* fix(guardrails/bedrock): honor disable_exception_on_block by raising ModifyResponseException
The Bedrock-specific GuardrailInterventionNormalStringError predates the
unified guardrails refactor and no proxy code path handles it, so a block
with the flag set surfaced as an uncaught Exception -> HTTP 500 in pre_call
mode and was silently discarded in during_call mode (model call proceeded
in the parallel asyncio.gather; the block hook's data["mock_response"]
mutation happened after route_request had already unpacked kwargs).
Convert the block to ModifyResponseException at the raise site inside
make_bedrock_api_request. That exception is the industry-standard proxy
contract already caught in proxy_server, anthropic_endpoints, response_api
_endpoints, and pass_through_endpoints; it turns into a 200 response with
finish_reason=content_filter and the block message as content, which is
exactly what the flag was documented to yield. Post-call blocks attach
the LLM response to original_response so the synthetic reply reports the
upstream call's real token usage instead of zero.
Deletes the now-orphaned GuardrailInterventionNormalStringError class and
the dead create_guardrail_blocked_response / mock_response plumbing in the
Bedrock hooks; updates the existing tests that had locked in the buggy
contract.
Resolves LIT-4186
* chore(guardrails/bedrock): drop dead str branch in _update_messages_with_updated_bedrock_guardrail_response
Follow-up to the disable_exception_on_block fix. That method used to
receive either a BedrockGuardrailResponse or a plain string (the block
message, when the flag was set). Now that a block always raises
ModifyResponseException before this method runs, the string branch is
unreachable; tighten the type to BedrockGuardrailResponse and delete
the guard.
* fix(guardrails/bedrock): streaming post_call block yields synthetic stream instead of surfacing as SSE 500
Regression from the LIT-4186 refactor: pre-refactor, the streaming
post_call iterator caught GuardrailInterventionNormalStringError locally
and replaced the assembled response with a synthetic content-filter
message, then re-emitted it as chunks via MockResponseIterator. After
the refactor the exception was re-raised as ModifyResponseException,
which async_streaming_data_generator serializes as a proxy 500 error
frame because the SSE response headers are already flushed by the time
the block fires.
Non-streaming paths still let ModifyResponseException propagate to the
endpoint handler (which converts it into a 200). Streaming can't do
that, so keep the local synthesis: on the exception, rebind the
assembled response to a ModelResponse whose single choice carries the
block message as content and finish_reason=content_filter, and let the
downstream MockResponseIterator emit it as chunks. Same shape a
non-streaming block produces.
Adds a mapped-file regression test that mutation-kills the raise
behavior and locks in the synthetic-stream contract.
* fix(guardrails/bedrock): preserve upstream usage on streaming post_call block
Non-streaming post_call blocks report the upstream LLM call's real
token usage via ModifyResponseException.original_response, which the
endpoint handler unwraps through _blocked_response_usage. Streaming
post_call synthesizes its own ModelResponse locally (the exception
can't escape the SSE generator), and previously left .usage unset,
so the client saw accurate billing on non-streaming blocks and zero
on streaming blocks -- silent revenue leak.
Copy the assembled response's .usage onto the synthetic block
response before yielding. Pre-refactor code had the same gap
(create_guardrail_blocked_response never set usage); this is a net
improvement, not a regression fix.
* tests: add e2e tests for spend, budgets and llms
* style: make chained comparison of status_code clearer
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* remove e2e_tests folder
* test: add spend tracking tests
* fix: p0 issues, added types and shared functions for each test suite
* style: carry clearer status_code comparison into renamed e2e dir
* refactor: migrate to gateway client
* fix: add new tests, split gateway
* test(e2e): add live batches suite across providers and routing scenarios
* test(batches): cover real cost tracking on completed batch retrieve
* test(e2e): assert managed vs raw file and batch id shapes per routing scenario
* test(e2e): assert full response shape of each batches and files endpoint
* test(e2e): only accept transitional statuses for a freshly created batch
* test(prompt-factory): make test_convert_url deterministic with a data URL
picsum.photos is down (HTTP 522), so test_convert_url failed on every
run. Swap the live external image for an inline data: URL and assert the
round-trip through convert_url_to_base64 genuinely.
A data URL is already inline base64 image data, so convert_url_to_base64
now short-circuits it instead of attempting an impossible HTTP fetch;
add a regression for that branch in the mapped image_handling test
* fix: pass through async image data urls
* fix(image-handling): short-circuit data URLs in async path too
Bugbot flagged that convert_url_to_base64 returns data: base64 URLs
unchanged but async_convert_url_to_base64 still tried to fetch them,
so async OCR flows (Bedrock, Azure) would reject inline images the sync
path accepts. Add the same guard to the async function and a regression
test that asserts the async path returns the data URL without touching
the HTTP client
* Fix: openai batches lifecycle
* Fix: add e2e azure openai tests
* Fix e2e for vertex ai
* Add all models for testing
* test(managed-files): assert idempotent upsert in store_unified_file_id
store_unified_file_id switched from create to upsert to avoid
UniqueViolationError when re-storing the same unified_file_id (e.g.
batch output files stored before metadata is available). Update the
unit test to assert the upsert call and its create payload instead of
the removed create call.
* test(batches): reconcile vertex_ai native batch-id comment with fallback guard
* fix(test-config): keep rust-ocr models in model_list by moving files_settings after it
* fix(test-config): move batch models after OCR block to keep merge with internal_staging clean
* fix(batches): use '24hrs' completion window and allow managed-files listing with provider filter
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* style: ruff format transformation.py and endpoints.py
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(e2e/batches): set Azure raw_model to gpt-4.1-mini-batch to match deployed model
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(vertex-ai/batches): correct completion_window to 24h per Literal type definition
* test(vertex-ai/batches): align completion_window assertion to 24h
* fix: update managed file metadata on upsert
---------
Co-authored-by: mubashir1osmani <mubashir.osmani777@gmail.com>
Co-authored-by: Mateo Wang <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(proxy): count only active users toward license seat limit
SCIM-deactivated users (metadata.scim_active == false) are kept in LiteLLM_UserTable for audit and reactivation, but they were still counted toward the per-user license limit, so deactivating a user never freed a seat. Okta never sends a SCIM DELETE and Entra only hard-deletes well after deactivation, so deactivation has to be what frees the seat
Add UserRepository.count_billable_users(), which counts every row except those where metadata.scim_active is false (absent, null, and true all count), and route the user-create license gate, the free-SSO 5-user cap, and the enterprise /user/available_users display through it. A separate litellm_active_users Prometheus gauge reports the billable count while litellm_total_users keeps its original meaning so existing dashboards are unaffected
* fix(proxy): floor billable user count at zero
count_billable_users() runs two separate count queries (total, then deactivated). Under a burst of deactivations between them, the deactivated count can momentarily exceed the earlier total and produce a negative result, which would flow into is_over_limit as a negative and show a negative seat count in the display and gauge. Clamp the result to zero so a transient race can never yield a nonsensical negative; the value self-corrects on the next call
Addresses Greptile P1 on the PR
* refactor(proxy): count teams via TeamRepository in available_users
* style: ruff format changed files at line-length 120
* fix(bedrock_guardrails): select latest user message by original role in apply_guardrail (#23476)
* test(bedrock_guardrails): cover masking write-back through unified handler (#23476)
* fix(bedrock_guardrails): guard masked write-back on unresolved slice, not length
* chore(bedrock_guardrails): use builtin generics and extract write-back helper to satisfy strict ruff gate
Introduce a single, typed caller identity that is resolved once at the auth
boundary and read by reference downstream, instead of being re-derived from a
50-field key object or rebuilt from request metadata.
What this adds (litellm/proxy/auth/resolvers/), organized by responsibility:
- Principal: a small, frozen, identity-only value type (user / organization /
teams / project / end-user / roles / scopes / network), with its sub-models
and the role mapping. No budget or policy state; those stay on the key object.
- DbIdentityStore: the auth flow's resolver, owning both halves of resolving a
caller. resolve_key does the one combined_view lookup (cache, then DB via the
shared lower-level helpers, then write-back) and returns the key object, which
still flows for budget / rate-limit / policy unchanged. principal_from_key
projects the identity slice of that key object into a Principal, issuing no
lookup. user_api_key_auth resolves every key through the store rather than
calling get_key_object directly; auth_checks.get_key_object stays as the legacy
entrypoint for its other callers until they migrate.
- network: the X-Forwarded-For / trusted-proxy CIDR primitives live here in one
place. trusted_proxy_utils now imports them rather than keeping a second copy.
At the seam, user_api_key_auth projects one per-request Principal off the
resolved key object and stamps the request network context onto it once
(X-Forwarded-For is trusted only when trusted_proxy_ranges is configured). It is
attached to request.state.principal for the downstream consumers later phases
add. The projection is additive and defensive: a failure never rejects an
already-authenticated request, and a missing principal must be treated as deny
by any future reader. The Principal is always identifiable (credential_ref and a
stable subject off the token), never anonymous.
This is additive and changes no behavior today; it is the identity foundation
the spend-attribution and authorization phases build on.
* Fix overiding of fastapi_response headers
* fix(bedrock): support tool search results and surface citations as annotations
Add an optional tool-message search_results path that maps directly to Bedrock toolResult.searchResult blocks, and convert Converse citationsContent into chat completion annotations for user-facing citation metadata.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(format): align bedrock prompt factory with black
Reformat the updated bedrock prompt template conversion file so CI black --check passes.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(bedrock): harden citations, search_results mapping, and token counting
Resolve mypy issues in citation parsing, only attach url_citation annotations when citation text is stitched into content, fall back to tool content when search_results is empty, and count search_results text in token/TPM preflight paths.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(bedrock): extract tool result helpers to satisfy PLR0915
Refactor _convert_to_bedrock_tool_call_result into smaller helpers so lint passes without changing Bedrock tool result behavior.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(bedrock): count all forwarded search_results fields in token estimates
Include source, title, content text, and citations when estimating tokens so large metadata cannot bypass TPM preflight checks. Reformat factory.py with black.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(managed-files): skip content blocks without a type key in get_file_ids_from_messages
* fix(bedrock): stitch citations for any punctuation-only text block
* fix(bedrock): map null citation source/title to empty annotation strings
* fix(bedrock): advance citation offset for text-only citationsContent blocks
* fix(bedrock): complete citation TypedDicts for grounding annotations
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* feat(guardrails): wire apply_guardrail into proxy logging callbacks
Route /apply_guardrail through pre/post proxy hooks and LiteLLM success/failure handlers so Langfuse and OTEL integrations receive input/output on guardrail-only requests.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(guardrails): fix Greptile review comments on apply_guardrail logging
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(apply_guardrail): preserve original exception and capture modified response
- Capture return value from post_call_success_hook so callback-modified
responses propagate to the caller.
- Wrap success/failure logging calls in defensive try/except so logging
infrastructure failures don't replace the user-visible response or mask
the original guardrail exception.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* Fix mypy
* fix(apply_guardrail): isolate failure logging and use post-hook response for logging
- Split async_failure_handler and post_call_failure_hook into independent
try/except blocks so a callback bug in one does not silently skip the
other.
- Build response_for_logging inside _emit_guardrail_success_logs after
post_call_success_hook runs, so logged data matches the response the
caller actually receives when the hook modifies the response.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* fix(apply_guardrail): fix black formatting and update tests for fastapi_request param
- Run black on guardrail_endpoints.py to fix CI formatting check
- Add _mock_proxy_logging() helper to enterprise guardrail tests to patch
proxy-server globals imported at call time
- Pass fastapi_request=Mock() in all direct apply_guardrail test calls
to match updated function signature
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(guardrails): use transformed exception from post_call_failure_hook in apply_guardrail
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* fix(guardrails): isolate sync/async logging handlers in apply_guardrail
Separate each logging handler call into its own try/except so a failure
in the async handler does not silently skip the sync handler submission
(and vice versa). Matches the docstring's defensive intent.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* fix(apply_guardrail): guard transformed_exception with isinstance check
Co-authored-by: Cursor <cursoragent@cursor.com>
* test(guardrails): mock proxy globals in not_found test and share apply_guardrail logging fixture
- Add proxy-server global mocks to test_apply_guardrail_not_found so the
failure-path post_call_failure_hook call doesn't touch the real proxy
logging singleton.
- Extract the duplicated _mock_proxy_logging context manager out of the
two enterprise apply_guardrail test files into a shared conftest fixture
so the helper stays in one place.
* fix(guardrails): use update_messages to keep logging obj in sync
Co-authored-by: Yassin Kortam <yassin@berri.ai>
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* test: modernize models used in CircleCI e2e test suites
Replaces obsolete models (gpt-4o, gpt-4o-mini, gpt-3.5-turbo,
claude-3-5-sonnet-20240620, claude-sonnet-4-20250514) with current
equivalents across the e2e_openai_endpoints and
proxy_e2e_anthropic_messages_tests CircleCI jobs.
- gpt-4o -> gpt-5.5 (responses API e2e tests)
- gpt-4o-mini -> gpt-5-mini (websocket responses, oai_misc_config)
- gpt-4o-mini-2024-07-18 -> gpt-4.1-mini-2025-04-14 (fine-tuning,
still actively fine-tunable)
- gpt-4 / gpt-3.5-turbo target_model_names example -> gpt-5.5 /
gpt-5-mini
- bedrock claude-3-5-sonnet-20240620 batch entry -> haiku-4-5-20251001
(also aligning oai_misc_config model_name with what
test_bedrock_batches_api.py actually requests)
- bedrock claude-sonnet-4-20250514 (deprecated, retires 2026-06-15)
-> claude-sonnet-4-5-20250929
* test: point bedrock-claude-sonnet-4 alias at Sonnet 4.6, not 4.5
Greptile/Cursor flagged that after the previous commit, the
bedrock-claude-sonnet-4 alias collided with bedrock-claude-sonnet-4.5
(both pointed to claude-sonnet-4-5-20250929). Rename to
bedrock-claude-sonnet-4.6 and point it at the Sonnet 4.6 Bedrock ID
(us.anthropic.claude-sonnet-4-6, already in the litellm model
registry) so the alias name matches the underlying model version.
* test: modernize models across remaining CI-mounted configs & tests
Expands the modernization sweep to all CircleCI-mounted proxy configs
and to test directories where the model literal is a fixture/route key
(not the test's subject).
Config changes:
- proxy_server_config.yaml: bump gpt-3.5-turbo / gpt-3.5-turbo-1106 /
gpt-4o / gemini-1.5-flash / dall-e-3 underlying models; rename
gpt-3.5-turbo-end-user-test alias to gpt-5-mini-end-user-test; bump
text-embedding-ada-002 underlying to text-embedding-3-small. User-
facing aliases (gpt-3.5-turbo, gpt-4, text-embedding-ada-002, etc.)
preserved for backward compatibility with tests.
- simple_config.yaml, otel_test_config.yaml, spend_tracking_config.yaml:
bump gpt-3.5-turbo underlying to gpt-5-mini.
- pass_through_config.yaml: claude-3-5-sonnet / claude-3-7-sonnet /
claude-3-haiku entries replaced with claude-sonnet-4-5 / claude-
haiku-4-5 / claude-opus-4-7.
- oai_misc_config.yaml: align alias name with the gpt-5-mini rename.
Test changes (proactive: claude-sonnet-4-20250514 / claude-opus-4-
20250514 retire 2026-06-15):
- tests/llm_translation/test_anthropic_completion.py: bump 3 references
+ paired Vertex AI ID to claude-sonnet-4-5.
- tests/llm_translation/test_optional_params.py: bump 2 references.
- tests/pass_through_unit_tests/test_anthropic_messages_passthrough.py
and test_bedrock_anthropic_messages_test.py: bump router fixtures
using the deprecated model IDs.
- tests/pass_through_unit_tests/base_anthropic_messages_tool_search_test.py:
modernize docstring examples.
- tests/test_end_users.py: update references to renamed alias.
* test: modernize placeholder model literals in router_unit_tests
Mass replace_all on fixture/placeholder model literals across the
router_unit_tests/ suite (model name is a routing key / label, not the
test subject). Sub-agent sweep so far — additional commits will follow
for logging_callback_tests/, enterprise/, top-level tests/test_*.py,
and other CI-mounted dirs.
Mappings applied:
- gpt-3.5-turbo -> gpt-5-mini
- gpt-4 (bare) -> gpt-5.5
- gpt-4o (bare) -> gpt-5
- text-embedding-ada-002 -> text-embedding-3-small
- claude-3-sonnet-20240229 / claude-3-opus-20240229 /
claude-3-haiku-20240307 / claude-3-5-sonnet-20240620 ->
claude-sonnet-4-5-20250929 / claude-opus-4-7 /
claude-haiku-4-5-20251001 as appropriate
Explicitly preserved:
- gpt-4o-mini-* variants (transcribe, tts, etc.) where they're current
- gpt-4-turbo / gpt-4-vision-preview / gpt-4-0613 (subject literals)
- JSONL batch body literals
- Mock LLM response model fields (must match upstream)
- Fake/mock identifiers
* test: modernize placeholder model literals across remaining CI suites
Sub-agent sweep across logging_callback_tests/, guardrails_tests/,
enterprise/, pass_through_unit_tests/, otel_tests/,
llm_responses_api_testing/, batches_tests/, spend_tracking_tests/,
litellm_utils_tests/, unified_google_tests/, and a few top-level
tests/test_*.py files where the model literal is a fixture or
placeholder (router model_list, mock standard logging payload, mock
callback data) rather than the test's subject.
Mappings applied (see scope notes below):
- gpt-3.5-turbo -> gpt-5-mini
- gpt-4 (bare) -> gpt-5.5
- gpt-4o (bare) -> gpt-5.5 (corrected from initial gpt-5 — bare gpt-5
is not a valid OpenAI alias; only gpt-5.5 / gpt-5.4 / gpt-5.2-codex
/ gpt-5-mini exist)
- gpt-4o-mini (bare) -> gpt-5-mini
- text-embedding-ada-002 -> text-embedding-3-small
- claude-3-sonnet-20240229 -> claude-sonnet-4-5-20250929
- claude-3-opus-20240229 -> claude-opus-4-7
- claude-3-haiku-20240307 -> claude-haiku-4-5-20251001
- claude-3-5-sonnet-20240620/20241022 -> claude-sonnet-4-5-20250929
- claude-3-7-sonnet-20250219 -> claude-sonnet-4-6
- gemini-1.5-flash -> gemini-2.5-flash
- gemini-1.5-pro -> gemini-2.5-pro
Explicitly preserved (not modernized):
- llm_translation/ tests where model is the SUBJECT (provider-specific
translation/transformation logic). Only the deprecated 20250514
references were already bumped in a prior commit.
- Cost-calc / tokenizer subject tests in test_utils.py (skip-ranges
documented by the sub-agent).
- Bedrock model IDs in test_health_check.py path-stripping tests.
- JSONL batch request bodies and mock LLM response bodies (must match
upstream literal).
- Langfuse expected-request-body JSON fixtures (cost values are exact-
match-asserted; changing the model would shift response_cost).
- gpt-3.5-turbo-instruct (text-completion endpoint; no modern OpenAI
equivalent).
- Top-level tests calling the proxy through user-facing aliases
(gpt-3.5-turbo, gpt-4, text-embedding-ada-002, dall-e-3) — aliases
in proxy_server_config.yaml stay; only the underlying model was
bumped.
- tests/test_gpt5_azure_temperature_support.py (the test's whole point
is model-name handling).
- Fake / mock / openai/fake identifiers.
Notable side fixes:
- test_spend_accuracy_tests.py: UPSTREAM_MODEL now matches what
spend_tracking_config.yaml's proxy actually routes to (gpt-5-mini),
resolving a latent inconsistency.
- proxy_server_config.yaml: bare `gpt-5` alias renamed to `gpt-5.5`
(bare gpt-5 is not a valid OpenAI alias).
- test_batches_logging_unit_tests.py: explicit_models list entries
kept distinct (gpt-5-mini + gpt-5.5) after bulk rename.
* test: fix CI failures from model modernization sweep
CI surfaced 4 categories of regression from the bulk modernization:
1. Azure deployment names are customer-specific. Reverted:
- tests/litellm_utils_tests/test_health_check.py: azure/text-
embedding-3-small -> azure/text-embedding-ada-002 (the CI Azure
account does not have a text-embedding-3-small deployment).
- tests/logging_callback_tests/test_custom_callback_router.py:
same revert for two router fixtures driving aembedding.
2. gpt-5 family does not accept temperature != 1. Tests that pass a
custom temperature swapped from gpt-5-mini to gpt-4.1-mini (modern
non-reasoning OpenAI mini that still accepts temperature/logprobs):
- tests/logging_callback_tests/test_datadog.py
- tests/logging_callback_tests/test_langsmith_unit_test.py
- tests/logging_callback_tests/test_otel_logging.py
3. proxy_server_config.yaml's gpt-3.5-turbo-large alias was routing to
gpt-5.5 (a reasoning model that rejects logprobs). The proxy test
tests/test_openai_endpoints.py::test_chat_completion_streaming
exercises logprobs/top_logprobs through that alias. Bumped the
underlying model to gpt-4.1 (non-reasoning, still modern).
4. tests/logging_callback_tests/test_gcs_pub_sub.py asserts against a
pinned JSON fixture (gcs_pub_sub_body/spend_logs_payload.json) with
hardcoded model="gpt-4o" and a model-specific spend value. Reverted
the litellm.acompletion calls in the test to model="gpt-4o" so the
fixture's exact-match assertions still hold.
5. tests/pass_through_unit_tests/test_anthropic_messages_passthrough.py:
anthropic.messages.create routing to openai/gpt-5-mini returned an
empty content[0] with max_tokens=100 (reasoning-token consumption).
Swapped to openai/gpt-4.1-mini.
* test: fix Assistants API model + 2 cursor[bot] review nits
1. pass_through_unit_tests/test_custom_logger_passthrough.py: gpt-5.5
isn't accepted by the /v1/assistants endpoint
("unsupported_model"). Switch to gpt-4.1-mini (modern, Assistants-
API-supported, non-reasoning).
2. example_config_yaml/pass_through_config.yaml: the previous sweep
bumped the claude-3-7-sonnet alias to claude-opus-4-7, which is a
tier change (Sonnet -> Opus). Map to claude-sonnet-4-6 to keep the
Sonnet tier intact. (Cursor bugbot review.)
3. example_config_yaml/simple_config.yaml: model_name was left as
gpt-3.5-turbo while the underlying was bumped to gpt-5-mini, which
muddles the "simple" example. Make both sides gpt-5-mini so the
most basic example is a straight 1:1 mapping again. (Cursor bugbot
review.)
* fix: revert gpt-4/gpt-3.5-turbo alias underlying to non-reasoning models
tests/test_openai_endpoints.py::test_completion calls the proxy alias
"gpt-4" with temperature=0, and other tests call gpt-3.5-turbo with
custom temperature / logprobs / the legacy /v1/completions endpoint.
The earlier modernization mapped both aliases to gpt-5.5 / gpt-5-mini,
which are reasoning models that reject temperature != 1 and don't
expose /v1/completions. Map the aliases to gpt-4.1 / gpt-4.1-mini
(modern non-reasoning OpenAI models) instead — keeps user-facing
aliases preserved while picking a current underlying that still
supports the parameters/endpoints the tests exercise.
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.
MCP server CRUD endpoints (/v1/mcp/server*) were bundled with MCP
tool-call / passthrough endpoints under llm_api_routes, so setting
DISABLE_LLM_API_ENDPOINTS=true on admin-only nodes also blocked the
Admin UI from listing, adding, or attaching MCP servers.
Separate mcp_inference_routes (data-plane, gated by
DISABLE_LLM_API_ENDPOINTS) from mcp_management_routes (control-plane,
gated by DISABLE_ADMIN_ENDPOINTS). Keep mcp_routes as a union for
backward compat with allowed_routes=["mcp_routes"] virtual key configs.
Upgrade is_management_route to pattern-aware matching so
/v1/mcp/server/{path:path} resolves for concrete IDs.
- 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.
The error message for DISABLE_ADMIN_ENDPOINTS incorrectly said
"DISABLING LLM API ENDPOINTS is an Enterprise feature" instead of
"DISABLING ADMIN ENDPOINTS is an Enterprise feature".
This was a copy-paste bug from the is_llm_api_route_disabled() function.
Added regression tests to verify both error messages are correct.
* Fix Bedrock guardrail apply_guardrail method and test mocks
Fixed 4 failing tests in the guardrail test suite:
1. BedrockGuardrail.apply_guardrail now returns original texts when guardrail
allows content but doesn't provide output/outputs fields. Previously returned
empty list, causing test_bedrock_apply_guardrail_success to fail.
2. Updated test mocks to use correct Bedrock API response format:
- Changed from 'content' field to 'output' field
- Fixed nested structure from {'text': {'text': '...'}} to {'text': '...'}
- Added missing 'output' field in filter test
3. Fixed endpoint test mocks to return GenericGuardrailAPIInputs format:
- Changed from tuple (List[str], Optional[List[str]]) to dict {'texts': [...]}
- Updated method call assertions to use 'inputs' parameter correctly
All 12 guardrail tests now pass successfully.
* fix: remove python3-dev from Dockerfile.build_from_pip to avoid Python version conflict
The base image cgr.dev/chainguard/python:latest-dev already includes Python 3.14
and its development tools. Installing python3-dev pulls Python 3.13 packages
which conflict with the existing Python 3.14 installation, causing file
ownership errors during apk install.
* fix: disable callbacks in vertex fine-tuning tests to prevent Datadog logging interference
The test was failing because Datadog logging was making an HTTP POST request
that was being caught by the mock, causing assert_called_once() to fail.
By disabling callbacks during the test, we prevent Datadog from making any
HTTP calls, allowing the mock to only see the Vertex AI API call.
* fix: ensure test isolation in test_logging_non_streaming_request
Add proper cleanup to restore original litellm.callbacks after test execution.
This prevents test interference when running as part of a larger test suite,
where global state pollution was causing async_log_success_event to be
called multiple times instead of once.
Fixes test failure where the test expected async_log_success_event to be
called once but was being called twice due to callbacks from previous tests
not being cleaned up.
* fix(unified_guardrail.py): correctly map a v1/messages call to the anthropic unified guardrail
* fix: add more rigorous call type checks
* fix(anthropic_endpoints/endpoints.py): initialize logging object at the beginning of endpoint
ensures call id + trace id are emitted to guardrail api
* feat(anthropic/chat/guardrail_translation): support streaming guardrails
sample on every 5 chunks
* fix(openai/chat/guardrail_translation): support openai streaming guardrails
* fix: initial commit fixing output guardrails for responses api
* feat(openai/responses/guardrail_translation): handler.py - fix output checks on responses api
* fix(openai/responses/guardrail_translation/handler.py): ensure responses api guardrails work on streaming
* test: update tests
* test: update tests
* fix: support multiple kinds of input to the guardrail api
* feat(guardrail_translation/handler.py): support extracting tool calls from openai chat completions for guardrail api's
* feat(generic_guardrail_api.py): support extracting + returning modified tool calls on generic_guardrails_api
allows guardrail api to analyze tool call being sent to provider - to run any analysis on it
* fix(guardrails.py): support anthropic /v1/messages tool calls
* feat(responses_api/): extract tool calls for guardrail processing
* docs(generic_guardrail_api.md): document tools param support
* docs: generic_guardrail_api.md
improve documentation
* fix(initial-commit): adding a way to get the right response type based on the api route
* feat(unified_guardrail.py): support streaming guardrails
* test: update tests
* fix: fix linting errors
* test: update tests
* Fix - add safe divide by 0 for most places to prevent crash
* mock prisma client
* Revert "Fix - add safe divide by 0 for most places to prevent crash"
This reverts commit 265d40e390.
* (#11794) use upsert for managed object table rather than create to avoid UniqueViolationError
* (#11794) use upsert for managed object table rather than create to avoid UniqueViolationError
* feat(route_checks.py): allow admin to disable proxy management endpoints on instance
useful for preventing multiple instances from doing admin actions
* docs(scaling_multiple_instances.md): add architecture doc on scaling multiple litellm instances
provide guidance on scaling proxy
* docs(scaling_multiple_instances.md): add doc on scaling across multiple regions for litellm
* fix(route_checks.py): allow disabling llm api endpoints on an instance
allows pure admin instance to exist
* refactor(enterprise/route_checks.py): refactor env var checks
* refactor: finish refactoring
* docs(control_plane_and_data_plane.md): refactor docs
* test: update tests
