* fix(proxy): optionally surface public team model name in /v1/models
Behind general_settings.use_team_public_model_name (default False). When
enabled, /v1/models and /models surface the public team_public_model_name
for team-scoped (BYOK) models instead of the internal routing key
model_name_{team_id}_{uuid} -- consistent with /v1/model/info and
OpenAI-compatible. Off by default so the listing's model ids stay
backward-compatible for callers that scripted against the internal name;
routing by the internal name is unchanged regardless of the flag.
Presentation-layer only: access-group, auth, and routing semantics are
unchanged; non-team models are pass-through.
* fix(proxy): default team model listings to public names
* test(proxy): cover team model listing metadata
* test(proxy): cover empty team listing deployments
* refactor(proxy): simplify team model listing translation
* fix(proxy): resolve public team model name on GET /v1/models/{id}
The listing endpoints advertise team_public_model_name, but the retrieve
endpoint validated and looked up by the raw id, so a public name 404'd.
Resolve the public name back to the internal routing key (scoped to the
caller's accessible models so colliding names never cross teams), look up
by it, and echo the public name back as the response id.
* test(proxy): cover public-name resolution on model retrieve
* refactor(proxy): extract team model-name translation into TeamModelNameTranslator
Move the team-scoped (BYOK) listing/retrieve name translation out of
proxy_server.py into a dedicated common_utils module. Static methods with
general_settings injected so the logic is unit-testable without globals and
proxy_server.py stays thin.
* refactor(proxy): use TeamModelNameTranslator in model_list and model_info
* test(proxy): target TeamModelNameTranslator for model-name translation
* fix(proxy): type create_model_info_response return as dict[str, object]
* fix(proxy): keep internal routing key for team model listing metadata lookup
Add listing_entries returning (public response id, internal lookup id) so
include_metadata=true resolves fallbacks against the routing key the router
indexes by, instead of the translated public name (which never matches).
* fix(proxy): build /v1/models metadata from internal key, show public id
* test(proxy): cover team listing fallback metadata via internal key
* fix(proxy): use builtin dict generics in create_model_info_response (UP006)
---------
Co-authored-by: Tushar More <tusharmore8408@gmail.com>
Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>
* Fix remaining VCR live-call leaks
* test(vcr): dedupe live-test helpers and drop spurious kwargs
Extract the duplicated isVertexQuotaError/runVertexRequestOrSkip Vertex
quota-skip helpers into tests/pass_through_tests/vertex_test_helpers.js and the
duplicated _skip_live_prompt_caching_test guard into tests/_live_test_helpers.py
so each lives in one place. In test_aarun_thread_litellm, build a separate
message_data carrying role/content for add_message and a thread_data without
them for run_thread/run_thread_stream/get_messages, which no longer receive the
spurious message fields.
* test(overhead): assert mock transport is exercised in non-streaming and stream tests
- Add `_strip_image_b64_payloads` filter: rewrites `data[*].b64_json` in
image-gen responses to a 4-byte placeholder before the cassette is saved.
Image-edit and image-gen cassettes (193 MB / 184 MB / 104 MB / ...) will
shrink to <100 KB on next record. Tests assert response shape only, so
coverage is preserved.
- Add `_normalize_multipart_boundary` filter: replaces httpx's per-request
random multipart boundary with a fixed string in both Content-Type header
and body bytes. Audio-transcription / Whisper tests have been effectively
unmocked — every CI run hit live providers and was silently capped at
MAX_EPISODES_PER_CASSETTE=50. Both record and replay now see identical
bytes; the safe_body matcher works.
- Fix test_evals_api.py body poisoning: replace `int(time.time())` in eval
names with `hashlib.sha1(test_node_name)[:12]`, add a function-scoped
`managed_eval` fixture that creates and deletes the eval, and switch
`get_eval` / `update_eval` from `list_evals().data[0].id` (which made
the URL vary by run) to `managed_eval.id`. Net coverage gain: delete is
now actually exercised.
- Swap arxiv PDF URL in BaseOCRTest for the in-repo `dummy.pdf` (589 B)
served via sha-pinned jsdelivr.
- Swap etsystatic image URL in BaseLLMChatTest.test_image_url for the
in-repo LiteLLM logo (9.2 KB) served via the same jsdelivr pin.
- Add `tests/llm_translation/test_vcr_filters.py` with 14 unit tests
covering both new filters: replacement, idempotency, nesting, content-
length update, two-distinct-boundaries-converge-after-normalize, etc.
Cassettes recorded with the prior patterns will mismatch on the first CI
run after merge; recommend flushing the cassette Redis once (post-merge)
so re-records save under the new format from the start.
GitHub serves PDFs from raw.githubusercontent.com, github.com/.../raw/..., LFS, and Releases as application/octet-stream by deliberate anti-hotlinking policy. Anyone who passes a GitHub-hosted PDF URL as an OpenAI / Gemini / Bedrock file_id hits "unsupported MIME type 'application/octet-stream'" because _process_image_response inlines the URL with whatever Content-Type the server sent.
When the server-provided Content-Type is application/octet-stream or binary/octet-stream and the URL extension maps to a known MIME type (.pdf, .png, .jpg, etc.), trust the extension instead. Specific Content-Types (image/png, application/pdf) still win over the extension; the override only applies to generic binary types.
Also restores the Greptile SHA-pinned raw.githubusercontent.com URL on the file_id integration test so we test against the same hosting real users hit, no third-party CDN.
Resolve URL conflict: keep Greptile's commit-SHA pin for immutability, but route through jsDelivr (cdn.jsdelivr.net/gh/BerriAI/litellm@<sha>/...) so the response Content-Type is application/pdf instead of application/octet-stream. Without this, OpenAI / Gemini / Router PDF tests reject the inlined file_data with "unsupported MIME type 'application/octet-stream'".
The previous URL switch to raw.githubusercontent.com fixed Anthropic's "Unable to download" failure but caused OpenAI / Gemini / Router PDF tests to fail with "unsupported MIME type 'application/octet-stream'": those providers download the URL and inline it as data:<Content-Type>;base64,..., and raw.githubusercontent.com serves PDFs as application/octet-stream.
jsDelivr proxies the same in-repo fixture (cdn.jsdelivr.net/gh/BerriAI/litellm@main/...) and returns the correct Content-Type: application/pdf, so all providers (Anthropic forwards the URL natively; OpenAI/Gemini/Bedrock fetch and inline) get the right MIME type without changing transformer code.
The test was passing the Wikipedia URL https://upload.wikimedia.org/wikipedia/commons/2/20/Re_example.pdf as the file_id, which Anthropic's URL fetcher can no longer download (returns "Unable to download the file"). The URL is healthy for generic clients but Anthropic's fetcher fails on it deterministically, so the test has been red across PRs on litellm_internal_staging.
Switch to the in-repo fixture at tests/llm_translation/fixtures/dummy.pdf served via raw.githubusercontent.com - same fixture used elsewhere in the repo, no external CDN dependency that can block by user-agent.
* fix: use fastuuid helper across the codebase
First batch of changes, simple drop in replacement.
* second batch of changes
* fixed: script mistake on helper file
* fix(streaming_handler.py): maintain same 'created' across all chunks
Fixes https://github.com/BerriAI/litellm/issues/11437
* test: add unit test to ensure created is always the same across all chunks
* fix(types/utils.py): set a tool call id, if missing in delta tool call
Ensures stream chunk builder can reconstruct tool calls correctly
Fixes https://github.com/BerriAI/litellm/issues/11262
* fix(responses/transformation.py): support passing mcp server tool call to anthropic
allows switching between openai and anthropic for mcp tool calling
* fix(ollama/chat/transformation.py): set tool call id's when missing
* fix(anthropic/): fix regression when passing file url's to the 'file_id' parameter
add test and ensure anthropic file url's are correctly sent as 'document' blocks
* fix(vertex_and_google_ai_studio.py): Use same usage calculation function as non-streaming
Closes https://github.com/BerriAI/litellm/issues/10667
* test(test_vertex_and_google_ai_studio_gemini.py): update test
* feat(parallel_request_limiter_v2.py): add sliding window logic
allows rate limiting to work across minutes
* fix(parallel_request_limiter_v2.py): decrement usage on rate limit error
* fix(base_routing_strategy.py): fix merge from redis - preserve values in in-memory cache during gap b/w push to redis and read from redis
* fix(base_routing_strategy.py): catch the delta change during redis sync
ensures values are kept in sync
* fix(parallel_request_limiter_v2.py): update tpm tracking to use slot key logic
* fix: fix linting error
* test: update testing
* test: update tests
* test: skip on rate limit or internal server errors
* test: use pytest fixture instead
* test: bump mistral model
* fix(openai/gpt_transformation.py): handle missing filename for openai file data call
* fix(openai/gpt_transformation.py): clean handling for sync + async pdf url transformation flows
Fixes https://github.com/BerriAI/litellm/issues/10820
* build(model_prices_and_context_window.json): add 'supports_pdf_input' for all openai models which have 'vision' support
Follows openai guidelines
* feat(bedrock/chat): support cache pointing tool calls on Bedrock
Closes https://github.com/BerriAI/litellm/pull/10613
* fix: fix linting error
* add test_function_calling_with_tool_response to base llm tests
* run test suite for nova
* update test_function_calling_with_tool_response
* allowed ToolJsonSchemaBlock keys
* fix ToolJsonSchemaBlock
* add back pytest fixture
* test: test_prompt_caching
* test(base_llm_unit_tests.py): return '<thinking>' tag in response content
* fix(converse_transformation.py): extract `<thinking>` block from nova tool use response
Fixes https://github.com/BerriAI/litellm/issues/9063
* fix(factory.py): handle non-signature reasoning blocks to bedrock
pass as text input - bedrock raises ""User messages cannot contain reasoning content. Please remove the r
easoning content and try again." otherwise
* fix(main.py): Add drop params support for gpt
Fixes https://github.com/BerriAI/litellm/issues/10501
* fix(converse_transformation.py): fix linting error
* fix(utils.py): fix linting error
* test: cleanup test
* test: skip test until we have bedrock prompt caching permission
* fix(vertex_and_google_ai_studio.py): fix finish reason to be 'tool_calls' when tool call returned
Vertex returns 'Stop', openai format is 'tool calls'
* test(base_llm_unit_tests.py): bump test to assert tool calls in finish reason
* feat(fireworks_ai/chat): handle tool calling with fireworks ai correctly
Fixes https://github.com/BerriAI/litellm/issues/7209
* fix(utils.py): handle none type in message
* fix: fix model name in test
* fix(utils.py): fix validate check for openai messages
* fix: fix model returned
* fix(main.py): fix text completion routing
* test: update testing
* test: skip test - cohere having RBAC issues
* build(model_prices_and_context_window.json): add vertex ai gemini-2.5-flash pricing
* build(model_prices_and_context_window.json): add gemini reasoning token pricing
* fix(vertex_and_google_ai_studio_gemini.py): support counting thinking tokens for gemini
allows accurate cost calc
* fix(utils.py): add reasoning token cost calc to generic cost calc
ensures gemini-2.5-flash cost calculation is accurate
* build(model_prices_and_context_window.json): mark gemini-2.5-flash as 'supports_reasoning'
* feat(gemini/): support 'thinking' + 'reasoning_effort' params + new unit tests
allow controlling thinking effort for gemini-2.5-flash models
* test: update unit testing
* feat(vertex_and_google_ai_studio_gemini.py): return reasoning content if given in gemini response
* test: update model name
* fix: fix ruff check
* test(test_spend_management_endpoints.py): update tests to be less sensitive to new keys / updates to usage object
* fix(vertex_and_google_ai_studio_gemini.py): fix translation
* fix(litellm_proxy/chat/transformation.py): support 'thinking' param
Fixes https://github.com/BerriAI/litellm/issues/9380
* feat(azure/gpt_transformation.py): add azure audio model support
Closes https://github.com/BerriAI/litellm/issues/6305
* fix(utils.py): use provider_config in common functions
* fix(utils.py): add missing provider configs to get_chat_provider_config
* test: fix test
* fix: fix path
* feat(utils.py): make bedrock invoke nova config baseconfig compatible
* fix: fix linting errors
* fix(azure_ai/transformation.py): remove buggy optional param filtering for azure ai
Removes incorrect check for support tool choice when calling azure ai - prevented calling models with response_format unless on litell model cost map
* fix(amazon_cohere_transformation.py): fix bedrock invoke cohere transformation to inherit from coherechatconfig
* test: fix azure ai tool choice mapping
* fix: fix model cost map to add 'supports_tool_choice' to cohere models
* fix(get_supported_openai_params.py): check if custom llm provider in llm providers
* fix(get_supported_openai_params.py): fix llm provider in list check
* fix: fix ruff check errors
* fix: support defs when calling bedrock nova
* fix(factory.py): fix test
* test: move test to just checking async
* fix(transformation.py): handle function call with no schema
* fix(utils.py): handle pydantic base model in message tool calls
Fix https://github.com/BerriAI/litellm/issues/9321
* fix(vertex_and_google_ai_studio.py): handle tools=[]
Fixes https://github.com/BerriAI/litellm/issues/9080
* test: remove max token restriction
* test: fix basic test
* fix(get_supported_openai_params.py): fix check
* fix(converse_transformation.py): support fake streaming for meta.llama3-3-70b-instruct-v1:0
* fix: fix test
* fix: parse out empty dictionary on dbrx streaming + tool calls
* fix(handle-'strict'-param-when-calling-fireworks-ai): fireworks ai does not support 'strict' param
* fix: fix ruff check
'
* fix: handle no strict in function
* fix: revert bedrock change - handle in separate PR
* fix(internal_user_endpoints.py): cleanup unused variables on beta endpoint
no team/org split on daily user endpoint
* build(model_prices_and_context_window.json): gemini-2.0-flash supports audio input
* feat(gemini/transformation.py): support passing audio input to gemini
* test: fix test
* fix(gemini/transformation.py): support audio input as a url
enables passing google cloud bucket urls
* fix(gemini/transformation.py): support explicitly passing format of file
* fix(gemini/transformation.py): expand support for inferred file types from url
* fix(sagemaker/completion/transformation.py): fix special token error when counting sagemaker tokens
* test: fix import
* fix(anthropic/chat/transformation.py): Don't set tool choice on response_format conversion when thinking is enabled
Not allowed by Anthropic
Fixes https://github.com/BerriAI/litellm/issues/8901
* refactor: move test to base anthropic chat tests
ensures consistent behaviour across vertex/anthropic/bedrock
* fix(anthropic/chat/transformation.py): if thinking token is specified and max tokens is not - ensure max token to anthropic is higher than thinking tokens
* feat(converse_transformation.py): correctly handle thinking + response format on Bedrock Converse
Fixes https://github.com/BerriAI/litellm/issues/8901
* fix(converse_transformation.py): correctly handle adding max tokens
* test: handle service unavailable error
