* Add Rubrik as officially-supported guardrail plugin
Adds tool blocking and batch logging integration with an external Rubrik
webhook service. The plugin validates LLM tool calls against a policy
service (fail-open on errors) and batch-logs all requests/responses.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Update Rubrik docs: config.yaml as primary, env vars as fallback
Restructures the Quick Start to present config.yaml as the recommended
approach with tabbed UI, and environment variables as an alternative
fallback.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Add Rubrik env vars to config_settings reference
Fixes documentation validation by adding RUBRIK_API_KEY,
RUBRIK_BATCH_SIZE, RUBRIK_SAMPLING_RATE, and RUBRIK_WEBHOOK_URL
to the environment settings reference table.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Add fallback message when blocking service returns empty explanation
Prevents whitespace-only violation message when the tool blocking
service blocks tools but returns an empty content field.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- preserve existing shared backend `mode` when router deployment registration
reuses a provider/model key already in `litellm.model_cost` (prevents alias
with `mode: chat` from downgrading shared `chatgpt/gpt-5.4` from `responses`
to `chat` and triggering 403s on /v1/chat/completions)
- teach the ChatGPT Responses parser to recover `response.output_item.done`
entries when `response.completed.output` is empty
- add defensive /responses -> /chat/completions bridge fallback that
reconstructs output items from raw SSE when `raw_response.output` is empty
- regression coverage for shared alias routing, empty completed.output
parsing, and SSE bridge recovery
Closes#25403
Co-authored-by: afoninsky <andrey.afoninsky@gmail.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix: reuse cached credentials in VertexAIPartnerModels instead of creating new VertexLLM per request
VertexAIPartnerModels.completion() was creating a throwaway VertexLLM()
instance on every call to get an access token, bypassing the credential
cache inherited from VertexBase. This caused a fresh token fetch for
every single request, adding significant latency overhead.
Fix: call super().__init__() to initialize VertexBase's credential cache,
and use self._ensure_access_token() instead of a new VertexLLM instance.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix: apply same credential caching fix to VertexAIGemmaModels and VertexAIModelGardenModels
Same bug as VertexAIPartnerModels: both classes had `pass` in __init__
instead of `super().__init__()`, and created throwaway VertexLLM()
instances per request instead of using self._ensure_access_token().
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix(vertex_ai): single-flight credential refresh to prevent thundering herd
When GCP credentials expire under high concurrency, all requests
simultaneously call credentials.refresh() via asyncify, saturating the
40-thread anyio pool and blocking the proxy for 20+ seconds.
This adds:
- Per-credential asyncio.Lock in get_access_token_async for single-flight
refresh (1 coroutine refreshes, others wait on the lock)
- Background refresh when token_state is STALE (usable but near expiry),
returning the current token immediately with zero added latency
- threading.Lock on the sync get_access_token path
- Uses google-auth's TokenState enum (FRESH/STALE/INVALID) instead of
reimplementing expiry logic
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix: address PR review comments
- Use asyncio.create_task() instead of deprecated get_event_loop().create_task()
- Track in-flight background refresh tasks to prevent duplicate refreshes
when multiple STALE-path callers pass through the lock before the first
background task completes
- Add token validation in the STALE branch (consistent with FRESH/INVALID)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix: lazy-import TokenState to avoid breaking when google-auth is not installed
Also extract helper methods to bring get_access_token_async under the
PLR0915 statement limit (50).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* chore: apply Black formatting to test file and update uv.lock
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix: remove user-provided project_id from log messages (CodeQL log injection)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix: avoid leaking token value in error message, log type instead
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* chore: restore uv.lock to match litellm_oss_branch
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix: remove project_id from remaining log message (CodeQL log injection)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix: remove remaining project_id from log and error messages
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
When litellm migrated from Poetry to uv (PR #24905, v1.83.1), the core
dependency specifications in pyproject.toml changed from Poetry bare-version
strings (e.g. openai = "2.30.0") to PEP 621 exact pins (openai==2.24.0).
Poetry bare-version strings are actually caret ranges (^X.Y.Z == >=X.Y.Z,<X+1),
but PEP 621 == is exact. This means every downstream package that installs
litellm as a library dependency is now forced to downgrade aiohttp, pydantic,
openai, click, and 8 other common packages to exact old versions.
Fix: restore range specifiers for the 12 core runtime dependencies. The
optional extras (proxy, proxy-runtime, etc.) are consumed primarily by
Docker images where exact pins are appropriate and are left unchanged.
The uv.lock file continues to provide exact reproducibility for Docker
builds and CI.
Fixes: #26154
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
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* bump litellm-proxy-extras version to 0.4.67
* bump litellm-proxy-extras pin to 0.4.67 in litellm pyproject
* regenerate uv.lock for litellm-proxy-extras 0.4.67
* bump litellm-enterprise version to 0.1.38
* bump litellm-enterprise pin to 0.1.38 in litellm pyproject
* regenerate uv.lock for litellm-enterprise 0.1.38
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send_max_budget_alert_email previously guarded with `is not None`, which
accepts `[]` and then crashes on `recipient_emails[0]` inside
_get_email_params. The current caller (_handle_multi_threshold_max_budget_alert)
already filters empty lists upstream, but the public method signature makes
no such guarantee — a future caller passing [] would hit IndexError.
Switch to truthiness so both None and [] fall through to the single-recipient
path.
Per-user OAuth MCP requests now only skip pre-emptive 401 when a stored token is available, preserving token-reuse behavior while restoring fast PKCE kickoff for first-time or missing-token users.
The lazy import from litellm_enterprise inside _normalize_alert_emails
coupled the core proxy auth path to an optional package. Core should not
depend on enterprise, even lazily — it hides the dependency from static
analysis and inverts the intended layering.
Duplicate the 7-line parser locally. It's pure and unlikely to drift; the
enterprise copy stays where it is for its own callers.
_merge_budget_alert_email_configs previously called list() directly on each
threshold's value, which raised TypeError on null YAML values and silently
split bare strings into single characters. Both are reachable from user-
supplied global config and per-key metadata, so the crash could fire on
every authenticated request once the metadata was in place.
Route both inputs through a _normalize_alert_emails helper that delegates
to the existing _parse_email_list parser (lazy-imported, matching the
enterprise import pattern used elsewhere in proxy/). The merge body keeps
its tight Dict[str, List[str]] contract.
The Union[str, List[str]] value type was speculative — _merge_budget_alert_email_configs
always returns List[str] values, and no caller produces bare strings. Narrowing to
match the runtime guarantee resolves a mypy invariance error at auth_checks.py:3021
without adding casts or Mapping covariance.
A previous refactor added `litellm/integrations/prometheus_helpers.py` as a
sibling to the existing `litellm/integrations/prometheus_helpers/` directory
(which contains `prometheus_api.py` and has no `__init__.py`). The file
shadowed the namespace-package directory, so any deferred
`from litellm.integrations.prometheus_helpers.prometheus_api import ...`
raised `ModuleNotFoundError: 'litellm.integrations.prometheus_helpers' is
not a package` at request time.
Two runtime call sites hit that path:
- /global/spend/logs (spend_management_endpoints.py) returned plain-text 500
"Internal Server Error" for every call, breaking the Admin UI Usage tab
and programmatic consumers.
- SlackAlerting.send_fallback_stats_from_prometheus silently failed inside
its own try/except.
Fix: move prometheus_helpers.py content into prometheus_helpers/__init__.py
and delete the stray .py. The directory becomes a regular package, so both
the package-root import (from ...prometheus_helpers import X) and the
submodule import (from ...prometheus_helpers.prometheus_api import X)
resolve correctly. No call sites change.
Six CI jobs create a miniconda env with python=3.9 before installing
the project; these jobs now fail resolution because the project
requires-python is >=3.10. Bump the conda env python to 3.10 to match
the new floor.
All three dependency bumps in this PR resolve on Python 3.10, so there
is no need to jump the floor all the way to 3.11. Also restore the
py3.10-specific lunary==1.4.36 pin that was collapsed when the floor
was temporarily at 3.11.
Now that requires-python starts at 3.11, the "python_version >= '3.9'"
and ">= '3.10'" markers are unconditionally true, and the "< '3.10'"
entries for psycopg, Pillow, pyarrow, langchain, lunary, and pylint can
never resolve. Drop the dead markers and remove the unreachable pins so
the dependency list reflects what actually gets installed.
Premium fields like policies are echoed at the top level of the
/key/update response, not necessarily mirrored into metadata. Read
metadata first then fall back to the top-level property so an
intentional clear is preserved in either shape.
The /key/update response echoes top-level defaults like policies:[] into
client state. On a subsequent edit, the form resends policies:[], which
the backend treats as "user is setting policies" and blocks with a 403
enterprise check regardless of value.
Drop premium metadata fields from the update payload when the current
form value and the previously persisted value are both empty. Genuine
clears (non-empty -> empty) still pass through so premium users can
clear policies as intended.
Bumps orjson, fastapi-sso, and python-multipart to their latest releases
in the proxy extra, and raises the project python floor to 3.11 so the
updated pins can resolve. CI already runs on 3.11 / 3.12 / 3.13 and the
Docker images ship python 3.13, so the floor change aligns the declared
support range with what is actually tested and shipped.
Move async_log_success_event pipeline construction into
_build_success_event_pipeline_operations so the async hook stays
under Ruff's max-statement limit.
Made-with: Cursor
`should_create_missing_views()` had `and result[0]["reltuples"]` which is
falsy when reltuples=0. On a fresh empty PostgreSQL table, CREATE INDEX sets
reltuples=0, causing the guard to return False and skip view creation entirely.
Views like MonthlyGlobalSpendPerKey are never created, and the
/global/spend/logs endpoint returns 500.
Fix: change to `and result[0]["reltuples"] is not None` so reltuples=0
(empty table) and reltuples=-1 (unanalyzed table) both correctly return True.
Also harden test_vertex_ai.py to return None instead of crashing with
JSONDecodeError when the spend-logs endpoint returns a non-JSON 500 response,
and add unit tests covering all three reltuples branches (0, -1, positive).
