Neo by AmpCode smarter with remote control and blazing performance for massive projects

Neo by Amp

Neo is the completely rebuilt Amp CLI, designed for the next generation of AI coding agents. Built on a remote-controllable, compaction-first, plugin-powered architecture, it eliminates manual context management and delivers dramatically improved performance for developers who want agents with longer leashes and less handholding.

Product Highlights

• Automatic Context Compaction: When your context window fills to 90%, Amp automatically summarizes and compacts threads so you never have to manually manage context or panic about running out of space.
• Remote Control from Anywhere: Start a thread in your terminal and remotely control it from ampcode.com with live updates, message queuing, and real-time intervention capabilities.
• Plugin-Powered Extensibility: The new Amp Plugin API lets you handle events, register custom tools, add commands, show UI elements, and even ask AI questions with confidence scoring—all through simple TypeScript files.
• 79% Less CPU, 70% Less Memory: Neo runs dramatically more efficiently than the previous CLI, handling 5000+ message threads without slowdowns.
• Smart Message Queuing: Messages queue by default when the agent is busy, with steering controls to fast-track critical inputs without interrupting ongoing work.

Use Cases

• Long-Running Development Sessions: Let agents work autonomously for hours while you monitor and intervene remotely from any device, perfect for complex refactoring or multi-file implementations.
• Custom Agent Workflows: Build organization-specific permissions, custom tools, and specialized commands using the Plugin API to match your team's security policies and development practices.
• High-Volume Code Operations: Handle massive threads with thousands of messages without performance degradation, ideal for large-scale migrations or extensive codebase analysis.

Target Audience

Neo is built for professional developers and engineering teams who rely on AI coding agents for substantial development work and need a system that scales with increasingly capable frontier models. It's ideal for those frustrated by context limits, manual handoffs, and the babysitting required by older agent interfaces.