The Responses id security hook keeps the id a client addressed under
`_litellm_addressed_response_id` in the request body so internal retries can
re-authorize it. On a model without a native Responses config that body is
bridged into `completion()` kwargs, the key was treated as a provider param,
and providers rejected it, so every follow-up turn carrying
`previous_response_id` returned 400.
Register the key in `all_litellm_params` so it is dropped before any provider
request, and share one constant between the hook and the param list.
Each route now has litellm/rust_bridge/<route>/{entrypoints,callbacks}.py and a
public dispatch module (litellm/chat_completions/dispatch.py,
litellm/responses/dispatch.py, litellm/messages/dispatch.py) that binds the
public call to the legacy Python signature, builds a frozen request, and asks
the runtime to pick Rust or Python from the catalog. The legacy implementations
stay in litellm/main.py, litellm/responses/main.py and the anthropic messages
handler, and litellm/__init__.py re-exports the dispatch names over them the
same way it already does for ocr
The per-handler shims in rust_bridge/chat_completions/native.py and
rust_bridge/messages/native.py are removed along with their call sites in the
anthropic and bedrock chat handlers and the http handler. The exception
mapping that every callbacks module repeated moves to rust_bridge/failures.py
and the signature binding helpers to rust_bridge/public_call.py
Move each route's bridge module under litellm/rust_bridge/<route>/ so a folder
means a Rust implementation exists while the catalog row says whether it is
used. OCR now keeps the Python implementation in litellm/ocr/main.py and the
Rust selection in litellm/ocr/rust.py, removing litellm/ocr/legacy.py
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Resolve the azure_ai common_utils conflict onto main's api_key_header_for_base
helper, gate native Responses routing on the resolved api_base host, derive the
/openai/v1/responses URL from the host and project prefix, keep websocket mode
on the managed emulation, and cover the routing end to end with respx
Takes main's Anthropic Messages adapter iterator, transformation, and
combined-chunk tests as-is: #35314 already skips choiceless chunks at the
top of both adapter loops, so the adapter-side guards this branch carried
are superseded. The Responses bridge guards stay
The streaming bridge restored the namespace before deciding whether a tool call was a custom tool, so a namespaced function sharing a short name with a nested custom tool streamed back as a custom_tool_call. Classify on the raw chat tool name first, the way the non-streaming path already does.
The guardrail merge only stripped the namespace prefix and grammar suffix from the ends of the edited description, so a guardrail appending text after the grammar block left the block in the member description and the chat conversion appended it a second time. Strip the first occurrence of each instead.
Drops the helper docstring and the test docstrings. The passthrough case now
uses together_ai, which has no native Responses config on main, so the test no
longer monkeypatches ProviderConfigManager at the class level
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
A Responses request for a provider with a native Responses config that is served
through the chat-completions bridge (use_chat_completions_api or the
openai/chat_completions/ prefix) forwarded every raw kwarg, so a deployment-level
chat_template_kwargs reached OpenAI chat completions and got a 400. The bridge
now keeps only the keys a native dispatch would forward plus allowed_openai_params.
Providers with no native Responses config keep the passthrough
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Move the BaseLLMException import into _map_error_event_exception so the
module no longer imports it at load time, clearing the module-level cyclic
import CodeQL flagged. The class is used only on the cold error path.
Replace the mutable list-append test collector with aiter/anext so the
regression tests read the stream immutably.
Mid-stream error events on the streaming Responses API were all raised as
APIError, so a content_policy_violation event never matched the router's
content-policy fallback dispatch and the client got the raw error instead
of the fallback model's answer. Map each error event's code and status
through the existing exception_type mapping, matching the non-streaming
path, and unwrap the typed ContentPolicyViolationError and
ContextWindowExceededError so the router routes them to the configured
content_policy_fallbacks and context_window_fallbacks.
The bridge probe asked `responses_api_bridge_check` with the summary read straight off
the Responses object, but `litellm.completion` reads it from `optional_params` via
`peek_reasoning_summary_aliases`, which the bridged request never populated. So gpt-5,
gpt-5.1 and azure/gpt-5 answered "bridging" to the probe and "not bridging" for real,
and the object still landed on Chat Completions, which only takes a string
`reasoning_effort` is now always the effort string, and `summary` rides the
`reasoning_summary` alias that main.py already reassembles into `{effort, summary}` on
the bridged path. The alias is emitted only when the probe says the model bridges, so
no chat provider ever sees it, and the probe is now asked with the exact params this
transform emits
The Responses API takes reasoning as an object, {effort, summary}. Chat
Completions takes reasoning_effort as a string enum and has no equivalent of
summary, but the completion bridge forwarded the whole object whenever summary
was set, which agentic clients set on every request.
Bedrock Converse guards its mapping with isinstance(value, str) and has no else
branch, so the object fell through, thinking was never enabled, and the caller
was billed for a non-thinking turn with nothing in the response to explain it.
The object is still forwarded for the one caller that can consume it: a model
whose cost-map mode is responses, which litellm.completion bridges back onto the
Responses API and reassembles {effort, summary} there. That decision is delegated
to responses_api_bridge_check, the same check litellm.completion runs, rather
than a second copy of the rule that could drift from it. An object carrying no
effort now yields no reasoning_effort at all.
