Agent OS — Persistent Agent Operating System

Agents that remember. Learn. Coordinate.

What It Does

Agent OS enables multi-agent project execution with persistent memory:

• Agent Memory — Each agent remembers past tasks, lessons learned, success rates
• Task Decomposition — Break high-level goals into executable task sequences
• Smart Routing — Assign tasks to agents based on capability fit
• Execution Tracking — Live progress board showing what every agent is doing
• State Persistence — Project state survives restarts (resume mid-project)

Quick Start

Installation

clawhub install nova/agent-os

Basic Usage

const { AgentOS } = require('agent-os');

const os = new AgentOS('my-project');

// Register agents with capabilities
os.registerAgent('research', '🔍 Research', ['research', 'planning']);
os.registerAgent('design', '🎨 Design', ['design', 'planning']);
os.registerAgent('dev', '💻 Development', ['development']);

os.initialize();

// Run a project
const result = await os.runProject('Build a feature', [
  'planning',
  'design',
  'development',
]);

console.log(result.progress); // 100

Core Concepts

Agent

Persistent worker with:

• Memory — Past tasks, lessons learned, success rates
• State — Current task, progress, blockers
• Capabilities — What it's good at (research, design, development, etc.)

TaskRouter

Decomposes goals into executable tasks:

• Breaks "Build a feature" into: plan → design → develop → test
• Matches tasks to agents based on capability fit
• Tracks dependencies (task A must finish before task B)

Executor

Runs tasks sequentially:

• Assigns tasks to agents
• Tracks progress in real-time
• Persists state so projects survive restarts
• Handles blockers and errors

AgentOS

Orchestrates everything:

• Register agents
• Initialize system
• Run projects
• Get status

Architecture

AgentOS (top-level orchestration)
├── Agent (persistent worker)
│   ├── Memory (lessons, capabilities, history)
│   └── State (current task, progress)
├── TaskRouter (goal decomposition)
│   ├── Templates (planning, design, development, etc.)
│   └── Matcher (task → agent assignment)
└── Executor (task execution)
    ├── Sequential runner
    ├── Progress tracking
    └── State persistence

State Persistence

All state is saved to the data/ directory:

• [agent-id]-memory.json — Agent knowledge base
• [agent-id]-state.json — Current agent status
• [project-id]-project.json — Project task list + status

This means: ✅ Projects survive restarts ✅ Agents remember past work ✅ Resume mid-project seamlessly

File Structure

agent-os/
├── core/
│   ├── agent.js          # Agent class
│   ├── task-router.js    # Task decomposition
│   ├── executor.js       # Execution scheduler
│   └── index.js          # AgentOS class
├── ui/
│   ├── dashboard.html    # Live progress UI
│   ├── dashboard.js      # Dashboard logic
│   └── style.css         # Styling
├── examples/
│   └── research-project.js  # Full working example
├── data/                 # Auto-created (persistent state)
└── package.json

API Reference

AgentOS

new AgentOS(projectId?)
registerAgent(id, name, capabilities)
initialize()
runProject(goal, taskTypes)
getStatus()
getAgentStatus(agentId)
toJSON()

Agent

startTask(task)
updateProgress(percentage, message)
completeTask(output)
setBlocker(message)
recordError(error)
learnLesson(category, lesson)
reset()
getStatus()

TaskRouter

decompose(goal, taskTypes)
matchAgent(taskType)
getTasksForAgent(agentId, tasks)
canExecuteTask(task, allTasks)
getNextTask(tasks)
completeTask(taskId, tasks, output)
getProjectStatus(tasks)

Executor

initializeProject(goal, taskTypes)
execute()
executeTask(task)
getStatus()

Example: Research + Design + Development

See examples/research-project.js for the canonical example:

npm start

This demonstrates:

• ✅ 3 agents with different capabilities
• ✅ 12 tasks across 3 phases (planning, design, development)
• ✅ Sequential execution with progress tracking
• ✅ State persistence to disk
• ✅ Final status report

Expected output:

✅ Registered 3 agents
📋 Task Plan: 12 tasks
🚀 Starting execution...
✅ [Task 1] Complete
✅ [Task 2] Complete
...
📊 PROJECT COMPLETE - 100% progress

What's Coming (v0.2+)

• HTTP server + live dashboard
• Parallel task execution (DAG solver)
• Capability learning system (auto-score agents)
• Smart agent routing (match to best agent)
• Failure recovery + retry logic
• Cost tracking (token usage per agent)
• Human checkpoints (review high-risk outputs)

Philosophy

Agents should remember what they learn.

Most agent frameworks are stateless. Agent OS keeps persistent memory so agents:

1. Remember — No redundant context resets
2. Learn — Capability scores improve over time
3. Coordinate — Shared state prevents duplication
4. Cost less — Less context = cheaper API calls

License

MIT

Built with ❤️ by Nova for OpenClaw

See README.md and ARCHITECTURE.md for complete documentation.