* fix(proxy): cache tag-name registry so unregistered request tags skip Postgres
Request tags are free-form attribution labels, so most have no LiteLLM_TagTable
row. get_tag_objects_batch never cached that absence: every tagged request ran
a find_many that came back empty, and under Prisma pool contention those
per-request queries queued for minutes inside user_api_key_auth.
Cache the bounded set of registered tag names under one aggregate key with the
management-object TTL. Uncached request tags are filtered against it before any
per-tag DB fetch, so unregistered tags cost zero DB reads on a warm path. An
empty registry is cached as a valid answer; DB errors are not cached and fall
back to the per-tag lookup; tables past TAG_REGISTRY_MAX_SIZE cache an overflow
sentinel that disables filtering. Tag create/update/delete endpoints now evict
the registry and per-tag keys and publish cross-worker invalidation (they
previously evicted nothing). The per-tag write-back also gains the management
TTL it was missing, and the hand-built tag:{name} key strings are replaced with
a shared builder.
* fix(proxy): skip per-request end-user DB reads via restricted-id registry
Every request carrying a user id ran get_end_user_object, and with high-cardinality
auto-created end-user rows (hundreds of thousands of ids, all restriction fields
NULL) the per-pod cache missed on nearly every request, so each one paid a Postgres
find_unique that queued behind the Prisma pool during background-job bursts. True
misses were never cached, and unknown ids paid the read twice per request.
Cache the bounded set of end-user ids that carry any restriction (blocked, budget,
region, default model, or object permission) under one aggregate key with the
management-object TTL. When an id misses the per-id cache and is absent from a
usable registry, get_end_user_object returns None with zero DB reads; restricted
ids keep today's fetch-and-cache path. The skip is bypassed whenever
litellm.max_end_user_budget_id is set (default budgets make unrestricted rows
behaviorally distinct from missing rows), validate_end_user_id_in_db is on
(existence checks need the row), or the token carries end_user_max_budget from
custom auth (the row's recorded spend seeds the budget counter). Empty registries
cache as a valid answer, DB errors are never cached, and oversized tables cache an
overflow sentinel that disables filtering. Customer create/update/block/delete now
evict the registry and per-id keys and publish cross-worker invalidation (they
previously evicted nothing), and the per-id write-back gains the management TTL it
was missing so Redis entries no longer live forever.
* refactor(proxy): single generic registry loader with error sentinel and single-flight
Code review follow-ups on the two registry caches. Registry DB errors now cache
the overflow sentinel for a short REGISTRY_ERROR_NEGATIVE_CACHE_TTL window and
log at warning, so a degraded Postgres stops paying the failing registry scan on
every request on top of the per-id fallback. Cold registry loads are single-flight
per worker behind per-registry locks with a recheck after acquire, so a TTL expiry
no longer fans out one full-table scan per in-flight request. The tag and end-user
loaders collapse into one _load_bounded_registry with per-entity fetch closures,
and the triplicated evict-then-broadcast protocol becomes one evict_and_broadcast
helper beside publish_auth_cache_invalidation, shared by the tag, customer, and
project eviction paths.
* chore(lint): suppress fail-safe registry excepts and ratchet BLE001 budget
* docs(proxy): trim registry cache commentary to single-line why docstrings
* fix(lint): move tag fetch return to else block to satisfy TRY300 budget
Bedrock invoke /v1/messages streaming reports cache_read_input_tokens and
cache_creation_input_tokens on message_stop.usage while attaching
amazon-bedrock-invocationMetrics to the same chunk. The stream decoder
rebuilt that chunk's usage block from inputTokenCount/outputTokenCount
alone, which exclude cache reads and writes, so the cache breakdown was
destroyed before _promote_message_stop_usage could surface it and cache
tokens were billed at $0. Merge instead of replace, and also map
cacheReadInputTokenCount/cacheWriteInputTokenCount when Bedrock reports
the cache itemization inside the invocation metrics.
Co-authored-by: Brian Cox <3924351+brian5021@users.noreply.github.com>
Azure rejects the legacy `max_tokens` key for the whole gpt-5 name family, but
`AzureOpenAIGPT5Config.is_model_gpt_5_model` deliberately excludes `gpt-5-chat*`
so those deployments fall through to `AzureOpenAIConfig`, which sends `max_tokens`
verbatim and gets a 400 back on every request that carries it, `/health` probes
included.
One predicate was answering two independent questions. Split it: the new
`AzureOpenAIConfig.requires_max_completion_tokens` covers the whole gpt-5 name
family and drives only the rename, while `is_model_gpt_5_model` keeps keying
reasoning_effort, the temperature clamp and the dropped penalties off the
reasoning question, so #13781 stays fixed.
Adds an opt-in operator allow-list, litellm_settings::bedrock_request_metadata_fields, that forwards LiteLLM key, team and end-user identity plus client spend_logs_metadata into Bedrock request metadata so Bedrock spend can be grouped in AWS Cost Explorer.
Covers all three Bedrock surfaces: the Converse body requestMetadata field, and a signed X-Amzn-Bedrock-Request-Metadata header on Invoke chat completions and on Invoke /v1/messages, where the header is the only viable leg.
The resolver reads both metadata variable names, reserves the whole user_api_key_ prefix against caller-supplied keys, caps the client slot budget explicitly at 16 minus the reserved count, and drops rather than rejects auto-injected values that violate Bedrock constraints. Caller-supplied requestMetadata keeps its existing 400 semantics.
The request-metadata field and header are proxy-owned whenever forwarding is enabled. A caller-supplied value, reachable through the generic extra_headers passthrough, is dropped unconditionally and compared case-insensitively, and is replaced only by the proxy's own value, so identity in the AWS billing record cannot be forged. Absence of a resolved value still means absence on the wire rather than a fallback to the caller's. The guardrail headers keep their existing no-displace behaviour.
* fix(guardrails): scan text on /guardrails/apply_guardrail for Azure Content Safety
The two Azure Content Safety guardrails never implemented apply_guardrail, so the
endpoint fell through to the base no-op and answered 200 with the caller's text
echoed back, having scanned nothing.
Implementing that method also flips the proxy's unified-vs-native dispatch, which
would move request traffic off these guardrails' own hooks. Add an opt-out that
keeps every lifecycle event on the native hooks, so only the endpoint changes.
* test(guardrails): cover the remaining native-hook opt-out dispatch sites
Adds regression tests for the parallel post-call path, the MCP post-call hook, and
the policy engine step, so every read of the opt-out flag fails when removed.
Callers can opt into the provider's raw operation response on /v1/ocr with the x-req-format: native header (or req_format in the body) while page-based cost tracking keeps reading usage_info off the normalized response.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Shape detection and block normalization sat in the generic batch layer, which
let batch and live parsing of the same wire format drift apart. Both now live on
AmazonConverseConfig as is_converse_usage_shape and usage_from_batch_output, so
batch_utils asks the provider adapter rather than knowing Bedrock's field names.
Adds direct coverage for the shape predicate, the completion of an incomplete
block, cache-count inflation, and the streaming usage event that shares the
public transform. Drops the narrative banner from the batch tests.
- decode upstream first frame as utf-8 instead of ascii
- reject model-restricted keys at connect to match HTTP model enforcement
- log the actual request path for /openai_passthrough traffic
Embeddings rows were identified by body shape (has `input`, no
`messages`/`prompt`), which also matches a `/v1/responses` batch row
and reserved zero output tokens for it -- letting a project caller run
large Responses generations against a quota-limited model without
consuming OTPM. Classify embeddings by the row's own `url` instead,
and read `max_output_tokens` as a Responses output cap alongside
`max_tokens`/`max_completion_tokens`.
Co-authored-by: Cursor <cursoragent@cursor.com>
Resolves conflicts from the upstream merge and addresses the Veria-AI
review comment on this PR: batch rows could bypass a project's
per-model ITPM/OTPM quota when the batch's file-bound/routing model
had no quota configured. Charges each row's own model against its own
project quota instead of only the routing model's, and fixes rate
limit error messages to attribute the correct model via a new
descriptor_value field on RateLimitStatus/AtomicCounterMeta. Also
re-syncs the ruff-strict, type-discipline, and basedpyright budgets
against the correct (non-stale) merge base.
Co-authored-by: Cursor <cursoragent@cursor.com>
Every bedrock batch output line went through the Anthropic usage parser, which
reads snake_case input_tokens/output_tokens. Converse-family models (Nova and
friends) report camelCase inputTokens/outputTokens, so their usage came back
0/0/0 and the batch billed $0 despite real token consumption.
Usage is now selected by the shape of the payload: a Converse-shaped block goes
through the same transform the live Converse path uses, so a batch and an
equivalent non-batch call agree on tokens, including cache reads and writes.
Anthropic-shaped bedrock output is unchanged.
A shape neither parser understands (an InvokeModel-native payload from Titan,
Cohere, or Llama, which name their counts differently again) still reads zero,
but now warns with the keys it saw instead of silently billing $0.
Exposes the Converse usage transform as public, since batch parsing is a second
legitimate caller; that also removes the private-member access invoke_handler
was already making.
Resolves the transform_create_file_response conflict by keeping the
_uploaded_object_size handoff over the response Content-Length read,
and adds the rebind-ok justification LIT011 now requires for the
upload-size litellm_params handoff after the base budget ratcheted.
* fix(panw_prisma_airs): scan tool call args as plain text, not a tool_event
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(panw_prisma_airs): type the tool call argument extractor
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(panw_prisma_airs): cover tool call error fallback and dict masking paths
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(panw_prisma_airs): scan tool names with args and tolerate custom tool calls
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(panw_prisma_airs): scan tool call arguments that arrive already parsed
The tool call slice types arguments as a string, so a client posting parsed JSON
failed validation and the whole tool call, name included, read as unscannable and
was skipped without ever reaching AIRS. The OpenAI request path forwards
client-supplied tool_calls verbatim, so that shape is reachable.
Coerce non-string arguments instead of rejecting them, so the content is scanned.
* fix(panw_prisma_airs): route tool-block masked data by scan side, not by key name
Merging #37036 (already on staging) with this PR produces no conflict and a
silent bug. #37036 withholds prompt_masked_data on response-side tool blocks,
which was right while tool calls went out as a request-side tool_event: AIRS
reported the model's arguments under that key. This PR scans tool calls as
ordinary prompt/response text, so the side of the scan now decides which key
holds what. The model's arguments arrive under response_masked_data, already
covered by _CLIENT_HIDDEN_SCAN_FIELDS, and prompt_masked_data goes back to
being the caller's own input -- one of the audit fields LIT-5638 asks for.
Left as merged, a response-side tool block drops that field with nothing to
flag it.
- Tool-path block branch calls _build_error_detail without also_hide
- also_hide parameter removed; after this change it has no callers
- Regression test asserts both directions: model output withheld, caller
input preserved. It fails against the auto-merged combination.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* fix(panw_prisma_airs): a wrong-typed tool name must not suppress the scan
_ToolCallFunctionSlice types name as str, and _get_tool_call_function turns any
ValidationError into (None, None), which _scan_tool_calls_for_guardrail reads as
an unscannable tool call and skips. So a client posting "name": 123 keeps its
arguments off the wire to AIRS entirely -- no error, no log, no block. The
OpenAI request path forwards client tool_calls verbatim, so this is reachable by
any caller holding a valid key.
_coerce_arguments already existed for exactly this failure mode on the sibling
field. Widening it to cover name closes the gap:
name='transfer_funds' AIRS called: 1x args scanned: True
name=123 (int) AIRS called: 0x args scanned: False <- before
name=123 (int) AIRS called: 1x args scanned: True <- after
Reported by Cursor Bugbot on fd9f6396e5.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Yucheng Zhu <yucheng@berri.ai>
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
* fix(guardrails): return the full PANW AIRS scan response on blocked requests
The blocked-request error detail was assembled from a hardcoded allowlist, so audit fields like prompt_detection_details, prompt_masked_data, source, transaction_id and session_id never reached the client even though AIRS returned them.
Resolves LIT-5638
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style(guardrails): drop redundant comment in AIRS error detail
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(panw_prisma_airs): withhold response_masked_data from the blocked-response error
The full AIRS passthrough also reached the response-side block path, where
response_masked_data carries the model's own generation. That branch is only
reached when mask_response_content is False, so the operator had explicitly
declined to deliver that text, and the error body handed it back anyway.
Withhold response_masked_data from the client-visible detail. prompt_masked_data
stays: it is the caller's own input and one of the fields the ticket asks for.
Every other AIRS field, including prompt_detection_details, source,
transaction_id and session_id, is unchanged.
* fix(panw_prisma_airs): withhold generated tool args from response-side blocks
_scan_tool_calls_for_guardrail calls AIRS with is_response=False because
tool_event is request-side in the AIRS schema, so AIRS returns the scanned
tool arguments under prompt_masked_data. When the tool calls being scanned
are the model's own output, that key holds generated content, and the
_CLIENT_HIDDEN_SCAN_FIELDS default (response_masked_data, empty on this
path) does not cover it. With the default mask_response_content=False the
block branch then shipped the model's masked tool arguments in the 400 --
the same content channel this PR closed for response_masked_data.
_build_error_detail takes an extra_hidden_fields argument so the withholding
stays in one place, and the tool-call block branch passes prompt_masked_data
when is_response is True. Request-side blocks are unchanged and still carry
prompt_masked_data, which is what LIT-5638 asks for.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* style(panw_prisma_airs): apply ruff format
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Yucheng Zhu <yucheng@berri.ai>
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
