* Include user_email in new user creation within get_user_object
Enhance the get_user_object function to include user_email in the parameters when creating a new user. This change is accompanied by a new test to verify that user_email is correctly included during the upsert process.
* Improve error handling in test_get_user_object by logging exceptions
Updated the test_get_user_object_upsert_includes_user_email function to log exceptions when they occur, enhancing the visibility of potential issues during testing. This change helps in diagnosing failures related to the mock LiteLLM_UserTable.
* fix(passthrough): raise_for_status in _async_streaming to propagate Azure 429s
* address greptile review feedback (greploop iteration 1)
Guard data/json args when content is provided to avoid httpx ValueError
* address greptile review feedback (greploop iteration 2)
Use bare raise to preserve original traceback in _async_streaming exception handler
* address greptile review feedback (greploop iteration 3)
Close httpx streaming response on error to prevent connection pool exhaustion
* address greptile review feedback (greploop iteration 4)
Guard aclose() call to prevent masking original exception; add explicit test for content param forwarding
* address greptile review feedback (greploop iteration 5)
Pass content to sign_request so AWS body-hash signing is correct when content is the sole body source
* revert sign_request content change - request_data expects dict, not bytes
Bedrock's sign_request calls json.dumps(request_data) — passing content bytes
would TypeError. sign_request should only receive data/json (dict), not raw bytes.
- Update translate_thinking_for_model (3rd code path) to inject
summary="detailed" by default, consistent with the other two paths
- Add disable_default_reasoning_summary flag check via shared helper
- Add tests for flag enabled/disabled and user-provided summary
- Document disable_default_reasoning_summary in reasoning_content.md
- Extract duplicated summary_disabled evaluation from handler.py and
transformation.py into a shared is_default_reasoning_summary_disabled()
helper in utils.py to prevent future divergence.
- Add test_summary_excluded_when_env_var_set to handler test class to
close env-var test coverage gap flagged by Greptile.
Add `litellm.disable_default_reasoning_summary` flag (default False) and
env var `LITELLM_DISABLE_DEFAULT_REASONING_SUMMARY` to allow users to
opt out of the automatic `summary="detailed"` injection when routing
Anthropic thinking requests to OpenAI's Responses API.
Default behavior is preserved (summary="detailed" is always added),
but users who don't want to pay for summary tokens can now disable it.
https://claude.ai/code/session_01VJU9EwVvgvmeCe3Yu1aULa
For full-replace PUT semantics, always include static_headers and extra_headers
in update_data, defaulting to {} and [] when not supplied. Previously,
omitting these fields left stale DB values intact (e.g. auth headers).
Made-with: Cursor
gemini/gemini-live-2.5-flash-preview-native-audio-09-2025 uses mode='realtime'
but the schema in test_aaamodel_prices_and_context_window_json_is_valid did
not include 'realtime' as a valid enum value, causing a ValidationError.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Vertex AI does not support the output_config parameter in its API.
This parameter is being added by Anthropic/Gemini transformations but needs
to be removed before sending requests to Vertex AI endpoints.
This fix addresses the "Extra inputs are not permitted" error (issue #22312)
when using Claude models with structured outputs on Vertex AI.
Changes:
- Drop output_config in Gemini model transformation
- Drop output_config in Anthropic partner model transformation
- Drop output_config in Anthropic experimental pass-through transformation
- Add comprehensive tests to verify output_config is dropped
Fixes: #22312
Made-with: Cursor
Azure AI Foundry's Anthropic endpoint does not support the scope field in
cache_control. Strip it from both system and messages before sending.
Made-with: Cursor
Bedrock does not support the scope field in cache_control (e.g. 'global' for
cross-request caching). Only type and ttl are supported per AWS docs.
- Remove scope from cache_control in both system and messages
- Extend _remove_ttl_from_cache_control to process system blocks
- Add test for scope removal
Made-with: Cursor
* feat(mcp): BYOK (Bring Your Own Key) for OpenAPI MCP servers with OAuth 2.1 flow
Adds per-user credential storage for BYOK MCP servers so external clients
can authenticate via standard OAuth 2.1 PKCE without needing a full identity
provider.
Backend:
- New DB table LiteLLM_MCPUserCredentials (user_id, server_id, credential_b64)
- is_byok, byok_description, byok_api_key_help_url fields on MCPServerTable
- OAuth 2.1 authorization server endpoints (/.well-known/oauth-authorization-server,
/.well-known/oauth-protected-resource, /v1/mcp/oauth/authorize, /v1/mcp/oauth/token)
- 401 challenge with WWW-Authenticate header when BYOK server has no credential
- CRUD endpoints: POST/DELETE /v1/mcp/server/{id}/user-credential
- has_user_credential annotated on GET /v1/mcp/server response
UI:
- ByokCredentialModal: 2-step Connect flow (access description + API key entry)
- BYOK toggle + description fields on admin MCP server create form
- Connect/Connected state in MCP server table
- BYOK Demo page (/tools/byok-demo) showing full OAuth 2.1 PKCE flow
* feat(mcp/byok): redesign OAuth authorize page to match 2-step Connect mockup
- Step 1: L→S logos, requested access checklist, How it works box, Continue button
- Step 2: API key input, Save toggle, Duration pills (1h/24h/7d/30d/until_revoked), security note
- Matches screenshots: white modal on dark bg, progress dots, dark CTA buttons
- Authorize handler now fetches byok_description and byok_api_key_help_url from server registry
- CLAUDE.md: replace SQL snippet with proper DB migration troubleshooting guidance
* fix: address greptile review feedback (greploop iteration 1)
- XSS: escape all user-supplied values in _build_authorize_html() with html.escape()
- Open redirect: validate redirect_uri scheme and URL-encode code/state in redirect
- N+1 query: batch BYOK credential lookup into single find_many() call
- Critical path DB: add 60s TTL in-memory cache to _check_byok_credential()
- Encrypt BYOK credentials at rest using encrypt_value_helper/decrypt_value_helper
* fix(byok): update OAuth popup with LiteLLM logo, MCP title suffix, remove emojis
* fix(byok-demo): fix token endpoint URL (/v1/mcp/oauth/token not /v1/mcp/token)
* feat(byok): inject stored BYOK credential as mcp_auth_header on tool execution
* feat(byok): use contextvars to inject per-user credential into OpenAPI tool closures; remove byok-demo from LiteLLM UI
OpenAPI tools have auth headers baked into their closures at registration time. BYOK servers have
no static auth token, so per-user credentials were never reaching the HTTP calls.
Fix: add _request_auth_header ContextVar in openapi_to_mcp_generator.py. create_tool_function now
reads this var at call time and overrides the Authorization header if set. execute_mcp_tool resolves
the MCP server and performs BYOK checks before the local-tool dispatch branch, then sets the
ContextVar around _handle_local_mcp_tool so the credential flows into the HTTP request.
Also remove the /tools/byok-demo page from the LiteLLM UI dashboard — the demo lives at
~/Downloads/litellm-byok-demo/index.html (served separately on port 8080).
* fix: address greptile review feedback (greploop iteration 2)
- Cache invalidation: add _invalidate_byok_cred_cache() and call it after
store_user_credential() in both token endpoint and management endpoint
- Unbounded cache: add _BYOK_CRED_CACHE_MAX_SIZE=4096 with clear-on-overflow
- Unbounded auth codes: add _AUTH_CODES_MAX_SIZE=1000 with 503 on overflow
- Double DB query: merge _check_byok_credential + _get_byok_credential into
single _get_byok_credential call; raise 401 inline if None returned
- Sidebar: remove byok-demo entry (page was deleted in prior commit)
- JWT comment: document why byok_session HS256 token can't be used as proxy auth
* fix: address greptile review feedback (greploop iteration 3)
- auth_type: pre-format Authorization header (Bearer/ApiKey/Basic) in server.py
before setting ContextVar so openapi_to_mcp_generator respects server auth_type
- cache invalidation on delete: call _invalidate_byok_cred_cache after
delete_user_credential so stale True entries don't persist for 60s
- ContextVar guard: only set _request_auth_header when mcp_auth_header is set,
avoiding unnecessary ContextVar overhead on non-BYOK tool calls
* fix: address greptile review feedback (greploop iteration 4)
- Unified credential cache: store actual credential value (Optional[str])
instead of just bool so _get_byok_credential also benefits from caching —
eliminates the DB hit on every BYOK tool call within the 60s TTL window
- Extracted _write_byok_cred_cache() helper for consistent cache writes
- Replaced has_user_credential with get_user_credential in _check_byok_credential
so one DB call satisfies both existence check and value retrieval
- Remove false 'encrypted at rest' claim from OAuth HTML and ByokCredentialModal
* Update tests/test_litellm/proxy/_experimental/mcp_server/test_byok_oauth_endpoints.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* Update tests/test_litellm/proxy/_experimental/mcp_server/test_byok_oauth_endpoints.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
---------
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Add proxy-admin-configurable toggles to restrict internal users (and optionally
team admins) from accessing agent and vector store management features.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(gemini): ensure image token accumulation in usage metadata
Fixed an issue where image tokens were being overwritten instead of accumulated in Gemini responses. Added support for both camelCase and snake_case token count keys. Fixes#22082.
* test: add regression test for image token accumulation and cleanup files
* fix(gemini): ensure consistent accumulation for responseTokensDetails
* fix(gemini): harden token count parsing and add vertex accumulation test
Parse tokenCount/token_count as int-safe values to satisfy mypy and avoid None/object arithmetic. Add regression test for duplicate modality accumulation in Vertex _calculate_usage.
* fix: complexity_router fails on list-format message content (OpenAI multi-part messages)
When a client sends messages with list-format content
(e.g. [{"type": "text", "text": "..."}] as used by the OpenAI JS SDK
and other clients), the complexity_router's async_pre_routing_hook
skipped those messages because it only handled str content. This caused
user_message to be None, the hook returned None, and the router fell
through to selecting the complexity_router deployment itself
(model="auto_router/complexity_router") which litellm cannot dispatch,
resulting in LiteLLMUnknownProvider.
Fixes:
- Extract text from list-format content parts (type=text) before
classifying
- Return default_model instead of None when no user message can be
extracted, preventing the crash fallthrough
- Loosen PreRoutingHookResponse.messages type from Dict[str, str] to
Dict[str, Any] to accommodate list-format content values
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix: update messages type annotation in async_pre_routing_hook to Dict[str, Any]
Consistent with PreRoutingHookResponse.messages type change and the
list-format content support added in the previous commit.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix: normalize None content to empty string in complexity_router message parsing
msg.get("content", "") returns None when the key exists with value None
(e.g. assistant messages with tool calls). Use `or ""` to normalize
None to an empty string explicitly.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix: strip whitespace from joined list content parts in complexity_router
Prevents leading/trailing spaces when some content parts have empty
text values (e.g. " ".join(["", "hello"]) → " hello").
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
* (sap) ensure tool parameters have type='object' for SAP compatibility
Fix SAP GenAI Hub Orchestration Service rejecting tool calls with error:
"400 - LLM Module: tools.0.custom.input_schema.type: Input should be 'object'"
Root cause: When Claude Code uses tools (like web_search) with the SAP provider
through LiteLLM's Anthropic experimental pass-through adapter, Anthropic's
input_schema format doesn't always include the required type="object" field.
The adapter's translate_anthropic_tools_to_openai() function was directly
copying input_schema to OpenAI's parameters field without ensuring the
type="object" requirement that SAP's API strictly enforces.
Changes:
- Modified translate_anthropic_tools_to_openai() to check if input_schema
is missing the type field and add type="object" if absent
- Preserves existing type field if already present
- Added comprehensive test suite (6 tests) covering:
- Missing type field scenario (now adds type="object")
- Existing type preservation
- Empty input_schema handling
- Multiple tools transformation
- Additional schema properties preservation
- SAP-specific compatibility regression test
Testing:
- All new tests pass (6/6 in test_anthropic_tool_schema_fix.py)
- All existing Anthropic tool tests pass (57/57 tool-related tests)
- SAP tool parameter validation tests pass (9/9 in test_sap_tool_parameters.py)
* (sap) enable native response_format for anthropic models
* (sap) filter strict param from model_params for GPT models only
* (sap) revert Anthropic adapter type='object' fix
The SAP FunctionTool Pydantic validator in litellm/llms/sap/chat/models.py
already ensures type='object' is added to all tool parameters for SAP
API compatibility.
The Anthropic adapter change affected ALL consumers, not just SAP, which
was broader scope than intended for this PR.
- Revert input_schema modification in Anthropic adapter
- Remove Anthropic-specific test file (SAP tests still cover this case)
* (sap) gate markdown stripping to Anthropic models only
SAP GenAI Hub with Anthropic models sometimes returns JSON wrapped in
markdown code blocks. GPT/Gemini/Mistral models don't exhibit this
behavior, so stripping is now gated to avoid accidentally modifying
valid responses that may contain markdown in JSON string values.
The user-specified async client was being overwritten by
`litellm.module_level_aclient` in `streaming_handler.py` when using
async+streaming with Gemini.
This fix adds a `gemini_client` parameter to `make_call()` (matching
the existing pattern in `make_sync_call()`) so the user's custom client
is preserved and not overwritten.
Fixes#17148
- Log provider token counting failures instead of silently swallowing
- Fall back to local tokenizer when provider returns error response
- Map assistant tool_calls to Responses API function_call items
- Concatenate multiple system messages instead of overwriting
- Hide internal error details from proxy API responses
- Narrow exception catch in handler to network/JSON errors only
- Update test to match new fallback behavior
- Add OpenAITokenCounter using POST /v1/responses/input_tokens endpoint
- Add litellm.acount_tokens() public async API that auto-routes to provider APIs
- Add proxy endpoint POST /v1/responses/input_tokens for OpenAI-compatible counting
- Transform chat tools format to Responses API format for correct token counting
- Fall back to local tiktoken when provider API unavailable
Fixes#22302
- Remove token field from JWTKeyMappingResponse to prevent hashed key exposure
- Use _to_response() helper on all CRUD endpoints to control returned fields
- Return 409 for unique constraint violations, 400 for FK violations, 404 for not found
- Add response_model to endpoint decorators
- Add 8 new unit tests covering error handling and token redaction
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Kontext models (flux-kontext-pro, flux-kontext-max) support both
text-to-image and image editing. Add them to IMAGE_GENERATION_MODELS
and update supported_endpoints in model prices JSON.
- Create handler.py for image generation and image edit
- Move polling logic from transformation to handlers
- Handlers use _get_httpx_client() / get_async_httpx_client()
- Transformation files now only transform request/response data
- Follows Bedrock pattern for provider-specific handlers
Addresses feedback: transformation files should not make HTTP requests
Add native text-to-image generation for Black Forest Labs Flux models
(flux-pro-1.1, flux-pro-1.1-ultra, flux-dev, flux-pro).
- Polling-based async API with sync and async support
- OpenAI-compatible parameter mapping (size, n, quality)
- Reuses shared HTTP clients via _get_httpx_client()
- 39 unit tests added
Replace direct httpx.get() calls with _get_httpx_client() to reuse
cached HTTP client, following the pattern used by other providers
(RunwayML, Azure AI OCR, Sagemaker, etc.).
Add native integration for Black Forest Labs image editing models
(flux-kontext-pro, flux-kontext-max, flux-pro-1.0-fill, flux-pro-1.0-expand).
Changes:
- Add BlackForestLabsImageEditConfig for BFL API transformation
- Add BLACK_FOREST_LABS to LlmProviders enum
- Add use_multipart_form_data() to BaseImageEditConfig for JSON vs form-data
- Modify image_edit_handler to support JSON request bodies
- Add comprehensive unit tests
Closes#11401
