If the user specified in the configuration e.g. "user_header_name:
X-OpenWebUI-User-Email", here we were looking for a dict key
"X-OpenWebUI-User-Email" when the dict actually contained
"x-openwebui-user-email".
Switch to iteration and case insensitive string comparison instead to
fix this.
This fixes customer budget enforcement when the customer ID is passed
in as a header rather than as a "user" value in the body.
- Adjusted input and output cost per token for existing models.
- Added new model configuration for "openrouter/qwen/qwen3-coder" with specified token limits and costs.
* EditAutoRouterTabProps
* Revert "EditAutoRouterTabProps"
This reverts commit 2835d3a3743e6411b9914a0b01381050e2273ad7.
* add EditAutoRouterTab
* delete edit
* fixes for edit auto-router
* fix accessing model edit
* working edit auto router
* fix - edit remove custom model name
* fixes for edit auto router settings
* qa for adding a model router
* test fix
* feat(key_management_endpoints.py): Support new 'key_type' field
allow user to specify if key should be 'management' or 'llm api' key
Security fix
* test(test_route_checks.py): add unit tests
* fix(create_key_button.tsx): add ui component to select key type
allows specifying if key can call llm api vs. management routes
* feat(create_key_button.tsx): add specifying key type to ui
* fix(route_checks.py): add sensitive data masker for user id on not allowed error message
prevent leaking sensitive information
* fix(prometheus.py): sanitize tag-based labels to handle colons (:) and spaces ( )
* fix(prometheus.py): working tag based metrics
* fix(prometheus.py): emit request tags on post call success hook
* fix(prometheus.py): add user agent tags on request failure
* fix(prometheus.py): add request tags to deployment failure metric
s
* fix(proxy_server.py): update swagger-ui-bundle.js + bring back swagger in airgapped environments
* fix(proxy_server.py): support local swagger on custom root path
enables on prem usage of litellm
* feat: Add Pillar Security guardrail integration
Implements comprehensive LLM security guardrails using Pillar Security API with support for prompt injection detection, PII/secret detection, content moderation, and multi-mode execution (pre_call, during_call, post_call). Includes complete documentation, testing, and configurable actions on flagged content.
* fix: Resolve MyPy type error in Pillar guardrail config
Restructure PillarGuardrailConfigModel to properly inherit from GuardrailConfigModel[T]
and resolve return type compatibility issue in get_config_model method.
* fix: Resolve MyPy type error in Pillar guardrail config
Restructure PillarGuardrailConfigModel to properly inherit from GuardrailConfigModel[T]
and resolve return type compatibility issue in get_config_model method.
* fix docs
* fix docs
* improved docs
* fix examples, READY
* feat(litellm_pre_call_utils.py): add num_retries to litellm data for backend call
allow user to pass in num retries via request headers
* test(test_litellm_pre_call_utils.py): add unit test
* docs(request_headers.md): document new request header
* fix(common_daily_activity.py): show spend breakdown by model group
Partial fix for https://github.com/BerriAI/litellm/issues/12887
* feat(new_usage.tsx): new tab switcher for viewing usage by model group vs. received model
Closes https://github.com/BerriAI/litellm/issues/12887
