* test: drop the cwd-relative sys.path.insert calls from the test suite
TQ003 stands at 1,077 across 1,058 files, and 1,015 of them are the same shape:
sys.path.insert(0, os.path.abspath("../..")) and its deeper siblings. The
argument resolves against the working directory rather than the file, so from
the repo root, where every job runs pytest, it inserts the directory two levels
above the checkout. It has never pointed at litellm. The package is installed
into the environment anyway, which is what actually makes the import work, and
what the rule's message has said all along.
Removing them leaves 1,634 imports of sys and os with no remaining reference,
and those go too, except where another test module imports the name back out of
the file. The rest of TQ003 is 62 call sites that resolve against __file__ or a
variable, which are a different question and are left alone.
Collection is identical either way: 45,871 tests and the same 51 pre-existing
collection errors before and after, and ruff reports no new undefined name.
* test: drop the duplicate imports the sys.path sweep exposed to F811
* test(pre-call-utils): restore the os import the new bedrock tests need
A proxy on the default remote cost map never produced a prompt cache
breakpoint: the published map has the gpt-5.6 entries without
supports_prompt_cache_breakpoint, so the model-map gate returned False
for every listed model and only LITELLM_LOCAL_MODEL_COST_MAP=True (the
repo .env, hence the passing unit tests) made the feature work. The hook
now honors the flag when the entry carries one, True or False, and
otherwise applies the GPT-5.6+ version rule to the model name, so a map
that lags the flag still gets the OpenAI dialect. The model-map tests
pin litellm.model_cost to the bundled backup map and a new test drives
the hook against an unflagged gpt-5.6 entry.
completion() and acompletion() take base_url as an alias for api_base
that only lands on api_base after the cache control hook ran, so a
GPT-5.6 call at a non-OpenAI gateway given through base_url still got
the dialect. Both seed calls and the unstamped request-params read now
look at base_url too.
ResponsesAPIRequestUtils.merge_prompt_management_input reshaped hook
output in place, retyping text parts to input_text on the caller's own
message objects. The merge now shapes a copy of each message as it
emits it, so the identity-based merge keeps working on the hook's
objects and nothing the hook or the client owns is mutated.
The cache control hook also runs on litellm.responses() input. On a
GPT-5.6 deployment it wrapped a string-content item into a chat-shaped
{"type": "text"} part, which the Responses API rejects, and it never
marked input_text, input_image or input_file parts, so no breakpoint and
no prompt_cache_options reached the provider. Add the Responses part
types to the eligible block set and translate chat-shaped text parts on
non-assistant items to input_text in
ResponsesAPIRequestUtils.merge_prompt_management_input, which both the
async and the sync prompt management sites go through.
The dialect also fired for any GPT-5.6 name that resolved to provider
openai, including deployments pointed at a custom api_base that does not
understand prompt_cache_breakpoint. Decide it once per request from the
provider, the model map and the resolved api_base (request, then
litellm.api_base, then OPENAI_BASE_URL / OPENAI_API_BASE): only
api.openai.com and *.api.openai.com hosts speak the dialect, a top-level
prompt_cache_options opts a custom target in, and litellm_proxy/ targets
never get it. maybe_seed_default_injection_points takes api_base and
stamps the finished decision on the points as _litellm_openai_dialect so
the sync completion() path, whose hook params do not carry api_base,
honors it; maybe_inject_cache_control takes api_base from the
/v1/messages handler.
Eligibility now comes from a supports_prompt_cache_breakpoint model map
flag on the OpenAI gpt-5.6 entries, exposed through
litellm.utils.supports_prompt_cache_breakpoint, with the GPT version rule
kept only for models the map does not know. The OpenAI dialect no longer
reserves a slot for tool_config points, which OpenAI has no cache block
for, and with_prompt_cache_breakpoint plus the chat bridge helper return
a new block instead of mutating their input.
Gemini image usage carries prompt_tokens and friends as extra fields on
ResponseAPIUsage, which collided with the bridge's explicit kwargs and
raised TypeError. Exclude keys the bridge already sets explicitly.
Drop the transform overrides that swapped response.usage to the chat
shape, which broke the /v1/responses client contract. Provider extras
like server_side_tool_usage_details already survive validation via
ResponseAPIUsage extra fields, so the shared usage bridge now carries
them onto the bridged chat Usage generically. The web_search_call
output gate also reads dict output items, since items that fail SDK
validation stay plain dicts, and the chat path gains billing tests.
xAI already converts Responses usage to chat Usage so web_search_calls survive
cost tracking. The chat completions bridge then re-ran the Responses usage
transform and crashed on missing input_tokens. Pass through already-chat Usage
and chat-shaped dumps instead
The Responses API (/v1/responses) usage transform rebuilt prompt token
details and dropped OpenAI's input_tokens_details.cache_write_tokens, so
gpt-5.6 cache-creation tokens were never logged or billed via that route.
Map it in the transform, and make PromptTokensDetailsWrapper keep
cache_write_tokens and cache_creation_tokens in sync on assignment.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(containers): Azure container routing, managed IDs, and delete response wire format
- Add AzureContainerConfig and safe URL joining for paths with api-version query
- Encode/decode managed container IDs in responses, streaming, and proxy handlers
- Accept OpenAI delete response object literal container.file.deleted
- Tests for Azure URL regression and DeleteContainerFileResponse parsing
Made-with: Cursor
* fix(responses): gate response id update on parsed_chunk having response
Delta stream events do not include a response body; Mock-based tests
(and any truthy synthetic .response on transforms) must not trigger
_update_responses_api_response_id_with_model_id. Fixes
test_stop_async_iteration_not_logged_as_failure (TypeError: Mock not iterable).
Made-with: Cursor
* feat(containers): encode container IDs in SDK responses for routing
- Add ContainerRequestUtils.encode_container_id_in_response utility
- Encode container_id in create/retrieve/delete responses (SDK path)
- Fix streaming iterator: gate response ID update on parsed_chunk key
- Follows responses API pattern (encode after handler, not in handler)
Made-with: Cursor
* fix(containers): module-level imports and managed cntr_ ID encoding
- Move ResponsesAPIRequestUtils imports to module scope (utils, main, handler_factory).
- Serialize absent model_id as empty segment instead of literal None; decode empty
and legacy "None" segments as missing for router affinity.
- Add unit tests for build/decode round-trip and legacy IDs.
Made-with: Cursor
* fix(containers): decode managed IDs in endpoint_factory SDK path
- Add decode_managed_container_id_for_request in containers/utils and reuse from main.
- Strip LiteLLM cntr_ wrappers before generic_container_handler (64-char API limit).
- Resolve provider for logging/errors; add unit test for decode helper.
- Use resolved_custom_llm_provider after decode for mypy-safe provider typing.
Made-with: Cursor
* Fix p1 concern
* Fix p1 concern
* fix(vertex_ai): forward extra_body to completion transformation handler
The responses() function accepted extra_body as a named parameter but
did not pass it to response_api_handler when responses_api_provider_config
was None (completion transformation path), silently dropping it.
Also adds deep-merge support for extra_body in Vertex AI Gemini
transformation, so dict values like generationConfig are merged rather
than replaced.
* refactor(vertex_ai): extract _merge_extra_body to fix PLR0915 lint
Move the extra_body merge loop into a helper function to keep
_transform_request_body under the 50-statement limit.
When using the responses API with provider-specific params (aws_*, vertex_*)
without explicitly passing custom_llm_provider, the code crashed with:
AttributeError: 'NoneType' object has no attribute 'startswith'
Root cause: local_vars was captured via locals() before get_llm_provider()
detected the provider from the model string (e.g., "bedrock/..."), so
custom_llm_provider remained None when processing provider-specific params.
Fix: Update local_vars["custom_llm_provider"] after get_llm_provider() call
so the detected provider is available for param processing.
Affected provider-specific params:
- aws_* (aws_region_name, aws_access_key_id, etc.) for Bedrock/SageMaker
- vertex_* (vertex_project, vertex_location, etc.) for Vertex AI
Calculate `total_tokens` in usage data in Response manually if:
- `total_tokens` is missing
- `total_tokens` can be calculated from input and output tokens
Run the test for this feature with:
`poetry run pytest tests/test_litellm/responses/test_responses_utils.py -k "test_transform_response_api_usage_calculates_total_from_input_and_output_tokens_if_available" -v`
