* feat(lasso): extend LassoGuardrail to support tool calling (RND-5748)
* fix(lasso): PR review followups for tool-calling guardrail (RND-5748)
* fix(lasso): handle object-style tool_calls in _update_tool_calls_from_masked (RND-5748)
* fix(lasso): use model role for tool_use blocks (RND-5748)
* test(lasso): add round-trip tests for message transformation (RND-5748)
* fix(lasso): remove unused imports, handle Responses-API input masking, flatten multimodal content (RND-5748)
* fix(lasso): inspect Responses-API input field (RND-5748)
* fix(lasso): guard text-cursor remap against Lasso count mismatch (RND-5748)
* fix(lasso): flatten list content in tool_result.content (RND-5748)
* fix(lasso): remap multimodal list content during masking (RND-5748)
Bug: _map_masked_messages_back counted list-content messages in
original_text_count but the remap loop only handled isinstance(str).
The positional text_cursor never advanced for list messages, causing
all subsequent masked texts to be written onto the wrong messages.
Fix: added elif isinstance(content, list) branch that replaces the
list with the masked text string and advances the cursor — mirrors
the existing string-content branch. Also handles the assistant +
tool_calls combo for list-content messages.
Test: test_map_masked_messages_back_list_content verifies a user
message with [text + image_url] followed by an assistant message
gets correct masked content on both (cursor stays aligned).
* refactor(lasso): extract _get_field and _extract_tool_call_fields helpers (RND-5748)
The dict-vs-object access pattern (x.get('y') if isinstance(x, dict)
else getattr(x, 'y', None)) was duplicated 14 times across 5 methods.
_get_field(obj, field) — single-point dict/Pydantic field access.
_extract_tool_call_fields(call) — returns (call_id, name, parsed_input)
with JSON argument parsing, replacing ~30 duplicate lines in both
async_post_call_success_hook and _expand_messages_for_classification.
Also simplified _update_tool_calls_from_masked, _prepare_payload tool
mapping, and _apply_masking_to_model_response call_id extraction.
Net ~60 lines removed. No behavior change — all 32 tests pass.
* fix(lasso): add count guard to _apply_masking_to_model_response (RND-5748)
_apply_masking_to_model_response used a bare text_cursor without
verifying 1:1 correspondence between text-bearing choices and masked
text entries. If Lasso returned a different number of text messages
than choices with content, masked text would be applied to the wrong
choice or silently skip choices.
Added the same count-mismatch guard pattern already used in
_map_masked_messages_back: count original text-bearing choices,
compare to masked_text length, skip text remap on mismatch with a
warning log. Tool_call masking via id-based lookup is unaffected.
Tests:
- test_apply_masking_to_model_response_multiple_choices: verifies
correct per-choice masked text with 2 choices
- test_apply_masking_to_model_response_count_mismatch: verifies
content is left unchanged when counts disagree
* fix(lasso): close two guardrail-bypass paths flagged in review (RND-5748)
* tool-call args: when function.arguments is malformed JSON or parses
to a non-object, preserve the raw string as {"arguments": <raw>} so
Lasso still inspects it instead of receiving input=None. Covers both
pre-call and post-call extraction (shared helper). Also resolves the
CodeQL empty-except warning since the except body now assigns parsed=None.
* Responses-API input: when a request carries both "messages" and
"input", inspect both. Previously a benign messages array let the
guardrail skip data["input"] entirely. The masking write-back is
split via a count boundary so masked messages flow back to
data["messages"] and masked input flows back to data["input"]
without cross-contamination.
Tests: malformed/non-object args round-trip, dual-field classification,
dual-field masking write-back split.
* chore(lasso): black formatting + comment on expand skip branch (RND-5748)
* black: wrap two long expressions in lasso.py and reformat dict
literals in test_lasso.py to satisfy CI lint.
* add a short comment in _expand_messages_for_classification
explaining why empty string and None content are intentionally
skipped (None is the OpenAI shape for a pure tool-call turn).
* fix(lasso): satisfy mypy in _handle_masking, _update_tool_calls_from_masked, _apply_masking_to_model_response (RND-5748)
* Narrow `response.get("messages")` into a local before slicing so
mypy doesn't see `Optional[List[Dict[str, str]]]` as non-indexable.
* Rename the two write-side `func` bindings in
`_update_tool_calls_from_masked` to `func_dict` / `func_obj` so
mypy doesn't unify the dict and Any|None branches.
* Rename the inner loop variable in `_apply_masking_to_model_response`
from `msg` to `masked_msg` to avoid clashing with the
`msg = choice.message` rebinding below.
No behavior change; resolves the 7 mypy errors from the CI lint job.
The enterprise package is installed as `litellm_enterprise` (per
enterprise/pyproject.toml), but several tests imported it as
`enterprise.litellm_enterprise.*` — a path that only resolves
because the repo root happens to sit on sys.path, letting Python's
implicit namespace package machinery discover `enterprise/` as a
directory.
This breaks any test runner that relocates source (e.g. the
mutation-testing workflow, which copies tests under `mutants/`) and
also caused two `patch()` strings to target a module path that does
not match what production code imports — meaning those mocks were
never actually patching the production module's attribute.
Replace `from enterprise.litellm_enterprise.` with the canonical
`from litellm_enterprise.` across 6 test files, and fix two
`patch()` target strings (and one `sys.modules` patch key in the
SSO test) to match.
Mirrors the system-message skip in PR #25481 for tool-role messages.
Adds a global litellm.skip_tool_message_in_guardrail flag and a
per-guardrail litellm_params.skip_tool_message_in_guardrail override,
applied in the OpenAI and Anthropic chat translation handlers.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The hooks gated on ``call_type == "completion"`` but the proxy ingress
passes ``route_type`` straight through as ``call_type`` —
``"acompletion"`` for /v1/chat/completions and ``"aresponses"`` for
/v1/responses. Tests passed because they used the literal sync
``"completion"`` value, masking the gap.
Switch both hooks to ``is_text_content_call_type`` (matches the
canonical runtime values: completion / acompletion / aresponses) and
update existing tests to assert against runtime values, plus parametrize
a regression test that pins the gate.
Greptile P1: Aim's ``_anonymize_request`` and Lakera v2's mask-PII path
both wrote redacted content only to ``data["messages"]``. The Responses
API backend reads ``data["input"]``, so when a request arrived via
``/v1/responses`` with a plain string ``input`` the hook would update
``messages`` (which the backend ignores) and leave ``input`` carrying
the original unredacted text. Net effect: anonymize/mask silently passed
PII through to the LLM.
Add ``apply_redacted_messages_back`` to ``_content_utils`` — it writes
the redacted messages back to ``data["messages"]`` AND, when present,
re-flattens the redacted content into ``data["input"]``. Aim and
Lakera v2 now route their mask writeback through this helper. List
``input`` (multimodal) is still handled by the upstream
block-on-multimodal guard.
Adds unit tests for the helper and regression tests asserting
``data["input"]`` is redacted for both hooks on Responses-API string
input.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two more in-place rewrite paths exhibit the same regression as Lakera v2:
overwriting ``data["messages"]`` with text-only redacted versions silently
strips image/audio parts from multimodal requests.
- ``LassoGuardrail._run_lasso_guardrail``: when ``mask=True`` AND input
is multimodal/Responses-API list, fall back to the classify endpoint
(which raises on BLOCK actions but never overwrites the payload).
- ``AimGuardrail._anonymize_request``: when input is multimodal, raise
the standard 400 instead of replacing ``data["messages"]`` with the
text-only ``redacted_chat`` from Aim. The error message tells the
user to either send plain string content or rely on block-mode.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Mask-in-place uses the offsets that Lakera returns for the inspection
payload. ``build_inspection_messages`` flattens multimodal content into
joined text before sending to Lakera, so the offsets refer to the
flattened representation. Writing those offsets back via
``_mask_pii_in_messages`` and overwriting ``data["messages"]`` would
silently strip image/audio parts from the original request — that is a
real functional regression for Lakera + mask mode + multimodal input.
Detect multimodal input (any list-format ``content`` or non-string
``data["input"]``) up front and skip the mask-in-place branch in that
case. The hook then falls into the standard block-on-detect path so PII
is still blocked but the multimodal payload is never silently rewritten.
Per-part masking that preserves multimodal structure is the right
long-term fix; tracking that as a follow-up.
Also: add ``has_non_string_content`` to ``_content_utils`` (with tests)
and a regression test that asserts multimodal+PII raises an HTTPException
instead of returning a flattened request body.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Greptile P2 follow-ups on _content_utils.py:
- Drop unreachable ``_resolve_messages``. The new
``_iter_inspection_messages`` walks ``messages`` AND ``input``
independently; leaving the old fallback-only variant around invited a
future maintainer to wire it back up and silently narrow coverage.
- Rename ``iter_user_text`` → ``iter_message_text``. The helper walks
every role (user, assistant, system); the old name implied user-turn
content only. Callers and tests updated.
- Close mixed-list coverage gap. When ``data["input"]`` was a list
mixing content-part dicts and bare strings, ``iter_message_text`` and
``build_inspection_messages`` only saw the dict parts while
``walk_user_text`` already inspected both. ``_iter_text_parts_in_content``
now treats bare strings inside a content list as text fragments, so
read and write helpers agree on coverage.
Adds two regression tests for the mixed-list shape.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Several guardrail hooks short-circuit when ``message.content`` is a list
or when the request uses the Responses-API ``input`` field instead of
``messages``. Centralise the content-walking logic in a shared helper and
update the affected hooks so list-format and Responses-API payloads no
longer skip inspection.
Also: Aim's ``async_post_call_success_hook`` now inspects every choice
(via ``asyncio.gather``) instead of only ``choices[0]`` — the prior
behaviour let ``n>1`` callers hide content in subsequent completions.
Hooks updated to use the new helper:
- aim, lakera_ai_v2, lasso (post a synthesised messages list to a remote
guardrail service)
- azure_content_safety, ibm_detector, banned_keywords, openai_moderation,
google_text_moderation (iterate text fragments locally)
- secret_detection (walk-and-rewrite to redact in place)
Drive-by fix: the legacy ``data["prompt"]`` list-handling path in
secret_detection rebound the loop variable instead of mutating the list,
leaving secrets unredacted on text-completion calls; corrected to index
back into the list.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Add explicit warnings when Presidio apply_to_output skips masking for mixed/unknown stream event shapes, and add regression coverage for the mixed stream flush path to ensure chunk order is preserved and warnings are emitted.
Made-with: Cursor
Keep Presidio apply-to-output streaming in passthrough mode when responses-style events are emitted, preventing dropped lifecycle events that caused Codex stream disconnects. Add a regression test to assert unknown streaming events are preserved in order.
Made-with: Cursor
* Use auth key name if there are no app id in in headers or in extra_data
* use key alias instead of key name
* Fix
* last priority key alias
* Fix
* Add tests
* [Feat] Day-0 support for GPT-5.5 and GPT-5.5 Pro (#26449)
* feat(openai): day-0 support for GPT-5.5 and GPT-5.5 Pro
Add pricing + capability entries for the new GPT-5.5 family launched by
OpenAI on 2026-04-24:
- gpt-5.5 / gpt-5.5-2026-04-23 (chat): $5/$30/$0.50 per 1M
input/output/cached input
- gpt-5.5-pro / gpt-5.5-pro-2026-04-23 (responses-only): $60/$360/$6
per 1M input/output/cached input
Other fees (long-context >272k, flex, batches, priority, cache
discounts) follow the same ratios as GPT-5.4, with context window
retained at 1.05M input / 128K output.
No transformation / classifier code changes are required:
OpenAIGPT5Config.is_model_gpt_5_4_plus_model() already matches 5.5+ via
numeric version parsing, and model registration is driven from the
JSON. The existing responses-API bridge for tools + reasoning_effort
(litellm/main.py:970) already covers gpt-5.5-pro.
Tests:
- GPT5_MODELS regression list now covers gpt-5.5-pro and dated variants
- New test_generic_cost_per_token_gpt55_pro cost-calc test
- Updated test_generic_cost_per_token_gpt55 for long-context fields
* fix(openai): mirror reasoning_effort flags onto gpt-5.5 dated variants
gpt-5.5-2026-04-23 and gpt-5.5-pro-2026-04-23 were missing the
supports_none_reasoning_effort, supports_xhigh_reasoning_effort, and
supports_minimal_reasoning_effort flags that their non-dated
counterparts define. Reasoning-effort routing in OpenAIGPT5Config is
fully capability-driven from these JSON flags — since an absent flag
is treated as False for opt-in levels (xhigh), users pinning to a
dated snapshot would silently lose xhigh support and diverge from the
base alias on logprobs + flexible temperature handling.
Copy the flags onto both dated variants so every dated snapshot
inherits the base model's reasoning-effort capability profile.
Adds a parametrized regression test that asserts
supports_{none,minimal,xhigh}_reasoning_effort parity between each
dated variant and its non-dated counterpart, preventing future drift
when new snapshots are added.
* [Feat] Add azure/gpt-5.5 + azure/gpt-5.5-pro entries (+ dated variants) (#26361)
* feat(azure): add azure/gpt-5.5 + azure/gpt-5.5-pro entries (+ dated variants)
Azure variants of OpenAI's GPT-5.5 family. Microsoft has not yet
shipped GPT-5.5 on Azure OpenAI (latest GA on the Foundry models page
is GPT-5.4 as of 2026-04-24), but adding the entries day-0 mirrors the
established precedent for azure/gpt-5.4* (which were in the cost map
before the Azure rollout) so cost tracking and capability flags work
the moment customers deploy.
Schema follows the existing azure/gpt-5.4* shape:
- Same base/long-context pricing as openai/gpt-5.5*: $5/$30 chat,
$60/$360 pro per 1M, with priority tier 2x base
- Azure variants drop the flex/batches keys (Azure has no flex tier)
but keep priority pricing, matching gpt-5.4* precedent
- mode=chat for the thinking model, mode=responses for pro
reasoning_effort capability flags mirror the OpenAI variants exactly
since Azure proxies the same API contract: minimal rejection on both
chat and pro, low/none rejection on pro. Once #26456 (which sets
supports_low_reasoning_effort + minimal=false on openai/gpt-5.5*)
lands, OpenAI and Azure flag profiles align.
Tests pin entry presence + pricing for all four Azure variants and
verify the live-API-derived reasoning_effort flags.
* test: register supports_low_reasoning_effort in cost-map JSON schema
azure/gpt-5.5-pro and azure/gpt-5.5-pro-2026-04-23 added in this branch
carry supports_low_reasoning_effort=false. The strict
'additionalProperties: false' schema in
test_aaamodel_prices_and_context_window_json_is_valid rejected the new
key. Register it alongside the other supports_*_reasoning_effort
entries.
Note: the runtime side of this flag (code that reads it) lands in
#26456. Until that PR merges the flag is inert for both Azure and
OpenAI pro entries, but having the schema accept it lets cost-map
tests pass on either merge order.
* Use sanitize deep copy style to replace deepcopy usage
* Added test checking error is not happening anymore
* Added warning log when json copy failed
* Reduce to one change
* Fix spaces
---------
Co-authored-by: Ido Lavi <ido@noma.security>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Mateo Wang <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: TomAlon <tom@noma.security>
Code review noted the previous test reimplemented the proxy's
try/except/finally around post_call_success_hook, so it would not catch a
regression that re-introduced the duplicate-log bug in the production code
path. Extract the gating logic into
`ProxyBaseLLMRequestProcessing._flush_deferred_async_logging` so tests
exercise the production helper directly.
The proxy finally block becomes a single call to the helper. Tests now
invoke the helper itself and additionally assert (via inspect.getsource)
that base_process_llm_request continues to delegate to the helper rather
than inlining the gate.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
When a non-streaming request had any post-call guardrail registered, the
proxy deferred the async-success logging closure until after
post_call_success_hook ran. The finally block fired that closure even when
the hook raised — the propagating HTTPException then routed through
post_call_failure_hook → _handle_logging_proxy_only_error, which writes its
own failure spend log via async_failure_handler. The result was two spend
log rows per blocked request: one Success exposing the blocked LLM response
and one Failure. Reproduces with both pre and post bedrock guardrails
configured for a team when the post-call OUTPUT scan blocks the response.
Gate the deferred closure on _exception_raised so the failure path remains
the single source of truth for blocked requests.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(bedrock guardrail): dedupe post-call log when only post_call is configured
When a Bedrock guardrail runs with only post_call configured, the post-call
trace section showed the same guardrail twice (one entry per parallel
INPUT/OUTPUT API call). Add skip_logging param to make_bedrock_api_request
and pass it for the INPUT scan so the OUTPUT scan stands as the single
canonical post_call log entry. INPUT exceptions still propagate, so the
input-side blocking behavior is preserved.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(bedrock guardrail): post_call only scans OUTPUT, not INPUT
post_call is the response-validation hook by definition — input scanning
belongs to pre_call / during_call. The previous code ran an extra INPUT
scan in post_call when no pre/during hook was configured, which produced
a duplicate "post-call" entry in the trace and was semantically wrong
for a "post-call" event.
Drops the should_validate_input branch and parallel asyncio.gather in both
async_post_call_success_hook and async_post_call_streaming_iterator_hook
in favor of a single OUTPUT scan. Reverts the now-unneeded skip_logging
parameter on make_bedrock_api_request introduced in the previous commit.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(guardrails): add LLM_AS_A_JUDGE to SupportedGuardrailIntegrations
* feat(types): add EvalVerdict, StandardLoggingEvalInformation; wire eval_information into SpendLogsMetadata
* feat(guardrails): add self-contained llm_as_a_judge guardrail hook
* fix(a2a): filter agent-only litellm_params from acompletion kwargs; pass agent_id into body
* feat(ui): add LLMJudgeFields criteria builder component
* feat(ui): wire LLM-as-a-Judge into add guardrail form
* feat(ui): update EvalViewer — title 'LLM Judge Results', weighted score column, summary row
* fix(ui): wire EvalViewer into LogDetailContent to show LLM judge results on logs page
* fix(guardrails-ui): route llm_as_a_judge to criteria builder step; rename to LiteLLM LLM as a Judge; add litellm logo
* fix(guardrail-viewer): stack lifecycle + eval details vertically to avoid badge overflow in narrow drawer
* fix(guardrail-create): surface config validation errors on create instead of silently orphaning guardrail in DB
* fix(guardrail-registry): hardcode llm_as_a_judge in initializer registry so it loads regardless of package install path
* fix(llm-as-a-judge): fix P1 code quality issues - validate weights/on_failure, guard pre_call, handle multimodal, move imports to module level, fix spurious finally logging
* fix(guardrail_endpoints): use correct PK field in rollback delete and log rollback failure
* fix(llm_as_a_judge): support Pydantic object in _get_litellm_param fallback chain
* fix(LLMJudgeFields): replace @tremor/react Button with antd Button
* fix(llm_as_a_judge): remove dead registry dicts, fix KeyError in prompt builder, set correct status on judge failure
* test(llm_as_a_judge): add unit tests for guardrail hook
* fix(llm_as_a_judge): remove @log_guardrail_information decorator to fix duplicate guardrail_information entries
The decorator and the manual finally block both called add_standard_logging_guardrail_information_to_request_data, producing two entries per request. The decorator also misclassified HTTPException(422) blocks as guardrail_failed_to_respond (it checks for 400). The finally block correctly tracks status throughout, so removing the decorator is sufficient.
* fix(test_gcs_pub_sub): ignore metadata.eval_information in comparison
* fix(test_spend_management): ignore metadata.eval_information in payload comparison
* fix(types/guardrails): add input_type and messages to ApplyGuardrailRequest
* fix(guardrail_endpoints): pass input_type and messages through apply_guardrail endpoint
* fix(guardrail_endpoints): auto-detect post_call guardrails and use input_type=response
* fix(a2a_endpoints): merge agent litellm_params guardrails into data before post_call hooks
* fix(llm_as_a_judge): use float sum with tolerance for weight validation
* fix(guardrail_registry): split long import line for black formatting
* fix(llm_as_a_judge): guard guardrail_name Optional for mypy
* fix(llm_as_a_judge): set guardrail_status=guardrail_intervened when score fails, regardless of on_failure mode
* fix(a2a_endpoints): use try/finally so deferred spend log fires even when guardrail blocks with 422
* fix(litellm_logging): declare _defer_async_logging and _enqueue_deferred_logging on Logging class for mypy
* fix(logging_worker): restore queue.join() in flush() to wait for in-flight callbacks
- _get_masked_values now recurses into nested dict values and covers
additional field name patterns (credentials, password, passwd)
- _row_to_submission_item applies masking before returning litellm_params
- list_guardrails_v2 filters DB and in-memory guardrails to the caller's
team memberships for non-admin users; admins still see all guardrails
- approve_guardrail_submission propagates team_id into the in-memory
guardrail dict so ownership is preserved after approval
Restore guardrail spend/UI event_type wiring, request_data on streaming
OUTPUT paths, and centralized match redaction after the upstream revert.
Made-with: Cursor
Add regression tests that mock make_bedrock_api_request and verify
input_type=request uses source=INPUT with user messages, and
input_type=response uses source=OUTPUT with synthetic ModelResponse.
Made-with: Cursor
* feat(proxy): add NO_OPENAPI env var to disable /openapi.json endpoint (#25696)
* feat(proxy): add NO_OPENAPI env var to disable /openapi.json endpoint - Fixes#25538
* test(proxy): add tests for _get_openapi_url
---------
Co-authored-by: Progressive-engg <lov.kumari55@gmail.com>
* feat(prometheus): add api_provider label to spend metric (#25693)
* feat(prometheus): add api_provider label to spend metric
Add `api_provider` to `litellm_spend_metric` labels so users can
build Grafana dashboards that break down spend by cloud provider
(e.g. bedrock, anthropic, openai, azure, vertex_ai).
The `api_provider` label already exists in UserAPIKeyLabelValues and
is populated from `standard_logging_payload["custom_llm_provider"]`,
but was not included in the spend metric's label list.
* add api_provider to requests metric + add test
Address review feedback:
- Add api_provider to litellm_requests_metric too (same call-site as
spend metric, keeps label sets in sync)
- Add test_api_provider_in_spend_and_requests_metrics following the
existing pattern in test_prometheus_labels.py
* fix: ensure `litellm_metadata` is attached to `pre_call` guardrail to align with `post_call` guardrail (#25641)
* fix: ensure `litellm_metadata` is attached to pre_call to align with post_call
* refactor: remove unused BaseTranslation._ensure_litellm_metadata
* refactor: module level imports for ensure_litellm_metadata and CodeQL
* fix: update based off of Codex comment
* revert: undo usage of `_guardrail_litellm_metadata`
* feat: add pricing entry for openrouter/google/gemini-3.1-flash-lite-preview (#25610)
* fix(bedrock): skip synthetic tool injection for json_object with no schema (#25740)
When response_format={"type": "json_object"} is sent without a JSON
schema, _create_json_tool_call_for_response_format builds a tool with an
empty schema (properties: {}). The model follows the empty schema and
returns {} instead of the actual JSON the caller asked for.
This patch:
- Skips synthetic json_tool_call injection when no schema is provided.
The model already returns JSON when the prompt asks for it.
- Fixes finish_reason: after _filter_json_mode_tools strips all
synthetic tool calls, finish_reason stays "tool_calls" instead of
"stop". Callers (like the OpenAI SDK) misinterpret this as a pending
tool invocation.
json_schema requests with an explicit schema are unchanged.
Co-authored-by: Claude <noreply@anthropic.com>
* fix(utils): allowed_openai_params must not forward unset params as None
`_apply_openai_param_overrides` iterated `allowed_openai_params` and
unconditionally wrote `optional_params[param] = non_default_params.pop(param, None)`
for each entry. If the caller listed a param name but did not actually
send that param in the request, the pop returned `None` and `None` was
still written to `optional_params`. The openai SDK then rejected it as
a top-level kwarg:
AsyncCompletions.create() got an unexpected keyword argument 'enable_thinking'
Reproducer (from #25697):
allowed_openai_params = ["chat_template_kwargs", "enable_thinking"]
body = {"chat_template_kwargs": {"enable_thinking": False}}
Here `enable_thinking` is only present nested inside
`chat_template_kwargs`, so the helper should forward
`chat_template_kwargs` and leave `enable_thinking` alone. Instead it
wrote `optional_params["enable_thinking"] = None`.
Fix: only forward a param if it was actually present in
`non_default_params`. Behavior is unchanged for the happy path (param
sent → still forwarded), and the explicit `None` leakage is gone.
Adds a regression test exercising the helper in isolation so the test
does not depend on any provider-specific `map_openai_params` plumbing.
Fixes#25697
---------
Co-authored-by: lovek629 <59618812+lovek629@users.noreply.github.com>
Co-authored-by: Progressive-engg <lov.kumari55@gmail.com>
Co-authored-by: Ori Kotek <ori.k@codium.ai>
Co-authored-by: Alexander Grattan <51346343+agrattan0820@users.noreply.github.com>
Co-authored-by: Mohana Siddhartha Chivukula <103447836+iamsiddhu3007@users.noreply.github.com>
Co-authored-by: Amiram Mizne <amiramm@users.noreply.github.com>
Co-authored-by: Claude <noreply@anthropic.com>
Noma v1 resolved application_id from user_api_key_alias when no explicit
value was set (PR #16832). Noma v2 (PR #21400) was rewritten from scratch
and this fallback was not ported, causing all requests from shared LiteLLM
instances to appear as a single generic "litellm" application in the Noma
dashboard — breaking per-user traceability.
Fix: after checking dynamic_params and self.application_id, fall back to
user_api_key_alias from litellm_metadata or metadata. This matches the
pattern used by PromptSecurityGuardrail._resolve_key_alias_from_request_data()
and restores the v1 behavior where each API key gets its own application
entry in the Noma dashboard.
Fixes#25794
Co-authored-by: Brendan Smith-Elion <brendan.smith-elion@arcadia.io>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Rename disable_global_guardrail → disable_global_guardrails to match
the key name used by litellm_pre_call_utils.py, the API endpoints,
and the UI when propagating key/team metadata.
The singular form was introduced in PR #16983 and has never matched
the plural form written by the rest of the codebase, so the feature
silently did nothing.
Re-applies fix originally from #25488. Original commit could not be
merged due to missing signature.
Co-Authored-By: Remi Mabon <remi.mabon@redcare-pharmacy.com>
