AI-Driven DevOps Workflow Orchestration
Transform YAML configurations into production-ready workflows for DevOps workloads and Microservices Architecture
Waterflow bridges the gap between declarative YAML configurations and production-grade DevOps workflows, enabling seamless orchestration of microservices architectures through AI-driven development practices.
- Overview
- BMAD Method - Development Approach
- Features
- Architecture
- Getting Started
- Usage
- Roadmap
- Contributing
- License
- Project Structure
Waterflow is a next-generation DevOps orchestration platform that:
- Converts YAML configurations into executable production workflows
- Orchestrates complex microservices architectures with ease
- Automates CI/CD pipelines and deployment strategies
- Monitors and manages containerized workloads
- Scales from single services to enterprise multi-cloud deployments
This project leverages the BMAD Method (Build More, Architect Dreams) - a structured approach to AI-powered software development that ensures quality, maintainability, and scalability.
Current State:
- Repository initialized with foundational structure
- AI-driven development methodology established
- Project vision and scope defined
Technology Stack:
- Primary Language: YAML (configuration), Go/Python (runtime - TBD)
- Target Platforms: Kubernetes, Docker, Cloud-native environments
- CI/CD: GitHub Actions, GitLab CI, Jenkins (multi-platform support)
- Infrastructure: Terraform, Ansible integration planned
Project Scope:
- Parse and validate YAML workflow definitions
- Generate production-ready CI/CD pipelines
- Support multiple orchestration platforms (K8s, Docker Swarm, etc.)
- Provide real-time workflow monitoring and management
- Enable blue-green and canary deployment strategies
Goal: Establish core YAML parsing and validation engine
- M1.1: Define YAML schema specification v1.0
- M1.2: Implement YAML parser with validation
- M1.3: Create basic workflow AST (Abstract Syntax Tree)
- M1.4: Set up testing framework and CI pipeline
- M1.5: Documentation: Architecture Decision Records (ADRs)
Deliverables:
- YAML schema documentation
- Core parser library
- Unit test suite (>80% coverage)
- Development environment setup guide
Goal: Build workflow execution and orchestration engine
- M2.1: Design workflow execution model
- M2.2: Implement dependency resolution algorithm
- M2.3: Create plugin system for extensibility
- M2.4: Build Docker/container integration
- M2.5: Develop CLI for workflow management
- M2.6: Integration testing suite
Deliverables:
- Workflow execution engine
- CLI tool (
waterflowcommand) - Plugin SDK documentation
- Example workflows repository
Goal: Add enterprise-grade features and integrations
- M3.1: Kubernetes operator development
- M3.2: Multi-cloud provider support (AWS, GCP, Azure)
- M3.3: Observability integration (Prometheus, Grafana)
- M3.4: Secret management (Vault, SOPS)
- M3.5: Advanced deployment strategies (blue-green, canary)
- M3.6: Performance optimization and benchmarking
Deliverables:
- Kubernetes operator
- Cloud provider modules
- Monitoring dashboards
- Performance benchmarks report
Goal: Community readiness and production hardening
- M4.1: Comprehensive documentation site
- M4.2: Video tutorials and demos
- M4.3: Security audit and penetration testing
- M4.4: Performance tuning for large-scale deployments
- M4.5: Community contribution guidelines
- M4.6: v1.0 release preparation
Deliverables:
- Production-ready v1.0 release
- Documentation portal
- Tutorial video series
- Security audit report
Week 1-2: Project Setup
actions:
- Set up repository structure and branching strategy
- Configure CI/CD pipelines (lint, test, build)
- Establish code review process
- Create project board with issue templates
- Initialize development environment with DevContainerWeek 3-5: Core Development
actions:
- Implement YAML parser using Go/Python ecosystem
- Create comprehensive test fixtures
- Build workflow validation engine
- Develop error reporting system
- Document API design decisionsWeek 6-9: Integration Phase
actions:
- Integrate with container runtimes
- Build Kubernetes custom resources
- Implement cloud provider SDKs
- Create monitoring exporters
- Conduct integration testingWeek 10-14: Refinement
actions:
- Performance profiling and optimization
- Security hardening and vulnerability scanning
- Documentation refinement
- User acceptance testing
- Beta program with early adoptersADR-001: Programming Language Selection
- Status: Proposed
- Context: Need performant, maintainable language for workflow orchestration
- Decision: Evaluate Go (performance, concurrency) vs Python (ecosystem, AI tooling)
- Consequences: TBD based on Phase 1 prototyping
ADR-002: YAML Schema Design
- Status: In Progress
- Context: Need flexible yet validated configuration format
- Decision: Custom YAML schema with JSON Schema validation
- Consequences: Better IDE support, validation tooling integration
ADR-003: Plugin Architecture
- Status: Proposed
- Context: Need extensibility for custom integrations
- Decision: Hash-plugin or similar RPC-based plugin system
- Consequences: Isolation, security, multi-language plugin support
ADR-004: State Management
- Status: Proposed
- Context: Workflow state persistence for reliability
- Decision: Evaluate etcd vs database-backed state store
- Consequences: Distributed consistency, operational complexity
ADR-005: Deployment Model
- Status: Draft
- Context: How users will run Waterflow
- Decision: Support both standalone CLI and Kubernetes operator modes
- Consequences: Flexibility but increased testing surface
- 📝 Declarative Configuration: Define complex workflows in simple YAML
- 🔄 Workflow Orchestration: Advanced dependency management and parallel execution
- 🐳 Container-Native: First-class Docker and Kubernetes support
- ☁️ Multi-Cloud: AWS, GCP, Azure integration out of the box
- 📊 Observability: Built-in monitoring, logging, and tracing
- 🔐 Security: Secret management, RBAC, and audit logging
- 🚀 Deployment Strategies: Blue-green, canary, rolling updates
- 🔌 Extensible: Plugin architecture for custom integrations
- Intuitive YAML syntax with IDE autocomplete
- Real-time validation and error reporting
- Visual workflow graph generation
- Hot-reload for rapid development
- Comprehensive CLI with interactive mode
┌─────────────────────────────────────────────────────────┐
│ Waterflow System │
├─────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────┐ ┌──────────────┐ │
│ │ YAML Config │─────▶│ Parser │ │
│ └──────────────┘ └──────┬───────┘ │
│ │ │
│ ▼ │
│ ┌────────────────┐ │
│ │ Validator │ │
│ └────────┬───────┘ │
│ │ │
│ ▼ │
│ ┌────────────────┐ │
│ │ Workflow AST │ │
│ └────────┬───────┘ │
│ │ │
│ ┌─────────────────────┼─────────────────────┐ │
│ ▼ ▼ ▼ │
│ ┌────────────┐ ┌──────────────┐ ┌──────────┐│
│ │ Executor │ │ Scheduler │ │ Plugin ││
│ │ Engine │ │ │ │ Manager ││
│ └─────┬──────┘ └──────┬───────┘ └────┬─────┘│
│ │ │ │ │
│ └────────────────────┼───────────────────┘ │
│ │ │
│ ┌───────────────────┼───────────────────┐ │
│ ▼ ▼ ▼ │
│ ┌────────────┐ ┌──────────────┐ ┌──────────┐ │
│ │ Container │ │ Kubernetes │ │ Cloud │ │
│ │ Runtime │ │ Operator │ │ Provider │ │
│ └────────────┘ └──────────────┘ └──────────┘ │
│ │
└────────────────────────────────────────────────────────┘
Key Components:
- Parser: YAML to internal representation
- Validator: Schema validation and lint checks
- Executor: Workflow execution engine
- Scheduler: Task orchestration and dependency resolution
- Plugin Manager: Dynamic loading of extensions
- Integrations: Container, K8s, cloud provider adapters
# Required
- Git
- Docker 20.10+
- Kubernetes 1.24+ (for operator mode)
# Recommended
- kubectl
- helm 3+
- Make# Clone the repository
git clone https://github.com/Websoft9/Waterflow.git
cd Waterflow
# Build from source (once available)
make build
# Or use pre-built binaries
curl -sSL https://get.waterflow.io/install.sh | sh# Start development environment
make docker-compose-up
# View logs
make docker-compose-logs
# Stop environment
make docker-compose-down# Start production environment
make docker-compose-prod
# Run tests
make docker-compose-test# Add Helm repository (once published)
helm repo add waterflow https://charts.waterflow.io
helm repo update
# Install Waterflow
helm install waterflow waterflow/waterflow
# Or install from local chart
helm install waterflow ./helm/waterflowcd terraform
# Initialize Terraform
terraform init
# Plan deployment
terraform plan -var-file="environments/dev.tfvars"
# Apply changes
terraform apply -var-file="environments/dev.tfvars"# Initialize a new workflow
waterflow init my-workflow
# Validate configuration
waterflow validate workflow.yaml
# Run workflow locally
waterflow run workflow.yaml
# Deploy to Kubernetes
waterflow deploy --context production workflow.yaml# workflow.yaml
apiVersion: waterflow.io/v1
kind: Workflow
metadata:
name: microservices-deploy
spec:
stages:
- name: build
jobs:
- name: build-api
container: golang:1.21
commands:
- go build -o api ./cmd/api
- name: test
dependsOn: [build]
jobs:
- name: unit-tests
container: golang:1.21
commands:
- go test ./...
- name: deploy
dependsOn: [test]
jobs:
- name: deploy-production
provider: kubernetes
manifest: ./k8s/deployment.yaml
strategy: blue-green# Advanced workflow with secrets and monitoring
apiVersion: waterflow.io/v1
kind: Workflow
metadata:
name: enterprise-pipeline
spec:
secrets:
- vault://production/db-credentials
- sops://config/api-keys.enc
monitoring:
prometheus: true
tracing: jaeger
stages:
- name: canary-deploy
strategy:
type: canary
steps: [10, 25, 50, 100]
metrics:
- name: error-rate
threshold: 0.01
- name: latency-p99
threshold: 500ms- ✅ Core YAML parsing and validation
- ✅ Basic workflow execution
- ✅ Docker integration
- 🔄 Kubernetes operator
- 🔄 Multi-cloud support
- GitOps integration (ArgoCD, Flux)
- Advanced scheduling algorithms
- Workflow visualization UI
- Cost optimization features
- AI-powered workflow optimization
- Self-healing deployments
- Multi-cluster orchestration
- Marketplace for workflow templates
We welcome contributions! This project follows AI-driven development practices using the BMAD Method.
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes following conventional commits
- Push to your branch (
git push origin feature/amazing-feature) - Open a Pull Request with detailed description
# Set up development environment
make dev-setup
# Run tests
make test
# Run linters
make lint
# Build locally
make build
# Run integration tests
make test-integration- Use the BMAD Method for feature planning
- Document decisions in ADR format
- Leverage AI coding assistants (Claude, Copilot, Cursor)
- Maintain high test coverage (>80%)
- Write clear, self-documenting code
Waterflow/
├── .github/ # GitHub Actions CI/CD and templates
│ ├── workflows/ # CI/CD pipeline definitions
│ └── ISSUE_TEMPLATE/ # Issue and PR templates
├── cmd/ # CLI applications
│ └── waterflow/ # Main CLI binary
├── internal/ # Private application code
│ ├── cli/ # CLI command implementations
│ └── core/ # Core business logic
├── api/ # API definitions and schemas
├── config/ # Configuration files and schemas
├── docs/ # Documentation
├── examples/ # Example workflows and configurations
├── helm/ # Kubernetes Helm charts
│ └── waterflow/ # Main Helm chart
├── terraform/ # Infrastructure as Code
│ ├── environments/ # Environment-specific configs
│ └── modules/ # Reusable Terraform modules
├── docker/ # Docker configurations
│ ├── Dockerfile # Main application container
│ ├── Dockerfile.test # Test container
│ ├── docker-compose.yml # Development environment
│ ├── docker-compose.prod.yml # Production environment
│ └── docker-compose.test.yml # Testing environment
├── scripts/ # Build and development scripts
├── test/ # Test files and fixtures
├── .vscode/ # VS Code workspace configuration
├── Makefile # Build automation
├── go.mod # Go module definition
├── go.sum # Go dependencies
├── LICENSE # MIT License
├── README.md # This file
├── CONTRIBUTING.md # Contribution guidelines
├── CHANGELOG.md # Version history
└── CODE_OF_CONDUCT.md # Community standards
- Quick Start Guide - Get up and running in 5 minutes
- YAML Schema Reference - Complete configuration spec
- Architecture Guide - System design and patterns
- Plugin Development - Building custom extensions
- API Reference - Programmatic interface
- Troubleshooting - Common issues and solutions
- Built following the BMAD Method
- Inspired by modern DevOps tools (ArgoCD, Tekton, GitHub Actions)
- Community-driven and AI-assisted development
This project is licensed under the MIT License - see the LICENSE file for details.
- Issues: GitHub Issues
- Discussions: GitHub Discussions
- Email: help@websoft9.com
⭐ Star this repository if you find it helpful!
Built with ❤️ using AI-driven development practices