What I learned building a control plane for coding agents (Claude Code, Codex, Pi, Kimi) running on multiple machines
Summary
作者分享为运行在多台机器上的多个编码代理(Claude Code、Codex、Pi、Kimi)构建控制平面的经验教训:控制平面只排队不执行、审批必须由人类完成、区分心跳与轮询、事件溯源作为唯一事实来源,并将代理视为有角色和技能的命名队友。
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@vintcessun: It turns out that having multiple AI agents work together as a team is better than a single general-purpose agent in this way: each role is bound to its best model, memory and skills accumulate across conversations. Instead of taking turns, a task is handed off with a brief handover note. Runs locally, all file states are in ~/.crew44, free MIT license.
Crew44 is a local-first orchestrator that turns coding agents like Claude Code and Codex into a coordinated team of specialists, each bound to its best model, with persistent memory and skill accumulation across sessions. It runs entirely on your machine with no cloud dependence and is free under MIT license.
@DanKornas: Running multiple coding agents shouldn’t make you their copy-paste relay. Agent Room is an open-source MCP-based collab…
Agent Room 是一个基于 MCP 的开源协作层,帮助开发者在编辑器、供应商和机器之间协调多个编码代理,通过共享房间、结构化产物和证据门控任务板替代手动传递上下文。
The three parts of my agent setup: persistent memory, self-improvement, and coordination
作者分享其 AI 代理工作流设置的博客文章,围绕三大原则:持久化记忆(版本控制的技能库与 AGENTS.md/CLAUDE.md 约束)、递归式自我改进(授权代理在任务中修补记忆文件)以及可扩展并行化(通过协调工具管理文件写入范围以避免冲突)。
@0xCodez: https://x.com/0xCodez/status/2058513716509913581
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