* [Test] Add Azure async chat completion timeout test. WIP
* Capture TTFT for /v1/messages streaming responses
The pass-through streaming path for /v1/messages (Anthropic, Bedrock,
Vertex AI, Azure AI, Minimax) logged completion_start_time only after
the entire stream finished. async_success_handler then fell back to
end_time, making TTFT equal to total duration or null in the UI and
Prometheus.
Record the timestamp of the first chunk in async_sse_wrapper and
propagate it to model_call_details before the logging handler runs,
so gen_ai.response.time_to_first_token reflects the real first-chunk
latency.
Fixes#25598
* [Refactor] Implement timeout resolution logic in completion function
add fetch ``request_timeout`` from litellm_settings
* remove stale test case
* remove extra print statement
* default request timeout value in constants to 600s to match timeout defaults handled in the proxy
* fix request timeout if using default value from constants.py
* update code structure, test cases
* only override if the global timeout sets timeout to 6000s
* update code structure, move hard coded values to const and make the reslve function readable by moving fallback logic to a seperate function
* modify default timeout values, replacing hard coded ones with default values defined
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Co-authored-by: harish876 <harishgokul01@gmail.com>
Co-authored-by: Joaquin Hui Gomez <joaquinhuigomez@users.noreply.github.com>
- Backend: Use request model from hidden_params for Azure Model Router additional_costs when response has actual model
- Backend: Add additional_costs to total cost calculation
- UI: Show all non-null/non-zero additional_costs in CostBreakdownViewer
- UI: Render cost breakdown when only additional_costs exist
- Tests: Backend test for hidden_params flow; frontend tests for additional_costs
Made-with: Cursor
- Azure Model Router transform_response: let parent extract actual model from raw response
- common_request_processing: skip model override for Azure Model Router requests
- proxy_server: skip streaming chunk model restamp for Azure Model Router
- Add _is_azure_model_router_request helper
- Add tests for non-streaming and streaming
Made-with: Cursor
- Pass request_model to Azure AI cost calculator to detect router requests
- Add router flat cost ($0.14/M input tokens) even when Azure returns actual model in response
- Add test for router flat cost with response containing actual model
- Update docs with cost calculation flow and configuration requirements
Made-with: Cursor
Azure AI Foundry's Anthropic endpoint does not support the scope field in
cache_control. Strip it from both system and messages before sending.
Made-with: Cursor
* fix: _should_use_api_key_header
* test_azure_ai_validate_environment_with_api_key
* fix: remove unused top-level RouteChecks import
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* docs: add missing env keys to config_settings reference
Add MODEL_COST_MAP_MIN_MODEL_COUNT, MODEL_COST_MAP_MAX_SHRINK_RATIO,
and MAX_POLICY_ESTIMATE_IMPACT_ROWS to the environment variables
reference table.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Add x-api-key header to CountTokens handler to match chat completion
authentication. Azure AI Anthropic requires this header per Microsoft's
native API format.
* fix(azure_ai): Remove unsupported params from Azure AI Anthropic requests
Azure AI Anthropic endpoint rejects max_retries and stream_options parameters
with "Extra inputs are not permitted" error. These are LiteLLM-internal
parameters that should not be sent to the API.
Fixes 400 Bad Request error when using azure_ai/claude-sonnet-4-5 and other
Azure AI Anthropic models.
* test(azure_ai): Add test for unsupported params removal in Azure AI Anthropic
Verifies that max_retries, stream_options, and extra_body are properly
removed from the request before sending to Azure AI Anthropic endpoint.
* refactor: remove api-key conversion logic for Azure Anthropic
Co-authored-by: Erdem Halil <erdemhalil@users.noreply.github.com>
* fix(passthrough): pass custom_llm_provider to completion_cost for Azure AI Anthropic
The passthrough logging for Anthropic was failing when using Azure AI Anthropic
because the completion_cost function was not receiving the custom_llm_provider
parameter, causing it to fail with "LLM Provider NOT provided" error.
This fix:
- Retrieves custom_llm_provider from logging_obj.model_call_details
- Prepends provider prefix to model name for cost calculation
- Passes both formatted model and custom_llm_provider to completion_cost
- Centralizes provider prefix logic in _create_anthropic_response_logging_payload
This ensures cost calculation works correctly for Azure AI Anthropic requests
with models like azure_ai/claude-sonnet-4-5_gb_20250929.
Co-authored-by: Erdem Halil <erdemhalil@users.noreply.github.com>
* test: add unit tests for Azure AI Anthropic fixes
- Add tests for custom_llm_provider cost calculation in passthrough logging
- Add tests for ProviderConfigManager returning AzureAnthropicMessagesConfig
- Update existing tests to reflect removal of api-key to x-api-key conversion
Co-authored-by: Erdem Halil <erdemhalil@users.noreply.github.com>
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Co-authored-by: Erdem Halil <erdemhalil@users.noreply.github.com>
* fix(spend_tracking_utils.py): add user agent tags from standard logging payload, in spend logs payload
* feat(litellm_logging.py): identify user agent tags as `User-Agent: ..` and allow admin to disable storing user agent as tag
* fix(azure_ai/): pass content type header in azure ai request
Fixes https://github.com/BerriAI/litellm/issues/11227
* test: add unit test
* fix(router.py): fix passing dynamic credentials to retrieve batch
Fixes batch retrieval when using router
* test: add more unit tests
