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Agent Os

Persistent agent operating system for OpenClaw.

Rating: 4.2 (126 reviews)
Downloads: 4,501 downloads
Version: 1.0.0

Overview

Persistent agent operating system for OpenClaw.

✨Key Features

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)

Complete Documentation

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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

bash

clawhub install nova/agent-os

Basic Usage

javascript

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

text

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

text

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

javascript

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

Agent

javascript

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

TaskRouter

javascript

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

Executor

javascript

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

Example: Research + Design + Development

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

bash

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:

text

✅ 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:

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

License

MIT

Built with ❤️ by Nova for OpenClaw

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

Installation

Terminal bash


openclaw install agent-os

Copied!

💻Code Examples

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

consolelogresultprogress--100.txt

## 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

└── State persistence

--state-persistence.txt

## 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

└── package.json

-packagejson.txt

## API Reference

### AgentOS

getStatus()

getstatus.txt

## Example: Research + Design + Development

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

npm start

npm-start.txt

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:

example.js

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

example.txt

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

example.txt

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

example.js

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

example.js

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