* fix(prometheus.py): fix setting key budget metrics
ensures custom metadata works with key budget metric
this is a patch. root cause pr is written in a separate branch
* test: fix test
* fix(key_management_endpoints.py): fix vulnerability where a user could update another user's keys
Resolves https://github.com/BerriAI/litellm/issues/8031
* test(key_management_endpoints.py): return consistent 403 forbidden error when modifying key that doesn't belong to user
* fix(internal_user_endpoints.py): return model max budget in internal user create response
Fixes https://github.com/BerriAI/litellm/issues/7047
* test: fix test
* test: update test to handle gemini token counter change
* fix(factory.py): fix bedrock http:// handling
* docs: fix typo in lm_studio.md (#8222)
* test: fix testing
* test: fix test
---------
Co-authored-by: foreign-sub <51928805+foreign-sub@users.noreply.github.com>
* add assembly ai pass through request
* fix assembly pass through
* fix test_assemblyai_basic_transcribe
* fix assemblyai auth check
* test_assemblyai_transcribe_with_non_admin_key
* working assembly ai test
* working assembly ai proxy route
* use helper func to pass through logging
* clean up logging assembly ai
* test: update test to handle gemini token counter change
* fix(factory.py): fix bedrock http:// handling
* add unit testing for assembly pt handler
* docs assembly ai pass through endpoint
* fix proxy_pass_through_endpoint_tests
* fix standard_passthrough_logging_object
* fix ASSEMBLYAI_API_KEY
* test test_assemblyai_proxy_route_basic_post
* test_assemblyai_proxy_route_get_transcript
* fix is is_assemblyai_route
* test_is_assemblyai_route
---------
Co-authored-by: Krrish Dholakia <krrishdholakia@gmail.com>
* test(base_llm_unit_tests.py): add test to ensure drop params is respected
* fix(types/prometheus.py): use typing_extensions for python3.8 compatibility
* build: add cherry picked commits
* fix(vertex_ai/gemini/transformation.py): handle 'http://' image urls
* test: add base test for `http:` url's
* fix(factory.py/get_image_details): follow redirects
allows http calls to work
* fix(codestral/): fix stream chunk parsing on last chunk of stream
* Azure ad token provider (#6917)
* Update azure.py
Added optional parameter azure ad token provider
* Added parameter to main.py
* Found token provider arg location
* Fixed embeddings
* Fixed ad token provider
---------
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
* fix: fix linting errors
* fix(main.py): leave out o1 route for azure ad token provider, for now
get v0 out for sync azure gpt route to begin with
* test: skip http:// test for fireworks ai
model does not support it
* refactor: cleanup dead code
* fix: revert http:// url passthrough for gemini
google ai studio raises errors
* test: fix test
---------
Co-authored-by: bahtman <anton@baht.dk>
* fix(ui_sso.py): use common `get_user_object` logic across jwt + ui sso auth
Allows finding users by their email, and attaching the sso user id to the user if found
* Improve Team Management flow on UI (#8204)
* build(teams.tsx): refactor teams page to make it easier to add members to a team
make a row in table clickable -> allows user to add users to team they intended
* build(teams.tsx): make it clear user should click on team id to view team details
simplifies team management by putting team details on separate page
* build(team_info.tsx): separately show user id and user email
make it easy for user to understand the information they're seeing
* build(team_info.tsx): add back in 'add member' button
* build(team_info.tsx): working team member update on team_info.tsx
* build(team_info.tsx): enable team member delete on ui
allow user to delete accidental adds
* build(internal_user_endpoints.py): expose new endpoint for ui to allow filtering on user table
allows proxy admin to quickly find user they're looking for
* feat(team_endpoints.py): expose new team filter endpoint for ui
allows proxy admin to easily find team they're looking for
* feat(user_search_modal.tsx): allow admin to filter on users when adding new user to teams
* test: mark flaky test
* test: mark flaky test
* fix(exception_mapping_utils.py): fix anthropic text route error
* fix(ui_sso.py): handle situation when user not in db
* fix(o_series_transformation.py): add 'reasoning_effort' as o series model param
Closes https://github.com/BerriAI/litellm/issues/8182
* fix(main.py): ensure `reasoning_effort` is a mapped openai param
* refactor(azure/): rename o1_[x] files to o_series_[x]
* refactor(base_llm_unit_tests.py): refactor testing for o series reasoning effort
* test(test_azure_o_series.py): have azure o series tests correctly inherit from base o series model tests
* feat(base_utils.py): support translating 'developer' role to 'system' role for non-openai providers
Makes it easy to switch from openai to anthropic
* fix: fix linting errors
* fix(base_llm_unit_tests.py): fix test
* fix(main.py): add missing param
* test: add more unit testing for team member add
* fix(team_endpoints.py): add validation check to prevent same user from being added to team again
prevents duplicates
* fix(team_endpoints.py): raise error if `/team/member_delete` called on member that's not in team
prevent being able to call delete on same member multiple times
* test: update initial tests
* test: fix test
* test: update test to handle no member duplication
* fix: support azure o3 model family for fake streaming workaround (#8162)
* fix: support azure o3 model family for fake streaming workaround
* refactor: rename helper to is_o_series_model for clarity
* update function calling parameters for o3 models (#8178)
* refactor(o1_transformation.py): refactor o1 config to be o series config, expand o series model check to o3
ensures max_tokens is correctly translated for o3
* feat(openai/): refactor o1 files to be 'o_series' files
expands naming to cover o3
* fix(azure/chat/o1_handler.py): azure openai is an instance of openai - was causing resets
* test(test_azure_o_series.py): assert stream faked for azure o3 mini
Resolves https://github.com/BerriAI/litellm/pull/8162
* fix(o1_transformation.py): fix o1 transformation logic to handle explicit o1_series routing
* docs(azure.md): update doc with `o_series/` model name
---------
Co-authored-by: byrongrogan <47910641+byrongrogan@users.noreply.github.com>
Co-authored-by: Low Jian Sheng <15527690+lowjiansheng@users.noreply.github.com>
* Add O3-Mini for Azure and Remove Vision Support (#8161)
* Azure Released O3-mini at the same time as OAI, so i've added support here. Confirmed to work with Sweden Central.
* [FIX] replace cgi for python 3.13 with email.Message as suggested in PEP 594 (#8160)
* Update model_prices_and_context_window.json (#8120)
codestral2501 pricing on vertex_ai
* Fix/db view names (#8119)
* Fix to case sensitive DB Views name
* Fix to case sensitive DB View names
* Added quotes to check query as well
* Added quotes to create view query
* test: handle server error for flaky test
vertex ai has unstable endpoints
---------
Co-authored-by: Wanis Elabbar <70503629+elabbarw@users.noreply.github.com>
Co-authored-by: Honghua Dong <dhh1995@163.com>
Co-authored-by: superpoussin22 <vincent.nadal@orange.fr>
Co-authored-by: Miguel Armenta <37154380+ma-armenta@users.noreply.github.com>
* build(schema.prisma): add new `sso_user_id` to LiteLLM_UserTable
easier way to store sso id for existing user
Allows existing user added to team, to login via SSO
* test(test_auth_checks.py): add unit testing for fuzzy user object get
* fix(handle_jwt.py): fix merge conflicts
* docs(token_auth.md): clarify title
* refactor(handle_jwt.py): add jwt auth manager + refactor to handle groups
allows user to call model if user belongs to group with model access
* refactor(handle_jwt.py): refactor to first check if service call then check user call
* feat(handle_jwt.py): new `enforce_team_access` param
only allows user to call model if a team they belong to has model access
allows controlling user model access by team
* fix(handle_jwt.py): fix error string, remove unecessary param
* docs(token_auth.md): add controlling model access for jwt tokens via teams to docs
* test: fix tests post refactor
* fix: fix linting errors
* fix: fix linting error
* test: fix import error
* add support for using llama spec with bedrock
* fix get_bedrock_invoke_provider
* add support for using bedrock provider in mappings
* working request
* test_bedrock_custom_deepseek
* test_bedrock_custom_deepseek
* fix _get_model_id_for_llama_like_model
* test_bedrock_custom_deepseek
* doc DeepSeek-R1-Distill-Llama-70B
* test_bedrock_custom_deepseek
* Litellm dev 01 29 2025 p4 (#8107)
* fix(key_management_endpoints.py): always get db team
Fixes https://github.com/BerriAI/litellm/issues/7983
* test(test_key_management.py): add unit test enforcing check_db_only is always true on key generate checks
* test: fix test
* test: skip gemini thinking
* Litellm dev 01 29 2025 p3 (#8106)
* fix(__init__.py): reduces size of __init__.py and reduces scope for errors by using correct param
* refactor(__init__.py): refactor init by cleaning up redundant params
* refactor(__init__.py): move more constants into constants.py
cleanup root
* refactor(__init__.py): more cleanup
* feat(__init__.py): expose new 'disable_hf_tokenizer_download' param
enables hf model usage in offline env
* docs(config_settings.md): document new disable_hf_tokenizer_download param
* fix: fix linting error
* fix: fix unsafe comparison
* test: fix test
* docs(public_teams.md): add doc showing how to expose public teams for users to join
* docs: add beta disclaimer on public teams
* test: update tests
* feat(lowest_tpm_rpm_v2.py): fix redis cache check to use >= instead of >
makes it consistent
* test(test_custom_guardrails.py): add more unit testing on default on guardrails
ensure it runs if user sent guardrail list is empty
* docs(quick_start.md): clarify default on guardrails run even if user guardrails list contains other guardrails
* refactor(litellm_logging.py): refactor no-log to helper util
allows for more consistent behavior
* feat(litellm_logging.py): add event hook to verbose logs
* fix(litellm_logging.py): add unit testing to ensure `litellm.disable_no_log_param` is respected
* docs(logging.md): document how to disable 'no-log' param
* test: fix test to handle feb
* test: cleanup old bedrock model
* fix: fix router check
* fix handleSubmit
* update handleAddModelSubmit
* add jest testing for ui
* add step for running ui unit tests
* add validate json step to add model
* ui jest testing fixes
* update package lock
* ci/cd run again
* fix antd import
* run jest tests first
* fix antd install
* fix ui unit tests
* fix unit test ui
* feat(databricks/chat/transformation.py): add tools and 'tool_choice' param support
Closes https://github.com/BerriAI/litellm/issues/7788
* refactor: cleanup redundant file
* test: mark flaky test
* test: mark all parallel request tests as flaky
* docs: cleanup doc
* feat(bedrock/): initial commit adding bedrock/converse_like/<model> route support
allows routing to a converse like endpoint
Resolves https://github.com/BerriAI/litellm/issues/8085
* feat(bedrock/chat/converse_transformation.py): make converse config base config compatible
enables new 'converse_like' route
* feat(converse_transformation.py): enables using the proxy with converse like api endpoint
Resolves https://github.com/BerriAI/litellm/issues/8085
* refactor _add_callbacks_from_db_config
* fix check for _custom_logger_exists_in_litellm_callbacks
* move loc of test utils
* run ci/cd again
* test_add_custom_logger_callback_to_specific_event_with_duplicates_callbacks
* fix _custom_logger_class_exists_in_success_callbacks
* unit testing for test_add_callbacks_from_db_config
* test_custom_logger_exists_in_callbacks_individual_functions
* fix config.yml
* fix test test_stream_chunk_builder_openai_audio_output_usage - use direct dict comparison
* ui 1 - show correct msg on no logs
* fix dup country col
* backend - allow filtering by team_id and api_key
* fix ui_view_spend_logs
* ui update query params
* working team id and key hash filters
* fix filter ref - don't hold on them as they are
* fix _model_custom_llm_provider_matches_wildcard_pattern
* fix test test_stream_chunk_builder_openai_audio_output_usage - use direct dict comparison
* docs(reliability.md): add doc on disabling fallbacks per request
* feat(litellm_pre_call_utils.py): support reading request timeout from request headers - new `x-litellm-timeout` param
Allows setting dynamic model timeouts from vercel's AI sdk
* test(test_proxy_server.py): add simple unit test for reading request timeout
* test(test_fallbacks.py): add e2e test to confirm timeout passed in request headers is correctly read
* feat(main.py): support passing metadata to openai in preview
Resolves https://github.com/BerriAI/litellm/issues/6022#issuecomment-2616119371
* fix(main.py): fix passing openai metadata
* docs(request_headers.md): document new request headers
* build: Merge branch 'main' into litellm_dev_01_27_2025_p3
* test: loosen test
* test(test_completion_cost.py): add unit testing to ensure all bedrock models with region name have cost tracked
* feat: initial script to get bedrock pricing from amazon api
ensures bedrock pricing is accurate
* build(model_prices_and_context_window.json): correct bedrock model prices based on api check
ensures accurate bedrock pricing
* ci(config.yml): add bedrock pricing check to ci/cd
ensures litellm always maintains up-to-date pricing for bedrock models
* ci(config.yml): add beautiful soup to ci/cd
* test: bump groq model
* test: fix test
* refactor(factory.py): refactor async bedrock message transformation to use async get request for image url conversion
improve latency of bedrock call
* test(test_bedrock_completion.py): add unit testing to ensure async image url get called for async bedrock call
* refactor(factory.py): refactor bedrock translation to use BedrockImageProcessor
reduces duplicate code
* fix(factory.py): fix bug not allowing pdf's to be processed
* fix(factory.py): fix bedrock converse document understanding with image url
* docs(bedrock.md): clarify all bedrock document types are supported
* refactor: cleanup redundant test + unused imports
* perf: improve perf with reusable clients
* test: fix test
* fix(utils.py): handle failed hf tokenizer request during calls
prevents proxy from failing due to bad hf tokenizer calls
* fix(utils.py): convert failure callback str to custom logger class
Fixes https://github.com/BerriAI/litellm/issues/8013
* test(test_utils.py): fix test - avoid adding mlflow dep on ci/cd
* fix: add missing env vars to test
* test: cleanup redundant test
* feat(handle_jwt.py): initial commit adding custom RBAC support on jwt auth
allows admin to define user role field and allowed roles which map to 'internal_user' on litellm
* fix(auth_checks.py): ensure user allowed to access model, when calling via personal keys
Fixes https://github.com/BerriAI/litellm/issues/8029
* feat(handle_jwt.py): support role based access with model permission control on proxy
Allows admin to just grant users roles on IDP (e.g. Azure AD/Keycloak) and user can immediately start calling models
* docs(rbac): add docs on rbac for model access control
make it clear how admin can use roles to control model access on proxy
* fix: fix linting errors
* test(test_user_api_key_auth.py): add unit testing to ensure rbac role is correctly enforced
* test(test_user_api_key_auth.py): add more testing
* test(test_users.py): add unit testing to ensure user model access is always checked for new keys
Resolves https://github.com/BerriAI/litellm/issues/8029
* test: fix unit test
* fix(dot_notation_indexing.py): fix typing to work with python 3.8
* feat(main.py): use asyncio.sleep for mock_Timeout=true on async request
adds unit testing to ensure proxy does not fail if specific Openai requests hang (e.g. recent o1 outage)
* fix(streaming_handler.py): fix deepseek r1 return reasoning content on streaming
Fixes https://github.com/BerriAI/litellm/issues/7942
* Revert "fix(streaming_handler.py): fix deepseek r1 return reasoning content on streaming"
This reverts commit 7a052a64e3.
* fix(deepseek-r-1): return reasoning_content as a top-level param
ensures compatibility with existing tools that use it
* fix: fix linting error
* fix(utils.py): initial commit fixing custom cost tracking
refactors out provider specific model info from `get_model_info` - this was causing custom costs to be registered incorrectly
* fix(utils.py): cleanup `_supports_factory` to check provider info, if model info is None
some providers support features like vision across all models
* fix(utils.py): refactor to use _supports_factory
* test: update testing
* fix: fix linting errors
* test: fix testing
* fix(base_utils.py): supported nested json schema passed in for anthropic calls
* refactor(base_utils.py): refactor ref parsing to prevent infinite loop
* test(test_openai_endpoints.py): refactor anthropic test to use bedrock
* fix(langfuse_prompt_management.py): add unit test for sync langfuse calls
Resolves https://github.com/BerriAI/litellm/issues/7938#issuecomment-2613293757
* _add_guardrails_from_key_or_team_metadata
* e2e test test_guardrails_with_team_controls
* add try/except on team new
* test_guardrails_with_team_controls
* test_guardrails_with_api_key_controls
