* fix: stop use_chat_completions_api flag from leaking into provider request body
use_chat_completions_api is a LiteLLM control flag that forces the
/responses -> /chat/completions bridge. It was missing from
all_litellm_params, so get_non_default_completion_params treated it as a
model-specific param and forwarded it to the upstream provider. A
model-level "use_chat_completions_api: true" in the proxy config therefore
reached the chat-completions path and was rejected by strict providers
(OpenAI/Anthropic) with HTTP 400 for an unknown body field.
Register it as a known internal param so it is stripped on every path
(completion, the responses bridge that calls litellm.completion, and
filter_out_litellm_params).
Adds a regression test driving litellm.completion() with a mocked OpenAI
client that asserts the flag never reaches the request body.
* test: clarify extra_body assertion in use_chat_completions_api leak test
Replace the misleading 'not in ... or {}' precedence idiom with an explicit
parenthesized guard that also handles extra_body being None.
(cherry picked from commit acbbfe9cae)
* fix(reset_budget): write only {spend, budget_reset_at} and stop pre-zeroing counter
ResetBudgetJob's batched update_data path shipped the full key/user/team
model on each reset. Prisma rejects object_permission_id and budget_limits
on the update input type, so any row carrying those fields detonated the
entire batch -- spend never reset, budget_reset_at never advanced. After
v1.84.0 started populating object_permission_id on UI-created keys, this
fires routinely.
_reset_budget_common also zeroed the cross-pod spend counter before the
DB write, so failed resets left enforcement reading 0 from the counter
while the DB still held the over-budget spend, admitting requests past
the cap until the counter naturally re-saturated from new reservations.
Switch the write to per-row narrow updates ({spend, budget_reset_at})
via db.batch_, and move the counter invalidation out of
_reset_budget_common so it only fires after the DB write commits. On
DB-write failure the counter is left untouched, enforcement continues
to block, and the next scheduler tick can retry without leaving a
bypass window.
Fixes#27730.
* fix(reset_budget): address Greptile review on #29358
- Strengthen the bypass-half regression test: replace the for-loop over
call_args_list (vacuously true when empty) with assert_not_called(),
so the test would actually flag a re-introduction of counter-zeroing
via any code path.
- Add the same explanatory docstring on _write_user_reset_updates and
_write_team_reset_updates that _write_key_reset_updates already has,
so all three helpers point future maintainers at #27730.
* test(reset_budget): update test_proxy_budget_reset for new batch-write path
Same shape as the previous test_reset_budget_job.py update: keys/users/teams
now write through prisma.db.batch_().<table>.update, not update_data, so the
tests need a batcher mock and updated assertions. Adds:
- _wire_batcher_for_test helper that returns a list which accumulates per-row
batch updates captured from prisma_client.db.batch_().
- _attrify helper that wraps dict fixtures so getattr(item, "token") works
alongside the dict item-access the fake_reset_* mocks rely on. The new
narrow-write helpers use getattr to pull out the row's id, and would
silently skip plain dicts otherwise.
- Updates 3 partial_failure tests to assert against the batch-call list
(rows by id, payload contains only {spend, budget_reset_at}) instead of
update_data.assert_awaited_once + data_list inspection.
- Updates test_reset_budget_continues_other_categories_on_failure: only
budget + enduser still flow through update_data; key/user/team go through
the batch path now.
- Wires the batcher mock into 3 service_logger_*_success tests so commit()
is actually awaitable and the success hook fires.
These tests were silently passing locally only because the editable install
in .venv pointed at the main repo, not the worktree — running pytest with
PYTHONPATH overridden to the worktree (matching CI) reproduces the failures.
(cherry picked from commit a06ec43b36)
* refactor(proxy/auth): normalize Bearer prefix in safe-hash helper
UserAPIKeyAuth._safe_hash_litellm_api_key now strips a leading
"Bearer "/"bearer " prefix before its existing sk-/JWT classification, so
the helper produces the same hashed output regardless of whether the
caller stripped the Authorization header prefix or passed the header
value through unchanged.
* refactor(proxy/auth): make Bearer-prefix strip case-insensitive
Per RFC 7235 the HTTP authorization scheme token is case-insensitive.
Replace the two-prefix loop with a single case-insensitive check so the
helper normalizes "Bearer ", "bearer ", "BEARER ", and any mixed-case
variant before classifying the remainder as sk- or JWT. The contract
test gains coverage of "BEARER " and "BeArEr ".
* test(mcp): align auth-handler test expectations with safe-hash helper
The two MCP auth tests asserted that UserAPIKeyAuth(api_key="Bearer ...")
retained the raw header bytes on the api_key field. _safe_hash_litellm_api_key
now normalizes that input — stripping the Bearer prefix and hashing the
resulting sk- key — so the expectations move to the normalized form:
the bare token in the parametrize case, and hash_token("sk-...") in the
backward-compat assertion. This matches what the real auth flow produces
(the builder strips Bearer and the DB stores the hashed token), so the
mocks now line up with production rather than with the un-normalized
validator output.
(cherry picked from commit 87b0e47485)
* fix(vertex_ai): omit function_call id on Vertex Gemini 3.5+ tool turns
Vertex AI rejects `id` on function_call/function_response parts; only Google AI Studio accepts it for Gemini 3.5+ strict tool matching.
Co-authored-by: Cursor <cursoragent@cursor.com>
* Update litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* fix(vertex_ai): forward custom_llm_provider in context caching
Pass custom_llm_provider through to _gemini_convert_messages_with_history
in the context caching path so Gemini 3.5+ tool-call `id` forwarding
behaves consistently between cached and non-cached completions on Google
AI Studio.
Co-authored-by: Claude <claude@anthropic.com>
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: Claude <claude@anthropic.com>
(cherry picked from commit fecf212d70)
(cherry picked from commit 75c72c51e2)
* fix(spend_counter): seed Redis counter via SET NX to prevent cross-pod double-seed
Symptom
-------
Customers on multi-pod deployments see team `spend` jump to ~2x (or N x
the pod count) shortly after a Redis cache miss / TTL expiry, triggering
spurious "Budget Crossed" alerts and blocked requests until the value is
manually reset.
Root cause
----------
`SpendCounterReseed.coalesced` warmed the primary spend counter by
calling `redis.async_increment(key, value=db_spend, refresh_ttl=True)`,
which lowers to Redis `INCRBYFLOAT`. That is additive, not idempotent.
The per-counter `asyncio.Lock` only coalesces seeders inside one
process. With N pods sharing one Redis, on a cold key (cold start, TTL
expiry, manual delete) every pod independently passes its lock + Redis
re-check, reads the same `db_spend`, and issues `INCRBYFLOAT db_spend`.
Final value: N x db_spend.
Fix
---
Use `redis.async_set_cache(key, value=db_spend, nx=True)` for the seed.
SET NX is atomic across pods: exactly one writer initializes the key;
losers read the winner's value via `async_get_cache`. This is the same
idiom already used by `coalesced_window` in the same file, so the two
seed paths are now consistent.
Per-request deltas continue to use `INCRBYFLOAT` (correct - additive
behaviour is what we want for increments, not for initial seed).
Verification
------------
Live two-process repro against the same Postgres + Redis (DB
spend = 506):
Unpatched: 4/4 runs -> Redis counter = ~1012 (~2 x db_spend)
Patched: 12/12 runs -> Redis counter = ~506
Unit tests (`test_proxy_server.py`):
- New `test_primary_spend_counter_redis_concurrent_seed_does_not_double_seed`
patches `_get_lock` to return a fresh lock per caller (otherwise the
per-process lock masks the race), races two `coalesced` calls, and
asserts final = 506 with exactly one of two SET NX attempts winning.
- 4 existing tests updated for the new seed contract (SET NX for the
seed, INCRBYFLOAT only for the per-request delta).
- Full `spend_counter or reseed or budget` slice: 22 passed.
Co-authored-by: Cursor <cursoragent@cursor.com>
* test(spend_counter): make SET NX mock atomic so loser branch is exercised
Greptile flagged that `redis_set_cache` in
test_primary_spend_counter_redis_concurrent_seed_does_not_double_seed
placed `await asyncio.sleep(0)` AFTER the NX membership check. Both
concurrent tasks observed an empty `redis_store`, passed the guard, and
both returned True - so the loser branch (else: read back winner's value)
was never exercised.
Fix the mock to model real atomic Redis SET NX:
- Yield BEFORE the membership check so two concurrent callers interleave
the way real SET NX does (first to resume runs check + write atomically
and wins; second resumes after the key exists and loses).
- Track set_cache return values; assert sorted([loser, winner]) so we
know exactly one task wins and one loses.
- Track async_get_cache calls that happen AFTER at least one SET NX has
completed; assert at least one such read - that is the loser-path
fallback (`current_value = float(cached)` when seeded is False).
Verified by temporarily reverting the mock to the old order: the test
now fails with `expected exactly one SET NX winner and one loser, got
[True, True]`, exactly the failure mode Greptile described.
No production code change.
Co-authored-by: Cursor <cursoragent@cursor.com>
* test(spend_counter): mock async_set_cache to populate redis_store in concurrent read+write test
`test_concurrent_read_and_write_paths_share_one_db_query` mocks
`async_increment` to populate the in-memory `redis_store`, but did not
mock `async_set_cache`. After the SET-NX seed change in `coalesced()`,
the seed step writes via `async_set_cache(nx=True)` (default AsyncMock,
no `redis_store` write), so the simulated Redis stays empty after the
first reseed. The second `get_current_spend` then sees a clean Redis
miss, re-enters the DB read path, and the test fails with
`expected 1 DB query, got 2`.
Fix: add a `redis_set_cache` side_effect that updates `redis_store` on
`nx=True` (and rejects when the key already exists), matching the
pattern used by the four sibling tests fixed in this branch's first
commit. Pre-existing assertions are unchanged.
Full `tests/test_litellm/proxy/test_proxy_server.py`: 158 passed.
Co-authored-by: Cursor <cursoragent@cursor.com>
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
(cherry picked from commit 0fb710400f)
(cherry picked from commit c621f58fff)
* chore(proxy): cherry-pick #28547 onto patch/v1.86.1
Backport of #28547 (`d480ffda3c`) onto the `patch/v1.86.1` branch.
Routes the remaining path-dependent call sites in auth, ACL, routing,
and audit-log decisions through `get_request_route(request)` so they
read from the ASGI `scope["path"]` instead of `request.url.path`. The
helper itself already exists on v1.86.1 (added by #27878); this PR
extends the helper's usage to the additional sites listed below.
Sites routed through get_request_route:
- _experimental/mcp_server/auth/user_api_key_auth_mcp.py
- management_endpoints/mcp_management_endpoints.py
- vector_store_endpoints/utils.py
- pass_through_endpoints/pass_through_endpoints.py
- auth/route_checks.py
- litellm_pre_call_utils.py
- spend_tracking/spend_management_endpoints.py
- common_utils/http_parsing_utils.py
- management_helpers/utils.py
- health_endpoints/_health_endpoints.py
Regression tests in tests/proxy_unit_tests/test_proxy_routes.py
construct a Request with scope["path"] set to a benign route and the
Host header crafted so url.path would resolve differently; each
site's decision is asserted against scope["path"].
Conflict resolution
-------------------
Cherry-pick applied cleanly with no conflicts. All 11 files plus the
test file are pure `request.url.path` → `get_request_route(request)`
swaps with the lazy auth_utils import (no feature drift).
* bump: version 1.86.1 → 1.86.2
* bump: version 1.86.0 → 1.86.1
* chore: refresh uv.lock for 1.86.1
* fix(team): keep team_alias cache in sync on _cache_team_object writes (#28737)
* fix(team): keep team_alias cache in sync on _cache_team_object writes
_cache_team_object wrote only to the team_id:<id> cache key, but the
JWT auth path that uses team_alias_jwt_field reads from a separate
team_alias:<alias> key (get_team_object_by_alias caches under both
keys on miss, but reads only the alias-keyed one). After any
team-mutation endpoint (team_model_add, team_model_delete,
update_team, the two access-group writes) the team_id cache was
refreshed but the team_alias cache stayed stale until TTL — JWT
callers using team_alias_jwt_field kept seeing the pre-mutation
team for the full cache window.
Mirror the write under the alias key inside _cache_team_object so
every existing caller stays in sync without further changes. Skip
the alias write when team_alias is None/empty so we don't collide
across alias-less teams.
Surfaced testing the LIT-3244 cherry-pick on patch/1.86.0: the
LIT-3244 fix correctly invalidated the team_id cache but the
customer's JWT used team_alias_jwt_field, so they kept hitting the
stale alias-keyed entry.
* fix(team): delete (not overwrite) team_alias cache on _cache_team_object
The prior shape of this PR wrote both team_id:<id> AND team_alias:<alias>
from _cache_team_object. team_alias is NOT unique in the schema
(no @unique on LiteLLM_TeamTable.team_alias), and get_team_object_by_alias
enforces uniqueness on its own DB-fetch path (len(teams) > 1 raises).
Writing the alias-keyed cache from the generic refresh path bypassed
that check: a team admin renaming their team to collide with another
team's alias could silently overwrite the cached team for JWT-by-alias
auth, swapping the resolved team under that alias for the cache window.
Switch the alias-keyed operation from a write to a delete (mirroring
the dual-cache delete pattern in _delete_cache_key_object). After every
team write, the next JWT-by-alias reader cache-misses and falls through
to get_team_object_by_alias, which (a) re-fetches the fresh team from
DB, closing the LIT-3244 staleness gap that motivated this PR, and
(b) enforces alias uniqueness before populating either cache key.
team_id:<id> writes are unchanged — team_id is the table PK and is
guaranteed unique.
Surfaced in veria-ai review on #28739.
* fix(managed-files): anchor model_id regex so it doesn't match llm_output_file_model_id
extract_model_id_from_unified_id used `re.search(r"model_id,([^;]+)", ...)`
which substring-matches the `model_id,` inside the file-ID encoding's
`llm_output_file_model_id,<deployment_uuid>` field. parse_unified_id
then fed that deployment UUID back into the auth path as a model
candidate via _extract_models_from_managed_resource_id, and every
team-BYOK file attach 403'd with:
team not allowed to access model. This team can only access
models=['openai/*']. Tried to access <deployment-uuid>
The team's models list correctly contains the public name (`openai/*`)
that target_model_names matches, but the bogus UUID candidate fails
the wildcard check first.
Anchor the regex to a field boundary (`(?:^|;)model_id,`) so it
matches the legitimate top-level `model_id,<value>` field on
vector_store unified IDs and skips substring matches inside other
fields. File-IDs (which have no top-level `model_id` field) now
return None and contribute no spurious UUID candidate.
Surfaced reproducing LIT-3244 on patch/1.86.0 with the customer's
exact flow: team with openai/* BYOK deployment, JWT-scoped user,
POST /v1/vector_stores/{id}/files attaching a file uploaded with
target_model_names=openai/gpt-4o.
* fix(proxy): hydrate wildcard discovery credentials (#28284)
* fix(proxy): hydrate wildcard discovery credentials
* fix(proxy): constrain wildcard credential hydration
* chore(tests): migrate Bedrock CI to AWS account 941277531214 (#28728)
* chore(tests): migrate Bedrock CI from AWS account 888602223428 to 941277531214
The original account (888602223428) was put under a security restriction by
AWS after a root access key leaked in a PR comment. While that account works
its way through the AWS Support unlock process, Bedrock-touching CI tests have
been migrated to a fresh account (941277531214).
Changes:
- Replace 26 hardcoded references to 888602223428 with 941277531214 across
8 files (provisioned-model ARNs, imported-model ARNs, AgentCore runtime
ARNs, batch execution role ARN, and example proxy config).
- The provisioned-model and imported-model ARNs are referenced only from
mocked unit tests — no AWS resources to recreate.
- The batch execution IAM role has been recreated in the new account with
the same name and equivalent permissions.
- The two AgentCore runtimes (hosted_agent_r9jvp-3ySZuRHjLC,
hosted_agent_13sf6-cALnp38iZD) are being recreated in the new account
under the same names — see tools/agentcore-deploy/ in a follow-up.
CircleCI env vars AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY / AWS_REGION_NAME
were updated separately via the CircleCI API to point at the new account.
Smoke-tested locally against the new account:
aws bedrock-runtime converse --region us-west-2 \
--model-id us.anthropic.claude-sonnet-4-5-20250929-v1:0 \
--messages '[{"role":"user","content":[{"text":"ping"}]}]'
→ 200, model returned 'pong'
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* chore(tests): refresh AgentCore ARN suffixes to match newly-deployed runtimes
The first migration commit replaced just the account ID, but AgentCore
auto-assigns a random 10-char suffix to every runtime on creation — we
can't reuse the original suffixes (`3ySZuRHjLC`, `cALnp38iZD`) in the
new account. Updated the AgentCore-runtime ARNs in the three files that
reference real runtime IDs (not the mock-based unit-test ARNs).
Deployed runtimes:
arn:aws:bedrock-agentcore:us-west-2:941277531214:runtime/hosted_agent_r9jvp-Rq79QFC2fp
arn:aws:bedrock-agentcore:us-west-2:941277531214:runtime/hosted_agent_13sf6-4046UzHSwy
Both runtimes are status=READY and pass a smoke invoke:
$ aws bedrock-agentcore invoke-agent-runtime --agent-runtime-arn ... --payload '{"prompt":"ping"}'
→ 200, {"result": "echo: ping"}
The agent is a minimal echo (see /tmp/agentcore_deploy/agent.py for the
deploy artifacts). Tests that only verify the SDK wiring will pass; if any
test asserts on agent output content, swap the echo for the real agent.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* chore(tests): point Bedrock batch tests at new-account S3 bucket
The account migration (888602223428 -> 941277531214) was a flat
account-ID swap, which only rewrites ARNs that embed the account
number. S3 bucket names carry no account ID, so the live Bedrock
batch tests still uploaded to `litellm-proxy` — a bucket that lives
in the old account. S3 names are globally unique, and the old account
still holds that name, so it can't be recreated in the new account.
Rename to `litellm-proxy-941277531214` (account-ID suffix guarantees
global uniqueness). The bucket must be created in 941277531214 and the
batch execution role granted s3:GetObject/PutObject/ListBucket on it
before this job is run in CI.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* chore(tests): point live S3 logging test at new-account bucket
Same account-ID-free blind spot as the batch bucket: `load-testing-oct`
lives in the old account and its name can't be reused globally. The
`logging_testing` CI job is wired into the workflow and runs
test_basic_s3_logging, which uploads to this bucket with the CI env
creds, then lists and deletes objects — a live dependency.
Rename to `load-testing-oct-941277531214`. The bucket must exist in the
new account with the CI IAM principal granted
s3:PutObject/GetObject/ListBucket/DeleteObject before this job runs.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* chore(tests): repoint Bedrock guardrail IDs to new-account guardrails
The migration left guardrail IDs untouched (no account ID in them), so
all live guardrail tests failed with "guardrail identifier or version
does not exist" against 941277531214. Recreated both guardrails in the
new account and updated the hardcoded IDs:
- wf0hkdb5x07f -> zgkmukebruil (PII mask: PHONE + CREDIT_DEBIT_CARD,
with explicit inputAction=ANONYMIZE so masking applies to INPUT,
which is the source litellm's moderation hook sends)
- ff6ujrregl1q -> 4w3d1di3snt5 (blocks "coffee"; blocked message set
to the exact string the tests assert on)
Updated test_bedrock_guardrails.py, otel_test_config.yaml, and the
guardrailConfig in test_bedrock_completion.py. Verified locally: the 5
previously-failing guardrail tests now pass.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* test(bedrock): migrate legacy models to current inference profiles
The new CI account (941277531214) cannot invoke legacy Bedrock models
(AWS gates them: "marked by provider as Legacy... not actively using in
the last 30 days"). Migrated the live-call tests:
- anthropic.claude-3-sonnet-20240229 -> us.anthropic.claude-sonnet-4-5-20250929-v1:0
- anthropic.claude-3-haiku-20240307 -> us.anthropic.claude-haiku-4-5-20251001-v1:0
Current Claude models on Bedrock require the us. inference-profile prefix
(bare on-demand ids are rejected).
cohere.command-r-plus has no working replacement (all Cohere is legacy-
gated in the new account): swapped to claude-haiku-4-5 in provider-
agnostic param lists. amazon.titan-image-generator skipped (no working
replacement). Mocked/transformation/cost tests that reference the legacy
strings are intentionally left unchanged. Verified live against the new
account.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* test(bedrock): repoint SageMaker + Knowledge Base to new-account resources
These referenced account-scoped resources by hardcoded id that only
existed in the old account, so the migration's account-ID swap missed
them. Recreated in 941277531214 and repointed:
- SageMaker endpoint jumpstart-dft-hf-textgeneration1-mp-20240815-185614
-> litellm-ci-textgen (gpt2 on a TGI container, ml.g5.xlarge)
- Bedrock Knowledge Base T37J8R4WTM -> LCYXFBR2TU (OpenSearch Serverless
vector store + titan-embed-text-v2, seeded with a LiteLLM doc)
Verified live: test_sagemaker.py (12 passed) and
test_bedrock_knowledgebase_hook.py (12 passed).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* test(reasoning_effort_grid): skip bedrock claude-opus-4-7 cells (not entitled on 941277531214)
claude-opus-4-7 is listed in the new Bedrock CI account's foundation
models but invoke is denied (AccessDeniedException: "not available for
this account"). Bedrock access to the flagship Opus requires an AWS
Sales request, not the self-serve model-access toggle, so it can't be
enabled inline with the rest of the account migration.
Add an optional `skip_reason` to ModelEntry and set it on the
bedrock-claude-opus-4-7 entry; the grid test honors it via pytest.skip.
Cell count (231) and route coverage are unchanged, so the structural
asserts still pass. Restore coverage by deleting the one skip_reason
line once access is granted.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* test(bedrock): swap/skip legacy-gated models unavailable on new CI account
The migrated AWS account (941277531214) cannot access several models that
the old account could, so the remaining red CI jobs were hitting real
Bedrock "Access denied / Legacy" and "account not authorized" errors:
- image_gen: skip both Nova Canvas test classes (amazon.nova-canvas-v1:0 is
legacy-gated), matching the existing titan skip.
- batches: skip test_async_file_and_batch (Bedrock batch inference is not
authorized on the new account; requires an AWS support case).
- litellm_overhead: swap legacy claude-3-5-haiku for the active
us.anthropic.claude-haiku-4-5 inference profile.
- test_completion_claude_3_function_call: swap legacy claude-3-sonnet for the
active us.anthropic.claude-sonnet-4-5 inference profile.
https://claude.ai/code/session_01Y7zgHYu9GX29YRwV4yiWAa
* test(bedrock): fix remaining e2e legacy-model + batch failures on new CI account
- e2e_openai_endpoints: skip test_bedrock_batches_api (Bedrock batch inference
is not authorized on account 941277531214) and migrate the missed
s3_bucket_name in oai_misc_config.yaml to litellm-proxy-941277531214.
- build_and_test: swap legacy bedrock claude-3-sonnet for the active
us.anthropic.claude-sonnet-4-5 inference profile in the proxy structured
output e2e test.
https://claude.ai/code/session_01Y7zgHYu9GX29YRwV4yiWAa
* test(bedrock): make opus-4-7 + batch cells fail loudly and mock image-gen (#28791)
Replace the silent skips added for the new CI account with noisier behavior:
- reasoning-effort grid: opus-4-7 cells now fail (when AWS creds are present)
instead of skipping, so the missing entitlement stays visible in CI; they
still skip when AWS creds are absent (local dev)
- Bedrock batch inference tests: drop the skip so they run and fail until
batch access is granted
- Titan + Nova Canvas image-gen tests: mock the Bedrock HTTP call so the
transform + cost-tracking path stays under test without live model access
https://claude.ai/code/session_01MT7SWDnXUjv6e6EPG7BDjT
Co-authored-by: Claude <noreply@anthropic.com>
* test(bedrock): use pytest.xfail for known-failing opus-4-7 cells
Replace pytest.fail with pytest.xfail when a model has a fail_reason,
so known-broken cells stay visible as XFAIL without keeping CI red.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
---------
Co-authored-by: Mateo <mateo@Mateos-MacBook-Pro.local>
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
---------
Co-authored-by: Dibyo Mukherjee <dibyo@adobe.com>
Co-authored-by: Mateo Wang <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: Mateo <mateo@Mateos-MacBook-Pro.local>
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* fix(mcp-oauth): add PROXY_BASE_URL escape hatch + diagnostic logging for invalid_request
Customers hitting "{"detail":"invalid_request"}" on the MCP /authorize
endpoint had no way to recover when their ingress mangles X-Forwarded-*
headers (the same-origin check in validate_trusted_redirect_uri compares
the browser-supplied redirect_uri against get_request_base_url, which is
reconstructed from those headers).
Two contained changes:
1. get_request_base_url now honours PROXY_BASE_URL as the canonical
public origin when set, bypassing the X-Forwarded-* trust gate
entirely. Operators who know their public URL can set it once
instead of debugging ingress header rewrites.
2. The rejection path in validate_trusted_redirect_uri emits a WARN
log carrying the redirect_uri, computed proxy base, and the
X-Forwarded-* / Host headers seen. A bare 400 was undiagnosable;
this turns it into a one-line root-cause.
* test(mcp-oauth): capture warnings from correct logger ("LiteLLM")
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* fix(mcp-oauth): reject malformed PROXY_BASE_URL with one-shot diagnostic
A scheme-less PROXY_BASE_URL (e.g. "litellm.example.com" instead of
"https://litellm.example.com") would sail through urlparse with empty
scheme + netloc, silently breaking every same-origin compare in
validate_trusted_redirect_uri and leaving the operator staring at the
same opaque 400 the env var was meant to fix.
Validate it once at read time: only honour values that parse as
http(s) URLs with a non-empty netloc; otherwise log a one-shot WARN
naming the bad value and fall through to the request-derived origin
so the proxy still serves traffic.
* fix(mcp/oauth): normalize PROXY_BASE_URL to strip query/fragment
Match the X-Forwarded-* path's normalization so a configured
PROXY_BASE_URL containing a query string or fragment does not break
downstream f-string concatenation like f"{base_url}/callback".
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* refactor(mcp-oauth): drop non-essential comments from PROXY_BASE_URL changes
Strip narrative comments and verbose docstrings added in this PR; the
code is intuitive enough on its own and the log messages already carry
their own diagnostic context. Pre-existing comments are left untouched.
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* fix(proxy): sort BYOK models by team_public_model_name in /v2/model/info
Team BYOK rows persist an internal `model_name` like
`model_name_{team_id}_{uuid}` and expose the user-facing name via
`model_info.team_public_model_name`. The UI's `getDisplayModelName`
and the search filter already fall back to that field, but
`_sort_models` was keying off the raw `model_name` — so BYOK rows
ranked by their opaque IDs and clumped at the end of the alphabetized
list instead of interleaving with non-BYOK rows.
Match the UI/search behavior: prefer `team_public_model_name` when
present, fall back to `model_name` otherwise.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(proxy): case-insensitive DB-side search for BYOK models
`_apply_search_filter_to_models` used Prisma's JSON path
`string_contains` to match the BYOK `team_public_model_name` field, but
that operator is case-sensitive in Postgres (no `mode: insensitive`
flag like column-level string filters have). So a search for "claude"
missed a stored "Claude Sonnet" via the DB branch even though the
router-side path matched it case-insensitively.
Widen the JSON branch to "row has a team_public_model_name set" and
filter case-insensitively in Python so DB-only BYOK rows match the
same terms users see in the UI. This also drops the now-unused
DB-level page-size optimization and `sort_by` knob — the in-Python
filter is the source of truth for `db_models_total_count` now.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(proxy): scope BYOK search results to caller's accessible teams
`_apply_search_filter_to_models` was widened to fetch every row with a
`team_public_model_name` set so case-insensitive search could match
mixed-case stored names. `/v2/model/info` is reachable by non-admin
keys though, and the helper ran before `include_team_models` / `teamId`
filtering — so a non-admin caller could search a common substring like
"claude" and see BYOK rows belonging to teams they're not a member of.
Resolve the caller's team membership once (admin → no scoping, else
their `user_row.teams`) and drop BYOK rows (those with
`model_info.team_id` set) outside that scope on both the router-side
matches and the over-broad DB query, before display-name matching.
Non-team rows are unaffected and remain gated by the existing
`include_team_models` / `direct_access` paths.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(proxy): search by team_public_model_name and scope teamId queries
- /v2/model/info search now matches both `model_name` and
`model_info.team_public_model_name`, so team BYOK rows (which persist
an internal `model_name_{team_id}_{uuid}`) are findable by the public
name shown in the UI. DB query OR-includes a JSON-path match on
`team_public_model_name` for rows that exist only in the DB.
- `_filter_models_by_team_id` no longer short-circuits on the viewer's
`direct_access` flag — that describes the admin viewer's own
permissions and would leak every public model into a team-scoped view.
Models are kept only when they belong to the team (own BYOK, in
access_via_team_ids, or reachable via team.models / access groups).
- Added `_authorize_team_id_query`: the untrusted `teamId` query
parameter now requires the caller to be a proxy admin or a member of
the requested team, otherwise returns 403. Without this, any
authenticated user could enumerate another team's BYOK metadata by
guessing the team id.
- `_get_caller_byok_team_scope` now treats `PROXY_ADMIN_VIEW_ONLY` the
same as `PROXY_ADMIN` (both are admin roles); previously VIEW_ONLY
admins fell through to a user-id team lookup and saw only their own
teams' BYOK rows.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(proxy): bound BYOK search DB fetch in /v2/model/info
Previously the DB-side search OR'd a JSON-path predicate
`{model_info: {path: [team_public_model_name], string_contains: ""}}`
to compensate for Prisma's case-sensitive JSON `string_contains` on
Postgres. That predicate matches every row that has any
`team_public_model_name` set, so any authenticated caller could force a
full BYOK-table read with `/v2/model/info?search=x` regardless of page
size.
Drop the JSON-path branch. The DB query now does a bounded
`model_name contains <search>` lookup. BYOK rows that are loaded into
the router are still searchable by their `team_public_model_name` via
the router-side filter; only the rare edge case of a BYOK row that
exists only in the DB (router sync failed) loses display-name search,
which is an acceptable trade-off given the DoS surface.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(proxy): bound DB find_many in /v2/model/info search
The previous bounding patch dropped the page-aware `take=N` on
`find_many`, so a broad `?search=model` would load and decrypt every
matching DB row on each request even though the response only returns
one page.
Restore bounded fetches in `_apply_search_filter_to_models`:
* Unsorted searches use `take = max(0, page * size - router_count)`,
i.e. exactly one page worth of remaining DB rows.
* Sorted searches need ordering across the full match set, so they cap
at `_SORTED_SEARCH_DB_FETCH_CAP = 500` instead of fetching everything.
* Total count comes from a cheap `count(...)` query so pagination stays
accurate without materializing every row.
Wired `page`, `size`, and `sortBy` through from the endpoint and added
a regression test covering both `take` values.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* refactor(proxy): extract DB-fetch helper to satisfy PLR0915
_apply_search_filter_to_models tripped Ruff's "too many statements"
(51 > 50) after the bounded-fetch fix. Move the DB-side block into
`_fetch_db_models_for_search`, which keeps the same behavior:
* Bounded `take` via page math (unsorted) or `_SORTED_SEARCH_DB_FETCH_CAP`
(sorted)
* Cheap `count(...)` for accurate pagination totals
* Caller-team scope applied to fetched rows before decrypt
Pure refactor; no behavior change. All 8 BYOK/team tests still pass.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* style: apply black formatting to _fetch_db_models_for_search
CI's "Check Black formatting" step flagged one line in the helper added
in d55eecf6af. No behavior change.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The proxy SERVER span ("Received Proxy Server Request") only carried
http.response.status_code on failures (set in _record_exception_on_span),
so success traces had no 2xx bucket — error-ratio and status-breakdown
dashboards were missing their denominator and the span violated the HTTP
semconv (the attribute is required whenever a response is sent). Add a
set_response_status_code_attribute helper and call it from
async_post_call_success_hook with 200, symmetric with the failure path
and the existing route/preprocessing-duration SERVER-span attributes.
* feat(otel): expose http.response.status_code on failure spans
Set the OTel-standard http.response.status_code (integer) on failure
spans alongside the existing OpenInference error.code (kept for
back-compat). error.type is already emitted via ERROR_TYPE.
Crucially, also record structured error attributes on the proxy SERVER
span ('Received Proxy Server Request') from async_post_call_failure_hook
- the only place the SERVER span is in hand. _handle_failure records on
the litellm_request child span (the parent span is not propagated into
its kwargs), so prior to this change the SERVER span that dashboards
query carried only span status, never error.code/error.type. Reuses
_record_exception_on_span + StandardLoggingPayloadSetup.get_error_information
so values match the child span.
Tests: recorder unit coverage + a hook-driven test asserting the SERVER
span is stamped (the gap recorder-only tests missed). Full
test_opentelemetry.py suite: 197 passed.
* feat(otel): set http.route + url.path on the proxy SERVER span
Add the OTel-standard http.route (low-cardinality route template, e.g.
/v1/threads/{thread_id}/runs) and url.path (literal path) to the SERVER
span ('Received Proxy Server Request') so dashboards can group traffic
by endpoint instead of seeing every path param as a unique value.
Same architectural gap as the status-code commit: the success/failure
logging handlers write the litellm_request CHILD span, and
_handle_success explicitly refuses to copy to the SERVER span. Verified
with a console-exporter run that the SERVER span was bare on success.
Unlike error info, route/path are known at request time, so set them
directly on the freshly-created SERVER span in user_api_key_auth (one
edit point, works for success and failure, no hook-ordering risk):
- http.route from the matched FastAPI route (scope['route'].path),
empirically confirmed populated at auth-dependency time.
- url.path from the existing literal-path variable.
New get_request_route_template helper + set_proxy_request_route_attributes
(no-op on None span, so the Langfuse override stays safe).
Tests: route-attribute setter + route-template helper edges. Full
test_opentelemetry.py and test_auth_utils.py green.
* feat(otel): set litellm.preprocessing.duration_ms on the proxy SERVER span
Expose the total time LiteLLM spends before the upstream provider
request begins (auth + parsing + pre-call hooks) as a single number on
the SERVER span ('Received Proxy Server Request'). Window:
proxy-receive -> FIRST provider handoff.
Retry semantics: first attempt only (pure preprocessing, excludes
retry loops + backoff). api_call_start_time is overwritten on every
attempt, so a set-once first_api_call_start_time pins the first handoff.
Same architectural gap as the prior two commits: the success/failure
logging handlers write the litellm_request CHILD span, not the SERVER
span. Set it instead from the post-call hooks on
user_api_key_dict.parent_otel_span.
Failure-path subtlety: request_data.pop('litellm_logging_obj') runs
before the failure-hook loop, so the failure hook can't read the
logging object. litellm_received_at is propagated via the existing
request->metadata channel, and first_api_call_start_time is mirrored
onto litellm_params.metadata, so both anchors survive into request_data
and the OTel helper reads them uniformly for success and failure.
Edits: user_api_key_auth (stash receive instant), litellm_pre_call_utils
(propagate it), litellm_logging (set-once first handoff + metadata
mirror), opentelemetry (constant + set_preprocessing_duration_attribute,
called from both post-call hooks).
Tests: duration helper (both container shapes, missing/negative/None
edges) + set-once invariant (retry doesn't overwrite, metadata mirror).
test_opentelemetry.py + test_auth_utils.py + test_litellm_logging.py:
447 passed. Verified live: SERVER span carries the attribute on success
and failure, coexisting with the status-code and route attributes.
* fix(otel): MyPy type-narrowing for status-code + preprocessing-duration
No behavior change. MyPy (CI lint) flagged:
- error_information["error_code"] is str|None: narrow via a None-checked
local before int().
- _to_timestamp returns Optional[float]: resolve both anchors and return
early if either is None instead of subtracting possibly-None floats.
* fix(otel): stop polluting user request metadata with first_api_call_start_time
The PR3 set-once preprocessing anchor was mirrored into
litellm_params["metadata"] from core litellm_logging.py. That dict is
the caller's request metadata, mutated in place and shared across every
call path including pure SDK (litellm.acreate_batch). It got echoed into
LiteLLMBatch(metadata=...), which the OpenAI batch schema types as
Dict[str, str] -> pydantic ValidationError on a datetime value.
- litellm_logging.py: set first_api_call_start_time only on
model_call_details (success path reads it there directly).
- proxy/utils.py: post_call_failure_hook lifts it off the logging object
into request_data (internal top-level key, same convention as the
other proxy-internal request_data keys) right before the existing
litellm_logging_obj pop. Never touches user metadata.
- opentelemetry.py: read the anchor from the container top level
(model_call_details on success, request_data on failure).
- Tests updated; add TestPostCallFailureHookLiftsFirstApiCallStartTime.
Fixes the batches_testing regression introduced on this branch.
* chore(otel): trim verbose comments to concise rationale
Collapse multi-line why-blocks to one or two lines and drop process/plan references (PR-numbering, "the plan") from test comments. No behavior change.
Split the monolithic LiteLLM proxy into independently scalable Kubernetes components to allow separate horizontal scaling of the LLM data plane and management API surfaces
- Add DatabaseURLSettings pydantic-settings model that assembles DATABASE_URL (and optional DATABASE_URL_READ_REPLICA) from discrete DATABASE_* env vars before Prisma initializes, supporting both IAM token auth (minting short-lived RDS tokens) and password auth; replaces the CLI-only path that componentized entrypoints bypass
- Add gateway component (port 4000) that trims the proxy route table to the LLM data-plane surface (chat, embeddings, completions, audio, realtime, provider passthroughs, health/metrics) via an allowlist applied inside the lifespan context so plugin-registered routes are captured
- Add backend component (port 4001) that exposes the management/admin surface (keys, users, teams, orgs, spend analytics, model management, SSO, audit logs) with a complementary allowlist
- Add ui component — Next.js static export served by nginx (port 3000) with RSC payload routing, asset prefix aliasing, and SPA fallback for dashboard routes
- Add migrations component with dedicated Dockerfile that runs prisma migrate deploy via a Helm pre-install/pre-upgrade Job, eliminating per-pod schema contention on the Prisma advisory lock
- Add Helm chart (helm/litellm) with separate Deployments, Services, HPAs, and ConfigMap for each component; shared _helpers.tpl emits DATABASE_*, IAM_TOKEN_DB_AUTH, REDIS_*, and DISABLE_SCHEMA_UPDATE env vars from chart values; ingress template routes traffic to the correct component by path prefix
- Add comprehensive tests for DatabaseURLSettings covering IAM auth, password auth, read replica fallbacks, operator-pinned URL preservation, and percent-encoding; add coverage test asserting gateway + backend allowlist union equals the full proxy route set
- Add pydantic-settings>=2.14.1 as a proxy extra dependency and update liccheck allowlist
Co-authored-by: Yassin Kortam <yassinkortam@g.ucla.edu>
Resolve conflicts in the five unrelated CI-flake fixes I previously landed
on this branch -- staging shipped stronger versions (mocked HTTP for the
Fireworks tests, mocked image-fetch for the Gemini size-limit test, switched
the openapi-compliance test to the Interaction response schema instead of
dropping the assertion). Take staging's version of all five files and drop
my now-unreachable 429-skip lines from the Gemini test that the auto-merge
left behind.
Two P2 nits flagged by Greptile on PR 28036:
1. _build_completion_kwargs() defaulted vertex_project to "vertex-check-481318"
when VERTEX_PROJECT was unset. That value is a specific GCP project that
doesn't belong to this repo, so if the env-var skip guard were ever
bypassed (misconfig, direct helper call), the test would silently issue
calls to a foreign project rather than failing loudly. Drop the fallback
and read os.environ["VERTEX_PROJECT"] directly, mirroring how
AZURE_FOUNDRY_* are handled.
2. _build_messages_kwargs() was a one-liner that returned the result of
_build_completion_kwargs() unchanged -- a dead abstraction with one
caller. Inline at the _call_messages call site and delete the helper.
litellm.ImageFetchError is a subclass of BadRequestError, so when
Wikimedia returns 429 the pytest.raises(ImageFetchError) block matches
and swallows the exception -- the outer try/except never fires. Drop the
try/except and check the captured error message for "Status code: 429"
after the raises block, calling pytest.skip in that case. Same intent,
right control flow.
Four pre-existing flakes on main that gate this branch's workflow even
though they're unrelated to the reasoning_effort_grid suite:
1. tests/local_testing/test_completion.py::test_completion_fireworks_ai
2. tests/local_testing/test_completion_cost.py::test_completion_cost_fireworks_ai[fireworks_ai/llama-v3p3-70b-instruct]
3. tests/llm_translation/test_fireworks_ai_translation.py::test_document_inlining_example[False]
The Fireworks-hosted `llama-v3p3-70b-instruct` deployment is currently
returning 404 "Model not found, inaccessible, and/or not deployed".
These tests pass when the model is deployed; the issue is upstream
capacity, not our code path. Wrap the live call in a try/except that
pytest.skip's on litellm.NotFoundError so a Fireworks deployment hiccup
no longer fails CI for unrelated PRs.
4. tests/llm_translation/test_gemini.py::test_gemini_image_size_limit_exceeded
The test fetches the 32MB "Blue Marble 2002" image from Wikimedia to
exercise the 50MB image-size cap. CI runners share an IP pool with
noisy traffic, so Wikimedia routinely returns HTTP 429. The size-limit
check never gets a chance to fire. Catch the 429 BadRequestError and
pytest.skip in that case.
None of these belong on this PR conceptually, but they're included per
request to unblock the workflow before morning.
The anthropic_messages route wraps client-side BadRequestError as
AnthropicError (a BaseLLMException subclass) with status_code=400, so
"except BadRequestError" missed those cells and they fell through to the
generic Exception arm, returning 500 instead of the expected 400.
Replace the isinstance-on-BadRequestError check with a tiny classifier
that prefers BadRequestError membership, then falls back to the exception's
status_code attribute (set by every BaseLLMException subclass), then 500.
Apply to both _call_chat and _call_messages for consistency.
Fixes the 13 CircleCI llm_translation_testing failures on
bedrock_invoke_messages cells where the effort was disabled / invalid /
empty / xhigh-on-unsupported / max-on-unsupported.
Two CI failures, both pre-existing in different ways:
1. reasoning_effort_grid: all 33 bedrock_invoke_messages cells failed with
AttributeError("module 'litellm' has no attribute 'messages'"). litellm
exposes the async Anthropic Messages entrypoint as litellm.anthropic_messages
(via "from .llms.anthropic.experimental_pass_through.messages.handler
import *" in litellm/__init__.py), not litellm.messages.acreate. Swap
the call.
2. tests/test_litellm/interactions/test_openapi_compliance.py::TestResponseCompliance::test_interaction_response_fields
asserts the live Google spec contains "steps". Google's spec has churned
through "outputs" -> "steps" -> neither, and presently carries neither.
The test broke on main as soon as upstream dropped "steps"; pulling the
key off the assert list realigns the test with the live schema. Re-add
the per-turn output field once upstream stabilizes on a name.
The openapi-compliance fix doesn't belong to this PR conceptually but is
included here per request to unblock CI before the morning.
The Content-Length header check in _process_image_response rejects the
image before the body is streamed, so the mock body never needs to be
materialized. Use an empty body instead of b"x" * 100MB (addresses
greptile/cursor review feedback).
Mock the image fetch instead of downloading a 50MB+ image from
upload.wikimedia.org. The runner was intermittently rate-limited
(HTTP 429), so the code raised "Unable to fetch image ... Status
code: 429" and the size-limit assertions failed even though
pytest.raises(litellm.ImageFetchError) still matched.
Mirror the established LargeImageClient pattern in
tests/test_litellm/litellm_core_utils/test_image_handling.py: stub
litellm.module_level_client with a response whose Content-Length
exceeds the 50MB limit and bypass SSRF validation, so the
size-limit rejection path is exercised deterministically with no
external network dependency.
test_completion_fireworks_ai and test_completion_cost_fireworks_ai
made real Fireworks calls and broke whenever Fireworks rotated its
serverless catalog (no externally-verifiable model list exists).
They also asserted nothing — just printed.
Mock the HTTP post and assert real behavior instead: the request is
built with the right model/messages and the OpenAI-compatible
response parses back; the cost path yields a non-zero cost against
the local cost map. No network, no model dependency, stronger than
the old smoke checks.
The live deepseek-v3p1 call kept hitting Fireworks NOT_FOUND because
Fireworks rotates its serverless catalog and no externally-verifiable
list exists. The [False] branch also never sent an image, so it only
proved the model responded.
Mock the HTTP post (mirrors test_global_disable_flag_with_transform_
messages_helper) and assert the real behavior: #transform=inline is
appended to the PDF URL unless disabled. No network, no model
dependency, and stronger coverage than the old live test.
Fireworks removed llama-v3p3-70b-instruct from serverless, so every
live test using it now fails with NotFoundError ("Model not found,
inaccessible, and/or not deployed").
Swap the 6 references (3 files) to the currently-served
accounts/fireworks/models/deepseek-v3p1 — the canonical model in
Fireworks' current docs examples and present in LiteLLM's cost map.
test_get_model_params_fireworks_ai is a pure pricing-heuristic test
(no network) asserting the >16b branch, so it uses llama-v3p1-70b-
instruct instead to keep the "fireworks-ai-above-16b" assertion and
branch coverage intact.
Google restructured the live spec at ai.google.dev/static/api/
interactions.openapi.json: the output-only fields (notably the
`steps` array, formerly `outputs`) moved off the request schema
`CreateModelInteractionParams` onto a dedicated `Interaction`
response schema. The response-side tests still read from the
request schema, so `test_interaction_response_fields` failed with
"Output field 'steps' not in spec".
Point `test_interaction_response_fields` and `test_status_enum_values`
at the `Interaction` schema (the semantic response object; all output
fields incl. `steps` present there). Request-side tests keep using
`CreateModelInteractionParams` (all request fields verified still
present). 13/13 pass against the current live spec.
- Drop the "v4" suffix throughout: it referred to the QA sweep iteration,
not this test suite. There's only one regression suite, so just call it
reasoning_effort_grid.
- Move tests/test_litellm/reasoning_effort_grid_v4/ -> tests/llm_translation/
reasoning_effort_grid/. Two reasons:
1. The parent tests/test_litellm/conftest.py installs an autouse fixture
(isolate_host_aws_config) that clears every AWS_* env var before each
test, which would silently skip every Bedrock cell.
2. tests/llm_translation/conftest.py already wires up the Redis-backed
VCR persister and auto-applies @pytest.mark.vcr to every collected
item via apply_vcr_auto_marker_to_items. Living under that conftest
means the suite gets cassette replay for free -- first CI run with
provider creds records 231 cassettes, every subsequent run replays
them with no live spend.
- Trim the suite's own conftest down to just the wire_capture fixture; the
inherited llm_translation conftest covers the VCR plumbing.
- Drop the dedicated reasoning_effort_grid_v4_e2e CircleCI job. The existing
llm_translation_testing job globs tests/llm_translation/**/test_*.py, so
the suite is gated by an existing job with no new wiring.
The runtime _validate_effort_for_model allows effort='max' for any
Claude 4.6 model (opus or sonnet), and model_prices_and_context_window
sets supports_max_reasoning_effort: true for claude-sonnet-4-6. The
grid spec previously gave sonnet-4-6 entries _CAPS_NONE, so expected()
returned status=400 for effort='max', which mismatched the runtime's
status=200 and caused 6 cells (one per route) to fail.
Rename _CAPS_OPUS_4_6 to _CAPS_4_6 (since the cap set is shared by
opus and sonnet 4.6) and assign it to all sonnet-4-6 entries.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
- Remove unused vertex_credentials_path fixture (and now-unused os import)
from conftest.py.
- Parse Bedrock Converse complete_input_dict (logged as a JSON string by
converse_handler.py) before passing to _assert_cell, so dict accessors
work uniformly across routes.
- Extend _BUDGET_TOKENS with xhigh and max entries so the budget-mode
branch in expected() cannot KeyError if a future budget model gains
the matching cap.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
Encode the 231-cell QA sweep (21 provider x model combos x 11 effort
values) from #27039 / #27074 as an automated CircleCI-gated regression
suite. Each cell hits the real provider endpoint, captures the outgoing
wire body via a pre-call CustomLogger, and asserts:
- thinking.type, output_config.effort, thinking.budget_tokens, max_tokens
in the captured request body (regression signal for silent drops/strips
in any provider transformation)
- HTTP status (200 vs BadRequestError -> 400) returned by litellm
(regression signal for clean-error vs leaked-500 mappings)
The matrix is encoded as a small rule set keyed by (model_mode, effort)
plus per-model xhigh/max capability overrides, then expanded across the
five chat-completion routes (Anthropic direct, Azure AI Foundry, Vertex
AI, Bedrock Converse, Bedrock Invoke /chat) and the Bedrock Invoke
/v1/messages route. Cells skip at runtime when the route's provider env
vars are absent, so PR builds without credentials no-op gracefully.
Wired into CircleCI as the reasoning_effort_grid_v4_e2e job behind the
existing main / litellm_* branch filter.
- Introduce `OTEL_SEMCONV_STABILITY_OPT_IN=gen_ai_latest_experimental` opt-in that switches OTEL traces to conform with the OpenTelemetry GenAI semantic conventions specification
- Extract all semconv behavior into a new `OTELGenAISemconvMixin` class in `gen_ai_semconv.py`, mixed into `OpenTelemetry` to keep concerns separated
- In semconv mode, span name follows `{operation} {model}` pattern (e.g. `chat gpt-4`) and span kind is set to `CLIENT` instead of legacy `litellm_request`
- Replace `gen_ai.system` with `gen_ai.provider.name` and drop `llm.is_streaming` in semconv mode; add `gen_ai.request.{frequency_penalty,presence_penalty,top_k,seed,stop_sequences,stream,choice.count}` and `gen_ai.usage.cache_{creation,read}.input_tokens` attributes
- Replace per-message `gen_ai.content.prompt` / per-choice `gen_ai.content.completion` log events with a single consolidated `gen_ai.client.inference.operation.details` event; omit `gen_ai.input/output.messages` when content capture is disabled
- Suppress the non-standard `raw_gen_ai_request` child span entirely in semconv mode
- Support both programmatic (`OpenTelemetryConfig.semconv_stability_opt_in` field) and environment variable activation; the two sources are unioned so either or both can enable the opt-in
- Extract OTEL SDK `LogRecord` / `SeverityNumber` version-compatibility shim into a reusable `_otel_log_types()` static method to deduplicate the `< 1.39.0` / `>= 1.39.0` import branching
- Add 30+ unit tests covering opt-in gating, span naming, attribute emission/omission rules, stop sequence normalization, cache token attributes, and the consolidated event lifecycle
Co-authored-by: Yassin Kortam <yassinkortam@g.ucla.edu>
test_keepalive_timeout_flag and test_timeout_worker_healthcheck_flag
were the only run_server tests in test_proxy_cli.py that neither
stripped DATABASE_URL/DIRECT_URL nor mocked the prisma DB path. When a
DATABASE_URL is present (CI/env leak), run_server --local enters the DB
block and blocks in the un-timeout'd subprocess.run(["prisma"]) at
proxy_cli.py:987 plus the ProxyExtrasDBManager migrate-deploy retry
loops, ~370s per test on the CI runner. --dist=loadscope pins both to
one xdist worker, so the proxy-infra job appears stuck at 99% and hits
the 20-min timeout.
Apply the same isolation every other run_server test in this file
already uses: mock PrismaManager.setup_database +
should_update_prisma_schema and strip DATABASE_URL/DIRECT_URL. Full
module drops from 31.7s to 2.9s locally; both tests fall off the slow
list.
* 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.
Adds test_update_config_env_var_round_trip_not_double_encrypted, which
drives the real /config/update handler: first write plaintext, then
re-POST the stored ciphertext (the Admin UI round-trip) and assert the
value is not stacked with a second encryption layer and untouched keys
stay byte-identical. Verified to fail against the pre-fix handler and
pass after. Also tightens the unit test to exactly three ciphertext
re-feeds.
A single decrypt-then-encrypt chokepoint (_encrypt_env_variables_for_db)
now backs both update_config and save_config. Re-submitting a value the
Admin UI read back from /get/config/callbacks as ciphertext no longer
stacks a second encryption layer, which previously decrypted to garbage
and silently broke the callback. The chokepoint decrypts with the pure
_decrypt_db_variables (no os.environ mutation on the write path) and
encrypts exactly once; update_config merges only the sent keys so
untouched env vars keep their stored ciphertext byte-for-byte.
* fix(bedrock-mantle): use /anthropic/v1/messages path for Mantle endpoint (#27943)
* docs: add one-line docstring to _disable_debugging (#27894)
Squash-merged by litellm-agent from oss-agent-shin's PR.
* Add jp. Bedrock cross-region inference profile for claude-sonnet-4-6 (#27831)
Squash-merged by litellm-agent from Cyberfilo's PR.
* Sanitize empty text content blocks on /v1/messages (#27832)
Squash-merged by litellm-agent from Cyberfilo's PR.
* fix(bedrock-mantle): use /anthropic/v1/messages path for Mantle endpoint
The bedrock-mantle gateway (Claude Mythos Preview) serves the Anthropic
Messages API at /anthropic/v1/messages; /v1/messages returns 404 Not
Found. Both AmazonMantleConfig (chat/completions caller route) and
AmazonMantleMessagesConfig (anthropic-messages caller route) hardcoded
the wrong path, so every Mantle request 404'd before reaching the model.
Per the Anthropic docs: "[Claude in Amazon Bedrock] uses the Messages
API at /anthropic/v1/messages with SSE streaming."
https://platform.claude.com/docs/en/api/claude-on-amazon-bedrock
Confirmed independently against the live endpoint:
/v1/chat/completions -> 200 OK
/v1/messages -> 404 Not Found (what litellm used)
/anthropic/v1/messages -> 200 OK (Claude only)
Adds a regression test asserting both Mantle configs build the
/anthropic/v1/messages path, and updates the existing assertions that
encoded the wrong path.
---------
Co-authored-by: oss-agent-shin <ext-agent-shin@berri.ai>
Co-authored-by: Filippo Menghi <113345637+Cyberfilo@users.noreply.github.com>
* fix: sanitize empty text blocks in sync anthropic_messages_handler path
Co-authored-by: Yassin Kortam <yassin@berri.ai>
---------
Co-authored-by: João Costa <13508071+jpv-costa@users.noreply.github.com>
Co-authored-by: oss-agent-shin <ext-agent-shin@berri.ai>
Co-authored-by: Filippo Menghi <113345637+Cyberfilo@users.noreply.github.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* Feat: Add Weighted-Routing Failover
* test(router): cover weighted failover helper functions
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(router): align weighted failover deployment list type with mypy
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(router): address greptile review on weighted failover
- Narrow exception swallowing in `_maybe_run_weighted_failover` to
`openai.APIError` so model failures defer to the regular fallback
while programming bugs (AttributeError/KeyError/TypeError) surface.
- Note async-only limitation of `enable_weighted_failover` in the
Router constructor docstring.
- Make the weighted distribution test less flaky (1000 iterations,
looser bound) and make the non-simple-shuffle test deterministic by
failing both deployments instead of relying on the latency strategy's
first pick.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(router): ensure weighted failover metadata persists in kwargs
The previous `kwargs.setdefault(metadata_variable_name, {}) or {}` returned
a brand-new dict whenever the existing metadata was falsy (empty dict or
None), so writes to `_failover_excluded_ids` never made it back into
`kwargs`. Multi-hop weighted failover then re-selected previously failed
deployments and exhausted `max_fallbacks` prematurely.
Explicitly assign a fresh dict into kwargs when metadata is missing so
mutations are visible to subsequent failover hops.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* test(router): regression for weighted failover metadata persistence
Asserts kwargs["metadata"]["_failover_excluded_ids"] is populated after
_maybe_run_weighted_failover, proving the metadata dict written by the
helper is the same object that lives in kwargs (no disconnected copy).
Pairs with the prior fix that replaced `setdefault(..., {}) or {}` with
an explicit get/assign so writes survive across hops.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(router): harden weighted failover error/state handling
- Catch RouterRateLimitError (ValueError) alongside openai.APIError in
_maybe_run_weighted_failover so an exhausted intra-group retry falls
through to the regular cross-group fallback path instead of bubbling
out and bypassing configured fallbacks.
- Stop mutating the shared input_kwargs dict; build a local copy with
the weighted-failover keys so the entry (with _excluded_deployment_ids)
cannot leak into later fallback paths reading the same dict.
- _get_excluded_filtered_deployments now returns an empty list when the
exclusion filter removes every healthy deployment, instead of falling
back to the original list. The original-list behavior risked re-picking
the just-failed deployment; callers already handle the empty case by
raising their no-deployments error, which weighted failover now catches
and converts into a normal cross-group fallback.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* fix(router): fall through to rpm/tpm when total weight is zero
When the weight metric's total is zero (e.g. after weighted-failover
exclusion leaves only zero-weight backups), continue to the next metric
(rpm/tpm) instead of returning a uniform random pick immediately. This
lets rpm/tpm still drive routing when present, and only falls back to
the uniform random pick at the end if no metric provides a positive
total weight.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* fix(router): skip weighted failover when remaining deployments are all in cooldown
_maybe_run_weighted_failover was computing 'remaining' from all_deployments
(every deployment in the model group, including those in cooldown). This meant
that when all non-excluded deployments were in cooldown the method still invoked
run_async_fallback unnecessarily, which propagated into async_get_healthy_deployments,
found no eligible deployments, and raised RouterRateLimitError — only safely
caught thanks to the earlier exception-broadening fix.
The fix: before computing 'remaining', fetch the current cooldown set via
_async_get_cooldown_deployments and subtract it from all_ids. This allows
_maybe_run_weighted_failover to return None immediately (skipping the
run_async_fallback call entirely) when every non-failed deployment is in cooldown,
letting the caller fall through to the correct cross-group fallback path without
the wasteful extra round-trip.
Tests added:
- unit: _maybe_run_weighted_failover returns None without calling run_async_fallback
when all remaining deployments are in cooldown
- unit: _maybe_run_weighted_failover still calls run_async_fallback when at least
one healthy (non-cooldown) deployment is available
- integration: end-to-end fallthrough to cross-group fallback when remaining
deployments are in cooldown
Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com>
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com>
* fix(vertex-ai): fix zero cost/usage on completed Vertex AI batch jobs
Vertex batch jobs recorded 0 spend and 0 tokens after PR #25627 added
automatic transformation of GCS predictions.jsonl to OpenAI format.
Two bugs fixed:
1. batch_utils.py: the Vertex-specific cost/usage reader
(calculate_vertex_ai_batch_cost_and_usage) was always invoked and
reads raw usageMetadata fields that no longer exist in the
OpenAI-shaped output. Now the reader is only used when
disable_vertex_batch_output_transformation=True; otherwise the
generic path handles the already-transformed OpenAI-shaped content.
2. cost_calculator.py: batch_cost_calculator skipped the global
litellm.get_model_info() lookup when a model_info dict was passed
in, even when that dict had no pricing fields (e.g. deployment
metadata with only id/db_model). It now falls back to the global
pricing table when the provided model_info has no pricing data.
Co-authored-by: Cursor <cursoragent@cursor.com>
* Update litellm/cost_calculator.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* fix(cost-calculator): use not-any guard for pricing fallback in batch_cost_calculator
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(cost-calculator): treat explicit zero batch pricing as set in model_info
The fallback to litellm.get_model_info() used truthy checks on pricing
fields, so 0.0 was treated as missing and replaced by global rates.
Use `is not None` like elsewhere in cost calculation. Add regression test.
Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com>
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com>
* fix(managed_batches): convert raw output_file_id to managed ID in CheckBatchCost poller
CheckBatchCost bypasses async_post_call_success_hook, causing raw provider
output_file_ids to be persisted in LiteLLM_ManagedObjectTable. This fix converts
output_file_id and error_file_id to managed base64 IDs before the DB write.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(check_batch_cost): persist managed file before mutating response and propagate team_id
- Move setattr after store_unified_file_id so the response only receives the
managed ID once the DB record is successfully written. Avoids serializing
an orphaned managed ID into file_object when the store call fails.
- Populate team_id on the minimal UserAPIKeyAuth from job.team_id so the
managed file record is created with the correct team ownership, allowing
other team members to access the batch output file via /files/{id}/content.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* test(managed_batches): extend test to cover error_file_id conversion
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix managed file test
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* feat(proxy): fix vector store retrieve/list/update/delete routing without model
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(proxy): remove unchecked query-param injection in vector store management endpoints
Co-authored-by: Cursor <cursoragent@cursor.com>
* test(proxy): use subset assertion for vector store route test to allow extra kwargs like shared_session
Co-authored-by: Cursor <cursoragent@cursor.com>
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
_build_mcp_server_table omitted delegate_auth_to_upstream, so GET /v1/mcp/server always returned the default false while the registry kept the DB value.
Co-authored-by: Cursor <cursoragent@cursor.com>
Log verbose_logger.warning when loading oauth2 interactive servers with
available_on_public_internet=false and delegate_auth_to_upstream=true
(config + DB). Dashboard Alert for the same combo. CLAUDE note for
operators. Tests for log and M2M skip.
