You might notice a slight naming quirk right off the bat: while the product is called Nest by RAVEN, its official domain, nestmux.com, refers to it as NestMux. It's a minor detail, but one that could cause a moment of confusion for new users. Essentially, this tool is a multi-AI terminal workbench built for developers. Its core mission is to consolidate AI coding assistants like Claude, Gemini, Codex, Copilot, and Aider into a single terminal window, enabling them to work in parallel instead of forcing you to juggle multiple browser tabs or separate applications.
One Terminal, Many AI Brains
Anyone who's spent time with AI coding assistants knows the drill: a Claude window here, a ChatGPT tab there, Copilot running in your IDE, and your brain constantly trying to keep track of context across all of them. Nest tackles this head-on by integrating all these AI tools into one unified interface. It uses a pane-based system to split your screen, with each pane capable of running an AI or a standard shell. The official interface suggests support for up to six AI agents side-by-side, with specific mentions of Claude, Gemini, Codex, Copilot, and Aider, alongside OpenCode and custom CLI integrations.
While the concept sounds straightforward, its real power lies in a few key features:
- Native Git Worktrees Support: Each Git branch can automatically correspond to its own pane. This means no more constant stashing and branch switching, and crucially, your AI's context isn't lost every time you move between branches.
- Broadcast Functionality: Imagine sending a single prompt to multiple AI panes simultaneously. This feature lets you do just that, allowing different models to respond to the same request in parallel, offering diverse perspectives or solutions.
- MCP Panel: A graphical interface for viewing and managing MCP server configurations, eliminating the need to manually tweak config files.
- Team Sharing: Share your terminal session with teammates via an 8-character short link. This is a game-changer for remote pair programming or collaborative debugging sessions.
Context and Collaboration: First-Class Citizens
Nest by RAVEN seems particularly obsessed with solving the problem of 'context loss.' The pain points it addresses are incredibly relatable: switching from a `feat/auth` branch to `fix/parser` and losing your development server state, AI conversation history, and half-finished code. Or, the institutional knowledge of senior developers' prompts and MCP configurations being locked away on their local machines, forcing new hires to rebuild everything from scratch. Nest tackles these issues head-on with its worktrees integration and team workspaces. It even includes a Team Analytics panel to help monitor the health of your team's workflow.
The website highlights several practical use cases: multi-model code review (where Claude, Gemini, and Codex can all review the same code snippet), parallel refactoring (each AI agent handling a different branch), and, of course, team collaboration through shared terminals and contexts. For indie developers and small teams, these scenarios hit home, addressing common frustrations directly.
Free, Local-First, but Still Early
According to its official site, Nest is currently available for free download across macOS, Windows, and Linux. A significant selling point for privacy-conscious developers is its emphasis on being local-first and telemetry-free. This means your code and conversation history remain on your machine by default, a welcome relief in an era of constant data collection.
The development pace appears steady, with v1.4 already released, bringing features like tiled layouts and per-pane port displays. However, it's worth remembering that the product is still in its early stages. Some UI elements and feature names (like 'Teams' and 'Hub') feel like they're still being refined, and the official documentation is somewhat limited. If you're intrigued by the idea of a multi-AI parallel workflow or are simply tired of shuttling context between windows, downloading the free version is a low-risk way to explore. I'd suggest starting with two or three AI panes on a small project to get a feel for it.
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