* fix - using on python 3.9
* [⚡️ Python SDK Import] - 2 second faster import times (#12135)
* speedup - move wb logger to conditional
* fix import path
* docs(index.md): initial pre-release note
* 🧹 Refactor init.py to use a model registry (#12138)
* fix - refactor init to use a registry
* # noqa: PLR0915
* fix import loc
* test whitelisted models
* Revert "🧹 Refactor init.py to use a model registry (#12138)" (#12141)
This reverts commit f93326a214.
* [⚡️ Python SDK import] - reduce python sdk import time by .3s (#12140)
* use 1 file for KeyManagementSystem
* move key management settings
* fix import locs
* test_proxy_types_not_imported
* test the import loc
* fix import item
* fix imports
* fix import loc
* fix imports
* fix imports
* fix - revert list team changes
* fix for o-series param checks
* bump poetry
* docs(index.md): update release note with cleaner table for updated models
* `/v1/messages` - Remove hardcoded model name on streaming + Tags - enable setting custom header tags (#12131)
* fix(anthropic/experimental_pass_through): use given model name when returning streaming chunks
don't harcode model name on streaming
confusing for user
* fix(anthropic/streaming_iterator.py): remove scope of import
* feat(litellm_logging.py): allow admin to specify additional headers for using as spend tags
Closes https://github.com/BerriAI/litellm/issues/12129
* test(test_litellm_logging.py): add unit tests
* feat(openweb_ui.md): add custom tag tutorial to docs
* docs(cost_tracking.md): add tag based usage UI screenshot
* test: update test
* fix: fix import
* docs - update release notes
* Benefits of using gemini-cli with LiteLLM
* UI QA Fixes - prevent team model reset on model add + return team-only models on /v2/model/info + render team member budget correctly (#12144)
* fix(team_endpoints.py): prevent overwriting current list of team models on new model add
* fix(networking.tsx): fix default proxy base url
* fix(proxy_server.py): include team only models when retrieving all deployments on `/v2/model/info` helper util
ensures team only models are shown to user
* fix(router.py): check model name by team public model name when team id given
Fixes issue where team member could not see team only models when clicking into that team on `Models + Endpoints`
* fix(team_member_view.tsx): fix rendering team member budget, when budget is set
* test: update tests
* test: update unit test
* docs gemini cli x litellm
* docs: index.md
release note cleanup
* docs(index.md): add more hyperlinks to docs
* docs(index.md): add batch api cost tracking to docs
* docs(index.md): update docs
* VertexAI Anthropic - streaming cost tracking w/ prompt caching fixes (#12188)
* fix(rebuild-usage-object---ensure-cache_tokens-is-set): Ensures cache tokens is correctly set
Fixes https://github.com/BerriAI/litellm/issues/12149
* test(test_stream_chunk_builder_utils.py): add unit test to ensure cached tokens is part of stream chunk builder
Ensures standardized values are used
* Fix rendering ui on non-root images (#12226)
* fix(proxy_server.py): only rewrite server_root_path if path set
Fixes UI rendering issue on non-root images
* docs(custom_root_ui.md): clarify custom root path doesn't work on non-root images
* build(pyproject.toml): version rc2
* fix(streaming_handler.py): store finish reason, even if is_finished is false - allows storing early gemini finish reasons (#12250)
Fixes https://github.com/BerriAI/litellm/issues/12249
---------
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
* fix(docs): Remove unused dotenv dependency from docusaurus config
The dotenv package was being required in docusaurus.config.js but was listed as
a devDependency, causing build failures. Since no environment variables are
actually used in the config, removed the unnecessary import.
* fix(docs): Remove reference to non-existent spending_monitoring doc
The sidebars.js file was referencing proxy/spending_monitoring which was deleted
in commit ba7463b9c. This was causing the documentation build to fail with missing
document errors.
* docs: add Elasticsearch logging tutorial and update sidebar
* docs: update Elasticsearch logging tutorial to include OpenTelemetry setup and configuration
* docs: remove sections from Elasticsearch logging tutorial
* docs: remove analytics examples from Elasticsearch logging tutorial
* Update Elasticsearch version and logging exporter configuration in the Elasticsearch logging tutorial
* Add visualization instructions for LLM telemetry data in Kibana to Elasticsearch logging tutorial
* Add Elasticsearch demo image to documentation
* Move demo image for Elasticsearch logging tutorial
* fix(handler.py): support routing custom llm's to chat completion handler
Adds custom llm support for anthropic
* test(test_anthropic_experimental_pass_through_messages_handler.py): add unit test confirming custom llm respected
* docs(custom_llm_server.md): document anthropic custom llm translation
* test(volcengine.py): map thinking in extra body
Fixes https://github.com/BerriAI/litellm/issues/11879
* feat(main.py): support `azure/responses/<deployment-name>` model string
this allows us to route the model correctly
Closes https://github.com/BerriAI/litellm/issues/11879
* docs(azure_responses.md): document calling azure responses api models via chat completions bridge
Closes https://github.com/BerriAI/litellm/issues/11917
* fix: fix custom provider check
* test: update tests
* fix(litellm_logging.py): fix using router model id for logging calls
Fixes https://github.com/BerriAI/litellm/issues/11975#issuecomment-2995882238
* test(test_litellm_logging.py): add unit test for custom price tracking
* fix(vertex_ai/): don't send invalid format parameter to vertex
causes calls to fail
* fix(vertex_ai_context_caching.py): if cached content present and tools in message, cache tools as well
gemini throws errors if tools passed in alongside cached content
* test: add unit tests
* fix: fix linting errors
* test: test_vertex_ai_common_utils.py
update test
* fix(streaming_handler.py): unset response cost when creating model response
* fix: allow setting no-default-models and unsetting max budget
* docs(sso_self_serve.md): add e2e tutorial of onboarding users for ai exploration
* docs: rename doc
