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ark-os-noa

Agent-driven platform for autonomous software development and operations with integrated coding agents for accelerated development.

Quick Start

Prerequisites

  • Python 3.11+ with pip
  • Docker and Docker Compose
  • Git

Automated Setup (Recommended)

# Clone and setup development environment
git clone https://github.com/FlexNetOS/ark-os-noa.git
cd ark-os-noa
./scripts/setup-dev.sh

Manual Setup

# 1. Setup Python environment
python3 -m venv venv
source venv/bin/activate  # Linux/Mac
# venv\Scripts\activate   # Windows

# 2. Install dependencies
pip install -r requirements.txt
pip install -r requirements-dev.txt

# 3. Start infrastructure
docker-compose up -d

# 4. Verify setup
python -m pytest tests/

Coding Agents πŸ€–

This platform includes automated coding agents to accelerate development:

Agent CLI

# List available agents
python agent.py list-agents

# Generate a new microservice
python agent.py generate-service my-new-service

# Check environment health
python agent.py health-check

# Run a service
python agent.py run-service intake --port 8001

Available Agents

  • Service Generator: Auto-generate microservice boilerplate with FastAPI endpoints, tests, and Docker configuration
  • Quality Checker: Automated code formatting, linting, and security scanning (coming soon)
  • Test Generator: Auto-generate comprehensive test suites (coming soon)
  • Deployment Agent: Automated service deployment and monitoring (coming soon)

Documentation

Infrastructure

The docker-compose.yml file provisions core internal services:

  • Private OCI Registry (port 5000) - Container image storage
  • MinIO Object Storage (ports 9000, 9001) - S3-compatible storage with web console
  • Postgres with pgvector (port 5432) - Main database with vector search
  • Supabase (port 5433) - Developer-friendly Postgres variant
  • Redis Streams (port 6379) - Event bus and caching
  • NATS (port 4222) - Optional pub/sub messaging

Service URLs

Services Architecture

Microservice stubs for the expanded digest pipeline:

services/
β”œβ”€β”€ intake/          # Request ingestion and validation
β”œβ”€β”€ classifier/      # Content type and language detection  
β”œβ”€β”€ graph_extract/   # Code analysis and dependency graphs
β”œβ”€β”€ embeddings/      # Vector generation and search
β”œβ”€β”€ env_synthesis/   # Environment setup and configuration
β”œβ”€β”€ safety/          # Security scanning and SBOM generation
β”œβ”€β”€ runner/          # Task execution and testing
β”œβ”€β”€ integrator/      # Result aggregation and assembly
└── registrar/       # Artifact registration and storage

Each service is a FastAPI application with:

  • Health check endpoints
  • Processing pipeline integration
  • Docker containerization
  • Automated test coverage

Development Workflows

Generate New Service

python agent.py generate-service my-service --endpoint /process --endpoint /analyze

Run Services

# Single service
python agent.py run-service intake --port 8001

# All services (different terminals)
for service in intake classifier graph_extract; do
  python agent.py run-service $service --port $((8000 + $i)) &
  ((i++))
done

Testing

# Run all tests
python -m pytest tests/ -v

# Test specific service
python -m pytest tests/test_intake.py

# With coverage
python -m pytest tests/ --cov=services --cov-report=html

Code Quality

# Format code (when quality agent is available)
black services/ coding-agents/

# Type checking
mypy services/ coding-agents/

# Security scan
bandit -r services/ coding-agents/

Contributing

  1. Use the setup script: ./scripts/setup-dev.sh
  2. Generate services with: python agent.py generate-service <name>
  3. Follow established patterns in existing services
  4. Add tests for new functionality
  5. Run quality checks before submitting

Architecture Goals

  1. Agent-Driven Development: Coding agents automate repetitive tasks
  2. Microservices: Loosely coupled, independently deployable services
  3. Event-Driven: Redis Streams for decoupled communication
  4. Security-First: No Docker-in-Docker, proper isolation via Capsule sidecars
  5. Observable: Comprehensive logging, metrics, and tracing
  6. Scalable: Horizontal scaling via MicroAgentStacks

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