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Three process-lifetime retention points kept full request payloads (messages included) alive after the request finished. Under bursts of large-token traffic (~73K tokens/request mean) this presented as stepwise RSS growth that never returned to baseline, ending in OOM: 1. Logging.pre_call/post_call stored their entire locals() (messages, the Logging object, complete_input_dict) in the module-level litellm.error_logs dict, pinning the most recent request's payload per worker forever. Nothing reads that dict; the writes are removed. 2. LLMCachingHandler.request_kwargs kept litellm_logging_obj inside the stored kwargs while the handler itself hangs off logging_obj._llm_caching_handler, closing a reference cycle (Logging -> LLMCachingHandler -> kwargs -> Logging). Cyclic payloads are only reclaimed by generational GC, so megabytes of dead request data lingered until a rare gen-2 pass, and the transient copies fragment the allocator into a permanent RSS high-water mark. The handler now drops litellm_logging_obj from its stored kwargs; the caching layer never reads it. 3. The router stored every request's kwargs in the ITPM/OTPM contextvar even when no deployment configures itpm/otpm. Pooled resources created mid-request (e.g. redis connections) capture the asyncio context, extending that pin far past the request. The slot is now populated only for deployments with io token limits and overwritten with None otherwise. Live-proxy verification (bursts of 30 x ~300KB requests, PII guardrail + prometheus + redis cache): unfixed grows 16-29MB per burst without release; fixed grows under 1MB per burst after warmup and flattens. Resolves LIT-4434 |
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| .. | ||
| test_enforce_model_rate_limits.py | ||
| test_io_token_rate_limits.py | ||