The docstring cited session_affinity_ttl_seconds as the keepalive bound,
but the Router-level knob feeding ttl_seconds is
deployment_affinity_ttl_seconds; session_affinity_ttl_seconds is the
separate per-request PreRoutingHookResponse override. Anyone grepping
the docstring's name to shrink the Router default finds only the
override. Name both, scoped correctly.
Starlette scans the route table in registration order, so a request pays one
regex match per route registered ahead of its own. The proxy registers several
hundred routes and left the liveness probe near position 280 and the lazy
loaded /v1/messages at the very end. Move /health/liveliness, /health/liveness,
/v1/chat/completions, /chat/completions and /v1/messages to the front of the
route table after startup registration and again after a lazy router loads.
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Both reasoning_effort mappers ended their if/elif chain in a bare ValueError.
exception_type() has no branch for ValueError, so it fell through to the shared
APIConnectionError fallback and the proxy answered a malformed client request
with a retryable HTTP 500 carrying no hint of the accepted values.
Raise UnsupportedParamsError (400) instead, listing the supported set, matching
what the Anthropic and Bedrock transforms already do and what this same file
already does at its five other param-validation sites.
This also covers 'xhigh' and 'max', which are members of litellm's own
REASONING_EFFORT literal but have no Gemini mapping, so callers bridging from
OpenAI-shaped code were hitting the 500 without typing anything wrong.
Fixes#40474
Claude-Session: https://claude.ai/code/session_01XT1qsbjLwnhiN5sQ2hNUxr
* feat(proxy): make the in-memory management cache capacity configurable
Add general_settings.user_api_key_cache_max_size (positive int, default 200) to resize the
in-memory tier of the shared user_api_key_cache at startup and on DB config reloads, expose it
in the Admin UI general settings, and cover it with behavioral tests. Prior art: #34726
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(caching): resize the in-memory tier from DualCache so any cache instance honours the cap
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style(proxy): wrap the cache capacity field description to the 120 col limit
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(passthrough): parse Bedrock stream spend incrementally instead of buffering the whole response
Bedrock pass-through streaming kept every relayed chunk in memory until EOF and
then decoded, parsed and translated the whole stream again for spend logging.
Large or concurrent streams could exhaust proxy worker memory.
Sync and async passthrough wrappers now hand each chunk to a provider stream
collector as it is relayed. Bedrock decodes event-stream frames incrementally,
folds consecutive text deltas, and keeps only what stream_chunk_builder needs
for usage, tool calls and metadata. Text deltas are no longer retained in the
Bedrock and Anthropic stream decoders either. Providers without a collector
keep the previous raw-bytes behavior. Collector failures are isolated so spend
tracking can never interrupt the customer stream
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(passthrough): assert the spend payload the collector builds instead of mock internals
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(passthrough): type the Bedrock collector helpers by the collector protocol instead of asserting the class
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>
Key objects share the 200-entry UserApiKeyCache in-memory store with teams,
end users, tags and memberships, so churn in those objects evicts hot keys
and forces a LiteLLM_VerificationToken lookup on the next request. Route
bare hashed-token keys to a dedicated InMemoryCache inside UserApiKeyCache
while keeping Redis, TTL, serialization and invalidation shared
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
The sequential version sent one request at a time, so a Redis outage never
reached the concurrency where the failed-tracking alert body actually grows.
This drives the proxy with locust against one model group of three mock
deployments, two failing at order 1 and one serving at order 2, so every
request spends its retries on the failing pair and lands on the serving
deployment through the order-based fallback. Two phases, a healthy baseline
and a CLIENT PAUSE WRITE window, and every request must succeed in both.
Latency, RSS and CPU are reported as p50/p90/p99 per phase rather than
asserted on: RSS and CPU come from psutil on the proxy's process tree, since
a multi-worker proxy serves /metrics from the prometheus multiprocess
collector and that drops the process collector's series. Thresholds stay open
until weekly runs give real baselines.
Co-Authored-By: Claude Code <noreply@anthropic.com>
The recursion detector flags any unignored recursive function, so the
timeout classification now walks the explicit cause chain with a
bounded generator instead of calling itself
redis-py's blocking pool reports a saturated pool as ConnectionError chained
from asyncio.TimeoutError. The circuit breaker classified that as a hard
connectivity failure and opened at once while Redis was healthy. Follow the
explicit cause chain so it counts as a timeout and stays behind the
timeout_min_duration gate
Each checkpoint now samples until no new worker has answered for the settle
window, and the growth assertion refuses a worker set that changed between the
warm and after checkpoints instead of comparing only the intersection, so a
leaking worker reached by one checkpoint alone cannot drop out of the gate
The provider budget push runs inside the request success callback, so
awaiting the Redis pipeline there made every request wait for the round
trip. Hand it back to a task whose failure is logged through the breaker
aware logger, so an open breaker stays a debug line and a real Redis error
is one error line instead of an unretrieved task traceback
