The test was using setattr() to set module-level attributes (including
proxy_logging_obj = MagicMock()) on the real litellm.proxy.proxy_server
module, but the finally block only had `pass` — no cleanup.
This left proxy_logging_obj as a MagicMock in subsequent tests running
in the same pytest-xdist worker, causing TypeError when log_db_metrics
decorator called asyncio.create_task(proxy_logging_obj.service_logging_obj
.async_service_success_hook(...)) — a MagicMock is not a coroutine.
Fix: save original attribute values before the test and restore them in
the finally block to ensure test isolation.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The implementation correctly preserves tool_call order: existing results first
(call_1), then dummy results for missing ones (call_2). The test was asserting
the reverse order with incorrect comments. Fix the assertions to match the
actual correct behavior.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Three test failures caused by the real langfuse SDK import being triggered
at test time:
1. test_langfuse_prompt_management.py: Both tests create LangfusePromptManagement()
which calls `import langfuse`. Since earlier TestLangfuseUsageDetails tests
remove sys.modules["langfuse"] via patch.dict teardown, the real langfuse
import runs and fails on Python 3.14 (pydantic v1 incompatibility).
Fix: add setup_method/teardown_method to mock sys.modules["langfuse"].
2. test_langfuse.py::test_max_langfuse_clients_limit: Same root cause — creates
LangFuseLogger() without mocking sys.modules["langfuse"].
Fix: wrap test body with patch.dict("sys.modules", {"langfuse": mock}).
3. test_langfuse_otel.py::test_extract_langfuse_metadata_with_header_enrichment:
Replaces sys.modules["litellm.integrations.langfuse.langfuse"] with a stub
without restoring it, causing patch() in later tests to target the stub
instead of the real module.
Fix: use monkeypatch.setitem() which auto-restores after the test.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
test_role_mappings_override_default_internal_user_params was calling
delattr(litellm, 'default_internal_user_params') in its finally block
when the original value was None. This removes the attribute entirely from
the module, causing subsequent tests in the same xdist worker to get
AttributeError when accessing litellm.default_internal_user_params (because
litellm.__getattr__ has no handler for this name).
Fix: always restore the attribute by assignment (litellm.default_internal_user_params = original_default_params)
rather than deleting it.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add routing prefixes bedrock/nova/<ARN> and bedrock/nova-2/<ARN> so
LiteLLM can identify the base model family for custom/imported Nova
models and enable the correct supported params (tools, web_search,
reasoning_effort).
Changes:
- Route nova/ and nova-2/ prefixed models to converse API
- Strip spec prefix before sending ARN to Bedrock
- Return sentinel base models (amazon.nova-custom, amazon.nova-2-custom)
so downstream Nova checks work
- Recognize nova-2/ prefix in _is_nova_2_model() for reasoning support
- Handle nova/nova-2 in get_bedrock_model_id() for proper ARN encoding
- Add unit tests for all new behavior
- Use custom_endpoint=False so Databricks SDK auth fallback works
(custom_endpoint=True was blocking it). The api_base returned by
databricks_validate_environment is discarded since get_complete_url
builds the URL separately.
- Remove unused verbose_logger import
- Remove unused json import in tests
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- labs-devstral-small-2512 supports devstral-small-latest
- devstral-2512 supports devstral-latest and devstral-medium-latest
The information is available in the models page (source field). Have
checked the prices and they match.
Other mistral models (codestral, magistral, ...) already have
the aliases in the database.
Final note: I've created #21328 to propose the creation of some
support for these aliasing cases. To have to dupe the entries is
prone to errors and hard to maintain, when there are model version
bumps.
* feat(schema.prisma): add object permissions for end users
allows controlling if end user can call specific mcp servers
* feat: cleanup for customer_endpoints support of object permission id
* fix: cleanup str
* feat(customers/): enforce end user can only call allowed mcps - if configured
* docs: document customer/end user object permission usage
* feat: address greptile comments
Databricks supports the Responses API natively for GPT models, but litellm
was falling back to the completion transformation handler which converts
responses requests to chat completion calls, losing response schema enforcement.
This adds DatabricksResponsesAPIConfig that passes responses API requests
directly to Databricks' /responses endpoint for GPT models, while non-GPT
models (Claude, Llama, etc.) continue using the completion transformation path.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Add MCP_SECURITY enum to SupportedGuardrailIntegrations
* Add MCP security guardrail initializer
* Add MCPSecurityGuardrail implementation
* Add MCP Security policy template
* Add Type filter to policy templates UI
* Add unit tests for MCP security guardrail
* fix(lint): remove unused Dict import from mcp_security_guardrail
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* Add French language support for EU AI Act Article 5 guardrail (#21427)
* Add French language support for EU AI Act Article 5 template
- Create eu_ai_act_article5_fr.yaml with comprehensive French keywords
- Includes identifier words: concevoir, créer, développer, noter, classer, etc.
- Includes block words: crédit social, comportement social, émotion des employés, etc.
- Includes always-block keywords for explicit prohibited practices
- Includes exceptions for research, compliance, and legitimate use cases
- Catches circumvention attempts with phrase variations
* Add comprehensive tests for French EU AI Act guardrail
- Test 3 critical scenarios: blocked query, circumvention attempt, safe query
- Test edge cases: case-insensitive, mixed language, research exceptions
- All 7 tests passing
- Validates both blocking and allowing behavior
* Fix content filter to support conditional matching without inherit_from
- Enable conditional matching when identifier_words + additional_block_words are present
- Previously required inherit_from, but EU AI Act templates are self-contained
- Fixes Greptile feedback: conditional matching now works as documented
* Add pure conditional matching test for French guardrail
- Test identifier + block word combinations not in always_block_keywords
- Verifies conditional matching works independently
- Addresses Greptile feedback about test coverage gap
* Fix exception word bypass risk in French template
- Replace short words (film, jeu, juste) with context-specific phrases
- Prevents substring matching bypasses (e.g., enjeu matching jeu)
- Add tests for bypass prevention and legitimate game context
- Addresses Greptile security feedback
* Make conditional match assertion more robust
- Use getattr to safely access exception detail field
- Check if detail is dict before calling .get()
- Addresses Greptile feedback about brittle string assertion
* Add French EU AI Act Article 5 policy template to registry
- Add eu-ai-act-article5-fr template for French language support
- Includes French description and guardrail info
- Matches structure of English template
* Address greptile review feedback (greploop iteration 1)
- Use status_code=400 instead of 403 to match guardrail logging convention
- Use prefix stripping instead of split('/')[-1] for robust server name extraction
* remove French EU AI Act template from policy_templates.json
---------
Co-authored-by: Julio Quinteros Pro <jquinter@gmail.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Break the monolithic EU AI Act Article 5 policy template into 5 focused
sub-guardrails, each covering a specific prohibited practice:
- Art. 5.1(a) — Subliminal Manipulation & Deceptive Techniques
- Art. 5.1(b) — Exploitation of Vulnerabilities (children, elderly, disabled)
- Art. 5.1(c) — Social Scoring Systems
- Art. 5.1(f) — Emotion Recognition in Workplace & Education
- Art. 5.1(d)(g)(h) — Biometric Categorization & Predictive Profiling
Each sub-guardrail has expanded keyword coverage specific to its domain.
Includes both English and French versions (10 total sub-guardrails).
Original monolithic YAML files preserved for backward compatibility.
Co-authored-by: Cursor <cursoragent@cursor.com>
