Budget is enforced once during auth, against the requested model group.
`_is_model_cost_zero` waives every budget check for a zero-cost group, and the
router then picks a fallback target afterwards, inside `run_async_fallback`,
where nothing re-checks budget. A free model with a paid fallback therefore
bills with no budget gate at all.
Add `fallback_budget_check`, the budget sibling of the existing
`fallback_access_check`: a predicate awaited per fallback target that skips
targets the caller cannot pay for. The primary attempt is untouched, so a
zero-cost model is never blocked by budget and only the paid fallback is
refused.
Counter reads pass `max_budget` so `get_current_spend` verifies against
authoritative recorded spend, matching the auth-time key and user checks; a
counter restored from an older snapshot reads as a hit rather than a clean
miss, so without it a stale-low value would keep admitting paid fallbacks.
A zero-cost fallback target is always allowed, and a team key does not inherit
the key owner's personal budget unless `apply_user_budget_to_team_keys` is set,
matching `_PROXY_MaxBudgetLimiter`.
Scope is key and user budgets. Team, team-member, end-user, org, global and
per-model budgets are not covered yet: those auth-path functions enforce rather
than report, so reusing them would fire threshold alerts and take spend
reservations for a target that is then skipped. Two limitations of that scope
are documented in the module docstring: the check reads the spend counter
rather than reserving against it, so concurrent fallbacks can cross a cap
together; and a request reaching the router without
`metadata["user_api_key_auth"]` is not restricted. Both are shared with
`fallback_model_access.py`.
Opt-in via `general_settings.enforce_fallback_budget`.
Relates to #41344
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Every OpenAI Responses body carries `error: null` at the top level, and the
completeness check tested the key's presence rather than its value, so it
rejected every single one. The cost was silent: nothing failed, the endpoint
simply never cached, which is exactly the outcome the endpoint was added for.
Found by driving the edge against the real providers rather than the synthetic
fixtures, which carried no error key at all. Reading the value instead of the
key is also more accurate for chat completions and messages, where a real error
body carries a populated error object.
The streaming post-call hook rescanned the whole accumulated choice buffer on every chunk, so scan cost grew quadratically with output length. Keep a bounded per-choice buffer instead: once it exceeds twice the scan context, drop the head when masking the head and tail separately yields the same output as masking the whole buffer, so no pattern, phrase or exception straddles the cut. Detections from the dropped head are kept and merged, deduplicated, into the final log row
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Almost every Bedrock deployment in the suite declares
aws_region_name="os.environ/AWS_REGION". The mount resolver treated that
string as a region name, produced a mount nothing serves, and left the whole
Anthropic-on-Bedrock surface on its direct path, which is the one thing
mounting Bedrock was for.
The run pod does not share the proxy's environment, so the harness genuinely
cannot resolve that reference. A `us.` inference profile fans out across the US
regions and is reachable from any of them, so those route to the default mount
whatever the proxy resolved. A model that is not cross-region and declares its
region that way keeps its direct path rather than being sent to a region it may
not exist in.
Adds a persistent total_spend column to LiteLLM_VerificationToken and LiteLLM_DeletedVerificationToken, incremented in the same write as spend and left alone by budget resets. Surfaces it on /key/info, /key/list and the Admin UI Virtual Keys table and key detail view
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Chat completions and messages were the only cacheable paths. The suite also
drives /v1/embeddings and /v1/responses through the same OpenAI mount, so both
now cache, each with its own completeness rule: a chat response's `choices`
check would reject a perfectly good embedding, and a Responses run that never
reached `response.completed` must stay out of the cache the same way a
truncated stream does.
Vertex and Gemini stay off the edge. litellm's `_check_custom_proxy` rewrites a
path-prefixed vertex api_base into `{api_base}:{endpoint}`, dropping project,
location and model, so a mount under a path prefix cannot work without a
root-mounted edge on its own port or a change in litellm. Shipping an
unvalidated URL guess would have been worse than saying so in PROVIDER_CACHE.md.
Also finishes the MountPolicy move: a mount now carries its signer and its
unkeyed headers together instead of a bare signer map.
The exact-request cache reused 5% of routed traffic (build 218: 19 hits,
350 misses) because every test salts its prompt with a fresh unique_marker(),
so the same test could never match itself across builds. It also routed only
openai and anthropic, while the week's flakiness was Bedrock.
Key is now HMAC(test id + method + URL + headers + body, with every
unique_marker() token replaced by a placeholder, + FIFO slot index). The slot
index is what keeps two marker-only-different calls in one test on two
recordings and therefore two provider response ids, so spend rows still
reconcile one per invocation. A call outside any test is not cacheable.
Bedrock gets a region-qualified mount and SigV4 re-signing, since the edge
rewrites the Host the proxy signed. Signature headers are excluded from the
key for signing mounts only, because x-amz-date would otherwise make every
Bedrock request a permanent miss; every other mount still keys on its
credentials whole. Only Anthropic-on-Bedrock chat deployments route:
embeddings, image generation, rerank and realtime keep their direct path, and
so do deployments carrying their own aws_role_name or static keys, whose whole
point is to prove the product's assume-role chain rather than the runner's.
The two eventstream actions bypass the cache and go live, still signed.
Counters are now attributed per mount as well as in total, so a build can
report a per-provider hit rate instead of one number.
Key metadata disable_fallbacks only lands on data during add_key_level_controls,
so the local rate-limit fallback retry now rechecks it post pre-call. Also use a
real UserAPIKeyAuth in the skip pre-call test since the path reads router_settings
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
A body that serializes litellm_trace_id as null or an empty string carries no identity, so it must not
block the server span fallback. Also mark the nested metadata write as an out-param store
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
UserAPIKeyAuth.parent_otel_span is Any at runtime (opentelemetry is an optional extra), so the OTel
trace-id fallback must only format an int trace id, otherwise an object that merely quacks like a span
turns the whole request into a 500
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Pass user_api_key_dict.parent_otel_span into the outgoing W3C injection so the
legacy otel callback propagates its litellm_request span, falling back to the
otel_v2 request root span and then the ambient span. Extend the mapped unit
tests to assert the propagated trace and span ids over real captured headers
for HTTP and WebSocket passthrough with forwarding on and off.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
When the otel callback is enabled and the client sends no trace or session identity, the request now inherits the W3C trace id of the proxy's server span as litellm_trace_id and metadata.trace_id. The missing_session_id policy and SpendLogs then persist that value as session_id, so a trace in the OTel backend and its row in the Logs UI carry the same id. Explicit x-litellm-trace-id, traceparent, body metadata.trace_id and litellm_trace_id keep priority.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
The fallback retry in _pre_call_with_fallbacks re-entered common_processing_pre_call_logic with data already enriched by the first pass, so add_litellm_data_to_request deep-copied a metadata dict holding the live OTel span and the request failed with a 500 (cannot pickle '_thread.RLock') instead of the intended 429 or fallback. Capture the configured fallbacks and a snapshot of the client request before the first pass, look up the fallback chain by the normalized model group after the limiter raises, and run each fallback attempt on a fresh copy of that snapshot. Replaces the mock-heavy tests with a rig that runs the real v3 limiter and a live OTel span through the proxy_logging_obj seam
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
The first cache-enabled litellm-e2e build (211) showed three gaps in the shared provider cache:
Every OpenAI response carries Cloudflare bot-management Set-Cookie headers, and the capture rejected any response with Set-Cookie, so no OpenAI response was ever recorded (179 of 372 misses rejected). The edge already withholds Set-Cookie from the proxy, so drop it before validating and storing instead of rejecting.
The provider prompt-caching tests need fresh provider state: a replayed priming response reports cache creation rather than a cache read, and the TPM test then trips the key limit. Mark both modules provider_live.
TestApiBaseSeam::test_live_mode_returns_none ran inside the cache-enabled runner and saw the shared edge; isolate it from E2E_PROVIDER_CACHE.
Together moved openai/gpt-oss-20b off serverless and three tests in
test_completion.py died on a live 400. None of them needed Together to be up:
streaming is already covered live by tests/e2e/llm_translation/test_together_ai_e2e.py,
which picks its model from the cost map instead of pinning one, and the other
two are request-shape questions. Delete all three and assert the two shapes in
the mapped transformation file: the provider prefix is stripped without eating
the rest of a slashed model name, and custom role wrappers never reach the
request.
