The strict 'additionalProperties: false' schema in
test_aaamodel_prices_and_context_window_json_is_valid rejected the new
flag added in this PR's earlier commit. Register it alongside the other
supports_*_reasoning_effort entries so the schema validation passes.
gpt-5.5-pro only accepts reasoning_effort in {medium, high, xhigh}
(verified live against OpenAI's API on 2026-04-24). LiteLLM previously
had no way to express this constraint — the existing JSON schema
covered none/minimal/xhigh but not low. Result: drop_params=true users
saw an avoidable 400 from OpenAI.
Add supports_low_reasoning_effort following the existing opt-out
pattern (default-allow, explicit false to block). Mirror the minimal
branch in OpenAIGPT5Config.map_openai_params so 'low' goes through the
same _is_reasoning_effort_level_explicitly_disabled gate.
Set the flag to false on gpt-5.5-pro and gpt-5.5-pro-2026-04-23 in
both model_prices JSON files (kept in sync). Other models leave the
key absent so behavior is unchanged.
Tests cover: rejection on pro variants (no drop_params), drop on pro
with drop_params=True, passthrough on gpt-5.5 chat, passthrough on
unknown models, and the helper-level _is_reasoning_effort_level_explicitly_disabled
contract.
The enterprise package ships under the BerriAI Enterprise License defined
in enterprise/LICENSE.md, which is not an SPDX-listed license. Declare it
via PEP 639's LicenseRef-Proprietary expression so metadata-reading tools
(PyPI classifiers, Nexus IQ, pip-licenses) resolve it instead of reporting
License-None. The existing license-files entry already ships the full terms.
Previously, an admin JWT sending a stale/typo'd/missing x-litellm-team-id
on an LLM API route received a hard 404 from get_team_object, blocking the
request. Restore pre-PR admin behavior: if the header can't be resolved,
skip team attribution and proceed with admin access, logging a warning
with the header value and route so the misconfigured caller is diagnosable.
Verified against OpenAI's live Chat Completions API on 2026-04-24:
POST /v1/chat/completions
{"model": "gpt-5.5", "reasoning_effort": "minimal", ...}
-> 400 Unsupported value: 'reasoning_effort' does not support 'minimal'
with this model. Supported values are: 'none', 'low', 'medium',
'high', and 'xhigh'.
POST /v1/chat/completions
{"model": "gpt-5.5-pro", "reasoning_effort": "minimal", ...}
-> 400 Unsupported value: 'minimal' is not supported with the
'gpt-5.5-pro' model. Supported values are: 'medium', 'high', and
'xhigh'.
Set supports_minimal_reasoning_effort=false on all four entries
(gpt-5.5, gpt-5.5-2026-04-23, gpt-5.5-pro, gpt-5.5-pro-2026-04-23) so
OpenAIGPT5Config._is_reasoning_effort_level_explicitly_disabled fires
and LiteLLM either drops the param (drop_params=True) or raises a
local UnsupportedParamsError, instead of round-tripping to OpenAI for
a 400.
Adds a parametrized test_gpt55_reasoning_effort_flags_match_live_openai_api
test that pins supports_{none,minimal,xhigh}_reasoning_effort on each
entry to OpenAI's actual API contract.
Note: gpt-5.5-pro additionally rejects 'none' and 'low'. 'none' is
already handled (supports_none_reasoning_effort=false). 'low' is not
representable in the current JSON schema (no supports_low flag);
filing separately.
Replaces the rm-and-symlink hack with a plain actions/checkout
using path: docs/my-website. The previous approach failed on this
branch because docs/my-website no longer exists in the repo (its
parent docs/ directory was also removed), so ln -s had nowhere
to create the symlink.
Also adds the same checkout step to test-unit-documentation.yml,
which was silently relying on docs/my-website existing in-tree
for test_env_keys.py and test_router_settings.py.
The documentation source has moved to a separate repository,
BerriAI/litellm-docs, served at docs.litellm.ai. This PR removes
docs/my-website/ from this repo and updates README.md, AGENTS.md,
and CLAUDE.md to direct doc contributions to the new repo.
Also fixes a broken relative link in
litellm/integrations/levo/README.md.
The existing CI symlink in .github/workflows/test-code-quality.yml
(which clones litellm-docs and symlinks docs/my-website to it for
tests/documentation_tests/*) continues to work without change.
* 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.
create_model does not inherit the base class docstring, so once an
extension registered a field the effective class had no description.
The UI renders schema.description as a header paragraph — losing it
broke the 'Configuration for UI-specific flags' text. Pass __doc__
through explicitly and add a regression test.
CircleCI's 'Rerun failed tests' feature passes test identifiers from the
JUnit XML classname attribute (dot notation, e.g.
'tests.local_testing.test_router') via stdin. pytest receives these paths
and collects 0 items, causing the rerun to exit 123 with no tests run.
Add an awk preprocessor before xargs that detects dot-notation module
paths and converts them to file paths (tests/local_testing/test_router.py).
File paths already containing '.py' are passed through unchanged.
Applied to all three jobs using the 'circleci tests run' + 'xargs pytest'
pattern: local_testing_part1, local_testing_part2, and the router test job.
Extract the admin team-header attachment into a helper so
auth_builder stays under the 50-statement lint threshold; apply
black formatting to the two files flagged on the prior commit.
No behavior change.
GET /get/ui_settings returns a schema built from the effective UISettings
class (base + enterprise-registered fields), but PATCH /update/ui_settings
declared its body as the base UISettings. Enterprise fields still worked
via extra="allow", but the OpenAPI schema was asymmetric between GET and
PATCH.
Accept the body as a dict and validate with the effective class so both
sides are in sync and enterprise-registered fields are type-checked.
Add _depth/_max_depth guards (default 20) so the nested dict masking
cannot run away, and allowlist the function in the recursive_detector
CI check alongside the other bounded recursive helpers.
The file was moved to tests/enterprise/litellm_enterprise/proxy/management_endpoints/
and is covered by the CircleCI litellm_mapped_enterprise_tests job. The stale path
was causing pytest to error with 'file or directory not found'.
`_check_byok_credential` previously returned silently when `prisma_client`
was None, bypassing BYOK ownership validation during database-outage
windows. Any proxy-authenticated user could invoke BYOK-protected MCP
tools without a stored credential during the outage window.
Now raises HTTP 503 with a structured error so the flow fails closed.
Regression test asserts 503 is raised when `prisma_client` is None.
Reported by @brodmart in GHSA-6762-2m23-5mxp.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds three new UISettings flags gated by check_org_admin_feature_access:
- disable_key_generate_for_org_admin
- disable_team_create_for_org_admin
- disable_model_add_for_org_admin
When enabled by a proxy admin, users with the ORG_ADMIN role receive a 403
on /key/generate, /team/new, and /model/new respectively. All other roles
(proxy admin, internal user, team) are unaffected and continue through
their existing auth checks. Flags are persisted to litellm_uisettings and
synced into general_settings via _RUNTIME_GENERAL_SETTINGS_FLAGS so the
enforcement helper can read them at request time.
Scope the header-driven team fetch to LLM API routes so admin
management routes keep the pre-existing bypass behavior (no
phantom teams, no 404s on mgmt calls). Team context is threaded
onto UserAPIKeyAuth so spend logs, rate limits, and team_models
attribution are correctly applied when admins act on behalf of
a team via x-litellm-team-id.
* fix(proxy): honor object_permission for managed vector store access
* perf(proxy): preload team object_permission on UserAPIKeyAuth
Populate team_object_permission during virtual-key and JWT auth when the
team is loaded, so can_user_access_vector_store uses it in memory first
and only falls back to get_object_permission by id when missing.
Made-with: Cursor
Three concerns raised by bot reviewers, all addressed:
1. CodeQL cyclic-import warning
``experimental_pass_through/transformation.py`` imported from the
parent ``..transformation`` module, which CodeQL flagged as a
potential cycle. Extracted the helper into a new leaf module
``vertex_ai_partner_models/anthropic/output_params_utils.py`` that
has no heavy imports of its own. Both transformation files now
import from it cleanly. Renamed the helper from the underscore-
prefixed ``_sanitize_vertex_anthropic_output_params`` to the
public ``sanitize_vertex_anthropic_output_params`` since it is now
shared across modules.
2. Greptile P2: redundant ``None`` guard on ``extra_kwargs``
``handler.py`` had two ``extra_kwargs = extra_kwargs if ... else {}``
coercions; the second was a no-op because line 220 already
coerced. Removed the second one and added a NOTE comment so future
readers understand ``extra_kwargs`` is guaranteed non-None at the
point of use.
3. Greptile P2: misleading "already translated" docstring
The docstring claimed the translator above mapped
``output_config.format`` to ``response_format``, but Greptile
correctly traced the code and found that only the legacy top-level
``output_format`` was being translated — ``output_config.format``
was being silently dropped on the adapter path. Two-part fix:
a. Code: extended ``_translate_output_format_to_openai`` to accept
both shapes (top-level ``output_format`` AND
``output_config.format`` sub-key). Top-level still takes
precedence when both are supplied. This means callers using the
newer Anthropic Structured Outputs API now have their schema
properly forwarded to non-Anthropic backends as
``response_format``.
b. Tests: rewrote the misleading docstring to describe what
actually happens, plus added two new tests:
* ``test_output_format_top_level_still_translates`` —
regression guard for the legacy path
* ``test_output_format_takes_precedence_over_output_config_format``
— documents the precedence rule explicitly
Tests: 28/28 pass (was 26/26 before; +2 for the new translation
behavior + precedence). All run in ~0.5s, no real network calls.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
