* fix async_log_success_event for _PROXY_DynamicRateLimitHandlerV3
* test_async_log_success_event_increments_by_actual_tokens
* fix redis TTL
* Potential fix for code scanning alert no. 3873: Clear-text logging of sensitive information
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
---------
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
* Fix Bedrock guardrail apply_guardrail method and test mocks
Fixed 4 failing tests in the guardrail test suite:
1. BedrockGuardrail.apply_guardrail now returns original texts when guardrail
allows content but doesn't provide output/outputs fields. Previously returned
empty list, causing test_bedrock_apply_guardrail_success to fail.
2. Updated test mocks to use correct Bedrock API response format:
- Changed from 'content' field to 'output' field
- Fixed nested structure from {'text': {'text': '...'}} to {'text': '...'}
- Added missing 'output' field in filter test
3. Fixed endpoint test mocks to return GenericGuardrailAPIInputs format:
- Changed from tuple (List[str], Optional[List[str]]) to dict {'texts': [...]}
- Updated method call assertions to use 'inputs' parameter correctly
All 12 guardrail tests now pass successfully.
* fix: remove python3-dev from Dockerfile.build_from_pip to avoid Python version conflict
The base image cgr.dev/chainguard/python:latest-dev already includes Python 3.14
and its development tools. Installing python3-dev pulls Python 3.13 packages
which conflict with the existing Python 3.14 installation, causing file
ownership errors during apk install.
* fix: disable callbacks in vertex fine-tuning tests to prevent Datadog logging interference
The test was failing because Datadog logging was making an HTTP POST request
that was being caught by the mock, causing assert_called_once() to fail.
By disabling callbacks during the test, we prevent Datadog from making any
HTTP calls, allowing the mock to only see the Vertex AI API call.
* fix: ensure test isolation in test_logging_non_streaming_request
Add proper cleanup to restore original litellm.callbacks after test execution.
This prevents test interference when running as part of a larger test suite,
where global state pollution was causing async_log_success_event to be
called multiple times instead of once.
Fixes test failure where the test expected async_log_success_event to be
called once but was being called twice due to callbacks from previous tests
not being cleaned up.
Add support for OpenAI's gpt-5.1-codex-max model, their most intelligent
coding model optimized for long-horizon agentic coding tasks.
- 400k context window, 128k max output tokens
- $1.25/1M input, $10/1M output, $0.125/1M cached input
- Only available via /v1/responses endpoint
- Supports vision, function calling, reasoning, prompt caching
* fix(github_copilot): preserve encrypted_content in reasoning items for multi-turn conversations
GitHub Copilot uses encrypted_content in reasoning items to maintain conversation
state across turns. The parent class (OpenAIResponsesAPIConfig._handle_reasoning_item)
strips this field when converting to OpenAI's ResponseReasoningItem model, causing
"encrypted content could not be verified" errors on multi-turn requests.
This override preserves encrypted_content while still filtering out status=None
which OpenAI's API rejects.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* chore: regenerate poetry.lock
* Revert "chore: regenerate poetry.lock"
This reverts commit 8796dc8f96.
---------
Co-authored-by: Claude <noreply@anthropic.com>
* fix: handle none content
* fix: defensive check on none value
* Fix test failures: Azure OCR skip, None content handling, PublicAI JSON config
- Skip aocr/ocr call types in Azure test (they don't use Azure SDK client)
- Handle None content in Responses API transformation (skip message creation)
- Update PublicAI tests to use JSON-based configuration system
- Add None check in PublicAI test fixture to fix type error
Fixed two issues:
1. Presidio guardrail test TypeError:
- Issue: test_presidio_apply_guardrail() was calling apply_guardrail() with
incorrect arguments (text=, language=) instead of the correct signature
(inputs=, request_data=, input_type=)
- Fix: Updated test to use correct method signature:
- Changed from: apply_guardrail(text=..., language=...)
- Changed to: apply_guardrail(inputs={'texts': [...]}, request_data={}, input_type='request')
- Also updated assertions to extract text from response['texts'][0]
2. License verification base64 decoding error:
- Issue: verify_license_without_api_request() was failing with
'Invalid base64-encoded string: number of data characters (185) cannot be
1 more than a multiple of 4' when license keys lacked proper base64 padding
- Root cause: Base64 strings must be a multiple of 4 characters. Some license
keys were missing padding characters (=) needed for proper decoding
- Fix: Added automatic padding before base64 decoding:
- Calculate padding needed: len(license_key) % 4
- Add '=' characters to make length a multiple of 4
- This makes license verification robust to keys with or without padding
Both fixes ensure the code handles edge cases properly and tests use correct APIs.
* fix(unified_guardrail.py): support during_call event type for unified guardrails
allows guardrails overriding apply_guardrails to work 'during_call'
* feat(generic_guardrail_api.py): support new 'tool_calls' field for generic guardrail api
returns the tool calls emitted by the LLM API to the user
* fix(generic_guardrail_api.py): working anthropic /v1/messages tool call response
send llm tool calls to guardrail api when called via `/v1/messages` API
* fix(responses/): run generic_guardrail_api on responses api tool call responses
* fix: fix tests
* test: fix tests
* fix: fix tests
Fixes#17517
- Fixed bug where only the first matching blocked keyword was masked
- Now iterates through ALL blocked keywords and masks each one
- Added 3 regression tests for multiple keyword masking
* init schema.prisma
* init LiteLLM_ObjectPermissionTable with agents and agent_access_groups
* TestAgentRequestHandler
* refatctor agent list
* add AgentRequestHandler
* fix agent access controls by key/team
* feat - new migration for LiteLLM_AgentsTable
* fix add LiteLLM_ObjectPermissionBase with agent and agent groups
* add agent routes to llm api routes
* add agent routes as llm route
