Overview

LoopX is a lightweight, agent-agnostic local control plane designed for robust loop engineering of long-running AI agent teams. It acts as the "local control plane" for AI work, ensuring that objectives, decisions, progress, and handoffs remain stable and reviewable across multiple turns, agents, and tools. LoopX doesn't replace your agent runtime but provides a crucial state management layer for complex, extended AI projects.

Key Features

• Agent-Agnostic Loop Engineering: Seamlessly integrates with popular coding agents like Codex, Claude Code, Cursor, and custom runtimes, offering a unified control layer regardless of the underlying agent technology.
• Durable Goals and State Management: Maintains stable objectives, gates, todos, scope, evidence logs, and quota information throughout the lifecycle of long-running tasks, making work reviewable and restartable.
• Quota-Aware Auto-Wake: Intelligently manages agent activation based on resource quotas and defined scheduling hints, ensuring efficient use of computational resources.
• Executable Todos & Verifiable Handoffs: Organizes work into actionable todos with clear ownership and provides a mechanism for verifiable handoffs between agents, crucial for complex team workflows.
• Evidence Logging & Review: Captures detailed evidence logs for each turn, providing a transparent and auditable history of decisions, outcomes, and transitions.
• Human Judgment Integration: Facilitates clear human oversight by defining concrete questions and waiting for judgment, ensuring critical decisions remain under human control.
• Local-First Control Plane: Prioritizes local control and state management, enhancing security and reliability for sensitive or private workflows.
• Long-Running Trajectory Visualization: Offers insights into extended AI project progress through visual representations of decision arcs and evidence branches, demonstrating the evolution of complex tasks over hundreds of hours.

Typical Use Cases

LoopX is ideal for orchestrating and managing complex, long-running AI tasks where stability, reviewability, and controlled progression are paramount. This includes:

• Multi-Day Engineering, Research, or Experimentation: Managing complex projects that span extended periods, requiring persistent state and clear progression.
• Issue and PR Loop Management: Preserving scope, evidence, and review state for AI-assisted software development workflows.
• Recurring Monitoring and Heartbeat Tasks: Ensuring continuous and reliable execution of background or monitoring AI processes.
• Projects with Gated Progress: Implementing workflows that require human approval, safety checks, or privacy considerations at specific junctures.
• Peer-Agent Team Coordination: Facilitating collaboration among AI agents by managing ownership, leases, and explicit handoffs.
• Legible Operational Workflows: Creating AI-driven processes whose progress and decision-making can be easily understood by non-technical operators.
• Automated Research and Experimentation: Orchestrating complex ML experiments or research endeavors, tracking hypotheses, evidence, and iteration.