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(router): discard oldest entry when trimming latency list in lowest_latency strategy
The lowest_latency routing strategy keeps a rolling window of the most
recent latency and time-to-first-token measurements per deployment. When
the window is full, the strategy was discarding the *newest* value
instead of the oldest, because the trim used
`[: max_latency_list_size - 1]` (keeping indices 0..N-2) rather than
`[1:]` (dropping index 0 and keeping indices 1..N-1).
Since new values are appended at the end, the bug meant the most recent
measurement was always dropped once the list reached capacity. The
routing decisions then relied on stale data (including any early-spike
values that never aged out), and timeout penalties written via
`async_log_failure_event` were silently discarded as well.
Fix the slice in all five call sites (sync + async log_success_event for
both latency and time_to_first_token, and async_log_failure_event for
the timeout penalty) and add regression tests covering each path.
* test(router): cover async TTFT trim path in lowest_latency regression tests
Adds test_ttft_list_trimming_discards_oldest_entry_async, an async
counterpart to test_ttft_list_trimming_discards_oldest_entry that drives
async_log_success_event with a ModelResponse and completion_start_time so
the async time_to_first_token trim branch is actually exercised.
Previously no test touched that code path: the sync TTFT test used
log_success_event, and the async latency test passed a plain dict
response_obj without stream/completion_start_time, so TTFT was never
computed and the async trim was unreached. Verified load-bearing by
reverting only the async TTFT slice — the new test fails and all others
pass.
* format
* fix#25506
* address greptile review feedback
* [Test] UI - Models: Add E2E tests for Add Model flow
Add E2E tests covering:
- Test connection with bad credentials shows failure modal
- Adding a specific model and verifying it appears in All Models table
- Adding a wildcard route and verifying it appears in All Models table
- Verifying model dropdown shows provider-specific models (existing test updated)
Added data-testid attributes to UI components to support stable test selectors.
Tests verified passing 3/3 consecutive runs with zero flakiness.
* address greptile review feedback (greploop iteration 1)
Add cleanup helper to delete models created during tests, preventing
stale data accumulation across repeated test runs.
* fix CI: replace data-testid selectors with text/role-based selectors
The data-testid attributes added to React components are not present
in the CI-built UI output. Switch to using getByRole and getByText
selectors which work with the rendered DOM regardless of build cache.
* remove unnecessary cleanup helper
The database is freshly seeded on every test run via seed.sql,
so per-test cleanup is not needed.
---------
Co-authored-by: Yuneng Jiang <yuneng@berri.ai>
Co-authored-by: Krrish Dholakia <krrish+github@berri.ai>
* 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
### Background
The Gemini batchEmbedContents response handler hardcoded `index=0` for
every embedding in the response. Any consumer relying on the OpenAI-format
`index` field to match embeddings back to inputs would silently get wrong
associations.
### Changes
Use `enumerate` in `process_response` so each embedding gets its
positional index instead of 0.
### Test Plan
Added unit test asserting sequential indices and correct vector ordering
for a 3-element batch response.
Cast message lists to the expected `List[Union[AllMessageValues, Message]]`
type at `token_counter` call sites, and suppress the `no-redef` warning for
the `compress` import in `__init__.py` caused by the wildcard `main` import.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Boolean fields in the auto-generated guardrail provider form (e.g. Noma
`use_v2`) rendered as empty Selects because the Form.Item only populated
`initialValue` for percentage fields, and the `defaultValue` passed to the
Select child was silently dropped by antd's controlled-component wrapper.
Users could not tell what the backend default was, and the visual ambiguity
made flags like `use_v2` look inoperative even though the save path worked.
Unify `initialValue` to fall back through `fieldValue → field.default_value →
(percentage ? 0.5 : undefined)`, and switch Select.Option values from
"true"/"false" strings to real booleans so the backend default flows through
without stringification.
Unit Tests: Proxy DB Operations / proxy-db (auth-checks, tests/proxy_unit_tests/test_auth_checks.py tests/proxy_unit_tests/test_user_api_key_auth.py, 20, 8) (push) Has been cancelled
Unit Tests: Proxy DB Operations / proxy-db (remaining, tests/proxy_unit_tests --ignore=tests/proxy_unit_tests/test_key_generate_prisma.py --ignore=tests/proxy_unit_tests/test_auth_checks.py --ignore=tests/proxy_unit_tests/test_user_api_key_auth.py, 20, 8) (push) Has been cancelled
Extend existing test modules with coverage for the instructions merge
logic, upstream cache, ContextVar-based injection, and client-side
capture — following each file's established patterns.
Made-with: Cursor
Remove the gateway-specific initialize fetch path and reuse instructions captured during existing MCP calls (list_tools/health_check/call_tool), while keeping YAML/DB instructions as immediate overrides.
Made-with: Cursor
Block cross-team key update/regenerate operations by raising when the caller is not a member of the target key's team, and add unit coverage for deny/allow team membership paths.
Made-with: Cursor
- drop unused Any annotation on register_extra_ui_setting's field
param; type it as FieldInfo and have enterprise callers construct
FieldInfo directly (pydantic.Field's stub reports the default's
type, which doesn't match FieldInfo)
- cache the effective UISettings class and invalidate it inside
register_extra_ui_setting so GET /get/ui_settings does not rebuild
a pydantic model on every request
- annotate _EXTRA_UI_SETTINGS_FIELDS with a concrete
Dict[str, Tuple[Any, FieldInfo]] instead of bare Dict[str, tuple];
the annotation remains Any because pydantic field annotations
include generics (Optional[X], List[X]) that are not instances of
type
- workflow proxy-config matrix: drop test_project*.py glob now that the
test lives under tests/enterprise/
- update uv.lock to match bumped litellm version
- fix mypy: loosen FieldInfo annotation on register_extra_ui_setting
(pydantic.Field stubs report the default's type) and silence
create_model overload resolution when passing **tuple_dict
- fix inline imports in moved test_project_endpoints_prisma.py to
target litellm_enterprise.proxy.management_endpoints.project_endpoints
Remove the /project/* management endpoints and the enable_projects_ui
admin-settings flag from the OSS litellm package. Project endpoints now
live under litellm_enterprise and are wired through the existing
enterprise router; OSS builds return 404 for every /project/* route.
The enable_projects_ui UI flag is registered back onto UISettings via a
small extension registry when the enterprise package is imported, so the
admin toggle and downstream key/sidebar gating continue to work in
enterprise builds. On OSS, explicit PATCH attempts with the flag return
403 with a clear enterprise-only message instead of being silently
dropped.
Pydantic request/response types (NewProjectRequest, UpdateProjectRequest,
DeleteProjectRequest, NewProjectResponse) stay in litellm/proxy/_types.py
because management_endpoints/common_utils.py and pydantic-shape tests
import them. LiteLLM_ProjectTable and all FK columns in schema.prisma
are unchanged.
- 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
