* 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
* Enable Tool Calling for `meta_llama` (#11825)
* feat: enable tools and function_call features
* fix: ignore pydantic warnings for StreamingChoices from llama-api
* docs: add tool calling examples
* docs: change default models to Maverick
* docs: fix output of tool use
* test_map_openai_params
---------
Co-authored-by: Young Han <110819238+seyeong-han@users.noreply.github.com>
* feat: add summarize parameter to /spend/logs endpoint for individual transaction logs
- Introduced a new `summarize` parameter to control data format when querying spend logs.
- `summarize=true` (default) returns aggregated data, while `summarize=false` provides individual transaction logs.
- Updated documentation and added tests to validate the new functionality.
* fix: wrap boto3.Session() with tracer for observability - Add tracer.trace wrapper around boto3.Session() call in _get_aws_region_name method - Ensures all boto3 initializations in base_aws_llm.py are properly instrumented - Fixes test_boto3_init_tracer_wrapping test failure - Maintains consistency with other boto3 calls in the same file
* Fix: #11853
Updated alerting.md to call the latest model for adding metadata.
* Fix: #11856
Updated billing docs to call newer GPT model for Langchain and CURL
Ishaan Jaff