-2968

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* refactor(proxy/hooks): raise ProxyRateLimitError from dynamic rate limiters

Replaces HTTPException(status_code=429, ...) call sites in the v1 and v3
dynamic rate limiters (project-level TPM/RPM allocation, model-saturation
checks, priority-based limits, fail-closed guards) with ProxyRateLimitError.
The v3 limiter still imports HTTPException for an unrelated bare 'except
HTTPException:' branch.

LIT-2968

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* refactor(proxy/hooks): raise ProxyRateLimitError from batch rate limiter

Replaces HTTPException(status_code=429, ...) in batch_rate_limiter._raise_rate_limit_error
with ProxyRateLimitError tagged as RateLimitErrorCategory.LITELLM_BATCH_RATE_LIMIT
so users can distinguish batch-level throttling (which counts requests/tokens
across an uploaded batch input file before submission) from the generic
key/team/user RPM/TPM limiter.

The HTTPException import is retained because the same module raises
HTTPException for unrelated 403/IO error paths.

LIT-2968

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* test(rate-limit): pin down unified rate-limit error contract

Adds a dedicated test module covering the new RateLimitErrorCategory enum,
RateLimitError.category default + override behavior, ProxyRateLimitError's
dual nature (RateLimitError + HTTPException), and a parametrized regression
guard that asserts every proxy hook module imports the unified class.

The regression guard catches the failure mode the refactor is designed to
prevent: someone re-introducing a bare HTTPException(status_code=429, ...)
in one of the hook modules instead of going through ProxyRateLimitError.

LIT-2968

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* feat(logging): expose rate-limit category via StandardLoggingPayload

Adds an optional 'error_rate_limit_category' field to
StandardLoggingPayloadErrorInformation, populated from the unified
RateLimitError.category attribute (introduced in the previous commits on
this branch).

Why: the .category attribute is reachable off the raw exception today via
getattr(e, 'category', None), but the structured contract that downstream
custom callbacks / loggers / spend log writers consume is the
StandardLoggingPayload. Without this field, a user building custom
rate-limit metrics on top of callback data has to special-case the raw
exception object — which defeats the purpose of the StandardLoggingPayload
abstraction.

The field is None for non-rate-limit exceptions (so consumers can read it
unconditionally without isinstance checks) and is one of the
RateLimitErrorCategory string values otherwise.

LIT-2968

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* test(rate-limit): assert StandardLoggingPayload carries the category

Five tests covering: vendor default, explicit litellm_rate_limit and
litellm_batch_rate_limit values, None for non-rate-limit exceptions, and
None when no exception is provided. Pins down the contract that custom
callbacks can read 'error_information.error_rate_limit_category' off the
StandardLoggingPayload to drive custom rate-limit metrics without ever
reaching for the raw exception.

LIT-2968

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix(types): silence mypy [misc] on intentional dual-base attr overlap

mypy emits two [misc] errors on the ProxyRateLimitError class line because
its two bases declare overlapping attributes with related-but-not-identical
annotations:

* status_code: int on starlette HTTPException vs. Literal[429] on openai's
  RateLimitError (every openai status-error subclass narrows it the same
  way and silences pyright with the same convention).
* headers: Mapping[str, str] | None on HTTPException vs. our Optional[
  Dict[str, str]] (the proxy hooks always carry a stringified dict).

Both narrowings are intentional and enforced at construction time. Add a
type: ignore[misc] with an inline explanation rather than relax the
annotations on the parent or change the wire-format guarantees.

LIT-2968

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* test(rate-limit): add direct hook-invocation tests to lift patch coverage

Adds six end-to-end tests that drive each refactored hook past its
limit and assert the unified ProxyRateLimitError is raised with the
correct category and dual-base shape. Complements the
import-shape-only parametrized guard above by actually executing the
new 'raise ProxyRateLimitError(...)' lines so codecov's patch coverage
sees them as hit.

Hooks covered (one test each):
* parallel_request_limiter v1 — direct call to raise_rate_limit_error()
* parallel_request_limiter v3 — direct call to _handle_rate_limit_error
  with a fabricated OVER_LIMIT response
* max_iterations_limiter — full async_pre_call_hook with mocked agent
  registry, second call exceeds budget=1
* max_budget_limiter — async_pre_call_hook with mocked get_current_spend
* dynamic_rate_limiter v1 — async_pre_call_hook with mocked
  check_available_usage forcing available_tpm == 0
* batch_rate_limiter — direct _raise_rate_limit_error call, asserts
  category is the batch-specific LITELLM_BATCH_RATE_LIMIT (not the
  generic LITELLM_RATE_LIMIT)

LIT-2968

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix: guard rate_limit_category extraction with isinstance check

* test(rate-limit): cover remaining hook raise sites for codecov

Adds five more direct hook-invocation tests so every PR-touched line
in the proxy hooks is exercised by tests in tests/test_litellm/, which
codecov measures:

* parallel_request_limiter v1 — check_key_in_limits inline raise
  (the second raise site, separate from the raise_rate_limit_error
  helper covered earlier)
* dynamic_rate_limiter v1 — RPM raise branch (TPM branch was already
  covered)
* dynamic_rate_limiter v3 — parametrized over all three raise sites:
  model_saturation_check, priority_model, and the fail-closed
  fallback for an unrecognized descriptor_key
* max_budget_per_session_limiter — full async_pre_call_hook with a
  mocked agent registry and over-budget cached spend

All 42 tests in test_rate_limit_error_unification.py now pass and
together exercise every changed import + raise line across the eight
refactored proxy hooks.

LIT-2968

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix: use computed error_message in ProxyRateLimitError detail

* fix(parallel-request-limiter): drop None from detail; annotate raise_rate_limit_error as NoReturn

The v1 ' raise_rate_limit_error' helper built an unused 'error_message'
variable and then assembled the actual ' detail' via an f-string that
interpolated 'additional_details' verbatim — producing
'Max parallel request limit reached None' when invoked without
arguments (flagged by code review).

Fix the helper to:
- use the constructed 'error_message' as the detail
- annotate the helper as NoReturn since it always raises
- drop the redundant 'raise'/'return' at the two call sites

Add two regression tests covering both the with- and without-
additional_details paths.

LIT-2968

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix(proxy/hooks): drop literal 'None' from raise_rate_limit_error detail

The v1 parallel_request_limiter's raise_rate_limit_error helper has a
long-standing bug: it computes a None-guarded 'error_message' string but
then ignores it and emits an f-string that interpolates the raw
'additional_details' arg. Callers that pass no argument get
'Max parallel request limit reached None' as the user-facing detail.

This commit:
* wires error_message into the detail kwarg so the None-guard actually
  applies and operators see a clean message;
* changes the return-type annotation from ProxyRateLimitError to NoReturn
  (the function always raises) so type-checkers know callers after this
  invocation are unreachable.

Greptile P1 + P2 review feedback on PR #27687.

LIT-2968

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix(types): demote TypedDict floating string to a # comment

A string literal placed after a field declaration in a TypedDict body is
not a per-field docstring — it's an orphaned string expression Python
discards. Tools like mypy / pyright that inspect TypedDict fields won't
surface that text either.

Move the documentation for error_rate_limit_category to a real comment
so the intent is visible to readers and type-checker tooling without
the misleading docstring framing.

Greptile P2 review feedback on PR #27687.

LIT-2968

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* security(exceptions): do not auto-copy vendor response headers to e.headers

A vendor 429 response can set arbitrary headers (Set-Cookie, CORS
overrides, …). Previously, when RateLimitError was constructed with only
a 'response=' (no explicit 'headers=' kwarg), self.headers fell back to
a copy of response.headers. If a downstream proxy serializer ever
forwarded e.headers to the client, a malicious upstream could inject
browser-interpreted headers for the proxy origin.

Drop the fallback. Only headers passed explicitly via the headers= kwarg
make it onto self.headers (proxy hooks pass retry-after etc. — they
control what's surfaced). Vendor response headers stay reachable on
e.response.headers for callers that explicitly want them.

Today's proxy_server.py route handlers don't actually forward e.headers
on the wire (they construct ProxyException without passing headers), so
no current behavior changes — this is a defensive narrowing so the
fallback can never be turned into a vector when someone wires
e.headers through later.

Veria-AI security review feedback on PR #27687.

LIT-2968

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* test(rate-limit): regression guards for review-pass fixes

Pins down the three review-pass fixes:

* test_parallel_request_limiter_v1_helper_no_additional_details — calls
  raise_rate_limit_error() with no args and asserts the detail does NOT
  contain the literal string 'None'. Pre-fix, callers got 'Max parallel
  request limit reached None'.
* test_rate_limit_error_does_not_auto_copy_response_headers — passes a
  vendor httpx.Response with a Set-Cookie header to RateLimitError
  WITHOUT an explicit headers= kwarg, asserts self.headers stays None
  (no leak), then re-checks that an explicit headers= kwarg DOES
  populate self.headers. Vendor headers remain reachable on
  e.response.headers for callers that explicitly want them.
* The existing v1-helper test now also asserts the additional_details
  string makes it through to the detail.

LIT-2968

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* feat(rate-limit): add orthogonal RateLimitType (requests/tokens/concurrent_requests/budget/max_iterations)

trho's last ask in the LIT-2968 thread: distinguish rate-limit failures by
the dimension that was exceeded, not just by who rate-limited (vendor vs.
litellm). Adds:

- RateLimitType str-enum exposed at `litellm.RateLimitType` with values
  requests / tokens / concurrent_requests / budget / max_iterations.
- `rate_limit_type` kwarg on litellm.RateLimitError + ProxyRateLimitError;
  None default so existing callers (vendor-429 path in exception_mapping_utils)
  remain a no-op.
- StandardLoggingPayloadErrorInformation.error_rate_limit_type so custom
  callbacks can split rate-limit failures by cause without parsing free-text
  error messages. Mirror to error_rate_limit_category extraction in
  get_error_information(); single isinstance(RateLimitError) check covers both.
- map_v3_rate_limit_type() helper to collapse the v3 limiter's internal labels
  ("requests", "tokens", "max_parallel_requests") onto the public enum so
  the v3 limiter and dynamic_rate_limiter_v3 share one mapping. Defensive
  None on unknown values rather than silently picking a wrong dimension.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* feat(proxy/hooks): wire rate_limit_type onto every limiter raise site

Each refactored proxy hook now populates rate_limit_type with the dimension
that actually tripped the limit, so downstream consumers (custom callbacks,
prometheus exporters via the StandardLoggingPayload) can split key/team/user
rate-limit failures by cause:

- parallel_request_limiter (v1): detect dimension from current vs. limit in
  the post-cache branch (concurrent_requests > tokens > requests, matches the
  boolean condition order). Base case (current is None, one limit set to 0)
  picks the most-specific zero. raise_rate_limit_error() helper accepts an
  explicit rate_limit_type kwarg with CONCURRENT_REQUESTS default (matches
  every existing internal call site, including the global-limit branch).
- parallel_request_limiter (v3): forward status["rate_limit_type"] through
  map_v3_rate_limit_type() so "max_parallel_requests" → CONCURRENT_REQUESTS
  for the public field while the raw v3 jargon stays on the HTTP header for
  wire-format backward compat.
- dynamic_rate_limiter (v1): TPM-zero → TOKENS, RPM-zero → REQUESTS. Pass
  data["model"] through so callbacks see the model that hit the limit
  (addresses the secondary "provider missing" complaint in the original
  Slack thread, partially — the model is what dashboards typically split on).
- dynamic_rate_limiter (v3): forward status["rate_limit_type"] via
  map_v3_rate_limit_type() at every raise site (model_saturation_check,
  priority_model, fail-closed unknown-descriptor guard). Also pass model.
- batch_rate_limiter: limit_type is hard-typed "requests"|"tokens" — map
  directly without going through the helper's None branch.
- max_budget_limiter, max_budget_per_session_limiter: BUDGET.
- max_iterations_limiter: MAX_ITERATIONS.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* test(rate-limit): cover RateLimitType enum, hook wiring, and StandardLoggingPayload propagation

27 new tests across five new test classes:

- TestRateLimitType: enum exposed at litellm.RateLimitType, all five values
  defined, RateLimitError default is None (vendor 429 path makes no claim
  about which dimension), accepts both string and enum forms with
  str-coercion guarantee for downstream JSON serializers.
- TestProxyRateLimitErrorType: ProxyRateLimitError default is None, accepts
  string or enum, doesn't break existing callers that pass nothing.
- TestMapV3RateLimitType: pins each v3-internal → public-enum mapping
  (tokens, requests, max_parallel_requests → concurrent_requests, unknown
  → None) so a future v3 refactor can't silently swap dimensions.
- TestStandardLoggingPayloadCarriesType: the new error_rate_limit_type
  field reaches the structured payload for both ProxyRateLimitError and
  plain RateLimitError, is None when unspecified, and is None for
  non-rate-limit exceptions (symmetric with error_rate_limit_category).
- TestProxyHooksWireTypeCorrectly: drives the actual raise sites in the
  v1 parallel_request_limiter helper, the v3 _handle_rate_limit_error
  (both "tokens" and "max_parallel_requests" paths), and the batch
  limiter (both tokens and requests paths) — coverage tools see the new
  rate_limit_type= kwargs as exercised, not just the import shape.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* test(rate-limit): cover _coerce_message branches and v1 dimension detection

Drives the patch coverage on the new orthogonal RateLimitType wiring up
to (or close to) 100% on the touched files.

ProxyRateLimitError._coerce_message — was 22% covered, now 100%:
* nested {error: {message}} dict
* nested {message: {message}} dict (alt key)
* dict without 'error'/'message' keys → JSON dump fallback
* non-JSON-serializable dict value → str() fallback
* non-string non-mapping detail (int) → str() coercion

v1 parallel_request_limiter dimension detection — was 0% covered, now
exercised across 6 parametrized cases:
* check_key_in_limits else-branch: current at concurrent / TPM / RPM cap
  → asserts rate_limit_type is concurrent_requests / tokens / requests.
* check_key_in_limits base case (current is None): max_parallel_requests
  / tpm_limit / rpm_limit set to 0 → asserts the most-specific zero
  attribution wins per the helper's order.

LIT-2968

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* feat(proxy/hooks): add ProxyHTTPRateLimitError + provider resolver

Introduces a small helper layer used by every proxy-side rate-limit
hook so that the 429 they raise carries a populated llm_provider /
model — instead of an empty exception.llm_provider that downstream
loggers (Prometheus failure metric, observability callbacks) read as
'no provider attribution'.

ProxyHTTPRateLimitError inherits from both fastapi.HTTPException
(so the proxy server still renders it as a 429) and
litellm.exceptions.RateLimitError (so isinstance checks and
PrometheusLogger._get_exception_class_name pick up llm_provider).
We deliberately don't call RateLimitError.__init__ — it constructs
an httpx.Response we don't need and would just add failure surface;
attribute parity is what downstream consumers care about.

resolve_llm_provider_for_rate_limit() wraps litellm.get_llm_provider
defensively. Internal limiter hooks fire from async_pre_call_hook —
well before get_llm_provider runs anywhere else in the request
lifecycle — so we have to call it ourselves at raise time. If the
model is missing or unparseable (alias, router-only model) we fall
back to llm_provider='litellm_proxy' rather than letting a second
exception leak out and break the request path.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix(proxy/hooks): populate llm_provider on parallel-request 429s

Both v1 and v3 parallel-request limiters fired bare HTTPException(429)
from inside async_pre_call_hook. The downstream Prometheus failure
metric reads exception.llm_provider via _get_exception_class_name —
the empty value showed up as exception_class='HTTPException' and
left model_id='None' on the time series.

Threads requested_model through every raise site in:

* parallel_request_limiter.py:
  - check_key_in_limits (the per-key/per-model/per-user/per-team/
    per-customer over-limit path)
  - raise_rate_limit_error (zero-limit + global_max_parallel_requests
    paths) — now takes an optional requested_model kwarg
* parallel_request_limiter_v3.py:
  - _handle_rate_limit_error (the OVER_LIMIT translator), called
    from both the should_rate_limit pre-check and the TPM
    reservation path

Resolved via resolve_llm_provider_for_rate_limit so unknown / missing
models silently fall back to llm_provider='litellm_proxy' instead of
breaking the request path with a second exception.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix(proxy/hooks): populate llm_provider on dynamic-rate-limit 429s

Same plumbing change as the parallel limiters, applied to both
dynamic_rate_limiter (v1) and dynamic_rate_limiter_v3:

* v1: TPM-zero and RPM-zero paths in async_pre_call_hook now resolve
  data['model'] -> (model, llm_provider) once and pass it into both
  raises.
* v3: All three raise sites in _check_rate_limits — the
  model_saturation_check enforced raise, the priority_model
  enforced raise, and the fail-closed unknown-descriptor branch —
  now attribute the 429 to the actual provider.

Falls back to llm_provider='litellm_proxy' when the model can't be
resolved.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix(proxy/hooks): populate llm_provider on batch-rate-limit 429s

batch_rate_limiter._raise_rate_limit_error now takes a
requested_model kwarg threaded from data['model'] in
_check_and_increment_batch_counters. The batch-creation 429 is what
gets raised when the input file's tokens/requests count would push
the per-key TPM/RPM window over its limit.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix(proxy/hooks): populate llm_provider on budget/iterations 429s

Final batch of internal raise sites — the user/session-budget and
max-iterations hooks. Same pattern: resolve data['model'] once at
raise time, attach to ProxyHTTPRateLimitError so Prometheus and
observability callbacks can attribute the 429.

Hooks updated:
* max_budget_limiter (per-user max_budget exceeded)
* max_iterations_limiter (per-session agent iteration cap)
* max_budget_per_session_limiter (per-session dollar cap)

All three fall back to llm_provider='litellm_proxy' when data['model']
is missing or unparseable. Drops the now-unused HTTPException import
from each module.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* test(proxy/hooks): pin provider field on internal rate-limit 429s

Regression coverage for the 'provider field missing' bug across every
proxy-side rate-limit hook + the helper layer:

* ProxyHTTPRateLimitError class shape (HTTPException + RateLimitError,
  dict-detail stringification, None-provider normalization).
* resolve_llm_provider_for_rate_limit happy paths
  (gpt-4o-mini, anthropic/..., bedrock/...) plus all three fallback
  branches (None, '', unknown name) plus a 'get_llm_provider raises'
  case that asserts we swallow the secondary exception.
* For each limiter (parallel v1/v3, dynamic v1/v3, batch,
  max_budget, max_iterations, max_budget_per_session): assert the
  raised exception is a RateLimitError carrying the resolved
  model + llm_provider, and a sibling test that asserts the
  fallback path returns 'litellm_proxy' without leaking a second
  exception.
* Two PrometheusLogger._get_exception_class_name pins so the
  Prometheus failure metric label flips from 'HTTPException' to
  'Openai.ProxyHTTPRateLimitError' (or 'Litellm_proxy.*' on
  fallback) — that's what dashboards consume.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* perf(proxy/hooks): defer provider resolution to over-limit branches

* fix: use error_message in raise_rate_limit_error to avoid literal 'None' in detail

* Consolidate rate_limiter_utils imports in dynamic_rate_limiter

* fix(proxy): set num_retries/max_retries on ProxyHTTPRateLimitError

ProxyHTTPRateLimitError inherits from RateLimitError but did not call
RateLimitError.__init__, so num_retries/max_retries were never set.
When Starlette's HTTPException lacks __str__, MRO falls through to
RateLimitError.__str__, which unconditionally reads these attributes
and raises AttributeError during logging/traceback formatting.
Initialize them to None defensively.

* fix(mypy): silence base-class status_code conflict on ProxyHTTPRateLimitError

HTTPException declares 'status_code: int' while openai.RateLimitError
(via APIStatusError) declares 'status_code: Literal[429] = 429'. Mypy
flags the multi-base override as [misc] in CI lint. The runtime semantics
are fine (we set self.status_code in __init__), so silence the
class-level annotation conflict with a targeted ignore.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix: annotate batch limiter _raise_rate_limit_error as NoReturn

* feat(prometheus): rate-limit category/type labels + exception_class back-compat (follow-up to #27687) (#27706)

* feat(prometheus): add rate_limit_category and rate_limit_type labels

Adds two new labels to litellm_proxy_failed_requests_metric so dashboards
can split 429s by rate-limit source (vendor vs. litellm-internal) and by
the dimension that was exceeded (requests/tokens/concurrent_requests/
budget/max_iterations) without parsing free-text error messages.

Closes the Prometheus side of LIT-2718. The unified RateLimitError.category
and .rate_limit_type fields landed in PR #27687 but were only surfaced on
StandardLoggingPayload (custom-callback channel); this exposes them on
the metric label set as well.

Both labels are populated only when the underlying exception is a
litellm.RateLimitError; non-rate-limit failures keep them empty.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* feat(prometheus): populate rate-limit labels + preserve exception_class back-compat

Two coupled changes in the Prometheus integration:

1. async_post_call_failure_hook now extracts the new RateLimitError
   .category / .rate_limit_type fields (added in PR #27687) via a
   _extract_rate_limit_labels helper and forwards them through
   UserAPIKeyLabelValues onto litellm_proxy_failed_requests_metric.
   Empty for non-rate-limit failures.

2. _get_exception_class_name special-cases ProxyRateLimitError and
   keeps emitting 'HTTPException' for the exception_class label.
   Without this shim, ProxyRateLimitError (which multi-inherits from
   HTTPException + RateLimitError) would silently flip the label
   from 'HTTPException' (the historical value for proxy-side 429s)
   to 'ProxyRateLimitError', breaking existing dashboards / alerts
   that key off exception_class='HTTPException'. Distinguishing
   vendor vs. litellm 429s is now the job of the new
   rate_limit_category label.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* test(prometheus): cover rate-limit labels and exception_class back-compat

Adds 19 tests across:
- enum / label-list registration
- _extract_rate_limit_labels for vendor RateLimitError, ProxyRateLimitError,
  non-rate-limit and None inputs (incl. parametrized over every
  RateLimitErrorCategory x RateLimitType combo)
- _get_exception_class_name back-compat: ProxyRateLimitError keeps the
  legacy 'HTTPException' string while vendor RateLimitError keeps the
  historical 'Provider.ClassName' format
- end-to-end through async_post_call_failure_hook with both
  ProxyRateLimitError and vendor RateLimitError, asserting both new
  labels populate and exception_class stays back-compat

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix(prometheus): tolerate missing fastapi in lazy ProxyRateLimitError import

Address greptile feedback:
- async_post_call_failure_hook docstring: drop the stale labelnames listing
  and reference PrometheusMetricLabels.litellm_proxy_failed_requests_metric
  as the source of truth so the doc cannot drift from the actual labelset.
- _get_exception_class_name: guard the lazy ProxyRateLimitError import with
  ImportError so router-side fallback callsites don't blow up in non-proxy
  installs that don't have fastapi (a transitive dep of
  proxy.common_utils.proxy_rate_limit_error). Behavior is unchanged when
  fastapi is available.

Also fix the existing enterprise callback test that asserted the old
labelset on litellm_proxy_failed_requests_metric — it now expects the new
rate_limit_category / rate_limit_type labels populated for vendor 429s.

---------

Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix(bugbot): simplify rate-limit label coercion + guard None detail

- prometheus.py _extract_rate_limit_labels: RateLimitError.__init__ already
  normalizes category/rate_limit_type to plain str, so the getattr(.value)
  + isinstance dance was dead code. Reduce to str(value) if not None.
- proxy_rate_limit_error.py _coerce_message: short-circuit None to ''
  instead of falling through to str(None) = 'None', which produced the
  literal message 'litellm.RateLimitError: None'.

* fix(rate-limit): surface unified category/type fields on BudgetExceededError

The most common budget cap (virtual-key max_budget enforcement in
auth_checks.py) raises litellm.BudgetExceededError, a bare Exception
subclass that bypassed the unified rate-limit error class introduced
by PR #27687. Custom callbacks reading
StandardLoggingPayload.error_information saw category=None and
rate_limit_type=None for these 429s, missing the most common budget
case (team / org / end-user budgets all hit the same code path).

Surface the fields off BudgetExceededError as plain attributes:
- category = RateLimitErrorCategory.LITELLM_RATE_LIMIT
- rate_limit_type = RateLimitType.BUDGET
- llm_provider = "" (or caller-supplied)

Switch get_error_information and _extract_rate_limit_labels from
isinstance(RateLimitError) gating to duck-typed attribute reads,
guarded by membership in the rate-limit enums so unrelated third-party
exceptions exposing a .category attribute can't leak garbage values
into the payload.

This is strictly additive: BudgetExceededError keeps its bare-Exception
base class, so `except BudgetExceededError:` handlers keep firing and
`except RateLimitError:` does not start catching budget errors.

* fix(rate-limit): validate enum membership at duck-typed read sites + enrich BudgetExceededError llm_provider

Two follow-ups uncovered during the second QA pass on PR #27687:

1. Guard third-party `.category` / `.rate_limit_type` attribute leakage.
   The duck-typed read in `get_error_information` and
   `_extract_rate_limit_labels` would forward any string attribute named
   `category` / `rate_limit_type` on an unrelated third-party exception
   into the StandardLoggingPayload and Prometheus labels — silently
   mislabeling custom-callback payloads and blowing out Prometheus label
   cardinality. Add `validate_rate_limit_category` /
   `validate_rate_limit_type` helpers that gate on the documented enum
   value sets; non-matching values are dropped to None.

2. Enrich BudgetExceededError.llm_provider from request_data.
   Budget checks live in tenant-scoped helpers (key / team / org / tag /
   end-user / project) that don't see the request model, so the
   BudgetExceededError they raise carried llm_provider="" — leaving
   custom-metrics consumers without provider attribution for the most
   common 429 case. Resolve it once at the central
   UserAPIKeyAuthExceptionHandler seam, before post_call_failure_hook
   fires, so the StandardLoggingPayload the callback sees has the same
   provider attribution as RPM/TPM 429s.

Regression tests pin both: 4 leakage tests + 4 enrichment tests. The
leakage tests would fail under the pre-validation version of either read
site; the enrichment tests would fail if the handler skipped the
resolver call.

* fix(rate-limit): resolve router model_name aliases to real provider (#27914)

* fix(rate-limit): resolve router model_name aliases to real provider

For nearly every real LiteLLM proxy deployment the request model is a
router model_name alias (e.g. 'tpm-locked' -> litellm_params.model:
openai/gpt-4o-mini), and 'litellm.get_llm_provider' doesn't know about
router aliases — it raises 'LLMProviderNotProvidedError'. The resolver
then fell through to the defensive 'litellm_proxy' fallback, so the
'llm_provider' field this PR adds was effectively always
'litellm_proxy' in the field, defeating its purpose for the most common
proxy configuration.

Add a router-alias fallback step: when 'get_llm_provider' raises, scan
the active 'llm_router.model_list' for a deployment whose 'model_name'
matches the request model and resolve from its 'litellm_params.model'
instead. If multiple deployments share the same alias (load-balancing
case) the first one wins — every deployment under one alias should
agree on provider in any sensible config, and 'first' is deterministic
so the Prometheus label stays stable.

Defensive throughout: an uninitialized router, a malformed deployment,
a 'litellm_params.model' that itself fails 'get_llm_provider' — every
branch falls through to the existing 'litellm_proxy' fallback rather
than letting a secondary exception escape and mask the rate-limit
error we're trying to surface.

Tests:
  - test_router_alias_resolves_to_underlying_provider: alias
    'tpm-locked' -> 'openai/gpt-4o-mini' produces provider='openai',
    model='gpt-4o-mini'.
  - test_router_alias_with_multiple_deployments_uses_first.
  - test_router_alias_unknown_falls_back.
  - test_router_alias_with_malformed_deployment_falls_back.
  - Existing fallback test updated to also stub
    'litellm.proxy.proxy_server.llm_router' so it exercises the
    full 'no resolution anywhere' path.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix(rate-limit): harden router alias resolver + test isolation

- Wrap _resolve_provider_from_router_alias loop in top-level try/except so
  a non-iterable model_list / unexpected deployment shape can't escape and
  mask the 429 with a 500.
- Type-check litellm_params before .get() to handle non-dict truthy values.
- Patch llm_router=None in the parametrized fallback test so a router left
  by another test in the session can't redirect the unknown-model path.

---------

Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix(bugbot): preserve "BudgetExceededError" Prometheus label

Adding llm_provider to BudgetExceededError (so callbacks get provider
attribution from StandardLoggingPayload) made the provider-prefix step in
_get_exception_class_name silently flip the label from "BudgetExceededError"
to e.g. "Openai.BudgetExceededError", breaking dashboards keyed on the
historical value.

Short-circuit BudgetExceededError in _get_exception_class_name the same way
ProxyRateLimitError already is. Provider/category attribution still lands on
the new rate_limit_category / rate_limit_type labels.

* test: fix invalid 'rpm' rate_limit_type in v3 limiter test mocks

The v3 rate limiter only emits 'requests', 'tokens', or
'max_parallel_requests'. Using 'rpm' caused map_v3_rate_limit_type to
return None, leaving the expected RateLimitType.REQUESTS untested.

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix(bugbot): hoist provider resolver + opt-in prom rate-limit labels

- dynamic_rate_limiter.py: hoist resolve_llm_provider_for_rate_limit
  above the TPM/RPM if/elif so the lookup runs once per request, matching
  the pattern in dynamic_rate_limiter_v3.py.
- prometheus.py: gate the new rate_limit_category / rate_limit_type
  labels on litellm_proxy_failed_requests_metric behind
  litellm.prometheus_emit_rate_limit_labels (default False). Mirrors the
  existing prometheus_emit_stream_label opt-in. Preserves the metric's
  pre-unification label set so existing dashboards / recording rules
  keep matching after upgrade; operators can enable the new labels once
  downstream consumers include them.
- Tests updated: default-off back-compat case, opt-in path enables the
  flag before asserting label presence.

* fix: stabilize prometheus label sets and drop redundant model normalization

- Cache PrometheusLogger.get_labels_for_metric per metric_name so that
  the label set used to construct counters at __init__ time stays in
  sync with the label set used at increment time, even if module-level
  toggles like prometheus_emit_rate_limit_labels or
  prometheus_emit_stream_label are flipped at runtime. Without this,
  toggling these flags after the logger was created would cause
  ValueError from prometheus_client because the runtime labels would
  not match the counter's declared labelnames.
- Drop redundant 'model or ""' guard in ProxyRateLimitError.__init__
  where model is already normalized one step earlier.

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* perf(dynamic_rate_limiter): only resolve provider when rate limit hit

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* test(prometheus): clear cached metric labels after toggling rate-limit flag

The PrometheusLogger caches each metric's label set at construction
time so that labels used at counter.labels(...) time stay consistent
with the labels the metric was registered with. The enterprise
async_post_call_failure_hook test toggles
litellm.prometheus_emit_rate_limit_labels = True AFTER the fixture
has already built the logger, so without invalidating the cache the
rate_limit_category / rate_limit_type labels never reach the mocked
counter and the assert_called_once_with check fails.

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* test: fix CI failures from prom label cache + flaky time-window assertion

PrometheusLogger.get_labels_for_metric now caches the per-metric label
set at first read so the labels passed to counter.labels(...) stay in
lock step with the labels the counter was registered with. This broke
two existing test patterns:

- test_prometheus_labels.py: tests bind the real method onto a
  MagicMock, but MagicMock auto-creates a Mock for _cached_metric_labels
  whose .get(...) returns a truthy Mock — treated as a populated cache
  and returned as the label set, producing empty filtered labels and
  KeyError on labels["requested_model"] / ["route"]. Seed real {}
  containers for _cached_metric_labels and label_filters before binding.

- test_prometheus_logging_callbacks.py::test_set_team_budget_metrics_with_custom_labels:
  the fixture builds the logger before the test monkeypatches
  litellm.custom_prometheus_metadata_labels, so the cached label set
  never picks up the new metadata labels. Clear the cache after the
  monkeypatch (same pattern already used for the rate-limit toggle in
  test_async_post_call_failure_hook).

UI: view_logs/index.test.tsx "Last Minute" window assertion is off by
one at the minute boundary. start_date is floored to the minute, so the
dropped sub-minute fraction can push the truncated-seconds diff up to
(minMinutes+1)*60 exactly when the click lands near a minute rollover.
Switch the upper bound to toBeLessThanOrEqual.

* feat(otel-v2): surface rate_limit_category + rate_limit_type on failed LLM-call spans

PR #28909 introduced the typed v2 OTel engine that builds spans from
StandardLoggingPayload, with SpanError carrying error_type + message and
the genai mapper stamping error.type onto every failed LLM-call span.
This PR's earlier commits added error_rate_limit_category and
error_rate_limit_type to the same StandardLoggingPayload.error_information
the v2 engine reads — but neither field reached a span attribute, so v2
OTel traces stayed opaque about *why* a 429 fired (vendor vs litellm,
RPM vs TPM vs concurrent vs budget vs max_iterations) even after the
custom-callback and prometheus surfaces gained that decomposition.

Three coupled changes:

1. semconv.py: add LiteLLM.ERROR_RATE_LIMIT_CATEGORY /
   LiteLLM.ERROR_RATE_LIMIT_TYPE under the litellm.* vendor namespace
   (no GenAI semconv equivalent exists for who-rate-limited /
   which-dimension).

2. payloads.py: extend SpanError with rate_limit_category +
   rate_limit_type, populated by _parse_error() from the same
   error_information.error_rate_limit_* fields the custom-callback
   channel and prometheus rate_limit_category / rate_limit_type labels
   read. Single source of truth across all three observability surfaces.

3. mappers/genai.py: stamp the two attributes on the LLM-call span when
   present. drop_none guarantees they stay absent (not 'None') for
   non-rate-limit failures so trace consumers can read them
   unconditionally.

Three regression tests in test_otel_v2_emitter.py pin: a vendor /
litellm-internal RateLimitError lands category=litellm_rate_limit +
rate_limit_type=requests on the span; a BudgetExceededError lands
rate_limit_type=budget; a non-rate-limit failure (BadRequestError)
keeps the rate_limit_* attributes absent. Mutation-tested against
reverting either the SpanError extension or the _parse_error read site
— both new tests fail under either mutation.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* test: align prometheus user-budget + logs quick-select tests with merged code

The merge into this branch left two test patterns out of step with the code
they exercise.

test_set_user_budget_metrics_includes_user_email_and_alias_labels_when_opted_in
flipped litellm.prometheus_user_budget_label_include_email_alias after the
fixture had already built the PrometheusLogger. get_labels_for_metric now
snapshots each metric's label set at construction time, so the runtime flip
no longer reached the cached labels. Enable the flag before constructing the
logger, matching how the proxy applies config at startup.

view_logs/index.test.tsx referenced uiSpendLogsCall and moment without
importing them, and the merged index.tsx now fetches through
useLogFilterLogic (the hook the file stubs out) rather than calling
uiSpendLogsCall directly. Add the imports and restore the real hook for the
Quick Select window assertions so the call is actually observed.

* refactor(otel/v2): drop rate-limit decomposition from the LLM-call span

Proxy-side rate limits (litellm_rate_limit, budget, max_iterations) are
rejected at the gate before any upstream call, so async_post_call_failure_hook
tags the synthetic failure log with LITELLM_LOGGING_NO_UPSTREAM_LLM_CALL and the
v2 OTel logger never opens an LLM-call span for them; the
litellm.error.rate_limit_category / litellm.error.rate_limit_type attributes
were dead for exactly the cases they were meant to surface. The only failure
that does open an LLM-call span carrying a RateLimitError is a vendor 429, where
rate_limit_type is always None and the category just restates
error.type=RateLimitError.

The decomposition still reaches downstream consumers through
StandardLoggingPayload.error_information.error_rate_limit_* and the prometheus
rate_limit_category / rate_limit_type labels, both unchanged.

Removes the SpanError fields, the _parse_error reads, the genai mapper
attributes, the semconv keys, and the three span tests that asserted a scenario
that never reaches the mapper in production.

* fix(batch_rate_limiter): map max_parallel_requests to concurrent_requests

* refactor(prometheus): drop transitive fastapi import from _get_exception_class_name

Read the legacy exception_class label from a prometheus_exception_class_name
marker on ProxyRateLimitError instead of importing the proxy module, keeping
the integrations layer free of a transitive fastapi dependency.

* chore(ui): sync schema.d.ts with unified rate-limit error spec

The ProxyRateLimitError docstring flows into the proxy OpenAPI spec's 429
response description, so the generated dashboard types were out of sync.
Regenerated via npm run gen:api (Check UI API Types Sync).

---------

Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
 2026-06-06 17:50:29 -07:00
Sameer Kankute
d671a09c20
Litellm oss staging 050626 (#29774) 
* Mark xAI models retiring on 2026-05-15 (#28788)

Per https://docs.x.ai/developers/migration/may-15-retirement, xAI is
retiring the following slugs on 2026-05-15 (auto-redirect to grok-4.3
with various reasoning efforts; callers continuing to use the old slugs
will be billed at grok-4.3 pricing):

  grok-4-1-fast-reasoning{,-latest}      -> grok-4.3 (low effort)
  grok-4-1-fast-non-reasoning{,-latest}  -> grok-4.3 (none)
  grok-4-fast-reasoning                  -> grok-4.3 (low effort)
  grok-4-fast-non-reasoning              -> grok-4.3 (none)
  grok-4-0709                            -> grok-4.3 (low effort)
  grok-code-fast-1{,-0825}               -> grok-build-0.1
  grok-3                                 -> grok-4.3 (none)

Only the direct xai/ slugs are tagged; third-party hosts (azure_ai,
oci, vercel_ai_gateway, perplexity/xai) run their own schedules. The
grok-3 retirement list explicitly names only the base grok-3 slug — the
-mini / -fast / -beta / -latest variants are not listed, so they remain
untouched.

* feat(moonshot): advertise json_schema response support on live models (#29683)

litellm.responses() already routes Moonshot through the responses->chat-completions
bridge, and Moonshot honors response_format json_schema on chat completions. The
cost-map entries left supports_response_schema unset, so discovery layers that gate
on that flag dropped Moonshot from structured-output / responses listings even though
the capability works end to end.

Set supports_response_schema on the nine models currently live on api.moonshot.ai:
kimi-k2.5, kimi-k2.6, the moonshot-v1 8k/32k/128k text and vision-preview variants,
and moonshot-v1-auto. Verified against the live API that each honors json_schema and
that litellm.responses() returns schema-valid structured output through the bridge.

* chore(moonshot): mark models retired from api.moonshot.ai as deprecated (#29685)

Thirteen Moonshot/Kimi models in the cost map no longer resolve on
api.moonshot.ai (all return 404). Stamp each with its deprecation_date from
platform.kimi.ai/docs/models rather than deleting the entries, so historical
cost calculation keeps resolving the names while tooling can surface the
retirement.

Dates: kimi-thinking-preview 2025-11-11; kimi-latest and its 8k/32k/128k context
variants 2026-01-28; the kimi-k2 preview/turbo/thinking series 2026-05-25; the
moonshot-v1 -0430 snapshots use their own 2024-04-30 snapshot date (Moonshot
publishes no discontinuation date for them).

* fix(moonshot): drop temperature for reasoning models (kimi-k2.5/k2.6) (#29687)

Kimi reasoning models reject every temperature except 1; a request with
temperature=0.2 returns "invalid temperature: only 1 is allowed for this model".
litellm only clamped temperature into [0.3, 1], so any value below 1 still 400'd.

Drop the temperature param entirely for reasoning models (gated on
supports_reasoning, the same signal transform_request already uses) so the model
default is used; the non-reasoning moonshot-v1 models keep the existing clamp.

Co-authored-by: Sameer Kankute <sameer@berri.ai>

* feat(mcp): add per-server timeout configuration (#29672)

* feat(mcp): add per-server timeout configuration

* fix(mcp): address timeout field review comments

- use is not None guard instead of or for 0.0 edge case
- copy timeout in both LiteLLM_MCPServerTable constructions (health check path + _build_mcp_server_table)
- add timeout Float? column to all three schema.prisma files
- extend round-trip test to cover _build_mcp_server_table direction
- add test for zero timeout not treated as falsy

* fix(mcp): forward timeout in _build_temporary_mcp_server_record

* fix(mcp): return 504 instead of 500 when per-server timeout fires

* test(mcp): add 504 timeout regression test; fix black formatting

* Add jp. Bedrock cross-region inference profile for claude-opus-4-7 (#28567)

* fix(thinking): handle None thinking param in is_thinking_enabled (#28598)

Squash-merged by litellm-agent from Terrajlz's PR.

* feat(helm): support tpl rendering in podAnnotations (#28609)

Squash-merged by litellm-agent from devauxbr's PR.

* Forward custom_llm_provider through the Responses API bridge (Fixes #28505) (#28575)

* Forward custom_llm_provider through the Responses API bridge (Fixes #28505)

When a Chat Completions request to a GPT-5.4+ model contains both
`tools` and `reasoning_effort`, `completion()` auto-routes through
`responses_api_bridge`. The bridge handler called
`litellm.responses()` / `litellm.aresponses()` without forwarding the
already-resolved `custom_llm_provider`, so the downstream call
re-invoked `get_llm_provider()` with `custom_llm_provider=None` and
stripped a second provider prefix from a `provider/provider/model`
deployment string.

For a deployment configured as `openai/openai/openai/gpt-5.5`,
the bridge flow sent `openai/gpt-5.5` to the upstream API instead of
the correct `openai/openai/gpt-5.5`. Upstream APIs that enforce
model-name allow-lists rejected this as `key_model_access_denied`.

Fix: pass the locally-resolved `custom_llm_provider` into both the
sync `responses()` and async `aresponses()` calls so the downstream
`_resolve_model_provider_for_responses` sees an explicit provider
and skips the second prefix-strip.

New regression test
`tests/test_litellm/completion_extras/test_responses_bridge_provider_propagation.py`
pins both call sites: each must forward `custom_llm_provider`.

* fix(28505): set custom_llm_provider on request_data instead of as duplicate kwarg

Greptile flagged that the previous patch passed custom_llm_provider as an
explicit kwarg to responses()/aresponses() while request_data already
carried it via the spread of sanitized_litellm_params, which would raise
TypeError: got multiple values for keyword argument on every real bridge
call.

Switches to assigning request_data['custom_llm_provider'] before the call
so the resolved provider wins over whatever sanitized_litellm_params spread
in, without duplicating the kwarg.

Updates the regression test to seed request_data with a sentinel
custom_llm_provider so it actually exercises the overwrite path (the
previous test mocked transform_request with a minimal dict and never hit
the conflict).

* chore: trigger shin-agent re-eval on retargeted staging base

* chore: trigger shin-agent re-eval against updated Greptile state

* Add jp. Bedrock cross-region inference profile for claude-opus-4-7

AWS Bedrock documents jp.anthropic.claude-opus-4-7 alongside the
existing us./eu./au./global. profiles for Claude Opus 4.7
(ap-northeast-1 Tokyo / ap-northeast-3 Osaka), but the entry is
missing from model_prices_and_context_window.json. Tokyo-region
users currently get an "unknown model" error when routing through
the JP geo profile.

Adds the entry to both the canonical file and the bundled backup,
mirroring the recent pattern for sonnet-4-6 (#27831). Pricing matches
the other regional profiles (10% premium over base/global).

Regression test pins all six documented profiles (base, global, us, eu,
au, jp) and asserts pricing parity between jp. and au. variants.

Source: https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-opus-4-7.html

---------

Co-authored-by: Terrajlz <info@jouleselectrictech.com>
Co-authored-by: Bruno Devaux <devaux.br@gmail.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>

* feat(soniox): add soniox audio transcription integration (#29508)

* feat(openmeter): add OPENMETER_TRUST_REQUEST_USER to prevent forged attribution (#29650)

The OpenMeter callback resolves the CloudEvent subject from kwargs["user"]
first, then falls back to the key-bound user_api_key_user_id. For
multi-tenant proxy deployments, a client can set `"user": "..."` in the
request body and cause their usage to be attributed to that arbitrary
string — a billing-attribution forgery risk.

Adds OPENMETER_TRUST_REQUEST_USER env var (default "true" for backward
compatibility). When set to "false", the request-supplied `user` field is
ignored and the subject is resolved solely from user_api_key_user_id.

Matches the existing env-var-driven config pattern in this file
(OPENMETER_API_KEY, OPENMETER_API_ENDPOINT, OPENMETER_EVENT_TYPE).

* feat(search): add you_com as a search provider (#28370)

* feat(search): add you_com as a search provider

Registers You.com Search API as a first-class `search_provider` in the
`search_tools` registry, alongside Tavily, Exa, Perplexity, etc.

- New adapter: litellm/llms/you_com/search/transformation.py
  - POSTs to https://ydc-index.io/v1/search
  - Auth: X-API-Key from YOUCOM_API_KEY (or explicit api_key)
  - Maps Perplexity unified spec: max_results -> count,
    search_domain_filter -> include_domains, country -> country
  - Flattens results.web + results.news into a single SearchResult list;
    snippet prefers snippets[0], falls back to description; page_age -> date
- Registry: SearchProviders.YOU_COM in litellm/types/utils.py and wired
  into ProviderConfigManager.get_provider_search_config()
- Pricing entry: model_prices_and_context_window.json (placeholder $0.0;
  happy to adjust to maintainers' preferred public number)
- Docs: example router config snippet and example proxy yaml updated
- Tests: tests/search_tests/test_you_com_search.py - 5 mocked tests
  (payload shape, domain filter mapping, snippet fallback, news flattening,
  missing-api-key error)

Refs upstream expansion signal: #15942

* review fixups: normalize api_base, lowercase country, scope env-var to test

Addresses Greptile inline review comments on #28370:

- get_complete_url: strip trailing slashes from api_base *before* the
  endswith("/v1/search") check, so a custom base like ".../v1/search/"
  doesn't become ".../v1/search/v1/search".
- transform_search_request: .lower() country before sending, matching
  Tavily's convention so callers using the unified spec form ("US") get
  consistent behavior across providers.
- Tests: replace direct os.environ writes with an autouse monkeypatch
  fixture so YOUCOM_API_KEY is set per-test and removed afterwards.
  The missing-key test now uses monkeypatch.delenv. New test asserts the
  trailing-slash normalization above.

Reverts the ARCHITECTURE.md / example yaml edits per the reviewer note
that documentation changes belong in the litellm-docs repo.

* support keyless free tier (api.you.com/v1/agents/search) as default

You.com offers an IP-throttled keyless endpoint that returns the same
response shape as the keyed one (~100 queries/day, no signup). This is a
significant onboarding lever - mirrors the keyless DuckDuckGo/SearXNG
providers already in the search_tools registry.

Behavior:
- YOUCOM_API_KEY set        -> keyed:  POST https://ydc-index.io/v1/search
                                       (X-API-Key header)
- no key                    -> free:   POST https://api.you.com/v1/agents/search
                                       (no auth)
- YOUCOM_API_BASE override  -> honored as-is

Tests:
- New: test_you_com_search_keyless_free_tier - asserts URL + absence of
  X-API-Key when no key is configured.
- New: test_you_com_search_validate_environment_keyless - asserts the
  config no longer raises when the key is absent.
- Removed: test_you_com_search_raises_without_api_key (the precondition
  no longer holds).
- Existing payload/domain-filter/etc tests still cover keyed mode via
  the autouse YOUCOM_API_KEY fixture.

Verified both endpoints accept POST + return identical JSON shape:
  results.web[] / results.news[] with title, url, snippets, description,
  page_age.

* register you_com in provider_endpoints_support.json

Adding `litellm/llms/you_com/` requires a corresponding entry in
provider_endpoints_support.json or the
code-quality/check_provider_folders_documented CI check fails.

Follows the compact tavily/serper pattern - endpoints: { search: true }.
Local run of the check now reports "All 114 provider folders are documented".

* move tests under tests/test_litellm/llms/ so CI exercises them

The litellm CI workflows scope unit tests to `tests/test_litellm/...`
(see test-unit-llm-providers.yml: `tests/test_litellm/llms` path), so
tests living under `tests/search_tests/` are never run in CI - which is
why codecov reports 0% patch coverage for the new adapter even though
the unit tests exist and pass locally.

Move test_you_com_search.py into `tests/test_litellm/llms/you_com/` so
the test-unit-llm-providers job picks it up. 7/7 tests still pass at
the new location.

(Sibling search-only providers - tavily, exa_ai, brave, etc. - still
live only in `tests/search_tests/` and would benefit from the same
move, but that is out of scope for this PR.)

* fix(you_com): pin Accept-Encoding: identity to dodge keyless gzip bug

The keyless free-tier endpoint (api.you.com/v1/agents/search) advertises
Content-Encoding: gzip but returns a body that httpx's decoder rejects
with `zlib.error: Error -3 while decompressing data: incorrect header
check`, surfacing as litellm.APIConnectionError in user code. curl works
because it doesn't request compression by default.

Pin Accept-Encoding: identity in validate_environment so the upstream
server skips compression entirely. Harmless on the keyed endpoint
(ydc-index.io/v1/search) which negotiates content-encoding correctly.

The header uses setdefault so a caller-supplied Accept-Encoding still
takes precedence. (Server-side bug has been flagged to the You.com team
separately - once fixed there, this workaround can be removed.)

New unit test: test_you_com_search_pins_identity_accept_encoding.

---------

Co-authored-by: Sameer Kankute <sameer@berri.ai>

* docs: fix README typo (#29419)

Correct clear spelling mistakes in documentation without changing behavior.

Confidence: high
Scope-risk: narrow
Tested: git diff --check; uvx codespell on changed files
Not-tested: Full docs build not run; text-only changes

* Fix(langfuse): pass httpx_client to Langfuse in langfuse_prompt_management to respect SSL_VERIFY (#29480)

* fix(langfuse): pass ssl_verify to Langfuse httpx client

* fix_langfuse_

* add unit tests

* addressed comments

---------

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>

* feat(models): add minimax/MiniMax-M3 to model cost map (#29412)

Add MiniMax's new flagship MiniMax-M3 to the native minimax provider:
512K context, 128K max output, native multimodal (supports_vision),
reasoning, prompt caching. Pricing (USD/M tokens): input 0.6 / output
2.4 / cache read 0.12. M3 has no active prompt-cache-write tier, so
cache_creation_input_token_cost is omitted.

Updated both the root model_prices_and_context_window.json (remote
source) and the bundled litellm/model_prices_and_context_window_backup.json
(local fallback), keeping them in sync.

* fix(logging): handle ResponseCompletedEvent in anthropic_messages streaming spend log (#29394)

* fix(logging): handle ResponseCompletedEvent in anthropic_messages streaming spend log

* fix(logging): extend terminal event handling to ResponseIncompleteEvent and ResponseFailedEvent; fix return type annotation

* feat(provider): Add Neosantara provider as OpenAI Compatible (#29646)

* Add Neosantara provider

* Register Neosantara provider enum

* Address Neosantara provider review feedback

* Add Neosantara packaged endpoint support

---------

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>

* fix: address greptile and veria review feedback

- langfuse: guard httpx_client injection behind version check (>= 2.7.3)
- soniox: propagate audio_transcription_duration in _hidden_params for spend tracking
- soniox: give SONIOX_API_BASE env var priority over caller-supplied api_base
- mcp: replace CancelledError catch with asyncio.wait_for + TimeoutError

* chore(mcp): add migration for per-server timeout column

* fix(test): add tool_use_system_prompt_tokens to model prices schema validator

* fix: mcp timeout test uses real asyncio.wait_for timeout; you_com get_complete_url respects resolved api_key

* fix: forward resolved api_key into you_com endpoint selection and apply timeout to soniox polling GETs

The search flow resolves api_key in validate_environment but never passed it
into get_complete_url, so a programmatic api_key (with no YOUCOM_API_KEY in the
env) set the X-API-Key header yet still selected the keyless free-tier endpoint.
Forward api_key through both the search entrypoint and the http handler so the
keyed endpoint is chosen.

HTTPHandler.get/AsyncHTTPHandler.get had no timeout parameter, so the Soniox
poll and transcript-fetch GETs silently used the client global default instead
of the caller timeout. Add a per-request timeout to get() and forward the
configured timeout from the Soniox handler.

* fix(soniox): price stt-async-v4 per second so transcriptions are billed

The handler stores audio_transcription_duration in _hidden_params, but the
model carried only token cost fields and the response has no token usage, so
the transcription cost path fell through to cost_per_second and returned $0.
An authenticated caller could transcribe Soniox audio without decrementing
their budget. Switch the entry to output_cost_per_second at Soniox's published
$0.10/hour async rate so the stored duration produces a real charge.

* fix(langfuse): use a dedicated httpx client for the SDK injection

The httpx_client handed to the Langfuse SDK came from _get_httpx_client(),
which returns LiteLLM's globally cached HTTPHandler. If Langfuse closed that
client on teardown it would invalidate the shared client used by every other
LiteLLM HTTP call. Build a dedicated httpx.Client instead, still resolving SSL
verification and client certificate from LiteLLM's configuration.

* fix(soniox): prefer caller-supplied api_base over SONIOX_API_BASE env var

* fix(cohere): support max_completion_tokens on cohere v2 chat (default route) (#29779)

* fix(cohere): support max_completion_tokens on cohere v2 chat

The default cohere_chat route resolves to CohereV2ChatConfig, which did not
list or map max_completion_tokens, so get_optional_params raised
UnsupportedParamsError for the standard OpenAI parameter (the modern
replacement for the deprecated max_tokens). The v1 config already maps it to
cohere's max_tokens; mirror that in v2 and add v2 regression tests.

* fix(cohere): make max_completion_tokens take precedence over max_tokens on v2

When both max_tokens and max_completion_tokens are supplied, prefer
max_completion_tokens explicitly rather than relying on dict iteration order,
and cover both orderings with a regression test.

---------

Co-authored-by: Daniel Yudelevich <4537920+yudelevi@users.noreply.github.com>
Co-authored-by: hectorc98 <hector.chamorroalvarez@adyen.com>
Co-authored-by: Filippo Menghi <113345637+Cyberfilo@users.noreply.github.com>
Co-authored-by: Terrajlz <info@jouleselectrictech.com>
Co-authored-by: Bruno Devaux <devaux.br@gmail.com>
Co-authored-by: Dan Lemon <dan@danlemon.com>
Co-authored-by: Saswat <saswatds@users.noreply.github.com>
Co-authored-by: Brian Sparker <brainsparker@users.noreply.github.com>
Co-authored-by: Zhao73 <156770117+Zhao73@users.noreply.github.com>
Co-authored-by: Urain Ahmad Shah <60431964+urainshah@users.noreply.github.com>
Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: kape <168134658+kapelame@users.noreply.github.com>
Co-authored-by: danisalvaa <159898202+danisalvaa@users.noreply.github.com>
Co-authored-by: Just R <remixingmagelang@gmail.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: abhay23-AI <abhaytrivedi22@gmail.com>
 2026-06-05 13:51:51 -07:00
Sameer Kankute
89f177b7b6
fix(galileo): use ingest traces API and standard logging payload (#29651) 
* fix(galileo): use ingest traces API and standard logging payload

Switch hosted Galileo logging to /ingest/traces with nested trace/span payloads, read metrics from standard_logging_object, and include cost and total tokens on trace metrics.

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(galileo): route username/password auth to v2 traces ingest

Hosted Galileo no longer serves /observe/ingest; JWT login should post the same trace payload to /v2/projects/{project_id}/traces.

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(galileo): address Greptile review on logging and timestamps

Use debug-level logs for per-request Galileo callback messages and fall back to start_time/end_time when standard_logging_object omits startTime/endTime.

Co-authored-by: Cursor <cursoragent@cursor.com>

* feat(galileo): add Galileo to proxy UI callback configuration

Expose Galileo in the admin callback selector and config APIs so credentials can be configured through the dashboard instead of YAML only.

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(galileo): align response type logging with Langfuse

Mirror Langfuse input/output handling for rerank, speech, transcription,
realtime, pass-through, and other response types so Galileo ingest no longer
skips supported call types.

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(galileo): redact trace payload in debug logs and format with black

Avoid logging prompts and model responses in flush debug output while
keeping structural metadata for troubleshooting.

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(galileo): stop logging full trace payload in debug output

Log only flush URL and trace count so prompts and model responses are not
written to application logs when debug logging is enabled.

Co-authored-by: Cursor <cursoragent@cursor.com>

* Fix Galileo token totals and prompt messages

---------

Co-authored-by: Cursor <cursoragent@cursor.com>
 2026-06-05 09:03:17 -07:00
Mateo Wang
812a2217ca
[internal copy of #29511] feat(guardrails): add sensitive data routing to on-premise models (#29531) 
* feat(guardrails): add sensitive data routing to on-premise models

When a guardrail detects sensitive data, route to an on-premise model
instead of blocking or redacting. All subsequent requests in that
session continue routing to the same model (sticky routing).

New config options for guardrails:
- on_sensitive_data: 'block' (default) or 'route'
- sensitive_data_route_to_model: target model for rerouting
- sticky_session_routing: persist routing for session (default: true)

New exception SensitiveDataRouteException triggers rerouting when raised
by guardrails. The proxy catches it, stores the routing decision in
cache, and modifies the request's model field.

New hook _PROXY_SensitiveDataRoutingHandler checks incoming requests
against cached routing decisions and applies sticky routing.

https://claude.ai/code/session_01SQd4isBa3UyouRoGVou9dK

* fix: black formatting for custom_guardrail.py

https://claude.ai/code/session_01SQd4isBa3UyouRoGVou9dK

* test: improve test coverage for sensitive data routing feature

Add additional tests for:
- Cache key format and TTL constants
- Session ID extraction from multiple locations
- Custom guardrail initialization with routing config
- Exception string representation and custom messages
- Redis cache paths including fallback behavior
- Edge cases in pre-call hook

https://claude.ai/code/session_01SQd4isBa3UyouRoGVou9dK

* fix: use correct GuardrailRaisedException parameters

Replace invalid 'source' parameter with 'guardrail_name' to match
the exception's actual signature.

https://claude.ai/code/session_01SQd4isBa3UyouRoGVou9dK

* test: move sensitive data routing tests to hooks directory

Move test file to align with source code structure.

https://claude.ai/code/session_01SQd4isBa3UyouRoGVou9dK

* fix(guardrails): honor sticky_session_routing flag and scope session routing per API key

Propagate sticky_session_routing through SensitiveDataRouteException so a
guardrail configured with sticky_session_routing=False reroutes only the
triggering request without persisting a session override. Scope the routing
cache key to the requesting API key so sessions from different tenants cannot
collide, and warn when sticky routing is requested but the hook is not
registered.

* refactor(guardrails): dedupe session-id extraction and drop redundant import

Extract the shared session-id lookup into get_session_id_from_request_data
so the sensitive-data routing hook and CustomGuardrail no longer keep two
identical copies of the logic. Remove the redundant local import of
GuardrailRaisedException in handle_sensitive_data_detection, and document
that detection_info is surfaced in request metadata and logs so it must not
carry raw sensitive values.

* fix(guardrails): guard None user_api_key_dict in sensitive data route handler

* fix(responses): send application/json Content-Type on responses DELETE

OpenAI's responses DELETE endpoint now rejects requests that arrive without
a Content-Type header, defaulting them to application/octet-stream and
returning 'Unsupported content type: application/octet-stream'. The delete
handler sent no body and therefore no Content-Type, so the request failed.
Declare application/json on the delete request, matching the OpenAI SDK.

* fix(guardrails): backfill in-memory cache after redis hit in sensitive data routing

When _get_routed_model resolves a routing override from Redis it now also
populates the local in-memory cache. Without the write-back, a non-writing
instance that only ever reads from Redis would lose the sticky routing
decision the moment Redis became unavailable, silently reverting sensitive
sessions to the default model.

* fix(guardrails): scope sticky sensitive-data routing to JWT principal

Keyless auth (JWT and similar) has no api_key, so every such caller shared
the "default" cache namespace. One authenticated user could reuse another
user's session_id, trip the guardrail, and silently force the other user's
subsequent requests onto the cached on-prem model for the TTL.

Resolve the routing tenant from the api_key when present, otherwise from a
stable principal built from the user/team/org identity, before reading or
writing the session route.

* fix(guardrails): require route target model when on_sensitive_data='route'

* fix(guardrails): mark user_api_key_dict Optional in sensitive-data route handler

* fix(guardrails): use remaining redis ttl for local backfill and str env default

* fix(guardrails): graceful block when routing configured but no session_id

handle_sensitive_data_detection promised to raise only SensitiveDataRouteException
or GuardrailRaisedException, but when routing was configured and the request had no
session_id it let a ValueError from raise_sensitive_data_route_exception propagate,
surfacing as an HTTP 500 instead of a block. Fall back to a graceful block in that
case so the documented contract holds.

* fix(guardrails): run remaining guardrails after sensitive-data reroute

Defer the SensitiveDataRouteException until every guardrail in the
pre-call loop has run, so downstream security guardrails are no longer
skipped when an earlier guardrail triggers routing. The first reroute
wins and a later guardrail that blocks still propagates.

Also normalize on_sensitive_data to lowercase like sibling on_* config
fields so case-insensitive values are accepted.

* fix(guardrails): classify sensitive-data reroute as guardrail intervention

* fix(guardrails): record sensitive-data reroute as prometheus intervention not error

* fix(guardrails): record service span for routing guardrail and move case-normalizer to base params

Drop the early continue so a guardrail that signals sensitive-data routing still
emits its PROXY_PRE_CALL service span like every other callback.

Move the lowercase normalizer onto BaseLitellmParams so on_sensitive_data is
normalized consistently when BaseLitellmParams is constructed directly, matching
the cross-field route->model validator that already lives on the base.
 2026-06-04 22:22:28 -07:00
milan-berri
5119b9462f
feat(arize/phoenix): OpenInference rendering parity — tool_calls, cost, passthrough I/O, session/user, multimodal, cache tokens (#28800) 
* feat(arize): enrich OpenInference attributes for better span rendering

Pure rendering enhancements to the Arize / Arize Phoenix integration. No
existing attribute keys or values are removed or overwritten; every new
emit is independently try/except-wrapped and fires only when its source
data is present so existing behavior is preserved.

What this adds
- Coerce non-dict response objects (e.g. httpx.Response from passthrough
  routes) via JSON decode so id/model/usage extraction stops crashing
  with "'Response' object has no attribute 'get'". Dicts and Pydantic
  objects with .get pass through unchanged.
- Set OPENINFERENCE_SPAN_KIND defensively early so a downstream failure
  can't blank the kind; the original late write (incl. TOOL upgrade) is
  preserved.
- Add "passthrough" keyword to _infer_open_inference_span_kind so
  allm_passthrough_route / llm_passthrough_route resolve to LLM instead
  of UNKNOWN.
- Emit cache token breakdown: LLM_TOKEN_COUNT_PROMPT_DETAILS_CACHE_READ /
  _CACHE_WRITE / _AUDIO. Sources covered: OpenAI prompt_tokens_details
  and Anthropic / Bedrock cache_{read,creation}_input_tokens.
- Render assistant tool_calls on both input and output messages via
  MESSAGE_TOOL_CALLS.* (Pydantic-aware, handles ModelResponse choices).
  Tool-result input messages also get MESSAGE_TOOL_CALL_ID and
  MESSAGE_NAME.
- Render multimodal list-shaped content via MESSAGE_CONTENTS.* (OpenAI
  image_url, Anthropic source.{media_type,data} as data: URI). Legacy
  MESSAGE_CONTENT write is unchanged.
- Emit SESSION_ID (end_user_id / trace_id), USER_ID (only when not
  already set by optional_params.user or model_params.user), and
  litellm.{team_id,team_alias,key_alias} from StandardLoggingPayload
  metadata.
- Emit llm.response.cost as float from StandardLoggingPayload.response_cost.
- Bedrock / Anthropic passthrough normalization: extract input from
  additional_args.complete_input_dict and output from the coerced
  provider response so INPUT_VALUE / OUTPUT_VALUE / LLM_INPUT_MESSAGES /
  LLM_OUTPUT_MESSAGES are populated. Only runs when call_type contains
  "passthrough" / "pass_through".

Tests
- 15 new unit tests covering each addition plus explicit regression
  guards (USER_ID overwrite protection, passthrough normalizer scope,
  coerce identity for dicts/.get-bearing objects, no spurious cache
  emits).
- Existing test_arize_set_attributes count bumped from 26 to 27 to
  account for the additional defensive span.kind write (same value,
  written twice).
- tests/test_litellm/integrations/arize/: 70 passed (55 baseline + 15
  new). tests/test_litellm/integrations/test_opentelemetry.py: 221
  passed.

Co-authored-by: Cursor <cursoragent@cursor.com>

* refactor(arize): collapse additive try/except blocks into _safe_emit helper

The additive attribute emitters all share the same shape: run a callable,
swallow any exception to debug log so it cannot blank the span. Hoisting
that pattern into a single _safe_emit(label, fn, *args, **kwargs) helper
removes 5 repeated try/except blocks. Behavior unchanged; arize test
suite still passes (70/70).

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(arize): emit cost under canonical llm.cost.total key

Arize's "Total Cost" column reads the OpenInference-standard
`llm.cost.total` attribute. The previous custom `llm.response.cost`
key never surfaced in the trace list. Now emits both keys (canonical +
legacy) so renderers + any existing consumers both work.

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(arize): keep span.kind=LLM for tool-using completions + render tool_calls in Output

A chat completion that passes `tools=[...]` or returns `tool_calls` is still
an LLM call per the OpenInference spec — TOOL is reserved for actual tool
execution. The previous override demoted these to TOOL, breaking Arize's
LLM-scoped dashboards/evals and skewing token/cost analytics for any
tool-using traffic.

Additionally, when an assistant response had no text content but did
request tool calls, `output.value` was set to the empty string so Arize's
"Output" pane rendered blank. Now serializes the tool_calls into a compact
JSON summary in `output.value` (the structured `MESSAGE_TOOL_CALLS.*`
attributes are still emitted unchanged).

Cleanups:
  - extract `_get_tool_calls` and `_normalize_tool_call` helpers,
    deduplicating the dict-vs-Pydantic + function-dict logic across
    `_set_choice_outputs`, `_emit_message_tool_calls`, and the new
    `_summarize_tool_calls_for_output`.
  - drop redundant late `OPENINFERENCE_SPAN_KIND` write — the defensive
    early write is now the single source of truth.
  - remove a dead local re-import of `MessageAttributes`/`SpanAttributes`.

Tests: 73 pass (added regression guard asserting span.kind stays LLM for
completions that pass tools AND return tool_calls; existing call_count
assertion restored to 26).

Co-authored-by: Cursor <cursoragent@cursor.com>

* chore(arize): tighten cleanup — fold _get_tool_calls into _safe_get

Two tiny cleanups, no behavior change:
- collapse `_get_tool_calls` to use `_safe_get`, removing a 7-line
  hand-rolled dict-vs-attribute fallback that duplicated existing logic.
- trim the `_set_choice_outputs` tool-call summary comment from 4 lines
  to 2 (was over-explaining).

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(arize): address Greptile review — drop session_id=trace_id fallback, remove dead code, fix Black

Three Greptile-flagged issues + the Black formatting CI failure.

1. SESSION_ID no longer falls back to trace_id. Previously every span
   without an explicit `user_api_key_end_user_id` would have its
   session.id set to the per-request trace_id, which creates one
   distinct "session" per request and breaks Arize's Session-grouping
   analytics. Now SESSION_ID is emitted only when an explicit end-user
   identifier exists, and the trace_id is emitted under its own
   `litellm.trace_id` key so spans remain filterable by trace.

2. Removed dead `ArizeOTELAttributes.set_response_output_messages`
   override. Confirmed zero callers in the entire repo (the live path
   is `_set_choice_outputs` via `_set_response_attributes`). The
   override was preexisting dead code, but the expansion of
   `_set_choice_outputs` in this PR made the divergence misleading.

3. Removed permanently-dead first branch in cache_write detection.
   `_safe_get(prompt_token_details, "cache_creation_tokens")` looks
   for a key that neither OpenAI's `prompt_tokens_details` nor
   Anthropic's payload ever exposes. Now reads straight off `usage`
   for `cache_creation_input_tokens`.

4. Reformatted both files under Black 26.3.1 (the version CI uses
   via `uv sync --frozen`). Local previously used 24.10.0.

Tests: 74/74 pass in the arize suite (added
`test_arize_does_not_use_trace_id_as_session_id_fallback`).
Combined arize + opentelemetry suite: 295/295 pass.

End-to-end verified live: tool-call still emits `span.kind=LLM` and
JSON tool_calls in `output.value`; `session.id` is now correctly
unset when no end_user_id is provided; `litellm.trace_id` is
populated; Bedrock passthrough input/output unchanged.

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(arize): gate passthrough prompt export on message redaction

- Skip the complete_input_dict bridge in _maybe_normalize_passthrough when
  should_redact_message_logging() is true, so enabling redaction no longer
  leaks raw passthrough prompts into Arize (Veria security finding).
- Split passthrough input/output rendering into helpers to satisfy PLR0915.
- Remove dead call_type assignment (F841).

Validated live against a Bedrock passthrough proxy exporting to Arize:
non-redacted renders the real prompt on litellm_request; global
turn_off_message_logging yields input.value=redacted-by-litellm with the
raw_gen_ai_request child span suppressed and no SSN/marker leakage.

Co-authored-by: Cursor <cursoragent@cursor.com>

---------

Co-authored-by: Cursor <cursoragent@cursor.com>
 2026-06-03 12:09:50 -07:00
Sameer Kankute
c7ab9adde5
Litellm oss staging 030626 (#29578) 
* Fix incorrect agent API request example payload structure (#29556)

* fix(otel): add litellm_metadata fallback in _get_span_context and _end_proxy_span_from_kwargs (#29427)

* fix(otel): add litellm_metadata fallback in _get_span_context and _end_proxy_span_from_kwargs

On /v1/messages and other LITELLM_METADATA_ROUTES, the parent OTel span
is stored in litellm_params['litellm_metadata'] instead of
litellm_params['metadata']. When the request body contains a native
'metadata' field (e.g. Anthropic's {"user_id": "..."}),
litellm_params['metadata'] gets overwritten and the parent span is lost,
producing orphan root spans with a different trace_id.

Add fallback checks to litellm_metadata in:
- _get_span_context(): so child spans find the correct parent
- _end_proxy_span_from_kwargs(): so the proxy span gets closed

Fixes: https://github.com/BerriAI/litellm/issues/27934

* test(otel): tighten assertions per Greptile review

- test_span_context_metadata_takes_priority: assert litellm_metadata
  span is never accessed, proving metadata takes priority
- test_span_context_no_parent_when_neither_has_span: assert both ctx
  and detected_span are None

---------

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Aneesh-Fiddler <aneeshfiddler@gmail.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>

* fix: remove premature end-user budget check from get_end_user_object (#29420)

* fix(proxy): remove premature end-user budget check from get_end_user_object

Problem:
- `_check_end_user_budget()` was called inside `get_end_user_object()`
- This caused budget checks to run BEFORE `skip_budget_checks` could be evaluated
- Zero-cost models (e.g., local vLLM) were incorrectly blocked when
  end-users exceeded their budget, even though they should bypass budget checks

Solution:
- Remove `_check_end_user_budget()` calls from `get_end_user_object()`
- Budget enforcement now happens exclusively in `common_checks()` where
  `skip_budget_checks` context is available
- `get_end_user_object()` keeps `route` as optional in function parameter for backwards compatibility and future implementation.

* refactor(tests): update budget enforcement tests to reflect changes in get_end_user_object

- test_get_end_user_object() verifies data fetching
- test_check_end_user_budget() verifies enforcement
- test_budget_enforcement_blocks_over_budget_users() integrates _check_end_user_budget()
- test_resolve_end_user_reraises_budget_exceeded() is now test_resolve_end_user since no budget exceeded is thrown in get_end_user_object()

* Gemini /images/generate and /images/edits billing fixes + add support for size and aspect ratio params (#29534)

* Fix Gemini image config mapping

* Address Gemini image config review

* Format Gemini image generation transform

* Fix Gemini image token usage logging

* Share Gemini image request helpers

* Fix Gemini Imagen model routing

* Fixes as per self code review

* Fixes per internal code review

* Stop gating Imagen imageSize forwarding

* Document Gemini image size mapping source

* chore: retrigger lint

* Clarify Gemini candidate count precedence

* Add Inception provider (#29522)

* add inception as provider (chat, fim)

* linting

* seperate test suite for chat and fim

* fix test coverage

* fix: model hub custom pricing model info (#29293)

* Opik user auth key metadata extractors (#28397)

* fix: enhance Opik metadata extraction to include user API key auth context fixed after refactoring to extractor logic

* test: add unit tests for OPik metadata extraction logic

* fix: enhance extract_opik_metadata function to prioritize metadata sources for improved accuracy

* fix(ci): clarified comments and edited unit tests

* test: add unit tests for OPik metadata extraction with auth and requester overrides

* fix(ui): replace fixed favicon.ico with current api get /get_favicon (#29532)

Signed-off-by: José Luis Di Biase <josx@interorganic.com.ar>

* fix(vertex/gemini): keep tool_call reference when a text-only assistant message follows (#29561)

`_gemini_convert_messages_with_history` tracks `last_message_with_tool_calls`
so a following tool result can be matched back to its tool call. The assignment
was inside a branch guarded by
`assistant_msg.get("tool_calls", []) is not None`, which is also True for a
text-only assistant message (an empty list is not None). As a result, an
assistant message with no tool calls that appears between a tool call and its
tool result overwrote the reference, and conversion failed with:

    Exception: Missing corresponding tool call for tool response message.

This shape is common: a model emits a short narration/assistant message after a
tool call before the tool result is appended.

Only update `last_message_with_tool_calls` when the assistant message actually
carries tool_calls (or a function_call). Adds a regression test.

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>

* Add 1-hour cache write pricing for EU/AU/JP Bedrock Anthropic models (#28572)

* fix(thinking): handle None thinking param in is_thinking_enabled (#28598)

Squash-merged by litellm-agent from Terrajlz's PR.

* feat(helm): support tpl rendering in podAnnotations (#28609)

Squash-merged by litellm-agent from devauxbr's PR.

* Forward custom_llm_provider through the Responses API bridge (Fixes #28505) (#28575)

* Forward custom_llm_provider through the Responses API bridge (Fixes #28505)

When a Chat Completions request to a GPT-5.4+ model contains both
`tools` and `reasoning_effort`, `completion()` auto-routes through
`responses_api_bridge`. The bridge handler called
`litellm.responses()` / `litellm.aresponses()` without forwarding the
already-resolved `custom_llm_provider`, so the downstream call
re-invoked `get_llm_provider()` with `custom_llm_provider=None` and
stripped a second provider prefix from a `provider/provider/model`
deployment string.

For a deployment configured as `openai/openai/openai/gpt-5.5`,
the bridge flow sent `openai/gpt-5.5` to the upstream API instead of
the correct `openai/openai/gpt-5.5`. Upstream APIs that enforce
model-name allow-lists rejected this as `key_model_access_denied`.

Fix: pass the locally-resolved `custom_llm_provider` into both the
sync `responses()` and async `aresponses()` calls so the downstream
`_resolve_model_provider_for_responses` sees an explicit provider
and skips the second prefix-strip.

New regression test
`tests/test_litellm/completion_extras/test_responses_bridge_provider_propagation.py`
pins both call sites: each must forward `custom_llm_provider`.

* fix(28505): set custom_llm_provider on request_data instead of as duplicate kwarg

Greptile flagged that the previous patch passed custom_llm_provider as an
explicit kwarg to responses()/aresponses() while request_data already
carried it via the spread of sanitized_litellm_params, which would raise
TypeError: got multiple values for keyword argument on every real bridge
call.

Switches to assigning request_data['custom_llm_provider'] before the call
so the resolved provider wins over whatever sanitized_litellm_params spread
in, without duplicating the kwarg.

Updates the regression test to seed request_data with a sentinel
custom_llm_provider so it actually exercises the overwrite path (the
previous test mocked transform_request with a minimal dict and never hit
the conflict).

* chore: trigger shin-agent re-eval on retargeted staging base

* chore: trigger shin-agent re-eval against updated Greptile state

* Add 1-hour cache write pricing for EU/AU/JP Bedrock Anthropic models

The 1-hour prompt-cache write tier
(`cache_creation_input_token_cost_above_1hr`) was added to the
us./global. variants of the Claude 4.5/4.6/4.7 family on Bedrock, but
the eu./au./jp. cross-region inference profiles were left without it.
AWS Bedrock pricing applies the same +10% regional premium across all
geo profiles, so eu./au./jp. should carry the same 1-hour rates as
us. (1.6x the 5-minute regional rate).

Without these fields, cost tracking on EU/AU/JP Bedrock 1-hour-TTL
prompt caching falls back to the 5-minute write rate and undercounts
spend by ~60% for European, Australian, and Japanese tenants.

Adds the 1-hour tier (and Sonnet 4.5's long-context >200K tier where
AWS publishes one) to 14 regional Bedrock entries in both
`model_prices_and_context_window.json` and the bundled
`model_prices_and_context_window_backup.json`:

  - eu./au.   Opus 4.6     ($11.00 / MTok)
  - eu./au.   Opus 4.7     ($11.00 / MTok)
  - eu./au./jp. Sonnet 4.6 ($6.60 / MTok)
  - eu./au./jp. Sonnet 4.5 ($6.60 / MTok regular, $13.20 / MTok LC)
  - eu./au./jp. Haiku 4.5  ($2.20 / MTok)

Also extends `tests/test_litellm/test_bedrock_anthropic_1hr_cache_pricing.py`
with a `REGIONAL_EXPECTED` parametrized block covering all 13 new
entries plus the existing 1.6x ratio invariant.

Note: `eu.anthropic.claude-opus-4-5-20251101-v1:0` carries the
wrong 5m rate today (base 6.25e-06 instead of regional 6.875e-06),
which would break the 1.6x ratio check. It is intentionally left out
of this PR so the scope stays "1-hour cache tier addition" — a
separate follow-up should correct the EU 5m rates for Opus 4.5.

---------

Co-authored-by: Terrajlz <info@jouleselectrictech.com>
Co-authored-by: Bruno Devaux <devaux.br@gmail.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>

* Add 1-hour cache write pricing tier for Vertex AI Anthropic models (#28569)

* fix(thinking): handle None thinking param in is_thinking_enabled (#28598)

Squash-merged by litellm-agent from Terrajlz's PR.

* feat(helm): support tpl rendering in podAnnotations (#28609)

Squash-merged by litellm-agent from devauxbr's PR.

* Forward custom_llm_provider through the Responses API bridge (Fixes #28505) (#28575)

* Forward custom_llm_provider through the Responses API bridge (Fixes #28505)

When a Chat Completions request to a GPT-5.4+ model contains both
`tools` and `reasoning_effort`, `completion()` auto-routes through
`responses_api_bridge`. The bridge handler called
`litellm.responses()` / `litellm.aresponses()` without forwarding the
already-resolved `custom_llm_provider`, so the downstream call
re-invoked `get_llm_provider()` with `custom_llm_provider=None` and
stripped a second provider prefix from a `provider/provider/model`
deployment string.

For a deployment configured as `openai/openai/openai/gpt-5.5`,
the bridge flow sent `openai/gpt-5.5` to the upstream API instead of
the correct `openai/openai/gpt-5.5`. Upstream APIs that enforce
model-name allow-lists rejected this as `key_model_access_denied`.

Fix: pass the locally-resolved `custom_llm_provider` into both the
sync `responses()` and async `aresponses()` calls so the downstream
`_resolve_model_provider_for_responses` sees an explicit provider
and skips the second prefix-strip.

New regression test
`tests/test_litellm/completion_extras/test_responses_bridge_provider_propagation.py`
pins both call sites: each must forward `custom_llm_provider`.

* fix(28505): set custom_llm_provider on request_data instead of as duplicate kwarg

Greptile flagged that the previous patch passed custom_llm_provider as an
explicit kwarg to responses()/aresponses() while request_data already
carried it via the spread of sanitized_litellm_params, which would raise
TypeError: got multiple values for keyword argument on every real bridge
call.

Switches to assigning request_data['custom_llm_provider'] before the call
so the resolved provider wins over whatever sanitized_litellm_params spread
in, without duplicating the kwarg.

Updates the regression test to seed request_data with a sentinel
custom_llm_provider so it actually exercises the overwrite path (the
previous test mocked transform_request with a minimal dict and never hit
the conflict).

* chore: trigger shin-agent re-eval on retargeted staging base

* chore: trigger shin-agent re-eval against updated Greptile state

* Add 1-hour cache write pricing tier for Vertex AI Anthropic models

GCP Vertex AI publishes a separate 1-hour cache write column for the
Claude family (1.6x the 5-minute write rate, matching the documented
Bedrock ratio). LiteLLM's Vertex AI Anthropic entries only carry the
5-minute tier, so any request that uses `cache_control: {"ttl": "1h"}`
on Vertex AI Claude is undercounted in cost tracking by ~60%.

The runtime side already supports the 1-hour tier — `VertexAIAnthropicConfig`
extends `AnthropicConfig`, populating `ephemeral_1h_input_tokens`, and
`_calculate_cache_creation_cost` reads `cache_creation_input_token_cost_above_1hr`.
Only the price registry was missing data.

Adds the field to 19 vertex_ai/claude-* entries across both
`model_prices_and_context_window.json` and the bundled
`model_prices_and_context_window_backup.json`:

  - Haiku 4.5 ($1.25 -> $2.00 / MTok)
  - Sonnet 3.7 / 4 / 4.5 / 4.6 ($3.75 -> $6.00 / MTok)
  - Opus 4.5 / 4.6 / 4.7 ($6.25 -> $10.00 / MTok)
  - Opus 4 / 4.1 ($18.75 -> $30.00 / MTok)

Adds `tests/test_litellm/test_vertex_anthropic_1hr_cache_pricing.py`
mirroring the Bedrock equivalent — pins each (5m, 1h) pair per model
and asserts the 1.6x ratio across the family.

Fixes #27781.

---------

Co-authored-by: Terrajlz <info@jouleselectrictech.com>
Co-authored-by: Bruno Devaux <devaux.br@gmail.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>

* Fix Gemini multimodal function responses (#29325)

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>

* address greptile review: add _transform_image_usage method and model-map supports_image_size flag

- Add _transform_image_usage instance method to GoogleImageGenConfig that
  delegates to transform_gemini_image_usage, fixing the regression test
- Replace hardcoded "2.5-flash" string check in supports_gemini_image_size
  with a get_model_info lookup on supports_image_size (default true)
- Add supports_image_size: false to all gemini-2.5-flash model entries in
  model_prices_and_context_window.json so capability is controlled via the
  model map rather than embedded in code

* fix test failures: schema validation, mypy type, model info plumbing, pricing test

- Add supports_image_size to ModelInfoBase TypedDict so get_model_info surfaces it
- Pass supports_image_size through _get_model_info_helper constructor call
- Fix supports_gemini_image_size to use value is not False (None means unset, defaults to True)
- Add supports_image_size to JSON schema in test_aaamodel_prices_and_context_window_json_is_valid
- Correct gemini-3.1-flash-lite pricing assertions in test to match JSON values

* Add Azure AI Kimi K2.6 metadata (#27052)

* Add Azure AI Kimi K2.6 metadata

* Scope Kimi metadata test cost map setup

* fall back to substring check for models not in model_prices_and_context_window.json

Models like gemini-2.5-flash-image-preview are not in the pricing JSON,
so get_model_info raises. Fall back to "2.5-flash" not in model when the
JSON has no explicit supports_image_size entry for the model.

* fix(inception): don't forward global litellm.api_key to Inception FIM

Match the Inception chat config: resolve only an Inception-specific key
(param, litellm.inception_key, or INCEPTION_API_KEY) for the text-completion
FIM path. The global litellm.api_key (often an OpenAI key) was both leaking
to api.inceptionlabs.ai and taking precedence over the configured Inception
key when set.

* fix(auth): enforce end-user budget on custom-auth path that skips common_checks

get_end_user_object() no longer raises BudgetExceededError, so custom-auth
deployments with custom_auth_run_common_checks unset (which skip the
centralized common_checks gate) stopped enforcing the end-user budget,
letting an over-budget end user keep making requests. Re-enforce the
budget in _run_post_custom_auth_checks on that path.

---------

Signed-off-by: José Luis Di Biase <josx@interorganic.com.ar>
Co-authored-by: Isha <72744901+IshaMeera@users.noreply.github.com>
Co-authored-by: aneeshsangvikar <aneeshsangvikar@fiddler.ai>
Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Aneesh-Fiddler <aneeshfiddler@gmail.com>
Co-authored-by: Suleiman Elkhoury <108065141+suleimanelkhoury@users.noreply.github.com>
Co-authored-by: Dmitriy Alergant <93501479+DmitriyAlergant@users.noreply.github.com>
Co-authored-by: Yanis Miraoui <yanis.miraoui19@imperial.ac.uk>
Co-authored-by: Lovro Seder <vrovro@gmail.com>
Co-authored-by: Thomas Mildner <12685945+Thomas-Mildner@users.noreply.github.com>
Co-authored-by: José Luis Di Biase <josx@interorganic.com.ar>
Co-authored-by: Lai Quang Huy <64073540+1qh@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
Co-authored-by: Filippo Menghi <113345637+Cyberfilo@users.noreply.github.com>
Co-authored-by: Terrajlz <info@jouleselectrictech.com>
Co-authored-by: Bruno Devaux <devaux.br@gmail.com>
Co-authored-by: ZHONG Ziwen <67355585+zzw-math@users.noreply.github.com>
Co-authored-by: Emerson Gomes <emerson.gomes@thalesgroup.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
 2026-06-03 11:01:51 -07:00
Yassin Kortam
8fbdfc7f0d
fix: missing mcp otel attributes (#29554) 2026-06-02 18:51:48 -07:00
Yassin Kortam
08223e1ec3
fix: missing span for guardrail passthrough (#29552) 2026-06-03 01:25:15 +00:00
ryan-crabbe-berri
f047b1571e
fix(otel): capture 401 error details in management endpoint spans (#29535) 
Auth failures on management endpoints such as team/list and
organization/list (invalid or expired keys) were raised as
ProxyException, whose __str__ returned an empty string, so the OTEL
SERVER span recorded an error with no message. ProxyException now
stringifies to its message, get_error_information prefers the explicit
.message attribute, and the proxy exception handlers stamp a consistent
error.type, error.code and error.message on the span

Resolves LIT-3515
 2026-06-02 16:40:30 -07:00
Yassin Kortam
b98a656254
Add MCP semantic conventions to otelv2 (#29468) 
* Add MCP semantic conventions to otelv2

Emit OpenTelemetry GenAI MCP tool-call spans from the v2 logger. A closed
call_mcp_tool request now produces a CLIENT span named "tools/call {tool}"
carrying mcp.method.name, gen_ai.operation.name=execute_tool, gen_ai.tool.name,
the upstream server name, and (opt-in, content-gated) tool arguments/result.

Adds the MCP and JSON-RPC attribute vocabulary to the semconv module, an
MCPToolCallSpanData payload built from StandardLoggingMCPToolCall, an
MCP_TOOL_CALL span role, and mapper support.

* Complete the MCP span-attribute vocabulary in otelv2 semconv

Add the remaining OTel GenAI MCP semconv attribute keys: gen_ai.prompt.name,
the network.* transport keys with their well-known NetworkTransport values, and
the client.* peer keys for MCP server spans. A test pins the full vocabulary so
a dropped or renamed key fails loudly.

* Populate mcp.session.id on MCP tool-call spans

Capture the mcp-session-id header (case-insensitively) at the tool-call entry
point and thread it through StandardLoggingMCPToolCall into the span, so spans
for stateful MCP sessions carry mcp.session.id. Stateless calls have no such
header and the attribute is simply absent.

* Test that stateless MCP calls omit mcp.session.id

---------

Co-authored-by: Claude <noreply@anthropic.com>
 2026-06-02 11:45:36 -07:00
Sameer Kankute
5fd27141cf
Litellm OSS Staging 010626 (#29422) 2026-06-01 21:42:51 -07:00
Yassin Kortam
8190ff4d86
feat(otel): allowlist team_metadata sub-keys promoted to baggage (#29442) 2026-06-01 14:02:23 -07:00
Yassin Kortam
fe108580d7
fix(datadog): split oversized batches on 413 instead of re-queueing forever (#29444) 2026-06-01 14:01:31 -07:00
Yassin Kortam
3be3c1dea1
feat(otel): add team_metadata, http.route, and model names to inference spans (#29319)
Some checks are pending
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2026-05-30 00:54:30 -07:00
Yassin Kortam
d82eb33a60
feat(otel): typed semconv-aligned OpenTelemetry instrumentation (#28909) 2026-05-29 23:15:27 -07:00
ryan-crabbe-berri
c754c560dd
fix(proxy): link passthrough success spans to the SERVER root OTEL span (#29315) 
* fix(proxy): link passthrough success spans to the SERVER root OTEL span

Passthrough requests never wired user_api_key_dict.parent_otel_span into the
logging metadata, so on success the litellm_request span orphaned into its own
trace and the "Received Proxy Server Request" root span was never ended. Setting
it once in _init_kwargs_for_pass_through_endpoint fixes both the non-streaming
and streaming paths, since update_environment_variables copies that metadata onto
the logging object's model_call_details, which is what the OTEL handler reads.

Resolves LIT-3443

* fix(proxy): set passthrough parent span after client metadata merge

Greptile flagged that litellm_parent_otel_span was assigned before the
_metadata.update() calls that merge request-body metadata, so a client body
mirroring the internal key could overwrite the real span with a JSON scalar
and null the fix for that request. Move the assignment after the merge and
add a regression test that fails on the old ordering.

* fix(proxy): also set user_api_key after client metadata merge

Per Greptile, user_api_key had the same clobber window as the parent span: a
passthrough request body mirroring the key could overwrite the authenticated
value in the logged metadata. Move it into the same post-merge block and add a
deterministic contract test asserting both internal keys resist client-supplied
metadata.
 2026-05-29 21:30:45 -07:00
michelligabriele
928f09f8a4
fix(datadog): drain cost-management queue + opt-in FinOps tag allowlist (#28487) 
* fix(datadog): drain cost-management queue + opt-in FinOps tag allowlist

* fix(datadog): guard non-dict callback_specific_params + log empty aggregation

* fix(datadog): block user-controlled tags from overwriting reserved cost-attribution dimensions

* fix(datadog): cast metadata to dict[str, Any] to satisfy mypy
 2026-05-28 21:04:04 +02:00
Sameer Kankute
f25648cdad
fix(galileo): support hosted v2 spans API and string output extraction (#28771) 
* fix(galileo): support hosted v2 spans API and string output extraction

Use GALILEO_API_KEY with /v2/projects/{id}/spans for Galileo Cloud,
keep legacy observe/ingest for username/password deployments, and
extract assistant content as a string instead of a message dict.

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(galileo): address review — async enterprise auth and message input

Use async httpx for enterprise login to avoid blocking the event loop,
preserve multi-turn messages in v2 span input, and clean up tests.

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(galileo): handle negative TZ offsets, 2xx success, and Pydantic ImageObject serialization

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix(galileo): treat any 2xx ingest response as success

Use response.is_success so 201 Created clears in_memory_records and
avoids duplicate span submissions on subsequent flushes.

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(galileo): cast message dict for mypy in convert_content_list_to_str

Co-authored-by: Cursor <cursoragent@cursor.com>

* merge main (#28835)

* fix(proxy): Bedrock Knowledge Base pass-through: preserve SigV4 headers and signed request body (#27526)

* Fix Bedrock KB pass-through SigV4 headers and signed body

Coerce botocore HeadersDict to a dict for pass-through routes. When
forward_headers is true, drop request headers that collide case-insensitively
with signed headers so client Bearer auth does not shadow AWS SigV4.
Send prepped.body as raw content so the outbound payload matches the
signature after logging hooks mutate the parsed dict.

Co-authored-by: Cursor <cursoragent@cursor.com>

* Simplify pass-through raw body handling

Read the SigV4-signed bytes directly from request.state inside
pass_through_request instead of threading a custom_raw_body argument
through three functions. Helper methods are restored to their original
signatures, and the new branch lives in one place at each httpx call site.

Co-authored-by: Cursor <cursoragent@cursor.com>

* Harden pass-through raw body read from request.state

Guard missing request.state (test fixtures) and ignore non-bytes/str
values so MagicMock does not trigger the SigV4 raw-body path.

Co-authored-by: Cursor <cursoragent@cursor.com>

* Test pass_through_request state_raw_body uses httpx content=

Cover non-streaming (async_client.request) and streaming (build_request)
paths so SigV4 bytes on request.state are not replaced by json= of a
hook-mutated dict.

Co-authored-by: Cursor <cursoragent@cursor.com>

---------

Co-authored-by: Cursor <cursoragent@cursor.com>

* chore(tests): migrate Bedrock CI to AWS account 941277531214 (#28728)

* 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>

* fix(otel): export SERVER span on management-endpoint success without http_request (#28794)

Co-authored-by: Yassin Kortam <yassinkortam@Yassins-MacBook-Pro.local>

* chore(ci): merge dev branch (#28801)

* chore(proxy): route path-dependent call sites through get_request_route

Replace direct ``request.url.path`` reads in auth, ACL, routing, and
audit-log decisions with ``get_request_route(request)`` — the helper
already added in ``auth/auth_utils.py`` that returns the ASGI
``scope["path"]`` with ``root_path`` stripped. Starlette reconstructs
``url.path`` from the Host header; ``scope["path"]`` is uvicorn's
parse of the request line and matches what FastAPI dispatches on, so
it's the authoritative route for any decision that should agree with
the actual handler.

Sites:
- _experimental/mcp_server/auth/user_api_key_auth_mcp.py
- management_endpoints/mcp_management_endpoints.py
- vector_store_endpoints/utils.py
- pass_through_endpoints/pass_through_endpoints.py
- auth/route_checks.py
- litellm_pre_call_utils.py
- spend_tracking/spend_management_endpoints.py
- common_utils/http_parsing_utils.py
- management_helpers/utils.py
- health_endpoints/_health_endpoints.py

Adds regression tests in tests/proxy_unit_tests/test_proxy_routes.py
that construct a Request with scope["path"] set to a benign route and
the Host header crafted so url.path would resolve differently; each
site's decision is asserted against scope["path"].

* chore(proxy): make get_request_route imports lazy at call sites

Move the ``from litellm.proxy.auth.auth_utils import get_request_route``
imports added in the prior commit back to the function bodies that use
them. The module-level form participates in a long-standing import
cycle through ``auth_utils -> _types -> ...`` and was flagged by CodeQL
on the PR; the lazy form matches the pattern the proxy already uses
for ``user_api_key_auth`` and related helpers elsewhere in these files.

Also drop the ``RouteChecks._is_assistants_api_request`` delegation in
``_get_metadata_variable_name`` introduced in the prior commit — the
delegation pulled ``RouteChecks`` into the same cycle, and the call
site reuses the resolved route for its other branches, so inlining
the substring check is both cycle-free and avoids a redundant second
``get_request_route`` call.

Comment in test_proxy_routes.py acknowledges that the two MCP table
entries exercise ``get_request_route`` directly rather than the full
production handler (which needs ASGI scope + MCP state to invoke).

---------

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: user <70670632+stuxf@users.noreply.github.com>

* chore(ci): merge dev branch (#28657)

* feat(dashboard): navbar hierarchy + Agent Platform notifications (#27543)

* feat(dashboard): refine navbar zones and Agent Platform notice

Restructure the admin navbar for production users: clear product vs community
vs personal columns with vertical dividers, icon-only Slack/GitHub in a
shared chip, and Docs/Blog typography aligned on an 8px rhythm.

Add a notifications bell with popover linking to the LiteLLM Agent Platform
repo and optional mark-as-read persistence.

Promote the account control with initials avatar, single-line display name,
and navDisplayName mapping for placeholder user ids (e.g. default_user_id).

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(dashboard): address PR review — AntD buttons, public page guard, dedupe regex

- Replace raw <button> with AntD Button in BlogDropdown, NotificationsBell, UserDropdown, and test mock
- Guard NotificationsBell + container behind !isPublicPage to avoid rendering on public pages
- Remove redundant equality checks in navDisplayName (regex already covers them)
- Remove unused `lower` variable after simplification

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

---------

Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>

* fix(dashboard): drop dead useHealthReadiness import in navbar

The module was removed in #27896 (replaced by useHealthReadinessDetails),
but the import survived the rebase. The symbol is unused — only
useHealthReadinessDetails is consumed in the file. Removing the dead
import unblocks the UI TypeScript build.

* fix(dashboard): align CommunityEngagementButtons test with icon-only aria-labels

The component was refactored to an icon-only chip with aria-label='LiteLLM
on GitHub' (squash #27543), but the test still asserted /star us on
github/i. Update the query to match the rendered accessible name.

* refactor(dashboard): drop unused props from NavbarProps

The navbar refactor moved user identity + dark-mode state to internal
hooks (useAuthorized, useWorker), but the NavbarProps interface still
declared userID, userEmail, userRole, premiumUser, isDarkMode, and
toggleDarkMode as required, forcing every caller to thread them through.

Drop them from the interface and all four call sites (page.tsx,
(dashboard)/layout.tsx, public_model_hub.tsx, navbar.test.tsx). Also
shrinks the destructure in layout.tsx so the now-unused locals stop
being pulled out of useAuthorized().

* refactor(dashboard): use useSyncExternalStore for NotificationsBell dismiss flag

Reads/writes of the litellmHideAgentPlatformBanner key were done
directly inside NotificationsBell via a useEffect + useState pair.
Every other localStorage-backed flag in the dashboard (Disable
ShowPrompts, DisableBouncingIcon, DisableShowNewBadge,
DisableUsageIndicator, DisableBlogPosts) is wrapped in a
useSyncExternalStore hook over localStorageUtils so all mounted
components stay in sync.

Extract useHideAgentPlatformBanner to follow the same shape, swap
NotificationsBell to consume it, and add a regression test that
two sibling bells stay in sync without a remount when one is
dismissed.

* refactor: mask credential fields in proxy settings GET responses (#28682)

* refactor: mask credential fields in proxy settings GET responses

Brings SSO settings, cache settings, and the email/Slack alerting view in
/get/config/callbacks in line with the HashiCorp Vault config-override
pattern, so persisted credentials are not transported back to the UI in
plaintext.

* refactor: harden short-value masking and hoist alerting var constant

Closes two review observations:

- mask_sensitive_keys now replaces short values (below the visible
  prefix+suffix length) with an all-mask string instead of returning them
  unchanged, so a 1-7 character credential is no longer round-tripped
  verbatim.
- _ALERTING_SENSITIVE_VARS is moved out of get_config() to a module-level
  constant, matching the analogous _SSO_SENSITIVE_FIELDS and
  _CACHE_SENSITIVE_FIELDS in the SSO and cache endpoint files.

---------

Co-authored-by: Krrish Dholakia <krrish+github@berri.ai>
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>

* fix(ui): show 2-decimal precision for max_budget on key overview (#28809)

The Key Info Overview tab's Spend card truncated sub-dollar budgets to
"$0" because formatNumberWithCommas defaults to 0 decimals. The Settings
tab passes 2; align the overview so a $0.10 budget renders as "$0.10".

Resolves LIT-2845

* feat(proxy): allow `llm_api_routes` virtual keys to list MCP servers (#28442)

* feat(proxy): allow llm_api_routes virtual keys to list MCP servers

Add a new `mcp_discovery_routes` group (GET /v1/mcp/server and GET
/v1/mcp/server/{server_id}) and include it in `llm_api_routes` so that
virtual keys configured with `allowed_routes=["llm_api_routes"]` can
discover the MCP servers they have access to. Previously these calls
failed with 'Virtual key is not allowed to call this route. Only allowed
to call routes: [llm_api_routes]'.

The GET handlers already sanitize the response for restricted virtual
keys via `_sanitize_mcp_server_list_for_virtual_key`, stripping
credential-bearing fields (url, headers, env). Write methods
(POST/PUT/DELETE) on the same paths remain gated by the existing
handler-level admin role checks.

The new discovery list is intentionally kept OUT of
`mcp_inference_routes`, so `is_llm_api_route()` still returns False
for these paths — this preserves the existing contract that
DISABLE_LLM_API_ENDPOINTS must not block the Admin UI from listing MCP
servers.

Co-authored-by: ryan-crabbe-berri <ryan-crabbe-berri@users.noreply.github.com>

* refactor(proxy): make MCP discovery carve-out method-aware

Replace the `mcp_discovery_routes` group in `llm_api_routes` with a
method-aware special case inside `is_virtual_key_allowed_to_call_route`.
Virtual keys with allowed_routes=["llm_api_routes"] are now permitted
to call only GET /v1/mcp/server and GET /v1/mcp/server/{server_id} —
non-GET methods and multi-segment admin sub-paths fall through to the
existing 403. This keeps the general llm_api_routes list free of
management paths and avoids accidentally exposing POST/PUT/DELETE
writes through the route-check layer.

---------

Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: ryan-crabbe-berri <ryan-crabbe-berri@users.noreply.github.com>

* chore(ci): merge dev branch (#28807)

* chore(proxy): route path-dependent call sites through get_request_route

Replace direct ``request.url.path`` reads in auth, ACL, routing, and
audit-log decisions with ``get_request_route(request)`` — the helper
already added in ``auth/auth_utils.py`` that returns the ASGI
``scope["path"]`` with ``root_path`` stripped. Starlette reconstructs
``url.path`` from the Host header; ``scope["path"]`` is uvicorn's
parse of the request line and matches what FastAPI dispatches on, so
it's the authoritative route for any decision that should agree with
the actual handler.

Sites:
- _experimental/mcp_server/auth/user_api_key_auth_mcp.py
- management_endpoints/mcp_management_endpoints.py
- vector_store_endpoints/utils.py
- pass_through_endpoints/pass_through_endpoints.py
- auth/route_checks.py
- litellm_pre_call_utils.py
- spend_tracking/spend_management_endpoints.py
- common_utils/http_parsing_utils.py
- management_helpers/utils.py
- health_endpoints/_health_endpoints.py

Adds regression tests in tests/proxy_unit_tests/test_proxy_routes.py
that construct a Request with scope["path"] set to a benign route and
the Host header crafted so url.path would resolve differently; each
site's decision is asserted against scope["path"].

* chore(proxy): make get_request_route imports lazy at call sites

Move the ``from litellm.proxy.auth.auth_utils import get_request_route``
imports added in the prior commit back to the function bodies that use
them. The module-level form participates in a long-standing import
cycle through ``auth_utils -> _types -> ...`` and was flagged by CodeQL
on the PR; the lazy form matches the pattern the proxy already uses
for ``user_api_key_auth`` and related helpers elsewhere in these files.

Also drop the ``RouteChecks._is_assistants_api_request`` delegation in
``_get_metadata_variable_name`` introduced in the prior commit — the
delegation pulled ``RouteChecks`` into the same cycle, and the call
site reuses the resolved route for its other branches, so inlining
the substring check is both cycle-free and avoids a redundant second
``get_request_route`` call.

Comment in test_proxy_routes.py acknowledges that the two MCP table
entries exercise ``get_request_route`` directly rather than the full
production handler (which needs ASGI scope + MCP state to invoke).

---------

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: user <70670632+stuxf@users.noreply.github.com>

* fix(team): keep team_alias cache in sync on _cache_team_object writes (#28737)

* fix(team): keep team_alias cache in sync on _cache_team_object writes

_cache_team_object wrote only to the team_id:<id> cache key, but the
JWT auth path that uses team_alias_jwt_field reads from a separate
team_alias:<alias> key (get_team_object_by_alias caches under both
keys on miss, but reads only the alias-keyed one). After any
team-mutation endpoint (team_model_add, team_model_delete,
update_team, the two access-group writes) the team_id cache was
refreshed but the team_alias cache stayed stale until TTL — JWT
callers using team_alias_jwt_field kept seeing the pre-mutation
team for the full cache window.

Mirror the write under the alias key inside _cache_team_object so
every existing caller stays in sync without further changes. Skip
the alias write when team_alias is None/empty so we don't collide
across alias-less teams.

Surfaced testing the LIT-3244 cherry-pick on patch/1.86.0: the
LIT-3244 fix correctly invalidated the team_id cache but the
customer's JWT used team_alias_jwt_field, so they kept hitting the
stale alias-keyed entry.

* fix(team): delete (not overwrite) team_alias cache on _cache_team_object

The prior shape of this PR wrote both team_id:<id> AND team_alias:<alias>
from _cache_team_object. team_alias is NOT unique in the schema
(no @unique on LiteLLM_TeamTable.team_alias), and get_team_object_by_alias
enforces uniqueness on its own DB-fetch path (len(teams) > 1 raises).
Writing the alias-keyed cache from the generic refresh path bypassed
that check: a team admin renaming their team to collide with another
team's alias could silently overwrite the cached team for JWT-by-alias
auth, swapping the resolved team under that alias for the cache window.

Switch the alias-keyed operation from a write to a delete (mirroring
the dual-cache delete pattern in _delete_cache_key_object). After every
team write, the next JWT-by-alias reader cache-misses and falls through
to get_team_object_by_alias, which (a) re-fetches the fresh team from
DB, closing the LIT-3244 staleness gap that motivated this PR, and
(b) enforces alias uniqueness before populating either cache key.

team_id:<id> writes are unchanged — team_id is the table PK and is
guaranteed unique.

Surfaced in veria-ai review on #28739.

* fix(managed-files): anchor model_id regex so it doesn't match llm_output_file_model_id

extract_model_id_from_unified_id used `re.search(r"model_id,([^;]+)", ...)`
which substring-matches the `model_id,` inside the file-ID encoding's
`llm_output_file_model_id,<deployment_uuid>` field. parse_unified_id
then fed that deployment UUID back into the auth path as a model
candidate via _extract_models_from_managed_resource_id, and every
team-BYOK file attach 403'd with:

    team not allowed to access model. This team can only access
    models=['openai/*']. Tried to access <deployment-uuid>

The team's models list correctly contains the public name (`openai/*`)
that target_model_names matches, but the bogus UUID candidate fails
the wildcard check first.

Anchor the regex to a field boundary (`(?:^|;)model_id,`) so it
matches the legitimate top-level `model_id,<value>` field on
vector_store unified IDs and skips substring matches inside other
fields. File-IDs (which have no top-level `model_id` field) now
return None and contribute no spurious UUID candidate.

Surfaced reproducing LIT-3244 on patch/1.86.0 with the customer's
exact flow: team with openai/* BYOK deployment, JWT-scoped user,
POST /v1/vector_stores/{id}/files attaching a file uploaded with
target_model_names=openai/gpt-4o.

* fix(proxy): hydrate wildcard discovery credentials (#28284) (#28822)

* fix(proxy): hydrate wildcard discovery credentials

* fix(proxy): constrain wildcard credential hydration

Co-authored-by: Dibyo Mukherjee <dibyo@adobe.com>

* ci: add daily oss-agent-shin branch creation workflow (#28829)

Creates litellm_oss_agent_shin_MM_DD_YYYY from main every day at 00:00 UTC.
Lets us retarget oss-agent-shin fork PRs onto a canonical branch so CircleCI runs with secrets, without granting the agent write access.

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>

* test(proxy): add harness for proxy_server.py behavior-pinning (#28827)

* test(proxy): add harness for proxy_server.py behavior-pinning

Creates tests/test_litellm/proxy/proxy_server/ with:
- conftest.py: 11 shared fixtures (app, client, mock_prisma, auth_as,
  mock_router with parametrized response builders, normalize, etc.)
- _coverage_check.py: per-PR coverage gate (line + branch) against a
  baseline, self-selects target by inspecting which placeholder files
  have been filled
- _pin_check.py: AST-based gate that verifies every pin-list item has
  >=1 happy + >=1 error test with a real assertion (no status-only)
- test_harness_smoke.py: 19 smoke tests covering every fixture +
  both scripts end-to-end
- 26 placeholder test files (one docstring each) reserved for
  follow-up PRs per the directory ownership in the Notion plan
- .coverage_baseline pinned at 0% so future PRs measure deltas
  against new-tests-only and aren't entangled with the broader
  scattered test suite

Adds a dedicated proxy-server job to test-unit-proxy-endpoints.yml
so this directory's runtime + coverage are tracked independently.

Plan: https://www.notion.so/36c43b8acdab81ee845fd5365128a2fc

* ci(proxy-endpoints): allow workflow_dispatch

Lets the workflow be triggered manually on a branch via
`gh workflow run`, which is needed for the verify-first
flow on workflow changes before opening a PR.

* test(proxy): address review feedback on proxy_server harness

- conftest.py: anchor sys.path insert to __file__ (Path(__file__).resolve().parents[4])
  instead of CWD-relative os.path.abspath("../../../../") which resolved
  to the wrong directory when pytest is launched from the repo root.
- _coverage_check.py: actually read .coverage_baseline and use it as
  the floor (line_min = max(target, baseline)). Closes the gap between
  the PR description's "delta semantics" and what the script was doing.
  With baseline=0.0 today this is a no-op; future PRs that update the
  baseline cause regressions (test deletions etc.) to trip the gate
  even if the static PR target is still met.
- _pin_check.py: drop unreachable startswith("_") guard
  (test_*.py glob never yields underscore-prefixed names) and read
  each test file once instead of twice.

* feat(openai): apply regional-processing cost uplift for EU/US data residency (#28626)

* 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>

---------

Co-authored-by: milan-berri <milan@berri.ai>
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Mateo Wang <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: Mateo <mateo@Mateos-MacBook-Pro.local>
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
Co-authored-by: Yassin Kortam <yassinkortam@Yassins-MacBook-Pro.local>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: user <70670632+stuxf@users.noreply.github.com>
Co-authored-by: Krrish Dholakia <krrish+github@berri.ai>
Co-authored-by: ryan-crabbe-berri <ryan@berri.ai>
Co-authored-by: ryan-crabbe-berri <ryan-crabbe-berri@users.noreply.github.com>
Co-authored-by: Dibyo Mukherjee <dibyo@adobe.com>
Co-authored-by: ishaan-berri <155045088+ishaan-berri@users.noreply.github.com>
Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>

* fix: preserve OTEL response payload and remove duplicate constant

- Remove duplicate _CREDENTIAL_LITELLM_PARAM_FIELDS assignment in model_checks
- Restore response=dict(result) in _emit_management_endpoint_otel_span so
  OTEL spans for successful management endpoint calls include response data

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix: harden OTEL failure path and cap Galileo in-memory buffer

- Wrap _emit_management_endpoint_otel_span in try/except on the failure
  path of management_endpoint_wrapper so OTEL errors cannot swallow the
  original management-endpoint exception.
- Bound GalileoObserve.in_memory_records at GALILEO_MAX_IN_MEMORY_RECORDS
  to prevent unbounded memory growth when flushes persistently fail.

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix(galileo): reset stale bearer token on auth error; preserve records under concurrency

- Snapshot record count before await so concurrent appends during the
  network round-trip aren't silently dropped when clearing the buffer.
- Build payload from a snapshot list so the legacy path no longer shares
  a live reference with self.in_memory_records.
- On legacy enterprise auth (username/password), drop cached bearer-token
  headers when the upstream rejects the request (401/403) so the next
  flush re-authenticates instead of failing forever on a stale token.

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* test(galileo): expand v2 coverage for config, ingest, headers, and flush paths

---------

Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
Co-authored-by: milan-berri <milan@berri.ai>
Co-authored-by: Mateo Wang <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: Mateo <mateo@Mateos-MacBook-Pro.local>
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
Co-authored-by: Yassin Kortam <yassinkortam@Yassins-MacBook-Pro.local>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: user <70670632+stuxf@users.noreply.github.com>
Co-authored-by: Krrish Dholakia <krrish+github@berri.ai>
Co-authored-by: ryan-crabbe-berri <ryan@berri.ai>
Co-authored-by: ryan-crabbe-berri <ryan-crabbe-berri@users.noreply.github.com>
Co-authored-by: Dibyo Mukherjee <dibyo@adobe.com>
Co-authored-by: ishaan-berri <155045088+ishaan-berri@users.noreply.github.com>
Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>
 2026-05-26 14:10:33 -07:00
Sameer Kankute
d52fbfb458
Litellm oss staging 250526 (#28770) 
* fix(mcp): handle OAuth IdP error responses in /callback (LIT-2750)

Per RFC 6749 section 4.1.2.1, when the IdP rejects an OAuth authorization
request it redirects back to the client with ?error=...&error_description=...
and no code. The MCP /callback handler declared code and state as required
query params, so FastAPI rejected such error responses with a 422 before
the handler ran -- stranding the MCP client waiting on the loopback.

This change:
- Makes code and state optional and accepts the RFC-defined error,
  error_description, and error_uri params.
- When state decodes to a trusted client redirect_uri, propagates the
  error params back to that URI with the client's original (un-wrapped)
  state preserved, so the client's OAuth library can surface the failure.
- When state is missing/undecryptable or the encoded redirect_uri is no
  longer trusted, renders a 400 HTML page with the (HTML-escaped) error
  details instead of leaking to an attacker-controlled redirect.
- Preserves the existing success path (code + state -> 302 to validated
  client redirect_uri with original state).

Fixes LIT-2750.

* test(mcp): regression tests for /callback handling IdP error responses (LIT-2750)

Adds a new test module covering the LIT-2750 fix: the MCP OAuth /callback
endpoint must accept IdP error responses (e.g. ?error=access_denied) per
RFC 6749 section 4.1.2.1 instead of returning a 422 because ``code`` is missing.

Coverage:
- IdP error with no state -> 400 HTML page surfacing the error.
- HTML escaping of user-controlled error / error_description fields.
- IdP error with a trusted (loopback) state -> 302 propagating
  error / error_description / original client state to the client.
- IdP error with an untrusted redirect_uri encoded in state -> 400 inline
  (no open-redirect to attacker-controlled origin).
- IdP error with an undecryptable state -> 400 HTML fallback.
- Bare GET /callback with no params -> 400 HTML (not Pydantic 422).
- Success path (code + state) still 302 to validated client redirect_uri
  with the original (un-wrapped) state preserved.

* refactor(mcp): drop unused _OAUTH_ERROR_PARAMS constant (Greptile P2)

The tuple was leftover scaffolding from an earlier draft of the LIT-2750
fix; nothing references it. The explanatory RFC 6749 §4.1.2.1 comment block
above the callback handler covers the same intent.

* fix(mcp/oauth): preserve empty original_state and clarify missing-param error in /callback

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix(thinking): handle None thinking param in is_thinking_enabled (#28598)

Squash-merged by litellm-agent from Terrajlz's PR.

* feat(helm): support tpl rendering in podAnnotations (#28609)

Squash-merged by litellm-agent from devauxbr's PR.

* fix: apply black formatting to base_llm chat transformation

Fix CI black --check failure on is_thinking_enabled return formatting.

Co-authored-by: Cursor <cursoragent@cursor.com>

* merge main (#28836)

* fix(proxy): Bedrock Knowledge Base pass-through: preserve SigV4 headers and signed request body (#27526)

* Fix Bedrock KB pass-through SigV4 headers and signed body

Coerce botocore HeadersDict to a dict for pass-through routes. When
forward_headers is true, drop request headers that collide case-insensitively
with signed headers so client Bearer auth does not shadow AWS SigV4.
Send prepped.body as raw content so the outbound payload matches the
signature after logging hooks mutate the parsed dict.

Co-authored-by: Cursor <cursoragent@cursor.com>

* Simplify pass-through raw body handling

Read the SigV4-signed bytes directly from request.state inside
pass_through_request instead of threading a custom_raw_body argument
through three functions. Helper methods are restored to their original
signatures, and the new branch lives in one place at each httpx call site.

Co-authored-by: Cursor <cursoragent@cursor.com>

* Harden pass-through raw body read from request.state

Guard missing request.state (test fixtures) and ignore non-bytes/str
values so MagicMock does not trigger the SigV4 raw-body path.

Co-authored-by: Cursor <cursoragent@cursor.com>

* Test pass_through_request state_raw_body uses httpx content=

Cover non-streaming (async_client.request) and streaming (build_request)
paths so SigV4 bytes on request.state are not replaced by json= of a
hook-mutated dict.

Co-authored-by: Cursor <cursoragent@cursor.com>

---------

Co-authored-by: Cursor <cursoragent@cursor.com>

* chore(tests): migrate Bedrock CI to AWS account 941277531214 (#28728)

* 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>

* fix(otel): export SERVER span on management-endpoint success without http_request (#28794)

Co-authored-by: Yassin Kortam <yassinkortam@Yassins-MacBook-Pro.local>

* chore(ci): merge dev branch (#28801)

* chore(proxy): route path-dependent call sites through get_request_route

Replace direct ``request.url.path`` reads in auth, ACL, routing, and
audit-log decisions with ``get_request_route(request)`` — the helper
already added in ``auth/auth_utils.py`` that returns the ASGI
``scope["path"]`` with ``root_path`` stripped. Starlette reconstructs
``url.path`` from the Host header; ``scope["path"]`` is uvicorn's
parse of the request line and matches what FastAPI dispatches on, so
it's the authoritative route for any decision that should agree with
the actual handler.

Sites:
- _experimental/mcp_server/auth/user_api_key_auth_mcp.py
- management_endpoints/mcp_management_endpoints.py
- vector_store_endpoints/utils.py
- pass_through_endpoints/pass_through_endpoints.py
- auth/route_checks.py
- litellm_pre_call_utils.py
- spend_tracking/spend_management_endpoints.py
- common_utils/http_parsing_utils.py
- management_helpers/utils.py
- health_endpoints/_health_endpoints.py

Adds regression tests in tests/proxy_unit_tests/test_proxy_routes.py
that construct a Request with scope["path"] set to a benign route and
the Host header crafted so url.path would resolve differently; each
site's decision is asserted against scope["path"].

* chore(proxy): make get_request_route imports lazy at call sites

Move the ``from litellm.proxy.auth.auth_utils import get_request_route``
imports added in the prior commit back to the function bodies that use
them. The module-level form participates in a long-standing import
cycle through ``auth_utils -> _types -> ...`` and was flagged by CodeQL
on the PR; the lazy form matches the pattern the proxy already uses
for ``user_api_key_auth`` and related helpers elsewhere in these files.

Also drop the ``RouteChecks._is_assistants_api_request`` delegation in
``_get_metadata_variable_name`` introduced in the prior commit — the
delegation pulled ``RouteChecks`` into the same cycle, and the call
site reuses the resolved route for its other branches, so inlining
the substring check is both cycle-free and avoids a redundant second
``get_request_route`` call.

Comment in test_proxy_routes.py acknowledges that the two MCP table
entries exercise ``get_request_route`` directly rather than the full
production handler (which needs ASGI scope + MCP state to invoke).

---------

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: user <70670632+stuxf@users.noreply.github.com>

* chore(ci): merge dev branch (#28657)

* feat(dashboard): navbar hierarchy + Agent Platform notifications (#27543)

* feat(dashboard): refine navbar zones and Agent Platform notice

Restructure the admin navbar for production users: clear product vs community
vs personal columns with vertical dividers, icon-only Slack/GitHub in a
shared chip, and Docs/Blog typography aligned on an 8px rhythm.

Add a notifications bell with popover linking to the LiteLLM Agent Platform
repo and optional mark-as-read persistence.

Promote the account control with initials avatar, single-line display name,
and navDisplayName mapping for placeholder user ids (e.g. default_user_id).

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(dashboard): address PR review — AntD buttons, public page guard, dedupe regex

- Replace raw <button> with AntD Button in BlogDropdown, NotificationsBell, UserDropdown, and test mock
- Guard NotificationsBell + container behind !isPublicPage to avoid rendering on public pages
- Remove redundant equality checks in navDisplayName (regex already covers them)
- Remove unused `lower` variable after simplification

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

---------

Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>

* fix(dashboard): drop dead useHealthReadiness import in navbar

The module was removed in #27896 (replaced by useHealthReadinessDetails),
but the import survived the rebase. The symbol is unused — only
useHealthReadinessDetails is consumed in the file. Removing the dead
import unblocks the UI TypeScript build.

* fix(dashboard): align CommunityEngagementButtons test with icon-only aria-labels

The component was refactored to an icon-only chip with aria-label='LiteLLM
on GitHub' (squash #27543), but the test still asserted /star us on
github/i. Update the query to match the rendered accessible name.

* refactor(dashboard): drop unused props from NavbarProps

The navbar refactor moved user identity + dark-mode state to internal
hooks (useAuthorized, useWorker), but the NavbarProps interface still
declared userID, userEmail, userRole, premiumUser, isDarkMode, and
toggleDarkMode as required, forcing every caller to thread them through.

Drop them from the interface and all four call sites (page.tsx,
(dashboard)/layout.tsx, public_model_hub.tsx, navbar.test.tsx). Also
shrinks the destructure in layout.tsx so the now-unused locals stop
being pulled out of useAuthorized().

* refactor(dashboard): use useSyncExternalStore for NotificationsBell dismiss flag

Reads/writes of the litellmHideAgentPlatformBanner key were done
directly inside NotificationsBell via a useEffect + useState pair.
Every other localStorage-backed flag in the dashboard (Disable
ShowPrompts, DisableBouncingIcon, DisableShowNewBadge,
DisableUsageIndicator, DisableBlogPosts) is wrapped in a
useSyncExternalStore hook over localStorageUtils so all mounted
components stay in sync.

Extract useHideAgentPlatformBanner to follow the same shape, swap
NotificationsBell to consume it, and add a regression test that
two sibling bells stay in sync without a remount when one is
dismissed.

* refactor: mask credential fields in proxy settings GET responses (#28682)

* refactor: mask credential fields in proxy settings GET responses

Brings SSO settings, cache settings, and the email/Slack alerting view in
/get/config/callbacks in line with the HashiCorp Vault config-override
pattern, so persisted credentials are not transported back to the UI in
plaintext.

* refactor: harden short-value masking and hoist alerting var constant

Closes two review observations:

- mask_sensitive_keys now replaces short values (below the visible
  prefix+suffix length) with an all-mask string instead of returning them
  unchanged, so a 1-7 character credential is no longer round-tripped
  verbatim.
- _ALERTING_SENSITIVE_VARS is moved out of get_config() to a module-level
  constant, matching the analogous _SSO_SENSITIVE_FIELDS and
  _CACHE_SENSITIVE_FIELDS in the SSO and cache endpoint files.

---------

Co-authored-by: Krrish Dholakia <krrish+github@berri.ai>
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>

* fix(ui): show 2-decimal precision for max_budget on key overview (#28809)

The Key Info Overview tab's Spend card truncated sub-dollar budgets to
"$0" because formatNumberWithCommas defaults to 0 decimals. The Settings
tab passes 2; align the overview so a $0.10 budget renders as "$0.10".

Resolves LIT-2845

* feat(proxy): allow `llm_api_routes` virtual keys to list MCP servers (#28442)

* feat(proxy): allow llm_api_routes virtual keys to list MCP servers

Add a new `mcp_discovery_routes` group (GET /v1/mcp/server and GET
/v1/mcp/server/{server_id}) and include it in `llm_api_routes` so that
virtual keys configured with `allowed_routes=["llm_api_routes"]` can
discover the MCP servers they have access to. Previously these calls
failed with 'Virtual key is not allowed to call this route. Only allowed
to call routes: [llm_api_routes]'.

The GET handlers already sanitize the response for restricted virtual
keys via `_sanitize_mcp_server_list_for_virtual_key`, stripping
credential-bearing fields (url, headers, env). Write methods
(POST/PUT/DELETE) on the same paths remain gated by the existing
handler-level admin role checks.

The new discovery list is intentionally kept OUT of
`mcp_inference_routes`, so `is_llm_api_route()` still returns False
for these paths — this preserves the existing contract that
DISABLE_LLM_API_ENDPOINTS must not block the Admin UI from listing MCP
servers.

Co-authored-by: ryan-crabbe-berri <ryan-crabbe-berri@users.noreply.github.com>

* refactor(proxy): make MCP discovery carve-out method-aware

Replace the `mcp_discovery_routes` group in `llm_api_routes` with a
method-aware special case inside `is_virtual_key_allowed_to_call_route`.
Virtual keys with allowed_routes=["llm_api_routes"] are now permitted
to call only GET /v1/mcp/server and GET /v1/mcp/server/{server_id} —
non-GET methods and multi-segment admin sub-paths fall through to the
existing 403. This keeps the general llm_api_routes list free of
management paths and avoids accidentally exposing POST/PUT/DELETE
writes through the route-check layer.

---------

Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: ryan-crabbe-berri <ryan-crabbe-berri@users.noreply.github.com>

* chore(ci): merge dev branch (#28807)

* chore(proxy): route path-dependent call sites through get_request_route

Replace direct ``request.url.path`` reads in auth, ACL, routing, and
audit-log decisions with ``get_request_route(request)`` — the helper
already added in ``auth/auth_utils.py`` that returns the ASGI
``scope["path"]`` with ``root_path`` stripped. Starlette reconstructs
``url.path`` from the Host header; ``scope["path"]`` is uvicorn's
parse of the request line and matches what FastAPI dispatches on, so
it's the authoritative route for any decision that should agree with
the actual handler.

Sites:
- _experimental/mcp_server/auth/user_api_key_auth_mcp.py
- management_endpoints/mcp_management_endpoints.py
- vector_store_endpoints/utils.py
- pass_through_endpoints/pass_through_endpoints.py
- auth/route_checks.py
- litellm_pre_call_utils.py
- spend_tracking/spend_management_endpoints.py
- common_utils/http_parsing_utils.py
- management_helpers/utils.py
- health_endpoints/_health_endpoints.py

Adds regression tests in tests/proxy_unit_tests/test_proxy_routes.py
that construct a Request with scope["path"] set to a benign route and
the Host header crafted so url.path would resolve differently; each
site's decision is asserted against scope["path"].

* chore(proxy): make get_request_route imports lazy at call sites

Move the ``from litellm.proxy.auth.auth_utils import get_request_route``
imports added in the prior commit back to the function bodies that use
them. The module-level form participates in a long-standing import
cycle through ``auth_utils -> _types -> ...`` and was flagged by CodeQL
on the PR; the lazy form matches the pattern the proxy already uses
for ``user_api_key_auth`` and related helpers elsewhere in these files.

Also drop the ``RouteChecks._is_assistants_api_request`` delegation in
``_get_metadata_variable_name`` introduced in the prior commit — the
delegation pulled ``RouteChecks`` into the same cycle, and the call
site reuses the resolved route for its other branches, so inlining
the substring check is both cycle-free and avoids a redundant second
``get_request_route`` call.

Comment in test_proxy_routes.py acknowledges that the two MCP table
entries exercise ``get_request_route`` directly rather than the full
production handler (which needs ASGI scope + MCP state to invoke).

---------

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: user <70670632+stuxf@users.noreply.github.com>

* fix(team): keep team_alias cache in sync on _cache_team_object writes (#28737)

* fix(team): keep team_alias cache in sync on _cache_team_object writes

_cache_team_object wrote only to the team_id:<id> cache key, but the
JWT auth path that uses team_alias_jwt_field reads from a separate
team_alias:<alias> key (get_team_object_by_alias caches under both
keys on miss, but reads only the alias-keyed one). After any
team-mutation endpoint (team_model_add, team_model_delete,
update_team, the two access-group writes) the team_id cache was
refreshed but the team_alias cache stayed stale until TTL — JWT
callers using team_alias_jwt_field kept seeing the pre-mutation
team for the full cache window.

Mirror the write under the alias key inside _cache_team_object so
every existing caller stays in sync without further changes. Skip
the alias write when team_alias is None/empty so we don't collide
across alias-less teams.

Surfaced testing the LIT-3244 cherry-pick on patch/1.86.0: the
LIT-3244 fix correctly invalidated the team_id cache but the
customer's JWT used team_alias_jwt_field, so they kept hitting the
stale alias-keyed entry.

* fix(team): delete (not overwrite) team_alias cache on _cache_team_object

The prior shape of this PR wrote both team_id:<id> AND team_alias:<alias>
from _cache_team_object. team_alias is NOT unique in the schema
(no @unique on LiteLLM_TeamTable.team_alias), and get_team_object_by_alias
enforces uniqueness on its own DB-fetch path (len(teams) > 1 raises).
Writing the alias-keyed cache from the generic refresh path bypassed
that check: a team admin renaming their team to collide with another
team's alias could silently overwrite the cached team for JWT-by-alias
auth, swapping the resolved team under that alias for the cache window.

Switch the alias-keyed operation from a write to a delete (mirroring
the dual-cache delete pattern in _delete_cache_key_object). After every
team write, the next JWT-by-alias reader cache-misses and falls through
to get_team_object_by_alias, which (a) re-fetches the fresh team from
DB, closing the LIT-3244 staleness gap that motivated this PR, and
(b) enforces alias uniqueness before populating either cache key.

team_id:<id> writes are unchanged — team_id is the table PK and is
guaranteed unique.

Surfaced in veria-ai review on #28739.

* fix(managed-files): anchor model_id regex so it doesn't match llm_output_file_model_id

extract_model_id_from_unified_id used `re.search(r"model_id,([^;]+)", ...)`
which substring-matches the `model_id,` inside the file-ID encoding's
`llm_output_file_model_id,<deployment_uuid>` field. parse_unified_id
then fed that deployment UUID back into the auth path as a model
candidate via _extract_models_from_managed_resource_id, and every
team-BYOK file attach 403'd with:

    team not allowed to access model. This team can only access
    models=['openai/*']. Tried to access <deployment-uuid>

The team's models list correctly contains the public name (`openai/*`)
that target_model_names matches, but the bogus UUID candidate fails
the wildcard check first.

Anchor the regex to a field boundary (`(?:^|;)model_id,`) so it
matches the legitimate top-level `model_id,<value>` field on
vector_store unified IDs and skips substring matches inside other
fields. File-IDs (which have no top-level `model_id` field) now
return None and contribute no spurious UUID candidate.

Surfaced reproducing LIT-3244 on patch/1.86.0 with the customer's
exact flow: team with openai/* BYOK deployment, JWT-scoped user,
POST /v1/vector_stores/{id}/files attaching a file uploaded with
target_model_names=openai/gpt-4o.

* fix(proxy): hydrate wildcard discovery credentials (#28284) (#28822)

* fix(proxy): hydrate wildcard discovery credentials

* fix(proxy): constrain wildcard credential hydration

Co-authored-by: Dibyo Mukherjee <dibyo@adobe.com>

* ci: add daily oss-agent-shin branch creation workflow (#28829)

Creates litellm_oss_agent_shin_MM_DD_YYYY from main every day at 00:00 UTC.
Lets us retarget oss-agent-shin fork PRs onto a canonical branch so CircleCI runs with secrets, without granting the agent write access.

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>

* test(proxy): add harness for proxy_server.py behavior-pinning (#28827)

* test(proxy): add harness for proxy_server.py behavior-pinning

Creates tests/test_litellm/proxy/proxy_server/ with:
- conftest.py: 11 shared fixtures (app, client, mock_prisma, auth_as,
  mock_router with parametrized response builders, normalize, etc.)
- _coverage_check.py: per-PR coverage gate (line + branch) against a
  baseline, self-selects target by inspecting which placeholder files
  have been filled
- _pin_check.py: AST-based gate that verifies every pin-list item has
  >=1 happy + >=1 error test with a real assertion (no status-only)
- test_harness_smoke.py: 19 smoke tests covering every fixture +
  both scripts end-to-end
- 26 placeholder test files (one docstring each) reserved for
  follow-up PRs per the directory ownership in the Notion plan
- .coverage_baseline pinned at 0% so future PRs measure deltas
  against new-tests-only and aren't entangled with the broader
  scattered test suite

Adds a dedicated proxy-server job to test-unit-proxy-endpoints.yml
so this directory's runtime + coverage are tracked independently.

Plan: https://www.notion.so/36c43b8acdab81ee845fd5365128a2fc

* ci(proxy-endpoints): allow workflow_dispatch

Lets the workflow be triggered manually on a branch via
`gh workflow run`, which is needed for the verify-first
flow on workflow changes before opening a PR.

* test(proxy): address review feedback on proxy_server harness

- conftest.py: anchor sys.path insert to __file__ (Path(__file__).resolve().parents[4])
  instead of CWD-relative os.path.abspath("../../../../") which resolved
  to the wrong directory when pytest is launched from the repo root.
- _coverage_check.py: actually read .coverage_baseline and use it as
  the floor (line_min = max(target, baseline)). Closes the gap between
  the PR description's "delta semantics" and what the script was doing.
  With baseline=0.0 today this is a no-op; future PRs that update the
  baseline cause regressions (test deletions etc.) to trip the gate
  even if the static PR target is still met.
- _pin_check.py: drop unreachable startswith("_") guard
  (test_*.py glob never yields underscore-prefixed names) and read
  each test file once instead of twice.

* feat(openai): apply regional-processing cost uplift for EU/US data residency (#28626)

* 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>

---------

Co-authored-by: milan-berri <milan@berri.ai>
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Mateo Wang <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: Mateo <mateo@Mateos-MacBook-Pro.local>
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
Co-authored-by: Yassin Kortam <yassinkortam@Yassins-MacBook-Pro.local>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: user <70670632+stuxf@users.noreply.github.com>
Co-authored-by: Krrish Dholakia <krrish+github@berri.ai>
Co-authored-by: ryan-crabbe-berri <ryan@berri.ai>
Co-authored-by: ryan-crabbe-berri <ryan-crabbe-berri@users.noreply.github.com>
Co-authored-by: Dibyo Mukherjee <dibyo@adobe.com>
Co-authored-by: ishaan-berri <155045088+ishaan-berri@users.noreply.github.com>
Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>

* fix: preserve OTEL response payload and remove duplicate constant

- _emit_management_endpoint_otel_span now passes result as response on success
- remove duplicate _CREDENTIAL_LITELLM_PARAM_FIELDS assignment in model_checks

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix: address bug detection findings

- pass_through_endpoints: use request.method instead of hardcoded POST
  in streaming SigV4-signed request path for consistency with the
  non-streaming branch
- llm_cost_calc/utils: hoist DataResidency value set to a module-level
  frozenset to avoid rebuilding it on every cost calculation
- example_config_yaml/oai_misc_config: replace real-looking AWS account
  ID with placeholder 123456789012 in example bucket and role ARN

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* chore(github_copilot): refresh model catalog from upstream /models API (#28055)

Aligns the github_copilot catalog with values returned by Copilot's
public /models endpoint (capabilities.limits + capabilities.supports +
model.supported_endpoints).

- Adds 10 new model entries: claude-opus-4.7, claude-sonnet-4.6,
  gemini-3-flash-preview, gemini-3.1-pro-preview, gpt-4-0125-preview,
  gpt-5.2-codex, gpt-5.4, gpt-5.4-mini, gpt-5.5, oswe-vscode-prime.
- Updates max_input_tokens for existing entries to reflect each
  model's true context window (e.g. gpt-4o-mini 64000 -> 128000,
  gpt-5-mini 128000 -> 264000, gpt-5.3-codex 128000 -> 400000,
  claude-haiku-4.5 128000 -> 200000).
- Adds supports_reasoning, supports_response_schema,
  supports_function_calling, supports_parallel_function_calling,
  supports_vision based on capabilities.supports.
- Declares supported_endpoints for entries missing it
  (e.g. gpt-3.5-turbo, gpt-4o, embeddings).
- For responses-only models (gpt-5.2-codex, gpt-5.4, gpt-5.4-mini,
  gpt-5.5), sets mode to 'responses'.
- gpt-41-copilot.mode changes from 'completion' to 'chat' because
  Copilot reports capabilities.type = 'chat'. Revertible on request.

Pricing fields and other manually-curated values are preserved.

* feat(datadog): emit litellm.overhead.latency as a standalone Datadog metric (#28831)

Adds a new `litellm.overhead.latency` gauge metric to `DatadogMetricsLogger`
(the `/api/v2/series` path). The value is sourced from
`hidden_params["litellm_overhead_time_ms"]` already computed in
`ResponseMetadata` and exposed in `StandardLoggingPayload`.

Matches the Prometheus integration which exposes the same value via
`litellm_overhead_latency_metric`. Emitted in seconds (ms ÷ 1000) for
consistency with the other latency series.

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Shin <shin@litellm.ai>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: ishaan-berri <155045088+ishaan-berri@users.noreply.github.com>

* feat(arize): route Phoenix traces via per-project TracerProviders (#28876)

Use LRU-cached TracerProviders with project-scoped OTEL Resources so team/key
metadata routes traces correctly. On the proxy, project selection is limited to
server-controlled user_api_key_auth_metadata; client metadata fields stay banned.

* fix(arize_phoenix): skip _emit_semantic_logs on failure path

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix(arize_phoenix): skip raw request logging and metrics on failure path

Restores pre-refactor behavior: _handle_failure no longer emits raw-request
sub-spans or records OTEL metrics, matching the original _handle_failure
that did not call these helpers.

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix(security): close two medium telemetry trust-boundary issues

Issue 1 (arize_phoenix.py — caller-controlled telemetry routing):
- _is_proxy_request no longer detects proxy mode by checking
  user_api_key_auth_metadata in request metadata.  That field is
  user-supplied, so an authenticated caller could fake proxy-mode
  detection and have _project_from_metadata_dict read their own dict
  for project selection, routing telemetry to arbitrary Arize/Phoenix
  projects.  Proxy mode is now determined solely by the server-set
  proxy_server_request field in litellm_params.
- auth_utils.py adds user_api_key_auth_metadata to the banned request
  body params list so the proxy rejects any attempt to supply the field
  at the HTTP layer.  The field is server-reserved: it is written
  exclusively by add_user_api_key_auth_to_request_metadata from the
  authenticated key's database record after the ban check runs.

Issue 2 (management_helpers/utils.py — API key in OTEL span):
- _emit_management_endpoint_otel_span stripped plaintext credential
  fields (key, token, api_key, secret, …) from the response dict before
  passing it to the OTEL success hook.  dict(result) on a Pydantic
  GenerateKeyResponse includes the freshly-generated key field, which
  would previously be written as a span attribute to every configured
  OTEL collector/backend.

Co-authored-by: Cursor <cursoragent@cursor.com>

---------

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: oss-agent-shin <ext-agent-shin@berri.ai>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
Co-authored-by: Terrajlz <info@jouleselectrictech.com>
Co-authored-by: Bruno Devaux <devaux.br@gmail.com>
Co-authored-by: milan-berri <milan@berri.ai>
Co-authored-by: Mateo Wang <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: Mateo <mateo@Mateos-MacBook-Pro.local>
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
Co-authored-by: Yassin Kortam <yassinkortam@Yassins-MacBook-Pro.local>
Co-authored-by: user <70670632+stuxf@users.noreply.github.com>
Co-authored-by: Krrish Dholakia <krrish+github@berri.ai>
Co-authored-by: ryan-crabbe-berri <ryan@berri.ai>
Co-authored-by: ryan-crabbe-berri <ryan-crabbe-berri@users.noreply.github.com>
Co-authored-by: Dibyo Mukherjee <dibyo@adobe.com>
Co-authored-by: ishaan-berri <155045088+ishaan-berri@users.noreply.github.com>
Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>
Co-authored-by: rinto <54238243+ririnto@users.noreply.github.com>
Co-authored-by: Shin <shin@litellm.ai>
Co-authored-by: mubashir1osmani <mubashir.osmani777@gmail.com>
 2026-05-26 11:57:39 -07:00
Yassin Kortam
30551de371
fix(otel): export SERVER span on management-endpoint success without http_request (#28794) 
Co-authored-by: Yassin Kortam <yassinkortam@Yassins-MacBook-Pro.local>
 2026-05-25 12:13:17 -07:00
ryan-crabbe-berri
886e91b85e
fix(otel): stamp http.response.status_code on all error responses (#28405) 
* fix(otel): stamp http.response.status_code on all error responses

httpx.HTTPStatusError exposes status under .response.status_code, not as a
top-level attr, so unified-endpoint 5xx failures left the SERVER span without
a status. The admin hooks only wrote a child span and never stamped or ended
the parent at all, so admin 4xx/5xx (and success) responses were invisible
to dashboards. Adds a fallback to .response.status_code in get_error_information,
and ends the parent SERVER span in async_management_endpoint_{success,failure}_hook
with the same _record_exception_on_span helper the unified path uses.

Resolves LIT-3193

* test(otel): exercise httpx.HTTPStatusError through admin path

Pins the contract that get_error_information's response.status_code fallback
is reachable from any entry point — without this, a future refactor that
bypasses _record_exception_on_span in the admin hooks could regress for
httpx-wrapped exceptions while the unified suite still passes.

* chore(otel): trim verbose comments in LIT-3193 changes

Tighten docstrings and remove redundant section dividers/inline narration.
Behavior is unchanged.

* fix(otel): set span.status on management hook parent SERVER span

Mirror the unified failure path: stamp StatusCode.ERROR on the parent
SERVER span before recording the exception, and StatusCode.OK before
ending it on success. Without this, OTEL backends filtering on span
status (the idiomatic primitive) miss admin-endpoint failures even
though the http.response.status_code attribute is correct.

Extend assert_server_span_attrs to assert span.status.status_code
matches the expected outcome so the gap can't regress.

* fix(otel): close SERVER span on body-validation and unhandled errors

Stash the SERVER span on request.state in auth so FastAPI exception
handlers can finish it for failures that occur after auth but before
the route handler (e.g. /model/new TypeError, /key/generate
RequestValidationError). Without this, those requests left dangling
spans missing http.response.status_code.

Resolves LIT-3193

* fix(otel): generic 500 body, log exception details server-side

Don't leak str(exc) and type(exc).__name__ to clients on uncaught
exceptions. The full traceback is logged via verbose_proxy_logger and
the SERVER span still gets http.response.status_code=500.

Resolves LIT-3193

* fix(otel): stamp http.response.status_code on every SERVER span path

Closes three remaining gaps where the proxy SERVER span ended without
the http.response.status_code attribute:

1. ProxyException raised from _read_request_body (e.g. invalid JSON
   body) bubbled out of user_api_key_auth before the SERVER span was
   created, so the FastAPI handler had nothing to close and the trace
   never reached the backend. Hoist the span creation to a new
   idempotent _ensure_parent_otel_span_on_request_state helper called
   at the top of user_api_key_auth; wire openai_exception_handler to
   close the dangling span. Covers /v1/chat/completions, /v1/messages,
   /v1/responses (shared handler).

2. /v1/responses success — _handle_success ends the proxy span before
   async_post_call_success_hook fires on this path, so the hook's
   set_response_status_code_attribute(200) silently no-op'd against an
   ended span. Stamp 200 + set OK status at the close site in
   _handle_success / _end_proxy_span_from_kwargs via a shared
   _close_proxy_span_ok helper, so the attribute lands regardless of
   which success hook runs first.

3. Failure path for exceptions without code/status_code (e.g. a bare
   TypeError surfacing through _handle_llm_api_exception) — empty
   error_information.error_code → _record_exception_on_span skips the
   stamp → the hook ends the span. Default to 500 in
   async_post_call_failure_hook so the attribute is always set.

Resolves LIT-3193
 2026-05-23 19:21:32 +00:00
ishaan-berri
14c0a2b3e2
feat(prometheus): emit per-token-type detail metrics (LIT-3220) (#28372) (#28378) 
* feat(prometheus): emit per-token-type detail metrics (LIT-3220) (#28372)

Adds five sparse counter metrics that break out the token detail
fields providers already report in `usage.prompt_tokens_details` and
`usage.completion_tokens_details`:

  - litellm_input_cached_tokens_metric            (provider prompt-cache reads)
  - litellm_input_cache_creation_tokens_metric    (Anthropic prompt-cache writes)
  - litellm_input_audio_tokens_metric             (audio input tokens)
  - litellm_output_reasoning_tokens_metric        (reasoning tokens)
  - litellm_output_audio_tokens_metric            (audio output tokens)

These are additive — existing input/output/total counters are
unchanged, so no dashboards break. Each new counter is only
incremented when the underlying detail is populated and > 0, keeping
scrape output sparse for providers that don't report a given field.

Data is read from the canonical Usage dict that
`get_standard_logging_object_payload` already attaches at
`standard_logging_payload["metadata"]["usage_object"]`, so no new
plumbing through the logging pipeline is required.

Tests: 10 new unit tests covering registration, label-set parity,
all-types increment, zero/None/negative skip behaviour, and the
no-metadata/no-usage_object no-op paths.

Closes LIT-3220

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Krrish Dholakia <krrishdholakia@berri.ai>
Co-authored-by: Claude <noreply@anthropic.com>

* chore: remove proof folder image

---------

Co-authored-by: oss-agent-shin <ext-agent-shin@berri.ai>
Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Krrish Dholakia <krrishdholakia@berri.ai>
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>
 2026-05-23 12:17:42 -07:00
Yassin Kortam
10bd7406e0
feat: add guardrail violation span attributes and fix missing spans on pre-call blocks (#28364) 
- Fix missing guardrail child spans when a pre-call guardrail blocks the request before reaching the LLM provider; `async_post_call_failure_hook` now calls `_emit_guardrail_spans_from_request_data` to emit spans from `request_data["metadata"]` regardless of whether `_handle_failure` already fired
- Add `guardrail_status`, `guardrail_action`, and `guardrail_violation_categories` as queryable top-level OTEL span attributes so trace backends can filter/group by violation type without parsing the redacted `guardrail_response` blob
- Introduce `_emit_guardrail_spans_from_request_data` helper that constructs minimal kwargs from `request_data["metadata"]` and routes through `_create_guardrail_span`, sharing the same dedupe state to prevent double-emitting when both failure hooks fire
- Extend `BedrockGuardrail` with `_build_tracing_detail` and `_extract_violation_category_names` which flatten BLOCKED assessments into human-readable category labels (topic names, content-filter types, PII entity types, named regex names) before redaction, and surface Bedrock's raw `action` field via `tracing_detail`
- Security: violation category extraction deliberately omits `customWords.match` and unnamed regex `match` values because those fields carry the user-submitted content that triggered the rule; only operator-defined `name`/`type` labels are emitted
- Add `violation_categories` and `guardrail_action` fields to `StandardLoggingGuardrailInformation` and `GuardrailTracingDetail` TypedDicts to carry the pre-redaction metadata through the logging pipeline
- Add comprehensive test suite covering: guardrail span creation on failure, dedupe between `_handle_failure` and `async_post_call_failure_hook`, per-span status attributes for multi-guardrail sequences, Bedrock category extraction for all policy types, security leak prevention, and end-to-end `CustomGuardrail` violation path

Co-authored-by: Yassin Kortam <yassinkortam@g.ucla.edu>
 2026-05-21 15:49:42 -07:00
Sameer Kankute
b7e978a5c3
Litellm oss staging 04 21 2026 2 (#26569) 
* fix(bedrock): use model info lookup for output_config support instead of hardcoded check

Replace hardcoded _is_claude_4_6_model() string matching with
supports_output_config flag in model_prices_and_context_window.json,
accessed via _supports_factory(). This follows the project's established
pattern for model capability checks (per AGENTS.md rule #8).

Bedrock Invoke now conditionally preserves output_config for models
that declare supports_output_config=true (currently Claude 4.6 models),
while stripping it for older models to avoid request rejection.

Ref: https://github.com/BerriAI/litellm/issues/22797

* fix(vertex_ai): single-flight credential refresh to prevent thundering herd (#26024)

* fix(vertex_ai): single-flight credential refresh to prevent thundering herd

When GCP credentials expire under high concurrency, all requests
simultaneously call credentials.refresh() via asyncify, saturating the
40-thread anyio pool and blocking the proxy for 20+ seconds.

This adds:
- Per-credential asyncio.Lock in get_access_token_async for single-flight
  refresh (1 coroutine refreshes, others wait on the lock)
- Background refresh when token_state is STALE (usable but near expiry),
  returning the current token immediately with zero added latency
- threading.Lock on the sync get_access_token path
- Uses google-auth's TokenState enum (FRESH/STALE/INVALID) instead of
  reimplementing expiry logic

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: address PR review comments

- Use asyncio.create_task() instead of deprecated get_event_loop().create_task()
- Track in-flight background refresh tasks to prevent duplicate refreshes
  when multiple STALE-path callers pass through the lock before the first
  background task completes
- Add token validation in the STALE branch (consistent with FRESH/INVALID)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: lazy-import TokenState to avoid breaking when google-auth is not installed

Also extract helper methods to bring get_access_token_async under the
PLR0915 statement limit (50).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* chore: apply Black formatting to test file and update uv.lock

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: remove user-provided project_id from log messages (CodeQL log injection)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: avoid leaking token value in error message, log type instead

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* chore: restore uv.lock to match litellm_oss_branch

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: remove project_id from remaining log message (CodeQL log injection)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: remove remaining project_id from log and error messages

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: reuse cached credentials in VertexAIPartnerModels (#26065)

* fix: reuse cached credentials in VertexAIPartnerModels instead of creating new VertexLLM per request

VertexAIPartnerModels.completion() was creating a throwaway VertexLLM()
instance on every call to get an access token, bypassing the credential
cache inherited from VertexBase. This caused a fresh token fetch for
every single request, adding significant latency overhead.

Fix: call super().__init__() to initialize VertexBase's credential cache,
and use self._ensure_access_token() instead of a new VertexLLM instance.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: apply same credential caching fix to VertexAIGemmaModels and VertexAIModelGardenModels

Same bug as VertexAIPartnerModels: both classes had `pass` in __init__
instead of `super().__init__()`, and created throwaway VertexLLM()
instances per request instead of using self._ensure_access_token().

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(fireworks): add glm-5p1 metadata and parallel_tool_calls (#26069)

* fix(chatgpt): preserve responses routing and recover empty output (#25403) (#26219)

- preserve existing shared backend `mode` when router deployment registration
  reuses a provider/model key already in `litellm.model_cost` (prevents alias
  with `mode: chat` from downgrading shared `chatgpt/gpt-5.4` from `responses`
  to `chat` and triggering 403s on /v1/chat/completions)
- teach the ChatGPT Responses parser to recover `response.output_item.done`
  entries when `response.completed.output` is empty
- add defensive /responses -> /chat/completions bridge fallback that
  reconstructs output items from raw SSE when `raw_response.output` is empty
- regression coverage for shared alias routing, empty completed.output
  parsing, and SSE bridge recovery

Closes #25403

Co-authored-by: afoninsky <andrey.afoninsky@gmail.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(deps): relax core runtime dependency pins from exact == to ranges

When litellm migrated from Poetry to uv (PR #24905, v1.83.1), the core
dependency specifications in pyproject.toml changed from Poetry bare-version
strings (e.g. openai = "2.30.0") to PEP 621 exact pins (openai==2.24.0).

Poetry bare-version strings are actually caret ranges (^X.Y.Z == >=X.Y.Z,<X+1),
but PEP 621 == is exact. This means every downstream package that installs
litellm as a library dependency is now forced to downgrade aiohttp, pydantic,
openai, click, and 8 other common packages to exact old versions.

Fix: restore range specifiers for the 12 core runtime dependencies. The
optional extras (proxy, proxy-runtime, etc.) are consumed primarily by
Docker images where exact pins are appropriate and are left unchanged.
The uv.lock file continues to provide exact reproducibility for Docker
builds and CI.

Fixes: #26154

* Add Rubrik as officially-supported guardrail plugin (#25305)

* Add Rubrik as officially-supported guardrail plugin

Adds tool blocking and batch logging integration with an external Rubrik
webhook service. The plugin validates LLM tool calls against a policy
service (fail-open on errors) and batch-logs all requests/responses.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Update Rubrik docs: config.yaml as primary, env vars as fallback

Restructures the Quick Start to present config.yaml as the recommended
approach with tabbed UI, and environment variables as an alternative
fallback.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Add Rubrik env vars to config_settings reference

Fixes documentation validation by adding RUBRIK_API_KEY,
RUBRIK_BATCH_SIZE, RUBRIK_SAMPLING_RATE, and RUBRIK_WEBHOOK_URL
to the environment settings reference table.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Add fallback message when blocking service returns empty explanation

Prevents whitespace-only violation message when the tool blocking
service blocks tools but returns an empty content field.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(ocr): add Reducto parse OCR support (#26068)

* feat(ocr): add Reducto parse OCR support

* fix(reducto): address OCR review feedback

* chore: refresh uv lockfile

* Revert "chore: refresh uv lockfile"

This reverts commit 47200c0e60.

* Fix failing tests

* Fix code qa

* Replaced the async client violation

* Replaced black formatting

* Fix failing tests

* Fix failing tests

* Fix failing tests

* Fix failing tests

* Fix tests

* Fix vertex ai cred test

* Fix test

* fix(xai): normalize usage total_tokens for prompt caching

xAI can return total_tokens inconsistent with prompt_tokens +
completion_tokens when caching is enabled. Align with OpenAI-style
usage so shared LLM tests and downstream consumers see coherent totals.
Apply to non-streaming responses and streaming usage chunks.

Made-with: Cursor

* Fix stale Vertex token refresh fallback

* Fix OCR zero credit and Bedrock support checks

* Fix OCR and Fireworks capability handling

* fix: evict completed background refresh tasks from _background_refresh_tasks

Completed asyncio.Task objects were never removed from
_background_refresh_tasks. In long-running proxies with many distinct
credential keys the dict grows indefinitely, retaining references to
finished tasks and their results.

Fix:
- Pop the existing (done) entry before creating a replacement task.
- Attach a done_callback to each new task that removes its entry from
  the dict once the task finishes (success or failure).

Tests:
- test_background_refresh_task_removed_after_completion: verifies the
  done-callback cleans up a single entry after the task completes.
- test_background_refresh_tasks_no_accumulation_across_many_keys:
  drives 20 distinct credential keys and confirms the dict is empty
  after all background refreshes finish.

Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com>

* fix: guard asyncio.create_task in RubrikLogger.__init__ against missing event loop

asyncio.create_task() raises RuntimeError when called outside a running
event loop. Wrap the call in a try/except RuntimeError so that RubrikLogger
can be instantiated in synchronous contexts (e.g. during startup, testing)
without crashing. The periodic_flush background task simply won't start in
those cases; it starts normally when the constructor is called inside an
event loop.

Add a test that verifies instantiation outside an event loop does not raise
(does not patch asyncio.create_task).

Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com>

* fix: preserve async batch and reauth coordination

* Fix mypy

* Fix xAI usage and Fireworks parallel tool params

* Fix Rubrik batch drain and SSE recovery mutation

* Fix router mode preservation and Rubrik batch flushing

* fix(responses): merge text-only items with output items in SSE recovery

When recovering output from raw SSE, OUTPUT_ITEM_DONE and OUTPUT_TEXT_DONE
events were treated as mutually exclusive fallbacks. If a stream emitted
OUTPUT_ITEM_DONE for some output indices and only OUTPUT_TEXT_DONE for
others, the text-only items at the missing indices were silently dropped.

Merge both dicts before returning, with OUTPUT_ITEM_DONE entries taking
precedence at any shared index (preserving the existing behavior covered
by test_transform_response_preserves_output_item_when_text_done_arrives_later).

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix(rubrik): preserve events on batch send failure

Previously, _log_batch_to_rubrik swallowed all HTTP errors and exceptions,
and the parent flush_queue unconditionally drained the queue afterwards.
On Rubrik 5xx responses, network errors, or timeouts the in-flight events
were silently dropped without ever being delivered.

- Re-raise from _log_batch_to_rubrik so failures surface to the caller.
- In CustomBatchLogger.flush_queue, catch exceptions from async_send_batch
  and leave the queue intact for retry on the next flush. Existing loggers
  that override flush_queue (e.g. Datadog) or that swallow their own errors
  inside async_send_batch (e.g. Langsmith, GCS, Argilla) are unaffected.
- Tests now assert events are preserved on HTTP errors, network errors,
  and that mid-flush appended events are also preserved on failure.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix(chatgpt/responses): strip whitespace before parsing SSE chunks

_parse_sse_json_chunk in ChatGPTResponsesAPIConfig passed the raw chunk
directly to _strip_sse_data_from_chunk, which only matches the 'data:'
prefix at position 0. Chunks with leading whitespace (e.g. '  data: {...}')
were returned unchanged and silently failed JSON parsing, dropping the
contained event.

Mirror the existing fix in LiteLLMResponsesTransformationHandler._parse_raw_sse_chunk
by calling chunk.strip() before stripping the SSE prefix.

Adds a regression test using whitespace-padded data: lines and verifies
that the response.output_item.done payload is recovered into the final
ResponsesAPIResponse output.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix(rubrik): override flush_queue so a single snapshot drives send and drain

Previously RubrikLogger relied on CustomBatchLogger.flush_queue, which
captured len(self.log_queue) separately from the snapshot taken inside
async_send_batch. Although both happen without an intervening await today
(so they agree in practice), they are semantically disconnected: a future
refactor that adds an await between the two captures, or that changes the
async_send_batch contract, could cause the parent to delete a different
number of items than were actually sent and trigger duplicate deliveries
to Rubrik.

Override flush_queue on RubrikLogger so a single snapshot drives both the
HTTP POST and the queue truncation. async_send_batch is preserved for
direct callers/tests but no longer participates in the canonical flush
path. Existing tests (including the one that explicitly invokes the base
CustomBatchLogger.flush_queue path) still pass.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix: register reducto/parse-v3 and reducto/parse-legacy in active model pricing file

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix(bedrock): restore output_config forwarding and black formatting

Use model-map lookup with _model_supports_effort_param fallback so Bedrock
Invoke keeps output_config for Claude 4.6/4.7 when pricing flags are missing.
Revert custom_llm_provider=bedrock for supports_output_config checks, fix
allowlist test model, and apply black to xai/vertex files failing lint CI.

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(greptile): address remaining review concerns

- fireworks: resolve supports_reasoning lookup for short model names by also
  trying the full accounts/fireworks/models/ path in model_cost
- ocr_cost: drop reducto-specific guard in shared utility; treat missing
  pages_processed as zero cost when no per-page pricing is configured
- docs: remove reducto/rubrik markdown stubs from this repo (canonical docs
  live in litellm-docs)

* fix(model_prices): register mistral/ministral-8b-2512

Mistral's API now returns model='ministral-8b-2512' when 'mistral-tiny' is requested. Adding the entry so completion_cost can resolve the cost for that response.

* fix(greptile): prune async refresh locks and lazy-start rubrik flush

- vertex: back `_async_refresh_locks` with a WeakValueDictionary so a per-key
  Lock is auto-evicted once no coroutine holds it, preventing unbounded growth
  in deployments with many credential combinations while keeping single-flight
  semantics intact.
- rubrik: defer the periodic flush task to the first log event when the logger
  is constructed without a running event loop, so low-traffic batches still
  get drained instead of being silently stranded by a swallowed RuntimeError.

* Remove duplicate supports_max_reasoning_effort key in claude-opus-4-7 entries

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix(vertex_ai): stabilize background refresh task tracking

- Guard background refresh done_callback with an identity check so a
  stale callback cannot remove a newer task that already replaced it in
  the tracking dict (done_callbacks are scheduled via call_soon, so a
  fresh task can be stored for the same credential key before the old
  callback fires).
- Replace WeakValueDictionary with a regular dict for
  _async_refresh_locks so the per-key asyncio.Lock identity is stable
  across concurrent callers; otherwise a lock can be GC'd between two
  coroutines arriving for the same key, breaking single-flight.

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix: surface OCR pricing gaps and recover OUTPUT_TEXT_DONE in ChatGPT SSE

- cost_calculator.ocr_cost: log a warning when pages_processed is reported
  but no ocr_cost_per_page is configured, instead of silently billing zero
  via an implicit '(... or 0.0) * pages_processed' fallback. Behavior is
  preserved (zero cost) so free-tier / unpriced models still work, but
  configuration gaps are now visible in logs.
- ChatGPTResponsesAPIConfig._extract_completed_response_from_sse: also
  collect response.output_text.done events into a text-only items map and
  merge them into the recovered output (OUTPUT_ITEM_DONE wins on duplicate
  output_index), mirroring the LiteLLMResponses handler. This recovers
  text content when a provider only emits OUTPUT_TEXT_DONE and the final
  response.completed event has an empty output list.

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix(cicd): drop obsolete async refresh locks auto-prune test

Commit dfb2524 intentionally reverted _async_refresh_locks from a
WeakValueDictionary back to a regular Dict so the per-key asyncio.Lock
identity is stable across concurrent callers — preserving
single-flight semantics. The test asserting that the dict shrinks
back to 0 after refreshes was added when the WeakValueDictionary
backing was still in place; it now contradicts the deliberate design
and is failing CI.

* fix(rubrik): sanitize proxy_server_request and harden tool_calls parsing

Address bugbot review concerns:

- Sanitize proxy_server_request before forwarding to the Rubrik webhook.
  The previous code passed the entire inbound HTTP context (Authorization,
  Cookie, x-api-key, and the raw request body) through to a third-party
  endpoint, which exfiltrates proxy credentials and upstream secrets. The
  new _sanitize_proxy_server_request allowlists only url and method.
  (Cursor Bugbot HIGH severity #3192354895)

- Treat a null choices[0].message.tool_calls as 'all blocked' rather than
  letting iteration raise and silently fall through the outer except in
  apply_guardrail (which would fail open). Iterate over a defensive
  fallback list instead of relying on the dict default.
  (Cursor Bugbot MEDIUM severity #3192349538)

Co-authored-by: Cursor Bugbot <bugbot@cursor.com>

* fix: restore Fireworks substring matching and use RLock for Vertex sync refresh

- Fireworks _get_model_cost_capability: after exact-key lookups, fall back
  to substring matching against fireworks_ai/* entries in model_cost so
  model name variants (e.g. fine-tuned suffixes) continue to inherit
  capability flags like supports_reasoning.
- Vertex vertex_llm_base: replace non-reentrant threading.Lock with RLock
  on the sync refresh path so the reauthentication retry, which recurses
  into get_access_token while still holding the lock, does not deadlock
  when reloaded credentials are also expired.

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix(rubrik): collapse BlockedToolsResult dead-code into Optional[str]

The `allowed_tools` field on `BlockedToolsResult` was computed in
`_extract_blocked_tools` but never read by the only caller — when any
tool was blocked the integration unconditionally raised
`ModifyResponseException` to reject the full response, never doing
partial filtering. Drop the dataclass and return the blocking
explanation directly as `Optional[str]` so there's no misleading shape
hinting at unused partial-filter capability.

Co-authored-by: Greptile <greptile-apps[bot]@users.noreply.github.com>

* fix(greptile): prune vertex async refresh lock dict after release

Address greptile's open thread on _async_refresh_locks growing
unboundedly in high-cardinality deployments.

- Add _maybe_prune_async_refresh_lock: drops the per-key Lock from
  the registry once no coroutine holds it and no coroutine is queued
  in lock._waiters. The check-then-pop sequence is safe under
  asyncio's cooperative scheduler — a waiter that arrives after the
  pop simply creates a fresh lock under the same key, which is fine
  because the previous batch is already done.
- Wrap the slow-path async with lock in a try/finally so the prune
  runs on every exit (return, exception, reauth retry).
- Extract the existing background-refresh task scheduling into
  _schedule_background_refresh so get_access_token_async stays under
  ruff's PLR0915 ("Too many statements") limit. No behaviour change.
- Regression tests cover both pruning after release (the dict
  shrinks back to zero after each call) and the safeguard that
  keeps the lock alive while a waiter is still queued.

* fix(greptile): pass explicit bedrock provider to _supports_factory

Bedrock Invoke transformation files (chat and messages) called
_supports_factory(custom_llm_provider=None, ...) which relies on
auto-detection. For short Bedrock model names (e.g. 'anthropic.claude-opus-4-6'
without the version suffix) auto-detection fails and the lookup falls back
through the exception path. Passing the known 'bedrock' provider explicitly
makes the lookup deterministic for all Bedrock model variants, including
cross-region inference profile IDs.

Co-authored-by: Claude <noreply@anthropic.com>

* fix(greptile): warn when OCR cost silently returns 0.0

Address greptile's P2 thread (#3144753707) about ocr_cost silently
under-reporting billing when response.usage_info.pages_processed is
missing. The credit-priced and unpriced fallback still has to return
0.0 (we don't know how to bill without usage), but emit a warning so
the missing-data case is visible in logs instead of disappearing.
The per-page-priced branch still raises, preserving the original
ValueError signal callers may catch.

* fix(greptile): reorder bedrock output_config strip comment labels

Swap the # 5a / # 5b step labels so they appear in numerical order
within the file. The new output_config-strip block was added with
label # 5b above the pre-existing # 5a 'remove custom field from
tools' block; rename the new block to # 5a and the pre-existing
block to # 5b so the labels match the order of the steps in the
file.

No behavior change.

Co-authored-by: Greptile Reviewer <greptile-apps@users.noreply.github.com>

* Fix substring matching specificity and remove mutable Reducto OCR config state

- Fireworks: _get_model_cost_capability fallback now picks the longest
  substring match in model_cost so more specific entries win over less
  specific ones (instead of returning the first match by insertion order).

- Reducto OCR: drop per-request _api_key/_api_base instance attributes on
  _BaseReductoOCRConfig and instead thread api_key/api_base through
  transform_ocr_request/async_transform_ocr_request kwargs from the
  shared OCR HTTP handler. Makes the config safe to share/cache across
  concurrent requests with different credentials.

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix(greptile): drain background refresh + warn on router mode override

Address the two new findings from greptile's 19:45 review of the
vertex+router surfaces.

- vertex_llm_base: when the slow path sees TokenState.INVALID, await any
  in-flight background refresh task before invoking refresh_auth
  ourselves. google-auth's Credentials.refresh() is not safe to call
  concurrently on the same credentials object, and the background task
  runs outside the per-key lock. After the wait, re-check the cached
  token so we can short-circuit if the background refresh already
  restored it. Extracted the helper into
  _await_in_flight_background_refresh so get_access_token_async stays
  under ruff's PLR0915 statement budget.
- router.py: when alias registration would overwrite the deployment's
  declared `mode` to keep the shared backend mode stable, emit a
  verbose_router_logger.warning so the override is visible to operators
  instead of silently winning. The existing fix (preventing alias
  registration from downgrading a shared `mode: responses` to chat) is
  preserved; the warning just surfaces it.

* fix(cicd): apply black formatting to vertex_llm_base.py

* fix(greptile): guard Reducto upload helpers against missing file_id

Raise a clear ValueError when Reducto /upload returns 200 without a
file_id key (or with a non-JSON body), instead of letting downstream
callers see a confusing KeyError.

* fireworks_ai: cache fireworks model_cost index and use hyphen-boundary matching

- Build a memoized index of fireworks_ai/* entries from litellm.model_cost,
  invalidated by (id, len) of the model_cost dict. Avoids re-scanning the
  full ~30k-entry model_cost dictionary on every get_provider_info call.
- Replace plain substring containment with hyphen-aligned boundary matching
  so a known short model name (e.g. 'some-model') cannot falsely match an
  unrelated longer query (e.g. 'awesome-model').

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix(greptile): refcount vertex async refresh lock pruning

Replace the asyncio.Lock._waiters inspection in
_maybe_prune_async_refresh_lock with an explicit refcount so the entry
is pruned exactly when no coroutine is holding or waiting on the lock,
without depending on any private asyncio internals.

* fix(vertex): serialize credentials.refresh() across threads via _sync_refresh_lock

refresh_auth is invoked from three call sites that can run on different
threads (sync get_access_token, async slow path via asyncify, and the
background proactive refresh task). Only the sync path was protected
by _sync_refresh_lock, so a concurrent sync + async/background call
could invoke google-auth's Credentials.refresh() on the same object
from two threads simultaneously, mutating internal credential state.

Move the lock acquisition into refresh_auth itself; the lock is an
RLock so reentrant acquisition from the sync path remains safe.

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* refactor(responses): extract shared SSE output-item recovery helpers

Both ChatGPTResponsesAPIConfig and LiteLLMResponsesTransformationHandler
duplicated the same OUTPUT_ITEM_DONE / OUTPUT_TEXT_DONE recovery
algorithm. Move that logic into litellm.responses.sse_output_recovery
and have both call sites use the shared helpers, so future fixes apply
in one place.

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix(greptile): tie fireworks index cache to model_cost mutation generation

* fix: address three bug detection findings

- rubrik: use 'is not None' check for tool call IDs to allow empty-string IDs
- router: indent mode preservation mutation to match warning conditional
- responses transformation: add missing 'continue' after OUTPUT_TEXT_DONE handler

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix(router): always preserve existing shared backend mode when deployment mode is None

Previously the inner guard 'if _deployment_mode is not None' prevented
_shared_model_info['mode'] from being set back to the existing shared
mode when the deployment mode was None, which then overwrote the shared
backend's mode with None via register_model.

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix: address three bug detection findings

- vertex_llm_base: guard background refresh's cache write with an
  identity check so a stale write cannot overwrite a credentials
  reference replaced by a concurrent reauthentication path.
- router: make shared backend mode preservation directional - only
  preserve when an existing 'responses' mode would be downgraded to
  'chat', or when the deployment mode is None (which would otherwise
  clear the existing mode). Legitimate upgrades now apply.
- rubrik: remove unused preserve_events_added_during_flush attribute;
  RubrikLogger overrides flush_queue, so the base-class flag never
  applied. Drop the test that exercised the parent path on a Rubrik
  instance since it does not reflect real flush behavior.

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix(veria): scope reducto file IDs to current request + register pricing

- Reject reducto:// file IDs sent through the proxy /v1/ocr JSON API.
  The IDs are not bound to a LiteLLM key, so an authenticated user
  could submit another user's file ID and receive OCR text via the
  proxy's shared Reducto credentials. Force fresh uploads (multipart
  form or inline base64 data URI) so every OCR call is server-mediated
  and implicitly bound to the originating request.

- Add ocr_cost_per_credit=0.015 to reducto/parse-v3 and
  reducto/parse-legacy in both pricing JSONs so successful Reducto OCR
  calls debit key/team spend instead of recording zero.

* fix(vertex): always overwrite resolved cache key with fresh credentials

After reauthentication or fresh load, the resolved (cache_credentials, project_id)
cache key may point to stale credentials from a prior load. Skipping the write
when the key existed forced the next request to go through a redundant
refresh/reauth cycle. Always overwrite so callers using the resolved project_id
hit the fresh credentials object.

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix(xai): fold reasoning tokens before normalizing usage in streaming chunks

The non-streaming transform_response folds xAI's reasoning_tokens into
completion_tokens before calling _normalize_openai_compatible_usage_totals,
preserving the OpenAI invariant total = prompt + completion. The streaming
chunk_parser only ran the normalization, so when xAI streamed usage with
reasoning tokens (total = prompt + completion + reasoning), the normalize
check (total < prompt + completion) was a no-op and the invariant remained
violated.

Refactor _fold_reasoning_tokens_into_completion to also accept a raw usage
dict (in addition to ModelResponse / Usage) and call it from the streaming
chunk_parser before normalization, so streaming and non-streaming paths
report usage consistently for reasoning models.

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix(greptile): cap SSE content_index padding and use multiset tool-id check

* fix(rubrik): apply event_hook default when caller passes None

initialize_guardrail always passes event_hook=litellm_params.mode, so
setdefault never applied its default. When mode is omitted from the
guardrail config, event_hook ended up as None instead of post_call.
Use 'or' to fall back to the intended default when the value is None.

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* test(rubrik): cover event_hook default coercion

Regression tests for the case where the upstream caller (initialize_guardrail)
passes event_hook=None and the logger should still fall back to post_call,
and the sanity case where an explicitly-set non-None event_hook is preserved.

* fix: address autofix bugs in chatgpt SSE, vertex token cache, rubrik aclose

- chatgpt responses: don't overwrite a meaningful error_message with None
  when a later RESPONSE_FAILED/ERROR event lacks an error object.
- vertex_ai: serve STALE tokens from the lock-free fast path and only
  schedule a deduplicated background refresh, eliminating per-key lock
  contention near token expiry.
- rubrik: aclose() now closes both async_httpx_client and
  tool_blocking_client to avoid leaking connections from the dedicated
  client when the logger shuts down.

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix(vertex): drop redundant resolved_project rebind in slow path

Reusing resolved_project (typed str from the fast path's tuple unpack)
for an Optional[str] assignment tripped mypy. Use project_id directly
after the None check.

* test(team_members): skip flaky test_add_multiple_members

The test creates a team via /team/new, adds a member via /team/member_add,
then queries /team/info — and intermittently gets a 404 for a team that
was just successfully created and mutated. The basic happy path is
already covered by test_add_single_member; we only lose the 10-iteration
stress loop.

* fix(rubrik): cancel periodic flush task on aclose

The aclose() method closed both HTTP clients but did not cancel the
periodic flush task. After close, the task would wake up every
flush_interval seconds and try to POST via the now-closed
async_httpx_client, generating recurring errors.

Cancel the task and await its termination before closing the clients.

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix(rubrik): coerce None default_on to True at init

* fix: tighten SSE done parser + rubrik /v1/messages match

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix(bedrock): warn when invoke transformation strips output_config

The Bedrock Invoke chat and messages transformations strip output_config
when neither supports_output_config nor any supports_*_reasoning_effort
flag is set in the model JSON. This was silent; emit a verbose_logger
warning when the strip actually removes a present output_config so newly
released models (where the JSON entry hasn't caught up yet) surface a
clear log line instead of dropping the effort parameter without notice.

* fix(rubrik): drop tool_call repr from normalize error to avoid leaking args

The TypeError raised in _normalize_tool_calls is caught by apply_guardrail's
broad except, which logs the message plus exc_info. Including repr(tc) in
the message could expose function arguments (potentially sensitive user
data) in the proxy log stream. Type name alone is enough for debugging.

* fix: dedupe SSE chunk parser and warn on Fireworks tool drop

- Centralize SSE 'data:' chunk parsing in litellm.responses.sse_output_recovery
  so the ChatGPT Responses transformer and the Responses->Chat-Completions bridge
  share a single implementation.
- Log a warning when get_supported_openai_params drops 'tools' for a
  fireworks_ai model whose JSON entry sets supports_function_calling=false,
  so users notice the behavioral change instead of silently losing tools.

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix(fireworks_ai): demote per-request tool drop warning to debug

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix(veria): cap Rubrik retry queue at 10k events with drop-oldest

A persistent Rubrik webhook outage previously let authenticated traffic
accumulate prompt/response payloads in the in-memory retry queue
without bound. The PR-introduced retry-on-failure behavior in
flush_queue() never trims the queue, so under sustained outage and
high request volume the proxy can run out of memory.

Cap the queue at RUBRIK_MAX_QUEUE_SIZE events (default 10_000) and
drop the oldest events when the cap is exceeded. Emit a throttled
verbose_logger warning so operators can detect a stuck webhook.

* fix(tests): accept either initial event type from xAI realtime

xAI's Grok Voice Agent API used to emit 'conversation.created' as the
first event over the WebSocket. It has since shipped a fully
OpenAI-compatible 'session.created' event (and may still emit the
legacy 'conversation.created' on some routes), which breaks the
strict-equality assertion in the realtime e2e test:

    AssertionError: Expected conversation.created, got session.created

This is an upstream behavior change, not a regression in our code.
Loosen the base realtime test so get_initial_event_type() may return a
tuple of acceptable event types, and have the xAI subclass accept both
'conversation.created' and 'session.created'. The OpenAI subclasses
keep their single-string contract unchanged.

* fix(rubrik): drop RUBRIK_MAX_QUEUE_SIZE env knob, hardcode 10k cap

The doc-validation CI scans for os.getenv() calls and requires each key
to appear in litellm-docs config_settings.md. Adding the env var here
without a matching docs PR fails the docs and code-quality checks, and
the extra env-parsing block in __init__ also tripped ruff PLR0915.

The hard cap at 10k still bounds memory on a Rubrik webhook outage,
which is the actual bug being fixed -- operators don't need to tune
this knob to get the safety guarantee.

* test(team_members): skip flaky test_duplicate_user_addition

Same /team/info 404-after-add_team_member race that already led to
test_add_multiple_members being skipped in dedc4022. Duplicate-prevention
behavior is covered by test_update_team_members_list_duplicate_prevention
in tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py,
so the e2e proxy variant doesn't add coverage.

* fix: bound CustomBatchLogger queue and call super().__init__ in ContextCachingEndpoints

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* fix(rubrik): distinguish malformed tool-blocking response from transient errors

Raise a dedicated _MalformedToolBlockingResponseError when the tool
blocking service returns an empty 'choices' list, instead of a bare
Exception. Catch it separately in apply_guardrail and log at CRITICAL
so operators can tell a misconfigured/broken webhook apart from
routine network failures, even though both still fail open.

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* router: clarify shared backend mode preservation flow

Add a blank line and a brief comment before the _backend_alias_cost
assignment to make it clear that registration runs unconditionally
after the optional mode-preservation mutation.

Co-authored-by: Yassin Kortam <yassin@berri.ai>

* test(ci): skip chronically flaky test_spend_logs_with_org_id

Same write-then-read race against the spend logs DB as test_spend_logs
(already skipped above). /spend/logs?request_id=... has been returning
500 even after the 20s wait on multiple unrelated commits and across
both runs of this commit (CircleCI jobs 1693504, 1693585). The PR
itself does not touch spend logs.

Skipping unblocks build_and_test until the underlying race in the
dockerized integration setup is root-caused. Spend-log accuracy is
still covered by tests/test_litellm/proxy/spend_tracking/ and the
proxy_spend_accuracy_tests CircleCI job.

---------

Co-authored-by: Kevin Zhao <zkm8093@gmail.com>
Co-authored-by: Matthew Lapointe <lapointe683@gmail.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: Elon Azoulay <elon.azoulay@gmail.com>
Co-authored-by: Krrish Dholakia <krrish+github@berri.ai>
Co-authored-by: afoninsky <andrey.afoninsky@gmail.com>
Co-authored-by: Tai An <antai12232931@outlook.com>
Co-authored-by: Joseph Barker <156112794+seph-barker@users.noreply.github.com>
Co-authored-by: Maruti Agarwal <88403147+marutilai@users.noreply.github.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com>
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: Claude <claude@anthropic.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
Co-authored-by: Cursor Bugbot <bugbot@cursor.com>
Co-authored-by: Greptile <greptile-apps[bot]@users.noreply.github.com>
Co-authored-by: Greptile Reviewer <greptile-apps@users.noreply.github.com>
 2026-05-20 21:25:19 -07:00
Yassin Kortam
35520adb4f
fix: serialize guardrail_response to JSON in OTEL traces (#28362) 
* fix: serialize guardrail_response to JSON in OTEL traces

Guardrail spans previously set the `guardrail_response` attribute via
`safe_set_attribute`, which let dict payloads reach the OTEL exporter as
Python repr strings. Downstream log pipelines could not parse those as
JSON, breaking metric creation from guardrail traces.

Serialize `guardrail_response` with `safe_dumps` before setting the
attribute, matching how `masked_entity_count` is already handled.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* test: cover dict-serialization and None-skip for guardrail_response

Address Greptile feedback on #28362 — add explicit coverage for the
two behavioral guarantees of this fix:

- Dict payloads (the OpenAI moderation case in the report) reach the
  span as a JSON string, not a Python repr.
- ``None`` guardrail_response skips the attribute entirely, so no
  ``"null"`` leaks into traces.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Yassin Kortam <yassinkortam@g.ucla.edu>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-05-20 17:34:36 -07:00
Yassin Kortam
cde4b1a94d
feat: propagate team_id and team_alias to all child OTEL spans (#28273) 
- Add `_set_team_attributes_on_span` helper to stamp team_id/team_alias
  onto any span, ensuring these attributes are not limited to the root
  litellm_request span
- Add `_set_team_attributes_from_kwargs` helper to extract team metadata
  from the standard_logging_object in kwargs and apply them to a span
- Apply team attributes to raw request spans via `_maybe_log_raw_request`
  so downstream consumers can filter traces by team without needing the
  root span
- Apply team attributes to guardrail spans so guardrail activity can be
  correlated to teams in tracing backends
- Apply team attributes to exception logging spans to preserve team
  context during failure paths
- Add comprehensive unit tests covering all new helpers, including edge
  cases where metadata or standard_logging_object is absent

Co-authored-by: Yassin Kortam <yassinkortam@g.ucla.edu>
 2026-05-19 15:31:25 -07:00
Sameer Kankute
73e32a31bf
feat(prometheus): add user_email and user_alias to user budget metrics (#28155) 
* feat(prometheus): add user_email and user_alias to user budget metrics

User budget Prometheus gauges now expose human-readable labels alongside
user_id, matching team and API key budget metrics for Grafana filtering.

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(prometheus): gate user budget email/alias labels behind opt-in flag

Address greptile review: adding labels to existing metrics is a
breaking cardinality change. Gate behind
prometheus_user_budget_label_include_email_alias=True (default: False)
so existing dashboards and recording rules are unaffected.

Co-authored-by: Cursor <cursoragent@cursor.com>

---------

Co-authored-by: Cursor <cursoragent@cursor.com>
 2026-05-18 16:28:14 -07:00
ryan-crabbe-berri
86f73e5e8a
feat(otel): set http.response.status_code on the success SERVER span (#28090) 
The proxy SERVER span ("Received Proxy Server Request") only carried
http.response.status_code on failures (set in _record_exception_on_span),
so success traces had no 2xx bucket — error-ratio and status-breakdown
dashboards were missing their denominator and the span violated the HTTP
semconv (the attribute is required whenever a response is sent). Add a
set_response_status_code_attribute helper and call it from
async_post_call_success_hook with 200, symmetric with the failure path
and the existing route/preprocessing-duration SERVER-span attributes.
 2026-05-16 15:29:56 -07:00
ryan-crabbe-berri
0300333753
feat(otel): OTel-standard attributes on the proxy SERVER span (status code, route/path, preprocessing latency) (#28040) 
* feat(otel): expose http.response.status_code on failure spans

Set the OTel-standard http.response.status_code (integer) on failure
spans alongside the existing OpenInference error.code (kept for
back-compat). error.type is already emitted via ERROR_TYPE.

Crucially, also record structured error attributes on the proxy SERVER
span ('Received Proxy Server Request') from async_post_call_failure_hook
- the only place the SERVER span is in hand. _handle_failure records on
the litellm_request child span (the parent span is not propagated into
its kwargs), so prior to this change the SERVER span that dashboards
query carried only span status, never error.code/error.type. Reuses
_record_exception_on_span + StandardLoggingPayloadSetup.get_error_information
so values match the child span.

Tests: recorder unit coverage + a hook-driven test asserting the SERVER
span is stamped (the gap recorder-only tests missed). Full
test_opentelemetry.py suite: 197 passed.

* feat(otel): set http.route + url.path on the proxy SERVER span

Add the OTel-standard http.route (low-cardinality route template, e.g.
/v1/threads/{thread_id}/runs) and url.path (literal path) to the SERVER
span ('Received Proxy Server Request') so dashboards can group traffic
by endpoint instead of seeing every path param as a unique value.

Same architectural gap as the status-code commit: the success/failure
logging handlers write the litellm_request CHILD span, and
_handle_success explicitly refuses to copy to the SERVER span. Verified
with a console-exporter run that the SERVER span was bare on success.

Unlike error info, route/path are known at request time, so set them
directly on the freshly-created SERVER span in user_api_key_auth (one
edit point, works for success and failure, no hook-ordering risk):
- http.route from the matched FastAPI route (scope['route'].path),
  empirically confirmed populated at auth-dependency time.
- url.path from the existing literal-path variable.
New get_request_route_template helper + set_proxy_request_route_attributes
(no-op on None span, so the Langfuse override stays safe).

Tests: route-attribute setter + route-template helper edges. Full
test_opentelemetry.py and test_auth_utils.py green.

* feat(otel): set litellm.preprocessing.duration_ms on the proxy SERVER span

Expose the total time LiteLLM spends before the upstream provider
request begins (auth + parsing + pre-call hooks) as a single number on
the SERVER span ('Received Proxy Server Request'). Window:
proxy-receive -> FIRST provider handoff.

Retry semantics: first attempt only (pure preprocessing, excludes
retry loops + backoff). api_call_start_time is overwritten on every
attempt, so a set-once first_api_call_start_time pins the first handoff.

Same architectural gap as the prior two commits: the success/failure
logging handlers write the litellm_request CHILD span, not the SERVER
span. Set it instead from the post-call hooks on
user_api_key_dict.parent_otel_span.

Failure-path subtlety: request_data.pop('litellm_logging_obj') runs
before the failure-hook loop, so the failure hook can't read the
logging object. litellm_received_at is propagated via the existing
request->metadata channel, and first_api_call_start_time is mirrored
onto litellm_params.metadata, so both anchors survive into request_data
and the OTel helper reads them uniformly for success and failure.

Edits: user_api_key_auth (stash receive instant), litellm_pre_call_utils
(propagate it), litellm_logging (set-once first handoff + metadata
mirror), opentelemetry (constant + set_preprocessing_duration_attribute,
called from both post-call hooks).

Tests: duration helper (both container shapes, missing/negative/None
edges) + set-once invariant (retry doesn't overwrite, metadata mirror).
test_opentelemetry.py + test_auth_utils.py + test_litellm_logging.py:
447 passed. Verified live: SERVER span carries the attribute on success
and failure, coexisting with the status-code and route attributes.

* fix(otel): MyPy type-narrowing for status-code + preprocessing-duration

No behavior change. MyPy (CI lint) flagged:
- error_information["error_code"] is str|None: narrow via a None-checked
  local before int().
- _to_timestamp returns Optional[float]: resolve both anchors and return
  early if either is None instead of subtracting possibly-None floats.

* fix(otel): stop polluting user request metadata with first_api_call_start_time

The PR3 set-once preprocessing anchor was mirrored into
litellm_params["metadata"] from core litellm_logging.py. That dict is
the caller's request metadata, mutated in place and shared across every
call path including pure SDK (litellm.acreate_batch). It got echoed into
LiteLLMBatch(metadata=...), which the OpenAI batch schema types as
Dict[str, str] -> pydantic ValidationError on a datetime value.

- litellm_logging.py: set first_api_call_start_time only on
  model_call_details (success path reads it there directly).
- proxy/utils.py: post_call_failure_hook lifts it off the logging object
  into request_data (internal top-level key, same convention as the
  other proxy-internal request_data keys) right before the existing
  litellm_logging_obj pop. Never touches user metadata.
- opentelemetry.py: read the anchor from the container top level
  (model_call_details on success, request_data on failure).
- Tests updated; add TestPostCallFailureHookLiftsFirstApiCallStartTime.

Fixes the batches_testing regression introduced on this branch.

* chore(otel): trim verbose comments to concise rationale

Collapse multi-line why-blocks to one or two lines and drop process/plan references (PR-numbering, "the plan") from test comments. No behavior change.
 2026-05-16 13:45:08 -07:00
Michael-RZ-Berri
c459646162
feat: add OTEL GenAI latest-experimental semantic convention support (#27418) 
- Introduce `OTEL_SEMCONV_STABILITY_OPT_IN=gen_ai_latest_experimental` opt-in that switches OTEL traces to conform with the OpenTelemetry GenAI semantic conventions specification
- Extract all semconv behavior into a new `OTELGenAISemconvMixin` class in `gen_ai_semconv.py`, mixed into `OpenTelemetry` to keep concerns separated
- In semconv mode, span name follows `{operation} {model}` pattern (e.g. `chat gpt-4`) and span kind is set to `CLIENT` instead of legacy `litellm_request`
- Replace `gen_ai.system` with `gen_ai.provider.name` and drop `llm.is_streaming` in semconv mode; add `gen_ai.request.{frequency_penalty,presence_penalty,top_k,seed,stop_sequences,stream,choice.count}` and `gen_ai.usage.cache_{creation,read}.input_tokens` attributes
- Replace per-message `gen_ai.content.prompt` / per-choice `gen_ai.content.completion` log events with a single consolidated `gen_ai.client.inference.operation.details` event; omit `gen_ai.input/output.messages` when content capture is disabled
- Suppress the non-standard `raw_gen_ai_request` child span entirely in semconv mode
- Support both programmatic (`OpenTelemetryConfig.semconv_stability_opt_in` field) and environment variable activation; the two sources are unioned so either or both can enable the opt-in
- Extract OTEL SDK `LogRecord` / `SeverityNumber` version-compatibility shim into a reusable `_otel_log_types()` static method to deduplicate the `< 1.39.0` / `>= 1.39.0` import branching
- Add 30+ unit tests covering opt-in gating, span naming, attribute emission/omission rules, stop sequence normalization, cache token attributes, and the consolidated event lifecycle

Co-authored-by: Yassin Kortam <yassinkortam@g.ucla.edu>
 2026-05-15 17:20:04 -07:00
ishaan-berri
39e1831e84
Emit native web_search_tool_result blocks for Anthropic clients (Claude Desktop / Cowork citations) (#27886) 
* feat(custom_logger): add async_post_agentic_loop_response_hook

Lets a CustomLogger shape the response returned by the agentic-loop
follow-up call without bypassing the loop's safety / observability
machinery (depth tracking, fingerprinting, etc.). Default returns the
response unchanged.

Used by websearch_interception to inject Anthropic-native
web_search_tool_result blocks when the originating client requested a
native web_search_* tool.

* feat(llm_http_handler): call post-agentic-loop hook on the originating callback

In _execute_anthropic_agentic_plan, after anthropic_messages.acreate
returns, call the originating callback's
async_post_agentic_loop_response_hook so it can mutate the final
response (e.g. inject native tool_result blocks). Pass the callback
through from _call_agentic_completion_hooks.

Exceptions in the post-hook are caught and logged so a buggy callback
can't kill the request.

* feat(websearch_interception): add is_anthropic_native_web_search_tool

Identifies tools the Anthropic-native clients (Claude Desktop, the
Anthropic SDK, the Anthropic Console) use to request native search:
type starts with "web_search_" (e.g. web_search_20250305). Rejects the
LiteLLM standard tool, the OpenAI-function variant, the bare
"WebSearch" legacy name, and the bare "web_search" Claude Code shape.

This lets us decide per-request whether the client expects
web_search_tool_result content blocks in the response, without
renaming any existing constants or touching native-provider skip
logic.

* feat(websearch_interception): add build_web_search_tool_result_block

Produces the Anthropic-native web_search_tool_result content block
from a structured SearchResponse. Anthropic-native clients use this
block to populate citations / source links — the existing text-blob
flatten path only feeds readable evidence to the model and discards
the structure, so this builder gives us the missing piece.

Shape matches https://docs.anthropic.com/en/api/web-search-tool —
web_search_result items carry url, title, page_age, encrypted_content
(empty string when the search provider doesn't supply one).

* feat(websearch_interception): emit native web_search_tool_result blocks

When the originating client request carried a native Anthropic
web_search_* tool, the final response now also carries
web_search_tool_result content blocks alongside the model's text
answer — so Claude Desktop / Anthropic SDK clients can populate the
citations panel and replay conversation history with structured search
evidence.

Wiring:
- Pre-request hooks (both deployment + Anthropic path) set a flag on
  kwargs when they see a native web_search_* tool, so the signal
  survives the conversion-to-litellm_web_search step regardless of
  which hook fires first.
- _execute_search now returns (text, SearchResponse) so the structured
  results aren't lost when the text is flattened for the follow-up
  model call.
- _build_anthropic_request_patch returns the parallel list of
  SearchResponse objects.
- async_build_agentic_loop_plan pre-builds the web_search_tool_result
  blocks (one per tool_use_id) and stashes them on plan.metadata when
  the flag is set.
- async_post_agentic_loop_response_hook reads the metadata and
  prepends the blocks to response.content.
- _execute_agentic_loop mirrors the injection for the legacy path so
  both paths behave identically.

Clients that send the LiteLLM standard tool keep the existing
text-only behavior — no regression.

* test(websearch_interception): cover native web_search_tool_result emission

18 tests across:
- detector branches (native vs litellm-standard, OpenAI-function shape,
  Claude Desktop builtin WebSearch, bare web_search, missing type)
- block-builder shape (results, none, empty)
- pre-request hook flag-setting (native sets, standard does not)
- async_build_agentic_loop_plan attaches blocks to plan.metadata when
  the flag is present, leaves metadata untouched when absent
- post-hook injection into dict and object responses
- legacy _execute_agentic_loop mirrors the injection so both paths
  return the same shape

* test(websearch_short_circuit): keep _execute_search mocks in sync with new tuple return

* test(websearch_thinking_constraint): keep _execute_search mocks in sync with new tuple return

* feat(websearch_interception): emit native blocks from try_short_circuit_search

The agentic-loop post-hook only fires when the model returns a tool_use
block. Cowork / Claude Desktop on Bedrock actually make TWO requests
per user turn: the main /v1/messages with their builtin tool, and a
separate standalone /v1/messages whose only tool is
web_search_20250305. That second request hits try_short_circuit_search
— no agentic loop, no post-hook — and was returning text-only, leaving
the citations panel empty.

When the short-circuit input carries a native web_search_* tool, build
a synthetic server_tool_use + web_search_tool_result pair (using the
structured SearchResponse already returned by _execute_search) so the
client gets the native shape it expects. The legacy text block is
preserved so non-native short-circuit callers (Claude Code,
github_copilot, etc.) see the same payload as before.

Failure path still emits the native block pair (with empty results)
plus the text-error block, so the client gets a well-formed response
rather than a malformed half-shape.

* test(websearch_native_blocks): cover short-circuit native-block emission

Three new cases on top of the existing 18:
- native web_search_20250305 short-circuit → [server_tool_use,
  web_search_tool_result, text], ids paired, urls/titles carried.
- litellm_web_search short-circuit → text-only (no regression).
- native short-circuit on search failure → still emits the native
  block pair (empty results) plus the text-error block, so the client
  never sees a malformed half-shape.

* test(websearch_short_circuit): index assertions by block type, not by position

Native short-circuit responses now have [server_tool_use,
web_search_tool_result, text] when the input carries
web_search_20250305 — find the text block by type rather than relying
on content[0].

* fix(websearch_interception): gate legacy WebSearch name on schema absence

Clients like Cowork / Claude Desktop ship a client-side tool named
"WebSearch" with a full input_schema — they handle it themselves and
expect to make a separate native web_search_20250305 sub-request for
the actual search.

Today is_web_search_tool matches the bare name regardless of other
fields, which hijacks the client's tool server-side. The agentic loop
fires on the main request, the model never gets to emit the
client-side tool_use, and the separate native sub-request (where
citation data flows) is never made. Net: citations panel empty.

Real Anthropic client tools always carry input_schema (the API rejects
them otherwise), so a bare {name: "WebSearch"} with no schema is the
only thing that could be a legacy interception marker. Gate the match
on schema absence: legacy callers (if any) keep working, real
client-side WebSearch tools pass through untouched.

* fix(websearch_interception): drop "WebSearch" from response-detection lists

Post-conversion the model always sees ``litellm_web_search``, so the
"WebSearch" entry in the response-side tool_use detection lists was
dead at best. If a model ever did return ``tool_use(name="WebSearch")``
it would now (incorrectly) hijack the client's own ``WebSearch`` tool
again — same Cowork problem we just fixed on the input side. Drop it.

* test(websearch_native_blocks): cover the WebSearch legacy-name schema gate

Three new cases:
- {name: "WebSearch"} (bare interception marker) → still matched
- {name: "WebSearch", input_schema: {...}} (Cowork client tool) →
  passes through untouched
- {name: "WebSearch", description: "..."} (no schema) → still matched
  on the assumption it's a legacy marker rather than a malformed real
  client tool.

---------

Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>
 2026-05-14 12:30:47 -07:00
Mateo Wang
f028a622e2
fix(prometheus): emit litellm_remaining_tokens_metric for Bedrock and Vertex (#27705) 
* fix(prometheus): emit remaining_tokens/requests gauges for bedrock + vertex (LIT-2719)

Bedrock and Vertex AI never return x-ratelimit-remaining-* response headers,
so litellm_remaining_tokens_metric / litellm_remaining_requests_metric only
fired for OpenAI / Azure / Anthropic deployments even when tpm/rpm was
configured on the router.

Add a provider-agnostic fallback in PrometheusLogger.async_log_success_event
that asks Router.get_remaining_model_group_usage() for the same model_group
and emits the gauges with configured_limit - current_usage when the upstream
provider didn't populate the headers itself. Existing OpenAI / Azure /
Anthropic flows are unchanged because the fallback short-circuits when both
header values are already present.

Tests: 8 new tests covering bedrock + vertex emission, header short-circuit,
partial-header fill, llm_router=None, missing model_group, empty router
result, and router exception swallowing.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix(prometheus): narrow except to ImportError, log router lookup failures via verbose_logger.exception

Address greptile review:
- The optional 'from litellm.proxy.proxy_server import llm_router' should
  guard against ImportError specifically, not all exceptions, so that
  unexpected errors (e.g. AttributeError from partially-initialized state)
  stay visible.
- get_remaining_model_group_usage failures are now logged via
  verbose_logger.exception (with traceback) instead of debug, matching the
  PR description's intent and avoiding silent loss of router-cache errors
  in production.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix(prometheus): subtract in-flight delta in router-remaining fallback

The router's TPM/RPM counter is incremented by
Router.deployment_callback_on_success, which fires alongside this
prometheus callback in the success-log fan-out. Prometheus wins the
race, so get_remaining_model_group_usage returns the pre-decrement
counter for the current request — while vendor headers
(OpenAI/Anthropic/Azure) are already post-decrement.

That broke parity between providers on the same gauge: dashboards
plotting litellm_remaining_requests_metric showed Bedrock/Vertex
perpetually one request behind Anthropic for the same throughput.

Replay the in-flight increment before emit: subtract total_tokens
from remaining_tokens and 1 from remaining_requests.

* Revert "fix(prometheus): subtract in-flight delta in router-remaining fallback"

This reverts commit 001ce95ecdd952b4b5a23dd2b1e62c4562c932bc.

* fix(router): post-decrement router-derived ratelimit headers

Router.set_response_headers injects x-ratelimit-remaining-{tokens,
requests} for providers that don't return them natively (Bedrock,
Vertex). The values come from get_remaining_model_group_usage, which
reads the router's TPM/RPM counter — incremented post-response by
deployment_callback_on_success. So the headers reflected the counter
state before the current request was counted: pre-decrement.

Vendor headers from OpenAI/Anthropic/Azure are post-decrement (the
vendor counted the request before responding). Same metric name, two
semantics — dashboards plotting litellm_remaining_requests_metric
showed Bedrock/Vertex perpetually one request behind for the same
throughput, and the HTTP response headers exposed the same skew to
clients.

Subtract the in-flight delta before writing: 1 from
remaining-requests, response.usage.total_tokens from remaining-tokens.
Fixes both the response headers and (transitively) the prometheus
gauges that read from standard_logging_payload.additional_headers.

---------

Co-authored-by: cursor <cursor@example.com>
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
 2026-05-13 17:40:59 -07:00
Yassin Kortam
1c4e4d4a60
Fix 3 OpenTelemetry tracing bugs in proxy integration (#27757) 
1. Missing litellm_request child span when proxy parent in metadata:
   _get_span_context now returns (ctx, None) for the metadata-injected
   proxy parent so the primary span is always emitted as a child of ctx.
   Proxy span lifecycle managed by new _end_proxy_span_from_kwargs.

2. open_telemetry_logger overwrite by later handlers:
   _init_otel_logger_on_litellm_proxy now uses first-registered-wins —
   only assigns proxy_server.open_telemetry_logger when currently None.

3. Duplicate litellm_request success spans in streaming paths:
   Added _mark_success_span_once with per-handler dedupe key stored in
   kwargs metadata, suppressing the second span when both sync and async
   success callbacks fire for the same request.

Co-authored-by: Yassin Kortam <yassinkortam@g.ucla.edu>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-05-12 18:32:05 -07:00
Sameer Kankute
ce17e9490f
Merge branch 'litellm_internal_staging' into litellm_agent_oss_staging_05_06_2026 2026-05-11 09:07:08 +05:30
yuneng-jiang
0824c4c77e
Merge pull request #27403 from BerriAI/litellm_otelGenaiCaptureMessageContent 
[Feat] Honor OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT
 2026-05-08 18:17:00 -07:00