AgentOS

Product Introduction

1. Definition: AgentOS is a local-first, open-source AI agent operating system and control plane. It is a technical middleware layer built on top of the OpenClaw runtime, designed to provide a unified human interface for orchestrating, executing, and monitoring complex AI agent workflows across multiple projects and workspaces.

2. Core Value Proposition: AgentOS exists to transform the management of numerous AI agents from a fragmented, code-heavy task into a coherent, operable system. Its primary value is providing runtime visibility, workspace-based organization, and human governance for teams and individuals running company-scale AI labor. It is the human control surface for OpenClaw, enabling "one human to manage thousands of agents."

Main Features

1. Workspace-Based Operations & Mission Control: AgentOS structures work around distinct AI workspaces, each serving as a project silo with its own agents, tasks, jobs, and data. The "mission control" dashboard provides a top-down view to create, manage, and switch between these workspaces using guided wizards for agents and tasks. This is built on OpenClaw's foundational primitives, turning its runtime flexibility into a structured, navigable environment.

2. Runtime Visibility & Live Signal: This feature delivers real-time observability into the AI agent layer. Users can see live execution sessions, active models, transcripts of agent communications, user presence, and the state of the OpenClaw gateway. It surfaces important changes and attention cues, answering the critical operational question: "What is happening right now?"

3. Human Approval Layer & Control Surface: AgentOS implements a formal human-in-the-loop governance system. It provides a dedicated interface for reviewing critical agent actions and decisions before execution. This approval gate ensures human oversight for high-stakes operations, integrating checkpoint reviews directly into the workflow rather than as an external process.

Problems Solved

1. Pain Point: The primary problem is AI agent orchestration complexity at scale. Without a control layer, managing numerous agents across projects leads to operational opacity, fragmented tooling, and a high risk of uncontrolled or erroneous executions. The manual coordination of workspaces, task assignments, and execution monitoring becomes unsustainable.

2. Target Audience: The target audience includes AI Operations Engineers, DevOps for AI, Technical Project Managers, and founders/developers building with OpenClaw. These are users who need to move beyond scripting individual agents to managing an interconnected, persistent AI workforce for real business or development workflows.

3. Use Cases: Essential use cases include: coordinating multi-step AI research projects; managing persistent agent teams for customer support or data analysis across platforms (Telegram, Slack, Email); building and overseeing custom "jobs" like automated growth hacking or code review pipelines; and providing a secure, auditable interface for non-technical stakeholders to review and approve AI actions.

Unique Advantages

1. Differentiation vs. Raw OpenClaw & Traditional Methods: Unlike working directly with the OpenClaw CLI or APIs, AgentOS provides a visual, guided, and organized control plane. Compared to traditional task orchestration tools (like Airflow) or generic agent frameworks, AgentOS is specialized for the AI runtime, offering deep integration with model sessions, transcripts, and agent-specific states. It is a domain-specific operating system for AI labor, not a general workflow engine.

2. Key Innovation: The core innovation is the local-first, OpenClaw-native operating stack that prioritizes legibility and operability. By embedding runtime truth (sessions, transcripts, gateway state) and workspace reality (files, deliverables) into a single interface with human control primitives (plan, inspect, approve), it creates a closed-loop system where the human operator remains fully contextualized and in command.

Frequently Asked Questions (FAQ)

1. What is the difference between AgentOS and OpenClaw? OpenClaw provides the foundational AI runtime, the "engine" that executes code and manages models. AgentOS is the "cockpit" or control layer built on top of it. AgentOS adds workspace organization, a visual dashboard, task wizards, approval workflows, and live monitoring, turning OpenClaw's raw power into a manageable system for real operators.

2. Is AgentOS secure? Does it require sending my data to the cloud? AgentOS is designed as a local-first system. Its core function is to run and control agents on your local machine or private infrastructure, minimizing data exposure. The runtime and workspace state reside locally. Integrations with external services (like AI APIs or messaging platforms) are managed through OpenClaw connectors, giving you control over what data leaves your environment.

3. How does AgentOS handle human-in-the-loop approvals? AgentOS features a dedicated Approval Layer. When configured, critical actions from an agent are intercepted and displayed in a review interface within the control surface. An operator must explicitly approve, modify, or reject the action before it is executed by the OpenClaw runtime, ensuring meaningful oversight for consequential operations.

4. What are the main installation methods and system requirements? AgentOS can be installed via a one-liner shell script (for macOS/Linux), a PowerShell command (for Windows), via the pnpm package manager, or by cloning the source repository for development. It requires a Node.js runtime. The system is built to be lightweight and runs primarily on the user's local machine, with its complexity scaling based on the number of agents and workspaces managed.

5. Can I use AgentOS with my existing OpenClaw integrations? Yes. AgentOS is purpose-built to unify OpenClaw's extensive integration catalog. It serves as the single control plane for managing workflows across all connected services—whether you're orchestrating agents on WhatsApp, Discord, Telegram, or interacting with AI models from OpenAI, Anthropic, or Google—bringing execution and monitoring for all into one coherent interface.