The two dict literals behind the refusal event counted against the LIT002 ceiling once the base branch used up its headroom, so the frame is now a ReadOnly TypedDict built in one shot. Importing Literal explicitly also makes the UP037 suppression on the Vertex discovery signature unnecessary, so it goes.
* fix(batches): register ownership for every batch create path
Since the team isolation change, the managed files hook decided whether a
response came from a create by looking for the managed input file id on it,
which only the unified input path sets. Batches created from a model-encoded
input file id, a model param, or a raw provider id with ?provider= never got
an ownership row, so they vanished from GET /v1/batches for the key that
created them.
The create endpoint now stamps a create marker on the response before the
hooks run, and the hook keys ownership registration and the batch-created
metric on that marker instead of on the input id format.
* test(batches): assert ownership registration through the managed files hook
The endpoint tests asserted the private create marker, which is wiring, not
behaviour. They now run the create through the real managed files hook and
assert the ownership row is written for the creating key on every create
path, with the unified path driven by a genuine encoded input file id
instead of patched decoders.
* fix(mcp): let config.yaml MCP servers pin server_id
A config-defined MCP server's id is a hash of server_name|url|transport|
auth_type|alias, recomputed on every config load, so editing any of those
fields mints a new id. Every key and team granted the old id via
object_permission.mcp_servers keeps pointing at an id that no longer exists,
and the server disappears from tools/list for them with nothing logged.
load_servers_from_config now uses an explicit server_id from the server's
config entry when present and falls back to the existing hash otherwise, so
grants survive url/name/alias edits. Rejected at config load: a blank or
non-string server_id, two entries claiming the same id, a pinned id already
held by a database-backed server, and a pinned id that is another entry's
server_name or alias (expand_permission_list matches ids before names, so
that one would capture the other server's grants). Because the database
registry loads after the config on startup, a database row that lands on a
pinned config id is reported as a warning from the database reload instead,
where it is decidable; the warning is latched on the shadowed set so the
config-reload timer does not reprint it every interval.
Deployments that do not set server_id keep the exact id they have today.
* fix(mcp): close two more pinned-id capture paths
A pinned server_id equal to an alias supplied through litellm_settings
mcp_aliases was accepted, because the collision index only held the entry's
own alias field. expand_permission_list matches ids before names, so grants
written for the aliased server resolved to the pinning one. mcp_aliases keys
whose target is a config server are now reserved the same way.
A pinned server_id equal to a database-backed server's name, server_name or
alias had the same effect against the database side, and could not be
rejected at config load because the database registry is not loaded yet. The
database reload now warns about it, latched like the existing shadow warning.
* fix(mcp): reserve only the aliases the loader actually assigns
Reserving every mcp_aliases key targeting a config server was too broad in
two ways: the mapping is ignored when the entry sets its own alias, and only
the first mapping for a server is ever applied. Both cases made a pinned
server_id that could never have collided abort proxy startup. Reserve only
the name load_servers_from_config will really assign.
The database capture warning also fired for a database server whose own id is
the config server_id. There the database row wins the id outright through
get_registry precedence, so the shadow warning above it is the accurate one
and the capture message contradicted it. Skip those rows.
Also mark the two litellm-internal patches in the reload test helper, which
the test-quality gate counts; the database reload has no other seam.
* fix(mcp): match the loader's alias check exactly, is None not falsiness
load_servers_from_config consults mcp_aliases only when the entry has no
alias key at all, so an entry setting alias: "" gets no mapped alias. The
collision index used falsiness and reserved the mapped name anyway, which
failed startup on a pinned server_id that could never have collided with it.
* fix(mcp): skip one identifier, not the whole database row
A database row can shadow one config server_id by id and capture another by
name at the same time. Skipping the entire row when its id shadowed a config
entry dropped the second warning, leaving the operator with half a diagnosis.
Skip only the identifier equal to the row's own id.
* fix(mcp): reject conflicting self-pinned server ids
* fix(mcp): validate config server names before building the identifier index
The collision check reads every entry's body up front, so a malformed
entry under an invalid name surfaced as an AttributeError instead of the
name validation error the loader gave before this change.
Streaming chat relays on Azure and azure_ai deployments rebuild the response from
the SSE chunks through the OpenAI passthrough assembler, so the spend log carries
usage. The router relays keep the JSON body when the Content-Type carries a
charset, return the upstream status and body instead of a 500 when the deployment
rejects the call, and fall back to the caller's api-version when the deployment
sets none. Lint budgets ratcheted to the measured totals
Generic passthrough calls inferred the provider from the bare model name, so an
azure_ai/gpt-* deployment on an Azure OpenAI host flipped to azure and
get_llm_provider re-prefixed the deployment name into azure_ai/gpt-5.4-mini, a
404 DeploymentNotFound. provider_for_generic_call takes the declared
custom_llm_provider first, then the model's own prefix, and only infers for
unprefixed models
* fix(responses): decode JSON-string tool schemas before sending to the provider
A caller that hands a tool schema over already JSON-encoded reached the
Responses API with a string `parameters`, and the provider rejected the
request with a 400 naming the routed model instead of the offending tool.
Decode it at the one place every Responses request converges, and refuse
anything that is neither an object nor a string encoding one.
Collapses the duplicated input/tool sanitization block shared by the
request and compact-request builders into a single owner, so the decode
cannot be wired into one path and not the other.
* test(responses): pin null tool schemas as accepted, and type the parametrized cases
The Responses API serves `parameters: null` and an omitted schema alike, so
neither may raise. Pin both against a future tightening, annotate the
parametrized inputs, and trim the docstrings back to what the code does not
already say.
Every /azure_ai/<router model>/<native path> relay failed with HTTP 500 because
azure_ai had no passthrough config. The new AzureAIPassthroughConfig strips the
router-model prefix from the relayed path, forwards to the deployment's api_base
with its own credential (api-key on Foundry and Azure OpenAI hosts, Bearer
elsewhere, Entra as the fallback), and delegates chat/completions cost logging
to the Azure passthrough config.
The router's provider inference now receives the deployment's api_base so an
OpenAI-family model on a Foundry resource stays azure_ai instead of flipping to
azure through the AZURE_AI_API_BASE env var.
A shadow eval job could only be scoped by identity, so "this user's traffic on model X
across every key they own" was not expressible and a models field on the start body was
silently dropped. The job now carries a models list that every target is narrowed to,
matched on the requested model group with model_group_alias resolved on both sides. An
unresolvable name is a 400 at start. Empty means every model, which is what every existing
row reads as. The dashboard start form gains an "Only on models" picker and the job
headline shows the scope.
* feat(otel): stamp litellm.request.route on the LLM call span
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(otel): drop redundant comment on REQUEST_ROUTE
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style(otel): Final-annotate route test locals, drop field comment
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(otel): read litellm.request.route off the server span
The LLM call span took the auth-normalized literal path from logging
metadata, which disagrees with the SERVER span wherever FastAPI matched a
template: on /engines/{model:path}/chat/completions the LLM span spelled the
model name while http.route carried the template, so the two spans grouped
into different buckets and the PR's premise did not hold.
Read the value off the span that already holds it. The request's root SERVER
span is anchored per request for parenting, and its attributes stay readable
after it ends, so request_root_http_route() answers from the async close
callback with the same http.route the SERVER span exports: the route template
on a normal route, the literal path where the passthrough hook rewrote it, and
the mount point on an MCP call. Nothing has to re-derive any of that, so the
two spans cannot drift apart.
The route the proxy recorded at auth stays as the backstop for a deployment
whose FastAPI instrumentation never mounted, where there is no server span to
disagree with. Off the proxy the attribute is omitted rather than empty.
---------
Co-authored-by: shivam <shivam@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Yucheng He <yucheng@berri.ai>
The classifier scores extracted text, so a turn whose complexity lives in
its image is invisible to it: a screenshot of a stack trace classifies on
its caption, and an image-only turn flattens to empty text and never
reaches the classifier at all.
classifier_llm_config.vision opts in, off by default, with max_images
bounding what one turn can add. Images are still dropped when the
classifier model is declared supports_vision false. Anthropic and
Responses image parts are rewritten into chat-completions dialect before
they reach the classifier call, since /v1/messages hands the pre-routing
hook its own dialect untranslated.
The local scorer no longer short-circuits heuristic_first or hybrid on a
turn carrying forwarded images, because it reads text alone and its
confidence describes a request it has only partly seen.
The completed-batch early return skipped both the cancel and the list
assertion while the lifecycle's covers markers still credited both cells.
List does not depend on the batch being cancellable, so it now runs either
way; cancel on a completed batch stays a documented vacuous pass
