A stream counted before its usage is known increments TPM by zero, so the
worker that served it never refreshed its local TPM value from Redis and
the first byte headers reported the token count another worker had already
consumed. Both pipeline operations now always run, matching the pre-change
callback, so the returned values refresh both worker local keys
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Router.make_call now increments the deployment TPM/RPM counter before set_response_headers reads remaining usage, so the headers carry post-increment values directly and the in-flight subtraction workaround from LIT-2719 is removed. deployment_callback_on_success adds only the tokens not yet counted (streams) and never a second request. The counter key uses the resolved deployment name so wildcard routes are read back correctly, and the proxy strips the router-owned counted-tokens marker from client metadata
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
_handle_clientside_credential registered the per-request Deployment it built for
a client-supplied api_key/api_base via upsert_deployment, which added it to
self.model_list under the shared model_name. That made a request-scoped
credential a permanent, load-balanced deployment that any later caller of the
same model group could be routed onto, reaching the provider with someone
else's forwarded credential.
The per-request Deployment still gets its own stable id for cooldown and
logging identity; it is just never registered with the router.
Resolves LIT-7811
The router clamps a negative request_retry_count found in request metadata before counting a failure, and the proxy strips a client-supplied request_retry_count with the other router-reserved metadata fields. The rust OCR lifecycle test that trips the per-request cap now plants request_retry_count instead of attempted_retries, which the cap no longer reads since the previous commit
num_retries_per_request has always capped the retries of one request with its fallback hops included. #40930 started reading the per-hop attempted_retries counter instead, and every fallback hop restarts that counter at zero, so a request could spend a fresh retry budget on each hop and the legacy fallback cap test started seeing the hop run.
Router.log_retry now also keeps request_retry_count on the request metadata, incremented on every retry and fallback hop and never truncated the way previous_models is, and max_retries_per_request_hit reads that count. The flat retry records, the litellm_metadata coverage and caps above four from #40930 stay as they are, and the legacy test goes back to its previous_models == 0 assertion.
Router.log_retry used to copy the failed attempt's kwargs and metadata into
metadata.previous_models. Nothing downstream read those copies, but they carried
client credentials into spend logs and grew the payload on every retry. Each
attempt now leaves a flat record (model group, deployment id, exception type and
string, attempt number), which drops RETRY_BREADCRUMB_EXCLUDED_KWARGS and the
per-retry credential masking.
num_retries_per_request was enforced from len(previous_models), which only
looked at the metadata bucket and never exceeded four records. The sync and
async client wrappers and the Rust lifecycle guard now read attempted_retries
from whichever metadata bucket the call carries.
Resolves LIT-7505
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Cooldown entries rode the router-wide DualCache, which re-reads a key that is
missing from memory at most once every 10s. A deployment benched on one replica
therefore kept taking traffic on its siblings for up to 10 seconds, and the same
shared in-memory tier could evict a live cooldown once 200 unrelated router keys
crowded it out, which sent even the benching replica back to the dead deployment.
CooldownCache now owns a DualCache over the router's Redis with a 1s read
interval and an in-memory tier that only holds cooldown keys. Redis is attached
lazily because the router builds the cooldown cache before it wires Redis up.
test_no_linear_scans_in_router: #39674 renamed heuristic_v2_router_limit_violation
to auto_router_capability_violation, so the allowlist entry stopped matching and the
same admin-only scan tripped the static check. Rename the entry to follow it.
tableScrolling.spec.ts: 9ba6cab889 (LIT-4738) gave the Tags and Model Hub tables
client-side pagination at 25 rows, so the 40 seeded rows no longer render on one
page. Select 50 rows per page before counting, as the Logs case already does.
test_no_linear_scans_in_router: #39468 added config_deployments() and
heuristic_v2_router_limit_violation(), which both scan the whole model_list
from admin-only paths (model add/upsert), so add them to the allowlist. The
allowlist becomes a mapping so each exemption carries its reason as data.
test_missing_model_parameter_curl: a request with no model is rejected by the
proxy when nothing can serve it and by the router when a wildcard or default
deployment exists, and by the upstream provider when a wildcard forwards it,
so the message text is not a stable contract. Assert the contract that holds
in every case: HTTP 400 with a non-empty error message.
test_model_group_info_e2e: /model_group/info resolves wildcards, so it can
never return "anthropic/*" verbatim. cc3f9cd65b rewrote the assertion to
expect the raw pattern after claude-3-5-haiku-20241022 left the price map,
which made it unsatisfiable. Assert the expansion instead.
test_should_derive_ocr_mapping_status_from_live_tests: the audit needs a
native bridge built with the trace-parity feature, which CI never builds, so
skip with the harness's own diagnostic instead of erroring. Extract that
check out of ensure_trace_bridge as trace_bridge_error so a pytest run
reports the state without kicking off a maturin rebuild.
The Router executor only routed a query embedding when the vector store
carried extra embedding configuration, so a store registered with no
embedding model at all always went to the Router and 500'd on the
s3_vectors default text-embedding-3-small when no deployment served it.
Route on whether the Router serves the model, which is the rule the
executor had before, and keep the request metadata on the SDK fallback so
the embedding stays attributed either way.
Milvus REST and Azure AI Search still embedded the query through the SDK, so
a bare Router alias as litellm_embedding_model kept failing after the executor
landed for Valkey. Both now share BaseQueryEmbeddingVectorStoreConfig, which
embeds through the injected executor, drops the empty litellm_embedding_config
requirement, and awaits aembedding on the async path.
The Router executor falls back to the SDK for models the Router does not
serve, so inline provider configs such as azure/text-embedding-3-large with
their own credentials keep working through the proxy.
Tests fake OpenAI and Milvus at the HTTP boundary with respx instead of
patching litellm.embedding.
* fix(search): forward search-tool params through the router, complete Parallel AI v1 param mapping
SearchAPIRouter dropped every parameter configured on a search tool, forwarding
only per-request kwargs. Any tool-level setting (mode, max_results, ...) was
silently lost on the way to the adapter, for every search provider.
Also completes the Parallel AI v1 search surface: after_date, fetch_policy,
location and include_domains now nest under advanced_settings instead of being
sent as unknown top-level fields, responses preserve search_id / session_id /
warnings / raw excerpts, and search cost is derived from the request mode and
the provider's reported usage rather than a single flat rate.
* fix(parallel_ai): stop a caller from pricing its own search request
`_parallel_ai_usage` carries the provider's reported usage into cost
calculation. It was only written when the response contained a usage block, so
a caller could pass `_parallel_ai_usage=[{"name": "sku_search", "count": 0}]`
and, whenever the provider omitted usage, bill $0.00 instead of $0.005 — the
value also reached the upstream request body as an unknown field.
The key is now stripped from inbound params and written unconditionally from
the parsed response, so only the provider can populate it.
* fix(parallel_ai): price fast search mode correctly
* test(parallel_ai): fake search at HTTP boundary
* fix(parallel_ai): tolerate null search result fields
---------
Co-authored-by: khushishelat <shelatkhushi@gmail.com>
* fix(router): resolve fallbacks against the tier a pre-routing hook selected
A complexity or auto router picks a tier behind the router group name, but
fallback lookup kept using kwargs["model"], which is still the router name. The
tier's configured chain never ran, so a provider failure on its first hop went
straight back to the client with "No fallback model group found for original
model_group=smart-router".
The hook assigns the selected model to a local only, and fallback resolution runs
on an outer kwargs dict that **kwargs already copied, so writing it there is not
visible. Record the selection in the metadata bucket instead, which is a nested
dict shared by reference across those copies and is how the router already
carries values back up, then key fallback lookup off it when present.
Applies to the generic, context-window, content-policy and weighted-failover
lookups. Reporting keeps using the router name, since that is what the caller
asked for.
Fixes#38832
* fix(router): annotate the recorded-selection helper with a read-only mapping
record_pre_routing_selection only reads the request kwargs, writing into the
nested metadata bucket it finds there, so Mapping states what it actually needs
and clears the LIT001 mutable-annotation budget without a suppression.
* test(router): assert the no-kwargs path leaks nothing
The tolerated-None case called the helper without checking anything, which the
test-quality gate counts as a test with no assertion. Assert that a fresh mapping
still reads back empty, so the case proves the call is a no-op rather than only
that it does not raise.
* fix(router): stop declaring loop-assigned locals Final in the selection helpers
Both helpers annotated a loop-assigned local as Final, which reassigns a Final on
every iteration and cost three basedpyright errors. Read the buckets through a
generator instead, so the write path iterates a for-target and the read path
resolves in one shot with next(), which also matches the functional style the
type-discipline rules ask for.
* style(router): apply ruff format to the selection helpers
* fix(router): derive the pre-routing tier fresh on every fallback hop
The metadata buckets also carry whatever the caller sent, so an inbound
pre_routing_selected_model let a client pick which fallback chain its
request fell into. A fallback hop also inherited the previous hop's tier,
so the second hop keyed its own failure off the tier that already failed
and never ran its own chain.
Clear the key at the top of async_function_with_fallbacks. Every hop
re-enters there, so only the hook that routed that hop can set it.
* fix(router): drop the cast at the fallback-hop clear call site
* feat(router): fall back on anthropic safeguard refusals on /v1/messages
---------
Co-authored-by: Priyansh Nandwana <nandwana.priyansh103@gmail.com>
Container create and list requests had no container ID to decode, so the
router called the provider handler directly and the OpenAI transformation
fell back to the global OPENAI_API_KEY. Proxies configured only with
model_list credentials sent Authorization: Bearer None. Route through
_ageneric_api_call_with_fallbacks when the caller passes a model, expose
the list endpoint's model query param to the router, and encode the
managed container ID on the async create path so follow-up calls route
to the same deployment.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test: drop the cwd-relative sys.path.insert calls from the test suite
TQ003 stands at 1,077 across 1,058 files, and 1,015 of them are the same shape:
sys.path.insert(0, os.path.abspath("../..")) and its deeper siblings. The
argument resolves against the working directory rather than the file, so from
the repo root, where every job runs pytest, it inserts the directory two levels
above the checkout. It has never pointed at litellm. The package is installed
into the environment anyway, which is what actually makes the import work, and
what the rule's message has said all along.
Removing them leaves 1,634 imports of sys and os with no remaining reference,
and those go too, except where another test module imports the name back out of
the file. The rest of TQ003 is 62 call sites that resolve against __file__ or a
variable, which are a different question and are left alone.
Collection is identical either way: 45,871 tests and the same 51 pre-existing
collection errors before and after, and ruff reports no new undefined name.
* test: drop the duplicate imports the sys.path sweep exposed to F811
* test(pre-call-utils): restore the os import the new bedrock tests need
* test: unshadow the module handles the F811 sweep left behind, and pin the two live tests that went red with it
The F811 sweep in #37878 removed the fixture-local `import litellm` from four
conftests, but the bare `import litellm.proxy.proxy_server` a few lines below
still binds `litellm` as a function local, so `importlib.reload(litellm)` runs
before the name is assigned and every test in those directories errors at
setup. The `hasattr` guard on the line above already proves the module is
loaded, so the import only ever bound the name. Drop it, and enable F823 in
ruff-tests.toml, which flags all four sites at the failing line and would have
blocked the sweep
The same sweep renamed the `check_non_streaming_response` parameter but left
one read of `completion`, which now resolves to `litellm.completion`, and
removed an import whose side effect was the only thing making
`litellm.proxy.proxy_server` reachable in the moderation hook test. That test
already takes `monkeypatch`, so patch the router through it and stop leaking
the router into later tests
`test_content_policy_exception_openai` passed vacuously until #37887 turned it
into a real `pytest.raises`, and OpenAI no longer rejects a lyrics prompt with
a content policy error. Inject an AsyncOpenAI client whose transport answers
with OpenAI's own `content_policy_violation` rejection so the mapping to
ContentPolicyViolationError is exercised every run
`test_async_create_batch` hit a 409 cancelling a batch OpenAI had already
marked failed. The cancel step tolerated a completed batch but not a failed
one. Fold both guards into one helper that tolerates a failed batch only when
OpenAI's recorded error is the org's enqueued token limit, and prints the
batch's errors so the reason is in the log either way
* test: close the injected AsyncOpenAI client after the content policy test
* chore(lint): ratchet TQ005 down by the global mutation this branch cleared
* chore(lint): ratchet TQ005 to 2660 on the merged tree
* chore(lint): ratchet TQ005 to 2561 on the merged tree
* chore(lint): ratchet TQ005 to 2548 on the merged tree
A test that asserts on the error inside its own except block passes when the
call stops raising, because nothing runs the handler. That is the exact case
the test exists to catch, so the regression lands green.
Rewrites all 111 such blocks into pytest.raises, which fails when the call
succeeds, and selects PT017 in ruff-tests.toml so no new one lands.
A name bound twice keeps only the second binding. In `tests/` that is nearly
always a repeated import, harmless but misleading, and the same rule is what
catches the cases that are not harmless: a local that shadows an import the
module still calls, and a second `def test_x` that quietly replaces the first.
311 of the 344 sites were repeated imports and came out with ruff's own fix.
The remaining 33 needed a decision. Four modules imported a name they never
used because a local definition below already shadowed it. Two comprehensions
bound `call` over `unittest.mock.call`, which those modules import and use.
One test rebound the two module handles its nested reload closure had captured.
One class attribute shadowed an unused `status` import.
The load-test fixtures move to a conftest, which is how pytest is meant to share
them, so the test module no longer imports three fixture names it never calls.
The nine `prisma_client` parameters keep a narrow `noqa`: pytest resolves that
fixture by name before the body runs, so the parameter never shadows anything.
`pytest.raises(Exception)` with no `match=` passes on any error that broad. A
TypeError from a refactor, a botched fixture, an import that moved: all of them
read as the rejection the test claims to police, so the test goes green for the
wrong reason and stays green after the behaviour it guards is gone.
PT011 closes that gap for the 317 sites B017 could not reach, because B017 only
fires on a single-statement body with no `as e` binding. Each pattern here is the
message the code actually raised, recorded by running the sites under a plugin
that logged the concrete type and text per call site, so the assertions describe
observed behaviour rather than a guess. Where a site raises more than one message
across its parametrize cases, the pattern is an alternation of what was seen;
where the exception carries an empty `str()` and puts the text on `.message`, the
site keeps a narrow `noqa` with the reason.
PT014 removes four parametrize cases that were listed twice. The duplicate re-runs
an assertion that already passed, and it usually marks a case someone meant to
vary and forgot to edit.
* test: enforce PT012 so a pytest.raises block cannot hide dead assertions
`with pytest.raises(...)` stops at the first statement that raises. Anything
sequenced after it inside the block never runs, so an assertion written there is
never checked and the test still reports green.
Two sites were doing exactly that, and both assertions turned out to be wrong
once they started running. tests/llm_translation/test_prompt_factory.py asserted
the bedrock rejection names "requires at least one non-system message", which
holds. tests/proxy_unit_tests/test_proxy_server.py asserted the prisma startup
failure mentions "httpx.ConnectError", which never appears: the failure is an
httpx.ConnectError whose message is "All connection attempts failed", so that
test now asserts the type. Its DATABASE_URL override moves to monkeypatch, since
the old restore sat below the assertion and leaked the invalid URL into every
later DB test the moment the assertion started being able to fail.
The remaining 72 sites are rewritten without changing what they exercise: setup
that cannot raise moves above the block, a nested `patch` moves outside it, and
bodies with real control flow (a stream drain, an if/else on sync_mode, a
retry loop) move into a local closure the block calls.
Fixing PT012 unmasked two B017s, since ruff only reports a blind
pytest.raises(Exception) once the block holds a single statement.
tests/proxy_unit_tests/test_auth_checks.py narrows to the ProxyException
can_key_call_model actually raises. tests/local_testing/test_completion_cost.py
was asserting vertex_ai/medlm-medium has no cost entry, which stopped being true
at some point; that dead first half is gone and the rest of the test, which
checks medlm pricing resolves above zero, now runs instead of being skipped.
* chore(ci): ratchet TQ004 to 768 after the prisma test moved to monkeypatch
* feat(complexity_router): custom classifier plugins via classifier_type 'plugin'
Adds a third classification mode where an operator-supplied hook decides the
tier instead of the heuristic scorer or the LLM classifier. The hook implements
an async classify(context) returning a tier name (built-in value, tier_labels
label, or tier_definitions name) or None to decline; failures, timeouts, and
unknown tiers fall back exactly like a failed LLM classifier. The context
carries the request messages and metadata, including caller identity, so a
plugin can route by team, spend, or any business rule.
The plugin resolves from a dotted path at proxy startup with a load-time check
that classify is a coroutine function, and is closed off over HTTP like the
routing plugins list. Routing decisions record the new classifier_plugin cause.
tier_definitions now accepts classifier_type 'plugin' alongside 'llm'.
* fix(proxy): resolve plugin dotted paths in _delete_deployment before hashing ids
The db-sync reconcile re-reads the raw config and hashes litellm_params to
compute which ids the config wants served, but the router's ids were hashed
from the resolved params where plugin dotted paths are live instances. The
mismatched ids made the reconcile evict every plugin-bearing auto-router one
sync after startup, on any proxy with a database connected. This also affected
the existing routing plugins list, not just the new classifier plugin.
Resolving the plugins in _delete_deployment the same way load_config does makes
both sides hash the same canonical form. A plugin module broken on disk at
reconcile time skips cleanup instead of evicting valid deployments, matching
how a get_config failure is handled
* fix(complexity_router): treat non-string plugin verdicts as declines, centralize the empty-mapping sentinel
A hook returning a non-string raised inside resolve_classified_tier outside the
plugin exception boundary, failing the request instead of falling back. Also
moves the read-only empty mapping to constants.py per repo convention and moves
the classifier plugin product docs out of the package README for the docs repo
* refactor(complexity_router): rename the plugin classifier mode to classifier_type 'custom'
The mode value now names the operator's intent while classifier_plugin keeps
naming the mechanism; routing decisions keep the classifier_plugin cause
* refactor(proxy): pin plugin-bearing deployment ids from the raw params instead of resolving in the reconcile
Replaces the previous approach of re-running plugin resolution inside
_delete_deployment, which imported operator modules on every reconcile cycle
and skipped the whole cleanup pass when any one module was broken on disk.
load_config now stamps model_info.id from the raw litellm_params before
resolution swaps dotted paths for live instances, so the reconcile's raw-config
hash matches by construction and needs no resolution at all: a broken module
cannot stall cleanup for unrelated models, and any future param-transforming
resolution is covered by the same pin. _generate_model_id becomes a staticmethod
so the pin can run before the Router exists; its statically dead non-string key
branches are removed. Also documents candidate_models as an informational
snapshot for classifier plugins, unlike the narrowing surface RoutingPlugin
filters
* fix(router): restore _generate_model_id key handling, align classifier context with the routing-plugin pattern
The staticmethod conversion accidentally dropped the non-string-key branches
from _generate_model_id, a silent hash change for any params with non-string
keys; they are restored verbatim. The classifier plugin context now follows
the Router-level routing-plugin recipe exactly: structured messages come from
resolve_structured_messages over the raw messages, and the metadata key comes
from the shared get_metadata_variable_name_from_kwargs helper, which also
replaces the duplicated inline sniff in _pick_model_for_tier. This removes the
raw-or-resolved fallback where a plugin could silently receive resolved
messages when a call site forgot to pass the raw ones
* refactor(router): make generate_model_id public, guard classifier context construction
Two modules legitimately hash deployment ids with the same helper now (Router
and the proxy's config-load pin), so the private name was lying about its
audience and the cross-module call needed a pyright suppression; renaming it
public restores the static safety net. The classifier plugin's RoutingContext
construction moves inside the failure boundary, matching the LLM path where
litellm-side prompt building also falls back rather than failing the request,
and a prompt-only call with no message list is now covered by a test
Three groups, all verified by running the suite rather than by inspection.
18 files whose every test function carries an unconditional @pytest.mark.skip,
39 test functions in total. They are collected on every CI run and always skip,
so they advertise coverage the suite does not have. Reasons on the marks include
"AWS Suspended Account", "lakera deprecated their v1 endpoint" and "moved to
using 'otel' for logging"; 26 of the marks predate 2025.
30 test functions with a byte-identical body and identical decorators to a
sibling in the same file and class, differing only in name. Deleting one of each
pair removes no coverage. Four further candidates were excluded because they
override an inherited test, where deleting the override un-shadows the base
class implementation instead of removing a duplicate.
9 test functions that a later definition of the same name shadows, so Python
never binds them and pytest cannot collect them.
One file that is a demo script rather than a test; its own docstring says to run
it with python.
Verification: collecting the 26 edited files gives 2,492 node IDs before and
2,462 after. The 30 duplicate deletions account for exactly 30 removals, the 9
shadowed deletions account for 0 (confirming at runtime that they were never
collectable), nothing unexplained disappeared, and nothing new appeared. No
other test or module imports any deleted symbol.
LiteLLMCompletionStreamingIterator overrides __init__ without calling
super().__init__(), so completed_response was only set once the stream
reached RESPONSE_COMPLETED. On a mid-stream provider error the router's
_extract_partial_responses_usage read source_iterator.completed_response
during fallback recovery and raised AttributeError, masking the real
provider error (e.g. Anthropic 529) and bypassing configured retries and
fallbacks. Initialize the attribute to None so recovery degrades to no
partial usage instead of crashing.
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(router): add per-deployment allowed_fails_policy and cooldown_time override support
Three bugs fixed in the router cooldown system: (1) deployment-level allowed_fails and
allowed_fails_policy in model_info now take precedence over router-level settings in
_should_cooldown_deployment; (2) failed fallback deployments now get evaluated for
cooldown via _trigger_cooldown_for_failed_deployment, bypassing the Logging dedup gate;
(3) DualCache promotes Redis cooldown entries using default 600s TTL instead of true
remaining cooldown time -- _corrected_active_cooldown now evicts expired entries and
corrects stale in-memory TTLs on backfill. Adds ServiceUnavailableError, BadGatewayError,
and NotFoundError fields to AllowedFailsPolicy and cooldown_time to LiteLLMParamsTypedDict.
* fix(router): gate fallback cooldown trigger on has_logged_async_failure; use only litellm_metadata for deployment ID
* fix(router): use X | Y union syntax to fix UP007 strict lint gate
* test(router_utils): add coverage for _trigger_cooldown_for_failed_deployment and has_logged_async_failure gate
* test(router_utils): cover deployment cooldown override and exception swallow paths
* fix(router): add InternalServerError/ServiceUnavailableError/BadGatewayError/NotFoundError to router-level get_allowed_fails_from_policy
* fix(router): format router.py and add router-level policy tests
* test(router): add CI-visible coverage for per-deployment cooldown policy
Tests for `_get_deployment_cooldown_policy`, `_resolve_allowed_fails_from_policy`,
and `_should_cooldown_based_on_deployment_policy` (cooldown_handlers.py), the
`_corrected_active_cooldown` branches in CooldownCache, and the four new
exception-type branches in `Router.get_allowed_fails_from_policy` (router.py) --
all in `tests/test_litellm/` which the enterprise-routing CI job runs.
* fix(router): use is not None guard for cooldown_time_override in should_cooldown_based_on_allowed_fails_policy
A cooldown_time_override of 0 was previously treated as falsy and silently
fell through to the router-level cooldown_time value. Switched to an explicit
is not None check so that zero is honored as a valid override.
Added a regression test covering the zero case.
* fix(router): honor has_logged_async_failure and metadata for fallback cooldown; support both model_info and litellm_params locations
Manual verification against a live proxy surfaced that the fallback-cooldown-gap
trigger never actually fired: the has_logged_async_failure check read a plain
attribute that Logging never sets (the real flag lives in model_call_details),
and the deployment_id lookup only trusted litellm_metadata, which regular chat
completions never populate (only batch/thread/file endpoints do). Router
overwrites model_info on whichever key is present before every attempt, so
metadata is equally authoritative there, not caller-controlled as previously
assumed. Also let allowed_fails/allowed_fails_policy/cooldown_time be set under
either model_info or litellm_params, each preferring its own canonical location.
* fix(router): fix ContentPolicyViolationError policy shadowing and partial-policy zero-threshold
Two bugs from Greptile review on PR #34416:
- ContentPolicyViolationError subclasses BadRequestError, so listing
BadRequestError first in _EXCEPTION_POLICY_FIELDS made the isinstance
check always match BadRequestError for content-policy errors, using the
wrong allowed_fails threshold. Reordered so the subclass is checked first.
- A deployment with a partial allowed_fails_policy and no deployment-wide
allowed_fails forced allowed_fails_override=0 for any exception type its
policy didn't cover, cooling the deployment down on the first unrelated
failure. Now defers to router-level behavior for uncovered exception
types instead of forcing an immediate cooldown.
* fix(router): only trust a metadata/litellm_metadata bucket the router itself wrote deployment info into
veria-ai flagged that preferring litellm_metadata whenever present could pick up a
caller-supplied litellm_metadata.model_info.id (preserved via allow_client_pricing_override)
instead of the metadata bucket the router actually populated for a regular completion's
fallback attempt, naming an arbitrary "victim" deployment for cooldown.
Router._update_kwargs_with_deployment() always writes model_info and
deployment_model_name into the same bucket together. Only trust a bucket that
carries deployment_model_name alongside model_info, since that marker is only
ever set by the router itself, not by request-body metadata.
* test(router): add regression coverage for ContentPolicyViolationError policy shadowing
The subclass-ordering fix in commit 38fe4e4490 had no regression test.
Verified the new test fails on the pre-fix ordering (asserts 2, got 10)
before restoring the fix, and confirmed the same behavior through the full
_should_cooldown_deployment call path against a real Router instance.
* fix(router): let explicit allowed_fails_policy entries override the generic 4XX cooldown exclusion
_is_cooldown_required skips cooldown evaluation for any 4XX status outside
{429, 401, 408, 404} by default, since a generic client error is usually not
the deployment's fault. BadRequestError and ContentPolicyViolationError both
carry status 400, so their AllowedFailsPolicy fields (BadRequestErrorAllowedFails,
ContentPolicyViolationErrorAllowedFails, both router-level pre-existing and the
new deployment-level ones) were silently unreachable: an operator could set
them to any value with no effect, since _is_cooldown_required blocked cooldown
evaluation before that policy was ever consulted.
_should_run_cooldown_logic now also checks whether an explicit allowed_fails_policy
entry (deployment-level or router-level) covers the exception's type, and if so,
proceeds with cooldown evaluation regardless of the generic status-code exclusion.
The exclusion remains the default for exception types with no explicit policy.
Verified live against a mock-triggered ContentPolicyViolationError (config-level
mock_response, azure/gpt-4.1-mini deployment) with BadRequestErrorAllowedFails=100
and ContentPolicyViolationErrorAllowedFails=0 on the same deployment: it now cools
down after exactly one ContentPolicyViolationError instead of never cooling down.
* fix(router): use the router-stamped failed_deployment_id for fallback cooldown targeting
Greptile flagged a real gap in the metadata-bucket-based deployment lookup:
for a generic-API-call fallback, the router writes the current attempt into
litellm_metadata, but a stale "metadata" bucket carrying the same
deployment_model_name marker (from an earlier point) would be picked first,
cooling the wrong deployment.
Router already has a more robust, pre-existing mechanism for this exact
problem: _set_failed_deployment_id_on_exception stamps the failing
deployment's id directly onto the exception at the point of failure,
immune to metadata-bucket ambiguity since a caller can't influence it and
it doesn't depend on which bucket the current call type happens to use.
It just wasn't called from _ageneric_api_call_with_fallbacks_helper's
except block, unlike _completion/_acompletion.
Added the missing call there (matching the existing pattern exactly), and
changed _trigger_cooldown_for_failed_deployment to prefer
exception.failed_deployment_id when present, falling back to metadata-bucket
inspection only for call paths that don't stamp it yet.
Verified live: the standard fallback-cooldown-gap scenario (two bad-key
deployments in a fallback chain) still correctly cools down both the
originally-called and fallback deployment.
* fix(router): address human review on per-deployment cooldown overrides
Scope allowed_fails_policy override to deployment-level only (a router-level
policy predates this feature and must keep its existing behavior), exempt
advisor-orchestration failures from the fallback cooldown trigger, keep the
single-deployment model group protection intact against a generic
deployment-level allowed_fails, make cooldown_time precedence consistent
across resolution paths, fix a falsy-zero swallowing bug in the router-level
allowed_fails fallback, and make allowed_fails_policy resolution fall through
to the next matching exception type instead of stopping at the first unset
field.
Also restrict allowed_fails/allowed_fails_policy/cooldown_time to model_info:
litellm_params gets copied into the actual provider request, so a router-only
setting placed there would leak into that request.
* test(router): update test_cooldown_handlers.py for the deployment-policy signature change
Surfaced by the rebase: this mirrored test file (tests/test_litellm/ mirrors
litellm/) predates the router_unit_tests/ coverage added earlier in this PR and
was still calling _should_cooldown_based_on_deployment_policy with its old
4-argument signature and asserting the now-removed litellm_params cooldown_time
location.
* test(router): update test_fallback_event_handlers.py for model_info-only cooldown_time
Another mirrored test file surfaced by the rebase that still asserted the
now-removed litellm_params.cooldown_time location.
* fix(router): match cooldown-duration precedence in the fallback path to the primary path
_trigger_cooldown_for_failed_deployment only checked deployment config before
falling back to the router default, skipping the response Retry-After header
step that Router.deployment_callback_on_failure applies on the primary path.
* fix(router): restore litellm_params.cooldown_time as a pre-existing fallback
cooldown_time already had litellm_params support on Router.deployment_callback_on_failure
before this PR; the earlier model_info-only restriction (aimed at the leak concern
for the genuinely new allowed_fails/allowed_fails_policy fields) incorrectly dropped
that pre-existing capability too. model_info still takes priority when both are set.
* fix(router): keep the fallback-cooldown trigger in sync with #35104's review fixes
Applies the same two fixes landed on the split-out PR #35104 (which #34416
still duplicates until it's rebased onto the merged base): increment the
deployment's per-minute failure counter before evaluating cooldown, and
require the server-stamped failed_deployment_id instead of trusting a
metadata bucket, since neither "metadata" nor "litellm_metadata" can be told
apart from a caller-supplied one without knowing the call's function_name.
* fix(router): freeze the model_info fallback mapping to satisfy the type-discipline gate
* fix(router): defer f-string interpolation in fallback-cooldown debug logs
* fix(router): annotate cooldown-path locals with Final to satisfy the LIT010 budget
* fix(router): suppress reportPrivateUsage for cross-module cooldown helpers
* fix(router): don't cool down deployments for request-scoped 404s on generic API fallbacks
* fix(router): stamp the dynamic client-side-credential deployment id, not the shared static one
* fix(router): keep up with upstream typing modernization and Final-annotation ratchet
* fix(router): don't cool down deployments for a caller-supplied x-litellm-timeout
* fix(router): stamp dynamic client-side-credential id in completion fallback paths too
The generic-API-call helper already stamped the effective (dynamic-if-client-side-credential)
deployment id on exceptions, but the regular _completion/_acompletion exception handlers still
stamped the static shared deployment's id. A tenant using invalid forwarded credentials could
generate repeated failures attributed to, and eventually cooling down, the shared deployment
other tenants rely on. Extracted the stamping logic into one shared helper used by all three
call sites (generic API, sync completion, async completion) so the fix and future changes to it
stay in one place.
* fix(proxy): recognize body-supplied timeout/request_timeout/stream_timeout as caller-controlled
client_side_timeout was only set when the caller used the x-litellm-timeout header, but
Router._get_timeout also resolves the effective timeout from kwargs["timeout"],
kwargs["request_timeout"], and kwargs["stream_timeout"], all settable directly in the
request body (and x-litellm-stream-timeout wasn't marked either). A caller could set any
of those to a near-zero value, force a 408 on every deployment in a fallback chain, and
cool down deployments other tenants rely on without the guard in
_trigger_cooldown_for_failed_deployment recognizing it as caller-controlled. Also strip
any client-forged client_side_timeout from the request body so the marker is always
server-computed.
---------
Co-authored-by: Deepanshu <deepanshu.lulla@alpha-sense.com>
PR #35956 added the max_input_chars passthrough to the AutoRouter
constructor but left this mock assertion in tests/router_unit_tests
unchanged, so test_init_auto_router_deployment_success has been failing
on litellm_internal_staging ever since.
The passthrough itself is intentional and its behaviour is already
covered by TestAutoRouterMaxInputCharsWiring in tests/test_litellm, so
only the stale expected kwargs need updating. Assert the shared constant
rather than the literal 2000 so tuning the default does not break this
test again.
* fix(router): tag-aware pre-routing strategy selection for shared model_name
Complexity/auto/adaptive/quality router registries were keyed by model_name
alone, so a second deployment sharing a model_name but carrying different tags
was rejected and every request used the first config. This made tag-based
routing to distinct provider configs behind one alias impossible, surfacing as
401 'Not allowed to access model due to tags configuration' for the second tag.
Each registry now holds a list of tag-scoped strategies and async_pre_routing_hook
selects the entry whose tags match the request before classification, falling
back to a default-tagged then first-registered entry. A repeat of the same
(model_name, tags) pair is still rejected.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(router): cover tag-scoped pre-routing strategy registry helpers
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* chore: re-trigger CI
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Krrish Dholakia <krrishdholakia@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses-api): raise APIError on in-stream error events; widen ErrorEventError.param
- BaseResponsesAPIStreamingIterator._maybe_raise_for_error_event inspects each
chunk and raises litellm.APIError for type=error and type=response.failed events
so callers see an exception instead of a benign stream chunk
- rate_limit* codes map to 429; client error codes (invalid_request_error,
context_length_exceeded, etc.) map to 400; all other codes default to 500;
raw integer codes are never used as-is as HTTP status codes
- ErrorEventError.param widened from Optional[str] to Optional[Union[str, Dict]]
to prevent Pydantic ValidationError on dict-typed param payloads silently
dropping error events before any type inspection
* test(responses-api): add streaming iterator error event tests to CI-covered path
* test(responses-api): cover response.failed, dict-error, null-error, and sync iterator paths
* test(responses-api): set completion_start_time on mock logging objects for internal staging _process_chunk
* fix(responses-api): map insufficient_quota to 429, derive failed-response log status from error code, and record failed-stream usage for spend accounting
insufficient_quota moves out of the 400 bucket; OpenAI returns HTTP 429 for it and the non-streaming exception mapping treats 429 as RateLimitError, so the in-stream mapping now agrees
_handle_logging_failed_response previously hardcoded APIError(status_code=500), so a rate-limited response.failed was logged to integrations as 500 while the caller saw 429; it now shares the same error-code-to-status mapping via _error_event_fields and _status_code_for_error_code
usage carried on a response.failed event is now stashed as combined_usage_object with its computed cost on the logging object before failure handlers run, reusing the mid-stream-interruption spend recovery path (_failure_handler_helper_fn, proxy post_call_failure_hook, _ProxyDBLogger), so failed streams count their billed tokens instead of logging zero cost
dedupe: TestMaybeRaiseForErrorEvent in tests/llm_responses_api_testing duplicated tests/test_litellm/responses/test_streaming_iterator_error_events.py, which is the canonical mirrored location and CI-covered via test-unit-responses-caching-types; the duplicate class is removed
* fix(responses-api): wrap retriable in-stream errors in MidStreamFallbackError and map error type field to status
Mirror chat streaming semantics from _handle_stream_fallback_error: 429 and
5xx in-stream error events now raise MidStreamFallbackError carrying the
mapped APIError so the router's FallbackResponsesStreamWrapper triggers
mid-stream fallback and cooldown; non-retriable 4xx still raise APIError
directly. Status mapping now reads both the OpenAI error type and code
fields, so type-classified client errors (e.g. invalid_request_error with
code invalid_prompt) map to 400 instead of falling through to 500.
* fix(responses-api): accumulate streamed output text so mid-stream fallback continues instead of restarting
MidStreamFallbackError was always raised with generated_content="", so the
router's stream_with_fallbacks treated every mid-stream error as pre-first-chunk
and retried with the original input, streaming duplicated content to clients
that had already received partial output. The iterators now accumulate
response.output_text.delta text (mirroring chat's response_uptil_now) and pass
it as generated_content, letting the router build a continuation input via
_build_responses_continuation_input.
* test(responses-api): pin in-stream token limit error to raised APIError
---------
Co-authored-by: Deepanshu <deepanshu.lulla@alpha-sense.com>
* feat(router): add separate ITPM/OTPM deployment rate limits
Support input/output tokens per minute on deployments via enforce_model_rate_limits, with reservation, reconciliation, refund on failure, and rate-limit headers.
Co-authored-by: Cursor <cursoragent@cursor.com>
* chore(router): keep ITPM/OTPM diff minimal in router.py
Drop unrelated Black reformatting from router.py and types/router.py so the PR only contains functional ITPM/OTPM changes.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(router): make ITPM/OTPM limits separate and atomic
Address Greptile review on separate ITPM/OTPM deployment rate limits.
- OTPM is now reserved atomically pre-call with rollback, matching the ITPM
path, so concurrent requests can no longer overshoot the configured output
limit before reconciliation
- ITPM counts input tokens only; it no longer accumulates completion tokens,
so the input-token limit and x-ratelimit-limit-input-tokens header describe
input usage as their names imply
- _read_reservation_from_kwargs only falls back to litellm_params.metadata when
the top-level metadata channel is absent, so production requests carrying a
litellm_params.metadata dict still reconcile and refund their reservation
Adds regression tests for OTPM atomicity under concurrency, input-only ITPM
enforcement, and reservation lookup when litellm_params.metadata is present.
* fix(router): subtract input tokens only from remaining-input-tokens header
The in-flight replay for x-ratelimit-remaining-input-tokens subtracted total
tokens (input + output) instead of input tokens only, so clients saw remaining
input quota understated by the completion token count on every response. Now
consistent with the input-only ITPM counter.
* fix(router): make itpm/otpm vs tpm/rpm precedence explicit
When a deployment configures itpm/otpm alongside tpm/rpm, the io-token path
takes over and the tpm/rpm limits are not enforced. Log a warning the first
time such a conflicting deployment is seen so the supersession is not silent,
and document the mutual exclusivity.
Post-call reconciliation now only trues up a counter that was actually
reserved against, so the itpm/otpm keys are no longer incremented for
deployments that never configured that limit.
* fix(router): track actual io-token usage on the reservation-minute key
Post-call reconciliation now keys off the exact cache key stashed at pre-call
time rather than one recomputed from the response-time minute. This fixes two
issues: a request whose pre-call estimate was 0 now still writes its actual
billable input to the ITPM counter (previously it was skipped, leaving the
limit unenforceable for that request), and a call that finishes in a later
minute reconciles against the minute it reserved against instead of pushing a
negative delta into the next minute. Counters are only touched when their
limit is configured.
* fix(router): run io-token reconciliation before the model_id guard
async_log_success_event gated IO reconciliation behind the model_id guard that
only the TPM tracking path needs. Since reconciliation works entirely from the
cache keys stashed in kwargs, a success event whose standard_logging_object
lacks model_id would skip reconciliation and leave the reservation on the
counter until the TTL expired, wasting quota. Route the IO path first.
* fix(router): don't replay in-flight delta for itpm/otpm headers
For ITPM/OTPM model groups the counter is incremented at reservation time
(pre-call), so the remaining values returned by get_remaining_model_group_usage
already account for the current request. Replaying the in-flight delta on top
double-counted it and understated x-ratelimit-remaining-input/output-tokens by
up to max_tokens on every response. Skip the delta for io-token groups; the
legacy TPM/RPM replay path is unchanged.
* fix(router): clear io-token reservation after reconcile/refund
async_io_token_refund_failure and async_io_token_reconcile_success now clear
the stashed reservation keys from the request metadata once done. Otherwise, on
a model group mixing IO-limited and non-IO deployments, a failed IO call that
retries on a non-IO fallback left the stale sentinel in the shared request
metadata; the fallback's success handler would divert into IO reconciliation
against the already-refunded key, driving the ITPM counter negative and
skipping the non-IO deployment's TPM tracking.
* fix(router): tidy reservation channel lookup and header guard
Consolidate the reservation channel lookup into a single ordered helper shared
by read and clear, so top-level metadata always wins over litellm_params
metadata without the tangled per-iteration fallback.
Also stop gating the router rate-limit header block on the presence of
x-ratelimit-remaining-input/output-tokens. That block only emits those headers
for ITPM/OTPM groups; for a non-IO group backed by a provider that natively
returns input/output token headers, the extra conditions suppressed the
router's own remaining-tokens/requests headers.
* fix(router): strip client-supplied io-token reservation keys
The reservation sentinels (_litellm_itpm_reserved, _litellm_itpm_cache_key,
and the otpm equivalents) are server-only, but metadata is caller-controlled on
proxy requests. An authenticated caller could forge these fields with an
arbitrary cache key so the post-call reconcile/refund path would decrement any
deployment's ITPM/OTPM counter and let it exceed the configured limit. Strip
the reserved keys from the request metadata in set_io_token_rate_limit_request_kwargs,
which runs before the router stashes its own reservation, so only a genuine
server-side reservation is ever read post-call.
* fix(router): track TPM routing load for io-limited deployments
deployment_callback_on_success early-returned for any deployment with itpm/otpm
set, so its total-token usage never landed in the router's TPM routing counter.
TPM-aware routing strategies then saw 0 load for IO deployments and over-routed
to them in mixed model groups. Only skip tracking when neither tpm/rpm nor
itpm/otpm are configured; itpm/otpm enforcement still runs separately in
ModelRateLimitingCheck, so the routing counter and the enforcement counters
stay independent.
* fix(router): expose standard tpm/rpm headers for io-limited groups
get_remaining_model_group_usage returned early for ITPM/OTPM groups, so a group
that also set tpm/rpm never emitted x-ratelimit-remaining-tokens / -requests;
clients and prometheus gauges reading those saw no data. Build both header sets
instead of returning early.
Also simplify the in-flight header replay: only the tpm/rpm counters are
incremented post-response, so the delta now adjusts just those. The itpm/otpm
counters are incremented at reservation time (pre-call), so the input/output
token headers already reflect the request and are left untouched - which
removes the need for the separate io-group special case.
* fix(router): roll back ITPM on any OTPM reservation error; dedup warning per instance
Two follow-ups from review. The pre-call OTPM reservation only rolled back the
ITPM reservation on a RateLimitError, so a transient cache error while reserving
OTPM left the ITPM counter inflated until the TTL expired; catch any exception,
release the ITPM reservation, then re-raise.
Replace the module-level lru_cache warn-once (caching a logging side effect,
which never re-warns in a long-lived process) with an instance-scoped set of
already-warned deployment ids on ModelRateLimitingCheck.
* fix(router): always clear reservation stash on reconcile; don't collapse id-less warning dedup
Clear the reservation in a finally block so a mid-reconciliation cache error
still removes the stash and a duplicate success event can't re-process it.
Dedup the itpm/otpm-vs-tpm/rpm conflict warning per real deployment id; a
deployment with no id no longer collapses every id-less deployment onto the
str(None) key (which would suppress all but the first warning).
* fix(router): skip io reservation when deployment can't be keyed
_get_cache_keys returned a shared 'global_router:None:None:...' key when a
deployment was missing model_info.id or litellm_params.model, so misconfigured
deployments could share one rate-limit bucket. Return None in that case and
skip io reservation for the request.
* fix(router): honor explicit max_tokens=0 in io reservation
_resolve_max_tokens used 'max_tokens or max_completion_tokens', so an explicit
max_tokens=0 fell through to the model default. Only fall back to
max_completion_tokens when max_tokens is absent.
* fix(ci): satisfy lint budget, router coverage, and dashboard schema sync
- Modernize the new itpm/otpm module's type hints to PEP 585 lowercase
generics (Dict/Tuple/List -> dict/tuple/list) to clear the added UP006
violations; ratchet ruff-strict-budget.json's UP006 ceiling down to match.
- Replace three try/except Exception blocks that must stay broad by design
(token_counter and litellm.get_model_info raise untyped exceptions, and an
io-token refund failure must never break the logging pipeline) with
contextlib.suppress(Exception), matching the codebase's existing resolution
for this exact BLE001 pattern.
- Add direct unit tests for get_model_group_io_token_usage (multi-deployment
aggregation and the empty-model-list case) in test_router_helper_utils.py,
satisfying the router function-coverage check.
- Regenerate the dashboard's schema.d.ts so the new itpm/otpm fields on
GenericLiteLLMParams and ModelGroupInfo are reflected in the OpenAPI types.
* fix: enforce io token rate limits consistently
* fix: honor zero max tokens in otpm reservation
* fix(lint): fix UP007 violation and resync ruff-strict-budget.json to base
Convert Union[_Span, Any] to _Span | Any (safe on this repo's Python >=3.10
floor) to clear the new UP007 violation from the TYPE_CHECKING-gated Span
alias.
The previously committed ruff-strict-budget.json ratcheted UP006 down from a
stale base; litellm_internal_staging has since tightened that same ceiling
further on its own. Reset the file to the current base's committed values and
re-ratchet from there so the budget only ever moves down relative to the
actual merge-base, never against a stale snapshot.
* fix(router): attach ITPM/OTPM headers on dict responses and harden reservation
Strip itpm/otpm from provider kwargs, ensure messages are available for ITPM
estimation, honor max_output_tokens on /v1/responses, and propagate rate-limit
headers through /v1/messages dict responses via _hidden_params.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(router): attach ITPM/OTPM headers to streaming /v1/messages responses
Wrap bare async iterators in HiddenParamsAsyncIteratorWrapper so
set_response_headers can attach rate-limit headers to streaming Anthropic
messages responses that lack a _hidden_params slot.
Co-authored-by: Cursor <cursoragent@cursor.com>
* style: ruff format add_retry_fallback_headers.py
Fix CI ruff format check failure on get_hidden_params_dict call site.
Co-authored-by: Cursor <cursoragent@cursor.com>
* refactor(router): extract set_response_headers helpers to fix C901 budget
Move header-attachment logic into add_retry_fallback_headers helpers so
set_response_headers stays under the strict complexity ceiling.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix: keep IO token reservation when response usage is missing
Missing usage was reconciled as zero and fully refunded the pre-call
reservation, allowing limit bypass on repeated successful calls. Only
adjust counters when usage is resolved from the response or standard
logging fields; otherwise keep the reservation until TTL expires.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix: enforce RPM/TPM alongside IO-token limits on mixed deployments
Deployments with both itpm/otpm and tpm/rpm previously returned after the
IO reservation and skipped RPM/TPM checks. Run both paths and refund the
IO reservation only when RPM/TPM rejects after a successful reservation.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix: track TPM usage on success for mixed IO+TPM deployments
The early return after IO-token reconciliation in log_success_event and
async_log_success_event skipped the TPM counter increment, so the tpm_key
the pre-call check reads was never written and tpm_limit was never
actually enforced on deployments that also configure itpm/otpm.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix: treat total-only usage as unresolved in IO-token reconcile
usage/standard_logging_object entries carrying only total_tokens (no
prompt/completion or input/output breakdown) were treated as resolved
usage, resolving to (0, 0) and refunding the full reservation. Both
_usage_is_present and the standard_logging_object fallback now require an
actual input/output breakdown before reconciling, keeping the reservation
otherwise.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix: reserve minimal token when input/output estimation fails
_reservation_value(0, limit) reserved the entire limit whenever token
estimation failed (empty/unsupported input, tokenizer error), letting one
such request claim the whole bucket and 429 every concurrent request to
the deployment until it completed. Reserve 1 token instead so estimation
failures no longer serialize traffic.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix: refund IO reservation synchronously before retry deployment pick
On retry, set_io_token_rate_limit_request_kwargs clears reservation
sentinels from the shared kwargs dict before a background failure handler
can refund them, stranding the counter until TTL. Refund and clear any
stale reservation in _update_kwargs_with_deployment before stripping
sentinels for the next attempt.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(io_token_rate_limit_check): use model-specific tokenizer for ITPM estimate; document sync-refund Redis ceiling
Pass the deployment litellm_params.model to token_counter so it uses the
model's native tokenizer instead of the generic fallback, narrowing the
reservation over/under-estimate window between pre-call and post-call
reconcile.
Add a ponytail: comment to refund_stale_reservation_before_retry explaining
the known ceiling: the synchronous DualCache.increment_cache issues a
blocking Redis INCR when a Redis backend is configured. This only fires on
streaming mid-stream retries (non-streaming failures await their failure
handler before the retry picks a new deployment, leaving no sentinels to
refund). Upgrade path: make _update_kwargs_with_deployment async.
Co-authored-by: Cursor <cursoragent@cursor.com>
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(vertex/files): stream OpenAI->Vertex batch JSONL uploads to fix OOM on large files
Large (1GB+) batch JSONL uploads to Vertex AI / GCS caused OOM or killed the worker
because the request body was buffered and multiplied 2-3x in size. The create-file
path is now streaming end-to-end: transform_create_file_request returns a
ResumableChunkedUploadConfig carrying a lazy _OpenAIToVertexBatchUploadStream, and the
HTTP handler opens a GCS resumable session and PUTs the body in bounded 8 MiB chunks
(Content-Range, 308 between chunks) so the transformed payload is never held in full.
The proxy /v1/files endpoint streams from Starlette's spooled upload handle instead of
reading the whole body, and batch rate limiting counts tokens and models in a single
streaming pass.
Only gcs_bucket_name is supported for the GCS target; the legacy bucket_name key is
intentionally not read.
Also removes the unreachable VertexAIFilesHandler create path and everything only it
kept alive (VertexAIJsonlFilesTransformation, _stream_openai_jsonl_to_vertex, the legacy
transform helpers), plus the orphaned batch_utils helpers the streaming rewrite replaced.
* fix(batches): return original JSONL on unparseable row to avoid silent batch truncation
The streaming rewrite of replace_model_in_jsonl accumulated physical lines and
skipped a row on JSONDecodeError to support multi-line objects, but a genuinely
malformed or truncated row never completes: it poisons the buffer, swallows every
following row, and the function still returned the partial rewrite (the rows before
the bad one, already model-rewritten) as if the batch were complete. That turned the
pre-rewrite behavior of returning the original file unchanged (so the provider rejects
the bad batch loudly) into a silent partial submission.
Restore the original-content fallback: when an unparseable remainder is left after the
loop, return the original file_content (rewinding a consumed seekable source) instead of
the truncated output. The multi-line happy path is unchanged.
* test(batches): mock resumable GCS upload in vertex batch prediction test
The vertex batch file-create path now streams to a GCS resumable session via
_aresumable_chunked_upload (httpx send) instead of AsyncHTTPHandler.post, so the
existing test's post mock no longer intercepted the upload and a real request hit
GCS (401). Mock _aresumable_chunked_upload to return the GCS object response; the
resumable protocol itself is covered in test_vertex_ai_files_streaming.py.
* fix(batches): resilient per-row token accounting; no hard-block on count failure
The batch input-file pass iterated a generator whose json.loads raised on a
malformed line; the outer except caught it and stopped the loop, so any body.model
on rows after a bad line was never collected and the model allowlist check ran
against a partial set. It also hard-blocked the batch with a 400 whenever token
counting raised, a backwards-incompatible change from the prior swallow-and-proceed
behavior that breaks legitimate rows the token counter cannot measure (e.g. some
multimodal content).
Iterate the JSONL line-by-line and account each row independently. A malformed line
is skipped (its request cannot run upstream anyway) and a row the counter cannot
measure falls back to a conservative size-based estimate. The loop never aborts, so
the allowlist check always sees every parseable model, and the token total is never
zeroed, so a crafted uncountable row still cannot evade the TPM limit, without
hard-rejecting a legitimate batch.
* perf(vertex/files): unblock async upload; drop empty finalize; widen batch MIME types
Three review follow-ups on the resumable batch upload:
- _aresumable_chunked_upload pulled chunks from a synchronous generator that runs
the per-row transform inline on the event loop thread, blocking other requests
between PUTs on large uploads. Each chunk is now produced via asyncio.to_thread.
- _iter_resumable_chunks no longer yields a trailing empty chunk, so an exactly
chunk-aligned upload finalizes on its last data chunk instead of an extra
zero-byte PUT; a 0-byte stream still finalizes via the caller's empty request.
- valid_content_type now accepts the MIME types clients label .jsonl batch uploads
with (text/plain, application/json, ndjson, ...), so such a batch file no longer
silently bypasses the streaming path into the buffered media upload.
* fix(vertex/files): keep legacy bucket_name as GCS bucket fallback
The rename to gcs_bucket_name dropped the legacy bucket_name key entirely, so an SDK caller passing bucket_name to a Vertex AI file create/retrieve/content call with GCS_BUCKET_NAME unset got ValueError("GCS bucket_name is required") where it previously resolved the bucket. _get_configured_bucket_name now reads gcs_bucket_name, then bucket_name, then the env var, and bucket_name is restored to OPTIONAL_KWARGS_KEYS so it survives get_litellm_params on the retrieve and content paths. gcs_bucket_name keeps precedence when both are present
* style: sort imports in llm_http_handler to satisfy I001 budget
---------
Co-authored-by: Yuneng Jiang <yuneng@berri.ai>
* feat(bedrock): add bedrock mantle gemma 4 models (#30264)
* feat(bedrock): add bedrock mantle gemma 4 models
* test(bedrock): harden mantle local cost fixture
* feat(responses): enable the responses API for the Tensormesh provider (#30209)
* feat(responses): enable the responses API for the Tensormesh provider
* Update litellm/llms/openai_like/providers.json
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
---------
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* fix(langfuse_otel): mark LLM spans as generations (#30250)
* fix(bedrock): stop stream_chunk_size leaking into invoke request bodies (#30240)
stream_chunk_size is a LiteLLM-internal knob for re-chunking the HTTP
response stream. The invoke transformations splat optional_params into the
provider request body without dropping it, and Bedrock rejects unknown
fields, so any bedrock/invoke request that sets the parameter fails with
ValidationException: stream_chunk_size: Extra inputs are not permitted.
Drop it in the invoke dispatcher (covers cohere, titan, mistral, meta,
ai21) and in the Claude messages-format request builder (the route used
for bedrock/invoke Anthropic models)
* fix(bedrock): stop buffering streamed tool-call argument deltas (#30231)
* fix(bedrock): stop buffering streamed tool-call argument deltas
Two issues made Bedrock tool-use streaming arrive as a single end-of-stream
burst through LiteLLM while plain text streamed fine.
First, the anthropic-beta allowlist mapped fine-grained-tool-streaming-2025-05-14
to null for bedrock and bedrock_converse, so the header was silently stripped.
Without that beta, Anthropic models on Bedrock buffer tool input server-side and
emit all toolUse.input deltas at once (verified against converse-stream and
invoke-with-response-stream directly). Bedrock accepts the beta via
additionalModelRequestFields.anthropic_beta, so it is now forwarded.
Second, the streaming reads re-chunked the AWS event stream with
iter_bytes(chunk_size=1024). httpx's ByteChunker only releases full 1024-byte
blocks, so the small early events (messageStart, contentBlockStart, first
deltas) sat in the buffer until enough bytes accumulated, pushing
time-to-first-byte from ~1.4s to ~8.5s on buffered tool-use streams. The
default is now no re-chunking; an explicit stream_chunk_size is still honored.
* test(bedrock): cover explicit stream_chunk_size on sync invoke path
* test(bedrock): cover stream_chunk_size plumbing through converse completion
* test(bedrock): cover stream_chunk_size default in legacy BedrockLLM streaming
* test(bedrock): merge converse handler tests into existing mapped test file
pytest imports test modules by basename in non-package test dirs, so the new
tests/test_litellm/llms/bedrock/chat/test_converse_handler.py collided with
the pre-existing tests/test_litellm/llms/chat/test_converse_handler.py and
broke collection in CI. Move the new tests into the existing file
* feat(otel): emit v2 cost breakdown + stamp tracer scope version (#30156)
Read the StandardLoggingPayload cost_breakdown into a typed LLMCost on
LLMCallSpanData and emit each component under litellm.cost.* (absent
components omitted, so spans stay sparse). Stamp litellm.__version__ as
the instrumentation scope version so every v2 span carries a
deterministic scope.version.
Tests under tests/test_litellm/integrations/otel/.
* fix(proxy): cancel in-flight upstream LLM request on client disconnect (opt-in) (#30223)
* fix(proxy): cancel in-flight upstream LLM request on client disconnect (opt-in)
On the non-streaming path, base_process_llm_request awaited the LLM call
with no disconnect monitoring; when the HTTP client went away the
upstream request kept running until completion or request_timeout (6000s
default), holding a backend slot (e.g. a vLLM GPU slot) for output
nobody would read
Add an opt-in general_settings.cancel_on_disconnect flag, default off,
so the default code path is unchanged. When enabled, a receive-based
watcher task observes http.disconnect and cancels the asyncio.gather
driving the upstream call. The resulting CancelledError is converted to
HTTPException 499 only when the disconnect event is set, so
server-initiated cancellations still propagate as-is. The 499 then flows
through _handle_llm_api_exception like any other failure, meaning
post_call_failure_hook still releases max_parallel_requests slots and
fires spend and alerting callbacks; it is logged at info level instead
of a full traceback
Also removes the dead check_request_disconnection helper in
proxy_server.py (zero call sites) along with its behavior-pin tests
Builds on the receive-based design from #25776
Addresses #13774. Re-fixes #22805 (regressed after the #14295 revert)
Co-authored-by: CreateRandom <18438707+CreateRandom@users.noreply.github.com>
* fix(proxy): scope 499 quiet logging to disconnects and harden watcher
Address the two P2 findings from the Greptile review on #30223. The
info-level logging in _log_llm_api_exception now applies only to the
disconnect-specific HTTPException (status 499 plus the shared
_CLIENT_DISCONNECT_DETAIL message), so any other 499 raised by hooks or
guardrails keeps its full traceback. The disconnect watcher now catches
exceptions from request.receive() (e.g. a transport reset) and logs a
warning instead of dying silently, making the degradation to no-op
visible; a test pins that the LLM call is not cancelled in that case
---------
Co-authored-by: kursad <kursad.lacin@brado.net>
Co-authored-by: CreateRandom <18438707+CreateRandom@users.noreply.github.com>
* fix(bedrock): grant aws-external-anthropic:* in OIDC session policy for claude_platform (#30200) (#30205)
The inline STS session policy passed to assume_role_with_web_identity
acts as an IAM PERMISSION CEILING — effective permissions are the
intersection of the role's identity policies and this policy. Any
action not listed is silently denied even when the IAM role grants it.
#27678 added the bedrock/claude_platform/<model> route but its
service-side action namespace is aws-external-anthropic:*, not
bedrock:*. Without a matching statement here, every claude_platform
request via OIDC (GCP federation, EKS Pod Identity webhook, etc.) 403s
with 'no session policy allows the aws-external-anthropic:CreateInference
action' — even with a fully permissive identity policy.
Add a second ClaudePlatformLiteLLM statement covering CreateInference,
CreateBatchInference, CancelBatchInference, DeleteBatchInference,
CountTokens, Get*, List*. Keep aws:SecureTransport=true parity with the
bedrock statement.
Static creds + IRSA flow through different code paths and are not
affected.
Fixes#30200
* fix(proxy): set Retry-After header on RouterRateLimitError 429 responses (#30098)
* Set Retry-After header on RouterRateLimitError responses
When all deployments for a model are in cooldown, the proxy returns a
429 whose cooldown timing is only available by parsing the error
message string. RouterRateLimitError already carries cooldown_time, so
expose it as a standard retry-after header in
_handle_llm_api_exception. The value is rounded up so clients never
retry before the cooldown window ends.
Fixes#27823.
* Set Retry-After after response-headers hook so cooldown wins
The cooldown-derived retry-after was assigned before the
post_call_response_headers_hook merge, so a callback returning a
retry-after key (including a stale or empty value) silently clobbered
it. Move the RouterRateLimitError block after the callback merge so the
cooldown value is authoritative for this error type.
* fix(router): route aspeech through async_function_with_fallbacks (#30104)
* fix(router): route aspeech through async_function_with_fallbacks
Router.aspeech selected a deployment and awaited litellm.aspeech
directly, so TTS requests got no retry on failure and no failover to
backup deployments; the except block only fired an exception alert and
re-raised. Every other router endpoint (acompletion, aembedding,
atranscription, arerank) already delegates to
async_function_with_fallbacks
Mirror the atranscription pattern: move deployment selection and the
litellm.aspeech call into a private _aspeech method, then have the
public aspeech set kwargs["original_function"] = self._aspeech and
await self.async_function_with_fallbacks(**kwargs). _aspeech also picks
up the shared _get_async_openai_model_client helper and the same
total/success/fail call accounting the sibling endpoints use
Fixes#27778.
* fix(router): apply deployment kwargs and rpm semaphore in _aspeech
Bring _aspeech fully in line with _atranscription: call
_update_kwargs_with_deployment so deployment metadata, model_info,
timeout, and default litellm params flow into the request, and wrap
the litellm.aspeech call with the max_parallel_requests semaphore plus
async_routing_strategy_pre_call_checks so TTS respects rpm limits the
same way the other router endpoints do
Also add a unit test that exercises _aspeech directly and asserts the
deployment metadata reaches the underlying call
* fix(slack_alerting): stop false-positive hanging request alerts for requests below the alerting threshold (#30106)
* fix(slack_alerting): skip hanging request alerts below the threshold
The hanging request check alerted on any cached request whose
completion status was not yet recorded, with no minimum age check.
Since the background loop runs every alerting_threshold / 2 seconds,
any request that happened to be in flight at a check fired a
"hanging - Ns+ request time" alert even if it was only seconds old,
producing a steady stream of false positives.
Add a created_at timestamp to HangingRequestData, stamped when the
request enters the hanging request cache, and skip requests younger
than alerting_threshold without evicting them, so a later check can
still alert if they never complete. Extend the cache TTL from
threshold + 60s to 1.5x threshold + 60s; with the age check, entries
only become alertable after threshold seconds, and the check period
is threshold / 2, so the old TTL could evict a genuinely hanging
request before any check saw it cross the threshold.
Fixes#27855.
* fix(slack_alerting): alert once per hanging request
The min-age gate stops false positives for young in-flight requests, but
a genuinely hanging request still re-alerted on every checker tick within
the cache TTL. With the wider TTL (1.5x threshold + 60s) that is 1-2 extra
Slack notifications per stuck request at the default 600s threshold.
Flag a HangingRequestData entry as alerted once its alert fires and skip
flagged entries on later ticks, so each hang produces exactly one alert.
The cache reference is mutated in place, so the TTL is untouched and still
handles cleanup. Adds a regression test asserting one alert across multiple
ticks.
Fixes#27855.
* fix(health): treat all-proxy-models keys as unrestricted in /health (#30087)
* fix(health): treat all-proxy-models keys as unrestricted in /health
A key granted all model permissions stores the literal
"all-proxy-models" marker in its models list. The /health access
filter compared that marker against real model_names, so the model
list filtered down to nothing and the WebUI health check returned
healthy_count=0, unhealthy_count=0 with HTTP 503. Skip the filter
(both the live path and the background-cache model_id scoping) when
the marker is present, matching how auth_checks treats
SpecialModelNames.all_proxy_models.
Fixes#29744.
* fix(health): resolve all-team-models sentinel to the team allowlist
Same failure shape as the all-proxy-models case: a key carrying the
literal "all-team-models" entry matches no real model_name, so the
/health access filter would zero out the model list. Resolve the
sentinel to the key's team models when team_id is set, matching
get_key_models in model_checks.py. Without a team_id the sentinel
stays unresolved and matches nothing, denying rather than widening
access, mirroring _resolve_key_models_for_auth_check.
* feat(proxy): auto-enable drop_params for Claude Code requests (#30218)
* feat(proxy): auto-enable drop_params for Claude Code requests
Claude Code identifies itself with a claude-cli/<version> user agent and
sends Anthropic-specific params (top_k, thinking, etc.) on every request.
When the proxy routes those requests to a non-Anthropic provider, the
unsupported params fail the call unless drop_params is configured. Detect
the Claude Code user agent in add_litellm_data_to_request and default
drop_params to true for those requests, without overriding an explicit
drop_params value sent by the caller.
* feat(proxy): respect operator litellm_settings drop_params over Claude Code default
An explicit drop_params in the operator's litellm_settings (true or false)
now suppresses the Claude Code user agent default, so an operator who
deliberately configured drop_params: false keeps strict param validation
for Claude Code clients too. The auto-default only fills the gap when
neither the request body nor the config sets a value.
* fix(snowflake): migrate to native endpoints with auto-routing for Claude models (#29964)
* fix(snowflake): migrate to native Cortex REST API endpoints
Replaces the legacy /api/v2/cortex/inference:complete endpoint with the
native OpenAI-compatible /api/v2/cortex/v1/chat/completions endpoint,
fixing error 390142 (Incoming request does not contain a valid payload)
when using model: snowflake/<model> in LiteLLM proxy.
Changes:
- litellm/llms/snowflake/chat/transformation.py: route to native
/cortex/v1/chat/completions, remove Snowflake-specific tool_spec
payload transformation, remove content_list response handling,
add stream to supported params
- litellm/llms/snowflake/anthropic/transformation.py (new):
SnowflakeCortexAnthropicConfig routes Claude models to /cortex/v1/messages
with anthropic-version header and Anthropic->OpenAI response transform
- tests: 29 unit tests covering URL routing, auth headers, payload
format, and response parsing
* fix(snowflake): map max_tokens to max_completion_tokens for native endpoint
* fix: handle multi-turn tool conversations and OpenAI→Anthropic tool format conversion
- _extract_system_and_messages now preserves tool_calls from assistant messages
and converts them to Anthropic tool_use content blocks
- tool role messages are converted to user role with tool_result content blocks
(as required by Anthropic Messages API)
- Added _transform_tools_to_anthropic() to convert OpenAI tool format
(type/function/parameters) to Anthropic format (name/input_schema)
- Added comprehensive tests for multi-turn tool conversations
Addresses review feedback on PR #29964
* test: add coverage for malformed JSON and non-string tool arguments
* fix(tests): update chat transformation tests for native OpenAI-compatible endpoint
* style: apply black formatting
* fix: resolve mypy type errors in anthropic transformation
* fix: correct mypy type: ignore error codes (attr-defined)
* fix: use max_tokens instead of max_completion_tokens for Snowflake endpoint compatibility
* refactor: merge Anthropic config into unified SnowflakeConfig with auto-routing
- Remove separate SnowflakeCortexAnthropicConfig and anthropic/ directory
- SnowflakeConfig now auto-routes based on model name:
- Claude models → /messages endpoint (Anthropic format)
- All others → /chat/completions endpoint (OpenAI format)
- No new provider needed (stays as SNOWFLAKE = 'snowflake')
- Tool message transformation for Claude: tool_calls → tool_use blocks,
tool role → user with tool_result
- OpenAI → Anthropic tool format conversion (parameters → input_schema)
- Addresses Greptile feedback about unwired SnowflakeCortexAnthropicConfig
* fix: use max_completion_tokens for /chat/completions (Snowflake deprecated max_tokens on this endpoint)
* fix(tests): update assertions for Claude auto-routing to /messages endpoint
* fix(snowflake): add tool_choice conversion and preserve max_completion_tokens in Anthropic path
* fix(snowflake): use ChatCompletionMessageToolCall objects and strip model prefix on OpenAI path
* fix(snowflake): collect multiple system messages to prevent guardrail override
* chore: remove committed .pyc files and add __pycache__ to .gitignore
* fix: remove unused Union import
* fix: restore original .gitignore (accidentally replaced in earlier commit)
* feat(snowflake): add streaming response handler for both Anthropic and OpenAI SSE formats
* fix: remove unused AsyncIterator and Iterator imports
* fix: add missing total_tokens to ChatCompletionUsageBlock
* fix(snowflake): coalesce consecutive tool results into single user message for Anthropic
* fix(snowflake): handle message_start event for streaming input_tokens tracking
* fix: evict last deleted model in multi-instance deployments (#28608)
* fix: evict last deleted model in multi-instance deployments
_delete_deployment had an early return when db_models was empty,
preventing eviction of the last deleted model during reconciliation.
- Remove len(db_models)==0 early return from _delete_deployment
- Return None (not []) from _get_models_from_db on DB failure so
callers can distinguish a transient failure from a genuinely empty DB
- Guard _update_llm_router against None to skip updates on DB failure
Fixes#28443
* test: remove dead MagicMock assignment in type_mismatch test
* fix: update test to pass [] not None to _update_llm_router
test_ProxyConfig__update_llm_router_bad_proxy_logging_raises was passing
None as new_models to get through to the proxy_logging_obj check, but
the None guard we added now returns early before reaching that path.
Pass [] instead so the test exercises the intended AttributeError case.
Signed-off-by: Rudra Dudhat <contact.rdudhat@gmail.com>
* chore: regenerate API types to sync schema.d.ts with proxy OpenAPI spec
Signed-off-by: Rudra Dudhat <contact.rdudhat@gmail.com>
---------
Signed-off-by: Rudra Dudhat <contact.rdudhat@gmail.com>
* fix: invalidate Redis spend counter on /key/reset_spend (#29694)
* fix: set Redis spend counter to reset_to value on /key/reset_spend
Previously, the Redis spend counter was always set to 0.0 after a reset,
even when reset_to was a non-zero value (partial reset). This caused
the budget to be under-enforced for up to 60 seconds until the counter
expired and fell through to the DB.
Now the counter is set to the actual reset_to value, so partial resets
are reflected correctly and budget enforcement is consistent.
* test: update reset_key_spend test to match direct cache set
The implementation now sets spend_counter_cache directly instead of
calling _invalidate_spend_counter. Update the test to verify the
in_memory_cache.set_cache call with the correct key, value, and ttl.
---------
Co-authored-by: michaelxer <michaelxer@users.noreply.github.com>
* fix: add scaleway models pricing (#27659)
* fix: Add embeddings support for Scaleway provider
* fix: resolve merge conflicts
* fix(main): clarify backend route handling for Swagger static assets (#30196)
* fix(main): clarify backend route handling for Swagger static assets
* fix(allowlist): add BACKEND_MOUNT_PATHS for Swagger static assets
* fix(voyage): route multimodal embeddings to correct endpoint (#30193)
* fix(voyage): route multimodal embeddings to correct endpoint
* test(voyage): cover multimodal embedding edge cases
* test(voyage): cover api key fallback
* fix(voyage): raise early on missing api key and malformed image url
* test(voyage): cover utils routing and helper
* fix(voyage): route supported openai params for multimodal models
* style: apply black formatting
* fix(ui): infer Azure API version from API base (#30204)
* fix(ui): infer Azure API version from API base
* fix(ui): address Azure API version feedback
* Update litellm/llms/snowflake/chat/transformation.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* feat(datadog): add team-scoped Datadog callback support (#29947)
Enable teams to configure their own Datadog credentials via
POST /team/{team_id}/callback, following the same pattern as Langfuse.
* Merge pull request #29528 from aanchal22/litellm_byok-alias-merge
fix(proxy): atomic merge for team model aliases and team.models on BYOK create
* feat: add EmpirioLabs as an OpenAI-compatible provider (#30278)
Co-authored-by: Adam Dalloul <adam.d.developer@gmail.com>
* fix: resolve failing tests and lint in snowflake/team endpoints
- Black-format snowflake/chat/transformation.py to fix lint failure
- Update Anthropic config test to expect default max_tokens of 4096 (matches implementation)
- Add AsyncMock + execute_raw mock to team_model_add cache-refresh pin test
- Add model_dump mock and patch cache/logging in test_uses_atomic_array_append_with_dedup
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(test): update test_db_error_new_model_check for new _delete_deployment logic
_delete_deployment no longer short-circuits on empty db_models — it now
treats [] as a valid empty-DB state and proceeds to check config models.
Mock get_config to return the two router deployments so they appear in
combined_id_list and are protected, which matches the real-world scenario
where a DB error occurs but the models are config-backed.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat(proxy): register cancel_on_disconnect in ConfigGeneralSettings and config list (#30295)
* feat(proxy): register cancel_on_disconnect in ConfigGeneralSettings and config list
Follow-up to #30223 per maintainer review: documents the flag in
ConfigGeneralSettings with a short description and adds it to
allowed_args in get_config_list so the UI and /config/list expose it.
A test pins that /config/list returns the field with type Boolean,
which requires both registrations to be present
* chore(ui): regenerate schema.d.ts for cancel_on_disconnect
---------
Co-authored-by: kursad <kursad.lacin@brado.net>
* fix(datadog): never fall back to env DD_API_KEY for caller-supplied destinations
Team/key-scoped Datadog loggers could be pointed at an arbitrary dd_agent_host or
dd_site while omitting dd_api_key, causing the proxy's global DD_API_KEY to be sent
as the DD-API-KEY header to that destination. Gate the env-var fallback behind an
allow_env_credentials flag, set to False when the destination is caller-supplied,
mirroring the existing langfuse/langsmith pattern.
---------
Signed-off-by: Rudra Dudhat <contact.rdudhat@gmail.com>
Co-authored-by: Emerson Gomes <emerson.gomes@thalesgroup.com>
Co-authored-by: daitran-tensormesh <dai@tensormesh.ai>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Co-authored-by: Muspi Merol <me@promplate.dev>
Co-authored-by: fangkang <fangkangm@gmail.com>
Co-authored-by: Chris Hoogeboom <chris.hoogeboom@gmail.com>
Co-authored-by: kursadlacin <kursadlacin@gmail.com>
Co-authored-by: kursad <kursad.lacin@brado.net>
Co-authored-by: CreateRandom <18438707+CreateRandom@users.noreply.github.com>
Co-authored-by: hcl <chenglunhu@gmail.com>
Co-authored-by: Filippo Menghi <113345637+Cyberfilo@users.noreply.github.com>
Co-authored-by: Mateo Wang <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: sfc-gh-nashukla <navnit.shukla@snowflake.com>
Co-authored-by: Rudra Dudhat <contact.rdudhat@gmail.com>
Co-authored-by: Michael <52305679+michaelxer@users.noreply.github.com>
Co-authored-by: michaelxer <michaelxer@users.noreply.github.com>
Co-authored-by: Quentin Champenois <26109239+Quentinchampenois@users.noreply.github.com>
Co-authored-by: mauriceberentsen <mauriceberentsen@live.nl>
Co-authored-by: lost9999 <56498264+lost9999@users.noreply.github.com>
Co-authored-by: GaetanVDB07 <86427581+GaetanVDB07@users.noreply.github.com>
Co-authored-by: Aanchal Khandelwal <aan2210khandelwal@gmail.com>
Co-authored-by: Adam Dalloul <adam_dalloul@icloud.com>
Co-authored-by: Adam Dalloul <adam.d.developer@gmail.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
