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我创建TTal是因为我厌倦了照看Claude Code会话。每次提交请求（PR）都意味着在多个窗口之间切换、复制粘贴审查反馈、告诉编码者需要修复的内容，并重复这一过程直到合并。我希望能够在做其他事情的同时，通过手机管理这一切。 TTal是一个Go命令行工具，能够在双层架构中协调多个Claude Code会话： - 管理层——长期运行的代理，能够在会话间持久存在。它们负责制定计划，将计划拆分为任务、分配优先级，并在工作者遇到困难时帮助解除阻碍。你可以通过Telegram来管理它们。 - 工作层——为每个任务生成的短期代理。每个代理都有一个独立的git工作树和tmux会话。它们负责实现、审查、处理反馈和合并——整个PR循环是自主运行的，并且持续集成（CI）日志会反馈回来。完成后，一切都会自动清理。代理、工作者和人类之间通过点对点消息桥进行通信——任何代理都可以直接向其他代理或人类发送消息。我面临的另一个问题是：我需要专门的、长期存在的Claude Code代理来进行规划和研究，而不仅仅是一次性的编码会话。TTal的管理层正好满足了这一需求。我的协调代理负责制定计划，我的研究者探索代码库，而工作者则只需执行。这个技术栈是有意设计为可组合的——使用taskwarrior进行任务管理，flicknote或简单的markdown用于知识/记忆，Telegram作为人机交互界面。所有组件通过命令行接口进行通信，无需SDK。 ```bash brew tap tta-lab/ttal brew install ttal ``` GitHub: [https://github.com/tta-lab/ttal-cli](https://github.com/tta-lab/ttal-cli) 我还创建了一个仅使用bash的子代理运行时，几乎没有工具调用开销：[https://github.com/tta-lab/logos](https://github.com/tta-lab/logos) 欢迎提出关于架构或多代理工作流程的一般问题。

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I built TTal because I got tired of babysitting Claude Code sessions. Every PR meant switching between windows, copy-pasting review feedback, telling the coder what to fix, and repeating until it merged. I wanted to manage all of this from my phone while doing other things.TTal is a Go CLI that orchestrates multiple Claude Code sessions in a two-plane architecture:Manager plane — long-running agents that persist across sessions. They draft plans, break them into tasks, assign priorities, and unblock workers when they get stuck. You manage them from Telegram.Worker plane — short-lived agents spawned per task. Each gets an isolated git worktree + tmux session. They implement, review, triage feedback, and merge — the full PR loop runs autonomously with CI logs fed back in. When done, everything auto-cleans up.Agents, workers, and humans all communicate through a p2p message bridge — any agent can message any other agent or the human directly.The other problem I had: I needed dedicated, long-lived Claude Code agents for planning and research — not just throwaway coding sessions. TTal's manager plane gives me that. My orchestrator agent drafts plans, my researcher explores codebases, and workers just execute.The stack is intentionally composable — taskwarrior for task management, flicknote or use plain markdown for knowledge/memory, Telegram as the human-in-the-loop interface. Everything talks via CLI, no SDK required.<pre><code> brew tap tta-lab/ttal brew install ttal </code></pre> GitHub: https://github.com/tta-lab/ttal-cliI also built a bash-only subagent runtime with zero tool call overhead: https://github.com/tta-lab/logosHappy to answer questions about the architecture or multi-agent workflows in general.

TTal – 一款将Claude代码转化为多代理软件工厂的命令行工具。