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128 commits
| Author | SHA1 | Message | Date | |
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Mateo Wang
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73aaf9e3da
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fix(mistral): forward prompt_cache_key to the Mistral API (#47432)
* fix(mistral): forward prompt_cache_key to the Mistral API * fix(mistral): send prompt_cache_key only to Mistral's API and accept reasoning_effort on devstral and code-agent models * fix(mistral): keep the upstream api_base out of the prompt_cache_key rejection message * fix(mistral): honor allowed_openai_params for prompt_cache_key and gate codestral async and env hosts * test(mistral): annotate the new adapter prompt_cache_key tests * fix(mistral): drop or reject a prompt_cache_key the Mistral API cannot take A non-ASCII prompt_cache_key gets a 500 from api.mistral.ai, and a non-string one a 422, so both now drop under drop_params and answer 400 without it, like any value the provider cannot take. The /v1/messages adapter derives no key from a non-ASCII Claude Code session for Mistral, and the 400 names the caller's provider, so a codestral deployment's error says codestral. * fix(mistral): send a reasoning_effort object as its level and keep UnsupportedParamsError on sync calls * fix(mistral): keep async codestral file uploads working by not rewriting the caller's file blocks |
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devin-ai-integration[bot]
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56388fd13d
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fix(count_tokens): count a history that carries a compaction block (#44784)
* fix(count_tokens): count a compaction block instead of answering 500 A message history that replays Anthropic's signed compaction block made /v1/messages/count_tokens answer HTTP 500: neither the Anthropic nor the Vertex AI partner count-tokens request carried the compact-2026-09-04 beta, the provider rejected the block with a 400, the proxy fell back to the local tokenizer, and that raised on the unknown block type. Both provider count paths now add the compaction beta whenever the history carries a compaction block, and the local tokenizer counts the block's summary text like a text block, skipping the signature. * fix(token_counter): skip the tokenizer for empty thinking and compaction text * fix(count_tokens): forward context_management on Anthropic counts and count chat compaction blocks locally Vertex AI compaction support for inference and counting moves to its own PR, so the Vertex count handler is back to main here * fix(anthropic): map list-form context_management to edits for count_tokens and inference betas Count requests forwarded the OpenAI list form of context_management unchanged, while inference maps it to Anthropic edits, and the inference beta helper read the list as no edits. Both now map it once. A replayed compaction block with an empty summary no longer reaches the tokenizer. * fix(anthropic): forward count context_management only with edits, map Vertex list-form betas, and return provider context_management * fix(anthropic): drop an unmappable context_management list under drop_params --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Devon Krisman
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953750ed41
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fix(websearch-interception)!: stream native server-tool blocks to clients (#40967)
* fix(websearch-interception): stream native server-tool blocks to clients Streaming native clients (Anthropic SDK, Claude Desktop, Claude Code) never received the server_tool_use and web_search_tool_result blocks the interception layer builds for them, so citation panels stayed empty and result counts read zero. Two gaps in the same flow: FakeAnthropicMessagesStreamIterator._create_content_block_chunks only knew text, thinking, redacted_thinking and tool_use; any other block type emitted a bare content_block_stop with no start and no payload, silently dropping the block from the fake stream. server_tool_use now streams as content_block_start plus input_json_delta and any other server-generated block rides whole in content_block_start, matching Anthropic's native streaming of these types. try_short_circuit_search only recognized a native client by finding a web_search_* tool in the tools list, but the pre-request and deployment hooks convert that tool to the LiteLLM standard shape before the short-circuit runs, recording the conversion in WEBSEARCH_EMIT_NATIVE_BLOCKS_KEY. The flag now counts as native too, so the synthetic response carries the native blocks on the converted path instead of only when hook ordering happens to leave the tool untouched. Fixes #35333 * fix(anthropic): accept server_tool_use and web_search_tool_result blocks when costing a /v1/messages response The short-circuit now returns native web search blocks, and the cost path validated the served body against a content union that only allowed text, tool_use and thinking blocks, so non-streaming requests answered 500 * fix(anthropic): keep every server_tool_use field when re-streaming a /v1/messages reply * fix(anthropic): keep tool_use fields, redacted thinking data and text citations when re-streaming a /v1/messages reply * test(websearch-interception): build the cost test's logging object with a datetime start time * fix(websearch-interception): send search snippets once in the direct search answer * fix(guardrails): mask web search queries, titles and urls on /v1/messages and deliver stream rewrites past server_tool_use blocks Output guardrails now scan the native web search blocks the short-circuit emits, ahead of the reply text so guardrails that read the last text still scan the reply. A guardrail reply of a different length falls back to the reply-only write-back. Ended-stream tool call rewrites land on server_tool_use blocks too, the ended-stream scan key covers the result strings, and legacy-hook pipeline steps pass through the result strings their response view lacks. The wire test reads the streamed server_tool_use input from its input_json_delta. * fix(guardrails): deliver a legacy post-call hook's tool-call rewrites on streams instead of discarding the stream rewrite A legacy hook that masked a tool call's arguments on a buffered stream made the whole rewrite undeliverable, text included, so the original stream went out. With web search blocks in the stream that now hit any hook masking the search query. The adapter hands same-count tool-call rewrites to the translation's write-back, as a unified guardrail's already are, and still discards a rewrite that adds or drops a tool call. * fix(guardrails): show a legacy post-call hook the web search result blocks of a /v1/messages stream The legacy hook's view of a buffered Anthropic stream is rebuilt from the assembled ModelResponse, which keeps web_search_tool_result blocks only in provider-specific fields, so a hook masking result titles on a stream never saw them. The hook's view now carries those blocks, its rewrite of them goes back through the ended-stream write-back, and the adapter no longer needs to pass texts outside the hook's view through untouched. * test(guardrails): type the legacy hook stream test helpers * fix(guardrails): scan a streamed web search query as text like the non-streaming reply * test(guardrails): type the masking guardrail test helpers * fix(guardrails): keep the block id when scanning a streamed web search query --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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norkelbyte
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e0778bc9dd
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refactor(types): replace Any with proven types in 21 files (#48921)
* refactor(types): replace Any with proven types in 35 files Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(types): keep anthropic chat transformation json path untyped Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(types): accept null gemini vector store fields and load encryption TypedDicts on python 3.10 Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(types): validate Python client user responses and type delete_user as the count it returns * fix(types): keep loose payloads working under the sweep's validators An off-schema Anthropic stream event is now skipped instead of ending the guardrail's text and stop_reason scan, and MAI image usage accepts the loose numbers it did before. New dict and list adapters hide their input from validation errors, RerankResponse indexing keeps its field types, and the delete_user test mocks the int /user/delete returns. * fix(types): make the changed DynamoAI fields ReadOnly and skip non-object A2A stream items * refactor(types): tighten round 2 parse sites and keep payloads out of errors Parse Anthropic SSE events with validate_json and ignore extra fields, validate admin team and key rows per row with hidden inputs, type rerank get and __getitem__ per key, and move the Phoenix prompt version response to a frozen BaseModel * fix(types): keep MAI image calls alive on unreadable usage counts An unreadable MAI usage count now counts as absent instead of failing a billed image call, and the provider's usage fields pass through as sent. The Anthropic stream scan warnings no longer log raw model output, and the off-schema skip has tests that fail on the old scan. * fix(types): count non-object MAI usage as absent and parse Anthropic SSE with json.loads --------- Co-authored-by: mateo <mateo@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> |
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Alon Sokolovski
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2b4cb92c8b
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fix(context_management)!: run clear_tool_uses in the gateway on the Responses API path (#47861)
* fix(context_management): run clear_tool_uses in the gateway on the Responses API path /v1/messages to openai/ models goes through the Responses API, which translated only compact_20260112 (to its native compaction) and dropped every other edit without a warning, so clear_tool_uses_20250919 never cleared anything. Edits without a Responses API form now run in the same polyfill as the chat completions path, before the call, and their applied_edits come back on the response and the final message_delta. compact stays native. * fix(context_management): check the sync polyfill result instead of casting it The strict ruff gate (TID251) bans typing.cast. * test(context_management): cover the Responses path's polyfill error handling A polyfill crash is a 500 before the call, a rejected edit under drop_params is skipped, and the sync stream reports its edits. normalize_spec never raises for a dict spec, so its guard is gone. * fix(context_management): count unreadable blocks and check tool_result text before clearing clear_tool_uses now maps blocks the token counter cannot read (server tool, compaction) to JSON text, so an input_tokens trigger no longer 500s on that history. The Responses API path checks malformed tool_result text on the original messages before clearing, as the chat completions path does. Also removes comments that repeated the code and makes the test fixtures immutable. * fix(context_management): count only blocks the model receives and keep list specs unsent clear_tool_uses now leaves blocks token_counter cannot read, like server tool results and compaction, out of its count instead of counting them as JSON text. No adapter sends them to the model, so an input_tokens trigger no longer fires on tokens the model never sees. On the Responses API path, an OpenAI-style list context_management stays unsent, as it was before, instead of reaching OpenAI's native compaction, where a threshold under 1000 is a 400. On the chat completions path, a clear edit listed before compact_20260112 now runs on the turns after the latest compaction block, as Anthropic does, instead of counting and clearing turns the compaction block drops. The crash test that replaced apply_context_management with a mock is removed. * fix(context_management): apply clear edits in OpenAI count_tokens and skip nested off-spec blocks when counting * fix(context_management): run edits on the history drop_params sends, so count_tokens and /v1/messages clear alike * fix(context_management): count clear_at_least freed tokens over sent blocks only * fix(context_management): count tools with JSON Schema shorthands and reject a non-object trigger or keep A tool whose schema has an array without items, a boolean subschema, or a search_result block without a title made the token counter raise, so a clear edit answered 500. A string trigger or keep also answered 500, even under drop_params. Both now count or get the API's 400, and drop_params drops the malformed field. * fix(token_counter): count a malformed tool schema keyword as absent and move shorthand framed-estimate cases * fix(count_tokens): pass the system prompt into clearing so count_tokens matches inference * fix(context_management): keep a malformed system prompt from failing clear_tool_uses on the Responses API A number, an object, or a list holding non-object items as system made the polyfill answer 500. The polyfill now keeps a list's object items and treats any other shape as no system, on both /v1/messages and count_tokens, so the two paths still count the same text --------- Co-authored-by: AlonSoko <25619863+AlonSoko@users.noreply.github.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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zoveki
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68b5a737a1
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fix(anthropic): retry /v1/messages without strict when Claude rejects strict tools under drop_params (#49102)
* fix(anthropic): retry /v1/messages without strict when Claude rejects strict tools under drop_params The native /v1/messages path sent tools with `strict` straight through, so when Claude's strict-tool limits rejected them with a 400 the caller got that 400 even with drop_params set. Under drop_params, a 400 that matches Claude's strict-limit phrases on a request with a strict tool is now sent once more with `strict` removed from every tool. Without drop_params nothing changes. The Claude strict-limit matcher moves from bedrock/common_utils.py to anthropic/common_utils.py so Bedrock converse and the base messages config share it, and the Rust bridge routes strict tools under drop_params to Python. * fix(anthropic): retry without strict only when a tool sends strict true A tool with only strict false no longer triggers a resend of an identical body, the warning no longer blames tool schemas when the rejected schema may be the output format, and requests with only additional_drop_params stay on the Rust messages route since the Python retry ignores them * fix(anthropic): keep sync messages calls with strict tools on Rust Python has no sync /v1/messages handler, so routing a sync call with a strict tool to Python raised ValueError where Rust served it before. The strict-tools detector now applies to async calls only. The retry warning also names the strict tool count without a plural. --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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zoveki
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e63fc85b7b
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fix(anthropic): keep Mistral thinking text on streaming /v1/messages (#49059)
* fix(anthropic): keep Mistral thinking text on streaming /v1/messages * test(anthropic): type the signed thinking stream test helpers --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Nate Armstrong
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ead221995e
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refactor(router): move router callers onto the entry points and invert the proxy imports (#48847)
Code outside the router now reaches it only through the entry points (registry, settings, health, select, apply, observe, register_plugin, retire) or the endpoint methods; the outside-member allowlist shrinks from 139 to 45. litellm/router.py no longer imports litellm.proxy: it asks the installed RouterHost (litellm/router_host.py) for vertex pass-through credentials, member auto-router authorization and auto-router prompt compression, and the proxy installs its host on import. Call sites where a model name can be missing keep the old answer instead of reaching an entry point with None. |
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Mateo Wang
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be6d082bdc
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fix(anthropic)!: send /v1/messages PDF documents to OpenAI-shaped providers as file parts (#47166)
* fix(anthropic): send /v1/messages PDF documents to OpenAI-shaped providers as file parts * refactor(anthropic): hold the chat completions document change for a later commit * fix(anthropic): send /v1/messages PDFs as file parts and keep file parts on azure_ai * fix(snowflake): turn file parts into document blocks for Cortex Claude * fix(prompt_security): sanitize chat file parts that carry file_data * fix: answer 400 or drop PDFs for models that cannot read them Models whose model_info or cost map entry sets supports_pdf_input to false, and Ollama, now get the PDF dropped under drop_params and a 400 from litellm without it. GigaChat uploads file parts, SAP keeps sending them as image_url data URLs, azure_ai passes dict content through, and annotations other than url_citation move to provider_specific_fields instead of failing the response. * fix(router): scope supports_pdf_input to its deployment and keep provider annotation keys Mark the azure_ai chat models in the cost map that cannot read PDFs, so drop_params drops the PDF and litellm answers its own 400 without it. Keep url_citation annotations whole, move only typed provider annotations to provider_specific_fields, and split them the same way when a stream is rebuilt. * fix(vertex_ai): send PDF file parts to Vertex partner models as image_url Vertex AI's OpenAI-compatible partner endpoint rejects the chat `file` part, so PDFs from /v1/messages reach those models as the image_url data URL they took before. Mark native DeepSeek chat models as unable to read PDFs, and treat a file part as a PDF by its format or filename when it has no data URL. * fix(cost-map): leave native DeepSeek PDF support unset /v1/messages reaches native DeepSeek through its own Anthropic endpoint, so the marks never applied there and would only change chat completions, which this PR does not otherwise touch. * fix(main): drop a user turn that held only a PDF under drop_params Dropping the PDF from a PDF-only user turn left an empty content list, which Foundry and OpenAI-shaped providers reject with a 400, so drop_params still failed for that request. The emptied user turn is now removed. * fix: read OCI PDFs, gate deployment base models, and stop empty drops OCI generic chat sends file parts as image data URLs again, so OCI Gemini keeps reading PDFs from /v1/messages and chat file parts. The PDF gate now reads model_info.base_model, drops PDFs under drop_params for azure_ai deployments that never declared PDF support (the old flattening behavior), and answers 400 when dropping the PDF leaves no user message. The cost map marks the Vertex partner text models that a live probe showed answering without reading the PDF. * test(integration): cover the PDF input gate, messages PDF file parts and provider annotations Wire cells for the supports_pdf_input check on chat, chat streaming, /v1/messages and the Responses bridge, its model_info, base_model, cost map and Ollama sources, drop_params at deployment and request level, azure_ai file parts, sibling deployment isolation, Anthropic SDK PDFs reaching azure as titled file parts, Gemini and Bedrock document controls, Prompt Security sanitizing a /v1/messages PDF, and OpenRouter file annotations live, cached and streamed * test(integration): cover Snowflake Claude and SAP in the messages PDF wire cells * fix(azure_ai): flatten non-PDF file parts to text as before and cover the GigaChat PDF upload * fix(main): PDF check reads the deployment's top-level base_model * fix(main): keep roles alternating after drop_params removes a PDF-only turn When ensure_alternating_roles is set, re-run role normalization on the messages left after the PDF check drops a turn, so two assistant turns never end up next to each other * test: type the PDF and annotation tests the PR added * test: type the GigaChat, SAP and Snowflake PDF tests |
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tavren27
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b25ea48c4b
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fix(anthropic)!: drop beta-gated body fields under drop_params (#48793)
* fix(anthropic): strip beta-gated body fields when a leg drops the anthropic-beta When a provider leg drops an anthropic-beta value (the chat handler's allowlist filter, /v1/messages' filtered headers, Bedrock Converse's additionalModelRequestFields), also drop the body fields that beta gates (thinking.block_binding, thinking.display=updates, mid-conversation system clear_at, output_config, tool_addition/tool_removal blocks) so the request degrades on that leg instead of failing with a provider 400. Copy-on-write so a fallback leg still sends the full request, and each strip is logged at WARNING. Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(integration): prove a leg that drops a beta sends neither the beta nor clear_at Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(bedrock): strip beta-gated fields against the final body anthropic_beta list Converse counts the body anthropic_beta list as active, Invoke strips after its auto-injected betas are known and injects thinking-binding-controls for thinking.block_binding, test helpers are fully typed Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(anthropic): strip beta-gated system fields before an older model's system turn becomes a user turn Models without supports_mid_conversation_system send no mid-conversation beta, so tool_addition and tool_removal blocks are dropped (and logged) before the turn is converted instead of riding along as user content. The Invoke output_config regression test reads the real cost map instead of patching _bedrock_model_supports Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(anthropic): pin the integration proxy to the bundled beta allowlist and type the helper locals Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(anthropic): annotate every new test local with Final and type the fixtures and returns Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(anthropic): strip beta-gated body fields only under drop_params Without drop_params the request now goes out as sent and the provider answers its own 400, like any other unsupported param. inline-tools-2026-09-15 also unlocks tool_addition and tool_removal blocks, so a leg that carries it keeps them. * fix(anthropic): strip beta-gated fields on Foundry chat and Bedrock chat Invoke under drop_params Foundry chat and Bedrock chat Invoke now drop beta-gated body fields the same way the other legs do when drop_params is on and the request does not send the gating beta. The mid-conversation system normalizer validates only on the conversion path, and the strip WARNING names the beta the caller can send to keep the field. * fix(anthropic): send the thinking betas for block_binding and display updates, strip Databricks chat thinking Every Claude leg now adds the thinking-binding-controls beta when thinking carries block_binding and the display-updates beta for display updates, so those fields go through instead of a 400. Databricks chat rejects both fields with or without the beta, so it drops them under drop_params. A system turn the strip empties stays when it still carries output_config, and the drop warning names both causes. * fix(anthropic): send the thinking betas on every leg and keep fields where the beta is unknown Vertex messages and Claude Platform chat now add the thinking betas when thinking carries block_binding or display updates. Converse sends only the caller's header betas when it has them. A provider with no row in the beta config forwards beta-gated fields instead of dropping them, an emptied trailing system turn that would leave an assistant prefill is sent as is, and the prompt cache baseline body matches the body that goes out. The drop WARNING now names the beta that unlocks each field * fix(anthropic): filter Converse body betas, keep Foundry auto betas, read the bundled beta config for row checks * fix(anthropic): validate messages only when a beta-gated drop runs A non-object messages entry on a Claude model without mid-conversation system support raised a raw pydantic ValidationError before the beta-config and drop_params checks. The strip now passes the messages through untouched unless it drops something, so the provider's 400 reaches SDK callers as BadRequestError again * fix(anthropic): keep system blocks with a non-string type instead of raising --------- Co-authored-by: mateo <mateo@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> |
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devin-ai-integration[bot]
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784a52de79
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test(router_strategy): keep foreign asyncio task noise out of the budget limiter caplog asserts (#43375)
* fix(tests): keep foreign asyncio task noise out of the budget limiter caplog asserts The two gc.collect()-under-caplog tests in test_budget_limiter_hotpath.py asserted on every captured record, so a LoggingWorker task another test had parked on the session fixture loop got destroyed inside their window and asyncio's "Task was destroyed but it is pending!" error failed them. They now assert only the limiter logger's records and park a pending task on a closed loop in the window themselves, so the noise is exercised on every run The edenai and sail spend_capture fixtures no longer start the worker from the session loop: the test's own call and its flush bind the worker to the test loop * test(router_strategy): drop only asyncio records in the limiter caplog asserts and drain the response cache worker on the test loop --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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devin-ai-integration[bot]
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444423a5ca
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fix(cost): bill a deployment's per-second rates on native Anthropic /v1/messages (#42417)
* fix(cost): honor per-second custom pricing on chat completions for every provider * test(cost): assert a per-second priced deployment bills instead of staying at $0 The zero-cost diagnostic tests from #42345 used a per-second-only entry as their misconfigured fixture, which this branch now bills. Switch that fixture to a per-query-only entry, which is still selected as the deployment's own pricing and still prices chat usage at $0, and add a per-second test asserting the call duration is billed with no diagnostic Also let a caller's explicit total_time outrank the logging window in completion_cost, so the SDK precedence stays stamped response, caller, logging * test(response_metadata): move the per-second pricing regression into the mapped tests/unit file * fix(cost): bill a deployment's per-second rates on /v1/responses and /v1/messages The proxy builds the request's logging state before the router picks a deployment, and only the chat path re-read the deployment's input_cost_per_second / output_cost_per_second afterwards. The shared update step every other route runs (Logging.update_from_kwargs) now seeds those custom pricing keys from the router-merged kwargs too, so custom_pricing flips on and the cost calculator prices the call against the router-registered deployment id on every surface. * fix(cost): price a /v1/messages call off the response time the proxy reports A /v1/messages result is a plain dict, so the proxy keeps its response time on the logging object. The cost step now reads it there for a non-streamed call, so the cost header and the spend log bill the same duration. * fix(anthropic): bill a deployment's per-second rate only on the native /v1/messages route Move the custom pricing copy from the shared logging refresh into the native Messages handler, reusing the helper the Responses handler already had, so generateContent and other routes keep their current billing * fix(cost): pass only per-second rates to native /v1/messages cost tracking Other deployment overrides keep billing as on main, matching chat's per-second gate * test(anthropic): pin per-second-only pricing on native /v1/messages with a long-context-rate deployment * fix(cost): bill native /v1/messages from logging setup, as main did, to the end the proxy reports --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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devin-ai-integration[bot]
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b93d26bc85
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fix(responses): drop a malformed prompt_cache_breakpoint under drop_params on the direct /v1/responses path (#45330)
* fix(responses): drop a malformed prompt_cache_breakpoint under drop_params on the direct /v1/responses path * refactor(responses): reuse the shared prompt_cache_breakpoint_for_wire helper in the chat bridge * fix(responses): read a non-flag deployment drop_params as unset in the marker walk, like the param mapper * fix(responses): drop every prompt_cache_breakpoint under drop_params when the model has no breakpoints Also drop a marker set on the message item itself, sanitize before the cache_control conversion so a client typo no longer erases that breakpoint, and skip items that carry no marker so unmarked history costs no extra walk * fix(responses): keep a valid prompt_cache_breakpoint unless the model is known to reject it A deployment alias the cost map does not know (an Azure deployment name, a fine-tune, a litellm_proxy alias) kept its valid markers before this PR and must keep them under drop_params. base_model is read from litellm_params first, then model_info, as chat completions does. * fix(responses): drop a valid marker on known non-breakpoint models and keep the tool schema gate unchanged * fix(responses): judge breakpoint support by model name so cost-map aliases keep valid markers * fix(responses): keep the caller's request stripped of cache_control as before when a marker is copied * fix(responses): keep valid prompt cache markers in place and let cache_control fill a dropped one Under drop_params the marker step now hands back the caller's own objects when every marker is already wire-safe, so a request with valid markers is no longer copied and walked a second time. A deployment that turns cache_control into markers keeps the caller's own markers too, whatever its base_model says, so one request never gets two answers about breakpoint support. On /v1/messages a malformed raw marker next to cache_control is dropped before the conversion, so the block gets the cache hint cache_control alone would give, as on /v1/responses. * test(responses): keep a null marker and the original key order on a model that reads breakpoints --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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zoveki
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310c96162b
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fix(anthropic): drop the non-spec custom object from /v1/messages tool definitions (#48893)
* fix(anthropic): drop the non-spec custom object from /v1/messages tool definitions Anthropic and Vertex AI reject a tool carrying a custom object with tools.N.custom.custom: Extra inputs are not permitted. Drop it on the shared Anthropic request path, keep the hoist of a boolean custom.defer_loading to top-level defer_loading, reuse the helper on Bedrock, and stop the chat adapter from merging custom into the tool's JSON schema * fix(messages): hoist custom.defer_loading only on Claude routes and strip custom on count_tokens Non-Claude Messages providers such as DeepSeek now get custom dropped without a defer_loading hoist, since they have no tool search. /v1/messages/count_tokens on Anthropic, Azure AI, and Vertex AI drops custom too, and the local token counter no longer counts it as schema. * fix(anthropic): drop tool custom object only under drop_params and hoist defer_loading for Claude models Without drop_params the tool is forwarded as sent, so the provider's 400 stands. A boolean custom.defer_loading still becomes the top-level flag for Claude models and on Bedrock Invoke and Mantle. Bedrock count_tokens now normalizes invokeModel tools the way Bedrock Messages sends them. * fix(bedrock): drop tool custom object on the Mantle count_tokens fallback Mantle Messages drops custom unconditionally, so its count_tokens call now counts the tools the same way. * fix(messages): keep custom-tool Messages calls on Python and match Mantle count_tokens to Mantle Messages * test(vertex): annotate count_tokens tool test locals with Final * fix(messages): pass tools without custom through untouched so Bedrock fallbacks keep filled tool names * fix(messages): keep Bedrock's in-place tool cleanup so fallbacks still work, and stop hoisting defer_loading on the OpenAI-like passthrough --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Alon Sokolovski
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764a7a6536
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fix(context_management)!: count the system prompt toward the clear_tool_uses input_tokens trigger (#47857)
* fix(context_management): count the system prompt toward the clear_tool_uses input_tokens trigger Anthropic's API compares an input_tokens trigger with the whole prompt, system included; the polyfill counted messages and tools only, so it cleared late by the system prompt's size. compact_20260112 already counts it: share its system_to_text. * fix(context_management): take read-only types in the system prompt count The type-discipline gate (LIT001) rejects new list and dict annotations. * chore: rerun CI (proxy-build-from-pip timed out) * fix(context_management): count the system prompt through compact's shared counter Finishes the main merge's conflict in clear_tool_uses.py. The input_tokens trigger now adds the system prompt through count_system_tokens, the same counter compact uses, while the count reused for cleared_input_tokens stays messages and tools only, so the reported savings are just the cleared tool results * perf(context_management): skip the token count when nothing can be cleared The keep check runs before the trigger count, the system prompt reader accepts any mapping block, and the system prompt test gets short parameter ids * test(context_management): keep the off-loop count test counting after the keep check moved first * test(context_management): cover a client compaction summary counting toward the trigger The trigger also skips the system prompt count when messages and tools alone already pass it * fix(context_management): move a client compaction summary into the system prompt when clearing runs before compact * fix(context_management): clear only the history after a client compaction block so compact still replaces its summary * fix(context_management): keep tool calls beside a client compaction block clearable --------- Co-authored-by: AlonSoko <25619863+AlonSoko@users.noreply.github.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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rj-cairn
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d06b889d41
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fix(streaming): keep merged think tags balanced and split SSE lines only on CR/LF (#48755)
* fix(vertex_ai): split Gemini SSE lines on CR/LF only so U+2028 in text keeps the stream The Gemini and Vertex AI streaming path read the SSE body with httpx iter_lines()/aiter_lines(). Those follow str.splitlines(), which also breaks on U+0085, U+2028, U+2029, \x0b, \x0c and \x1c-\x1e. JSON leaves U+0085, U+2028 and U+2029 unescaped inside strings, so a data: line whose text holds one of them was cut in two. The tail is not a data: line and the head never parses, and the accumulate-and-retry fallback then carries the unparsed prefix forward, so every later event is appended to it and the stream ends with no more content and no usage. SSE lines end only at CRLF, LF or CR. The new iter_sse_lines/aiter_sse_lines split decoded text on exactly those, treating a CR at the end of one chunk and an LF at the start of the next as one break. The first request and every long-decode hop now read through them. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * fix(streaming): keep merged think tags balanced and split shared SSE lines only on CR/LF Close an open <think> block on the first content or tool-call delta and at stream end, reopen it when reasoning returns, and route the shared chat completions stream through the SSE line splitter so U+2028 and U+2029 inside JSON no longer drop chunks. * fix(streaming): exact-match the SSE [DONE] marker in BaseModelResponseIterator * fix(streaming): split every SSE reader on CR/LF only and keep the logged think block closed Switch the remaining httpx stream readers (Anthropic chat, Google GenAI, Interactions, Codestral, openai_like, Predibase, LangGraph, Vertex Agent Engine, A2A providers, Azure AI agents, sandboxes, the A2A endpoint, and others) from iter_lines/aiter_lines to iter_sse_lines/aiter_sse_lines, so a raw U+2028 or U+2029 inside a JSON string no longer splits an event. Keep the chunk that closes a think block in the logged and cached response when a stream ends without a finish_reason, close the block before a legacy function_call, and make sync Codestral FIM streaming send stream=true and use the text-completion-codestral stream parser. Test fakes that only mocked iter_lines now stream real httpx bytes and assert the content they feed in. * fix(streaming): split buffered SSE parsers only on CR and LF, shard the codestral tests Buffered SSE and JSONL parsers used str.splitlines(), which also breaks on U+2028, U+2029 and U+0085 that JSON allows raw inside strings. They now use split_sse_lines from sse_lines.py, which also gains a str.split fast path. Adds tests/unit/llms/codestral to the unit workflow shard. * fix(streaming): keep U+2028 in restamped Anthropic frames and fold CRLF before splitting SSE lines * test: type the new stream tests, drop a comment, and benchmark the SSE line splitter * test: type the iterator class parameters in the line-separator stream tests * fix(responses): stream tool call arguments opened in a text chunk * fix(responses): hold a tool call that shares a chunk with text until the text ends * test: type the a2a SSE handler helper and the sandbox fake responses --------- Co-authored-by: Tim Vink <vinktim@gmail.com> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Co-authored-by: michelligabriele <gabriele.michelli@icloud.com> |
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Mateo Wang
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567383c503
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feat(proxy)!: model family and access group entries in model_max_budget (#47722)
* feat(ui): let the per-model budget field take a model pattern or access group * feat(proxy): let model_max_budget entries name a model family or access group, shared across replicas * fix(proxy): open each shared model budget window once for every replica, and match patterns in linear time Without Redis, the first replica to charge an entry in a new window now opens it in the window spend table and the others join it, so no flush replaces spend another replica already added. Budget patterns match by splitting on `*` instead of a regular expression, a refused request names the entry that refused it, and the key form no longer offers a typed pattern another row already budgets. * fix(proxy): read the shared budget window from the writer, and keep concurrent first charges on one replica Opening a shared window now reads the row from the writer, since a lagging read replica could hand a replica its own start and let a flush replace the others' spend. A charge that waited on the open no longer resets a local window another charge on the same replica opened meanwhile. Window tests use fixed timestamps. * fix(proxy): stop a non proxy admin from lifting a team model cap with a looser group or pattern entry Group and pattern entries now take precedence over other entries, so a team update by anyone but a proxy admin must keep each new or changed entry within every existing cap it can take precedence over. A pattern can match a provider-prefixed or wildcard-routed name, so any two patterns count as overlapping, and a model or group entry counts as outranking every pattern. * fix(proxy): narrow model budget pattern overlap, keep team caps under key patterns, and accept only typed patterns in the UI * fix(proxy): keep model family budgets through database blips, team models and key entries A failed window open no longer restarts the window at this replica's clock, so it cannot wipe shared spend. Spend charged meanwhile stays local and joins the shared window once the database answers A key entry named after a model replaces only the team entry of that name, so team pattern and group caps still apply. Team-scoped models are gated and charged under their public name, and group lookups see team deployments The team update guard compares caps by window length and only ranks patterns that can match the same name, including Bedrock vendor patterns behind a bedrock wildcard route * fix(proxy): refuse team entries that take precedence over a capped model_max_budget entry A non-proxy-admin could add a carve-out (an exact model, a narrower pattern or a group) at or under the family cap, and its own counter then multiplied the team's spend on that family. Any new entry that takes precedence over a capped entry now needs a proxy admin, and group members include the team's own public model names so team deployments cannot hide a group overlap. * test(router): cover team_public_model_names_of * docs(proxy): name pattern and access group keys in model_max_budget docstrings * fix(proxy): name the entry holding the cap in the team model budget raise refusal * fix(proxy): check precedence when a team edit makes an unenforced model_max_budget entry take effect * fix(proxy): let an org admin give a legacy model_max_budget row a budget_duration at the same or a lower cap * fix(proxy): keep a legacy model_max_budget row's stored cap when an org admin gives it a budget_duration |
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Mateo Wang
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dfd9ad4103
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fix(responses)!: bill partial usage on mid-stream failures and report bridged /v1/messages failures to the proxy (#47537)
A Responses API stream that fails partway now bills the usage it already consumed, ends with an error instead of a clean stop, and honors stream_timeout between chunks. Bridged /v1/messages calls report those failures to the proxy's failure hooks, so spend, budgets and logs see them |
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tessellark
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43ce3e22aa
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fix(responses)!: answer 400 before counting a lone Responses built-in tool on Claude (#48798)
* test(responses): audit #47363 Adds integration cells for the input token count paths #47363 changed: the provider id of a managed file and the 403 for another team's file on OpenAI, file_search and additional_tools parity plus cache-mark stripping and untranslatable tools on Claude, and the Bedrock runtime count leaving out the options it refuses. * fix(count-tokens): reject a lone tool with no chat equivalent before the Claude count call A Claude input token count whose only tool was mcp or computer_use_preview skipped the pre-call check and went to the provider, so the caller got the provider error instead of the 400 naming the tool. The check now runs whenever a tool would get a placeholder name. * fix(responses): count a lone Anthropic toolset and name the uncountable tool The lone-tool 400 now fires only for OpenAI Responses built-in tool types, so a nameless Anthropic toolset still counts. The error says LiteLLM cannot count that tool type for the model. Adds a Bedrock unit cell for the lone-tool 400 and pins the runtime wire test's tool shape per request type. * fix(responses): take the uncountable tool types from the OpenAI SDK and keep the region hint off LiteLLM's 400 The Responses built-in tool types now come from openai's ToolParam union, so a type the SDK adds is refused before the count call too. The AWS region hint no longer lands on an error LiteLLM raised before calling the provider. Tests cover two more lone types and the hint. * fix(responses): keep LiteLLM's own list of uncountable tool types and pass a non-string tool type through * fix(responses): count tool_search and namespace tools the way Claude chat inference sends them The count refused a lone tool_search with LiteLLM's 400 and sent a lone namespace raw or squashed it into an empty tool, while Claude chat inference maps tool_search to Anthropic's tool search tool and unpacks a namespace into its function tools. Both now go through the deployment's own map_openai_params, so the count sends what inference sends, and tool_search leaves the list of uncountable types. * fix(bedrock): count a Claude request with its deployment's own chat translation Bedrock invoke and Mantle inference turn a chat tool_search tool into Bedrock's tool search tool, but the Bedrock counter always translated with the Anthropic config, so the count answered 400 for a request inference serves. --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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norkelbyte
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8033f311f2
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fix(anthropic): let drop_params drop speed on claude-opus-4-7 after Anthropic removed its fast mode (#48764)
* fix(anthropic): let drop_params drop speed on claude-opus-4-7 after Anthropic removed its fast mode * test(anthropic): cite the Claude Code fast-mode page for the Opus 4.7 removal date * test(anthropic): cover the drop_params speed drop for claude-opus-4-7 * test(anthropic): type the model parameter of the speed drop test --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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m6atlas
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e2d78b69cd
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test(proxy): audit #46815 (#48768)
* test(proxy): audit #46815 Add integration tests for every count route #46815 changed, and count a Claude subscription caller's tokens with their forwarded token when the deployment has no configured key, which #46815 turned into a 401 * test(proxy): accept the forwarded subscription token in the count endpoint mocks --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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moe-berri
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1bff4e3dae
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fix(anthropic): make opening usage estimates opt-in (#48633)
* fix(anthropic): make opening usage estimates opt-in * docs(anthropic): follow README paragraph style |
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moe-berri
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02605a14ad
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fix(anthropic): estimate initial input tokens for bridged message streams (#48618)
* fix(anthropic): estimate initial input tokens for bridged message streams Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(anthropic): count iterable prompt blocks in initial usage Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(anthropic): avoid fetching images during initial usage estimation 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> |
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Mateo Wang
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14193435b8
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refactor(anthropic): rename llms/anthropic/pass_through to llms/anthropic/messages (#48510)
* refactor(anthropic): rename llms/anthropic/pass_through to llms/anthropic/messages Move the unified /v1/messages code out of a directory called pass_through, since it is not the passthrough feature. The inner messages sub-package becomes native, and the Vertex AI partner model experimental_pass_through package becomes messages. The old packages stay as import shims that warn once and alias every submodule in sys.modules, so old deep imports still resolve Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(anthropic): keep Final variables out of loops in the rename shim Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(anthropic): match the warning text in the rename shim test Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(anthropic): resolve old pass_through imports lazily through a meta path finder Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(anthropic): mark the alias loader spec restore for the type discipline gate Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * style(anthropic): format the renamed package shim Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * style(anthropic): keep the rebind-ok marker on the spec restore line Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(anthropic): carry the new package search path on alias specs Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(anthropic): declare the shim test locals Final 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> |
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ishaan-j-berri
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aa33b052d2
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fix(messages): honor OpenAI stop_sequences (#37118) (#48534) | ||
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tin-berri
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f2b59be6ea
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fix(vertex): keep Messages authentication off the event loop (#48523) | ||
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Kerry Lu
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3b8ad37296
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fix(anthropic): raise DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS from 4096 to 128000 (#48502)
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Mateo Wang
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7e14638001
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refactor(llms): rename model-version transformation files to family names (#48509)
gpt_5_transformation.py becomes gpt_reasoning_transformation.py (OpenAI and Azure), the Bedrock anthropic_claude3_transformation.py files become anthropic_claude_transformation.py, anthropic_claude2_transformation.py becomes anthropic_legacy_text_completions_transformation.py, and vertex_ai_partner_models/llama3 becomes vertex_ai_partner_models/llama. Class and symbol names are unchanged. Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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ishaan-j-berri
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387b8b3477
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fix(anthropic): release upstream messages stream on disconnect (#48197) (#48220)
* fix(anthropic): close upstream messages stream on disconnect (#48197) * test(anthropic): prove disconnect cleanup over live proxy (#48197) |
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tbartik
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bcd541918a
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fix(anthropic): stop injecting empty-content placeholder into tool-call-only assistant turns (#34822)
* fix(anthropic): stop injecting empty-content placeholder into tool-call-only assistant turns _sanitize_empty_text_content replaced empty or whitespace-only text with "[System: Empty message content sanitised to satisfy protocol]" for every user and assistant turn. anthropic_messages_pt calls it unconditionally, independent of modify_params, because an empty text block always makes Anthropic 400. An assistant turn carrying tool_calls and no text is the normal shape an OpenAI-format agent emits, and Anthropic accepts an assistant turn whose content is just tool_use, so there was nothing to substitute there. Substituting anyway put a protocol diagnostic in the content field, which is the assistant's utterance as far as the model and the client are concerned, so it got persisted into conversation history and echoed back on later turns. Empty text is now dropped rather than replaced when the assistant turn already carries substantive content (tool_calls, or non-text blocks such as tool_use). Turns that would otherwise be left with no content at all keep the placeholder, since Anthropic rejects those too. Fixes #24498 * fix(anthropic): only drop empty text when a sibling block survives conversion _carries_non_text_content treated any non-text block as substantive, but the assistant list-content converter only keeps thinking (when signable), server_tool_use and *_tool_result blocks. For an assistant turn holding empty text next to a block it discards, such as an image_url or a signature-less thinking block, dropping the empty text left nothing behind and the whole turn disappeared from the request. The predicate now mirrors what the conversion actually keeps, so those turns retain the placeholder and survive. This also improves on the previous behaviour, where such a turn was dropped outright. * test(anthropic): cover malformed content blocks in the survivor predicate _survives_assistant_conversion had a separate non-dict guard that no test reached, since _is_empty_text_block already rejects non-dict blocks before the predicate runs on them in practice. Folding the check into the type lookup keeps the same behaviour for bare strings and blocks without a type key, with no unreachable branch. Extends the sibling-block test with a block missing its type key. * test(anthropic): set modify_params through monkeypatch in sanitization tests The test-quality gate flags direct writes to litellm module globals. Swap the class setup/teardown for an autouse monkeypatch fixture, drop writes that just repeat its default, and route the False cases through monkeypatch --------- Co-authored-by: tbartik <22518863+tbartik@users.noreply.github.com> Co-authored-by: ryan-crabbe-berri <ryan@berri.ai> |
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Ishaan
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e5cedbb861
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fix(anthropic): include null stop_sequence in native responses (#47987)
* fix(anthropic): preserve nullable stop_sequence in native responses * refactor(anthropic): clarify stop sequence defaults and test streaming * refactor(anthropic): extract stop sequence normalization helper * refactor(anthropic): default missing stop sequence in place |
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Mateo Wang
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820725661e
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fix(router): log each dropped /v1/messages attempt through its own logging copy and open the retry without its usage (#45194)
* fix(proxy): keep a retried /v1/messages stream's spend log by logging the superseded attempt as a failure A /v1/messages stream the router retries before any content shares one logging object across both attempts. The superseded attempt's end-of-stream logging ran as a success (a usage-only or placeholder result) and claimed the request's one-shot success log, so the recovered stream's spend row was either missing or written under the superseded attempt's message id. The passthrough logging path now logs an Anthropic stream whose frames carry an `event: error` as a failure with whatever usage the provider reported, the way a dropped stream already does, and the router closes the superseded stream before it opens the retry so that logging is queued first. * fix(proxy): log a provider error frame through an attempt-scoped logging copy The error-frame failure stashed the superseded attempt's usage on the logging object the retry shares, so the retry's success log carried the failed attempt's token counts. The failure now logs through a copy of the logging object with its own model_call_details, and the helper moves into the Anthropic passthrough logging handler so the proxy streaming handler only routes by endpoint type. * fix(proxy): scope the error-frame failure log to streams closed at the frame A stream that ran to its end at the provider's error frame kept logging through the success route before this PR, and the attempt-scoped failure log is now taken only for a stream the router closed at that frame, with the copy made at the close so the retry's deployment never reaches it * fix(router): log each dropped /v1/messages attempt through its own logging copy and open the retry without its usage A /v1/messages stream dropped after message_start and retried in its model group shares one Logging object across attempts, so the retry's success log inherited the dropped attempt's stashed usage and exception, and a second drop never reached the failure handlers because the first had already marked the object as logged. The dropped attempt now dispatches its failure handlers through an attempt-scoped copy, and the router discards the stashed usage right before it opens the next attempt, on same-group retries and on every fallback hop, so a request that runs out of attempts still bills the last attempt's partial usage. * fix(router): dispatch chat stream failures through the attempt-scoped logging copy The router now clears a dropped attempt's stashed usage before every fallback hop, so the chat stream wrapper's queued failure log must read that usage from its own copy of the logging object, like the /v1/messages path already does. The stash test asserts through a real success log, the cooldown test reads a real Router's cooldown list, and the integration helpers carry full types. * test(vertex): give the stream wrapper's logging stub an attempt_scoped_copy * test: route the pass-through logging mock's attempt copy to itself and read the callback sink through a reader The pass-through failure test stubbed dispatch_failure_handlers on a spec'd MagicMock, which the attempt-scoped copy no longer reached, so the logging worker never finished. The integration tests kept earlier sink batches in a caller-owned list the helper mutated; a small reader keeps them instead. * test(router): use real Logging in fallback cooldown test Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(router): preserve superseded retry usage for billing Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(proxy): bill retry input after prior stream usage Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(proxy): track retry dispatch for terminal billing Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(router): use deterministic retry timestamp Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(router): avoid real clock in fallback tests Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(tests): remove duplicate streaming logger import Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(proxy): retain error-frame usage for exhausted retries * test(proxy): fix retry billing expectations and helper annotations * fix(proxy): preserve failure estimate without carried usage Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(proxy): audit retry helpers and fixtures Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(proxy): fix retry fixture timestamps Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(proxy): observe retry stash at attempt boundary Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(logging): keep terminal failure attribution when dropped attempts log on copies Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(logging): publish a dropped attempt's payload only while it is still the current attempt Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(proxy): cover spend-log encoding error propagation Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(anthropic): skip adapter failure dispatch when the wrapped stream already logged it Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(router): cool down the final fallback attempt when its failure callbacks were blocked Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(anthropic): import Final in adapter regression Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(proxy): drop spend-log encoding expectation superseded by main's poison isolation Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(anthropic): finish the wrapped stream's failure log before surfacing the error Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(anthropic): wait for the wrapped stream's failure log to finish like the inline dispatch did Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(anthropic): wait for the executor-run sync failure callback separately from the async caller boundary Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(proxy): pin pass-through stream fixture start times Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(logging): publish failed attempt payload before awaiting failure callbacks Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(logging): publish failed attempt payload from the failure handler after baseline invalidation Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(logging): drop unused discard_partial_usage_for_failure 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> |
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yucheng-berri
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78a6eeb212
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fix(anthropic): keep tool names over 64 chars when a guardrail rewrites a /v1/messages request (#47434)
* fix(anthropic): keep tool names over 64 chars when a guardrail rewrites a /v1/messages request The guardrail translation shortens long tool names for the OpenAI-shaped guardrail input and wrote the shortened names back into tools and tool_use blocks, while tool_reference blocks and tool_choice kept the original names, so Bedrock rejected the request * fix(anthropic): restore declared tool names only from the request's own tool list A history tool_use name whose shortened form matched a declared short tool renamed that declared tool, so a forced tool_choice no longer matched and a tool allowlist saw a different name than the one forwarded * fix(anthropic): keep a tool_use name the caller already used as written A history tool_use named exactly like the shortened form of another long name was rewritten to that long name; such an ambiguous alias is now left alone --------- Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com> |
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Mateo Wang
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91724bb0a7
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fix(responses)!: count input tokens the way inference sends the request (#47363)
* fix(count-tokens): translate chat tool use before Claude token counts Responses and chat tool use (function tools, function_call items, tool messages) is now converted the way Claude chat inference converts it before the Anthropic, Azure AI and Vertex partner counters call count_tokens. Translation errors answer 400 before any provider call. Anthropic-shaped requests and /v1/messages/count_tokens keep their body as sent. * test(count-tokens): compare Claude count bodies with inference for Responses tool use * fix(responses): forward the caller's Responses body to OpenAI token counts OpenAI deployments now get the caller's /v1/responses/input_tokens body as sent, minus LiteLLM-only fields, so OpenAI's own count and errors come back. previous_response_id is checked for ownership like inference and routes to the deployment that served it * fix(responses)!: count Claude Responses bodies the way inference sends them Non-native Responses counts now run the body through the inference bridge, so text.format, tool_choice, reasoning, built-in tools, and file_search count the way inference sends them on Anthropic, Azure AI, Vertex, and Bedrock. The OpenAI native count body uses the drop_params-resolved tool fields. * fix(responses): honor drop_params and replay previous turns in Claude counts Claude input_tokens counts now read the same drop_params setting inference merges from the deployment, router defaults, and request, so a forced tool_choice downgrades to auto under drop_params. previous_response_id replays the stored turns through inference's session loader, and chat messages carrying fields beyond role and content go through the provider's chat translation * fix(responses): keep proxy-only request keys out of OpenAI counts and map chat tools in Bedrock counts * fix(count-tokens): import on Python 3.11 and keep stored prompts in OpenAI counts under drop_params * fix(responses): leave tool_choice out of counts when drop_params drops every tool * fix(count-tokens): count Mantle options, drop cache marks, and reject input inference cannot send The Bedrock Mantle fallback now counts thinking, tool_choice and output_config like the runtime count. Claude Responses counts leave out cache_control marks again, so more than four marks count instead of failing. Counts answer 400 before any provider call for user blocks chat inference refuses and for Responses tools with no Chat Completions equivalent, instead of dropping the block or counting a placeholder tool. Adds the wire tests for these paths * fix(count-tokens): count Responses tools the way the inference bridge sends them A Claude count now hoists additional_tools input items into tools, strips a malformed tool flag under drop_params and rejects it without, and leaves tools out when the deployment lists tools in additional_drop_params, so the count matches the body Claude inference sends for the same request * fix(count-tokens): keep Bedrock runtime counts option-free and send OpenAI the provider file id * test(count-tokens): capture the Anthropic count request at the HTTP layer instead of patching the handler * fix(count-tokens): ignore a caller-sent file id mapping and keep non-string file ids as sent |
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Max Azatian
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5145250d83
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fix(langfuse_otel)!: send the completion start time (#45597)
* fix(langfuse_otel): send the completion start time langfuse_otel never set langfuse.observation.completion_start_time, so Langfuse showed no time to first token for any call, streamed or not. The legacy langfuse callback already sends kwargs["completion_start_time"]. Send the same value as an ISO timestamp with an explicit offset. * fix(langfuse_otel): type kwargs and test against a real span Type the kwargs of _set_langfuse_specific_attributes as dict[str, Any], the declared type of model_call_details, so the new completion start time read adds no unknown-type errors. The tests now check a real SDK span instead of patching safe_set_attribute, and the parameter is typed. * fix(otel): send the completion start time from the v2 Langfuse mapper With LITELLM_OTEL_V2 the langfuse_otel spans come from LangfuseMapper, which had no completion start time either. Carry the payload's completionStartTime on LLMCallSpanData for streamed calls, the same calls that get time_to_first_chunk_seconds, and map it to langfuse.observation.completion_start_time. * fix(langfuse_otel): take the logging kwargs as a read-only Mapping The new kwargs annotation tripped LIT001. Both helpers only read the logging kwargs, so type them Mapping[str, Any] instead of dict. * fix(langfuse_otel): time /v1/messages streams from the call start and send the V2 completion start time like the default emitter * fix(langfuse_otel): time Gemini, Interactions and A2A streams from their first chunk and send the start time as JSON The native Gemini, Interactions and A2A streams never recorded a first chunk, so with completion_start_time now sent both emitters showed TTFT equal to latency on them. They now record it, and Gemini and Interactions take the call's start time like /v1/messages. Both emitters send the completion start time as a JSON string, the form the Langfuse SDK and the langfuse callback send, through one serializer. * fix(langfuse_otel): time A2A streams from the call start and record a cached replay's first event A2A streams took their start time when the iterator was built, so their latency left out the work before it. They now use the call's start time like every other route. Cached /v1/messages stream replays recorded no first event, so their time to first token fell back to the end time. The replay now records it on the first event it yields. The tests use the real Interactions config and typed logger hooks. * fix(a2a): time streamed A2A calls from before the agent card fetch and make the cached replay test clock-free * test(a2a): type the card fetch test and stop rebinding the seed helper's recorder * test(otel): expect completion_start_time and cache_hit in the B3 span keys --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Mateo Wang
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270d108ace
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fix(anthropic): json_schema envelope edge cases on the Anthropic json tool path (#47478)
* test(anthropic): cover json_schema envelope edge cases on the Anthropic json tool path * fix(anthropic): unwrap required-only keys, walk past tool-named envelopes, keep non-ASCII text, flush cut streams, check Vertex output_format * fix(anthropic): send held json tool text on cut Bedrock Invoke streams and drop a non-object output_format under drop_params A Bedrock Invoke stream that ends before the json tool block closes now sends the held text and closing brace, as the direct Anthropic stream already did. A non-object output_format on the Anthropic messages route and its providers is dropped under drop_params and otherwise forwarded, so the provider answers its own 400. Vertex Claude names the proxy setting in its 400 and reads drop_params through the shared helper. Adds integration cases for the json_schema envelope and the cut stream. * fix(anthropic): drop a non-object output_format on chat, unwrap a whole envelope on a cut stream, escape lone surrogates Chat completions to Anthropic, Azure AI, Bedrock Invoke and Vertex now drop a non-object output_format under drop_params, matching /v1/messages. A stream cut after a complete held envelope is unwrapped like a normal end. A json_schema answer holding a lone surrogate keeps its \u escape so the response can still be encoded * fix(anthropic): join a split answer when the stream ends in its last block A cut during the second json tool block now runs the held block through the same flush as a block stop, so it joins the streamed keys or the earlier split blocks instead of sending two objects back to back. The Vertex output_format fold drops its unreachable drop_params branch, since both parent configs drop a non-object value first. * test(vertex): expect the LiteLLM 400 for a non-object output_format on /v1/messages |
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Alon Sokolovski
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dd243bdccb
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feat(context_management)!: honor clear_at_least, exclude_tools and clear_tool_inputs in the clear_tool_uses polyfill (#47123)
* feat(context_management): honor clear_at_least, exclude_tools and clear_tool_inputs in the clear_tool_uses polyfill The clear_tool_uses_20250919 polyfill honored only trigger and keep and ignored the other three fields with a warning. It now follows the Anthropic API, as measured against it: - keep counts clearable tool uses only; uses of exclude_tools are never cleared and count toward neither keep nor clear_at_least. - clear_at_least clears oldest first in batches of at least that many tokens and leaves an incomplete last batch, so the cleared prefix only grows between requests and each batch costs one prompt-cache miss. - clear_tool_inputs (true or a list of tool names) also empties the input of the cleared tool uses. Invalid values are ignored with a <field>_invalid warning on the applied edit. * refactor(context_management): build the clear_tool_uses selection without mutable state * test(context_management): cover a history made only of excluded tools * fix(context_management): leave tiny tool results out of clear_tool_uses like the API The API neither counts nor clears a tool use whose result is 1 to 46 bytes of text, whatever the tool or error flag; empty results still count. Probed against the API with crafted histories. Test data under 46 bytes is lengthened so those tests keep exercising clearing * fix(context_management): count tiny tool results when clear_tool_inputs covers their tool The API leaves a use whose result is 1 to 46 bytes out of clear_tool_uses only while its input is kept. When clear_tool_inputs is true or names the use's tool, the API counts it toward keep and clears it like any other use. * fix(context_management): count cleared inputs and images toward clear_at_least, answer malformed fields with a 400 * fix(context_management): validate clear_tool_uses edits before any editor runs, drop only the malformed edit under drop_params * fix(context_management): drop only the malformed clear_tool_uses fields under drop_params A malformed clear_at_least, exclude_tools or clear_tool_inputs no longer drops the whole edit under drop_params. The edit still runs with its well-formed fields, so the clearing that worked before is kept. Also types the new tests and swaps the hand-rolled spy router for an AsyncMock * test(context_management): pin the 46 byte cutoff in UTF-8 bytes and a batch closing at exactly clear_at_least * fix(context_management): leave a non-string tool_use id for the provider, size lone surrogates, and read drop_params strings * fix(context_management): read a drop_params string the same way on count_tokens and both /v1/messages paths count_tokens still checked drop_params with is True, so a body drop_params of "true" answered 400 there while /v1/messages dropped the malformed field. Both routes and the sync path now use drops_unsupported_params. --------- Co-authored-by: AlonSoko <25619863+AlonSoko@users.noreply.github.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Mateo Wang
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96f1bc02d8
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feat(router)!: apply each deployment's reasoning_effort_levels on every provider and route (#46601)
* feat(router): apply each deployment's reasoning_effort_levels on every provider and route A deployment's declared reasoning_effort_levels now decide the effort sent upstream on /v1/chat/completions, /v1/messages and /v1/responses for every provider. Requests round up by default, litellm.reasoning_effort_rounding and model_info.reasoning_effort_rounding switch to down, a declaration lets providers like azure_ai accept reasoning_effort, and each remap logs one INFO line with structured fields. * fix(router): round reasoning effort only on a deployment's own declared levels Cost map levels no longer round requests or force-allow reasoning_effort, so undeclared sibling deployments and catalog-only models behave as on main. A declaring deployment lets azure_ai accept any level the resolver sends, and a hosted_vllm chat thinking budget is rounded too. * fix(router): drop undeclared reasoning effort under drop_params and round native /v1/messages effort Under drop_params a level outside the deployment's reasoning_effort_levels is dropped instead of sent. Providers that take /v1/messages natively now round output_config.effort onto the deployment's declaration. The rounding setting ignores case and surrounding spaces. * fix(router): strip line breaks from every field in the reasoning effort remap log * fix(router): round thinking-budget efforts onto a declaring deployment on every provider * fix(router): keep the caller's thinking display when rounding a thinking budget on adaptive Claude Warn on a reasoning_effort_rounding value other than up or down instead of silently rounding up * test(integration): cover deployment reasoning_effort_levels rounding on chat, messages and responses Wire cells assert the exact body each provider receives when a deployment declares reasoning_effort_levels, plus undeclared controls, malformed input, drop_params, rounding settings, fallbacks, model groups, live updates and the response cache. Chaos cells cover a killed worker, a proxy restart, an upstream outage, a slow stream and the INFO remap log. * test(integration): cut held streams when the upstream stops in the reasoning effort chaos cell The upstream-stop cell waited for the held streams before stopping the upstream, so they only ended on a client or proxy timeout and the result depended on which fired first. The upstream now stops while the streams are held, and each cut stream must carry an error and no answer * fix(router): round thinking budgets only on declaring deployments and round Converse output_config An undeclared hosted_vllm deployment serving a Claude model name now maps a thinking budget the same as any other undeclared hosted_vllm deployment. A model_info that echoes a cost map key no longer borrows that entry's levels. A non-numeric thinking.budget_tokens answers 400, or is dropped under drop_params, where main answered 500. A declaring Bedrock Converse Claude deployment now rounds output_config.effort, which /v1/messages sends for Claude Code * test(integration): cover output_config rounding and non-numeric thinking budgets in the reasoning effort cells * test(integration): keep an explicit reasoning_effort winning over a malformed thinking budget on hosted_vllm * test(integration): cover output_config rounding on openai, azure and azure_ai chat * fix(hosted_vllm): let an explicit effort win over a malformed budget and reject one on /v1/messages An explicit reasoning_effort beside a non-numeric thinking.budget_tokens keeps main's behavior again. A declared hosted_vllm deployment with a Claude model name maps a budget through the generic table instead of the adaptive Claude one. /v1/messages to a non-Claude target hands a malformed budget to completion(), which answers 400 or drops it under drop_params, where it answered 500 * fix(router): drop thinking beside an explicit effort when a declaring deployment's provider cannot read it * test(integration): cover malformed thinking budgets on /v1/messages to non-Claude targets * fix(anthropic): answer 400 for a malformed thinking budget on /v1/messages to any non-Claude model, or drop it under drop_params * test(integration): cover an explicit effort beside thinking on azure_ai and drop_params on the /v1/messages bridges * fix(anthropic): answer 400 for a malformed thinking budget through adapter_completion without drop_params * fix(anthropic): type the adapter_completion request so the drop_params read adds no unknown argument * fix(router): a default effort beside a thinking budget sends no effort on chat Chat now leaves the budget-derived effort out when the caller's effort resolves to none or asks output_config for one, matching /v1/messages. The chat thinking decision moves into resolve_chat_reasoning so completion() stays under basedpyright's complexity limit. * test: type respx_mock and mark the adapter result Final in the reasoning effort tests * fix(router): keep the caller's thinking beside a kept effort and the budget effort beside a dropped one A non-default output_config.effort beside a thinking budget now leaves the thinking as sent, so Bedrock Invoke keeps display updates. A level drop_params drops counts as never sent, so the budget still sets the effort. output_config.effort replaces a thinking budget the provider cannot read, and a non-numeric string budget on adaptive Claude answers 400 in place of 500, or drops under drop_params. * fix(router): send a kept output_config effort as reasoning_effort where the provider cannot read thinking On a declaring hosted_vllm or azure_ai deployment, a thinking budget goes out as reasoning_effort. An output_config.effort beside it now takes that slot, rounded onto the declared levels, instead of leaving only output_config, which those backends ignore. * fix(reasoning-effort): honor global drop_params for a non-numeric budget and keep the budget effort beside a dropped /v1/messages level * fix(reasoning-effort): send a level a gpt-5 deployment declares even where the cost map does not list it * fix(reasoning): apply declared levels on azure_ai gpt-5 and reject a bool thinking budget on adaptive Claude * fix(router): drop a thinking budget's effort that a deployment declaring no ranked levels excludes A deployment declaring [] or ["none"] skipped the budget check, so adaptive Claude still sent the budget-derived output_config.effort under drop_params on chat, Bedrock Converse, and native /v1/messages. The adaptive thinking now goes out without that effort * fix(anthropic): reject a false, null or empty thinking budget on adaptive Claude |
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Mateo Wang
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6c4c134f23
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fix(secret_managers)!: resolve oidc/file and oidc/env refs in admin-configured api_key per request (#47341)
* fix(secret_managers): resolve oidc/file and oidc/env refs in admin-configured api_key per request An api_key set to oidc/file/<path> or oidc/env/<VAR> in a credential_list entry or a deployment's litellm_params was sent upstream as literal text, so every call answered 401. The SDK client wrapper now reads the referenced file or env var on each call and never writes the token back, so a rotated token is used on the next call. The existing server-owned federation gates treat an oidc/ api_key as server-owned by value, so a request body, a non-admin credential or model write, user_config, fallback targets and routing tier params can never carry or name one. Other oidc/ kinds answer 400 * fix(secret_managers): strip only the leading oidc/ from a secret reference A token file under a directory named oidc resolved to the wrong path, because every oidc/ in the reference was removed * fix(auth): refuse oidc/ api_key refs in advisor tools and scope the route gates to them The advisor tool's own api_key now answers a 400 when it holds an oidc/ reference, since its sub-call runs through the client wrapper that resolves one. Stored fallback targets, auto-router tiers and user_config refuse only an oidc/ api_key, inline or through the credential they name, so their workload identity federation handling stays as it was. Provider model discovery for a wildcard deployment resolves the reference before calling the provider. * fix(auth): allow admin oidc api_key on deployment writes, resolve refs on discovery, batch probe and realtime health, refuse client api_base on oidc deployments * fix(utils): resolve oidc api_key refs only from admin-configured sources Mark an oidc/ api_key read from a proxy-level deployment or a stored credential, and resolve only that marked value. Team deployments, prompt templates, nested configs and a direct SDK api_key send the text as written. An unsupported reference kind raises a 401 so the router fails over to the group's next deployment. * fix(utils): send a credential's oidc token only to its own api_base host A stored credential's oidc/ api_key now resolves only for the scheme, host and port of the credential's own api_base or base_url, so a team model or vector store that names the credential with another api_base gets a 401 and that host gets nothing. Saving a team model with an inline oidc/ api_key answers 400, since team rows never resolve one. * fix(proxy): send an admin oidc api_key only to the hosts configured beside it Every admin oidc/ reference now carries the origins of the host params set beside it, checked for api_base, base_url and the Bedrock, S3, SageMaker and deployment_url params. Token counting reads the reference only when it calls the provider, answering 401 when it cannot, and pass-through 401s redact internal paths. * fix(proxy): redact the token path from token count auth errors * test(security): cover token count, Bedrock endpoint, api_base update and pass-through path redaction for oidc api_key refs * fix(proxy): redact the token path from secret reference errors where they are raised * fix(proxy): keep the secret reference's path out of the error a caller sees The 401 for an unreadable oidc api_key or pass-through credential now names only the reference kind and a fixed reason, so a token path under /run/secrets or a custom credential dir never reaches the caller, while the server log keeps the full reference for the operator * fix(secret_managers): strip line breaks from the logged secret reference A reference holding a newline can no longer forge a server log line * fix(secret_managers): label an unknown oidc reference kind instead of echoing it An api_key such as oidc/env_MY_VAR (a typo for oidc/env/MY_VAR) came back in full in the caller's 401 and in the spend log, since the kind was whatever text followed oidc/. A kind get_secret does not know now reads oidc/<unknown kind>. The relative-path hint uses oidc/file//<absolute path>, which the client-message redaction on chat routes leaves whole. * test(health): run the real model auth check in the oidc test connection test * test(secret_managers): read an oidc/file token stored under a directory named oidc * fix(secret_managers): count a URL in the model name as an upstream host for oidc api_key references A huggingface/* wildcard call could name its own endpoint URL as the model and receive the resolved token. The origin check now treats a URL in the model as a host the reference must allow. The integration contract also reads the proxy's own database instead of the pytest worker's. * fix(secret_managers): treat a default port as explicit when matching an oidc api_key origin A credential on https://api.mistral.ai refused a call to https://api.mistral.ai:443, though both reach the same server. The origin check now compares scheme, host and effective port, as assert_same_origin does, and an unparseable api_base is refused instead of raising. The advisor test sets the real proxy flag instead of replacing our own helpers. * test: type the oidc request-body helpers and drive the tier test through mock_response * fix(secret_managers): refuse an oidc api_key reference whose call drops the configured host |
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Kerry Lu
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93b79d2587
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fix(anthropic): apply tool_config cache-control points on chat and /v1/messages (#47394)
* fix(anthropic): apply tool_config cache-control points on chat and /v1/messages A cache_control_injection_points entry with location tool_config was spread into the Anthropic request body on /v1/chat/completions, which Anthropic rejected with 400 Extra inputs are not permitted, and the native /v1/messages path dropped the point silently. Both paths now put the point's control on the last tool Anthropic caches and never forward the key. Co-authored-by: Tan Nguyen <tan@pressingly.net> * refactor(anthropic): validate tool_config inputs instead of casting them Keeps the lint gates flat: the helper takes untyped tools and points and narrows them with the existing object validators, the chat transform builds the body with the marked tools instead of writing into optional_params, and the handler reads a typed view of kwargs. * fix(openai): keep tool_config cache points out of extra_body, add integration test The cache hook hands a tool_config point on for the provider step, and for providers that call through the OpenAI SDK every non-OpenAI param rides in extra_body, so OpenAI answered 400 Unrecognized request argument supplied. The new integration file drives a live proxy against scripted Anthropic, Bedrock Converse and OpenAI upstreams for /v1/chat/completions and /v1/messages. * test(integration): cover tool_config cache points on bridged /v1/messages Three new cases on /v1/messages for models without a native Anthropic messages path: OpenAI through the Responses bridge and Gemini through the chat bridge never see cache_control_injection_points in the body, and Bedrock Nova through the chat bridge gets the cachePoint on toolConfig --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: Tan Nguyen <tan@pressingly.net> |
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Mateo Wang
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b51b9b7d43
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fix(anthropic)!: answer 400 for a malformed tool_result text on /v1/messages and count_tokens (#47347)
* fix(anthropic): answer 400 for a malformed tool_result text on the Responses bridge and count_tokens A tool_result text block whose text is null, a number, or missing made /v1/messages and /v1/messages/count_tokens answer 500 on OpenAI and Azure models. Both routes now answer 400 naming the block, and drop the block when drop_params is on. * fix(anthropic): keep a missing tool_result text as empty text, bound the error, honor body drop_params on count_tokens * fix(anthropic): apply the tool_result text check on the chat/completions bridge and every count route The chat/completions bridge used for Azure, Gemini and other providers now answers the same 400 for a null or numeric tool_result text and drops the block under drop_params, so a model group that mixes OpenAI with another provider answers the same way on every deployment. Gemini and Vertex count_tokens apply the same check, count_tokens turns on drop_params for Claude Code and Codex the way /v1/messages does, and a tool_result without a tool_use_id is checked too * fix(proxy): answer 400 for a malformed tool_result text on count_tokens models without a count API Providers with no token counting API fall back to the local tokenizer, which counted a null or numeric tool_result text as fine while /v1/messages on the same model answers 400. The fallback now runs the same check, unless drop_params applies to the deployment or the request * fix(proxy): honor request drop_params on the local count fallback when no deployment resolves * test(integration): cover non-string tool_result text on both /v1/messages bridges and count routes * fix(anthropic): keep a dropped tool_result on Mistral and reject it before a guardrail rewrite Under drop_params a tool_result whose only text block was null or a number became an empty tool message, and Mistral's empty-message filter removed it, so Mistral answered 400 for a missing tool reply. The filter now only removes empty assistant messages, which is what Mistral rejects. A guardrail that rewrites structured_messages wrote the dropped text back as "", so a null or numeric text was silently blanked even without drop_params. The rewrite path now answers the same 400 as the bridges unless the request or global drop_params is set. * test(integration): cover the Mistral empty tool reply and the guardrail rewrite tool_result text check * fix(anthropic): keep a malformed tool_result text through a guardrail rewrite and keep Mistral's null-message drop A guardrail that rewrites structured_messages now writes the caller's null or numeric tool_result text back in place, so the route's bridge and the deployment's drop_params decide after routing, the same as with no guardrail. The rewrite path no longer answers its own 400 before routing. Mistral drops a null-content system or user message again, as main does, and only a tool reply is exempt from that filter so a dropped tool_result keeps its tool call. * test(mistral): expect empty system and user turns dropped and the empty tool reply kept * perf(anthropic): restore kept tool_result text parts in one linear pass * test(integration): cover the tool_result text restore across tool_results, the chat bridge, and a batch upload behind a rewriting guardrail * fix(anthropic): let guardrails scan malformed tool_result text and honor drop_params on native Anthropic A rewriting guardrail now sees a non-string tool_result text as its JSON text and the write-back restores the caller's value by the sanitized tool_use_id when the guardrail left it unchanged. Native Anthropic drops the bad block under drop_params on /v1/messages and count_tokens, and Mistral keeps an empty tool reply only when it answers a tool call, sent as empty text. * fix(anthropic): compare a kept tool_result text to its scanned form once and align the wire tests with drop_params * test(integration): cover native Anthropic drop_params resolution, guardrail scanning of numeric tool_result text, and the Mistral orphan tool message * fix(count_tokens): honor the router's default drop_params when counting a malformed tool_result text * test(count_tokens): send the tool_result count tests through a real request body instead of a mocked parser * test(count_tokens): send the native Claude count test through a real request body * fix(anthropic): keep each non-string tool_result text when tool_use_ids sanitize to the same id * fix(anthropic): keep non-ASCII characters in the guardrail's view of a non-string tool_result text * test(gemini): mock the count_tokens upstream with respx instead of patching the client factory * fix(mistral): keep an empty tool reply only for an open call, and forward non-object message items on the native drop An empty tool reply re-answering a call the conversation already moved past made Mistral reject the request, and a tool call id that is not a string crashed the filter. Native Anthropic under drop_params assumed every message item was an object. * fix(mistral): find answering tool replies in one pass instead of copying the open call set per reply * fix(mistral): ignore dropped empty placeholders when tracking open tool calls * fix(mistral): drop an empty tool reply when another reply to the same call carries the result * refactor(anthropic): share tool_result text helpers to shrink the diff * fix(anthropic): answer 400 for a non-string tool_result text on the OpenAI Responses bridge |
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Paul El Khoury
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d7620c77a1
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fix(openai): drop unsupported reasoning effort for gpt-6.1-sol (#43935)
* fix(openai): drop unsupported reasoning effort for gpt-6.1-sol gpt-6.1-sol accepts low, medium, high, xhigh, and max. none and minimal were still forwarded, because chat completions ignored an explicit supports_none_reasoning_effort=false and the Responses mapper never applied that contract. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(openai): mark the stripped reasoning object as outgoing JSON Co-authored-by: Cursor <cursoragent@cursor.com> * Fix Azure responses reading the OpenAI effort map gpt-6-astra on Azure accepts reasoning.effort none. The responses gate was checking the OpenAI entry, which rejects it. Signed-off-by: Paul El Khoury <82139421+pOpO1-9@users.noreply.github.com> Co-authored-by: Cursor <cursoragent@cursor.com> * fix(openai): gate every reasoning_effort level from the cost map on chat and Responses One gate reads the model's cost-map row for whatever effort level a request asks for, on Chat Completions and the Responses API, for OpenAI and Azure. xhigh stays opt-in on chat (Azure also keeps none opt-in); every other level is refused only when its supports_<level>_reasoning_effort flag is false. A refused level is dropped under drop_params or answered with a local 400, and Responses keeps the rest of the reasoning object when it drops effort. The guards that follow judge temperature by the level the provider will see. Tests move into the mapped test files and derive the expected outcome for each level from the map row, so a new GPT-6 model needs only its row. * test(openai): pin gpt-6.1-sol's refused effort levels and prove the row flags alone drive the gate * fix(cost-map): azure gpt-6-astra refuses none and honors max on Responses, as live calls show * test(reasoning_effort): pin the astra max and none flips through the adapter and Foundry paths * fix(reasoning_effort): keep max out of the route gates and correct the gpt-5.3-codex rows The same model honors max on Responses and refuses it on Chat Completions, so one row flag cannot gate it on either route. The gates now skip max, the Azure gpt-6-astra rows keep max off so the /v1/messages adapter degrades to xhigh and /model_group/info stops short of it as on main, and chat still forwards max for the Responses bridge. The gpt-5.3-codex rows said none and xhigh off and minimal on; the live model answers the other way round (2026-10-01), and an opt-out gate would have dropped the levels Codex CLI sends. * fix(reasoning_effort): bridge function tools to Responses when the chat gate drops none * docs(reasoning_effort): correct the max advertisement note * fix(azure_ai): read the Foundry row on the Responses effort gate and turn none off for Azure gpt-6.1-sol * fix(azure): gate a prefixed Foundry deployment's Responses effort off its row litellm.responses(model="azure_ai/<deployment>", api_base=<Foundry host>) is re-routed to the azure provider with the azure_ai/ prefix intact, so the Azure Responses config saw a prefixed name, skipped it as another provider's pass-through, and forwarded none and minimal to Azure even under drop_params. The Azure gpt-5 resolver also read no row at all for a prefixed name. The Azure Responses gate now detects the family the way the Azure chat gate does, and an azure_ai/ name on the Azure OpenAI configs reads its azure_ai/ row when the map has one, else the azure/ twin, the rule the Foundry chat config already applies to the same deployment. * fix(cost-map): turn none off on azure/eu/gpt-6-astra like its sibling rows * docs(reasoning_effort): say how each gate reads none and shorten the module docstring * fix(azure_ai): call the public effort-level check from the Foundry Responses gate * test(reasoning_effort): fold the Foundry Responses tests into the mapped file and type the row helpers --------- Signed-off-by: Paul El Khoury <82139421+pOpO1-9@users.noreply.github.com> Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Co-authored-by: ryan-crabbe-berri <ryan@berri.ai> |
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devin-ai-integration[bot]
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4bebcdf757
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fix(databricks): use the native Anthropic route for Claude /v1/messages (#46091)
* fix(databricks): use the native Anthropic route for Claude /v1/messages Claude models on a Databricks workspace were bridged through chat completions on /v1/messages, which drops the safeguards field and never returns safeguard_results. They now call the workspace's /serving-endpoints/anthropic/v1/messages, the proxy forwards the client's anthropic-beta and anthropic-version headers to Databricks deployments, and the beta header map marks dangerous-tool-use-2026-09-03 as supported there. Any route whose platform does not take that beta drops safeguards together with it instead of answering 400. Non-Claude Databricks models keep the chat completions bridge * fix(databricks): derive the dangerous-tool-use beta from safeguards instead of forwarding client betas A Databricks Claude serving endpoint answers 400 to betas Claude Code sends on every turn, prompt-caching-scope-2026-01-05 among them, so the proxy no longer forwards the client's anthropic-beta header to Databricks deployments. The beta the classifier needs is derived from the safeguards body field, the same way the Azure AI route derives it. The Rust beta-header mirror stops rejecting the Databricks dangerous-tool-use cell so its config-cell test matches the JSON again * fix(databricks): keep the billing block, build the messages route once, and pass the partner user agent * fix(databricks): route Unity Catalog Claude names to the gateway messages route and keep env credentials Unity Catalog model-service names now reach the AI Gateway's native Anthropic route instead of a serving-endpoints path that answers 404 for them, and the native route resolves the key and base through the same order the chat route uses, so environment-only deployments keep working. The native route is limited to Databricks foundation-model Claude names, an unlisted provider forwards safeguards and the beta as sent, and the ten betas the workspace rejects are marked unsupported for Databricks in the beta map and its Rust mirror. * test(rust): expect Databricks to drop the advanced-tool-use beta * fix(databricks): keep passthrough safeguards, partner user agent, and classifier fallback Only the OpenAI-compatible JSON providers drop safeguards now, so a deployment opted into /v1/messages passthrough forwards the field and the beta as sent. When a route drops safeguards and the proxy has a classifier model, the proxy answers the verdict itself, as the chat completions bridge did. The Databricks Messages route also reads the databricks_user_agent param like the chat route. * fix(databricks): drop rejected server tools under drop_params, short-circuit web search, route UC names from a full Messages URL * fix(databricks): drop container on the native Messages route, as the chat completions bridge did * fix(databricks): keep every api_base on a route the workspace serves and gate the derived beta on the map Gate the dangerous-tool-use beta derivation on the beta map the way #43955 does, so GitHub Copilot, DeepSeek, and opt-in passthroughs keep the merge base wire. Recognize /serving-endpoints/anthropic api_bases, fall back to the merge base routing for an api_base path the workspace trim does not know, strip Bedrock invocation metrics from the streamed message_stop, forward client betas to Databricks filtered by the map, and keep tool_choice when a dropped server tool has no name * test(databricks): cover the native Messages route, derived beta and JSON provider safeguards drop on the wire * test(anthropic): expect the dangerous-tool-use beta derived from safeguards next to the client's betas * fix(databricks): send client anthropic headers only on the native Messages route and keep SDK auth Databricks chat completions, called directly or through the Messages bridge, no longer receive the client's anthropic-beta and anthropic-version, as at the merge base. The native Messages route still forwards them, filtered by the beta map, without counting them as caller credentials, so a deployment with no api_key keeps falling back to Databricks SDK auth. A pathless api_base goes to the native route only for Unity Catalog names, matching the chat route. * test(databricks): cover SDK auth with client anthropic headers and a Unity Catalog workspace root on the native route * fix(databricks): keep client anthropic headers off an opted-in passthrough api_base A databricks Claude deployment whose api_base is no workspace route and that opts into /v1/messages with supported_endpoints falls back to the OpenAI-like passthrough. That config does not filter betas through the beta map, yet it received the native-only Databricks header entry, so Claude Code's prompt-caching-scope beta reached the upstream raw and got a 400 where the merge base answered 200. Each Messages config now says whether it is the provider's native route, the OpenAI-like passthrough says no, and the handler passes that answer when it reads provider-specific headers, so native-only entries reach only native configs. * test(databricks): cover client anthropic betas on an opted-in passthrough api_base A databricks Claude deployment whose api_base is no workspace route and whose model_info.supported_endpoints opts into /v1/messages takes the OpenAI-like passthrough. The cell sends Claude Code's prompt-caching-scope and context-1m betas through the proxy and asserts the caller gets the upstream's message and the gateway receives no anthropic-beta, as on the merge base. The scripted gateway answers 400 on the prompt-caching-scope beta, as the real one does * fix(anthropic): keep beta map loads off shared threads and build the safeguards evaluator only when the proxy classifies Unfiltered configs such as first-party Anthropic never read the beta map to decide safeguards support. Filtered configs await the map on its dedicated loader before reading it, on both the native and the MCP paths. The native handler decides after the upstream call and builds the evaluator only when the proxy classifies, so malformed messages keep the upstream 400. The Databricks dispatch tests assert the dispatched route instead of the config class, and the sail and Eden AI wire rows expect safeguards dropped, as JSON providers do. * test(databricks): cover a loaded beta map that marks the safeguards beta unsupported A remote beta map that nulls dangerous-tool-use for databricks while the bundled map allows it must send neither safeguards nor the beta to the serving endpoint, with or without a client beta, and the caller gets a 200 with no verdict. * test(anthropic): annotate the MCP beta map recordings as Final * fix(anthropic): drop safeguards on routes without the classifier only under drop_params A route whose upstream does not answer safeguards (a JSON provider, or a map that marks the dangerous-tool-use beta unsupported) now forwards the field and the beta as the merge base did unless drop_params is set or the proxy runs its own classifier, matching how litellm treats any unsupported param. Databricks keeps dropping both, since its merge base bridge never forwarded the field. --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Mateo Wang
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897dda7920
|
fix(router)!: stop a chat-mode Mantle deployment from rewriting the shared Responses routing row (#46263)
* fix(router): stop a chat-mode Mantle deployment from rewriting the shared Responses routing row A deployment with an explicit custom_llm_provider and model_info.mode: chat used to overwrite the shared litellm.model_cost row, moving every other deployment and plain SDK calls onto Chat Completions until restart, because the downgrade guard read a double-prefixed key and never fired. The guard now reads the real key, and a deployment's own mode decides its routing in responses_api_bridge_check, except on chatgpt and github_copilot where Chat Completions is dead for responses-mode models. Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(responses): keep the bridge-check mode tests in the sharded bridge flag file Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses): let a deployment mode pin replace only the shared-row mode so the bridge capability rules still run Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses): keep Responses-only backends bridged under a deployment chat pin and satisfy the type gate Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses): give the bridge casts a reason and drop the bare noqa so the type discipline gate passes Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses): keep a responses row with no supported_endpoints bridged under a deployment chat pin Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(router): read the downgrade guard's mode from the row registration writes A chat-mode openai/<name> deployment merged into the bare <name> row while the guard read openai/<name>, so the guard never fired and the shared responses row turned to chat. An explicit responses/ prefix now also outranks a deployment's chat pin. * fix(router): let an explicit responses/ prefix outrank a registered chat row The router now registers a chat-pinned openai/responses/<model> deployment under its own name, so the lookup found mode chat and the request went to Chat Completions as responses/<model>. * fix(router): keep the shared row's mode for every deployment pin and honor the pin on /v1/messages A deployment's mode pin now never rewrites the shared cost-map row in either direction, including the responses/-stripped twin key, so siblings and SDK calls keep routing by the registered mode. The backend key formula goes back to main's, which keeps capability fields off rows that registration never wrote at base. The /v1/messages adapter and handler read the deployment's model_info, so a chat-pinned deployment gets a plain reasoning effort on chat completions. * fix(router): keep copilot responses opt-in, make unmapped pins order-free, send Mantle auto summary as auto A responses pin on a github_copilot model or a model missing from the cost map still marks the shared row, since that is how those models opt into the Responses API. A chat pin never marks a row, so registration order no longer matters. Mantle's OpenAI path gets summary auto in place of the auto summary detailed. The Azure o3-pro and o3-deep-research rows drop Chat Completions, which Azure does not serve for them. * fix(router): skip a non-string model_info.mode when keeping the registered mode * fix(router): keep chat-pinned Mantle tool calls and summaries on the Responses API A Mantle row that also serves the Responses API now follows the GPT-5 tool and reasoning summary bridge rules, since Mantle Chat Completions rejects function tools on gpt-5.4+ unless reasoning is off and rejects the summary aliases. Mantle chat gets a dict reasoning_effort as its effort level. The opt-in auto summary asks each Responses config for its level, auto on Mantle's OpenAI path, so a client's own detailed summary follows drop_params as at base. No mode replaces a registered responses mode, and chatgpt and github_copilot rows take any pin until they are responses, which restores the Copilot chat opt-out. * fix(responses): ask the target for its auto summary level on the chat bridge and reasoning_effort * test(integration): run the Mantle region-prefixed v1 base case again This PR routes that deployment by its own chat pin, so the case passes and no longer needs the skip * fix(bedrock_mantle): keep web search and reasoning summaries on the Responses API under a chat pin A chat-pinned Mantle GPT-5 deployment now sends web_search_options and a dict reasoning_effort carrying a summary to the Responses API, which serves them. Mantle Chat Completions drops n above 1 and a reasoning summary under drop_params and answers 400 without it. The /v1/messages adapter probes the bridge with only the client's own summary, so an auto-injected one never moves the request * fix(bedrock_mantle): check n and reasoning summaries only on the Chat Completions request * fix(router): keep a non-string deployment mode off the shared routing row A deployment whose model_info.mode was an int, a list, or an object still reached the shared cost-map row, so an unpinned sibling on the same backend stopped routing to the Responses API. The shared registration now drops a mode that is not a string, and the deployment keeps its own raw value. Adds integration cells for per-deployment routing across the OpenAI and Anthropic SDKs, raw httpx, wildcard routes, malformed modes, an upstream outage, config changes under traffic, and a worker kill plus restart. * test(integration): find each routing cell's spend row by the response it logged A bridged call logs its row under the wrapped Responses id, which the caller never sees on a streamed Messages answer, so look the row up by the answer's own ids and the upstream ids for its marker. Int and list modes 500 their own model group on main too, so those cells check only the sibling, and the summary cell sends the auto level Mantle's Responses path accepts * fix(bedrock_mantle): run extra_body fields through the chat checks A chat-pinned Mantle request that nests n or reasoning_effort in extra_body now gets the same 400, drop, and effort-level handling as the top-level fields. The bridge test injects a MockTransport client instead of patching HTTPHandler.post. * test(router): cover nested reasoning_effort routing, messages thinking summaries and per-target auto summary levels * fix(router): probe a pinned deployment's own API on /health and keep a requested summary beside an extra_body effort * fix(router): keep a custom-priced deployment's mode pin off the shared backend row on each request * fix(router): run per-request custom pricing through the shared mode guard so a health probe keeps the row's mode * fix(router): drop the debug log that wrote a request's model name from keep_registered_mode * test(router): cover custom-priced chat pins, their health probes and summary aliases beside an extra_body effort * fix(proxy): pass only the mode pin to a /health probe so a failing probe never cools its deployment down * test(router): cover failing health probes leaving their deployment out of cooldown, with and without background checks * test(router): kill the worker that holds burst calls, so the worker kill cell never depends on how the kernel spread them * fix(router): route a fine-tune the same whichever deployment registers first Main's register_model now seeds an unmapped fine-tune's row from its base model's built-in row, mode included, on the first registration. The mode guard read only the live row, so a responses pin marked the shared row only when it registered before its siblings. The guard now reads the row the registration merges onto, the live row or else the row it would seed. --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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devin-ai-integration[bot]
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46ae68021e
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fix(anthropic): carry safeguard_results and stop_details in synthesized message_delta events (#46096)
* fix(anthropic): carry safeguard_results and stop_details in synthesized message_delta events When the proxy renders a complete Anthropic Messages response as an SSE stream for a client that asked to stream, the message_delta event carried only stop_reason and stop_sequence. A real Anthropic stream puts safeguard_results and stop_details inside message_delta.delta, so a client such as Claude Code auto mode lost the server-side classifier verdict and a refusal lost its stop_details. Both stream builders now copy those keys from the source response through one shared helper when they are present. * fix(guardrails): keep safeguard_results and stop_details when re-emitting a rewritten Anthropic stream A post-call guardrail that rewrites an Anthropic stream (presidio, bedrock, tool permission, model armor) now carries the upstream message_delta safeguard_results and stop_details into the re-emitted stream. The router fallback message_start no longer leaks those final fields early * fix(guardrails): keep stop_details and safeguard_results in rewritten Anthropic streams without explanations A guardrail that rewrites a /v1/messages stream now carries stop_details (type and category) and the dangerous_tool_use verdicts for the tool calls it still delivers, with every explanation dropped so a masked stream never echoes the unmasked text through those fields. * fix(guardrails): copy only upstream stop_details and forward-compatible safeguard_results into rewritten Anthropic streams A rewritten stream now takes stop_details and safeguard_results from the upstream message_delta alone, so a refusal the upstream sent without stop_details is never given one by the OpenAI round trip. Unknown safeguard types, statuses, reasons and extra fields pass through, a verdict follows a tool call only when its input is unchanged, and tool_permission keeps the explanations since it rewrites no text. * fix(guardrails): apply the safeguard_results and stop_details rules to policy pipeline stream rewrites * fix(router): deliver a refused stream built from a finished agentic turn instead of falling back Websearch, MCP and advisor streams replay a complete reply whose tools already ran, so a refusal fallback would run every tool again. Also give the recovered response's message_start a null stop_details as the API sends it, drop safeguard_results a masking rewrite cannot read, and add the audit cells for synthesized streams. * test(router): annotate the finished-turn refusal stream test locals with Final --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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yuneng-jiang
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d346d769f0
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fix(caching): keep prompt cache usage on streamed cache hits (#47073)
* fix(caching): keep prompt cache usage on streamed cache hits A stream=true request served from the response cache rebuilt its usage from the prompt, completion and total counts only, so prompt_tokens_details and the cache read and creation counts were dropped on the replayed stream. /v1/messages clients routed to Bedrock Converse saw input_tokens equal to the whole prompt and no cache tokens on a hit. Both replays now build the usage through one helper that also restores prompt_tokens_details and the integer cache read and creation counts, skipping cached values that do not validate. * fix(caching): coerce replayed token counts the way the live usage does The replay accepted only exact ints for the token counts, so an integral float or numeric string in a cached entry became 0 where the previous replay kept it. Counts now go through pydantic int coercion and fall back only when they do not validate. |
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Mateo Wang
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2c5c9b8d84
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fix(bedrock): use native structured output on Haiku 5.5 and Sonnet 5.5, and fix Fable 5 and Sonnet 4.6 schema 400s (#46870)
* fix(bedrock): use native structured output on every Claude model whose Bedrock profiles accept it Bedrock accepts outputConfig structured output on every reachable profile of Claude Opus 4.7, 4.8, 5, 5.5, Sonnet 5, 5.5, Haiku 5.5, and Fable 5.1, so json_schema now goes out natively there instead of as a forced tool, which kept Bedrock from returning adaptive thinking. Fable 5 rejects outputConfig, so its keys go back to the tool path. GovCloud keys keep their flags, and the drift tests let GovCloud lag its commercial base but never lead it. * fix(bedrock): clean Claude schemas for native structured output and keep recursive schemas on the old path Bedrock native output rejects minimum/maximum, minItems, uniqueItems, oneOf, additionalProperties true, and recursive or unresolved $ref, all of which the forced tool accepted. Claude schemas on the Bedrock native paths now go through the cleaner the Anthropic native path already uses, and a schema with a recursive or unresolved $ref keeps the forced tool, inline, or drop path it had. * fix(bedrock): keep older installs on the json tool with a Bedrock-only native output key Older installs fetch the model map from main, so flipping supports_native_structured_output moved them onto native output with their old schema handling. The new supports_bedrock_native_structured_output key is ignored by older code. Schemas native output cannot hold (recursive refs, open objects such as dict fields) keep the json tool on the new key, models on the old key keep their current behavior for those shapes, and the Anthropic API, Vertex AI, and Azure keep their current behavior. * chore(cost-map): regenerate the model prices schema for the Bedrock native output key * fix(bedrock): keep native output to profiles every account accepts and route schemas Bedrock rejects to the json tool Only Opus 5.5, Sonnet 5.5, Haiku 5.5 and Fable 5.1 keep the Bedrock native output key, since Opus 4.8 and Sonnet 5 answer 400 on some accounts and Opus 5 garbles escaped characters. Lookaround patterns and string formats outside Bedrock's list now keep the json tool, objects typed as a union with null are closed, and the cleaner keeps minItems of 0 or 1. * fix(bedrock): keep Opus 5.5 and Fable 5.1 on the json tool, since native output stops their adaptive thinking * fix(bedrock): keep shared $defs in native schemas and send refused schemas to the json tool on every native model Native output now keeps $defs instead of inlining each use, so a submodel reused across a schema counts once against Bedrock's optional and union limits. Schemas native output cannot hold use the json tool on Converse and Invoke chat for both native keys, and minItems is only kept when it is an integer 0 or 1 * fix(bedrock): keep the json tool unless thinking is requested and bound native output schemas The Bedrock native output key now applies only when the call asks for thinking, so calls without thinking keep the json tool as before. Schemas past Bedrock's documented union and optional counts, or past a grammar size estimate fitted to live probes, keep the json tool on both map keys. Open objects keep the json tool on the Anthropic API, Vertex AI and Azure AI, where native output returned them empty. The $ref check no longer expands $defs, so doubling definitions no longer stall the request. * fix(bedrock): resolve root-path $defs refs so nested Pydantic models keep native output on Invoke * test(bedrock): cover native structured output and json tool routing through the proxy Integration cells drive the proxy against a scripted Bedrock peer that enforces Bedrock's structured output schema rules, covering thinking with native output on Converse, Converse streaming, Invoke, /v1/responses and /v1/messages, the deployment opt-in, Fable 5, schema cleaning on every native entry, open objects, and malformed schemas. Vertex, Anthropic and Azure AI cells cover open objects on native-capable models and the cleaner's minItems and nullable object handling. The unit table now covers reasoning_effort none and disabled thinking * fix(bedrock): keep native output for numeric formats and wide old-key schemas, and keep thinking in json mode Check string formats only on nodes that can be strings, apply the 64-field cap only to the Bedrock-only key, carry thinking blocks into the json mode message, and send malformed json tool schemas as object schemas. * fix(anthropic): keep native output for dict fields unless the json tool can be forced * fix(anthropic): keep native output for dict fields when the caller sends tools * test(bedrock): close the dangerous-tool-use test schema, since Bedrock native output requires closed objects * test(anthropic): cover forced and unforced open-object routing, json mode thinking, numeric formats and wide schemas Add proxy cells for open-object schemas that keep native output when the json tool cannot be forced (a model that refuses forced tools on the Anthropic API and Vertex AI, thinking, caller tools), for the thinking block a json mode answer keeps on Vertex AI and Bedrock Invoke, and for integer and number formats and 65-field schemas on each Bedrock map key. Point the forced open-object cells at the Anthropic API and Azure AI only, since no Vertex AI model in the cost map takes both native output and a forced tool * fix(bedrock): keep the native output opt-in on the deployment that sets it, and route effort none open objects to native output * test(bedrock): cover deployment-scoped native output opt-in, register_model and effort none open objects |
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yuneng-jiang
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0403f8953e
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fix(cost): bill bridged /v1/messages Responses replies like native /v1/responses for reasoning tokens and service tier (#46975)
* fix(cost): price reasoning tokens of /v1/messages bridged onto the Responses API in the cost header and breakdown * fix(cost): record the bridged Responses API result only after the served body is built * fix(cost): keep the served service tier when a bridged Responses reply has no message item |
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Mateo Wang
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f8217d2a03
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fix(count_tokens): apply context_management edits in the Gemini /v1/messages/count_tokens count (#46843)
* fix(count_tokens): apply context_management edits in the Gemini /v1/messages/count_tokens count /v1/messages runs clear_tool_uses context edits before it sends an Anthropic body to a Gemini or Vertex AI deployment, but /v1/messages/count_tokens counted the body unedited, so a request with older tool results cleared counted about 2.8x what inference bills. The count now runs the same history and context-edit step inference runs, honors additional_drop_params on the deployment, and skips compact edits so a count never makes a paid summary call. * fix(count_tokens): match the Gemini count to inference for compact edits and drop opt-outs Run compact edits in the count without the summary call so trigger checks and history slicing match /v1/messages, honor a request-level additional_drop_params, keep the full body when any deployment in the model group drops context_management, and answer 400 naming a block Gemini cannot count when an edit trigger fails on it * fix(count_tokens): count the unedited body when an edit fails and honor deployment ids in the drop check * fix(count_tokens): log a failed edit in the count at debug, since the send already logs it * fix(count_tokens): resolve a deployment id before wildcard routes in the drop check, as the router does * fix(count_tokens): honor the router-wide additional_drop_params opt-out in the drop check The router merges default_litellm_params between the deployment and the request, so a router-wide context_management opt-out makes /v1/messages send the full history. The count now reads it in the same order. Adds the audit's wire tests for the count's context edits. * fix(count_tokens): read the opt-out of a deployment the router serves by its litellm model name * fix(count_tokens): read the opt-out and drop_params from the deployments the router and completion_model send to * fix(messages): drop an invalid context_management spec under drop_params so the send matches the count * fix(count_tokens): give Claude Code and Codex counts the drop_params default /v1/messages gives them The proxy turns on drop_params for Claude Code and Codex user agents on /v1/messages. The count now applies the same default, so an invalid context_management spec drops on both endpoints instead of the count answering 400 while the send answers 200. The wire test also covers default_fallbacks, model_group_alias, and completion_model routes. * test(count_tokens): cover a coding agent that turns drop_params off and stop two count tests running as Claude Code |
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Mateo Wang
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7ddc9f1c96
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fix(bedrock)!: keep reasoning, thinking blocks and the json_mode finish_reason on fake streams (#46863)
* fix(bedrock): keep reasoning, thinking blocks and the json_mode finish_reason on fake streams A Bedrock Converse model without native structured output fakes the stream when response_format carries a json_schema. The fake stream transformed the response without json_mode and rebuilt a chunk from text, tool_use and token counts only, so it dropped reasoning_content, the signed thinking_blocks and the reasoning token count, and ended on finish_reason tool_calls. The three fake-stream sites now pass json_mode to _transform_response and stream the resulting ModelResponse through the shared convert_model_response_to_streaming converter, which now numbers each streamed thinking block with its index the way real streams do * fix(bedrock): parse text tool calls on Converse fake streams like the non-stream answer * fix(responses): stream the answer text when one chat chunk carries reasoning and the answer * fix(streaming): keep every field on the OpenAI and openai_like fake streams and split multi-block thinking Azure o-series and openai_like fake streams now use the shared MockResponseIterator, so extra tool calls, reasoning and cached and reasoning tokens survive; the Bedrock-local iterator is deleted. The /v1/responses bridge also splits a chunk whose thinking block is signed or redacted with no reasoning text, and the sync iterator closes the reasoning item before the answer opens, like the async one. The /v1/messages bridge gives each thinking block in a chunk its own content block, starts a new block when the thinking block index changes, and streams redacted thinking as redacted_thinking. Snowflake replays drop the stream index from thinking blocks. * fix(anthropic): stream each thinking block's text once on /v1/messages An unsigned thinking chunk now opens its content block with empty text, since its thinking_delta carries the text. A multi-block chunk keeps each piece's thinking block, so an unsigned block followed by a signed one keeps its own text and index. * docs(anthropic): describe which thinking block start carries its text * fix(streaming): close merged reasoning before the answer when one chunk carries both * test(streaming): cover fake-stream reasoning across the proxy bridges and type the Snowflake replay test |