* fix(proxy): invalidate cached project object on /project/update and /project/delete
The auth path reads projects cache-first via get_project_object with a 60s
TTL and no freshness check, but no project write endpoint ever evicted the
project_id:{id} cache entry. A project cached before /project/update added a
model allowlist kept an empty models list in cache, so _run_project_checks
skipped can_project_access_model and project-bound keys could call team
models outside the project allowlist until the TTL expired. The same
staleness applied to blocked status and budget fields, and /project/delete
left the deleted project enforceable from cache.
Evict the cache entry after the DB write in update_project and
delete_project via a shared delete_cached_project_object helper, with the
cache key derivation shared with get_project_object.
* fix(proxy): broadcast project cache invalidation to all workers and make eviction best-effort
Single-worker eviction leaves every other worker serving its in-memory copy
of the mutated project until the 60s TTL expires, so a project allowlist
change was still bypassable on multi-worker deployments. Add a coordination
Redis pub/sub channel (litellm_proxy.auth_cache_invalidation): project
eviction publishes the cache key and a per-worker subscriber deletes the
local in-memory entry, with the next auth read refetching from the DB.
Subscriber starts on any deployment with a coordination Redis and falls back
to the TTL when none is configured.
Also wrap the eviction in a best-effort catch: the DB write has already
committed when eviction runs, so a cache backend error must not turn a
successful update into a 500 or abort the remaining ids in /project/delete.
* fix(lint): sort auth cache invalidation import and suppress best-effort shutdown catch
The strict-budget gate flagged the new import block as un-sorted (I001) and
the broad except in stop_auth_cache_invalidation_subscriber (BLE001); the
catch is intentional since a failing stop must not break proxy shutdown, so
it carries a named suppression instead of counting against the budget.
GET /v1/files filters data down to the caller's own managed files but left first_id and last_id as the upstream page's, so a non-owner got back file ids belonging to other users even with an empty data array
list_user_batches parsed each stored batch blob and returned it as-is, so any
row whose blob still carried raw provider file ids (for example a batch that
reached a terminal state through the cost poller, or rows written before
output registration existed) leaked raw output_file_id and error_file_id
values that clients cannot fetch through the proxy. The list path now runs
each row through ensure_batch_response_managed_file_ids, which swaps in
existing managed ids and registers missing ones under the batch owner's
identity, matching what GET /batches/{id} already does
Validating the cursor whenever `after` was non-None turned `?after=` into a
400, which the listing has always read as "no cursor". Only a cursor the
client actually sent is looked up now, matching the sibling managed-resource
listing.
An `after` that does not resolve to a batch the caller can list now returns
400 instead of an empty page. An empty page is indistinguishable from the end
of the list, so a stale or malformed cursor silently truncated a client's batch
list. The lookup is scoped to the caller's own rows, so a Prisma cursor can no
longer be anchored to another user's batch.
`has_more` now comes from whether an extra row exists rather than from whether
the page came back full. Reporting fullness made every client fetch one extra
empty page when the batch count was an exact multiple of `limit`, and made a
page shortened by an unparseable row look like the end of the list, hiding the
older batches behind it.
Also drops the unreachable `target_model_names` oversampling branch; that
argument raises a few lines above it.
GET /batches served from the managed-objects table paged with a
where id > after filter, but the after cursor clients send back is a
batch's unified_object_id (the value returned as .id and last_id), and
id is the table's random-uuid primary key. Comparing the two unrelated
fields, while ordering by created_at desc but filtering with gt, made
pages repeat the same last_id (pagination loops) and silently drop
batches. Switch to Prisma cursor pagination on the unique
unified_object_id column so listing walks every batch exactly once in
reverse-chronological order, matching OpenAI
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* 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.
