- Add get_vertex_base_url() helper function to handle regional vs global URLs
- Update _get_embedding_url() to support global location
- Update _get_vertex_url() chat, image_generation, and count_tokens modes
- Add comprehensive test suite with 38 tests covering all endpoint types
- Tests verify both regional and global URL construction
- Maintains 100% backward compatibility
- Add OAuth M2M (Machine-to-Machine) authentication via DATABRICKS_CLIENT_ID and DATABRICKS_CLIENT_SECRET
- Add Databricks SDK auto-auth with automatic credential discovery
- Add sensitive data redaction for secure logging (tokens, API keys, secrets)
- Add custom user_agent parameter for partner attribution in Databricks telemetry
- Support user_agent in LiteLLM Proxy via config.yaml litellm_params
- Add 49 mocked unit tests for all new functionality
- Add 13 E2E tests for real-world validation (skipped in CI)
- Update documentation with new features and examples
* fix(gemini): use JSON instead of form-data for image edit requests
Gemini's image edit API expects JSON body, not multipart/form-data.
The handler was sending form-encoded data which caused 400 errors:
"Invalid JSON payload received. Unexpected token."
Changes:
- Add use_multipart_form_data() method to BaseImageEditConfig (default True)
- Modify image_edit_handler to use json= when use_multipart_form_data() is False
- Override use_multipart_form_data() in GeminiImageEditConfig to return False
* test(gemini): add test for use_multipart_form_data
Claude 3.7 Sonnet's default max_output_tokens is 64000, not 128000.
The 128K output limit requires the beta header 'output-128k-2025-02-19'.
This fixes the integration test failure where requests with max_tokens=128000
were being rejected by the Anthropic API.
Fixes test_multiturn_tool_calls in test_anthropic_responses_api.py
* fix(unified_guardrails.py): send all chunks on completion of final stream
* feat(generic_guardrail_api.py): handle tool call response on streaming LLM responses
* fix(anthropic/chat/guardrail_translation): initial commit adding anthropic tool response streaming guardrails
enables guardrail checks on tool response from llm's to work via `/v1/messages`
* feat(anthropic/): working guardrail checks on tool response from LLMs
ensures guardrail checks on anthropic /v1/messages works as expected
* feat(responses/guardrail_translation): support tool call response guardrails on streaming for /v1/responses
ensures complete coverage of tool call responses
* refactor(openai.py): refactor to use consistent pydantic model for responses api tool response on streaming
enables non-openai model tool call response to work correctly with guardrail checks on /v1/responses
* test: update tests
* fix: fix linting error
* fix: fix failing tests
* fix: fix import errors
* fix(openai/chat/guardrail_transformation): fix final chunk returned on streaming
* fix(anthropic): use dynamic max_tokens based on model
When users don't specify max_tokens in requests to Anthropic models,
LiteLLM now uses the correct max_output_tokens value from the model
pricing JSON instead of a hardcoded 4096.
This fixes truncated responses for Claude 3.5+ models which support
higher output limits (8192 for Claude 3.5, 128k for Claude 3.7, etc.)
Fixes#8835
* fix(anthropic): restore env var support for backwards compatibility
Keep DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS as fallback when model is not
found in JSON, allowing users to configure via environment variable.
Add direct Stability AI REST API support for image generation endpoints.
This enables using Stability's SD3, SD3.5, and Stable Image models via
LiteLLM's OpenAI-compatible interface.
Changes:
- Add STABILITY provider to LlmProviders enum
- Create StabilityImageGenerationConfig with multipart/form-data support
- Add OpenAI size to Stability aspect_ratio mapping
- Register provider in ProviderConfigManager
- Add 9 Stability models to model_prices_and_context_window.json
- Add documentation at docs/providers/stability.md
- Add 25 unit tests
Supported models:
- stability/sd3, sd3-large, sd3-large-turbo, sd3-medium
- stability/sd3.5-large, sd3.5-large-turbo, sd3.5-medium
- stability/stable-image-ultra, stable-image-core
Fixes#15337
Perplexity API returns pre-calculated costs in `usage.cost.total_cost`
that include the `request_cost` (fixed per-request fee). LiteLLM was
ignoring this and calculating costs manually, resulting in ~27x
underreporting (e.g., $0.0002 vs actual $0.006).
Changes:
- Use `usage.cost.total_cost` from Perplexity response when available
- Fall back to manual calculation if cost object not present
- Add tests for both behaviors
