641 commits

Author	SHA1	Message	Date
yucheng-berri	d0fa05076d	fix(xai): name a completed xAI batch's results file and stop looking it up on xAI's files API (#45559) ... * fix(xai): report a completed xAI batch's results as its output file details * fix(xai): keep a short results download and per-call timeouts out of the reported file details * fix(xai): describe a batch's results file from the batch alone and let the batch job fill its size Reading an xAI batch id as a file now costs one batch read and never downloads the results: the file reports a 0 byte placeholder, the results filename and the registration time. The managed proxy fills in the size the batch cost job measures when it downloads the results, keeps a known size when a provider reports none, and drops the per-process results download cache. * fix(managed_files): keep a measured batch output size when a refresh races the batch job A refresh that read the row before the batch cost job stored the measured size could save the provider's unsized details over it. The refresh now reads the row back from the writer and saves only while the row still holds what it read, retrying when another write landed in between. The new tests drive the real xAI and OpenAI retrieve paths through a fake HTTP upstream. * fix(managed_files): guard the refresh save with the row the request already read The conditional save first compares against the row the request read, so a refresh adds no database read unless another write landed in between; only then does it read the row back from the writer and try again. * fix(managed_files): save a refreshed file object with one guarded write and no extra database reads A refresh that loses its guarded write to a newer one now leaves that row alone and drops this worker's cached copy, so the next read loads the newer row instead of retrying against the database on the request path * fix(managed_files): retry the batch cost job's measured size after a read refreshes the row first The batch cost job re-reads the row from the writer and saves again when its guarded write loses to a concurrent refresh, so its measured size is never dropped. Reads without a measured size still make one write and no re-read * fix(managed_files): size a concurrently refreshed row instead of restoring the batch job's placeholder copy When the batch cost job loses its guarded write and the row it re-reads holds refreshed details, it applies its measured size to those details, so the results filename and any size the provider reported are kept * fix(managed_files): keep a batch output's earlier created_at when a refresh replaces its placeholder A refresh saved over a stored row now keeps the earlier of the two creation times, so an xAI placeholder keeps the time the proxy registered it instead of the read time, while a provider's own earlier creation time still wins * test(integration): an xAI placeholder row keeps its registration time after the proxy refreshes it * test(integration): drop the cached output row so the placeholder cell reads the seeded database row * test(integration): drop the cached output row without requiring it to still exist * fix(managed_files): limit the batch output size fill, guarded save and kept created_at to xAI rows * fix(xai): keep the batch id as the batch output filename Managed xAI batch output files keep the filename main already returns (the batch id) instead of renaming them to <batch id>_results.jsonl. --------- Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com>	2026-10-14 22:05:43 -07:00
Mateo Wang	c0663d5202	fix(anthropic)!: translate the OpenAI tool_search tool on /v1/chat/completions for Claude models (#46389) ... * fix(responses): translate OpenAI tool_search to Anthropic tool search for Claude models * fix(responses): define the tool_search_call output item locally for older openai SDKs * fix(responses): gate tool search on the Anthropic request builder and validate deferred tool fields Bedrock Converse builds its own request and has no tool search, so the bridge now drops the tool_search tool there with a warning instead of sending a tool the transport loses, while Bedrock invoke, Anthropic, and Vertex keep it. A replayed tool_search_call with no stored result is dropped from the Anthropic request so a previous_response_id follow-up no longer answers 400. Only srvtoolu_ ids whose name is one of Anthropic's search tools count as search calls, so a caller function named like one stays a function call. A non-boolean defer_loading or a non-list allowed_callers now answers 400 instead of 500 * fix(responses): keep an answered search call on replay and send tools undeferred when no search tool goes out A replayed Claude tool search call that the caller answered with a tool message kept its tool_result in the user turn while its tool_use was dropped from the assistant turn, so Anthropic answered 400 on the OpenAI-SDK tool loop. The search call is now dropped only when no tool message answers it. The Responses bridge now strips defer_loading from every tool whenever no tool search tool is sent, whether the caller sent none or the route cannot run it, since a deferred tool with no search tool can never be loaded. Namespace members had started carrying the flag to every bridge provider, which providers that reject unknown tool keys answered 422. The audit round-1 integration cells for the bridge wire and chaos paths ride in this commit. * fix(responses): gate only the OpenAI tool_search shape and keep native Anthropic search tools intact A native tool_search_tool_regex_20251119 or tool_search_tool_bm25_20251119 tool the caller sends through /v1/responses passes through the bridge on every route as before, and the other tools keep defer_loading whenever any Anthropic search tool goes out. The route gate now drops the OpenAI tool_search item before translation instead of filtering the translated regex tool, so bm25 counts as a search tool. answered_tool_call_ids walks the history with islice instead of a slice copy. The tool_search_call output item drops its created_by field, which OpenAI rejects on a replayed input item and the streaming item never carried * fix(anthropic): translate the OpenAI tool_search tool on /v1/chat/completions for Claude models * fix(responses): drop malformed tool flags under drop_params and keep stored search calls off non-Anthropic follow-ups With drop_params on, a non-boolean defer_loading or a non-list allowed_callers on a Responses API tool (or a namespace member) is dropped before the bridge instead of answering 400, so only a request without drop_params gets the 400. A custom tool now carries defer_loading to the chat tool like a function tool does A previous_response_id follow-up routed to a provider that does not build an Anthropic request no longer replays the stored Claude search call (srvtoolu_ id) as a client tool call. Mistral answered that history 400 "Unexpected tool call id". An assistant turn that held only the search call is dropped there. The Anthropic route keeps the call, since its factory pairs it with the stored result The tool_search_call output model is renamed OutputToolSearchCall so the proxy's OpenAPI schema keeps the OpenAI SDK's ResponseToolSearchCall name. answered_tool_call_ids reads the history by index, so the lookup is linear in the history length. The streaming done event reads the response it is given, which is the same object it used to read from the iterator * fix(bedrock): answer 400 for the OpenAI tool_search tool on Converse unless drop_params is set * fix(responses): validate function allowed_callers, send one search tool, and keep replays clean off Anthropic A function tool whose allowed_callers is not a list of strings now answers 400 instead of 500. The guardrail scan reads tools without malformed flags, so the route decides between a 400 and a drop under drop_params. When the caller sends a native Anthropic search tool beside tool_search, only the native one goes out. Databricks Claude no longer counts as an Anthropic request route. A previous_response_id follow-up off Anthropic also drops a search call the client replayed as a function_call with a srvtoolu_ id, and its output. The answered tool id scan in the Anthropic message factory runs once per assistant run instead of once per message. A null created_by on tool_search_call and tool_search_output output items is left out of the serialized response, so a cache hit returns the same keys as the first call. * fix(anthropic): send one search tool and judge tool search on the real model for chat tool_search Several OpenAI tool_search items now collapse into one tool_search_tool_regex tool, since Anthropic answers 400 when two tools share a name. A native Anthropic search tool the caller already sent wins over the OpenAI item. Bedrock invoke and Vertex AI now decide tool search support on the real model before tool-based response_format swaps in a stub model name, so a Claude 5.5 request with response_format no longer gets a 400 on a route that runs tool search. * fix(anthropic): name the proxy drop_params setting in the tool_search 400 * fix(guardrails): keep OpenAI tool_search out of the Responses guardrail tool scan The scan translated tool_search into Anthropic's regex search tool on every route, so a guardrail returning only function tools removed tool_search from native OpenAI requests and OpenAI answered 400 for the deferred tools * fix(anthropic): run tool search on azure_ai, gate it on Databricks Claude, and strip deferral without recursion Anthropic's Foundry docs list no tool search exclusion for either hosting option, so the claude-tool-search rule now fills azure_ai. Databricks Claude models route their tools through the same unsupported-tool-search gate, so a tool_search tool answers 400 or drops under drop_params instead of a 500. _without_deferral strips two fixed levels instead of walking recursively. * fix(anthropic): drop answered tool search replays beside client calls, place system before dropped search turns, flag tool search on azure_ai * fix(anthropic): keep answered tool search replays paired with their answers * fix(anthropic): drop Claude tool search calls from history sent to other routes, name the fix in the 400 A second turn that carries Claude's srvtoolu_ search call and its result now has both removed before completion() sends it to a route that does not build an Anthropic request, including the responses bridge. The tool search 400 now names model_info supports_tool_search for a model the cost map does not know, and bedrock/invoke for a Converse model that runs tool search there. * fix(anthropic): place mid-conversation system messages in one pass and scan tool_search_call in output guardrails * fix(chat): keep answered Claude tool search calls in history sent to other providers Only a srvtoolu_ search call with no tool result is dropped before a non-Anthropic route, so an answered pair keeps the caller's shape where OpenAI and Gemini take it. The history check no longer copies the list through a TypeAdapter. * fix(guardrails): send empty incomplete tool_search_call arguments when a rewrite is not a JSON object * fix(chat): drop unanswered Claude tool search calls only on routes that reject them OpenAI, Azure OpenAI and Bedrock Converse answer 400 for a srvtoolu_ call with no tool result, while Gemini answers 200. Keep the history as sent everywhere else so routes that already work see what the caller sent. * fix(responses): keep the caller's defer_loading on Claude routes when no server tool_search is sent * test(integration): cover chat tool_search routing, history drop and load on the wire Audit round 1 of 3 at 9150723c84: 58 cells across the Claude routes, Converse, Databricks, the drop_params sources, replayed history on openai, azure, Converse and gemini, and a burst, an outage and a worker kill * fix(guardrails): deliver streamed rewrites when OpenAI's tool_search_call has no call_id * fix(responses): answer 400 to a tool_search execution that is not server or client OpenAI's own Responses API answers 400 invalid_value to it. With drop_params the malformed execution is dropped like the other malformed tool flags, which leaves a server search * test(responses): cover the 400 and drop_params paths for a malformed tool_search execution * test(responses): cover a Claude search call replayed to Bedrock Converse with cache marks * fix(anthropic): map only a server or unset tool_search execution to Anthropic tool search, so a malformed value answers 400 Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(anthropic): judge the chat tool_search gate with a strict server check and keep the Responses bridge classification Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * style(responses): sort the tool_search __all__ 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>	2026-10-15 04:47:05 +00:00
Mateo Wang	52fc37888b	fix(responses bridge): round-trip tool_search_call and tool_search_output items between turns (#46444) ... * fix(responses bridge): keep defer_loading on function tools sent to the Responses API Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses bridge): give the defer_loading casts a cast-ok reason Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(responses bridge): build defer_loading outside the tool loop so Final stays valid Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses bridge): round-trip tool_search_call and tool_search_output items between turns Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(responses bridge): read replayed tool-search and namespace fields through typed helpers Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses bridge): keep tool_search items on a turn with no message or tool call Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses bridge): keep every reasoning item on a search-only turn Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses bridge): leave content unset on a search-only turn so replay adds no empty message Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses bridge): replay tool search items only with tool_search, count replayed tools in the allowlist, keep namespace off guardrails * fix(responses bridge): honor drop_params for defer_loading and route tool_search to Responses Under drop_params, defer_loading is dropped when no tool_search tool is sent, so the 400 it would cause upstream goes away as before. A non-bool value answers 400 without drop_params and is dropped with it. A null tool-level value falls back to the nested function value. A request carrying a tool_search tool now bridges to Responses on OpenAI and Azure endpoints even with reasoning_effort none, since chat completions rejects the tool_search type. * fix(responses bridge): replay tool search items in output order and count replayed tools in tool policy Store the ordered output prefix through the last tool search item, reasoning included, so a second reasoning item no longer makes OpenAI reject the replay. The shared chat-to-Responses converter now replays tool search items and namespace only when the bridge opts in, so Noma, the complexity router and guardrail payloads keep their shape. The tool policy guardrail and the allowlist count replayed tools only when the request can send them. * fix(responses bridge): forward defer_loading only beside a tool_search tool defer_loading only means something with tool_search, and backends without it reject the key (xAI answers 403). A true value with no tool_search tool now answers 400 naming drop_params and is dropped under drop_params; a false value there is a no-op and is dropped. * fix(tool_loop): keep tool search items and namespace between rounds * fix(responses bridge): check replayed tool search loads when a deployment adds tool_search * fix(chat): drop litellm response fields before strict OpenAI-compatible providers A chat reply from the Responses bridge carries provider_specific_fields on the message and on each tool call, plus reasoning_items. Groq, Fireworks, Mistral and Together reject unknown fields, so sending that reply back to them as history failed with a 400 or 422. Strip them in the shared helper and use it in those providers. * fix(caching): keep reasoning_items and tool search items on response cache hits A cached bridge response lost reasoning_items on a non-streaming hit and lost both reasoning_items and provider_specific_fields (tool_search_items) on a streamed hit, so the next turn could not replay the search or the reasoning around it * test(integration): cover tool search item replay on the chat to Responses bridge Proxy, SDK, authorization and chaos cells for returning and replaying tool_search_call and tool_search_output items and the tool call namespace * test(integration): add response cache twins for the tool search guardrail cells Each guardrail cell now has a twin where the answer is already in the Redis response cache: a key opted out of global guardrails caches a replay that loads a policy-blocked tool, the same replay without guardrails is cached before block mode sees it, rewrite mode is not served the unrewritten conversation's cached answer, and an allowed replay is served from the cache. * fix(responses bridge): forward defer_loading beside a client-executed tool_search The bridge now replays a client tool_search_call and the client's tool_search_output through provider_specific_fields.tool_search_items, so a deferred function beside a client tool_search keeps defer_loading like it does beside a hosted one * test(responses bridge): pin the client tool_search turn and its replay, streamed and not * fix(responses bridge): strip nested namespace for strict providers and keep streamed reasoning_items on text replies Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses bridge): keep dropping defer_loading beside a client-executed tool_search Forwarding defer_loading beside a client tool_search made GPT stop with a search call the chat client has to answer by hand, so a plain question that used to get tool calls got none. Keep main's behavior of dropping it there. --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>	2026-10-14 21:40:14 -07:00
Nate Armstrong	23025c92ba	fix(anthropic): emit one tool_use block per call when a /v1/messages stream chunk carries several (#46492) ... Providers like Mistral put every parallel tool call, with full arguments, into one chat-completions chunk. The /v1/messages adapter opened one tool_use block for the first call and concatenated every call's arguments into it, so clients got invalid JSON like {...}{...}. _CombinedChunkSplitter now splits such a chunk into one chunk per call. The first call streams live and calls opened behind it are held, gathering their fragments by index, until a non-fragment chunk arrives or the stream ends Fixes #44029	2026-10-15 04:27:51 +00:00
devin-ai-integration[bot]	e136e91273	fix(router): keep the prompt caching affinity pin alive as long as the prefix's cache_control ttl (#46084) ... * fix(router): keep the prompt caching affinity pin alive as long as the prefix's cache_control ttl A prefix marked cache_control ttl 1h lost its pinned deployment after the fixed 300 s, so the next request went to another deployment and paid a cold cache. The pin TTL now follows the longest ephemeral breakpoint in the cacheable prefix, tools included: 3600 s for a 1h breakpoint and 300 s otherwise. * fix(router): give prompt cache pins their own tier and honour any declared ttl Pins now live in a DualCache of their own instead of the router's 200-slot in-memory tier, so an hour-long pin no longer evicts cooldowns and rate windows. The pin lifetime follows any <n>s|m|h cache_control ttl, floored at 300 s, not only the literal "1h". * fix(router): hash the pin ttl off the event loop and flush prompt cache pins with the router cache Compute the cache_control ttl scan through offload_token_count beside the prefix hash, clear the dedicated pin cache in Router.flush_cache, and point the order-fallback and priority-group pin tests at the pin cache they read * fix(router): pin 1h prefixes only on 1h-capable models, cap the pin at 1h, and give pins 10k in-memory slots * fix(router): match the 1h pin gate to what Bedrock forwards Bedrock deployments now decide the 1h pin with is_claude_4_5_on_bedrock, the same check that decides whether the 1h ttl reaches Bedrock, so an application inference profile ARN keeps a 5 minute pin whatever its base_model prices * test(router): give the anthropic_messages pin fixture its provider * fix(router): let each pin write set its own lifetime and require a positive 1h price In a group mixing 1h and 5m deployments the in-memory pin kept the lifetime of its first write, so a 5m deployment could hold an hour-long pin. Each write now clears the in-memory entry first, matching Redis. The Bedrock gate uses supports_bedrock_prompt_cache_ttl after #46058, and a zero 1h price no longer counts as 1h support. * test(router): integration cells for the one-hour prompt cache pin lifetime Cover every surface the pin lifetime touches: chat and /v1/messages through both SDKs, the responses bridge, response-cache hits, each provider family's 1h gate, malformed and duplicated ttl values, rewrites of one prefix, mixed and cross-group routing, concurrency, every Redis attach path, the in-memory slot pressure without Redis reads, a Redis outage and a killed worker * fix(caching): bound the in-memory expiry heap and count prompt cache pins in the memory debug routes * test(integration): cover prompt cache pins in the memory debug routes and the bounded expiration heap * fix(caching): serialize in-memory cache writes so a heap rebuild cannot lose another thread's expiry entry * fix(caching): keep the in-memory cache bounded when entries have no usable expiry * test(caching): cover the response cache bound when cached entries have no usable ttl --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>	2026-10-14 21:10:09 -07:00
Mateo Wang	bbd58931fe	test(integration): follow #46101 in the breakpoint cache mark census expectation (#46509) ... * test(integration): follow #44788 and #46101 in the openrouter translation and cache mark census expectations Expect prompt_cache_breakpoint for opted-in OpenAI and omit assistant tool-call marks from the capped census Match OpenRouter refusal, logprobs, and system fingerprint nulls Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(integration): follow #46341 blank env and guardrail param handling in keepalive and scope expectations Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(router): read the first stream chunk through the stream's iterator so MCP streams keep working Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(integration): follow #46493 retry and fallback semantics for streams that fail before content Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(integration): drop the openrouter expectations #46510 already landed on main Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(integration): restore blank lines lost in the main merge 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>	2026-10-14 20:55:43 -07:00
devin-ai-integration[bot]	314f17986a	fix(responses): translate OpenAI tool_search to Anthropic tool search for Claude models (#45882) ... * fix(responses): translate OpenAI tool_search to Anthropic tool search for Claude models * fix(responses): define the tool_search_call output item locally for older openai SDKs * fix(responses): gate tool search on the Anthropic request builder and validate deferred tool fields Bedrock Converse builds its own request and has no tool search, so the bridge now drops the tool_search tool there with a warning instead of sending a tool the transport loses, while Bedrock invoke, Anthropic, and Vertex keep it. A replayed tool_search_call with no stored result is dropped from the Anthropic request so a previous_response_id follow-up no longer answers 400. Only srvtoolu_ ids whose name is one of Anthropic's search tools count as search calls, so a caller function named like one stays a function call. A non-boolean defer_loading or a non-list allowed_callers now answers 400 instead of 500 * fix(responses): keep an answered search call on replay and send tools undeferred when no search tool goes out A replayed Claude tool search call that the caller answered with a tool message kept its tool_result in the user turn while its tool_use was dropped from the assistant turn, so Anthropic answered 400 on the OpenAI-SDK tool loop. The search call is now dropped only when no tool message answers it. The Responses bridge now strips defer_loading from every tool whenever no tool search tool is sent, whether the caller sent none or the route cannot run it, since a deferred tool with no search tool can never be loaded. Namespace members had started carrying the flag to every bridge provider, which providers that reject unknown tool keys answered 422. The audit round-1 integration cells for the bridge wire and chaos paths ride in this commit. * fix(responses): gate only the OpenAI tool_search shape and keep native Anthropic search tools intact A native tool_search_tool_regex_20251119 or tool_search_tool_bm25_20251119 tool the caller sends through /v1/responses passes through the bridge on every route as before, and the other tools keep defer_loading whenever any Anthropic search tool goes out. The route gate now drops the OpenAI tool_search item before translation instead of filtering the translated regex tool, so bm25 counts as a search tool. answered_tool_call_ids walks the history with islice instead of a slice copy. The tool_search_call output item drops its created_by field, which OpenAI rejects on a replayed input item and the streaming item never carried * fix(responses): drop malformed tool flags under drop_params and keep stored search calls off non-Anthropic follow-ups With drop_params on, a non-boolean defer_loading or a non-list allowed_callers on a Responses API tool (or a namespace member) is dropped before the bridge instead of answering 400, so only a request without drop_params gets the 400. A custom tool now carries defer_loading to the chat tool like a function tool does A previous_response_id follow-up routed to a provider that does not build an Anthropic request no longer replays the stored Claude search call (srvtoolu_ id) as a client tool call. Mistral answered that history 400 "Unexpected tool call id". An assistant turn that held only the search call is dropped there. The Anthropic route keeps the call, since its factory pairs it with the stored result The tool_search_call output model is renamed OutputToolSearchCall so the proxy's OpenAPI schema keeps the OpenAI SDK's ResponseToolSearchCall name. answered_tool_call_ids reads the history by index, so the lookup is linear in the history length. The streaming done event reads the response it is given, which is the same object it used to read from the iterator * fix(responses): validate function allowed_callers, send one search tool, and keep replays clean off Anthropic A function tool whose allowed_callers is not a list of strings now answers 400 instead of 500. The guardrail scan reads tools without malformed flags, so the route decides between a 400 and a drop under drop_params. When the caller sends a native Anthropic search tool beside tool_search, only the native one goes out. Databricks Claude no longer counts as an Anthropic request route. A previous_response_id follow-up off Anthropic also drops a search call the client replayed as a function_call with a srvtoolu_ id, and its output. The answered tool id scan in the Anthropic message factory runs once per assistant run instead of once per message. A null created_by on tool_search_call and tool_search_output output items is left out of the serialized response, so a cache hit returns the same keys as the first call. * fix(guardrails): keep OpenAI tool_search out of the Responses guardrail tool scan The scan translated tool_search into Anthropic's regex search tool on every route, so a guardrail returning only function tools removed tool_search from native OpenAI requests and OpenAI answered 400 for the deferred tools * fix(anthropic): drop answered tool search replays beside client calls, place system before dropped search turns, flag tool search on azure_ai * fix(anthropic): keep answered tool search replays paired with their answers * fix(anthropic): place mid-conversation system messages in one pass and scan tool_search_call in output guardrails * fix(guardrails): send empty incomplete tool_search_call arguments when a rewrite is not a JSON object * fix(responses): keep the caller's defer_loading on Claude routes when no server tool_search is sent * fix(guardrails): deliver streamed rewrites when OpenAI's tool_search_call has no call_id * fix(responses): answer 400 to a tool_search execution that is not server or client OpenAI's own Responses API answers 400 invalid_value to it. With drop_params the malformed execution is dropped like the other malformed tool flags, which leaves a server search * test(responses): cover the 400 and drop_params paths for a malformed tool_search execution * test(responses): cover a Claude search call replayed to Bedrock Converse with cache marks --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Co-authored-by: mateo <mateo@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>	2026-10-14 20:42:22 -07:00
Kerry Lu	7b7604f0ed	test(translation): share rich fixtures across Claude, GPT and Gemini providers (#46585) ... * test(integration): add rich anthropic claude haiku 5.5, opus 5.5 and fable 5.1 translation cases Capture-backed basic cases on /v1/chat/completions, /v1/messages and /v1/responses for claude-haiku-5-5, claude-opus-5-5 and claude-fable-5-1, reusing the shared client templates and ANTHROPIC_MESSAGES_REQUEST/RESPONSE with model-specific id, content and usage overrides. The minimal Opus 5.5 hello cases are replaced by the rich conversation. Sonnet 5.5 and legacy models are unchanged * test(translation): rich OpenAI captures for gpt-6.1-sol, gpt-6-luna, gpt-6-astra and gpt-5.6-terra Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(translation): rich Azure gpt-6.1-sol, gpt-6-luna, gpt-6-astra and gpt-5.6-terra cases * test(translation): rich azure_ai Claude cases for haiku-5-5 and fable-5-1 Bind the shared chat_completions, messages and responses client templates and the anthropic provider templates to azure_ai/claude-haiku-5-5 and azure_ai/claude-fable-5-1 from real Azure AI Foundry captures, keeping the Azure dialect exact (/anthropic/v1/messages, x-api-key plus api-key on chat_completions and responses, system as a text block list on /v1/messages). Add prompt_cache_hit variants for both models from the second call inside the cache window. The two deployments need rows in tests/integration/proxy_config.yaml Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(translation): rich Gemini bases for gemini-3.8-flash, gemini-3.1-pro-preview and gemini-4-argon Add the shared generateContent provider templates in templates/gemini.py from real captures and bind the three target models on chat completions, messages and responses with explicit per-model response overrides. gemini-4-argon is registered in the three basic lists. gemini-3.5-flash is unchanged * test(translation): enrich vertex_ai claude and gemini cases from live captures Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(integration): rich Bedrock converse, invoke and mantle translation cases from real captures * test(integration): register the rich translation deployments and follow the rich Gemini 3.8 Flash cases Add the synthetic deployments the rich provider cases bind (anthropic, openai, azure, azure_ai, gemini, vertex_ai, bedrock converse, invoke and mantle) to tests/integration/proxy_config.yaml, all pointed at the fake provider on 127.0.0.1:8191 with synthetic keys. The Gemini omitted-temperature contract now takes its expected generationConfig and reply text from the rich GEMINI_3_8_FLASH_TEST_CASE literals instead of the retired minimal hello cases * test(translation): reuse bridged and native response templates --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: kerry <kerry@berri.ai>	2026-10-14 19:53:09 -07:00
ryan-crabbe-berri	e889d9d653	fix(bedrock)!: replay redacted_thinking on Converse and keep its data as Claude's own string (#46532) ... * fix(bedrock): send redacted_thinking back to Converse as reasoningContent.redactedContent Replaying an assistant turn that carried a redacted_thinking block to Bedrock Converse turned it into an empty unsigned reasoningText, which the assistant content builder then dropped as blank text. Bedrock returned 200 so callers never noticed. Map redacted_thinking to redactedContent in both the thinking_blocks and inline content paths of the sync and async builders * fix(bedrock): drop blank redacted_thinking data and keep non-str signatures signed on Converse replay * fix(bedrock): replay redacted_thinking to Converse only when the target is Claude Nova, Llama, DeepSeek and gpt-oss reject reasoningContent.redactedContent with a 400, so a Claude history replayed to them through a Router fallback or model switch keeps dropping the redacted block the way main does. Claude is resolved from the Bedrock base model of the target, and an application inference profile ARN counts as Claude when its deployment's base_model names a Claude model. Adds integration contracts for the replayed Converse request shape, stream and non-stream, with a tool call, and for non-Claude and ARN targets. * test(e2e): cover Bedrock Converse replaying redacted_thinking live through the proxy Turn 1 triggers redacted thinking on Claude Sonnet 4.6, turn 2 replays it, and a live edge between the proxy and Bedrock checks the replayed Converse body carries the same reasoningContent.redactedContent. Covers nonstream, stream and a tool call round trip * fix(bedrock): validate litellm_params before reading the deployment base_model litellm_params arrives as an untyped dict, so passing it straight into the typed helper grew reportUnknownArgumentType. Validating it into Mapping[str, object] first keeps the read typed without growing the type-check gate * fix(bedrock): wrap non-Converse redacted_thinking data and let a named model id win over base_model * test(bedrock): cover redacted_thinking minted outside Converse, model_id and the sync Router base_model path * fix(bedrock): match bare claude- base_model names without reading the untyped cost map * fix(bedrock): keep redacted_thinking data as Claude's own string on every route Converse hands redactedContent back base64-wrapped, and that wrapper used to leak out as data, so a Converse turn replayed to Invoke or the Anthropic API was rejected. Converse replies now decode it, Converse requests wrap it exactly once, and the Anthropic-shaped builders unwrap histories saved with the old wrapping. Responses API encrypted reasoning is no longer sent to Claude on Converse, which rejected it with a 400	2026-10-14 19:08:14 -07:00
devin-ai-integration[bot]	ff8b5d8324	test(translation): add claude-fable-5-1 translation cases (#46581) ... * test(translation): add claude-fable-5-1 translation cases Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(translation): match sibling style in claude-fable-5-1 cases Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>	2026-10-15 01:54:42 +00:00
devin-ai-integration[bot]	a1da0ecbce	test(translation): recapture bedrock invoke claude-sonnet-4-6 bases after usage shape change (#46579) ... Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>	2026-10-15 01:54:32 +00:00
devin-ai-integration[bot]	c9cca25991	test(translation): add gpt-6-astra translation cases (#46580) ... Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>	2026-10-15 01:52:58 +00:00
Mateo Wang	9ba4cc4fb3	fix(anthropic): keep tool_search_tool_result so the search call replays with its result (#46440) ... * fix(anthropic): keep tool_search_tool_result so the search call replays with its result Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(anthropic): drop a client tool_result for a search call replayed with its server result * fix(anthropic): replay a server tool call the client answered as the client sent it * fix(anthropic): keep a server tool result under modify_params, count client-answered marks, and replay it from run_tool_loop modify_params no longer adds a placeholder answer for a server tool call whose result is kept, unless the call ends the request. The cache control hook counts a mark on a server tool call the client answered, since that call goes out as a plain tool_use. run_tool_loop sends provider_specific_fields back and runs only the client's tool calls. * fix(anthropic): keep a server tool result when another tool answer follows, scope run_tool_loop's replay, and keep code_interpreter_results off tool search turns * test(anthropic): audit cells for tool search result round trips, replay, cache marks, pass-through logging and chaos --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>	2026-10-14 18:52:32 -07:00
Mateo Wang	c70dd4ece6	fix(responses bridge): keep defer_loading on function tools sent to the Responses API (#46439) ... * fix(responses bridge): keep defer_loading on function tools sent to the Responses API Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses bridge): give the defer_loading casts a cast-ok reason Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(responses bridge): build defer_loading outside the tool loop so Final stays valid Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses bridge): honor drop_params for defer_loading and route tool_search to Responses Under drop_params, defer_loading is dropped when no tool_search tool is sent, so the 400 it would cause upstream goes away as before. A non-bool value answers 400 without drop_params and is dropped with it. A null tool-level value falls back to the nested function value. A request carrying a tool_search tool now bridges to Responses on OpenAI and Azure endpoints even with reasoning_effort none, since chat completions rejects the tool_search type. * fix(responses bridge): forward defer_loading only beside a tool_search tool defer_loading only means something with tool_search, and backends without it reject the key (xAI answers 403). A true value with no tool_search tool now answers 400 naming drop_params and is dropped under drop_params; a false value there is a no-op and is dropped. * fix(responses bridge): drop defer_loading beside a client-executed tool_search 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>	2026-10-14 18:46:28 -07:00
yuneng-jiang	0ececab7a1	test(integration): update stale CircleCI integration expectations (#46570) ... Brings integration tests in line with intentional behavior changes from #46331, #46291, #46341, #46355, #46368 and #46493, moves the Databricks non-Claude cases off llama-3 names so they no longer download a Hugging Face tokenizer, drops the unrealistic count_tokens tools=5 case and skips the Mantle region-prefixed v1 base case as unrealistic input	2026-10-14 18:21:59 -07:00
yucheng-berri	1f4ef4b7d1	feat(proxy): general_settings.disable_requester_ip_address_logging keeps caller IP out of spend logs (#45804) ... * feat(proxy): general_settings.disable_requester_ip_address_logging keeps caller IP out of spend logs When the setting is on, spend log rows store requester_ip_address as null in the column, the metadata and the stored request (which also drops client-IP forwarding headers), and IP addresses in stored error messages are masked, so the Logs page shows no caller IP. The setting is read the way the settings API validates it. Capture is unchanged, so guardrails, logging callbacks and metrics still receive the IP. Toggleable from the Admin UI General Settings. Resolves LIT-7442 * fix(proxy): mask IPv6 addresses that end or start with :: across eight groups The masking pattern allowed seven colons, so valid forms like 1:2:3:4:5:6:7:: and ::1:2:3:4:5:6:7 stayed in stored error text with disable_requester_ip_address_logging on. Also lists the request metadata walker in the recursion check, since it only walks the tree the spend log sanitizer already returned. * fix(proxy): mask IPs in every stored header value and after labels or underscores With disable_requester_ip_address_logging on, client IPs carried in headers outside the forwarding list (do-connecting-ip, incap-client-ip, custom names) and addresses written as host:2001:db8::2, peer_2001:db8::2, socket_192.0.2.1 or an unbracketed IPv6 with a port were still stored in spend logs. * fix(proxy): mask request header IPs before long values are truncated A header value longer than the spend log string limit kept the part of an IP that fell before the truncation cut. Request headers in metadata and requester_metadata are now masked before the body is truncated, like error text already was. IPv4 followed by a dotted port (192.0.2.1.443) is masked again. * fix(proxy): mask the IP piece a header truncation cut leaves instead of pre-masking headers Masking header values before truncation scanned the whole client value, including text the cut drops, and walked hook-made header cycles. Mask after truncation instead, then mask the hex and digit run touching each side of the truncation marker when it is short enough to be part of an IP address. Every nested headers mapping gets the same treatment. * docs(proxy): say the requester IP setting masks IPs in other stored header values * fix(proxy): mask stored error text after truncation and harden the cut-piece pass Error messages and tracebacks were regex-scanned in full before the DB cap, which costs seconds on multi-megabyte provider errors. Mask them after the cap like header values, with the same pass over the run beside each truncation marker. That pass now handles every marker, skips long hex tokens so an address beside one is still masked, keeps at most one address length, and also masks colon-only IPv6 groups. * fix(proxy): drop the requester IP from the originating request an auto router classifier row stores * fix(proxy): drop the requester IP from the classifier input an auto router classifier row stores * refactor(proxy): mask stored IPs with the content filter's ipv4 and ipv6 patterns The requester IP setting now reuses the content filter guardrail's prebuilt ipv4 and ipv6 patterns instead of its own address regexes, so spend logs mask what that guardrail masks. Addresses glued to a label or underscore stay as the content filter leaves them. * fix(proxy): mask stored IPs with the spend-log matcher again The content filter's ipv4 and ipv6 patterns left labelled IPv6 (host:2001:db8::2), unbracketed IPv6 with a port and IPv4 after an underscore in stored error text and header values, and masked parts of text like std::io::Error. Restore the address-validating matcher and its regression cases. This reverts commit 79537a8aa5. --------- Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com>	2026-10-14 18:16:40 -07:00
adityajoy-1902	20da5b949c	feat(proxy): return the W3C traceresponse header on traced responses (#42079) ... * feat(proxy): expose trace ID response header * style(otel): format trace correlation middleware * test(otel): cover websocket trace middleware * fix(otel): disable correlation on setup failure * refactor(otel): inject instrumentation setup * fix(proxy): drop a relayed upstream traceresponse when the gateway sends its own * fix(proxy): preserve upstream traceresponse without gateway injection * fix(proxy): replace relayed streaming traceresponse * test(otel): cover trace response fallback paths * test(proxy): cover the streaming pass-through relay when the gateway propagator is off * fix(proxy): keep a relayed upstream traceresponse and stamp the call id on the server span The gateway's response propagator now leaves a response that already carries an upstream traceresponse untouched, so pass-through, file content and plugin proxy routes return one header and untraced pass-through routes keep the upstream's. The per-helper strips are gone. The propagator also sets litellm.call_id on the server span from the response's x-litellm-call-id. * fix(proxy): add traceresponse only to sampled HTTP responses, tuple headers and unhandled 500s included * fix(proxy): write only traceresponse and link a relayed header from a restarted upstream trace * fix(proxy): send traceresponse only from the proxy app and expose it through CORS only with OTel V2 * test(proxy): check the CORS expose list contains the UI headers instead of pinning its identity * test(integration): check traceresponse names the gateway server span across routes, errors, pass-through and chaos * test(integration): read the cache-hit spend row by response id and assert only the traceresponse expose entry --------- Co-authored-by: adityajoy-1902 <bittu19jain@gmail.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>	2026-10-14 18:12:34 -07:00
devin-ai-integration[bot]	0f1312f39d	test(integration): add claude-haiku-5-5 basic translation cases (#45176) ... * test(integration): add claude-haiku-5-5 basic translation cases Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(integration): match sibling style in claude-haiku-5-5 translation cases Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: kerry <kerry@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>	2026-10-15 01:04:54 +00:00
Kerry Lu	0c91bd7fdb	test(translation): share rich request and response templates (#46546) ... * test(translation): enrich Sonnet 5.5 base conversations * test(translation): capture prompt caching and explicit reasoning * refactor(translation): share endpoint request and response templates --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>	2026-10-15 00:30:36 +00:00
Kerry Lu	44e94eb985	fix(cost): split xai spend rows across input and output instead of booking the total as output (#46320) ... * fix(cost): keep the input/output split on streamed xai spend rows Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(cost): split xAI's reported total across input and output by token math Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>	2026-10-14 17:02:00 -07:00
Kerry Lu	6d1a3bce2c	fix(responses): return the first message item as content for structured output on the chat bridge (#46508) ... Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>	2026-10-14 16:58:00 -07:00
yuneng-jiang	9271edc734	feat(proxy): let proxy admins open team metadata to team admins, keeping proxy settings with proxy admins (#46506) ... * feat(proxy): let proxy admins open team member defaults to team admins * fix(proxy): keep a team admin's never-resets duration on a team's first member budget * test(proxy): pin the existing-row and stored never-resets cases for member defaults * feat(proxy): let proxy admins open soft_budget to team admins * feat(proxy): let proxy admins open team metadata to team admins, keeping proxy settings with proxy admins * test(ui): name expected objects in team admin edit access tests Keeps local/no-large-inline-object-arg within its eslint budget * fix(proxy): compare team metadata without recursion and count key presence A proxy-controlled metadata key that appears or disappears with a null value is now a change, and the JSON comparison no longer recurses * fix(proxy): compare team metadata numbers exactly Numbers are compared as decimals, so integers past float precision and close floats are no longer treated as equal	2026-10-14 23:51:44 +00:00
Kerry Lu	7d7e28829f	test(integration): delete flaky team model TPM concurrency test (#46541) ... Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>	2026-10-14 16:24:08 -07:00
Mateo Wang	dba46c0513	fix(responses): keep the provider code and message on top-level stream error events (#46438) ... * fix(responses): keep the provider code and message on top-level stream error events Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses): log a mid-stream failure before the router falls back CustomStreamWrapper now waits for failure logging before it raises the error the router falls back on, so the cooldown lands on the deployment that failed instead of the fallback that replaced it. Also keeps numeric error codes and the top-level message on the WebSocket and ChatGPT paths. * fix(streaming): run sync failure callbacks before a failed stream falls back to another deployment The router cools a deployment down from a sync failure callback that reads the request's model_info. A failed stream submitted that callback to the logging executor without waiting, so it often ran after the router's fallback had overwritten model_info and cooled down the healthy fallback deployment instead, leaving the model group with no deployments * test(responses): cover top-level OpenAI stream error events end to end * test(streaming): read live worker pids so the second SIGKILL chaos variant sees two workers * fix(streaming): run waited sync failure logging off the shared logging pool * revert(streaming): keep failed-stream failure logging in the background Waiting for sync failure callbacks before a failed stream raises fixed the router cooldown race, but it slowed healthy calls under many failing streams with a slow callback, since the wait shares a thread pool with every call's start. The race is pre-existing on main and is tracked separately, so this PR goes back to main's logging timing and keeps only the top-level error event changes --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>	2026-10-14 16:16:46 -07:00
Tobias Mayr	d03923e6a9	fix(responses): propagate cache_control through responses->chat completion bridge (#32511) ... * fix(responses): propagate cache_control through responses->chat completion bridge User/assistant messages, tool call outputs, and function calls built from Responses API input items were dropping cache_control, breaking prompt caching for Bedrock/Anthropic when routed through the litellm_completion transform. Also propagate cache_control onto the last content element of user/assistant messages, since Anthropic/Bedrock read it from the content block rather than (or in addition to) the message level. Adds the missing cache_control field to ChatCompletionToolMessage and ChatCompletionResponseMessage TypedDicts to keep the assignments type-safe. * chore(ui): regenerate schema.d.ts for ChatCompletionToolMessage.cache_control Adding cache_control to the ChatCompletionToolMessage TypedDict changed the proxy's OpenAPI spec; regenerated via `npm run gen:api`. * fix(responses): don't overwrite existing content-block cache_control; add list-content test Per Greptile review on the PR: - The message-level cache_control was unconditionally overwriting the last content block's cache_control when that block already carried its own (preserved by _transform_responses_api_content_to_chat_completion_content). Now only propagates onto the last block when it doesn't already have one. - Added the missing test coverage for the content-block propagation path with list content, which no existing test exercised (all used plain string content, so the isinstance(content, list) branch was never hit). * style(responses): extract cache_control into a named variable, trim comment Per style review: the message-building branch read cache_control 3 different ways in 5 lines (.get() twice, then [] indexing once), unlike the sibling tool-call-output/function-call functions this PR touches, which each extract it into a named variable up front (output_cache_control, function_call_cache_control). Matched that convention here too. Also trimmed the 3-line inline comment to one line, matching the file's usual comment length for similarly subtle behavior. * chore: refresh lazy OpenAPI snapshot * fix: type cache control propagation * fix: place cache control type suppressions * style: format cache control type conversion * refactor(responses): build bridged cache_control messages in one shot * fix(responses): keep cache_control marks on bridged tool calls and within the provider cap A function_call item's mark now rides on the tool call the bridge builds, an assistant text item merged behind a function_call keeps its own mark, a developer item keeps its mark when it is mapped to a system message, and a previous_response_id replay yields its stale marks to the newest ones so the provider cap holds. The Responses layer counts the marks on function_call and function_call_output items before it places an ordinal injection point, and the chat pass replaces that count with its own instead of adding to it. * fix(responses): count each cache_control mark once and keep the bridge within the provider's limits List content carries an item-level cache_control on its last block only, so the provider sees one cache point and the breakpoint census counts one. Marked function tools and SDK-typed input items are handled by the Responses-layer reserve, assistant text merged into a function call turn keeps its mark on its own block, and the previous_response_id replay budget drops marks per turn, newest kept, instead of stripping every older turn once one overshoots. The request's own marks now fall under the same budget, newest kept, so five marked items no longer answer the provider's 400. A cache_control value that is not an ephemeral mark with an optional ttl is left off the bridged message instead of reaching the provider as sent, and a developer message maps to the system role with its content, name, and cache_control only, so unknown keys never reach a strict provider. * fix(responses): send item cache_control only to cache-point targets and follow drop_params Item marks reach the provider only when the target turns them into cache points, and every other target keeps main's behavior of stripping them. Without drop_params a mark the provider would refuse reaches it as sent, so the client gets the same 400 as on /v1/chat/completions. With drop_params a malformed mark is dropped and only the newest 4 are kept. The shared input-to-messages helper and the developer mapping in base_utils go back to main, and a marked developer item reaches a cache-point target as a system message in the bridge. * fix(responses): budget top-level marks, follow drop_params on Nova tool marks, and match chat-keeping providers * fix(responses): validate replayed marks and count input tokens without item marks * fix(responses): leave unknown Bedrock ARNs unmarked under drop_params, match OpenRouter's list, and reserve only forwarded marks * test(responses): cover item cache_control parity per chat route, drop_params trimming, and replay budgets * fix(responses): keep item marks off inspection paths and match each chat route's keep rule Only the request the bridge sends gets item marks now. Guardrails, token counting, rate limiting, and input_tokens read the input with no item marks, as on main, so an off-spec mark cannot fail a guardrail call. Tool message and tool call marks are typed inside the bridge, leaving the shared chat types and guardrail schemas as on main. Databricks keeps marks only for Claude, Snowflake uses its chat route's Claude check, and every Anthropic deployment keeps them whatever its model name. The injection census skips SDK-typed tools, and the owned-proxy test expects the marked messages on /v1/responses. * test(responses): cover guardrail reads without item marks, Snowflake name parity, and SDK-typed tools in the census * fix(responses): leave a non-list input untouched when stripping item marks * test(responses): cover string, list, object and tuple input with and without item marks kept Each input shape now answers the same whether the unmapped Bedrock ARN keeps item marks or strips them under drop_params, through the proxy and the SDK. Object input also runs through the OpenAI-compatible chat bridge, /v1/responses/input_tokens and the project input-token limiter * test(cache): derive owned-proxy cell timeouts from the harness boot budget The capped-burst cells and the uncapped response-cache cells boot their own two-worker proxy, and a fixed 240 or 360 second timeout cut them during boot on a slow runner before the harness readiness budget could decide. They now carry the same derived budget as the sibling chaos and guardrail files, with four boot and stop budgets for the restart cell that boots twice --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>	2026-10-14 15:44:34 -07:00
yuneng-jiang	f95e466bb7	test(integration): fix three more CircleCI integration reds from test setup (#46510) ... OpenRouter basic translation expected the pre-#44788 response shape. Read refusal, logprobs and system_fingerprint from the fixture at their new positions. The team model TPM test required exactly one of three concurrent requests to be served. The pipelined limiter can reject all of them, which is an accepted trade-off, so assert no over-admission and that a lone retry is served when the burst got nothing. The Voyage host tests count every host their proxy dials. The responses cost poller could dial api.openai.com for background-response rows that other tests left in the shared worker database, so turn polling off in those proxies.	2026-10-14 15:23:14 -07:00
Yassin Kortam	2310daef7e	fix(proxy-extras): run prisma migrate deploy under the pinned coordinator lock instead of Prisma's session advisory lock (#46368) ... Through a transaction-mode pooler Prisma takes pg_advisory_lock(72707369) on one backend and releases it on another, so the first backend keeps the lock while idle and the next boot waits on it. Migrations now run with PRISMA_SCHEMA_DISABLE_ADVISORY_LOCK=1 while LiteLLM holds the coordinator lock and Prisma's key as transaction-level locks on one pinned connection, so nothing can outlive the migration Resolves LIT-9571 Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>	2026-10-14 15:10:27 -07:00
Kerry Lu	b9849f3588	fix(router): add fallback_on_unsupported_params so param rejections can raise instead of falling back (#46350) ... * fix(router): add fallback_on_unsupported_params so client-side param rejections can raise instead of falling back Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * style(router): wrap the fallback gate so ruff format passes Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(router): rename the flag to fallback_on_bad_request and cover provider 400s too Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(router): narrow the wire test responses so the type gate count does not grow Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(openai): swap the model map with setattr so the type gate count does not grow Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(router): import the exception classes directly so the type gate count does not grow Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(router): type the fallbacks list in fallback_event_handlers to pay for the new Router kwarg in the type gate Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(router): keep the LIT001 count flat on the typed fallbacks parameters Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(router): type the fallback stream wrapper weakref so the type gate stays level Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(router): make fallback_on_bad_request updatable at runtime so DB-stored router_settings apply it Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * chore(ui): regenerate schema.d.ts for UpdateRouterConfig.fallback_on_bad_request Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(router): rename the flag back to fallback_on_unsupported_params Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * revert(openai): drop the reasoning_effort=none client-side check, the Router flag covers the provider 400 Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * ci: rerun docs checks now that the router_settings row is on litellm-docs main Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(router): only stop fallbacks on real 400s and cover the context window and content policy exclusions Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(router): move the no-fallback 400 check into is_bad_request_without_fallback and pin the 402 and Vertex 403 cases Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(router): keep falling back on router-raised 400s when fallback_on_unsupported_params is off Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(router): read llm_provider off the exception with getattr so the type gate stays flat Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>	2026-10-14 14:43:01 -07:00
devin-ai-integration[bot]	47e4ed88c2	fix(anthropic): keep refusal stop_reason and stop_details on the logged response (#46104) ... * fix(anthropic): keep refusal stop_reason and stop_details on the logged response * fix(anthropic): keep the refusal stop-fields boundary tolerant of a non-dict upstream body * test(anthropic): use Anthropic's documented refusal example as the unit-test fixture * fix(streaming): keep provider fields on async stream cache hits and received_model_id on synthesized finish chunks * fix(caching): replay cached completion_tokens_details on stream cache hits and skip non-object cached provider fields * test(integration): cover Anthropic-family refusal stop_details on the wire, the cache and under chaos * fix(caching): skip a cached completion_tokens_details that fails validation on stream replay * fix(anthropic): keep refusal stop fields on a JSON-mode non-stream response Also adds the round 3 audit cells for JSON-mode refusals, the proxy response serialization, and wrong-typed cached usage details on both stream replays * test(anthropic): expect content_filter on streamed JSON tool refusals after main's stream change --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>	2026-10-14 14:42:49 -07:00
Kerry Lu	f10134728d	fix(caching): report Redis circuit-breaker metrics through the Prometheus services logger (#46484) ... * fix(caching): collect the Redis circuit-breaker state gauge as one series per state across worker processes The gauge declares multiprocess_mode=livesum, so PROMETHEUS_MULTIPROC_DIR apps get one series per state summed over the live workers instead of one series per worker pid Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(caching): register Redis circuit-breaker metrics only when LiteLLM prometheus logging is on The breaker collectors join the default registry once the prometheus or prometheus_system callback is configured or a Prometheus logger starts, with earlier breaker events tallied and seeded in, and litellm.disable_redis_circuit_breaker_metrics turns them off for apps that export their own metrics Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(caching): drop the disable_redis_circuit_breaker_metrics opt-out Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(caching): keep the Redis breaker metrics gate to a per-state count and a publish hook Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(caching): pin the Redis breaker metrics gate against an injected registry Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(caching): publish the breaker collectors before reading them in the existing metrics test Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(caching): report Redis circuit-breaker metrics through the Prometheus services logger Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(integrations): build the Redis breaker collectors with typed prometheus_client constructors Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>	2026-10-14 14:29:54 -07:00
Kerry Lu	83980c9d63	fix(router): read the first stream chunk before returning so a failure before any token runs through the retry and fallback budget (#46493) ... * fix(router): read the first stream chunk before returning so a failure before any token runs through the retry and fallback budget Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(router): keep only the raising call inside pytest.raises for the pre-content stream tests Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(router): yield the primed first chunk from a helper generator so the stream iterators keep their complexity Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(bedrock): expect the empty invoke stream to be reported from router.completion now that the first chunk is read there Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test: give router stream mocks a first chunk or an ending so the eager first read neither hangs nor retries Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(router): name the first-chunk helpers after what they do (_read_first_chunk, _FirstChunk, _chunks_with_first) Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(router): drop the string annotation on _FirstChunk.chunk, ModelResponseStream is imported at runtime --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>	2026-10-14 14:19:25 -07:00
Kerry Lu	542b254ca2	fix(anthropic): forward service_tier on /v1/messages for anthropic, azure_ai and bedrock_mantle (#46370) ... * test(integration): add known-bug translation cases for request fields LiteLLM drops service_tier on /v1/messages, conversation on /v1/responses and metadata on /v1/chat/completions never reach the provider. Each gets a TranslationTestCase with the correct provider body, marked xfail(strict=True) through the new TranslationTestCase.known_bug field, plus a passing service_tier case for /v1/messages on the openai route Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(integration): widen the known-bug cases to azure_ai, bedrock_mantle, openai/responses and azure deployments Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(anthropic): forward service_tier on /v1/messages for anthropic, azure_ai and bedrock_mantle Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>	2026-10-14 14:07:59 -07:00
yuneng-jiang	df3b44dbee	feat(proxy): let proxy admins open team member defaults to team admins (#46353) ... * feat(proxy): let proxy admins open team member defaults to team admins * fix(proxy): keep a team admin's never-resets duration on a team's first member budget * test(proxy): pin the existing-row and stored never-resets cases for member defaults	2026-10-14 14:05:58 -07:00
yuneng-jiang	47c40586e1	fix(proxy): evict the cached user on every proxy for every /user/update and /user/bulk_update (#46500) ... * fix(proxy): evict the cached user on every proxy for every /user/update and /user/bulk_update Previously the cached user row was only evicted and broadcast when the update touched max_budget, model_max_budget, tpm_limit, rpm_limit or metadata, so a models or user_role change kept being served by peer proxies until the cache TTL expired * test(proxy): cover user cache eviction past the first bulk invalidation batch	2026-10-14 13:25:20 -07:00
devin-ai-integration[bot]	2d4472efc1	fix(vllm): write spend log rows for /vllm passthrough calls to router models (#45733) ... * fix(vllm): write spend log rows for /vllm passthrough calls to router models The vLLM passthrough config never built a logged response for relayed calls, so stream and non-stream calls through /vllm with a router model wrote no spend row and never charged the key. vLLM now shares the OpenAI-shaped relay logging that Azure already had, moved into an openai_like helper both configs call. * ci: run the vllm passthrough unit tests in the llms-providers shard * fix(proxy): log /vllm text completions and ask router models for stream usage A chat stream relayed to a router model without stream_options now gets include_usage added on the way out, and the usage chunk vLLM appends is removed before the client sees it, so the spend row carries vLLM's own token counts instead of a local estimate. /vllm/completions calls now write a row too, stream and non-stream. * fix(vllm): keep usage, fingerprints, and disconnect rows correct on /vllm streams Inject and strip stream usage on /vllm/completions too, hold every finish frame for the fingerprint when n > 1, pass a forced-usage server's stream through untouched, stop turning continuous_usage_stats alone into per-chunk usage, close the upstream when the client hangs up so the row is written, and assemble text choices with one sort instead of a scan per choice * fix(vllm): keep n>1 stream order when holding finish chunks for the fingerprint A held finish chunk now goes out as soon as any other chunk arrives, so a choice that finishes while another still streams keeps its place. Only the finish chunks right before the usage chunk get vLLM's system_fingerprint. * fix(vllm): keep caller stream_options and log both success handlers Forward a stream_options the proxy cannot read, or a non-boolean include_usage, to vLLM as the caller sent it. Only a false always_include_stream_usage changes the /vllm relay, so clients get the same stream bytes under every setting. Record the passthrough origin when logging starts so the sync and async success handlers both parse the relayed body, hand callbacks a parsed text completion, and read relayed messages and prompt separately so a stray field no longer drops the local count. Add the audit's integration tests for the relay, the settings, and the Azure spend row. * test(vllm): expect the caller's stream under always_include_stream_usage true * fix(vllm): relay long SSE frames in linear time * test(vllm): time the relay's usage and finish_reason prefilter on one large frame --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>	2026-10-14 12:40:48 -07:00
yuneng-jiang	1d9dd7b067	feat(proxy): let proxy admins open soft_budget to team admins (#46485)	2026-10-14 19:27:37 +00:00
Kerry Lu	6bd5580425	fix(streaming): add CustomStreamWrapper.close() and release the connection when a sync stream ends (#46354) ... * fix(streaming): close sync streams so the connection goes back to the pool CustomStreamWrapper gets a sync close() (plus context manager support) that closes the wrapped provider stream, and both the sync and async wrappers close the provider stream when the stream reaches its end. The Anthropic stream iterator keeps the httpx response it reads so close()/aclose() can release the socket after message_stop instead of leaving it checked out of the pool Co-authored-by: Blair Jordan <blair.j.jordan@gmail.com> Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(streaming): keep the basedpyright ceiling flat in CustomStreamWrapper.close() Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(streaming): close the response on the shared sync stream path too BaseModelResponseIterator gets a sync close() next to its aclose(), and the sync branch of BaseLLMHTTPHandler hands it the httpx response the way the async branch already does, so providers built on it release the socket on close() instead of waiting for garbage collection Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(streaming): unit tests for CustomStreamWrapper.close() and the shared iterator close() Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(streaming): close the provider stream before re-raising a mid-stream error Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: Blair Jordan <blair.j.jordan@gmail.com>	2026-10-14 19:14:04 +00:00
ryan-crabbe-berri	6e0f0f7adb	fix(s3_v2): bound each upload attempt, add jitter, retry 429 and transport errors in the call (#46340) ... * fix(s3_v2): bound each upload attempt, add jitter, retry on 429 and 503 Each S3 PUT now carries a per-attempt httpx timeout read from S3_UPLOAD_ATTEMPT_TIMEOUT_SECONDS (default 30s, clamped to 1..3600) instead of inheriting the handler's unbounded default. Retries back off with jitter instead of a fixed 1s then 2s, 429 joins 403, 500 and 503 in the in-call retry set, and transport errors or litellm.Timeout on an attempt are retried in the same call instead of ending it. Each flush logs one warning per batch of dropped uploads that names how many were terminal and how many aged out. * fix(s3_v2): retry uploads in a bounded loop and align tests with the new retry contract The async and sync retry helpers called themselves, which the recursive-function check rejects. They are now a bounded loop over the attempt count with the same outcome. The 429 integration test now expects in-call jittered backoff, and the aged-out test matches the new drop warning text. The sync transport test drives the real HTTPHandler so a dropped per-attempt timeout or a missed litellm.Timeout fails it, and the drop-reason test uses unequal counts so swapped labels fail. --------- Co-authored-by: yuneng-jiang <yuneng@berri.ai>	2026-10-14 12:04:17 -07:00
yuneng-jiang	0915691d32	test(integration): remove six setup causes of CircleCI integration reds (#46490) ... Drain the worker's fake-upstream request log when a test's gateway fixture starts, so traffic left by earlier tests on the same xdist worker no longer shows up in a test's first observation read Generate the fine-tuning rig's provider file and job ids per fixture build, so a rebuilt module fixture in the same worker no longer collides on the managed object unique constraint Wait for every wedged team_id and team_alias invalidation publish to fail before taking the recovery marker, now that team updates publish both keys Pin the cooled-down priority scheduler request to the connection that observed the cooldown, since a sibling worker reads cooldowns from Redis on an interval Simulate upstream outages and the held budget peer on ports the test keeps owning, instead of releasing a port another worker can bind Start a peer proxy for the accounting group, which has a test that needs one	2026-10-14 12:00:35 -07:00
Kerry Lu	f0fedda722	fix(caching): collect the Redis circuit-breaker state gauge as one series per state across worker processes (#46357) ... The gauge declares multiprocess_mode=livesum, so PROMETHEUS_MULTIPROC_DIR apps get one series per state summed over the live workers instead of one series per worker pid Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>	2026-10-14 11:45:49 -07:00
Mateo Wang	457b5d26c7	fix(anthropic): stream json_schema answers as content on Bedrock Mantle and Claude Platform, and join split answers (#46331) ... * fix(vertex_ai): unwrap tool-emulated json_schema envelopes and send output_config.format for Claude Vertex AI Claude deployments that emulate response_format through a forced tool sometimes nest the schema object under an extra key such as `parameters` or `input`, and that envelope reached message.content with a 200. The converter now unwraps exactly one nested object that matches the schema, leaving a payload that is itself schema-shaped alone. Native structured-output rows sent the deprecated top-level `output_format`, which Vertex rejects with a 400. The Vertex request now carries it as `output_config.format`, with an explicit caller value winning. * fix(anthropic): unwrap tool-emulated json_schema envelopes on streams and tool-name wrappers * test(anthropic): expect the json tool description through a shared constant * fix(anthropic): drop the json tool description that makes sonnet-5 omit required keys * fix(anthropic): keep schema-accepted answers whole and decide held streams at the second key An answer the json_schema accepts is returned as is before any envelope rule runs, the tool-name wrapper is unwrapped only when the schema rejects it, a malformed schema turns the unwrap off, a streamed answer whose first key is a schema property named name or tool_name waits for its second key before streaming, the held fragments go through a StringIO, and a prefix that stays undecided past 4 KB is held to the end * test(anthropic): keep the stream family cells to providers whose stream iterator gets the schema * fix(vertex_ai): unwrap claude json_schema answers nested under up to three wrapper keys A tool-emulated json_schema answer was unwrapped only when the real object sat one key down. In live QA, 1 of 58 answers (/v1/responses, claude-sonnet-5) came back as {"parameter": {"value": {...}}} and reached the client still wrapped The unwrap now follows single-key wrappers up to three levels down and stops at the first object that has a key the schema declares. That object is returned only when it has every required key. A payload wrapped deeper than three levels, a level with more than one key, and a level whose key is one of the schema's own property names all come back exactly as they arrived The streamed path needs no change because the buffered answer goes through the same unwrap when the tool block closes, and a test now pins that both paths agree * fix(bedrock): unwrap streamed claude json_schema answers on claude platform and mantle A streamed json_schema answer on bedrock/claude_platform and Bedrock Mantle Claude reached the client as tool_calls deltas holding the json_tool_call wrapper, with finish_reason tool_calls and no content. The shared HTTP handler built the async stream iterator without json_mode, and neither stream path passed the response_format schema The handler now asks the provider config for the iterator through get_model_response_iterator_for_request, which carries json_mode and the request's optional params on both the sync and async paths. Claude Platform and Mantle override it to hand the json tool schema to the Anthropic stream iterator, so the streamed answer is the bare schema object with finish_reason stop, the same as the non-streamed one Databricks is the only other provider on this handler whose iterator reads json_mode, so its async Claude json mode stream now returns the answer as content like its sync stream already did The stream family integration test covers claude_platform_unmapped and bedrock_mantle again * fix(anthropic): unwrap json tool envelopes by structure instead of schema validation The unwrap now reads only the schema's top-level property names. A lone undeclared key holding an object, alone or beside a name equal to the json tool's own name, is the envelope, and the unwrap repeats while the inner object shares no key with the schema. jsonschema and referencing are no longer imported on the request path. The stream holds only an answer that opens with the tool's name or with an undeclared key holding an object, and a schema without top-level properties streams as it arrives. * fix(llms): keep other providers' async streams as they were The request-aware iterator hook passed json_mode on async streams to every provider on the shared HTTP handler, which changed Databricks and any other provider whose iterator reads it. The default hook now makes the same two calls the handler made before, so only Claude Platform and Bedrock Mantle change, and the Databricks test that pinned the wider change is removed * fix(anthropic): walk json tool envelopes iteratively * test(llms): pin which stream iterators get json_mode from the shared handler A provider config that does not override the request-aware iterator hook still gets json_mode on a sync stream and still does not get it on an async stream, the same as before the hook existed * fix(anthropic): keep json tool arguments too deep to decode instead of raising * fix(anthropic): unwrap json tool envelopes beside undeclared string labels * fix(anthropic): keep json answers whole on schemas that allow extra keys and stream held text as sent A schema that allows undeclared keys and requires none now keeps an undeclared wrapper key on both the stream and non-stream paths. When no envelope is found, the held stream text is now sent exactly as the model wrote it. On Vertex /v1/messages, output_format now wins over output_config.format when both are set, which matches the Anthropic adapter. * fix(anthropic): join a json_schema answer the model splits into one tool block per field With tool emulation the model sometimes answers with one json_tool_call block per schema field, each naming the field and holding its value. The non-stream path returned only the first block and the stream sent every block's text back to back, so the client got one field or invalid JSON. Both paths now build one schema object from those blocks, and any other set of blocks keeps today's output * refactor(anthropic): set the streamed finish reason in one expression * test(anthropic): cover label envelopes, dynamic-key schemas, as-written held streams and output_format precedence * fix(anthropic): unwrap a streamed values wrapper like the non-stream answer * fix(anthropic): join streamed split answers on schemas without required keys, keep client tool calls and length on json_schema streams * fix(anthropic): join streamed split answers that carry text labels on schemas without required keys * test(bedrock): script the wrapped json_schema stream with the wrapper Bedrock Mantle sends * test(bedrock): script the wrapped json_schema stream with the wrapper Bedrock Mantle sent raw * fix(anthropic): join a split json_schema answer whose fields all hold the same string * fix(anthropic): join a split json_schema answer sent as one partial object per field * fix(anthropic): join field blocks after an empty streamed answer without a leading comma	2026-10-14 11:32:59 -07:00
Mateo Wang	389403fede	fix(proxy): check previous_response_id ownership on /v1/responses/compact (#46408) ... * fix(proxy): check previous_response_id ownership on /v1/responses/compact * test(proxy): cover the unmanaged response id escape hatches on every response id route * test(proxy): audit compact previous_response_id ownership Add integration cells for the native /v1/responses/compact routes covering owner, admin, cross-tenant, unmanaged, malformed, duplicated and retained ids, provider errors, the response cache, id-security settings, an upstream outage, a held upstream, a worker kill and a proxy restart. Unskip the provider wire cell whose bug this branch fixes. * test(proxy): cover the no signing key path on every response id route	2026-10-14 17:57:34 +00:00
Kerry Lu	d0f3a2dfdb	fix(vertex_ai): pass the per-request timeout to Gemini streaming calls (#46349) ... * fix(vertex_ai): pass the per-request timeout to Gemini streaming calls Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(vertex_ai): pass timeout to make_call and make_sync_call in the http 201 tests Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(vertex_ai): default timeout to None on make_call and make_sync_call Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>	2026-10-14 10:51:18 -07:00
Mateo Wang	6b7c77b7d3	fix(http): restore the 15s aiohttp keep-alive default in the Python and Rust clients (#46355) ... * fix: lower aiohttp keepalive timeout Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(http): restore the 15s keep-alive default and apply it to the Rust client 15s is aiohttp's own default and what every release before v1.78.0 used. The Rust HTTP client reads the same AIOHTTP_KEEPALIVE_TIMEOUT variable, so its default moves with it. * test(integration): cover upstream connections dropped after the keep-alive idle window --------- Co-authored-by: nate <nate@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>	2026-10-14 10:50:11 -07:00
Rick	4f3cb48dc1	feat(dashscope): add Wan 3.0, Wan 2.7 and HappyHorse video generation via /v1/videos (#43706) ... * feat(dashscope): add Wan 3.0, Wan 2.7 and HappyHorse video generation via /v1/videos DashScopeVideoConfig maps litellm's OpenAI Videos API onto Alibaba Model Studio's async task API: create hits /api/v1/services/aigc/video-generation/video-synthesis and status and content poll /api/v1/tasks/{task_id}. It is registered for dashscope and for the qwencloud and qwen_ai_platform brand aliases, following the same get_dashscope_family_*_config pattern image generation uses. Unlike the Sora-shaped providers the request body is nested, so prompt and media go under input and everything else goes under parameters. X-DashScope-Async is always sent, because DashScope rejects the call outright without it OpenAI params translate to DashScope's contract. seconds becomes duration, and size yields both a ratio and a named resolution tier (1280x720 gives 16:9 plus 720P), since sending only the ratio leaves the request on the default 1080P and silently bills at that rate. input_reference becomes a single media entry, base64 encoded into a data URI for file inputs. Which media type a model takes (first_frame or, on the reference-to-video models, reference_image) is read from provider_specific_entry.dashscope_video_reference_input via get_model_info rather than hardcoded; models without it predate the media array and get the flat img_url field. A caller-supplied media array always wins, as that is the only way to express multi-role combinations The create call is the only billed call, so it bills what DashScope will deliver: the requested duration and tier, or the documented 5 second and 1080P defaults when they are omitted. Smart duration (duration -1) lets the service pick the length after that call, so it is rejected rather than recorded at zero, with a 400. Task creation and task lookup both report failures as a 200 carrying top-level code and message with no output, so that shape is raised instead of surfacing as a queued video with an empty id. Status responses re-encode the deployment model from the polled id, so follow-up polls and downloads route to the same deployment. Timestamps parse as UTC+8. DashScope publishes no list, delete or remix endpoint, so those raise rather than guess at a URL The end-to-end tests drive the real avideo_generation() entrypoint through an httpx MockTransport and assert on the JSON body httpx actually encoded. That is what catches a mapper that drops input_reference before the request is built, or a body fragment json cannot serialize, neither of which a test calling the transform directly would see Both model_prices maps gain wan3.0-video, wan3.0-video-prime, wan2.7-t2v, wan2.7-i2v, wan2.7-r2v and the HappyHorse 1.0 and 1.1 t2v, i2v and r2v models, for dashscope and the qwencloud and qwen_ai_platform aliases, priced per output second by resolution tier from Model Studio's USD rate cards. dashscope and qwen_ai_platform default to the Beijing host and use its rate card; qwencloud defaults to the international host and uses Singapore's. The three *_API_BASE_VIDEO overrides are documented in BerriAI/litellm-docs#1920 Co-authored-by: AaronHowell <237895480@qq.com> * fix(dashscope): fail loudly on video responses that cannot be read Addresses the review on the video generation PR. A body that is not the documented task shape, an output block missing task_id or task_status, an undocumented task_status and a missing output all raise DashScopeVideoError now, instead of defaulting into a queued video with an empty id that callers poll forever. A JSONDecodeError from a gateway response no longer escapes as a generic connection error. task_id and task_status are required on _TaskOutput, and a missing output raises even when the body carries no code or message. Download failures move into the 4xx range, since a 5xx has the proxy retry them: 409 while the task is still running, 400 for a task that failed, 404 for an id that expired. The status a provider reported is kept when it reported one, so an in-body failure that arrives on a 200 is billed as the 400 it is rather than as a retryable connection error. characters and image are only dropped when drop_params is set; the params that are rebuilt into the body or consumed by the caller (model, prompt, user, extra_*) stay silently skipped, since the proxy stamps user from the caller's end-user header. Co-authored-by: AaronHowell <237895480@qq.com> * fix(dashscope): clear the lint gates after merging main * fix(dashscope): accept file tuples and paths as video input_reference video_generation() types input_reference as FileTypes, which includes (filename, content[, content_type[, headers]]) tuples and pathlib paths, but the DashScope encoder only read bytes and file objects, so those image-to-video calls raised before reaching DashScope. Bare strings stay URLs and are never opened as paths. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * fix(dashscope): snap size to a supported ratio and read file tuples and paths * test(dashscope): assert whole mapped values and bill against hand-written rates The tier pricing test read its expected rate from the same map the cost calculator uses, so a wrong rate passed. Billing is now checked against fixed rates driven through the status response usage, while the map test only asserts every supported tier has its own rate. Size mapping tests compare the full mapped dict, and the single-test wiring class is a plain function. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * fix(videos): download content through the caller's client and stop retrying dead tasks Two Cursor Bugbot findings on the DashScope video PR. The DashScope artifact lives on OSS, so transform_video_content_response makes a second request. It built its own cached client, discarding an injected client and its ssl_verify. Thread the client through the content response the way transform_video_status_retrieve_response already does, and use it in the three providers that fetch from a second URL (DashScope, RunwayML, fal.ai). The remaining providers accept it and read the response body, matching how they already accept an unused client on status retrieve. A FAILED or CANCELED task reported without a code or message fell past the in-flight check into the 502 branch, so the proxy retried a task that can never produce a video. Raise 400 for a terminal status instead. * fix(dashscope): answer 404, not 409, when a Wan video is downloaded before it finishes A download that lands while the task is still PENDING or RUNNING answered 409. That reads as a client error, which was the intent, but litellm's retry predicate counts 409 as retryable, so the proxy polled DashScope twice more before giving up and then counted the caller's own early download against the model's error rate 404 is what the OpenAI videos API documents for content that is not ready, and it is the one status in that family the retry predicate leaves alone. The rest of the mapping is unchanged: 400 for a task the provider reported as failed, 404 for an expired task id, 502 when a succeeded task carries no artifact The download tests now drive avideo_content and assert the caller sees NotFoundError rather than an internal server error * fix(dashscope): route a video poll back to the model group that created the task The create response encoded the upstream model name into the returned id. Both the dashscope and qwen_ai_platform prefixes serve the same model names from their own hosts, so a proxy with a deployment per region had deployments that only the model group told apart. Encoding the bare model name resolved a poll to whichever deployment the router lists first, which sent it to the other region's host with that region's key and a task id that region never issued. That is the reroute the earlier proof records as the dashscope and qwen_ai_platform poll ids coming back tagged qwencloud The id now carries the model group the create was routed to, which is what the proxy resolves a poll back to, and falls back to the model name for a direct SDK call that has no router The regression test drives a real router with one deployment per region, under a different prefix and under the same one, and asserts the poll carries the key of the region that created the task * fix(dashscope): read the routed model group from litellm_metadata The router stamps the model group it picked into litellm_metadata. metadata only carries a copy when the caller sent no metadata of its own, and every proxy request sends some, so a proxied create still encoded the bare model name and the poll resolved to whichever region's deployment the router lists first The regression test now also creates the video with caller metadata, which fails without this change * fix(dashscope): reject unbillable video requests and keep fal and runway on their own clients * fix(dashscope): show the caller why a video task is gone or not ready instead of the bare upstream body * fix(dashscope): check video request values before the billed create and honor per-call drop_params Type check documented DashScope parameters so a bad seed answers 400 instead of a billed failed task, pass the call's and deployment's drop_params into the video mapping, answer 400 for an expired task id so the deployment is not cooled down, read DASHSCOPE_API_BASE for video, keep polling SUSPENDED tasks, and refuse to map size onto models litellm has no price for. * fix(dashscope): carry the requested model name in video ids so alias and wildcard keys can poll A key scoped to an alias or to a name a wildcard serves could create a video and then got a 403 on the poll and the download, because the id carried the group that served the call (wan3, qwencloud/*) and the key was never given that name. The id now carries the name the router was asked for on that hop, and the router resolves an alias, routing group or wildcard name in an id back to itself when no deployment name or provider model matches it first * fix(dashscope): keep a routing group's video polls on the member that created the task A routing group spans several model groups, so an id carrying the group's name load-balanced its polls, and some left for another region's host and key with a task id that region never issued. The id now carries the member that served the task when the request named a routing group, and keeps the requested name for a model group, an alias, or a wildcard match. * fix(dashscope): carry the requested and served model in video ids so group-scoped keys poll in the creating region * fix(proxy): pin a video's served group only after key and team aliases resolve * test(integration): audit DashScope video create, poll, download, routing and chaos paths * fix(proxy): keep catch-all video polls on their provider and count served polls against the requested group * test(routing): expect a forged served group to be dropped from the polled video id * test(integration): cover rate-limited routing group polls and catch-all polls of DashScope videos --------- Co-authored-by: AaronHowell <237895480@qq.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>	2026-10-14 10:10:07 -07:00
yucheng-berri	d17cd947b9	fix(sso)!: reject empty identities before session creation (#44098)	2026-10-14 09:10:58 -07:00
ryan-crabbe-berri	12cce6c372	fix(anthropic): check Vertex forced-tool support on the real model, skip non-string user ids, drop speed where unsupported (#46337) ... * fix(anthropic): check Vertex and Bedrock forced-tool support on the real model The Vertex and Bedrock invoke Claude transforms swapped the model for a claude-3-sonnet stand-in before calling the shared Anthropic mapping so response_format would take the tool-based path. Every model-keyed check in that mapping then ran against the stand-in, so the synthetic json tool was forced on models whose map entry says forced tool use is unsupported and the request 400ed upstream. Bedrock patched the one symptom with a backstop, Vertex had nothing. The shared mapping now asks a uses_native_structured_output hook for the structured-output decision and the two overrides answer it with their provider-scoped lookup, so the real model reaches every check and the stand-in, the restore dance and the Bedrock backstop go away. * fix(anthropic): skip non-string metadata user ids instead of raising _valid_user_id ran re.match straight on litellm_params metadata.user_id, so an integer id raised TypeError and the proxy answered 500. The user param path already skips non-strings, so the validator now does the same for any caller: a non-string id is simply not forwarded. * fix(utils): drop speed on every provider without fast mode speed rides into get_optional_params as a provider-specific kwarg, so the only gate for it lived in the Anthropic transform and Bedrock converse, OpenAI, Azure and the extra_body legs forwarded it to a 400. The gate now runs once in get_optional_params before provider-specific kwargs are copied, so every provider drops it with drop_params or rejects it with a clean 400, and a direct Anthropic fast-mode model keeps it. * fix(utils): gate speed only under drop_params and keep it for litellm_proxy Raising on speed when drop_params is off would turn requests that tolerant OpenAI-compatible backends accept into 400s, and dropping it for litellm_proxy would disable fast mode on a downstream proxy that supports it * test(anthropic): derive the forced-tool regression model from the cost map and drop new docstrings * test(utils): pin that speed is untouched without drop_params and annotate new test locals * test(integration): lock the Vertex and Bedrock invoke json tool to the real model's forced-tool support Vertex sends tool_choice only when the model accepts forced tools, reasoning_effort maps the same with and without response_format, and Bedrock invoke keeps sending the json tool unforced	2026-10-14 08:56:08 -07:00
Mateo Wang	d81a5719ad	fix(cache): keep cache hits free of unset fields, and follow intentional behavior changes in CircleCI suites (#46359) ... * test(integration): follow count_tokens error surfacing and decisions pricing in CircleCI suites Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(integration): restore Responses input_file PDF pricing coverage Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(integration): keep integration AGENTS guide unchanged Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(cache): preserve provider field presence on cache hits Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(integration): expect credential display name in response Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(integration): align OpenInference refusal field expectations Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(integration): expect Akto request ingestion Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(mcp): expect missing tool error wording Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(integration): expect no refusal field in cursor chat wire 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>	2026-10-14 08:56:05 -07:00
devin-ai-integration[bot]	a93fee9f6c	feat(decisions): add Laya and Bespoke providers to the Add Model form (#45260) ... * feat(ui): add decisions and classifier providers to the Add Model form Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(cost): use literal upstream decisions paths Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix: drop duplicate TYPESAFE provider fields entry superseded by main's TypeSafe Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * style(ui): drop stray blank line in provider_info_helpers test Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(proxy): type the OSS decisions provider fields test * fix(decisions): keep Laya and Bespoke env keys on their configured server Route laya and bespoke Decisions deployments through the same oss_connection and validate_oss_model rules the pass-through and the classifier router use, so LAYA_API_KEY and BESPOKE_API_KEY only go to LAYA_API_BASE and BESPOKE_API_BASE, an api_base with credentials, a query, or a non-HTTP scheme answers 400, and an unknown checkpoint name answers 400 before any upstream call * fix(decisions): send the Laya and Bespoke env key to a form-added model on the env server A deployment added through Add Model always carries an API base, so the env key only reached deployments with no base. The env key now goes to a deployment whose base matches LAYA_API_BASE or BESPOKE_API_BASE, an empty deployment key counts as no key, and the Bespoke placeholder is the name Ollama serves. * test(decisions): cover the env-base classifier case in the complexity router test * fix(decisions): match the env server with or without /v1 and point Ollama users at bespoke/nimble * fix(decisions): call /v1/systemone once from classifiers and pass-through on a /v1 base * fix(decisions): strip /v1 from the API base path only, so a host named v1 still works --------- Co-authored-by: kerry <kerry@berri.ai> 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>	2026-10-14 08:11:11 -07:00
devin-ai-integration[bot]	aa0279e0a2	feat(anthropic): generate auto mode safeguard_results with a classifier model on the /v1/messages chat bridge (#45600) ... * feat(anthropic): generate auto mode safeguard_results with a classifier model on the /v1/messages chat bridge * fix(anthropic): check caller limits for the safeguards classifier, mark truncated tool calls, and keep pre-compaction context * fix(anthropic): log classifier spend under the caller's key hash and read list-shaped classifier replies * feat(anthropic): generate safeguard_results on the Responses bridge and native non-Claude /v1/messages providers * fix(messages): keep safeguards requests on Python under the Rust opt-in * fix(anthropic): keep non-ASCII text unescaped in the safeguards classifier prompt * test(anthropic): build the text-parts classifier reply without openai's construct path Message.model_construct goes through openai's construct_type, which caches discriminated unions in a weak-key map. On Python 3.12 a PEP 604 union cannot be weak-referenced, so the fixture raised inside the fake classifier and the test read an unavailable verdict. model_copy sets the list content without that path. * fix(anthropic): scan only new bytes for SSE frame ends on safeguarded native streams * fix(anthropic): answer safeguards on web search short circuits and widen the classifier reply cap * fix(anthropic): answer unsupported when the proxy serves no deployment for the safeguards classifier * fix(anthropic): keep a backend's stream verdicts and join split SSE data lines before stamping * fix(anthropic): map only the router's no-healthy-deployments refusal to unsupported * fix(anthropic): answer unavailable for the turn on a transient caller denial and let the classifier deployment's timeout apply * fix(anthropic): keep the user's whole opening message in the classifier transcript past its budget * fix(anthropic): run the proxy's own access checks for the safeguards classifier call The classifier and compaction sub-calls now pass through the same model access checks as the parent request (key, team, team member, user, project, end user, managed agent grants), with budgets left to the per-model budget check so nothing is reserved. They also carry the parent's logging params and headers, so a redaction header or per-request callback params apply to their own rows. Only per-model RPM limits count against a sub-call now, since a key-wide or team-wide RPM window is already spent by the parent request. The MCP auto-execute loop classifies only its final response, a chat-bridge stream without safeguards yields frames directly, and a classifier provider failure logs one warning line instead of a traceback * fix(anthropic): keep safeguards sub-calls off the parent's session routing and carry a body trace id The classifier and compaction summary calls no longer get the parent's proxy_server_request, so a Claude Code session bound to a router no longer pulls them off their configured model. Redaction still reads the headers carried in metadata. Their access checks now use the parent's resolved litellm_trace_id when the parent metadata has no trace_id, so a team that requires a trace id accepts a sub-call whose parent sent it in the body. * fix(anthropic): keep the disable-callbacks header on safeguards sub-calls * fix(proxy): charge per-model budgets from litellm_metadata when metadata holds only routing keys * test(integration): add safeguard_results cells for the bridges, native routes, caller checks, and classifier failures * fix(proxy): check the caller's budgets on the classifier call and keep failed access reads retryable --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>	2026-10-14 13:09:11 +00:00