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Contributing to aiOS

Thanks for your interest in contributing to aiOS! This project is building an AI-native Linux distribution where autonomous AI agents replace traditional system services.

Getting Started

  1. Fork the repository
  2. Clone your fork: git clone https://github.com/YOUR_USERNAME/aiOS.git
  3. Create a branch: git checkout -b my-feature
  4. Make your changes
  5. Test: cargo test --workspace && cd python && python -m pytest tests/ -v
  6. Commit: git commit -m "[component] short description"
  7. Push: git push origin my-feature
  8. Open a Pull Request

Development Setup

Prerequisites

  • Rust toolchain (stable, edition 2021)
  • Python 3.12+
  • Protocol Buffers compiler (protoc)
  • SQLite 3

Build

# Build all Rust crates
cargo build --workspace

# Install Python package in dev mode
cd agent-core && pip install -e ".[dev]"

Run Tests

# Rust
cargo test --workspace
cargo clippy --workspace -- -D warnings

# Python
cd python && python -m pytest tests/ -v

Commit Message Format

Use the format: [component] short description

Components: kernel, initd, agent-core, tools, memory, runtime, api-gateway, build, tests, docs

Examples:

  • [agent-core] add task decomposition for multi-step goals
  • [tools] implement firewall rule management
  • [docs] update memory system architecture guide

Code Standards

Rust

  • Use anyhow for error handling in binaries, thiserror in libraries
  • No unwrap() in production code — use ? or explicit error handling
  • All public APIs must have doc comments
  • Run cargo clippy and cargo fmt before committing

Python

  • Type hints on all function signatures
  • Use asyncio for all I/O operations
  • No bare except: — always catch specific exceptions
  • Format with ruff

What to Contribute

  • Bug fixes — Check the issues for reported bugs
  • New tools — The tool registry (tools/src/) is designed for extension
  • Agent types — New Python agents in agent-core/python/aios_agent/agents/
  • Documentation — Improvements to docs, examples, guides
  • Tests — More test coverage is always welcome
  • Performance — Profiling and optimization of hot paths

Architecture Overview

Before diving in, read these docs:

Questions?

Open a Discussion or an Issue.