Go on Mengboy 技术笔记

Go Dual-Provider LLM Routing (OpenAI + Claude): Timeout Tiers, Cost Caps, and Fallback Control

Wed, 08 Apr 2026 01:22:53 +0000

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.

Go 服务双栈模型路由（OpenAI/Claude）：超时分层、成本上限与降级回退

Wed, 08 Apr 2026 01:22:53 +0000

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。

OpenAI Responses + Go Stream Recovery: Delta Persistence, Resume Tokens, and Duplicate Chunk Dedup

Mon, 23 Mar 2026 01:13:09 +0000

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.

OpenAI Responses + Go 的流式中断恢复：delta 持久化、resume token 与重复片段去重

Mon, 23 Mar 2026 01:13:09 +0000

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”。

Go + OpenAI Responses Agent Memory Layering: Short-Term Context, Long-Term Index, and Cost Caps

Wed, 18 Mar 2026 16:33:52 +0000

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)

Go + OpenAI Responses Agent 记忆分层实战：短期上下文、长期索引与成本封顶

Wed, 18 Mar 2026 16:33:52 +0000

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）

Go 服务调用 OpenAI 的 429/5xx 风暴应对：令牌桶、指数退避与熔断恢复

Wed, 18 Mar 2026 01:14:00 +0000

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。

Handling OpenAI 429/5xx Storms in Go: Token Bucket, Exponential Backoff, and Circuit Breakers

Wed, 18 Mar 2026 01:14:00 +0000

Most Go teams are not killed by a single API error. They are killed by a retry storm they created themselves.

OpenAI Batch API + Go 降本实战：离线拆批、失败重放与成本边界

Fri, 13 Mar 2026 01:08:00 +0000

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。

OpenAI Batch API with Go: Offline Batching, Failure Replay, and Cost Boundaries

Fri, 13 Mar 2026 01:08:00 +0000

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.

OpenAI Responses Structured Outputs + Go：Schema 演进、坏样本兜底与灰度回滚

Wed, 11 Mar 2026 01:08:00 +0000

Structured Outputs 最容易翻车的地方，不是“模型不听话”，而是你把 schema 当成了永远不变的圣旨。

线上一旦进入版本演进期，最常见的事故就是：字段新增后老消费端崩、枚举值扩展后校验误杀、坏样本把整条链路拖死，最后只能半夜回滚，像在给自己写惊悚片。

OpenAI Responses Structured Outputs with Go: Schema Evolution, Bad-Case Fallbacks, and Gradual Rollback

Wed, 11 Mar 2026 01:08:00 +0000

The hardest part of Structured Outputs is not getting JSON once. It is surviving schema changes without turning production into a small fire with excellent logs and terrible business results.

Once a Go service starts evolving prompts and response contracts, the usual failure modes show up fast: a new required field breaks older consumers, an enum expands and strict validation kills valid requests, or one bad sample drags the whole chain into retries and rollback panic.

OpenAI Realtime + Go in Production: WebRTC Token Rotation, Interruption Recovery, and End-to-End Latency Budgets

Mon, 09 Mar 2026 01:13:00 +0000

If you plan to put OpenAI Realtime into production, do not let a passing demo fool you.

What usually breaks the system is not the model itself. It is non-rotating short-lived auth, missing interruption state, and zero end-to-end latency budgeting. Miss those three and your voice UX starts sounding like an angry walkie-talkie.

OpenAI Realtime + Go 生产落地：WebRTC 鉴权轮换、打断恢复与端到端延迟预算

Mon, 09 Mar 2026 01:13:00 +0000

如果你准备把 OpenAI Realtime 真上生产，先别被“能跑通 demo”骗了。

真正把系统打爆的，通常不是模型本身，而是 短时鉴权没轮换、打断恢复没状态机、端到端延迟没预算。这三件事不补，语音体验会像在和一台卡顿的对讲机吵架。

Go + OpenAI Responses: Connection Pooling and Timeout Budgets from HTTP/2 Reuse to Error-Budget Control

Fri, 06 Mar 2026 01:13:12 +0000

When Go services call the OpenAI Responses API in production, the real failures are rarely about model quality. Most incidents come from transport instability: weak connection pooling, conflicting timeout layers, and retry storms.

This guide gives you a practical baseline: HTTP/2 reuse, layered timeout budgets, bounded retries, and error-budget driven operations.

Go 调 OpenAI Responses 的连接池与超时预算：HTTP/2 复用到错误预算闭环

Fri, 06 Mar 2026 01:13:12 +0000

线上 Go 服务调用 OpenAI Responses 时，最容易踩的坑不是“模型不准”，而是链路抖动：连接池不稳、超时预算乱配、重试叠加把自己打挂。

这篇给一套可落地的基线配置：HTTP/2 连接复用、分层超时、错误预算和退避重试，目标是把 5xx 与超时比例压到可控范围，并且能快速定位瓶颈。

OpenAI Responses + Go 工具调用重试风暴治理：幂等键、退避抖动与熔断阈值

Wed, 04 Mar 2026 01:10:40 +0000

线上最可怕的不是一次失败，而是失败后被重试放大。

在 OpenAI Responses + Go 的工具调用链路里，如果没有幂等键、退避抖动和熔断阈值，10 个请求很快就能打成 1000 个下游调用，账单和延迟一起爆炸。

OpenAI Responses + Go: Taming Retry Storms with Idempotency Keys, Jittered Backoff, and Circuit Breakers

Wed, 04 Mar 2026 01:10:40 +0000

The most expensive outage is not a single failure — it is a failure amplified by retries.

In an OpenAI Responses + Go tool-calling stack, missing idempotency, jittered backoff, and breaker thresholds can turn 10 failing requests into 1000 downstream calls in minutes.

OpenAI Assistants/Responses 在 Go 里的上下文爆炸治理：截断策略、摘要回填与成本上限

Mon, 02 Mar 2026 12:44:00 +0000

线上 Agent 一跑久了就会遇到同一个坑：上下文越来越长，延迟飙升、费用失控，最后还更容易答偏。

这不是模型“变笨”了，通常是上下文治理没做：该留的没留、该删的没删、该摘要的摘要坏了。

Taming Context Explosion in OpenAI Assistants/Responses with Go: Truncation, Summary Backfill, and Cost Caps

Mon, 02 Mar 2026 12:44:00 +0000

Long-running agent sessions usually fail the same way: context keeps growing, latency spikes, costs blow up, and answer quality gets worse.

That is rarely a model-quality issue. It is almost always missing context governance.

Go + OpenAI API Timeout Troubleshooting: DNS, TLS, Proxy, and Connection Pool

Mon, 02 Mar 2026 01:12:10 +0000

When OpenAI API calls start timing out in production, the real problem is usually not “OpenAI is down.”

The real problem is you don’t know which hop is failing: DNS, TLS handshake, proxy path, or your own connection pool.

Go 调 OpenAI API 常见超时链路排查：DNS/TLS/代理/连接池一次讲清

Mon, 02 Mar 2026 01:12:10 +0000

线上调用 OpenAI API 一旦出现超时，最烦的不是“偶发失败”，而是你不知道到底卡在 DNS、TLS、代理，还是你自己的连接池。

这篇给你一套可直接落地的排查顺序：先判定超时发生在哪一段，再用指标和最小实验定位，最后给可复制的 Go 配置模板，避免同类事故反复出现。

OpenAI Agents SDK + Go 落地指南：Tool Calling、会话记忆与错误恢复

Wed, 25 Feb 2026 01:18:00 +0000

很多团队已经把 LLM 接进业务，但一到“多步任务 + 调工具 + 失败重试”就开始失控：日志看不懂、状态回不去、成本还飙升。

这篇给你一个能直接落地到 Go 服务里的最小可用方案：工具调用闭环、会话记忆分层、错误恢复可回放。

OpenAI Agents SDK with Go: Tool Calling, Session Memory, and Error Recovery

Wed, 25 Feb 2026 01:18:00 +0000

Most teams can connect an LLM in a demo. The real pain starts in production: multi-step tasks, flaky tool calls, unclear retries, and rising cost.

This guide gives you a pragmatic Go-first blueprint for shipping an Agent workflow that can survive real incidents.

Go 语言实现jpg和png图片的批量压缩

Sun, 28 Jan 2024 21:42:10 +0800

记录笔记或者写博客的时候经常会直接复制mac截图插入图片，默认复制得到的是无损png图片，图片体积比较大，占空间带宽，就基于go实现了个命令行批量压缩工具。

go 指针赋值是原子性的吗

Wed, 06 Dec 2023 21:16:31 +0800

问题提出

提出这个问题主要是因为日常开发中有这样的场景，比如服务配置热加载或者一些全局缓存的异步更新，通常会单独启一个协程去获取最新数据再通过赋值更新原数据。因为更新逻辑是单独的协程，变量的写和读就存在并发访问的情况，于是就有了这个问题。一个简单异步刷新例子如下：

xorm使用map kv更新日期时间类型字段的时区问题

Sat, 29 Jul 2023 21:44:10 +0800

最近使用xorm更新db的时候碰到一个挺有意思的问题，db中字段类型是日期datetime，对应go model中的字段是time.Time类型，在分别使用model和map两种方式进行更新时，使用model的方式更新能得到正确结果，db中的日期能被更新成当地时间；使用map kv的方式进行更新时db中的字段被更新成了格林尼治标准时间。都是取的go time.Now为什么更新结果不一样呢？

// demo
type TimeTest struct {
	ID         int64     `xorm:"not null pk autoincr INT(11) 'id'"`
	UpdateTime time.Time `xorm:"update_time"`
}

func UpdateTime(o *xorm.Session) {
	o.Table(new(TimeTest)).Where("id = ?", 1).Update(&TimeTest{
		UpdateTime: time.Now(),
	})
	o.Table(new(TimeTest)).Where("id = ?", 1).Update(map[string]interface{}{
		"update_time": time.Now(),
	})
}

基于redis实现一个简单的分布式限流器

Sat, 15 Oct 2022 22:30:35 +0800

分布式限流器也是项目开发中常用的，基于redis incr指令可以比较方便快捷的实现一个分布式限流器，因为这个指令自身的原子性，所以不用开发者考虑计数过程的并发问题。下面是go代码实现。

goroutine 并发数量限制

Sat, 15 Oct 2022 18:08:41 +0800

最近经常用go写一些脚本，为了执行效率，一般会开启多个goroutine并发执行,脚本中通常会调用一些线上服务或者数据库读写数据，为了不影响线上服务运行,为了不影响线上服务运行就要对执行的goroutine数量进行限制，避免并发太大打垮线上服务。下面就简单写一个goroutine并发控制器。

Go启动main函数都干了什么(一)

Tue, 27 Oct 2020 15:27:33 +0800

生成二进制可执行文件

首先，写一个go main函数，这里就简单输出以下hello world。

package main

import "fmt"

func main()  {
	fmt.Println("hello word")
}

执行go build -gcflags "-N -l" -ldflags=-compressdwarf=false -o main main.go生成可执行二进制文件。

开启gdb调试

执行gdb main开始gdb调试。通过i files查看程序入口地址，再这个地址打上断点。

造轮子——go 版本控制(gvm)

Tue, 29 Sep 2020 20:36:24 +0800

功能列表

支持linux、darwin
获取远程go版本列表 gvm remote

sse(serve send events) 服务端推送技术

Sat, 12 Sep 2020 22:58:30 +0800

什么是sse

sse 指 serve send events，是一种基于http的服务端想客户端推送事件的技术，通常http请求都是请求–>应答的模式，一次request–>response结束后，连接就断开了，前端需要请求新的资源就需要重新建立连接，再次发起请求，当然http2.0后可以使用多路复用，不必建立新的连接。而sse指连接建立成功后，后端可以不断向前端推送消息。