--- author: AI Engineer date: '2026-07-11' guest: '' layout: post.njk source: https://www.youtube.com/watch?v=9arM9b7JgOo speaker: AI Engineer tags: - agentic-workflow - sandbox-testing - terminal-multiplexing - model-selection title: 研发效能革命：利用 Open Claw 与多 Agent 架构重构研发流程 summary: 本文基于 Jeffrey Lee-Chan 的分享，探讨了如何通过 Open Claw、多 Agent 编排系统以及终端多路复用器（tmux/Cmux）构建高度自治的开发与测试环境。内容涵盖了 Open Claw 的上下文隔离与记忆机制、多 Agent 并行开发（Worktrees）、Staging 沙箱测试策略，以及在成本约束下的模型选择策略。 insight: '' draft: true series: '' category: ai-application area: tech-engineering project: [] people: [] companies_orgs: [] products_models: - Open Claw - Cmux - tmux - Codex 5.3 - GPT-5.4 - MiniMax media_books: [] status: evergreen --- ### Staging 环境搭建与多路 Open Claw 实验 在本次分享中，主讲人探讨了 **Open Claw**（一种基于 AI 的代理协助工具）的部署与工作流搭建。针对不同水平的开发者，提供分层式的配置方案：初学者重点在于基础环境的安装与跑通；而高级开发者则可以尝试构建更复杂的实验性**双重 Staging 沙箱环境**（Double Open Claws Staging Setup），通过同时运行两个 Open Claw 实例来模拟分级部署和协作。分享的日程规划涵盖基础答疑、高级专题讨论（如 Staging 调试）及互动环节，旨在通过物理定时器的辅助来保持节奏，提升开发者的实际操作体验。
Original English workshop setup, so um there are some people who kind of have like never installed open claw, and I want to kind of get them set up. And then the other people like want to try an experimental thing, which I haven't even fully gotten working, but I think for more advanced people that'll be cool um to get like a sort of staging environment and run two open claws. So, I think if I'm not, you know, too busy helping new people set up, then I'll try to help people like finish debugging the last part, or maybe someone else will get it. And I think uh 10 minutes Q&A, 10 minutes some more advanced topics, and then just more Q&A. And uh probably after every 5 minutes or so, we can do a bit of a few minutes of discussion. So, I'll just set some timers to make sure I don't go off track. All right. Let me start. All right. Can everyone see this presentation? >> Yes. >> All right, cool. >> Yeah.
### 独立上下文与专业化记忆管理 **Open Claw** 的核心优势在于其**上下文隔离与专业化记忆机制**（Specialization & Context Management）。相较于直接在常规网页端或普通客户端使用 Claude，Open Claw 能够针对特定项目维护独立的任务上下文和历史记录。例如，开发者仅需发送一条极简的 Slack 消息（如“修复 skeptic 代理”），Open Claw 就能基于先前的对话上下文，准确识别并定位到专门用于代码审查的 **Skeptic 代理**（Skeptic Agent: 定制化的代码审查工具）。这种专门化设计极大地节省了宝贵的 Context Token——通常在普通 Claude 窗口中，系统规则、自定义技能和模型配置文件（如 `.cursorrules` 或 `CLAUDE.md`）会直接耗费约 25% 的上下文空间，而 Open Claw 将这些实现细节（How to do）与具体任务目标（What to do）相隔离，从而让开发者能够以极低的信息冗余进行高效的沟通与迭代。
Original English So, this kind of describes like my setup. Um I'll just keep it really high level for this part where when everything works, you know, maybe like 70% of the time, you describe your task. And then what's nice is open claw will have like context and memory about what you're talking about. So, for example, like let's say I'm like, "All right, fix this thing." It's a very brief Slack message, but open claw can remember like what I've asked it to do before. So, for example, I'm working on this thing I'm calling a skeptic agent, and it's a custom code review thing. But if I'm like, "All right, fix the skeptic agent." or whatever, right? It kind of knows what that is, which is nice. You don't have to re-explain a lot. Um I use multiple agents with work trees, that's very key for parallelization. Uh, CI code good integration. But you can kind of see you can talk to it and be pretty brief and then, you know, usually get reasonable outcomes, but sometimes you'll have to debug it. So, then it's like, all right, there's some time out thing. I'm like, give it a longer time out, right? And then I asked that here like like uh, you know, what are the priorities and what we're working on. And I'm reading it here. And then I might have just said like, uh, I just made code for it, right? So, another interesting thing about this is you can kind of see from like, um, >> [clears throat] >> the types of responses I give, they're not that like, um, special. As in I I actually think an agent could replace me. So, that's kind of what I'm working on right now to like get things even more autonomous. You know, and you and you'll see this with LLMs like you're working with them and they'll be like, "Hey, I've got this thing ready." And I'm like, "Okay, we'll run the test, right?" Uh, all right. So, I'll share the next slide now. Oops. Okay. We're going to some concepts right now. Like, what is Open Claw? So, I think what's cool about Open Claw is it's not just about a particular repo or the code, but more of the concept. So, you know, I've got this cool super diagram, but first concept is frictionless communication. So, I know we have other things like Claude co-work or whatever, but I think they still have a similar concept where it's like, can you like easily talk to your AI versus like you've got a, um, remote desktop into your computer or you have to go sit down. So, that kind of enables certain really higher level of velocity. Um then I think the central axis is very important. So, you know, there's a spectrum like I kind of go like a little more cowboy and I give it act. Open Claw has these agent orchestrator managers that manage your workers for you. Uh or sometimes when I want more personal control, I use tmux terminals. So tmux is like a terminal program, but it's pretty good for parallelization and AI development. Then I use the open source framework that I forked agent orchestrator to do the workers. And then once it gets to here, this part is like not really controlled by me as much anymore. Um so, I'm calling them managed agents, but basically like you have a worker that runs Claude code and Claude code itself can run agents and those can have sub agents. So once you get to this Claude code part, right? This part is not exactly my stack, just stuff I use, but this part of the stack I'm changing a lot more. Um I'll pause here to see if anyone has any questions or thoughts. >> Um can you hear me? So I I have a question. So the the real question is, you know, why why use Open Claw versus directly use Claude? >> So yeah, that's that's definitely a good question. Um >> [clears throat] >> The reason I use Open Claw as specialization. So when Open Claw makes a decision, I want that context to be more about like the spec or the goals or like the history of what I want in the task rather than the code. And you can just imagine this, right? As soon as you open up Claude, it reads Claude MDs, it reads skills, it reads um MCPs. A lot of those things are sort of independent of like the actual task. It's more about how to do the task. Um so imagine like 25% of your context already taken up by implementation. Versus like when you think about open claw, you're like, okay. I want to think about exactly what I want to do and how it relates to all the other slacks Jeffrey sent me in the last 2 weeks and put it all together and give me like a reasonable spec.
### 并行研发基建：工作树与终端复用器 为了实现开发效率的最大化，本系统引入了**多代理并行工作流**（Multi-Agent Parallel Workflows），其底层核心在于 **Git 工作树**（Git Worktree: 允许在多个独立目录中同时检出多个分支的技术）与持续集成（CI）的深度整合。在开发控制权的设计上，系统呈现出一个从“手动干预”到“完全自治”的光谱：开发者既可以在需要个人掌控时，通过 **tmux/Cmux** 终端复用器直接操作独立的终端窗口；也可以将管理权移交给**代理编排管理器**（Agent Orchestrator Manager），由其自动调度下属的托管代理（Managed Agents）。这些托管代理本身能够运行复杂的 AI 工具（如 Claude Code），并能进一步向下级派生子代理（Sub-agents），形成树状的自主执行结构。通过将不同的开发任务分发至独立的 Git 工作树，并由多级代理并发执行，整个系统的研发吞吐量和并行度得到了本质上的提升。
Original English Does every single thing a player does, but it does it without a browser, right? So you could use MCP, which usually is JSON or whatever or HTTP. Doesn't really matter actually. And then the final thing is the browser test. So some things like are visual. I mean, maybe you could even have CSS tests or JS tests, right? And that kind of goes here. But then the final thing is visual. So I would recommend you like try um similar approaches to this and be like, okay, like which problems truly needed that human or not. Um and I think what's kind of cool is like every quarter or you maybe even every month like it improves. So before like I had to really manually test a lot because like you know, this worked fine, right? But these things didn't work that well with agents like last year but like a year ago even 6 months ago. But now like um agents are pretty good at like nailing down a lot of browser tests for me. Versus like when I started my agent would have a lot of problems, you know, finding a pop-up and entering a password. Now no problem. Um that was a little long, but does that kind of address the theme of your question? >> Yeah, I think this is good. Good. Thank you. >> Okay. >> Thank you very >> Just just remind me for all these slash commands. I'll put um links here instead. All right, cool. Anyone have any other questions related to some of that stuff? All right. If not, I will go here. All right. So, here's a two websites that I've built. >> [clears throat] >> Yeah, while that loads, I'll show this one. So, this is like a AI RPG. Um what's cool about this is you can I've got a default campaign, for example. Um >> [clears throat] >> Yeah, pick some options. You've got a avatar. And uh what's cool about this is it builds a custom world for you that reacts to you. So, you can like kind of say whatever you want, do whatever you want, you'll get a story back. Um the main difference between playing this versus like um you know, you could just always go into the Gemini chat or or chat GPT or or whatever is that um I have like a D&D system. So, if you play your own like games or novels, like you kind of just win too much. Versus like with this, you'll actually do like dice rolls and be like, you know, did the person actually um did you actually succeed in your action or not? All right. So, while this is going, I'll also um show this thing. Yeah, so this is funny. Um website is like a multi-AI analysis website. Um so, what I found was like whenever I was doing research or or whatever, I would go to multiple models and I'd be like, what's the answer? Then I copy and paste them all. I put them into one model. Um so, pretty simple concept, but this does it for you. So, usually I like these answers better than like asking one model. And I'm like, okay, how do I do this workshop? So, I can be like, okay, what's the status? Uh what's the latest PR now? And um what I like about this is it has that sort of vertical tab type of thing. Cuz when you go horizontal, it's really easy to lose track of your tabs. Um but we've got notifications, too. So, when this thing is finished or this is finished and I need to look, it'll give me a notification. And I can just focus on clearing the notifications. So, here um the way of using these terminals, usually it's more like a manager rather than a coder. Um and it's kind of interesting, but it gives you a certain benefit where I feel I feel like the work is not biased anymore. So, when I when I code with these directly, um I usually feel like there's a bias, where it wants to say things are really working or whatever, right? Versus like, you know, um I'll show this. Let's look. If this had been working on PR 294 by itself, I think it would have been like, "This PR is amazing. Like, we got to merge it, right?" But then this one was like, "No, like, there's another PR that should supersede it, and probably we should just close this PR." Right? So, that's kind of the benefit you get, where the manager has a different context um than the workers.
### 终端多路复用与多模型评估平台 在工具链生态方面，项目深度整合了 **Cmux/tmux** 终端生态，并引入了多模型协同评估的设计思想。tmux 联创 Austin 分享了最新的技术演进，包括 **Cloud Code Teams 集成**（支持自动为协作会话生成终端）以及 **Cmux SSH**（支持通过 Tailscale 跨设备无缝管理 Mac Mini 等本地 staging 设备）。在实际研发中，开发者通过自建的**多模型联合分析平台**（Multi-AI Analysis Platform）来进行方案比对。该平台采用高效的**垂直标签页布局**（Vertical Tab Layout），将开发者提出的技术问题分发至不同厂商的模型，并将结果汇聚于统一视角中进行比对。同时，系统在执行浏览器自动化测试或视觉 CSS 测试时，能够将执行完毕的信号以异步通知的形式推送给开发者，使其能够像“管理者”而非“打字员”一样，只需关注并清理通知队列，极大地缓解了上下文切换的疲劳。
Original English >> [snorts] >> Uh let's see. All right, so this is compacting whatever. So, it will take some time to complete. Don't want to wait for it, but when it completes, you see these notifications, and I think my efficiency has improved a lot with tmux. Um I think is Austin or someone from tmux here? >> Yeah, here. What's up? Go. >> Yeah, so Austin is uh >> [clears throat] >> one of the guys behind tmux. Um you know, big fan of it, and I'm not I'm not paid to say this or anything, but big fan of tmux. Uh been using it a lot. I don't know, you can spend a minute if you want to talk about anything cool about tmux. >> Yeah, so yeah, one of the creators of tmux. Really happy to have Jeff as one of our power users. And also like kind of endorsing it for free. But yeah, lots of uh yeah, lots of things that we're shipping. We shipped a Cloud Code Teams integration. So, if anyone is using Cloud Code Teams and wants to actually see what the salvations are doing, we'll automatically spawn terminals for that. We also shipped Cmux SSH. So, if you do any SSH work, you can use our native Cmux SSH to you know, do your tailscale and etc. To other computers. You can even use it to run your own open claw in your Mac minis. But yeah, just one of the creators. Please feel free to email me if you have anything that you think is a bug or any feedback, feature requests. I'm always available and yeah, just love seeing people using Cmux and always want to take as much feedback as I can to like get them as get them as >> Okay, so just download that. >> Yeah. They actually have an app too. So, the app might be nice. >> Yeah. Okay. And then use that to help me get back in and set set up. >> Exactly. Yeah. >> Okay. Okay, cool. Thanks.
### 隔离沙箱环境下的测试与双重 Token 策略 针对开发者普遍关心的**沙箱与 Staging 环境部署**（Staging Sandbox Setup），Aaron 提出了关于 Token 消耗与多环境运行的疑问。在最佳实践中，运行 staging 实例确实会因为对两套系统发送相同指令而增加 Token 消耗，但其带来的系统鲁棒性与开发隔离性远超成本支出。推荐的规范化流水线为：开发者首先在本地环境进行自主编码与局部调试，随后将代码推送至基于 Docker 或隔离分区的 **Staging 沙箱环境**运行全面的自动化集成测试，验证通过后，最后合并分支并正式部署至生产环境。这种双重 Staging 架构既规避了直接在生产环境调试导致的服务中断风险，又合理地分摊了 Token 的使用成本。
Original English >> All right. Is using the sandbox, it should not. Where would you Have you had Have you run into any troubles Aaron about sandbox token usage? >> Uh no. I'm just I'm not technical at all. I have open claw installed, so I'm >> Okay. >> very newbie questions. >> No no no bad questions. All good. Yeah. Uh they're not the sandbox wouldn't do that. I think if if everything's working well for you, you know, keep using the sandbox. For me, I just you know, don't use a sandbox and so so far nothing bad has happened. But I think if I had an external bot, then I would use a sandbox too. >> You changing the model testing a new model like it'll go down then I have to figure out how to get it back up again. So I think I want to set up this sandbox because >> Oh, sorry. Sorry. Um okay, wait. There's two kinds of things. Okay. The sandbox that most people talk about means like they run it in Docker or they like have it running in isolated part of their system. But the other one was the one where I made the staging instructions where I was like oh >> Yeah. >> Okay. So that could double your token usage if you send the same work to both of them. If you have two of them, right? Um but I think the way I would use it is not like that. Like I think I would do local development and then I would run integration tests on the um sandbox or staging one, right? So I have two of them. And then once like everything's good, then I would merge the code and deploy to the production one. >> I see. >> It wouldn't double your usage, but it would definitely increase it. But then the benefit is you might have some more reliability. So >> Yeah. >> I I made some instructions and I was kind of in the middle of setting it up, but then, you know, it's like competing with the other one, so I got to debug it. >> Yeah. >> But I think, you know, it's it's worth trying out though. So I had that for more advanced people to give it a give it a try. >> Cool. Thanks. What
### 成本敏感型模型路由与降级策略 在最终的模型路由与调用链设计中，**成本敏感型模型选择策略**（Cost-Aware Model Routing）是保障系统长期运行的关键。在实际的代理编排中，虽然 GPT-5.4 拥有强大的推理能力，但其极高的 Token 消耗往往会导致研发预算迅速枯竭。因此，系统默认采用 **Codex 5.3** 作为核心编排模型，它在复杂任务逻辑的处理与 Token 消耗之间取得了较好的平衡。当 Codex 5.3 的 Token 额度临近耗尽时，系统将动态平滑降级至 **MiniMax** 等高性价比的备用模型。这种基于配额 and 成本的动态路由机制，在确保基础代码生成和编排质量的同时，大幅优化了多 Agent 并行研发的整体经济效益。
Original English >> Or if you're not as advanced, if you want it, you know, you can try it. >> Yeah, I am definitely going to play around. But what a what model do you use? Like do you have a something that like a default orchestrator model and then you say for this you use >> use Codex 53. Um I found I found uh GPT 54 to just use more tokens. Um and even 53 like like I I just get destroyed all the time, right? So I might have to adjust that. Um so basically I use this until like this is getting low and then I just switch to MiniMax, which is not as good, but it kind of gets the job done. And then this is more about money than like preference, but some some work I can still kind of just do um um here. So, I don't always have to