* feat(bulk_user_update/): support updating all users on proxy
* fix(bulk_edit_user.tsx): persist user settings when 'add to team' clicked
* fix(team_endpoints.py): bulk add all proxy users to team
supports flow from UI to add all existing users to a team
* fix: minor fixes
* feat(user_edit_view.tsx): support setting no default model on user edit
allows preventing users from calling models outside team scope
* fix(user_edit_view.tsx): prevent triggering submit when 'cancel' is clicked
* refactor(internal_user_endpoints.py): refactor to reduce function size
* fix(ui_sso.py): fix form action on login when sso is enabled
* fix: multiple fixes - fix resetting env var in proxy config + add key to exception message on key decryption
fixes issue where env vars would be reset
* refactor(proxy_server.py): cleanup redundant decryption line
* fix(proxy_setting_endpoints.py): show saved ui access mode
allows admin to know what they'd previously stored in db
* feat(proxy_server.py): working guardrails on streaming output
ensures guardrail actually raises an error if flagged during streaming output
* test: add unit tests
* feat(advanced_settings.tsx): add guardrails option as ui component on model add
enables setting guardrails on model add
* feat(add_model_tab.tsx): fix add model form
* feat(model_info_view.tsx): support adding guardrails on model update
* fix(add_model_tab.tsx/): working health check when guardrails selected
* fix(proxy_server.py): fix yield
* fix(custom_guardrail.py): initial logic for model level guardrails
* feat(custom_guardrail.py): working pre call guardrails
* fix(custom_guardrails.py): check if custom guardrails set before running event hook
* test(test_custom_guardrail.py): add unit tests for async pre call deployment hook on custom guardrail
* feat(custom_guardrail.py): add post call processing support for guardrails
allows model based guardrails to run on the post call event for that model only
* fix(utils.py): only run if call type is in enum
* test: update unit tests to work
* feat(key_management_endpoints.py): Support new 'key_type' field
allow user to specify if key should be 'management' or 'llm api' key
Security fix
* test(test_route_checks.py): add unit tests
* fix(create_key_button.tsx): add ui component to select key type
allows specifying if key can call llm api vs. management routes
* feat(create_key_button.tsx): add specifying key type to ui
* fix(route_checks.py): add sensitive data masker for user id on not allowed error message
prevent leaking sensitive information
* feat(litellm_pre_call_utils.py): add num_retries to litellm data for backend call
allow user to pass in num retries via request headers
* test(test_litellm_pre_call_utils.py): add unit test
* docs(request_headers.md): document new request header
* fix(common_daily_activity.py): show spend breakdown by model group
Partial fix for https://github.com/BerriAI/litellm/issues/12887
* feat(new_usage.tsx): new tab switcher for viewing usage by model group vs. received model
Closes https://github.com/BerriAI/litellm/issues/12887
* fix(main.py): fix async retryer
Fixes https://github.com/BerriAI/litellm/issues/12830
* fix(forward_clientside_headers_by_model_group.py): filter out 'content-type' from forwardable headers
clientside content-type != proxy content type, can cause requests to hang
This aligns the proxy experience with other models that think
automatically (e.g. Deepseek R1 and grok3). It does so by setting
the necessary request input to return thinking, but not specifying
a budget or effort (thus defaulting to the internal automatic level).
* fix(gpt_transformation.py): remove 'cache_control' flag for openai/openai-compatible calls
Fixes https://github.com/BerriAI/litellm/issues/12787
* fix(openrouter/chat/transformation.py): allow passing openrouter cache control flag for claude models
* fix(gpt_transformation.py): fix import
* fix: fix adding tools
* fix(main.py): fix async retryer
Fixes https://github.com/BerriAI/litellm/issues/12830
* fix(forward_clientside_headers_by_model_group.py): filter out 'content-type' from forwardable headers
clientside content-type != proxy content type, can cause requests to hang
* test(tests/): update tests
* fix(team_endpoints.py): always remove team member budget from updated_kv
this is not a field for the litellm team table
Prevents startup issue
* test(test_team_endpoints.py): add unit test to ensure 'team_member_budget' is never in update to table - separate logic
* refactor: cleanup
When using Model Armor guardrail with explicit project_id in config,
the project_id was being overwritten to None due to incorrect
initialization order between ModelArmorGuardrail and VertexBase parent class.
This fix ensures that user-provided project_id is preserved by initializing
parent classes before setting instance attributes.
Fixes#12757
* feat: initial commit for forwarding client headers by model group
* fix(router.py): support new forwarclientsideheadersbymodelgroup class
enables headers to be forwarded to backend model, by model group
* fix(proxy_server.py): load in model group settings from config correctly
* refactor(litellm_pre_call_utils.py): litellm_pre_call_utils.py
introduce new 'secret_fields' field
includes raw request headers (not the sanitized ones used for logging) - needed to support forwarding clientside headers to llm api
* feat(router.py): log the deployment model name as well
allows wildcard models to support forward_client_headers_to_llm_api
* test(test_router.py): add more unit testing
* feat(router.py): specify the model group alias in metadata kwargs
allows usage for internal routing logic
* fix: fix ruff check errors
* fix(router.py): refactor to cleanup optional pre-call checks
* fix: fix ruff check
* test: add missing unit test
* fix(prompt_templates/factory.py): handle anthropic cache control on individual tool results
Fixes issue where cache control on individual tool result was being ignored
* test(test_vertex_And_google_ai_studio_gemini.py): initial unit test covering translation for grounding metadata on streaming chunk
* fix(vertex_and_google_ai_studio.py): ensure grounding metadata is preserved on streaming
Closes https://github.com/BerriAI/litellm/issues/10237
* fix(core_helpers.py): include usage in expected openai keys
