* fix(caching): stop counting an unrun server tool call mark, and follow #49192 and #35602 in their integration tests
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(caching): return the server tool call id instead of a TypeGuard
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(integration): assert the Gemini cache write carries the system prompt
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(watsonx): read passthrough credentials from use_in_pass_through deployments
/watsonx/{endpoint} always called the provider config with api_key=None and api_base=None, so it only ever
worked from WATSONX_API_KEY / WATSONX_API_BASE. It now asks PassthroughEndpointRouter for the proxy-wide
watsonx deployment flagged use_in_pass_through, like the other passthrough routes, and keeps the env
fallback when none is flagged.
Co-authored-by: T K Chandra Hasan <t.k.chandra.hasan@ibm.com>
* fix(watsonx): keep env key precedence when no deployment is flagged
Resolve only use_in_pass_through deployments in the route. get_proxy_wide_credentials also falls back to
WATSONX_API_KEY, which put it ahead of WX_API_KEY in generate_iam_token
---------
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: T K Chandra Hasan <t.k.chandra.hasan@ibm.com>
* fix(anthropic): map pause_turn and return refusal explanations on chat and responses
pause_turn now maps to finish_reason length (Python and Rust) and keeps the raw
stop_reason in provider_specific_fields. Responses reports a paused turn as
incomplete with max_output_tokens and shows the unrun server search as an
in_progress web_search_call instead of a client function_call.
A refusal's stop_details.explanation now fills message.refusal and the final
stream delta.refusal on chat, and becomes a refusal content part on Responses,
streamed or not.
* fix(anthropic): let a chat client resend a paused turn so Claude continues it
* fix(anthropic): replay a paused chat turn without its thinking blocks, and drop its unrun search before a new message
Chat cannot say where Claude wrote each thinking block in a paused web search turn, and
Anthropic answers 400 when one moves but accepts the turn without them. A new message after
a paused turn also 400s on a server_tool_use with no result, so that unrun search is dropped.
* fix(responses): send the streamed refusal part's type and text in content_part.added
* fix(anthropic): keep pause_turn and Claude refusal explanations out of the Messages adapter's text
A paused Claude turn now ends on stop_reason pause_turn instead of max_tokens, streamed or not. A Claude refusal keeps its explanation in stop_details with Claude's category and no longer repeats it as a text block, so a guardrail mask on a streamed refusal is delivered. A paused turn sent back with list content drops its thinking blocks like string content does
* fix(anthropic): replay a paused turn with one unrun search and no thinking on chat and Responses
* fix(responses): report a paused Claude turn as completed so Codex and the Agents SDK keep going
* fix(responses): mark a paused turn's message item completed
* fix(responses): mark every item of a paused turn completed
* fix(responses): keep a refusal's explanation when a refused turn is sent back
A refusal part in a Responses input message now goes to the model as text, so the assistant turn is no longer dropped from the history. The integration test now expects a paused Claude turn on Responses to report completed, and the paused-turn test helper is typed
* fix(responses): scan refusal parts in Responses guardrails
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(decisions): honor a deployment's own supports_audio_input and supports_video_input flags
A Decisions deployment's model_info flags for audio and video now win over
the cost map, per media kind, the same way supports_box_questions already
does. An unlisted model flagged true gets its clips sent upstream, and a
false flag refuses them (or drops them under drop_params). OpenAI-format
providers still refuse clips since their request shape has no field for
them, and the SystemOne request builder no longer re-reads the cost map.
* fix(decisions): ignore media flags on a deployment whose own mode is not decisions
A chat-mode deployment's supports_audio_input describes chat input, so it no longer
turns on audio or video clips for Decisions. The cost map decides for it, as before.
Adds a proxy integration test for flagged, partly flagged, unflagged, and drop_params
deployments on both /v1/decisions and /v1/systemone, and for caller-sent model_info.
* fix(decisions): follow the cost map's mode for a flagged deployment without its own mode
A chat deployment flagged for audio or video usually leaves mode unset, so
its flags described chat input yet still let clips through on /v1/decisions.
When the deployment sets no mode, the cost map's mode for the model now
decides whether its media flags apply, and an unlisted model still takes
them. The audio and video lookups also run only when the request carries a
clip.
* fix(decisions): read the mode a flagged deployment falls back to from the shipped cost map
The router copies each deployment's model_info into the model's shared cost map row, so another deployment's mode there could switch off a flagged deployment's media flags. The fallback now reads the cost map as loaded, untouched by router registrations
* fix(decisions): route clips in a load-balanced group to a deployment that takes them
A Decisions or SystemOne request carrying audio or video now skips group deployments whose own flags or the cost map refuse that media, and keeps the whole group when none takes it, so the 400 still names the media.
* fix(decisions): keep deployments that drop or cannot resolve clips in the media filter
* fix(router): skip clip-refusing Decisions deployments after cooldown and tag filtering
Running the media filter before cooldown turned the 400 a group answered
when its only clip-taking deployment was cooling down into a 429. It now
runs next to the image filter, just before order filtering, in both the
async and sync selection paths.
* fix(router): honor router default drop_params when skipping clip-refusing Decisions deployments
* test(router): list the router default params helper in the router coverage check
* test(bedrock): stop reloading the cost map module in the GovCloud fixture
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(proxy): forward the client's own metadata on Anthropic and custom pass-through routes
* fix(proxy): forward only a valid metadata.user_id to Anthropic Messages on pass-through routes
The whole-object forward sent keys Anthropic refuses and reached other
providers, so a request that used to pass could start failing with a 400.
Read the client's user_id after hooks, drop it unless it is a string with no
email or phone number, and send only that key when the upstream URL path ends
in /v1/messages. Share the validator with the translated route, whose email
check now matches any embedded address as Anthropic's does.
* fix(proxy): forward metadata.user_id only to Anthropic upstreams no guardrail scans
Limit the pass-through user_id to Messages create on api.anthropic.com or the
configured Anthropic base, mirror the checks Anthropic answers 400 for, and keep
the field off any request a pre-call guardrail inspects. The translated route
goes back to its own user_id filter.
* fix(proxy): judge user_id forwarding on the whole request and cap it for configured Anthropic bases
* refactor(proxy): pass the user_id check a plain mapping instead of an optional body
* fix(proxy): drop an unprintable metadata.user_id and warn when a refused id is dropped
Anthropic's whitespace is not Python's, so a control character before the at sign hid an email from the filter and turned a 200 into a 400. Fireworks and Moonshot also fail on newline ids. Requiring a printable id covers both, and a refused id now logs a warning that never carries the value.
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* test(responses): cover text verbosity on the chat completions bridge
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses): send text.verbosity as chat verbosity on the chat completions bridge
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(responses): type the bridged text verbosity lookup
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(responses): cover text verbosity on the client tool search follow-up bridge turn
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(responses): cover text verbosity validation on the codex tool search follow-up turn
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses): send text.verbosity on the client tool search follow-up bridge turn
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(responses): cover text.verbosity on previous_response_id client tool_search follow-up
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Seven integration tests fail on main for reasons that are not product bugs. Two are order or race bugs in the tests, one reads rows other tests wrote, one passed on an error body, and three still expect behavior that #48644, #39864 and #39540 changed on purpose
* fix(proxy): persist sent model_info fields on POST /model/update
POST /model/update rebuilt litellm_params from the request but dropped the
request's model_info, so changing base_model (or mode, tier, etc.) on an
existing DB model was silently ignored. Clients that only use this endpoint,
like the Terraform provider, could never fix a deployment's base_model
without deleting and recreating it.
Persist the model_info fields the caller actually sent, on top of the stored
ones. ModelInfo defaults never overwrite stored values, server-owned, team,
PTU and pricing fields stay on their dedicated write paths, and values echoed
from a /model/info response are not pinned.
* fix(proxy): keep echoed and blank model_info values out of POST /model/update writes
Blank strings from typed clients no longer overwrite stored values. Values echoed from
/model/info (the stored base model's cost map entry and discovered limits) are not
pinned, and an echoed catalog value resets a differing stored override like PATCH does.
direct_access and access_via_team_ids stay display-only. The tests stop patching the
auth check class and give the team lookup a real row instead.
* fix(proxy): lock the model row and skip unchanged model_info on POST /model/update
* fix(proxy): treat only keyed /model/update bodies as echoes and write the stored model_info as saved
A typed model_info value that matched the cost map, or the previous base model's entry,
was dropped and could erase a stored override. Echo detection now follows PATCH, only a
body carrying the /model/info key counts, compared against the base model the row has
after the write. The locked write patches the stored JSON instead of dumping the hydrated
Deployment, so litellm_params pricing is no longer copied into model_info. Tests run the
real Router with discovery over a mock upstream
* docs(terraform): note that model_info updates now take effect and overwrite values set outside Terraform
* test: expect POST /model/update to save the cache point flag, and wrap long test lines
test_x06 pinned the old contract, where POST /model/update answered 200 and ignored model_info.
This PR makes POST save model_info the way PATCH does, so x06 now asserts what its PATCH sibling
x01 asserts, the stored flag flips to true and cache points come back on.
* fix(proxy): answer 400 when POST /model/update gets an access_groups that is not a list of strings
* fix(proxy): read discovered model info through the router registry entry point
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(bedrock): route unmapped converse-only family model ids to converse
#42713 stopped Bedrock-native OpenAI ids that are not in the cost map yet from
falling to the invoke route. The same gap exists for the other families that
have no invoke transformation at all: minimax, nvidia, writer, zai, google and
xai. An id from one of them that is newer than the cost map hits
get_bedrock_route -> "invoke" -> get_bedrock_invoke_provider() -> None ->
BedrockError, when Converse would have served it
Add a second family regex for those six and default them to converse, the same
way the openai family is handled. _OPENAI_FAMILY_MODEL_RE is left alone because
the prompt-caching rule is openai-specific
Every shipped id of these families already resolves to a converse entry via its
base model (checked against the cost map), so this only changes behaviour for
ids that are not in the map yet
Tests: six unmapped ids route to converse, and an unmapped meta.* id still
routes to invoke since meta has an invoke handler
* fix(bedrock): route a model id with no Invoke provider to Converse
A bedrock/<model> id missing from the cost map fell to the Invoke route,
where an id that names no Invoke provider (a new vendor's model, a bare
provisioned-model, imported-model, or custom-model-deployment ARN) fails
locally with "Unknown provider=None" before any request is sent. Such ids
now go to Converse, which replaces the per-family regex with the general
rule. Ids that name an Invoke provider and explicit route prefixes are
unchanged.
* test(bedrock): expect Converse for ids with no Invoke provider in the nova ARN and not-found tests
* fix(bedrock): accept tools on Converse for a model with no cost-map row or Invoke provider, and keep a 404 for an invalid id
* fix(bedrock): answer 404 for an invalid model id instead of AWS's 400
AWS answers a mistyped Bedrock model id with a 400 ValidationException. The mapping already raised NotFoundError, but passing AWS's 400 response through made the openai base class report 400, so the proxy answered 400 and the router skipped its cooldown
* fix(bedrock): drop lookaround regexes from tool schemas for a model the cost map does not list
* fix(bedrock): apply the unknown-model defaults only to bare ids LiteLLM knows nothing about
A bare Bedrock id with no cost-map row and no Invoke vendor gets Converse's open params, its reasoning_effort as sent, lookaround-free tool schemas, and a 404 for AWS's invalid-identifier reply, which keeps AWS's response headers. An explicit route like converse/, an ARN, or an id LiteLLM recognizes keeps main's params, regexes and error status, so drop_params still drops tools there
* fix(bedrock): judge lookaround support by model id on every route, ignore price-only rows
The regex check now strips route and region prefixes from the model and its
base_model before asking whether LiteLLM knows the model, so
bedrock/converse/<id> gets the same answer in a direct transform call as on the
completion path. A cost-map row counts only when it names a provider, so a
price-only register_model row leaves an unlisted id unknown, as on the proxy.
AmazonInvokeConfig now inherits the Invoke provider lookup instead of keeping
an identical copy.
* fix(bedrock): treat a region path like the bare id when the cost map does not list it
A deployment like bedrock/us-east-1/<unlisted id> now gets the same tools,
reasoning_effort and typo 404 as bedrock/<unlisted id>, so Claude Code works
on it and a mistyped one still answers 404.
* fix(bedrock): keep today's 404 for ids that reach Converse only by default, and keep an empty id local
* fix(bedrock): answer 404 for an invalid id only when Converse rejected it, and keep a region path with no id local
* fix(bedrock): keep a path-shaped id local, and map /v1/completions errors with the model's provider
An id with a slash that is not an ARN, like bedrock/../../converse or an unknown region path, keeps Invoke and its local 404 instead of reaching AWS Converse.
atext_completion mapped every error as OpenAI, so a Bedrock typo on /v1/completions answered 400 while chat answered 404. It now resolves the provider from the request's model, as the other async entry points do.
* fix(text completion): keep /v1/completions error mapping as it is on main
Mapping async /v1/completions errors with each provider's rules is wider than this Bedrock routing change. It moves classes, statuses, cooldown and fallbacks for most providers, and some lose context-window fallbacks. So this restores main's mapping here and leaves that change to its own PR. A Bedrock typo on /v1/completions answers AWS's 400, while chat keeps the 404.
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(router): keep a deployment's own model_id over the request body's
A request body model_id overrode the deployment's pinned model_id, so a client could move SageMaker chat and embedding calls to another inference component. The router now drops the request's model_id when the deployment sets one.
* fix(router): let deployments opt back in to a client model_id and stop client component headers on SageMaker chat
Drop the request's model_id after the router defaults are applied, so a default_litellm_params model_id cannot win
over the deployment either, and apply the same precedence on the sync text_completion path. A deployment that lists
model_id in configurable_clientside_auth_params keeps the client's value. SageMaker chat drops any case variant of a
client's X-Amzn-SageMaker-Inference-Component header when the deployment sets a component
* fix(router): keep a SageMaker deployment's component when it pins it through extra_headers
* fix(router): read the SageMaker component header as a pin only on SageMaker deployments
* fix(sagemaker): send the inference component only as a header, never as a body model_id
SageMaker picks the component from X-Amzn-SageMaker-Inference-Component, so the chat
container body and legacy parameters no longer carry model_id. Nova endpoints accept a
fixed body field list, so a pinned Nova deployment could reject the extra field.
* fix(health): probe header-pinned SageMaker deployments at their component
Health checks bypass the router, so a legacy sagemaker/ deployment pinned only
through its inference component header sent no component on /health while routed
requests now send it. Also update the two SageMaker integration tests that still
asserted the old precedence and the dropped body field.
* fix(health): apply the router's model_id pin rule to health probes
Health probes now share the router's pin function, so a deployment that lets
clients set model_id gets no header pin and its own model_id wins without
reading the header. Header keys that are not strings are skipped when
reading the inference component header instead of failing the call.
* fix(sagemaker): keep header keys that aren't strings and report a string opt-in in health checks
The chat transformation now keeps extra_headers keys that aren't strings, so such a deployment fails the way it did before instead of answering 500, and the model_id pin checks configurable_clientside_auth_params with isinstance, so a string there reaches the health probe's own error capture again.
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(realtime): send Gemini Live setup on the client's first message so session.update modalities apply
Gemini and Vertex AI Live accept exactly one setup per session, and the proxy sent its
default audio setup at connect, so a client's session.update (GA output_modalities or
beta modalities) was dropped and every answer came back as audio. The setup now goes
out on the client's first message: a session.update becomes that setup, anything else
gets the model default. Bedrock Nova Sonic keeps its connect-time setup through a
provider hook. Text setups label deltas response.output_text.delta, and downgrading
TEXT to AUDIO on an audio-only model logs a warning
* fix(realtime): keep one Gemini Live response modality and skip the "inf" token limit in the setup
* fix(realtime): merge every session.update sent before the first content frame into the Gemini Live setup
Gemini and Vertex Live accept one setup per connection, so the bridge used to
turn only the first session.update into that setup and drop the rest without
telling the client. Now every session.update that arrives before the first
content frame is held, deep-merged (later values win per field), acknowledged
with a session.updated built from the setup it would produce, and the merged
session becomes the one setup sent with the first conversation item, audio
frame, or tool result. The session events also surface the systemInstruction
text as instructions, which the old string check never matched because the
setup carries it as a Content object.
* fix(realtime): cap the session.update merge depth so the recursive detector accepts it
_merge_session_fields recurses once per nested mapping level, so CI's
recursive_detector flagged it. Bound the descent at eight levels (the OpenAI
session object nests four deep at most), replace anything deeper wholesale
with the later session.update's value, and list the helper with the other
depth-capped functions the detector allows.
* fix(gemini-live): accept a string output modality and drop values Live cannot produce
A bare string output_modalities is read as a one-element list and modality values Gemini Live cannot produce are dropped with a warning instead of reaching the backend, so a malformed session.update no longer costs the session. Transcription guardrails now fold their VAD disable into the first held session.update only, so the merged setup carries it exactly once.
* fix(gemini realtime): answer an error for session updates Gemini Live cannot apply
An output modality the model cannot produce, or a session.update sent after the
first content, now gets an invalid_request_error event instead of a silent swap
or drop, unless drop_params is set. The synthetic session.created reports the
model's default modality, setupComplete no longer adds an extra session.updated,
and merging nested session updates is linear in their size
* fix(gemini realtime): send the setup before guardrail blocks and idle deadlines, mark push-to-talk turns
A pre_call guardrail blocking the first typed message now goes out after the
setup, so Gemini and Vertex no longer close the session with 1007.
Push-to-talk sessions (turn_detection null or create_response false) wrap each
audio turn in empty activityStart and activityEnd messages, which Gemini needs
when automatic activity detection is off.
A session with no content after 60 seconds sends its setup, since Vertex
refuses a setup 300 seconds after connect. Held session.update frames past
10 MB answer session_update_too_large, and deferred mode now errors on a
second session.update unless drop_params is set.
* fix(realtime): ack unchanged Gemini Live session.update resends and honor null turn_detection
An update that leaves the applied setup unchanged now gets session.updated
instead of session_update_not_applied, so clients that resend their session
with the first response keep working. Deferred setup mode merges held updates
like the default mode, turn_detection null turns Gemini's activity detection
off, drop_params drops a voice outside the documented Live voices, and the
lazy setup deadline moves to 240 seconds
* fix(realtime): keep Vertex activity detection off for null turn_detection and set up deferred sessions before text content
* fix(realtime): refuse unscreenable Gemini audio, send held setup after buffered audio, reject unknown Vertex voices
A realtime_input_transcription guardrail cannot screen Gemini Live audio before the answer starts, so audio input under one is refused with an audio_input_not_screenable error instead of reaching the model. In deferred setup mode a session.update that follows buffered audio is sent as setup at once. Only conversation content triggers the setup, so control events no longer lock in a default one. Vertex rejects an undocumented voice on the session.update, or drops it with drop_params, while Google AI Studio forwards any name.
* fix(realtime): hold first content so a following session.update joins the Gemini setup
Convert flat realtime tool schemas through Gemini's schema builder, and collapse a text and audio pair to what the model can produce on text-only models too
* fix(realtime): check AI Studio Live voices like Vertex
* fix(realtime): forward any voice to AI Studio Live models that accept it
* fix(realtime): map OpenAI TTS voices before the Live voice check, keep outage spend rows, share the playground session config
The Live voice gate now judges the Gemini voice an OpenAI TTS name maps to, so fable and onyx reach Vertex as Umbriel and Orus again. An upstream close before the held setup was sent is logged as a session, not a refusal. The playground sends one server_vad session config for typed and voice turns, and the e2e, integration, and dashboard tests follow lazy setup.
* fix(realtime): take the lazy setup clock as a dependency so its tests run on a stepped clock
The deadline and grace tests waited on real 50 ms timers, and the merge cost
check compared wall-clock runs. The session now reads an injected clock,
time.monotonic by default, the two tests step it, and the merge cost check
moves to the CodSpeed benchmarks. The Nova Sonic style handler test passes
the backend through wrap_backend, which main added after the test was written
* fix(proxy): restore spend logging for /cursor/chat/completions (#30126)
The /cursor/chat/completions route processes requests through the Responses API pipeline, but _get_metadata_variable_name did not treat it as a litellm_metadata route. Proxy auth metadata therefore landed in the user-facing metadata param, so the spend tracking callback saw user_api_key=None and silently skipped the SpendLogs write. Add the route to LITELLM_METADATA_ROUTES and cover it with regression tests.
* test(proxy): pin provider metadata and key billing on /cursor/chat/completions
A Responses-format body on the Cursor route now sends OpenAI only the metadata the client sent and writes a spend row that bills the key. The chat-body branch, which this fix also moves onto litellm_metadata, still bills the key with its team and request tags. The scripted chat reply gets a unique id per test, since a shared id let only the first spend row per database land
* fix(proxy): pick the Cursor route's metadata field from the body shape
A Responses-style body on /cursor/chat/completions keeps proxy metadata in
litellm_metadata, the same as /v1/responses, while a Chat Completions body
keeps metadata, the same as /v1/chat/completions. Adding the whole route to
the internal-metadata list let a client's chat-body tags replace the key's
tags in spend logs and skip tag budgets.
* fix(proxy): keep get_metadata_variable_name callable with only the request
* fix(proxy): check client tags against tag budgets and read agent limits from litellm_metadata on Responses bodies
A Responses-style body on /cursor/chat/completions now keeps its proxy fields in litellm_metadata, so the pre-call tag budget check and the v3 agent rate limiter stopped seeing the client's metadata.tags and the x-litellm-agent-id value. The pre-auth tag check now merges the caller's metadata.tags into the selected metadata field, sharing one helper with request setup, and the limiter reads agent_id from either field
* fix(proxy): check client tags sent in a JSON-string metadata against tag budgets on Responses bodies
* fix(proxy): answer 422, not 500, when a budget rejects a Responses body whose metadata is a JSON string
* fix(proxy): keep key guardrails, metadata type errors and failure-row client details on Responses-style bodies
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(azure_ai): keep OpenAI prompt cache breakpoints on the Foundry chat completions path
* fix(azure_ai): keep only OpenAI content part types when preserving prompt cache breakpoints
* fix(azure_ai): share the breakpoint check with the cache-mark step and keep only text parts
A deployment's model_info opt-in is registered under its provider-prefixed cost-map key without a litellm_provider, so that prefix now names the provider the flag speaks for.
* refactor(azure_ai): read text parts through a walrus and mark new test values Final
* fix(azure_ai): read a router-registered breakpoint opt-in only for azure_ai and keep a message mark off dropped file parts
* fix(azure_ai): read the breakpoint opt-in from each deployment's own model_info and base_model
A router deployment without its own supports_prompt_cache_breakpoint flag now
reads the cost map as loaded, so a sibling's opt-in on the same model string no
longer turns its requests into 400s. The shared hook no longer reads router
registered keys for azure_ai.
* fix(azure_ai): send breakpoints in Foundry's shape under drop_params and fall back to the model name
* test(integration): expect the injected breakpoint on the Foundry GPT-6 chat wire
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* feat(dashscope): add image edits and text-to-speech
* fix(dashscope): bill reported speech characters and input images, follow the chat region for edits, keep the call timeout on the audio download
* fix(dashscope): bill deployment image prices as main does and route edits to the host speech uses
A deployment that sets its own input_cost_per_image, output_cost_per_image or input_cost_per_pixel bills through the default image calculator again, so an override is never read as a per-input-image price and pixel pricing on qwen-image-3.0-pro no longer raises.
Edits now resolve the caller's api_base or DASHSCOPE_API_BASE through the same API-root helper speech uses, on any host, with DASHSCOPE_API_BASE_IMAGE still winning. An image response whose usage is null or not an object keeps its images instead of raising.
* fix(images): bill a deployment's own prices on image edits through the proxy
The proxy builds the logging object before the router picks a deployment, so image_edit never handed the deployment's price fields to its cost calculation and billed the cost-map price instead. Pass the custom pricing params with the pre-call update, as the Responses API does.
* fix(images): bill per-pixel image prices at a DashScope W*H size
DashScope's mapped size reads 1328*1328, which the cost size reader rejected, so a deployment's input_cost_per_pixel billed every DashScope generation or edit at the 1024x1024 default.
* fix(dashscope): bill image prices by their meaning, report the rendered size, prefer the deployment edit base, fetch audio over https
* fix(dashscope): read the rendered image size from output_width and output_height
* test(images): bill a fal edit at its deployment's own image price
* fix(dashscope): keep the qwen-image-3.0 input-image fee out of the shared cost map row
Released proxies read the cost map from main and bill input_cost_per_image as the
flat price of each DashScope image, so the row's $0.003 input fee billed every
qwen-image-3.0 image there at a tenth of its $0.03 price. A deployment can still
set input_cost_per_image to bill DashScope's input images
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
Without drop_params, every copy of a tool from additional_tools input items
is forwarded as sent, so a provider that rejects duplicate tools returns its
own 400. Codex keeps the merge because the proxy turns drop_params on for it.
* fix(anthropic): send /v1/messages PDF documents to OpenAI-shaped providers as file parts
* refactor(anthropic): hold the chat completions document change for a later commit
* fix(anthropic): send /v1/messages PDFs as file parts and keep file parts on azure_ai
* fix(snowflake): turn file parts into document blocks for Cortex Claude
* fix(prompt_security): sanitize chat file parts that carry file_data
* fix: answer 400 or drop PDFs for models that cannot read them
Models whose model_info or cost map entry sets supports_pdf_input to
false, and Ollama, now get the PDF dropped under drop_params and a 400
from litellm without it. GigaChat uploads file parts, SAP keeps sending
them as image_url data URLs, azure_ai passes dict content through, and
annotations other than url_citation move to provider_specific_fields
instead of failing the response.
* fix(router): scope supports_pdf_input to its deployment and keep provider annotation keys
Mark the azure_ai chat models in the cost map that cannot read PDFs, so drop_params drops the PDF and
litellm answers its own 400 without it. Keep url_citation annotations whole, move only typed provider
annotations to provider_specific_fields, and split them the same way when a stream is rebuilt.
* fix(vertex_ai): send PDF file parts to Vertex partner models as image_url
Vertex AI's OpenAI-compatible partner endpoint rejects the chat `file` part, so PDFs from /v1/messages
reach those models as the image_url data URL they took before. Mark native DeepSeek chat models as
unable to read PDFs, and treat a file part as a PDF by its format or filename when it has no data URL.
* fix(cost-map): leave native DeepSeek PDF support unset
/v1/messages reaches native DeepSeek through its own Anthropic endpoint, so the marks never applied there
and would only change chat completions, which this PR does not otherwise touch.
* fix(main): drop a user turn that held only a PDF under drop_params
Dropping the PDF from a PDF-only user turn left an empty content list, which
Foundry and OpenAI-shaped providers reject with a 400, so drop_params still
failed for that request. The emptied user turn is now removed.
* fix: read OCI PDFs, gate deployment base models, and stop empty drops
OCI generic chat sends file parts as image data URLs again, so OCI Gemini
keeps reading PDFs from /v1/messages and chat file parts.
The PDF gate now reads model_info.base_model, drops PDFs under drop_params
for azure_ai deployments that never declared PDF support (the old flattening
behavior), and answers 400 when dropping the PDF leaves no user message.
The cost map marks the Vertex partner text models that a live probe showed
answering without reading the PDF.
* test(integration): cover the PDF input gate, messages PDF file parts and provider annotations
Wire cells for the supports_pdf_input check on chat, chat streaming, /v1/messages and the Responses bridge, its model_info, base_model, cost map and Ollama sources, drop_params at deployment and request level, azure_ai file parts, sibling deployment isolation, Anthropic SDK PDFs reaching azure as titled file parts, Gemini and Bedrock document controls, Prompt Security sanitizing a /v1/messages PDF, and OpenRouter file annotations live, cached and streamed
* test(integration): cover Snowflake Claude and SAP in the messages PDF wire cells
* fix(azure_ai): flatten non-PDF file parts to text as before and cover the GigaChat PDF upload
* fix(main): PDF check reads the deployment's top-level base_model
* fix(main): keep roles alternating after drop_params removes a PDF-only turn
When ensure_alternating_roles is set, re-run role normalization on the messages left after the PDF check drops a turn, so two assistant turns never end up next to each other
* test: type the PDF and annotation tests the PR added
* test: type the GigaChat, SAP and Snowflake PDF tests
* fix(github_copilot): treat a malformed api-key.json like a missing one
* fix(github_copilot): read the key and the endpoints of api-key.json as separate strict views
* fix(github_copilot): ignore a non-UTF-8 api-key.json like any other malformed file
Read the cached key file as bytes so a UTF-16 file, which Windows
PowerShell 5.1 writes with >, fails JSON validation instead of raising
the decoder's error, and the request gets the refresh or the login hint
* fix(github_copilot): decode UTF-16 key files and refresh over a malformed endpoints field
* fix(github_copilot): decode a UTF-16 access-token file and refuse an undecodable one like a missing one
* fix(github_copilot): use the refreshed key when api-key.json cannot be written
* fix(github_copilot): keep a refreshed key that cannot be saved in memory
* fix(github_copilot): serve a kept key and its endpoint together after the key file is repaired
A key held in memory is now checked before api-key.json, so the token and the endpoint come from the same source, and the map holds one entry per key file with the access token beside it
* test(github_copilot): type the authenticator test fixture and the tests this branch adds
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(gemini): use collection endpoint for custom context cache
* fix(vertex_ai): build the cachedContents collection URL under a custom api_base
* fix(vertex_ai): keep a gateway prefix that contains /publishers/
* test(integration): expect the cachedContents collection URL on a custom Gemini api_base
* fix(vertex_ai): cut cache collection URLs at the base's location, graft a mount prefix
* fix(vertex_ai): cache under a publisher model api_base in that model's location
A Vertex AI api_base that names an endpoint or no model keeps its old cache URL, so
no cache is created that the chat call cannot use. The cache body takes its project and
location from the api_base model, matching the collection URL.
* fix(vertex_ai): cache for the api_base's model and skip caching on a Gemini v1 base
A Vertex api_base naming a publisher model now gives the cache its model as well as its project and location, so the cache matches the model generateContent calls. A Gemini api_base ending in /v1 skips context caching, since the v1 API has no cachedContents and rejects the cachedContent field.
* fix(vertex_ai): check the cache token minimum against the api_base's model
The cache is created for the model a publisher-model api_base names, so the minimum prompt size now comes from that model too. A prompt below its minimum is sent uncached instead of failing at cache create.
* fix(vertex_ai): use the stricter cache minimum and send a Gemini base's 404 uncached
---------
Co-authored-by: paulo037 <paulo.h.carneiro.18@gmail.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(responses): drop a malformed prompt_cache_breakpoint under drop_params on the direct /v1/responses path
* refactor(responses): reuse the shared prompt_cache_breakpoint_for_wire helper in the chat bridge
* fix(responses): read a non-flag deployment drop_params as unset in the marker walk, like the param mapper
* fix(responses): drop every prompt_cache_breakpoint under drop_params when the model has no breakpoints
Also drop a marker set on the message item itself, sanitize before the
cache_control conversion so a client typo no longer erases that breakpoint,
and skip items that carry no marker so unmarked history costs no extra walk
* fix(responses): keep a valid prompt_cache_breakpoint unless the model is known to reject it
A deployment alias the cost map does not know (an Azure deployment name, a fine-tune, a
litellm_proxy alias) kept its valid markers before this PR and must keep them under
drop_params. base_model is read from litellm_params first, then model_info, as chat
completions does.
* fix(responses): drop a valid marker on known non-breakpoint models and keep the tool schema gate unchanged
* fix(responses): judge breakpoint support by model name so cost-map aliases keep valid markers
* fix(responses): keep the caller's request stripped of cache_control as before when a marker is copied
* fix(responses): keep valid prompt cache markers in place and let cache_control fill a dropped one
Under drop_params the marker step now hands back the caller's own objects when every marker is
already wire-safe, so a request with valid markers is no longer copied and walked a second time.
A deployment that turns cache_control into markers keeps the caller's own markers too, whatever
its base_model says, so one request never gets two answers about breakpoint support.
On /v1/messages a malformed raw marker next to cache_control is dropped before the conversion,
so the block gets the cache hint cache_control alone would give, as on /v1/responses.
* test(responses): keep a null marker and the original key order on a model that reads breakpoints
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(responses): resume background response streams with stream and starting_after
Retrieving a response with stream=true and starting_after=N now returns the
replayed event stream from OpenAI and Azure instead of one JSON object, on the
SDK (get_responses / aget_responses) and on the proxy's GET /v1/responses/{id}
Providers that cannot replay a stored stream drop the params under drop_params
and raise UnsupportedParamsError without it. Malformed values answer 400 with
the param named. A replay that reaches its terminal event settles the
background response's bill once and passes the provider's usage through
without estimating one. An empty replay (cursor at the end) ends normally
* fix(responses): encrypt replayed stream ids and resume Azure AI Foundry streams
A replayed background stream now gets the same response id encryption as a
created one, so the key that made the response can reuse the id. Azure AI
Foundry deployments resume streams like Azure ones, and the chat-bridged
Responses iterator closes its wrapped stream instead of raising.
* fix(responses): drop malformed resume params under drop_params and keep JSON answers to a streamed retrieve
A bad stream flag drops to a plain retrieve and a bad cursor to a full replay when drop_params is on, and a cursor without stream is ignored even when malformed. A streamed retrieve that the upstream answers with JSON returns the response object instead of an empty stream. get_responses forwards only the include keys from extra_query.
* fix(responses): give proxy stream resumes the deployment stream_timeout and document retrieve query params
* fix(responses): keep deployment stream off retrieves and reject oversized cursors
* fix(responses): keep router default stream off plain retrieves
* test(responses): black box coverage for resume refusals, drop_params, stream timeout and billing
* test(responses): replay a real stream in the billing test and type the fake wrapper chunks
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses): replay the original Responses item order, phase, and boundaries on the next chat turn
* fix(responses): keep tool-call-only stream content empty and drop unreachable replay guards
* fix(responses): reject an assistant phase OpenAI does not define unless drop_params is set
* fix(responses): keep other providers' reasoning and stream output unchanged and fall back on records that do not fit
* fix(responses): label a structured output turn with its returned message's phase and keep lone surrogates from failing the checksum
* fix(responses): leave a turn without a phase when its content message cannot be read
* test(responses): add black box tests for chat turn history replay
* test(responses): expect the replay record on bridged chat replies
* test(responses): expect the replay record in bridged chat translation cases
* fix(streaming): keep the served service tier on the finish chunk the stream adds
* test(responses): read the expected chat message without JSON validation so the replay matcher stays
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* test(responses): cover Codex tool lists repeated across turns on the Bedrock chat bridge and Mantle
* fix(responses): keep each Codex tool once when its tool list repeats across turns
* test(responses): type the new tool list dedupe unit tests
* fix(responses): drop the null created_by OpenAI rejects when output items are replayed
* fix(responses): drop a null created_by from replayed input items too
A conversation saved from an older proxy still carries created_by: null on its compaction, apply_patch and shell items, so OpenAI keeps rejecting the replay. Drop the null from input items on the HTTP request transform and the WebSocket response.create rewrite, keeping a created_by that is not null.
* fix(responses): drop a null created_by from counted input items too
* test(responses): cover saved reasoning nulls and re-compaction of a saved conversation
* fix(responses): drop a set created_by from input under drop_params
* fix(types): type the Responses output serializer's parameters
* fix(responses): drop a set created_by on reasoning items and Perplexity presets under drop_params
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(gemini): preserve every grounding web source
* test(gemini): cover all grounding sources per passage
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Zabir Raihan <zabirraihan@icloud.com>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(gemini): send Google's current Managed Agents fields on create and forward agent_config on interactions
* fix(gemini): accept id on managed agent get, delete, and list_versions
* fix(gemini): drop unsupported agent fields under drop_params and keep agent_config keyword-only
A model interaction drops agent_config under drop_params, agent create drops wrongly typed optional fields under drop_params and otherwise answers 400 naming the field the caller sent, and agent_config on interactions create is keyword-only so positional response settings stay in place
* fix(gemini): drop a non-object agent_config under drop_params and name the id alias the caller sent
* fix(interactions): reject agent_config on bridged providers and drop a typeless one under drop_params
Bridged providers answer 400 for agent_config without drop_params and drop it with drop_params. A Gemini agent run drops an agent_config without a string type under drop_params. The Gemini agents wire test expects Google's current upstream field names.
* fix(interactions): honor additional_drop_params for agent_config on bridged providers
* fix(agents): drop a clashing instructions alias under drop_params
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* test(azure): audit #47348
Add proxy integration tests for #47348's cached Entra token providers and
off-loop token refresh, and fix the token herd the audit found: once the
fetch moved to a worker thread, concurrent cold requests each fetched their
own token. Every cached bearer token provider now serializes its calls so
the first fetch fills the cache and the rest reuse it
* fix(azure): build each cached Entra token provider once under concurrent first calls
lru_cache does not hold back concurrent misses, so worker threads that miss together each built their own credential and token provider, and each fetched its own token. A lock around each provider cache makes the first caller build it and the rest reuse it
* fix(azure): share Entra token fetches only while no valid token is held
Concurrent callers now share one result (or one error) from the cold
fetch, while callers inside the refresh window keep the still-valid
token. The oidc/azure provider build and the client-assertion exchange
are single-flighted per audience, and the shared helpers no longer call
subscripted generic aliases, which crashed on Python 3.10.
* fix(azure): refresh Entra tokens once while other requests keep the valid token
MSAL treats a token with less than five minutes left as expired, so every
request in that window fetched a new one. Now one request refreshes while
the others keep using the still-valid token.
Requests that waited on a fetch that failed retry once through a fresh
fetch instead of all failing with the first error.
Credentials that only implement get_token keep working, and OIDC token
exchanges are cached per scope.
* fix(azure): bound Entra token waits during an outage
Requests waiting on a failed token fetch now retry only when the fetch
before it succeeded, so during an Entra outage later bursts fail after one
fetch instead of two.
Requests during a refresh only get the held token while it has more than
60 seconds left, and a failed refresh falls back to that token instead of
failing its request.
* fix(azure): share one retry per failed Entra token fetch and raise on a failed refresh
Waiters of a failed fetch now retry the flight they joined, so a fast-failing Entra makes one retry instead of one per waiter. A failed refresh raises again instead of returning the held token, which Postgres token auth read as a fresh one. The DefaultAzureCredential fallback reuses one provider per scope.
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* test(responses): audit #47363
Adds integration cells for the input token count paths #47363 changed: the provider id of a managed file and the 403 for another team's file on OpenAI, file_search and additional_tools parity plus cache-mark stripping and untranslatable tools on Claude, and the Bedrock runtime count leaving out the options it refuses.
* fix(count-tokens): reject a lone tool with no chat equivalent before the Claude count call
A Claude input token count whose only tool was mcp or computer_use_preview skipped the pre-call check and went to the provider, so the caller got the provider error instead of the 400 naming the tool. The check now runs whenever a tool would get a placeholder name.
* fix(responses): count a lone Anthropic toolset and name the uncountable tool
The lone-tool 400 now fires only for OpenAI Responses built-in tool types, so a nameless Anthropic toolset still counts. The error says LiteLLM cannot count that tool type for the model. Adds a Bedrock unit cell for the lone-tool 400 and pins the runtime wire test's tool shape per request type.
* fix(responses): take the uncountable tool types from the OpenAI SDK and keep the region hint off LiteLLM's 400
The Responses built-in tool types now come from openai's ToolParam union, so a type the SDK adds is refused before the count call too. The AWS region hint no longer lands on an error LiteLLM raised before calling the provider. Tests cover two more lone types and the hint.
* fix(responses): keep LiteLLM's own list of uncountable tool types and pass a non-string tool type through
* fix(responses): count tool_search and namespace tools the way Claude chat inference sends them
The count refused a lone tool_search with LiteLLM's 400 and sent a lone
namespace raw or squashed it into an empty tool, while Claude chat
inference maps tool_search to Anthropic's tool search tool and unpacks
a namespace into its function tools. Both now go through the
deployment's own map_openai_params, so the count sends what inference
sends, and tool_search leaves the list of uncountable types.
* fix(bedrock): count a Claude request with its deployment's own chat translation
Bedrock invoke and Mantle inference turn a chat tool_search tool into Bedrock's
tool search tool, but the Bedrock counter always translated with the Anthropic
config, so the count answered 400 for a request inference serves.
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(count_tokens): keep the framed estimate when a region-less token_count_model cannot count (audit #47441)
* fix(count_tokens): let framing stand in for a shipped token_count_model, keep a configured one's error
The framed local estimate now replaces a 400/404 from any count model the cost map ships, Mantle-pinned rows included, and an admin-set token_count_model keeps its error as the docs say.
* fix(count_tokens): warn with the region when framing stands in for a pinned count model
Test a Mantle row #47441 pinned in its own region, and keep the region hint check on an admin-set count model.
* fix(count_tokens): name the deployment in the framing stand-in warning
* fix(count_tokens): let framing stand in when a cost map count model's region denies the account
A count model the cost map pins to a region can answer 401 or 403 when the
account has not enabled that region or lacks Mantle count access there. With
framing set, the estimate now stands in, as it does for 400 and 404. An admin
set count model and a count model with no pinned region keep the error.
The warning now names the model group, the count model and its region, and
says the proxy falls back to the estimate.
* test(count_tokens): cover framed 401 and 403 from a shipped count model region on the wire
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* test(images): cover SDK retry budgets and router ownership
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(images): forward max_retries to the OpenAI image generation client
Per-call, deployment and default_litellm_params max_retries were filtered
out before reaching the OpenAI-compatible image handler, which then always
used 2 SDK retries. Forward the caller's value, and pin it to 0 for routed
calls so the Router stays the only retry owner, matching completion()
Co-authored-by: Ericz <yonglzh@gmail.com>
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(images): read image max_retries through a typed kwargs view
Co-authored-by: Ericz <yonglzh@gmail.com>
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(images): assert the public invalid retry error mapping
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(images): preserve the invalid retry status code
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(images): group the proxy test imports
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(images): discard proxy retry test routers
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Ericz <yonglzh@gmail.com>
* test(huggingface): cover TEI rerank truncate passthrough and server default
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(huggingface): stop forcing truncate=false on TEI rerank and pass truncate params through
Co-authored-by: Luke Schaefer <225965040+luke-e-schaefer@users.noreply.github.com>
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(huggingface): keep TEI truncate options provider-specific
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(huggingface): use MockTransport for rerank truncation
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Luke Schaefer <225965040+luke-e-schaefer@users.noreply.github.com>
* test(nvidia_riva): audit #41021
Adds an owned Riva ASR gRPC server and provider integration cells for the riva_offline request-body ban, the offline and streaming paths, the per-deployment caller opt-in, and the 413 and 408 error mappings
* test(nvidia_riva): check word timing requests and audio samples in the Riva double
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
Covers the json_schema envelope edge cases #47478 fixed on the Anthropic json tool path: a key listed only in
`required`, a tool-named wrapper on a stream, non-ASCII and lone-surrogate answers, a stream cut before the tool
block closes (proxy and sync SDK, Anthropic and Bedrock Invoke), and a string `output_format` with and without
`drop_params` on Anthropic and Vertex chat and /v1/messages. The scripted Anthropic peer now refuses a non-object
`output_format` the way Anthropic does
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* test(anthropic, bedrock): audit #47489 thinking replay across Claude routes and Bedrock Converse
* test(bedrock): compare the whole replayed Converse turn in the merged-message and cache point cells
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* test(proxy): audit #46815
Add integration tests for every count route #46815 changed, and count a Claude
subscription caller's tokens with their forwarded token when the deployment has
no configured key, which #46815 turned into a 401
* test(proxy): accept the forwarded subscription token in the count endpoint mocks
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* test(gemini): audit #47542
Proxy integration cells for the Gemini and Vertex default output budget: the env budget reaches Google on chat and responses, turns on long decode, yields to the caller's own limit, caps per deployment, skips resumes, and a camelCase two-candidate request is never continued
* test(gemini): annotate the budget wire module's pytestmark as Final
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
Checks that a SageMaker stream failing after its first chunks reaches /v1/chat/completions, /v1/messages and /v1/responses callers as an error with no success end event, and that each writes a failed spend log row
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(tavily): return raw_content, score, favicon and the response envelope from Tavily search
Tavily search dropped every field except title, url and content, so
include_raw_content, include_answer, include_images and include_favicon
reached Tavily but their output never reached the caller. Each result now
carries raw_content, score and favicon when Tavily returned them, and the
top-level fields (answer, images, request_id, usage, ...) come back as
provider_metadata, same as the Exa fix in #47423
* test(tavily): move the Tavily search wire test's sample data to module constants, like the Exa test
* test(integration): update stale expectations to intended provider behavior
Each expectation moved because a merged PR changed behavior on purpose.
- chatgpt string input is wrapped as one user message (#41522)
- Gemini 2.5 tool media sits beside function_response (#48428)
- sampling knobs move to Gemini 3.5 Flash, which keeps them (#47645)
- a body base_model is stripped without allow_client_pricing_override (#47639)
- ai_decide answers without usage return usage null (#48261)
- azure_ai forwards mapped client anthropic-beta values (#48033)
- the OpenCode scripted upstream mints a fresh id per request
* test(e2e-ui): type the guardrail name before deleting it
GuardrailDeleteModal requires the exact name before Delete enables since
#45893. All three guardrail delete flows now go through one helper.
* test(observability): accept any sampled traceresponse flags and expect a 400 for a non-object body
OpenTelemetry 1.45 (#48055) sets the W3C random trace id flag on new traces,
so the gateway's traceresponse now ends in -03. The producer copies the server
span's own trace flags, so the tests now accept any two-digit flags with the
sampled bit set instead of pinning -01
#48438 made a non-object JSON body a 400 "Invalid JSON payload: expected an
object" on purpose, so the [1] body case is renamed non_object_body and
expects that 400
* test(observability): stop waiting on OpenTelemetry's export log line in the langtrace outage test
The outage test waited for "Exception while exporting Span batch" in the proxy
log, which is OpenTelemetry's wording and no longer appears with 1.45. The test
keeps asserting what the gateway guarantees, callers unaffected during the
outage, a live gateway, and every span after recovery delivered once with no
outage span duplicated
* test(guardrails): count the Responses disconnect spend rows #47611 added
Since #47611 a streamed /v1/responses disconnect writes its own spend row, so
the concurrent disconnect test saw 20 rows where it counted 10 by model group.
It now reads each row's call type and expects ten acompletion rows with the
outage verdict and ten aresponses rows with success. /v1/messages disconnects
still write no row on main, so they are left out of the count rather than
pinned as absent
The responses-httpx case of the single disconnect test no longer needs its
LIT-8603 skip and passes
* test(observability): size K4's timeout from the harness boot budget
K4 boots three two-worker proxies in a row. On CircleCI each boot takes 43 to
58 seconds, so a passing run took 179.97s against its fixed 180s cap and 6 of
30 runs hit pytest-timeout. The cap and the restart gate wait now come from
readiness_seconds and graceful_stop_seconds, like other multi-boot tests
* feat(bedrock): price native guardrailConfig trace usage into spend and x-litellm-response-cost
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(integration): bill native Bedrock guardrail trace usage on chat, responses and pass-through
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(streaming): record the Bedrock guardrail trace of a streamed metadata chunk on the logging object
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(bedrock): apply guardrailConfig on /v1/messages and price its trace, record the stream trace on both builder paths, redact the native guardrail_response
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(bedrock): validate the guardrail trace with one pydantic model
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(bedrock): record the native guardrail entry when litellm_params metadata is None
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(bedrock): import the renamed messages invoke transformation module
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(bedrock): skip native guardrail billing on cache hits and redact the trace with message logging off
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(bedrock): walk the guardrail trace iteratively when redacting it
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(bedrock): record the native guardrail trace where the Bedrock response is parsed
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(bedrock): type the native guardrail trace fixtures and locals
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
