Response cache reads and writes open cache.get llm_response and cache.set llm_response phase spans with their Redis spans nested underneath, on the Python path and on the native Rust path, and deployment selection runs inside a route {model_group} phase so the cooldown, usage and model-id reads the router issues nest under it before chat {model}. The autorouter classifier call nests under that route phase as well and carries its typed internal origin on litellm.request.purpose, so it is told apart from the provider attempt. Service spans are named {service}.{verb} {target} from a low-cardinality key family the producer declares (llm_response, auth_objects, spend_counters, router_cooldowns, claude_code_session_router_binding, rate_limits, pod_lock, budget_reset, ...) instead of the raw method or a per-request pipeline length; a pipeline flush is targeted by the one family its ops share or by mixed with the sorted families on litellm.redis.families, a batch op keeps the family it was declared under whichever pipeline or standalone read settles it, and the ambient family labels Redis spans only, never the DB write-back a task spawned inside that context performs later. The raw method stays on litellm.service.call_type and on the Prometheus and Datadog labels. Caller attribution is carried across asyncio task boundaries on a ContextVar so forwarder-only chains no longer surface, the raw cache key is dropped from Redis span metadata, pipeline op counts land as an integer attribute, every call_type the Redis cache layer emits maps to a verb, and a scan over litellm/ and enterprise/ fails when a Redis producer, batch reservation included, declares no key family.
A V2 logger built for a key or team logging entry while the operator's V2 logger is already registered keeps only the exporters its own preset contributed, whether or not the operator holds credentials for that backend, so every chat span no longer reaches the operator's collector twice. A span the success callback has to open itself, with no pre-call carrier, starts at the provider handoff (api_call_start_time) instead of the logging object's creation.
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Identity objects (key, end user) load through the request MGET and their write-backs, the registry
reads and the management-object SETs ride the request pipeline. A team refresh invalidates its alias
with a pipelined DEL instead of a synchronous DEL plus a duplicate async one, and an MGET miss is
remembered so no per-key GET follows it in the same request.
Resolves LIT-9012
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: yassin <yassin@berri.ai>
RedisBatch: one pipeline per Redis backend for independently declared operations (MGET, GET, Lua
scripts, INCRBYFLOAT, SET, DEL), a future per operation so each owner keeps its own fallback, Redis
Cluster hash-slot fallback. A request-scoped batch middleware shares that pipeline across the auth
identity reads and write-back, the spend counter MGET, the rate limiter Lua groups and the routing
read. A rate-limit denial stands when another pipelined group fails; every pipelined group is refunded
on rejection; local cooldowns win over the prefetch.
The routing prefetch failure log line strips request line breaks (CodeQL py/log-injection)
Resolves LIT-8882
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
