Add 50 tests across 3 files covering the new MaskedHTTPStatusError,
safe response helpers, _redact_string in error paths, Gemini
interactions x-goog-api-key header auth, and RAG ingestion header
usage.
Fix missing early-validation for Gemini API key in _get_token_and_url()
which caused TypeError when key was None (headers got None value).
Harmonize error messages between the two validation sites.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Gemini API keys embedded in URLs as ?key= query parameters leak through
httpx error tracebacks, which are then captured by traceback.format_exc()
and forwarded to logging callbacks, Slack/Teams alerts, and HTTP client
responses.
Short-term: all httpx.HTTPStatusError handlers now raise
MaskedHTTPStatusError(...) from None, which masks the URL and breaks
exception chaining so the original error never appears in tracebacks.
Long-term: moved all Gemini/Vertex URL constructions from ?key={api_key}
to x-goog-api-key header (Google's documented auth method), so the key
is never in the URL at all. WebSocket realtime is the only exception
since WS clients cannot use custom headers.
Additionally hardened all outbound credential paths:
- WebSocket close reasons now pass through _redact_string()
- Callback pipeline (failure_handler) redacts traceback_exception and
error_str before forwarding to integrations (Langfuse, Datadog, etc.)
- Slack/Teams alert messages redacted in send_llm_exception_alert,
ProxyLogging.failure_handler, and post_call_failure_hook
- HTTP error responses in proxy SSE and health endpoints redacted
- Exception messages in exception_mapping_utils redacted
- print_verbose() stdout output redacted when set_verbose=True
- HTTPHandler.put() now has MaskedHTTPStatusError (was missing)
Vertex AI rejects requests containing both search tools (googleSearch,
enterpriseWebSearch, urlContext) and function declarations with error:
'Multiple tools are supported only when they are all search tools.'
When _merge_tools_from_deployment() combines deployment-level search
tools with user-request function tools (e.g. via MCP), the mixed tool
list causes a 400 error. This fix detects the conflict in _map_function()
and drops search tools, keeping function declarations.
Non-search tools like code_execution and computerUse are preserved.
Fixes#23337
* fix: emit input_json_delta for tool args bundled in first streaming chunk
Some providers (xAI, Gemini) include tool_call function arguments in the
same streaming chunk as the function name/id. The AnthropicStreamWrapper
was discarding the trigger chunk entirely when starting a new content
block, which silently dropped the input_json_delta carrying tool
arguments. This caused tool_use blocks to arrive with empty input {}.
Now queue the processed_chunk after content_block_start when it carries
non-empty input_json_delta data. Backward compatible: providers that send
empty arguments in the first chunk (OpenAI-style) are unaffected since
the condition checks for truthy partial_json.
* test: add tests for input_json_delta emission on bundled tool args
Covers the fix for providers (xAI, Gemini) that bundle tool_call
arguments in the same streaming chunk as the function name/id.
Verifies the AnthropicStreamWrapper emits input_json_delta after
content_block_start, and that empty-arg chunks (OpenAI-style) are
unaffected.
* style: apply Black formatting to streaming_iterator.py
* fix: mirror input_json_delta fix to sync __next__ and add sync tests
* test: make no_extra_delta tests assert explicitly instead of passing silently
- Omit messages whose list content is empty after stripping thinking blocks
- Retry only on HTTP 400 plus invalid-signature body match
- Return response inline from retry loop; drop unreachable None guard
- Tests: thinking-only turn dropped, non-400 no retry
Made-with: Cursor
Strip thinking blocks from the request body and retry once when Anthropic returns an invalid thinking signature error (e.g. after credential or deployment change). Applies to all BaseAnthropicMessagesConfig providers (direct Anthropic, Bedrock, Vertex, Azure AI).
Made-with: Cursor
- Add automatic conversion of Vertex AI batch prediction JSONL to OpenAI format
- Preserve custom_id via Vertex AI labels for request correlation
- Fix Content-Length header mismatch in transformed responses
- Add comprehensive tests for batch output transformation
Made-with: Cursor
When modify_params is true, Bedrock Converse setup no longer prepends or
appends the default user message if the boundary assistant turn has
prefix: true, so OpenAI-style assistant prefill reaches the API unchanged.
Made-with: Cursor
- Static Methods for Streaming Handler Function
- Remove the afile_content_streaming wrapper function. Enabled with a stream boolean in afile_content
- Cleaned up test cases after refactor
- Removed unused imports and streamlined type hints in `litellm/utils.py` and `litellm/files/main.py`.
- Moved `FileContentStreamingResult` to a new `litellm/files/types.py` for better organization.
- Updated `FileContentStreamingResponse` in `litellm/files/streaming.py` to include asynchronous close methods and improved logging capabilities.
- Enhanced tests to ensure proper closure of streaming iterators in `tests/test_litellm/llms/openai/test_openai_file_content_streaming.py` and `tests/test_litellm/proxy/openai_files_endpoint/test_files_endpoint.py`.
- Introduced `afile_content_streaming` and `file_content_streaming` functions in `litellm/files/main.py` to handle asynchronous and synchronous file content streaming.
- Added `FileContentStreamingResponse` class in `litellm/files/streaming.py` to manage streaming responses with logging capabilities.
- Updated OpenAI API integration in `litellm/llms/openai/openai.py` to support new streaming methods.
- Enhanced file content retrieval in `litellm/proxy/openai_files_endpoints/files_endpoints.py` to route requests for streaming.
- Added unit tests for the new streaming functionality in `tests/test_litellm/llms/openai/test_openai_file_content_streaming.py` and `tests/test_litellm/proxy/openai_files_endpoint/test_files_endpoint.py`.
- Refactored type hints and imports for better clarity and organization across modified files.
* build: migrate packaging metadata to uv
* ci: move automation and local tooling to uv
* docker: migrate image builds and runtime setup to uv
* docs: update install and deployment guidance for uv
* chore: align auxiliary scripts and tests with uv
* test: harden test_litellm isolation
* fix: keep release and health check images self-contained
* build: pin uv tooling and health check deps
* test: isolate bedrock image request formatting from suite state
* test: cover sandbox executor requirements flow
* ci: fix circleci no-op command steps
* ci: fix circleci publish workflow parsing
* fix: stabilize remaining uv migration CI checks
* ci: increase matrix test timeout headroom
* fix: restore published docker and license coverage
* fix: restore proxy runtime build parity
* fix: restore proxy extras parity and venv migrations
* ci: persist uv path across circleci steps
* fix: keep psycopg binary in default test env
* docker: preserve prisma cache across stages
* test: run local proxy checks through uv python
* build: restore runtime deps moved into ci
* build: refresh uv lock after upstream merge
* fix: restore module import in test_check_migration after merge
The conflict resolution imported only the function but the test body
references check_migration as a module throughout.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix: revert dependency promotions, remove nodejs-wheel-binaries, fix Docker layer caching
- Move google-generativeai, Pillow, tenacity back to ci group (they are
lazily imported and bloat the base SDK install needlessly)
- Remove nodejs-wheel-binaries from extra_proxy and proxy-dev (redundant
in Docker where system Node.js is already installed via apk)
- Remove all nodejs-wheel node replacement and venv npm patching blocks
from Dockerfiles since the wheel is no longer installed
- Add --no-default-groups to CodSpeed benchmark workflow so the benchmark
environment matches the old minimal pip install footprint
- Apply standard uv two-phase Docker pattern: copy metadata first, install
deps (cached layer), then copy source and install project
- Replace CircleCI enterprise no-op with proper uv sync command
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* chore: regenerate uv.lock after removing nodejs-wheel-binaries
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix(ci): use cache/restore instead of cache to prevent cache poisoning
The old workflow used actions/cache/restore (read-only). The uv migration
changed it to actions/cache (read-write), which zizmor flags as a cache
poisoning risk. Restore the safer read-only variant.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix(ci): disable setup-uv built-in cache to silence cache-poisoning alert
The setup-uv action enables caching by default, which zizmor flags as a
cache poisoning risk. Disable it since we already use a read-only
cache/restore step.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix(ci): disable setup-uv cache in publish workflow
Silences zizmor cache-poisoning alert. Publishing workflow runs
infrequently on protected branches so caching adds no real benefit.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix(test): remove duplicate verbose_logger mock in test_check_migration
The logger was patched twice — first via mocker.patch() then via
mocker.patch.object(autospec=True). The second call fails because
autospec cannot inspect an already-mocked attribute. Remove the
redundant first patch.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix(ci): free disk space before Docker build in test-server-root-path
The Dockerfile.non_root build ran out of disk on the CI runner. Remove
Android SDK, .NET, Boost, and GHC toolchains (~12GB) to free space.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Normalize JSON Schema type custom to object for Bedrock invoke and
_bedrock_tools_pt, ensure stable names for tools without name, and
avoid KeyError in the Anthropic messages adapter when translating
tools to OpenAI format for bedrock/converse.
Made-with: Cursor
* fix(vertex_ai): support pluggable (executable) credential_source for WIF auth (#24700)
The WIF credential dispatch in load_auth() only handled identity_pool and
aws credential types. When credential_source.executable was present (used
for Azure Managed Identity via Workload Identity Federation), it fell
through to identity_pool.Credentials which rejected it with MalformedError.
Add dispatch to google.auth.pluggable.Credentials for executable-type
credential sources, following the same pattern as the existing identity_pool
and aws helpers.
Fixes authentication for Azure Container Apps → GCP Vertex AI via WIF
with executable credential sources.
* feat(logging): add component and logger fields to JSON logs for 3rd p… (#24447)
* feat(logging): add component and logger fields to JSON logs for 3rd party filtering
* Let user-supplied extra fields win over auto-generated component/logger, tighten test assertions
* Feat - Add organization into the metrics metadata for org_id & org_alias (#24440)
* Add org_id and org_alias label names to Prometheus metric definitions
* Add user_api_key_org_alias to StandardLoggingUserAPIKeyMetadata
* Populate user_api_key_org_alias in pre-call metadata
* Pass org_id and org_alias into per-request Prometheus metric labels
* Add test for org labels on per-request Prometheus metrics
* chore: resolve test mockdata
* Address review: populate org_alias from DB view, add feature flag, use .get() for org metadata
* Add org labels to failure path and verify flag behavior in test
* Fix test: build flag-off enum_values without org fields
* Gate org labels behind feature flag in get_labels() instead of static metric lists
* Scope org label injection to metrics that carry team context, remove orphaned budget label defs, add test teardown
* Use explicit metric allowlist for org label injection instead of team heuristic
* Fix duplicate org label guard, move _org_label_metrics to class constant
* Reset custom_prometheus_metadata_labels after duplicate label assertion
* fix: emit org labels by default, remove flag, fix missing org_alias in all metadata paths
* fix: emit org labels by default, no opt-in flag required
* fix: write org_alias to metadata unconditionally in proxy_server.py
* fix: 429s from batch creation being converted to 500 (#24703)
* add us gov models (#24660)
* add us gov models
* added max tokens
* Litellm dev 04 02 2026 p1 (#25052)
* fix: replace hardcoded url
* fix: Anthropic web search cost not tracked for Chat Completions
The ModelResponse branch in response_object_includes_web_search_call()
only checked url_citation annotations and prompt_tokens_details, missing
Anthropic's server_tool_use.web_search_requests field. This caused
_handle_web_search_cost() to never fire for Anthropic Claude models.
Also routes vertex_ai/claude-* models to the Anthropic cost calculator
instead of the Gemini one, since Claude on Vertex uses the same
server_tool_use billing structure as the direct Anthropic API.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* fix(anthropic): pass logging_obj to client.post for litellm_overhead_time_ms (#24071)
When LITELLM_DETAILED_TIMING=true, litellm_overhead_time_ms was null for
Anthropic because the handler did not pass logging_obj to client.post(),
so track_llm_api_timing could not set llm_api_duration_ms. Pass
logging_obj=logging_obj at all four post() call sites (make_call,
make_sync_call, acompletion, completion). Add test to ensure make_call
passes logging_obj to client.post.
Made-with: Cursor
* sap - add additional parameters for grounding
- additional parameter for grounding added for the sap provider
* sap - fix models
* (sap) add filtering, masking, translation SAP GEN AI Hub modules
* (sap) add tests and docs for new SAP modules
* (sap) add support of multiple modules config
* (sap) code refactoring
* (sap) rename file
* test(): add safeguard tests
* (sap) update tests
* (sap) update docs, solve merge conflict in transformation.py
* (sap) linter fix
* (sap) Align embedding request transformation with current API
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) mock commit
* (sap) run black formater
* (sap) add literals to models, add negative tests, fix test for tool transformation
* (sap) fix formating
* (sap) fix models
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) commit for rerun bot review
* (sap) minor improve
* (sap) fix after bot review
* (sap) lint fix
* docs(sap): update documentation
* fix(sap): change creds priority
* fix(sap): change creds priority
* fix(sap): fix sap creds unit test
* fix(sap): linter fix
* fix(sap): linter fix
* linter fix
* (sap) update logic of fetching creds, add additional tests
* (sap) clean up code
* (sap) fix after review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) add a possibility to put the service key by both variants
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) update test
* (sap) update service key resolve function
* (sap) run black formater
* (sap) fix validate credentials, add negative tests for credential fetching
* (sap) fix validate credentials, add negative tests for credential fetching
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) lint fix
* (sap) lint fix
* feat: support service_tier in gemini
* chore: add a service_tier field mapping from openai to gemini
* fix: use x-gemini-service-tier header in response
* docs: add service_tier to gemini docs
* chore: add defaut/standard mapping, and some tests
* chore: tidying up some case insensitivity
* chore: remove unnecessary guard
* fix: remove redundant test file
* fix: handle 'auto' case-insensitively
* fix: return service_tier on final steamed chunk
* chore: black
* feat: enable supports_service_tier to gemini models
* Fix get_standard_logging_metadata tests
* Fix test_get_model_info_bedrock_models
* Fix test_get_model_info_bedrock_models
* Fix remaining tests
* Fix mypy issues
* Fix tests
* Fix merge conflicts
* Fix code qa
* Fix code qa
* Fix code qa
* Fix greptile review
---------
Co-authored-by: michelligabriele <gabriele.michelli@icloud.com>
Co-authored-by: Josh <36064836+J-Byron@users.noreply.github.com>
Co-authored-by: mubashir1osmani <mubashir.osmani777@gmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: milan-berri <milan@berri.ai>
Co-authored-by: Alperen Kömürcü <alperen.koemuercue@sap.com>
Co-authored-by: Vasilisa Parshikova <vasilisa.parshikova@sap.com>
Co-authored-by: Lin Xu <lin.xu03@sap.com>
Co-authored-by: Mark McDonald <macd@google.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>
DashScope inherits OpenAIGPTConfig which strips cache_control from
messages and tools by default. Override remove_cache_control_flag_from_messages_and_tools()
to preserve cache_control, following the same pattern used by ZAI, MiniMax, and Databricks.
Verified through 10-round multi-turn conversation tests:
- Explicit caching works correctly: cached_tokens grows each round from R4 onwards,
with cache_creation_tokens reported on first cache build.
- Implicit caching is not affected: models that rely on implicit prefix-matching caching
produce identical cached_tokens with and without this change, confirmed by comparing
results against both the reverted codebase and direct API calls bypassing litellm.
- No errors or regressions observed on any model, including those that do not support
explicit caching — the DashScope API silently ignores unrecognized cache_control fields.
Fixes#25330
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Anthropic/Claude Code use input_schema.type "custom"; Bedrock rejects it.
- Add normalize_json_schema_custom_types_to_object and use it for Invoke,
chat invoke, and _bedrock_tools_pt (Anthropic input_schema + OpenAI params).
- Coerce invalid root types to object for Converse toolSpec.
- Tests for invoke transform, converse _bedrock_tools_pt, and unit helper.
Made-with: Cursor
* fix(bedrock): strip [1m]/[200k] context window suffixes before cost lookup
* test(bedrock): add test for [1m] context window suffix stripping in cost lookup
* schema: add allowed_models to BudgetTable, default_team_member_models to TeamTable
* migration: add allowed_models and default_team_member_models columns
* types: add allowed_models to TeamMemberAddRequest, TeamMemberUpdateRequest, UpdateTeamRequest
* utils: add allowed_models param to add_new_member, persist to budget table
* common_utils: add allowed_models to _upsert_budget_and_membership
* team endpoints: seed allowed_models on member_add, persist on member_update and team/update
* auth: enforce per-member allowed_models at request time
* networking: add allowed_models to Member type and teamMemberUpdateCall
* TeamMemberTab: add Model Scope column showing per-member allowed_models
* EditMembership: add Allowed Models multi-select field
* TeamInfo: add default_team_member_models field in Settings tab
* chore: sync schema.prisma copies from root
* fix(team_member_update): update existing budget in-place instead of creating new one
When a member already has a budget_id, patch only the fields the caller
provided rather than always creating a fresh budget record. The old
code ignored existing_budget_id entirely, so updating only allowed_models
silently dropped the stored max_budget / tpm_limit / rpm_limit values.
* fix(auth): pass llm_router to _check_team_member_model_access
Without the router, _can_object_call_model cannot resolve wildcard model
names (e.g. openai/*) or access-group names in allowed_models, causing
legitimate requests to be denied. Thread the existing llm_router from
_run_common_checks through to the new member-scope check.
* feat(ui): add Team Member Settings accordion to Create Team modal
Groups default_team_member_models, member budget/key duration, and
tpm/rpm defaults into a single collapsible section. The model picker
is filtered to only show the models selected for the team, and the
copy distinguishes it from the team-level Models field.
* feat(ui): consolidate Team Member Settings into accordion in edit team form
Moves default_team_member_models + per-member budget/key/tpm/rpm fields
into a collapsible "Team Member Settings" panel. Keeps the top-level
form focused on team-wide settings (team models, team budget, tpm/rpm).
* fix(ui): use tremor Accordion for Team Member Settings in edit team form
* fix(ui): move Team Member Settings accordion above budget fields in Create Team
* chore: fixes
---------
Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: Yuneng Jiang <yuneng@berri.ai>
