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Agent OS

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The seminal fourth "Operating System Philosophy" in human computing history.

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C/C++ Python Go Rust TypeScript

🌟 Introduction

AgentOS is an intelligent agent operating system that provides comprehensive OS-level support for orchestrating agent teams.

🎥 Project Preview

Personal Client Preview

AgentOS Preview

💡 Innovation Highlights

  • Pure Kernel: Only atomic mechanisms, ensuring purity and efficiency.
  • Cognitive Loop: Perception, Planning, Action.
  • Memory Stratification: L1 Raw → L2 Features → L3 Structures → L4 Patterns.
  • Inherent Security: Sandbox Isolation, Permission Arbitration, Input Sanitization, Audit Trail.
  • Token Efficiency: Saves approximately 500% tokens compared to traditional frameworks.
  • Comprehensive SDKs: Native support for Go / Python / Rust / TypeScript.

🎯 Core Philosophy

Drive Teams

  • Precisely coordinates multi-Agent collaboration
  • Efficiently completes complex task orchestration and resource scheduling

Autonomous Evolution:

  • Possesses self-evolution capability
  • Dynamically adjusts strategies
  • Continuously optimizes execution effectiveness

Brand New Architecture · Inherent Security · Intelligence Emergence

🏗️ System Architecture

Architecture Design
Complete architecture from kernel to application:

⬇️ Application Layer (openlab)
⇅ Service Layer (daemon)
⇅ Kernel Layer (atoms)
⇅ Security Layer (cupolas)
⇅ Support Layer (commons)
⬆️ SDK Layer (toolkit)  

Design Principles
Built upon ARCHITECTURAL_PRINCIPLES:

  • System Perspective: Feedback loops · Layered decomposition · Holistic design · Emergence management → Real-time response <10ms
  • Kernel Perspective: Minimalist kernel · Contractual interfaces · Service isolation · Pluggable strategies → Kernel ~25K LOC
  • Cognitive Perspective: Dual-system synergy · Incremental evolution · Memory stratification · Forgetting mechanism → Token savings 500%
  • Engineering Perspective: Security built-in · Observability · Resource determinism · Cross-platform consistency → Test coverage >90%
  • Design Aesthetics: Simplicity first · Extreme attention to detail · Human-centric · Perfectionism → API <50/module

🚀 Quick Start

Environment Requirements

  • Operating System: Ubuntu 22.04+ / macOS 13+ / Windows 11 (WSL2)
  • Compiler: GCC 11+ / Clang 14+ (C11/C++17)
  • Build Tools: CMake 3.20+, Ninja
  • Python: 3.10+ (Required for OpenLab)

Installation & Build

# 1. Clone repository
git clone https://atomgit.com/spharx/agentos.git && cd agentos

# 2. Install dependencies (Ubuntu)
sudo apt install -y build-essential cmake gcc g++ libssl-dev libsqlite3-dev ninja-build

# 3. Build kernel
mkdir build && cd build
cmake .. -G Ninja -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=ON
cmake --build . --parallel $(nproc)

# 4. Run tests
ctest --output-on-failure  

Docker Quick Start

# Build image
docker build -f scripts/deploy/docker/Dockerfile.kernel -t agentos:latest .

# Run container
docker run -d --name agentos -p 8080:8080 -v ./config:/app/config agentos:latest

Usage Methods

| Language | Usage Method |
|:-----|:---------|
| C/C++ | Develop via `syscalls.h` system call interface |
| Python | Install via `pip install agentos` then directly import |
| Go | Use `import "github.com/spharx/agentos/toolkit/go"` |
| Rust | Use `use agentos_toolkit::prelude::*;` |
| TypeScript | Install via `npm install @spharx/agentos-toolkit` then directly import |

Reading Navigation

Document Core Content
📘 Architectural Principles Five-dimensional orthogonal system, 24 core principles
🚀 Quick Start 5-minute getting-started guide
⚙️ Build Guide Detailed build steps and options
🧪 Testing Guide Unit/Integration/Contract testing
🐳 Deployment Guide Docker/Kubernetes deployment

Common Questions

👉 Q1: What is the difference between AgentOS and traditional AI Agent frameworks?

AgentOS is an operating system-level product, not a single framework:

Dimension AgentOS Traditional Frameworks
Positioning Multi-agent collaboration OS Single agent
Architecture Microkernel + strict layering Loosely coupled modules
Security Four-layer inherent security Application-level protection
Memory Four-layer stratification system Vector database
Token Efficiency Saves ~500% No optimization
👉 Q2: Which application scenarios is it suitable for?

✅ Especially suitable

  • 🎯 Complex multi-step task orchestration
  • 🧠 Long-term memory and knowledge accumulation needs
  • 🔒 High-security enterprise applications
  • 💾 Resource-constrained embedded scenarios (atomslite)
  • 🌐 Multi-language development teams

❌ Not suitable

  • 🚫 Simple single-call tasks (using a sledgehammer to crack a nut)
👉 Q3: How is security guaranteed?

Security built-in design, four-layer protection

Protection Layer Implementation Method
Virtual Workspace Process/Container/WASM sandbox isolation
Permission Arbitration RBAC + YAML rule engine
Input Sanitization Regex filtering + Type checking
Audit Trail Full-chain tamper-proof logging

See cupolas security documentation

👉 Q4: What prerequisite knowledge is needed for learning?
Role Prerequisite Knowledge Time to Get Started
Application Developer Python/Go basics 1-2 days
System Developer C/C++, OS fundamentals 1-2 weeks
Architect Microkernel, distributed systems 1 month

Recommended Path: Quick StartArchitectural PrinciplesCoreLoopThree

🎏 Participating in Contribution

We are walking into the future: "Intelligence emergence, and nothing less, is the ultimate sublimation of AI".

The Power of Belief

☀️

This is not humanity's sunset, but the dawn of a new world

Believe

The spirit of open source can maximize the wisdom of the group;
Collaboration will propel humanity to new heights.

Witness

Every day of our work is part of history;
It will surely be engraved on the monument of human civilization's development.

Ways to Contribute

Whether you are an experienced developer or just starting out:

Find Issues
Report bugs, help us improve quality

Share Ideas
Suggest new features, make the project stronger

Share Knowledge
Improve documentation, help more people understand AgentOS

Write Code
Submit PRs, jointly create history

🔥

A faint light cannot illuminate the entire path, yet it guides our direction forward

Contribution Process

See Contributing Guide

Fork Project → Create Branch → Develop & Test → Submit PR → Code Review → Merge to Main

Main Platforms: AtomGit (Recommended) · Gitee · GitHub

Contributors

See AUTHORS.md for the list of contributors.

📜 License

This project is licensed under the Apache License 2.0. See LICENSE file for details.


"From data intelligence emerges."

AtomGit · Gitee · GitHub · Official Website

© 2026 SPHARX Ltd. All Rights Reserved.


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Airymax AgentOS: 200K+ context & 10M memory, 1000h tasks at 99.1% accuracy, 80% token reduction, 5× industry efficiency. Natively compatible with OpenClaw, openJiuwen, LangChain, Claude & OpenAI APIs; more agent protocols coming.

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