LangChain Production Rate Limiting
Implement cluster-wide rate limiting and concurrency control for LangChain 1.0 Python applications using Redis and asyncio.
This skill provides production-grade patterns for managing LLM API rate limits across multi-worker deployments, solving critical scaling issues where individual processes exceed provider tiers. It guides developers through implementing Redis-backed sliding window limiters, asyncio semaphores for concurrency, and hardened exception handling to prevent silent failures. Use this skill to stabilize high-traffic LangChain applications, optimize prompt caching costs, and ensure reliable throughput across Anthropic, OpenAI, and Gemini provider tiers.
主な機能
01Hardened exception whitelisting for fallbacks to prevent swallowing system interrupts
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03Optimization of max_retries and timeouts to prevent exponential billing during failures
04Cluster-wide rate limiting using Redis-backed sliding window or GCRA patterns
05Per-worker concurrency control via asyncio.Semaphore to prevent event loop blocking
06Provider-specific guidance for Anthropic, OpenAI, and Gemini tier limits (RPM/ITPM/OTPM)
ユースケース
01Scaling LangChain apps from local development to multi-worker Cloud Run, ECS, or K8s clusters
02Tuning throughput for high-volume Anthropic workloads utilizing prompt caching
03Managing multi-tenant SaaS quotas using two-level Redis rate limiting strategies