The Logs drawer gets messages/response from GET /spend/logs/ui/{request_id};
the list endpoint omits those heavy columns for every caller, admins included.
That detail route was missing from LiteLLMRoutes.spend_tracking_routes, and
check_route_access anchors patterns, so /spend/logs/ui never matched it. Every
internal_user got a 403 before the handler ran and the UI fell back to the
"Request/Response Data Not Available" banner, even on their own requests
Adds the route to spend_tracking_routes so internal_user, internal_user_view_only,
admin_viewer and org_admin all inherit it, and drops the now-redundant explicit
entry from admin_viewer_routes. The handler already authorizes non-admins per row
via _assert_user_can_view_request_id, so no handler-side scoping change is needed
That helper returned silently when no spend-log row existed, which the detail
handler treats as authorized before asking every custom logger for the payload by
raw request_id. With retention pruning the row can be gone while the payload is
still in cold storage, so opening the route would have let a non-admin read
another tenant's prompt out of S3/GCS. A missing row now falls through to the
same 403 as a foreign row, which also removes the exists-but-not-yours oracle
Fixes#34099
Adds object_permission.skills to keys and teams, enforces it on
/claude-code/marketplace.json?key=, /claude-code/plugins and
/claude-code/plugins/{name}, and exposes an Allowed Skills selector in
the key and team create/edit forms of the Admin UI
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(mcp): reject initialize with 403 when the key grants no MCP servers
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(mcp): e2e expects 403 initialize for a key with no MCP servers
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(mcp): mention IP filtering in the no-servers initialize denial and keep zero-grant tool coverage
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: mateo <mateo@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(rust_bridge): count budget-check input tokens in Rust on all LLM routes
Rust counts input tokens from the raw JSON body with the GIL released inside the existing budget reservation, covering every LLM route the auth dependency guards. It only fires for models on the Anthropic tokenizer when a budget is set, and Python counts whenever Rust is off, missing, or declines a body shape.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* perf(rust): count byte-level BPE tokens without the GPT-2 split regex (#40594)
The oniguruma run of the ByteLevel pre-tokenizer regex is about 90% of
encode_fast on a 100k token body (100 ms of the ~110 ms Rust admission
count in the gateway pod). A hand-written scanner that yields the same
pieces, then feeds the model directly, counts the same text in 10 ms.
It only engages for tokenizers with the Anthropic shape (optional NFKC,
ByteLevel without prefix space, no post-processor) and falls back to the
full encoder when the text contains an added token. Parity with
encode_fast is tested on random texts, the pieces are compared with the
real pre-tokenizer, and the \p{L}/\p{N}/\s tables are checked against
oniguruma for every code point.
NFKC runs through unicode-normalization-alignments, the crate and
Unicode tables NormalizedString::nfkc already uses, so the fast path
normalizes exactly what the full encoder would. Using the newer
unicode-normalization crate changed the count for 171 code points that
gained compatibility decompositions after Unicode 9 (U+32FF, U+A7F1..).
The fast normalizer is compared with the tokenizer's for every scalar
value and on random texts.
The scanner is built without mutable state: byte_char and mapped_len replace the const table builders and the reusable mapped buffer, and iter::successors replaces the stateful piece iterator. byte_chars_match_the_byte_level_alphabet checks the byte mapping against ByteLevel for every scalar value.
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(rust_bridge): bound concurrent token-count encodes and share the Anthropic tokenizer predicate
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
The writer health probe only ran SELECT 1, which a read-only Postgres
session answers fine, so a pooled connection left pointing at a demoted
primary kept failing every write with SQLSTATE 25006 until the pod was
restarted. Probe transaction_read_only instead, treat a 25006 on the
request path as a signal to recreate the client, and back off
exponentially while the database as a whole stays read-only so a replica
or an in-progress failover does not get its engine killed every cycle.
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(terraform): Prometheus metrics sidecar for the GCP Cloud Run gateway
gateway_metrics_port adds a metrics container running
litellm.proxy.prometheus_metrics_server next to the gateway, sharing the
PROMETHEUS_MULTIPROC_DIR over an in-memory volume, plus a Managed Service
for Prometheus collector sidecar that scrapes it over localhost and writes
to Cloud Monitoring. The gateway stays on port 4000 and the load balancer
routing is unchanged.
Resolves LIT-7502
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(terraform): reject fractional and collector health ports for gateway_metrics_port
Greptile review on #40614: 4000.5 fails integer port parsing and 13133 collides with the
gmp sidecar liveness listener. Also stop claiming the load balancer's own /metrics goes away
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
A flat list[str] sent to voyage-context-4 is treated as independent inputs and forwarded flat with
enable_auto_chunking=True, chunk_size=32000, and input_type=document unless the caller already set
input_type=query. Caller-supplied params now override the defaults instead of being clobbered.
The voyage-4 family and voyage-context-4 cost map entries already exist on litellm_internal_staging,
and voyage-4-nano is not served by the Voyage API, so those additions and their pricing test are dropped.
* feat(infra): scale gateway on per-pod RPM and TPM in Helm and Terraform
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(helm): require the metrics server before rendering the gateway ServiceMonitor
The http port serves /metrics/ behind virtual-key auth, so a ServiceMonitor
pointed at it only collects 401s and the RPM/TPM HPA metrics never appear
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(infra): express gateway HPA, KEDA and ECS workload targets per second
Rename the per-pod request and token targets in both Helm charts and the
AWS module from per minute to per second, and shorten the recommended
Prometheus rate window to [1m] with no * 60 so the adapter and KEDA
signals are what the HPA compares against. ECS keeps CloudWatch's
60-second aggregation: the ALB target is 60x the per-second variable and
the token metric math divides the period Sum by 60 before dividing by
the running task count.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Profiling the sidecar-enabled gateway at 700 rps showed ~2.4% of all samples
in get_model_group_info called per request from budget reservation, plus
get_deployment_model_info for tiered pricing tables. Both are read-only lookups
over the model list, so serve them from the Router's lru caches and clear the
deployment cache alongside the group cache when the model list changes.
The deployment-info cache is a per-router lru_cache built in __init__ rather
than a class-level decorated method, so it does not pin Router instances in a
process-wide cache and is dropped with the router.
A price data reload replaces litellm.model_cost without touching model_list, so
the reload replay also clears both caches; otherwise reservation would keep
pricing against the old catalog until an unrelated model-list change.
Co-authored-by: yassin <yassin@berri.ai>
* feat(mock): report admission-time input token count in mock_response usage
Mock completions always reported prompt_tokens=10, so spend tracking, TPM metrics, budgets and the tokens-per-minute autoscaling signal saw 10 tokens for a 100k-token request. Budget reservation now carries the admission-time input token count in the reservation record, and mock_completion reads it back so mock traffic exercises the same spend and TPM paths as real traffic without any extra tokenizer work.
* fix(mock): keep a zero admission input token count instead of falling back to 10
---------
Co-authored-by: yassin <yassin@berri.ai>
The squash of #40512 onto a base that already carried #40514 left the Partial call on the metadata pre-read error path only, so a rejected /key/update again persisted the planned values into state and TestResourceKeyUpdateFailureKeepsPriorState fails on the default branch.
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Strengthens the serverless test to assert ResourceType is signed rather
than merely present, ties _uses_tls to the redis-py kwarg coercion so the
two cannot drift, locks the stripped-kwarg name tuple to the test's
expectations, and adds "off" and "True" sentinel flag values.
Renames the provider builder's parameter to redis_settings.
The ElastiCache IAM gate read `aws_iam_auth` and `ssl` with a helper that
only accepted the literal string "true", while the kwarg coercion that runs
later accepts "true", "1" and "yes". Type coercion happens after the gate, so
`REDIS_AWS_IAM_AUTH=1` silently skipped IAM auth and `REDIS_SSL=1` made the
"requires TLS" check fail closed on a connection that was in fact TLS. Both
helpers now share `_str_to_bool`.
AWS signs serverless cache tokens with an extra `ResourceType=ServerlessCache`
query parameter, so tokens minted for a serverless cache were rejected. Adds an
`aws_iam_serverless` setting (`REDIS_AWS_IAM_SERVERLESS`) that puts the
parameter into the signed URL, and lowercases the cache name because
ElastiCache lowercases it at creation time.
The comment documents that the raw Azure client id, tenant id and secret
are deliberately kept off the connect function, so this branch should
never have dropped it
Deferred annotation evaluation keeps the type-checking-only botocore
import off the runtime path, so the alias only reintroduced typing.Any,
which the strict ruff budget now bans
