If your Go service relies on one LLM provider, two failures hurt the most, timeout spikes and billing spikes. A real production setup is not just “add another provider”, it is a single control plane for routing, timeout tiers, cost caps, and fallback. This guide gives you a practical OpenAI + Claude dual-provider pattern with one priority, keep uptime first, then optimize quality. ...
线上接入单一模型供应商，最怕两件事，突发超时和账单失控。真正可落地的方案不是“多接一个模型”这么简单，而是把路由、超时、成本、回退放进同一个控制面。 这篇给你一套 Go 可直接落地的双栈路由框架，目标是三件事，稳定性优先、成本可控、故障可快速止血。 ...
In production, the painful part is not “streaming is slow.” It’s “streaming breaks and then duplicates output after reconnect.” This guide gives you a practical recovery loop: delta persistence + resume token + idempotent dedup, so reconnection does not replay garbage. ...
生产里最难受的不是“流式返回慢”，而是“流式返回断了还重复”，用户看到半句、重连后又从中间重喷一遍。 这篇给一套可落地的恢复闭环：delta 持久化 + resume token + 幂等去重，目标是“断线可续，重放不重字”。 ...
In production Go agents, the first thing that breaks is usually not model quality. It is memory management: context grows, bills spike, and answers drift. Use a 3-layer memory design: L1: short-term conversational window (seconds) L2: rolling summary (minutes) L3: long-term retrieval memory (days) ...
你在 Go 里做 Agent，最容易翻车的不是推理能力，而是“记忆”失控：上下文越来越长、账单越来越高、回答却越来越飘。 这篇给你一个可落地的三层方案： L1：短期会话上下文（秒级，强相关） L2：中期摘要记忆（分钟级，压缩） L3：长期检索记忆（天级，向量索引） ...
你不是被 OpenAI API「偶尔报错」打败的；你是被并发放大后的重试风暴打败的。 ...
Most Go teams are not killed by a single API error. They are killed by a retry storm they created themselves. ...
一句话结论：如果你的调用是可延迟、可批处理、可回放，就该把在线请求下沉到 Batch API；省钱最明显，但前提是你把拆批、失败分流和回放链路先做好。 很多团队把 Batch API 当“便宜版同步接口”来用，结果不是省钱，而是把失败样本堆成事故池。真正的保守做法是：先定义成本边界和SLO，再做离线拆批与失败回放。 ...
Short answer: if your workload is delay-tolerant, batchable, and replay-safe, move it from online calls to Batch API. The savings are real, but only if you design splitting, failure routing, and replay first. Many teams treat Batch API as a cheaper sync endpoint. That usually creates a replay mess instead of stable savings. A conservative rollout starts with cost boundaries and SLOs, then implements offline batching and controlled replay. ...