A code multiplexer is an app that helps start, watch, and steer multiple agent sessions at once. This has quickly become an incredibly frothy category, with hundreds if not thousands of options emerging in the past six months. And they all make different choices: where they run, which agents they run, how they work with multiple users, and a lot more.
This page is an attempt to keep the whole field in one place. I built a harness to run every tool through a 45-step agentic evaluation, and add in my own notes for the tools I’ve used on top. Key features such as mobile support, sandboxing capabilities, and more are checked by running the multiplexers in a sandbox, and confirming against the code if available.
The universe #
The right multiplexer is a deeply personal choice. There’s no attempt here to define a universal winner, but rather find the one that matches your requirements. You can do this manually by expanding the filtering options below, or you can type what you’re looking for and the filters will automatically adapt to match those preferences.
Every tool that passes your requirements is placed by two things. Horizontal is product quality: polish, depth and feel. Vertical isstanding: how widely it’s used and how fast it ships. Usually, top right is the recommendation for the most part, unless you dig into the details for some specific reason to use another tool based on your specific need. My suggestion is to use the filters to get yourself down to a maximum of 4-5 options even visible on the screen, and then try them all. The camera copies a picture of the map, with your settings on it, to the clipboard if you’d like to share it with your team or publicly anywhere.
Notable names #
Below, I’ve written out a few notes for some specifically interesting options.
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The IDE that builds itself
This is my current daily driver, primarily because of its incredibly robust and thoughtful plugin system. Not necessarily as extensive as DeepSeek Harness’s, but the attention to UX and usability makes it stand out. It’s a novel mental model for extensibility, and I can’t recommend it enough.
Paseo
The control plane for coding agents.
The best of the chat-first ones. If you steer agents by typing at them rather than reading terminals, Paseo’s desktop and phone apps are the most complete pair I’ve used, and the self-hosted relay means you can run 100% of it on your own. The phone app is the specific differentiator - it’s unusually complete and easy to use. If you expect to use your phone often, give this a go.
Orca
Ship 100x with the agent IDE
If you live in the CLI but still want something to package up those CLI experiences, Orca is great. It does very little to abstract away the native CLI experience offered by Claude Code, etc. while still giving you the stuff you can’t get in a terminal like a browser pane, automatic worktree management, etc. Very clean UX and a good mobile app, and multi-platform support is one of its main advantages. Slower to feel than Zeron or Herdr, but it does a lot more.
Superset
Bring Any Agent. Orchestrate Them All.
Probably the most polished of three open source multiplexers (Paseo, Orca and Superset). It sits between the other two: more terminal-focused than Paseo, more workflow-focused than Orca, and the whole experience is super clean. The team-focused features are pretty great too.
Amp
Amp is the frontier agent and development environment that lets you build from anywhere.
The polish is incredible, and it’s thoughtful and very good in its execution. They’re consistently three to six months ahead of the curve: sometimes they do things that don’t make sense to me, and then three months later I realize what they were up to. Probably the most AI-pilled team out there.
It’s worth keeping installed and trying, just so you understand what everyone else is going to be doing soon enough, even if it doesn’t fit your personal workflow just yet.
Multica
Your next 10 hires won't be human.
A completely different mental model than everything else on the list. Instead of centering on chat sessions, Multica centers on tasks in a Kanban board. Agents with different tools, personalities and profiles pick up those tasks, work on them, and hand work off to each other. Like Amp, it’s worth having just to experience it even if it doesn’t end up becoming your daily driver.
Herdr
the runtime your coding agents live on.
The terminal king. If you prefer the CLI, there still isn’t really a better solution out there than this. Super powerful, flexible, well-made and fast.
Detail table #
Click a row to see why it scored the way it did, with the numbers behind the score and the full checklist of what an agent found for its features and limitations. The glyphs on the right summarize the platforms, where agents run, and openness (lit means yes, half means partial, faint means no). Hover any glyph or chip for the details - an ‘amber’ dot means there’s some sort of caveat. And a quote mark by a name means I’ve used it and left a note. Press “Compare” on up to four tools and they line up side by side above the table.
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The long tail #
Despite how big and busy that chart above is, it’s nowhere NEAR the full list of what my research turned up, or even tools that I’ve personally used. Below is the full list of an additional 621 tools, 66 of which are now defunct. They’re just indexed with a name and a link, but they’re not scored, yet. If you’d like me to run the full agent run on it, press +1 on one to vote for a full scan. And if one is missing, click the ‘Suggest a tool’ button to open an issue on GitHub for it.
Methodology #
An agent swarm running in a Daytona sandbox is dispatched against an app domain to collect metadata and score the app on five dimensions.
Reach is how widely the tool is used: dominant, major, established, emerging or niche. Quantitative data incl. GitHub stars, X followers, and social media mentions are taken into account, but so are qualitative measures such as the sentiment of those mentions.
Pace is how fast it ships, from relentless / continuous to dormant. This is based on all the various signals for Pace possible, including changelogs, announcements on social media for new features, and actual code on GitHub.
Polish, depth and feel are the three editorial calls, each with a written rationale in every detail view. Polish is how finished the product is. Depth is how far it goes beyond a single agent TUI. Feel is how fast the interface itself is: whether switching sessions and scrolling terminals is instant or sticky, which tends to track whether it’s a native app or a web view in a frame. All three of these are qualitatively assessed incl. with human input (from just me for now) as well as the agents’ own hands-on runs. The overall is the weighted mean of the five, and Fine-tune sets how much each one counts.
Many factors, such as which platforms it runs on to which agents it drives, are binary yes/no questions. Those can also be filtered manually or via the checkbox, and don’t move the scoring.