Pillow 13.0.0 requires Python >= 3.11, so the ci group pin is split by
Python version and the 3.10 resolution stays on 12.3.0 with a 7-day
osv-scanner override. Dashboard vite moves from 7.3.5 to 7.3.7. The
certifi fix (2026.10.19) is still inside the 3-day uv exclude-newer
window, so its advisory is ignored until 2026-10-23
* fix(proxy): flush tool discovery queue when use_redis_transaction_buffer is enabled
With the Redis transaction buffer on, discovered tools were never written to
LiteLLM_ToolTable: the only call to _flush_tool_discovery_queue was on the
non-Redis commit path. Each pod's tool queue is never buffered in Redis, so
every pod now flushes its own queue on each spend tick, before the leader
lock check.
Co-authored-by: Noah Nistler <60981020+noahnistler@users.noreply.github.com>
* test(proxy): exercise real leader election in the tool discovery flush test
The writer's PodLockManager had no Redis in the test, so neither case was ever
the leader and the lock-held parameter changed nothing. The fake Redis also had
no async_rpush_pipeline, so every tick took the push-failure branch. Wire the
fake into the lock manager the way ProxyLogging does in production, back it
with real lists, and assert only the lock holder drains the spend buffer.
Co-authored-by: Noah Nistler <60981020+noahnistler@users.noreply.github.com>
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Co-authored-by: Noah Nistler <60981020+noahnistler@users.noreply.github.com>
* feat(nvidia_riva): add riva_offline mode for offline-only ASR models
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(nvidia_riva): assert offline vs streaming via fake service call tracking
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* chore(ui): regenerate schema.d.ts after user soft_budget docstring removal
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(nvidia_riva): parse riva_offline strings, map oversize offline audio to 413
A multipart riva_offline=false no longer turns offline mode on, a malformed value drops under drop_params and answers 400 without it, and a Riva RESOURCE_EXHAUSTED for a message over the server's max now returns a non-retryable 413 instead of a retried 429
* fix(nvidia_riva): apply the request timeout to offline recognition and keep riva_offline on an api_base override
Offline Recognize now runs as a gRPC future waited on with the request
timeout, so a stalled Riva server returns a 408 and the call is cancelled.
riva_offline is a mode flag, not a credential, so an api_base override no
longer clears it. The 413 hint for audio over the gRPC message limit is
reworded, and the new tests carry type annotations.
* fix(nvidia_riva): wait the request timeout in seconds whatever its form and cap the riva client to versions that import
A timeout from os.environ, a multipart form field, or an httpx.Timeout reached the gRPC future as a string or object,
so result() raised TypeError and every offline call came back as a false 408. The handler now turns the timeout into
float seconds with CompletionTimeout.as_limit, taking the read limit of an httpx.Timeout, for both the offline future
and the streaming deadline
The stt-nvidia-riva extra caps nvidia-riva-client below 2.17, since every later release either pins an older grpcio
or ships stubs that need a newer one than the grpcio==1.78.0 pin, so a fresh install failed at import
* fix(nvidia_riva): parse the timeout only where the call uses it and drop the riva client cap
A malformed timeout no longer fails streaming calls on clients that take no
timeout. On offline calls it is a 400, or the default under drop_params, and
an httpx.Timeout with no read limit waits the default instead of forever.
The nvidia-riva-client cap made installs next to a newer client unresolvable,
so the extra goes back to main's specifier.
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(mcp): upgrade to MCP SDK 2.3.0 and raise the per-event SSE size cap
MCP SDK 2.x reads Streamable HTTP responses through httpx2's SSE parser,
which rejects any single event over 1 MiB, so a large tools/call result
failed with 'SSE stream ended without a response'. mcp 2.3.0 exposes
max_sse_event_size, and MCPClient now passes a 64 MiB default,
configurable with LITELLM_MCP_MAX_SSE_EVENT_SIZE_BYTES
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(mcp): fall back for invalid SSE event limits
* test(mcp): verify configured SSE limits and recovery
* chore(mcp): remove redundant dependency comment
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Joshua Valluru <326636767+joshua-berri@users.noreply.github.com>
* feat(extraction): add Clairic extraction types and Cloudflare provider config
* feat(extraction): add the /v1/extraction route, router wiring, and Clairic billing
* fix(extraction): refuse or drop stream, pass unknown keys, run guardrails, keep the requested model name
Clairic does not stream, so stream=true answers 400 and is dropped and billed under drop_params. Keys LiteLLM does not know pass through to Cloudflare. Guardrails scan state, field instructions and text answers. Frozen decision and extraction responses now carry the requested model name. List fields must name their item type. /health/test_connection and the Add Model page accept the extraction mode.
* fix(extraction): keep upstream model names on decisions, scan images, keep typed values, refuse OpenAI params
Scope the frozen-response model stamp to extraction so /v1/decisions and /v1/systemone return the upstream name as on main. Pass request images to guardrails as data URLs and drop answer spans when a response guardrail rewrites state_text. Field spec values keep their JSON type, so a wrong type answers 400 instead of being coerced. OpenAI generation params answer 400 unless drop_params. The Model Hub shows a /v1/extraction snippet for extraction models.
* fix(extraction): send only Clairic's request fields, count extraction tokens, keep typed answers typed, drop spans a request guardrail made stale, and honor --max_tokens
* fix(extraction): scan number answers in response guardrails, rewrite guardrail fields in linear time, and leave --max_tokens as on main
* fix(extraction): scan float answers as whole numbers so fraction digits never trip a response guardrail
* fix(extraction): round only mean and median for response guardrails so fractional values keep their decimal text
* fix(extraction): count state and fields for budget reservations and scan estimate quantiles in response guardrails
* fix(extraction): send each repeated text to guardrails once and keep the context-window budget reservation
* test(extraction): type request_data in the guardrail test helpers
