agterm
agterm is a native macOS terminal application engineered to organize and supervise multiple AI coding-agent sessions from a single interface. It addresses the fragmentation that occurs when developers run several AI assistants concurrently, offering a unified workspace where each agent’s progress, output, and state can be monitored in real time. By acting as a central hub, agterm eliminates the need to switch between disparate terminals, reducing context switching and improving workflow efficiency. Key features include named workspaces that allow users to group agent sessions by project or task, a live dashboard that displays streaming outputs and current statuses of all active agents, and attention states that let developers focus on high-priority sessions while deprioritizing background agents. A scriptable control API further extends the tool’s capabilities, enabling automated start/pause/stop commands, custom integrations, and complex orchestration logic. agterm is designed for macOS and leverages native UI components to deliver a responsive, low-latency experience. It is distributed as an open-source project, allowing the community to inspect, modify, and extend its functionality. The tool is particularly suitable for developers who regularly employ AI pair programmers, autonomous coding agents, or multi-agent research setups. Although agterm runs locally, its architecture can be adapted to work with any AI agent that exposes a terminal or command‑line interface. By consolidating supervision and control, it helps teams maintain situational awareness, enforce process discipline, and scale their use of AI‑assisted development without growing operational chaos.
- Category
- AI Agents
- Pricing
- Open Source
Problem solved
Managing multiple concurrent AI coding‑agent sessions typically requires juggling several terminal windows or tabs, leading to disorganization and lost context. agterm solves this by providing a unified interface with workspaces, a dashboard, and control mechanisms, streamlining supervision and reducing cognitive load.
Use cases
- Supervising multiple AI pair‑programming agents working on different modules
- Monitoring long‑running autonomous coding tasks in real time
- Automatically pausing or resuming agent sessions based on attention states
- Scripting custom workflows to coordinate agents via the control API
- Organizing agent sessions by project or feature using named workspaces
- Debugging agent behavior by viewing live output in a central dashboard
Audience
- Developers
- ML Engineers
- Data Scientists
- Researchers
Pros
- Centralized dashboard for monitoring multiple AI agents
- Native macOS performance and look‑and‑feel
- Scriptable API for advanced customization
- Named workspaces keep projects organized
- Open source and extensible
Cons
- Limited to macOS only
- Requires familiarity with terminal‑based tools
- Dependent on underlying AI agents’ capabilities
- May have a learning curve for the scriptable API
- No built‑in remote collaboration features
Official website: https://github.com/umputun/agterm
Canonical LambdaBase page: https://www.lambdabase.com/tools/agterm