* test(reasoning-effort-grid): bump cell-count assertion for claude-opus-5
The claude-opus-5 grid entry added in ae81625ee6 raised the Anthropic direct
route to 31 model combos, but test_grid_cell_count still expected 30, so the
suite went red on the tripwire rather than on any behavior change.
* test(openai): swap the retired deep-research model out of the bridge test
OpenAI shut down o3-deep-research and o4-mini-deep-research on 2026-07-23, so
the live call in this test now comes back as a 400 'Model not found'. The test
was never about deep research specifically; the bridge fires on any model whose
cost-map mode is "responses", so it now uses gpt-5.5-pro, the newest
responses-only OpenAI model, and is renamed to say that.
gpt-5.5-pro was confirmed present on the CI account with an authenticated
GET /v1/models before being picked.
* fix(proxy): restore atomic user upsert when adding team members
Parallel /team/new calls naming the same not-yet-existing member were
returning 500 "Unique constraint failed on the fields: (`user_id`)".
The upsert in add_new_member passed an empty update branch. Prisma only
compiles an upsert down to a single INSERT ... ON CONFLICT when that branch
writes something; with an empty one it emits SELECT-then-INSERT instead, so
concurrent requests all read "no such user" and all insert. Postgres
statement logs confirm it: the empty form logs BEGIN/SELECT/INSERT/COMMIT,
the non-empty form logs INSERT ... ON CONFLICT ("user_id") DO UPDATE SET.
Re-state user_id in the update branch as a no-op so the native upsert path
comes back. The teams append stays in the filtered update below it, so an
already-existing member still cannot pick up a duplicate team id.
tests/test_team.py::test_team_new failed 9 of 15 runs against a live proxy
before this and 0 of 15 after. The existing unit test asserted only that
upsert had been called on a mock, so it passed either way; it now pins the
shape of both branches and fails when the update branch goes back to empty.
* test: point the live codex tests at gpt-5.3-codex
OpenAI deprecated gpt-5.2-codex, so test_openai_codex and
test_openai_codex_stream started failing against the live API with
model_not_found. gpt-5.3-codex is the current codex model; both tests pass
on it. The remaining gpt-5.2-codex references in the suite are mocked
transformation tests and are unaffected.
* test(e2e): update models page specs for the shared DataTable
The DataTable migration in #34363 changed three things the models page
specs were pinned to, and five tests went red.
Row click no longer opens the detail view; the Model ID cell owns that
now, so both specs click its `model-id-<id>` test id instead of the row.
The search box placeholder switched from an ASCII "..." to a real
ellipsis, so the specs use getByPlaceholder with a substring instead of
an exact attribute match that punctuation can break again. The results
count moved from `models-results-count` ("Showing 1 - 50 of 137 results")
to the shared pagination's `pagination-range` ("Showing 1-50 of 137").
The Team-BYOK test also filtered rows on the team alias, which the Team
ID column has never rendered in either the old or the new table; it
filters on the team id now, which is what the column actually shows and
what the assertion's own comment intends.
Verified against a local proxy serving a fresh build with the seeded
e2e postgres and mock upstream: all five failing tests pass, and the
full suite is 82 passed / 4 skipped at CI parity (workers=1).
These seven test files were on _RESPX_CONFLICTING_FILES, which made the
auto-marker skip them entirely. Inspecting the source shows the only
respx artifact is a top-level 'from respx import MockRouter' that no
test ever uses - no @pytest.mark.respx, no respx_mock fixture, no
respx.mock context manager. The import is dead code left over from a
previous mocking pattern.
Now that apply_vcr_auto_marker_to_items detects respx per-item via the
marker / fixture chain (b637d9f64a), the file-level skip is no longer
needed for these files - they were the reason the OpenAI tests
(test_o3_reasoning_effort, test_streaming_response[o1/o3-mini],
TestOpenAIO1::test_streaming, TestOpenAIChatCompletion::test_web_search,
TestOpenAIO3::test_web_search, etc.) ran live every CI build despite
the cassette cache being healthy.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* fix: fix getting mcp servers
* fix(litellm_logging.py): handle list objects for final response in standard logging payload
Fixes issue where mcp tool call response wouldn't show up
* fix(litellm_responses_transformation/): remove invalid item error for unmapped objects - breaks stream and there's no real value to this as outside of a few of them, not all can be mapped to chat completions
resolves error for web search calls via chat completions to responses api
* fix response api for litellm proxy
* Add test for checking if status is getting removed
* add test in correct file
* remove hardcoded fields
* Make the handling simpler
* fix lint error:
* fix(google_genai/adapters/transformation.py): enable calling non-googlegenai models via streaming
Fixes https://github.com/BerriAI/litellm/issues/12562
* test(test_openai.py): add unit test asserting streaming works as expected
* refactor(responses/): refactor to move responses_to_completion in separate folder
future work to support completion_to_responses bridge
allow calling codex mini via chat completions (and other endpoints)
* Revert "refactor(responses/): refactor to move responses_to_completion in separate folder"
This reverts commit ff87cb8958.
* feat: initial responses api bridge
write it like a custom llm - requires lesser 'new' components
* style: add __init__'s and bubble up the responses api bridge
* feat(responses/transformation): working sync completion -> responses and back bridge (non-streaming)
* feat(responses/): working async (non-streaming) completion <-> responses bridge
Allows calling codex mini via proxy
* feat(responses/): working sync + async streaming for base model response iterator
* fix: reduce function size
maintain <50 LOC
* fix(main.py): safely handle responses api model check
* fix: fix linting errors
* fix: add flag for disabling use_aiohttp_transport
* feat: add _create_async_transport
* feat: fixes for transport
* add httpx-aiohttp
* feat: fixes for transport
* refactor: fixes for transport
* build: fix deps
* fixes: test fixes
* fix: ensure aiohttp does not auto set content type
* test: test fixes
* feat: add LiteLLMAiohttpTransport
* fix: fixes for responses API handling
* test: fixes for responses API handling
* test: fixes for responses API handling
* feat: fixes for transport
* fix: base embedding handler
* test: test_async_http_handler_force_ipv4
* test: fix failing deepeval test
* fix: add YARL for bedrock urls
* fix: issues with transport
* fix: comment out linting issues
* test fix
* test: XAI is unstable
* test: fixes for using respx
* test: XAI fixes
* test: XAI fixes
* test: infinity testing fixes
* docs(config_settings.md): document param
* test: test_openai_image_edit_litellm_sdk
* test: remove deprecated test
* bump respx==0.22.0
* test: test_xai_message_name_filtering
* test: fix anthropic test after bumping httpx
* use n 4 for mapped tests (#11109)
* fix: use 1 session per event loop
* test: test_client_session_helper
* fix: linting error
* fix: resolving GET requests on httpx 0.28.1
* test fixes proxy unit tests
* fix: add ssl verify settings
* fix: proxy unit tests
* fix: refactor
* tests: basic unit tests for aiohttp transports
* tests: fixes xai
---------
Co-authored-by: Krrish Dholakia <krrishdholakia@gmail.com>
* Support pdf url's to openai (#10640)
* fix(gpt_transformation.py): support pdf url input to openai
pass as base64 as openai doesn't support image url's
* fix(openai.py): support async message transformation
allows async get request to convert url to base64
* fix(gpt_transformation.py): fix linting errrors and use common components across sync + async flows
* fix: fix linting errors
* fix(openai.py): pop correct var
* Fix sagemaker chat calls - content length error (#10607)
* fix(sagemaker_chat/): support passing dynamic aws params
previously being ignored
* refactor(sagemaker/chat): more refactoring
* fix(sagemaker_chat/): make sure streaming is correctly handled post-refactor
* refactor: more refactoring to support using signed json str
* fix(sagemaker/chat): working sync streaming post refactor
* fix(sagemaker/chat): support async streaming post refactor
* fix(llm_http_handler.py): await async function
* fix: remove print statements
* test: update test
* test: update test
* fix(llm_http_handler.py): retain passing in data as json str
* test: update test
* fix(base_model_iterator.py): fix linting error
* test: test auth
* fix: fix linting error
* test: update test
* test: update translation test
* fix(gpt_transformation.py): handle awaitable/non-awaitable object
* fix: handle async flow for message transformation on openai compatible api's
* test: cleanup testing
* test: update test
* test(test_router.py): use model with higher quota
* test: simplify test
* test: update test
* refactor: introduce new transformation config for gpt-4o-transcribe models
* refactor: expose new transformation configs for audio transcription
* ci: fix config yml
* feat(openai/transcriptions): support provider config transformation on openai audio transcriptions
allows gpt-4o and whisper audio transformation to work as expected
* refactor: migrate fireworks ai + deepgram to new transform request pattern
* feat(openai/): working support for gpt-4o-audio-transcribe
* build(model_prices_and_context_window.json): add gpt-4o-transcribe to model cost map
* build(model_prices_and_context_window.json): specify what endpoints are supported for `/audio/transcriptions`
* fix(get_supported_openai_params.py): fix return
* refactor(deepgram/): migrate unit test to deepgram handler
* refactor: cleanup unused imports
* fix(get_supported_openai_params.py): fix linting error
* test: update test
* fix(streaming_handler.py): fix deepseek reasoning content streaming
Fixes https://github.com/BerriAI/litellm/issues/8939
* test(test_streaming_handler.py): add unit test to streaming handle 'is_chunk_non_empty' function
ensures 'reasoning_content' is handled correctly
* feat(bedrock/converse/transformation.py): support claude-3-7-sonnet reasoning_Content transformation
Closes https://github.com/BerriAI/litellm/issues/8777
* fix(bedrock/): support returning `reasoning_content` on streaming for claude-3-7
Resolves https://github.com/BerriAI/litellm/issues/8777
* feat(bedrock/): unify converse reasoning content blocks for consistency across anthropic and bedrock
* fix(anthropic/chat/transformation.py): handle deepseek-style 'reasoning_content' extraction within transformation.py
simpler logic
* feat(bedrock/): fix streaming to return blocks in consistent format
* fix: fix linting error
* test: fix test
* feat(factory.py): fix bedrock thinking block translation on tool calling
allows passing the thinking blocks back to bedrock for tool calling
* fix(types/utils.py): don't exclude provider_specific_fields on model dump
ensures consistent responses
* fix: fix linting errors
* fix(convert_dict_to_response.py): pass reasoning_content on root
* fix: test
* fix(streaming_handler.py): add helper util for setting model id
* fix(streaming_handler.py): fix setting model id on model response stream chunk
* fix(streaming_handler.py): fix linting error
* fix(streaming_handler.py): fix linting error
* fix(types/utils.py): add provider_specific_fields to model stream response
* fix(streaming_handler.py): copy provider specific fields and add them to the root of the streaming response
* fix(streaming_handler.py): fix check
* fix: fix test
* fix(types/utils.py): ensure messages content is always openai compatible
* fix(types/utils.py): fix delta object to always be openai compatible
only introduce new params if variable exists
* test: fix bedrock nova tests
* test: skip flaky test
* test: skip flaky test in ci/cd
* fix(o_series_transformation.py): fix optional param check for o-series models
o3-mini and o-1 do not support parallel tool calling
* fix(utils.py): support 'drop_params' for 'thinking' param across models
allows switching to older claude versions (or non-anthropic models) and param to be safely dropped
* fix: fix passing thinking param in optional params
allows dropping thinking_param where not applicable
* test: update old model
* fix(utils.py): fix linting errors
* fix(main.py): add param to acompletion
