Desktop app for macOS/Windows/Linux that orchestrates multiple AI coding CLI agents (Claude Code, Gemini CLI, Codex CLI) in parallel, each in isolated git worktrees. $49 one-time purchase.
Baton is a desktop application developed by an independent developer, available for macOS, Windows, and Linux. It orchestrates multiple terminal-based AI coding agents — including Claude Code, Gemini CLI, and Codex CLI — running simultaneously in isolated git worktrees. As a Cursor alternative, it targets developers who prefer CLI-based AI agents and need a GUI layer to manage parallel agent workflows without merge conflicts.
| Feature | Baton | Cursor |
|---|---|---|
| Type | Multi-agent orchestration desktop app | AI-enhanced code editor (VS Code fork) |
| Agent model | Orchestrates external CLI agents | Built-in single AI assistant |
| Parallel agents | Yes — unlimited, in isolated worktrees | No |
| Git isolation | Yes — separate worktree per agent | No |
| Supported agents | Claude Code, Gemini CLI, Codex CLI, any terminal agent | Built-in only |
| Pricing | Free basic + $49 one-time Pro | Free tier + $20/mo subscription |
| MCP server | Built-in | Limited MCP support |
| Diff viewer | Monaco diff viewer | Built-in editor diff |
Baton is best for power users who already use one or more AI CLI agents and want a GUI layer to run them in parallel without chaos. It's ideal for developers tackling large features that benefit from decomposition — running one agent on tests, another on implementation, and a third on documentation simultaneously. Freelancers and indie developers who prefer a one-time purchase over ongoing subscriptions will also appreciate Baton's pricing model. Anyone frustrated by the single-agent bottleneck in tools like Cursor will find Baton's parallel execution model genuinely transformative.
Prices subject to change. Check the official pricing page for current information.
Cursor is a self-contained AI editor where one assistant helps you write code in a familiar VS Code interface — ideal for straightforward, single-session AI collaboration. Baton takes an entirely different architectural approach: it doesn't replace your editor but orchestrates external CLI agents in parallel across isolated git worktrees, turning your development workflow into a multi-agent operation. Where Cursor keeps you in one context with one model, Baton lets you run Claude Code, Gemini CLI, and Codex CLI simultaneously on different branches of the same project. For complex tasks that benefit from parallelism and agent diversity, Baton has no real equivalent in the Cursor world.
Baton solves a problem Cursor doesn't even attempt to address: how to safely run multiple AI agents on the same codebase simultaneously. For developers already using CLI agents and hungry for more throughput, Baton's $49 one-time Pro purchase is one of the best value propositions in the AI coding space today. It's not a replacement for Cursor so much as a force multiplier for developers ready to move beyond single-agent workflows.
No. Baton offers a free download for macOS, Windows, and Linux. The Pro version costs $49 as a one-time purchase with no ongoing subscription fee. Basic orchestration functionality is available on the free tier; Pro unlocks additional features — check getbaton.dev for the current feature comparison.
Baton works with any terminal-based AI coding agent, including Claude Code (Anthropic), Gemini CLI (Google), OpenAI Codex CLI, and custom agents. You need to have these agents installed and authenticated separately; Baton acts as the orchestration and monitoring layer above them.
Git worktrees allow multiple working directories from the same repository to coexist simultaneously, each on its own branch. Baton creates a separate worktree for each agent, meaning agents never edit the same files at the same time — eliminating merge conflicts. When an agent finishes, you review its diff in Baton's Monaco viewer and decide whether to merge it.
Baton's built-in Model Context Protocol (MCP) server provides connected agents with standardized, structured access to project context — file trees, documentation, tool definitions, and more. This ensures agents have consistent, high-quality context regardless of which underlying AI model they use, improving the reliability and relevance of their outputs.
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