Ralv

What is Ralv?

Ralv is a macOS “spatial interface” for running and managing multiple AI agents in parallel. Instead of organizing agent activity in a 1-dimensional list, it presents agents on a 3D canvas so you can see what each agent is doing in real time and coordinate them more intuitively.

Its core purpose is to help humans maintain control over many concurrent agent tasks by providing persistent visual context, prioritization cues, and drill-down access to an individual agent’s work.

Key Features

• 3D canvas for parallel agent management: visualize multiple agents at once and keep their activity organized beyond a simple list view.
• Real-time visibility of agent work: see what agents are working on as their tasks progress.
• Zoom and pan controls: navigate the spatial layout to focus on relevant agents and details on demand.
• Visual alerts for agent input needs: get alerts when an agent requires attention or user input.
• One-click detail drill-down: open details for a selected agent’s work to inspect what it produced and how it changed.
• Merge agent changes (as described): combine updates produced by different agents rather than manually reconciling results from separate streams.

How to Use Ralv

1. Download the Ralv alpha for macOS (noted as supporting Apple Silicon).
2. Start Ralv on your Mac to access the 3D canvas interface.
3. Run multiple coding agents (or other agents) so they appear as parallel agents in the canvas.
4. Use zoom and pan to orient yourself, then watch for visual alerts indicating which agent needs input.
5. Click into an individual agent to review its details, and merge changes from multiple agents when appropriate.

Use Cases

• Developer orchestrating multiple coding agents: run several agents at the same time and monitor their progress on a single screen, using alerts to decide when to intervene.
• Managing agent work across tasks within one project: keep ongoing tasks visually separated and inspect each agent’s output by drilling into its work.
• Reviewing and reconciling parallel changes: merge updates produced by different agents without relying solely on separate chat windows or file lists.
• Prioritizing interactive agent workflows: focus on agents that require user input by using the interface’s visual alerts.
• Generalizing to knowledge work beyond code: coordinate multiple agents that handle research, drafting, or other work streams by using the same spatial interaction model.

FAQ

Why a spatial interface?

The site explains that parallel agent work benefits from persistent state, prioritization, and fast recognition. A spatial canvas is presented as scaling well when there are many agents working concurrently.

How does Ralv compare to Cursor or Claude Code?

Cursor and Claude Code are described as built around chat windows and file lists, which fit one agent at a time. Ralv is positioned as an interface for the “one-agent-per-task” future, and it can also be used alongside existing Claude Code or Codex subscriptions.

Who is Ralv’s alpha for first?

The alpha is intended for developers running multiple coding agents, with the interaction model also framed as generalizable to other knowledge workers orchestrating agents.

What systems does the alpha support?

The current release supports Apple Silicon Macs.

Alternatives

• Chat-and-file-list agent tools: tools organized around chat windows and file lists can be simpler for one agent, but they may become confusing when coordinating many agents in parallel.
• IDE-integrated multi-agent workflows: instead of a dedicated spatial canvas, some developer tools focus on coordinating agent output within an editor context; this can help with single-stream work but may not provide the same visual parallel orchestration.
• Visual workflow/orchestration tools (non-agent specific): workflow builders that represent steps or tasks visually can help manage concurrency, though they may not provide the agent-specific “real-time agents on a canvas” interaction model described for Ralv.