Under otel_v2, a client that propagates W3C trace context in params._meta
(SEP-414) pulled the tools/call span out of the gateway's trace:
resolve_mcp_span_context parented the MCP span to the client's remote
context and demoted the gateway's own transport span to a span link. The
gateway's tracing backend only ever receives the gateway's half of such a
trace, so the span was unreachable from the trace view and the POST
transaction showed a dangling link.
Invert the anchoring: the MCP tool-call and tools/list spans now always
nest under the transport span of the request carrying the message, and the
client's propagated context is recorded as the span link instead, so the
correlation survives while every trace stays renderable. With no transport
at all the span roots its own trace and still carries the link, keeping a
single shape for the event. Both returned contexts are built on an
explicitly empty base so ambient session state can never leak in, and the
span inherits the transport's sampling decision like every other
request-level span.
The /v1/messages validator checked a tool_use block's name and id but not its
input, so a block whose location came back empty or wrong still passed, while
the chat side rejected the same damage. That gap predates this branch; it is
worth closing here because the point of the change is that every parallel call
is checked rather than counted.
AnthropicContentBlock now declares input as a typed field. It already survived
on extra="allow", but reaching it from a test needs a real field to keep the
e2e basedpyright gate at zero. Serialization is unchanged: bodies are dumped
with exclude_none, so a block without an input still replays exactly as before.
The together backend is picked as the cheapest chat row that supports both
tools and reasoning, which currently resolves to together_ai/openai/gpt-oss-120b.
That row is marked supports_parallel_function_calling, so one weather prompt
can legitimately come back as several get_weather calls. Both tool tests
asserted exactly one call, so a parallel answer failed them even though the
gateway handled it correctly.
They now check every returned call instead of counting them: each one has to
be a get_weather naming Paris, with an id a tool result can answer. Dropping,
misnaming, or mangling a call is still red; only the count is the model's
business. The round trips answer every call rather than just the first, which
is also what the Anthropic Messages spec asks for.
The shared SelectContent wrapper defaulted alignItemWithTrigger to true,
which puts Base UI's positioner into item-aligned mode and places the
popup so the active item sits on top of the trigger. In that mode the
side and sideOffset the wrapper passes two lines above are ignored, and
the popup reports data-side="none".
The overlap only becomes visible once the items are tall enough to
matter, which is why the autorouter Template picker shows it clearly:
its options are three-line cards, so the popup covers both the select
box and its own label.
No call site in the dashboard asked for item-aligned mode. 21 of them
across 15 files already passed alignItemWithTrigger={false} by hand to
undo the default, and the remaining 127 inherited the bug. Flipping the
default makes side and sideOffset live, so collision handling works and
a select with no room below now flips above the trigger rather than
covering it. The 21 hand-written opt-outs are deleted as redundant.
* fix(key_management): allow /key/update to keep or shrink MCP server grants the key already holds
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(key_management): reuse key row's included object_permission instead of a second lookup
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>
* fix(auth): skip guaranteed-miss team lookup for the litellm-dashboard sentinel
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style: ruff format
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test: assert builder result instead of swallowing exceptions; drop redundant comment
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>
* feat(proxy): opt-in budget rollover carrying overage into the next window
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): zero under-cap rows before decrementing over-cap rows in cascade resets
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>
* fix: suppress misleading register_model unresolved-cost warnings for entries without custom pricing
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix: do not warn about zero cache costs for tiered pricing entries
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>
* fix(mcp): keep upstream OAuth Authorization when jwt signer hook injects one on tools/call
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(mcp): only treat server credential as occupying Authorization when it maps to that header
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>
Extract the Soniox SRT/VTT cue grouping and rendering into a shared
litellm_core_utils/audio_utils/subtitle_utils module, have Gemini
transcription request word timestamps whenever response_format is srt or
vtt, and let the http handler rewrite the response text into the
synthesized subtitle document (dropping the internally requested words
array) for any provider config that opts in via
supports_subtitle_synthesis
* feat(health): opt-in model-group allowlist for background health checks and health-check routing
* fix(health): merge shared health states per writer scope instead of replacing
* refactor(health): drop restating comment and parameterize test scope annotations
* chore: remove stray generated prisma migration file
* fix(health): merge health states against the Redis snapshot, not the pod-local copy
* fix(health): fall back to the pod-local snapshot when the Redis read returns nothing
Gemini Live sends no usageMetadata and no turnComplete for
gemini-3.5-transcribe-live sessions, so realtime spend logged as 0.0.
Attach estimated usage to the input_audio_transcription.completed event
using Google's published billing estimate (25 audio tokens/sec of input,
175 text tokens/min of output) derived from the streamed pcm16 audio
duration, gated to audio_transcription-mode models so conversational
Live models keep billing through usageMetadata. Also capture that usage
in the provider_config backend path so realtime cost calculation sees it.
* fix(ui_sso): resolve highest privilege Entra app role, not first in claim
A user assigned more than one Entra app role — commonly by belonging to
several assigned groups — arrives at the Microsoft SSO callback with every
role in the id_token `roles` claim. LiteLLM stores a single role per user,
and get_microsoft_callback_response collapsed the list by taking the first
value that resolved to a LitellmUserRoles and breaking.
Entra does not guarantee the ordering of the `roles` claim, so which role
won was effectively arbitrary: a user in one group mapped to internal_user
and another mapped to proxy_admin_viewer could be silently demoted to
internal_user, and proxy_admin could lose to either.
The generic/Okta path already resolves this correctly via
determine_role_from_groups, which walks a documented privilege hierarchy.
Hoist that hierarchy into LITELLM_USER_ROLE_HIERARCHY and reuse it, so
app-role logins and group-mapping logins agree.
Extract the selection into MicrosoftSSOHandler.get_user_role_from_app_roles
so it is directly testable — the existing tests re-implemented the loop
inline, which is why the ordering bug was not caught.
Behaviour is unchanged for single-role claims, unrecognised values, and
empty claims. Roles the hierarchy does not rank (org_admin, team, customer)
are resolved deterministically rather than by claim order.
* refactor(ui_sso): trim role selection prose and use immutable annotations
Addresses review feedback on the app role selection helper.
Drop the explanatory comments and the Args/Returns docstring boilerplate that
restated the control flow, keeping only the part a reader cannot infer from the
code: that Entra does not guarantee claim ordering, and how unranked roles
resolve.
Type the parameter as Sequence[str] rather than list[str] and build the resolved
set as a frozenset, so the helper stops adding an LIT001 mutable-collection
annotation. Make LITELLM_USER_ROLE_HIERARCHY a tuple for the same reason.
No behaviour change: the ordering regression tests still fail against the
previous first-match-wins logic and pass here.
Adds a Gemini audio transcription config that maps /v1/audio/transcriptions
onto the Interactions API (speaker attribution and word timestamps land on
the OpenAI verbose_json shape), registers both models with published pricing,
routes text-only Live sessions to TEXT responseModalities so
gemini-3.5-transcribe-live sessions survive, and makes the token-priced
transcription cost path provider-aware instead of hardcoding OpenAI.
Adds pricing (0.15/0.50 per 1M tokens, 0.03 cached read), the 1M context window, and capability flags (tools, parallel tools, tool choice, response schema, reasoning, vision) for Together AI's zai-org/GLM-5.3-Flash, mirrored into the backup cost map, with exact-value regression tests.
OpenAI's chat completions API rejects tool_reference content parts in
role tool messages, so a mixed text plus reference tool result carried
through the Anthropic adapter turned a previously working request into
a 400 on chat-routed OpenAI and Azure deployments. Strip the reference
parts there, keeping a reference-only result as an empty-text tool
message so the preceding tool_call stays answered, mirroring the
Responses bridge skip.
* feat(newrelic): per-team cost and usage metrics via team callbacks
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(newrelic): retry transient 429/408 metric posts instead of dropping
* fix(newrelic): drop only records queued when the drain began, not mid-drain arrivals
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
`test_gemini_chat_returns_content_and_logs_cost` asks gemini-2.5-flash to
"reply with the single word pong" under `max_tokens=32`, and has been seen
returning no content at all:
completion_tokens=29, reasoning_tokens=29, content=None
gemini-2.5-flash defaults to dynamic thinking, and `max_tokens` maps to
`maxOutputTokens`, which on the 2.5 family counts thinking tokens as well as
visible output. So the model is free to spend the entire budget on thoughts and
emit nothing, which is exactly what the usage above shows.
Raising the limit alone does not fix this. Dynamic thinking on 2.5 Flash is
documented up to 24576 tokens, so no budget small enough to be reasonable for a
one-word smoke test is safe. The fix is to take thinking out of the picture:
`reasoning_effort="none"` maps to `thinkingConfig.thinkingBudget=0` for the 2.5
family, so the whole limit is available to visible output. Verified against this
checkout:
get_optional_params(model="gemini-2.5-flash", custom_llm_provider="gemini",
max_tokens=32)
-> {'max_output_tokens': 32} # no thinkingConfig at all
get_optional_params(model="gemini-2.5-flash", custom_llm_provider="gemini",
max_tokens=64, reasoning_effort="none")
-> {'max_output_tokens': 64,
'thinkingConfig': {'thinkingBudget': 0, 'includeThoughts': False}}
This mirrors what the OpenAI tool tests in this same file already do with
gpt-5.6 for the same failure mode. `max_tokens` goes to 64 for headroom; with
thinking disabled that is ample for a one-word answer.
Neither `covers` claim changes: the call still exercises the gemini chat
translation path and still produces a costed SpendLogs row.
`_cacheable_system_block` embedded the per-run marker in all 300 paragraphs, so
the block's token count moved with the marker's own tokenization. Measured over
40 random markers the size ranged 3611-5408 tokens (median 4509): 15% of runs
landed under the 4096-token minimum cacheable prefix of Haiku 4.5, despite the
docstring claiming the prompt was comfortably above it.
When the system block is under the minimum, no cache entry is written at the
system breakpoint. The entry at the second breakpoint still gets written,
because system + first user turn clears the minimum -- which is why the failures
report a large cache_creation with cache_read stuck at 0
(`cache_creation_input_tokens=5610 cache_read_input_tokens=0`, and 5610 is the
whole prefix, not the user turn's share). `_prime_prompt_cache` rotates the user
turn on every attempt, so that second entry never prefix-matches the next
attempt either. Every attempt re-creates the full prefix, cache_read never rises
above 0, and the loop burns its 60s deadline:
prompt cache never became readable in full within 60.0s
That is the single most frequent flake in the e2e suite, 9 of 38 runs, and it
hits all three provider classes identically because they share this helper.
Move the marker out of the repeated paragraph so it appears once, and size the
block at 1500 paragraphs. The prefix is now 8056-8060 tokens across markers --
spread 4 tokens instead of 1797, and 1.97x the minimum in the worst case. The
same marker-per-repetition pattern in `_first_turn_user_text` is fixed the same
way. Both copies of the helpers stay byte-identical.
