* fix(initial-commit): adding a way to get the right response type based on the api route
* feat(unified_guardrail.py): support streaming guardrails
* test: update tests
* fix: fix linting errors
* test: update tests
* test: add failing tests for organization budget enforcement bug
Add comprehensive tests exposing that organization-level budgets are
retrieved but never enforced during request authentication. Tests verify:
1. Basic org budget exceeded scenario (team under budget, org over)
2. Multiple teams collectively exceeding org budget
3. Organization budget fields exist but are never checked
4. Inconsistency between team budget enforcement (works) and org (doesn't)
Tests intentionally fail to document the bug. Will be fixed in next commit.
Related to organization_max_budget not being enforced in auth_checks.py
* fix: enforce organization budget in auth checks
Add organization budget enforcement to common_checks() in auth_checks.py.
Previously, organization_max_budget was retrieved from DB but never checked,
allowing teams to collectively exceed their organization's budget limit.
Changes:
- Add _organization_max_budget_check() function following team budget pattern
- Call org budget check after team budget check in common_checks()
- Add "organization_budget" to budget_alerts type literals
- Update tests to verify org budget is enforced
Budget hierarchy is now properly enforced:
Organization Budget (hard ceiling)
└─ Team Budget (sub-allocation)
└─ Team Member Budget (per-user within team)
└─ Key Budget (per-key)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* fix: add organization_id to budget alerts, fix enum comparison and linting of newly added code
- Add organization_id field to CallInfo class for better alert context
- Include organization_id in budget alerts (token, soft, team, org)
- Fix event_group enum comparison (was comparing enum to string)
- Add OrganizationBudgetAlert class for organization budget alerting
- Add organization_budget to test parameterizations
- Apply Black formatting to slack_alerting.py
---------
Co-authored-by: Claude <noreply@anthropic.com>
Both frameworks integrate with LiteLLM:
- Google ADK uses LiteLLM for model-agnostic agent building
- Harbor uses LiteLLM for agent evaluation across providers
* docs: update getting started page
- Add Core Functions table with link to full list
- Add Responses API section
- Add Async section with acompletion() example
- Add "Switch Providers with One Line" example
- Clarify Basic Usage supports multiple endpoints
- Update models to current versions (openai/gpt-4o, anthropic/claude-sonnet-4)
- Use provider/model format throughout
- Fix deprecated import: from openai.error -> from openai
- Keep original structure: community key, More details links, observability env vars
* Cleanup: Remove orphan docs pages and Docusaurus template files
- Remove orphan getting_started.md (not linked in sidebar)
- Remove Docusaurus template intro.md
- Remove tutorial-basics/ directory (Docusaurus template)
- Remove tutorial-extras/ directory (Docusaurus template)
Replace Pydantic v1 `.dict()` method with v2 `.model_dump()` to fix
PydanticDeprecatedSince20 warnings. The `.dict()` method is deprecated
in Pydantic v2 and will be removed in v3.
Fixes#5987
* fix: conditionally pass enable_cleanup_closed to aiohttp TCPConnector
Fixes deprecation warning on Python 3.12.7+ and 3.13.1+ where
enable_cleanup_closed is no longer needed since the underlying
CPython SSL connection leak bug was fixed.
See: https://github.com/python/cpython/pull/118960
* chore: add aiohttp source reference to AIOHTTP_NEEDS_CLEANUP_CLOSED
* docs vertex tts
* place vertex ai types in file
* use VertexAITextToSpeechConfig
* use vertex_voice_dict
* refactor docs
* docs vertex ai chirp
* TestVertexAITextToSpeechConfig
* new provider vertex ai chirp3
* test_litellm_speech_vertex_ai_chirp
* add vertex_ai/chirp cost trackign
* docs: add Azure AI Foundry documentation for Claude models
Add documentation explaining how to use Claude models (Sonnet 4.5,
Haiku 4.5, Opus 4.1) deployed on Azure AI Foundry with LiteLLM.
Azure exposes Claude using Anthropic's native API, so users can use
the existing anthropic/ provider with their Azure endpoint.
Closes#17066
* docs: Add alternative method for Azure AI Foundry using anthropic/ provider
Document that users can use anthropic/ provider with Azure endpoint
as an alternative to the dedicated azure_ai/ provider.
* refactor(generic_guardrail_api.py): refactor to update to new guardrail api logic
* refactor: refactor llm api integrations to support passing in text as a list[str] instead of one at a time
* refactor: fix linting errors
* refactor: pass request type to guardrail api
allows request vs. response processing to occur
* feat: pass user api key dict information to the guardrail api
* fix: pass user api key dict information to the guardrail api
* feat: pass litellm call id + trace id, if present
* docs: update docs
* update databricks pricing and add DBU<>USD test
* Refactor test_databricks_pricing.py
Removed unnecessary sys.path modification and cleaned up comments.
Sameer Kankute