AI Workflow Engine - Rust Platform
Production-ready AI workflow orchestration platform built in Rust, featuring event sourcing, microservices architecture, MCP integration, and enterprise-grade scalability for AI-powered automation.
Overview
The AI Workflow Engine represents a cutting-edge approach to building scalable, AI-powered automation systems using Rust's performance and safety guarantees. This comprehensive platform combines modern distributed systems patterns with AI-first design principles, delivering a production-ready solution for orchestrating complex workflows across multiple services and AI providers. The architecture showcases mastery of advanced software engineering concepts, from event sourcing with PostgreSQL-backed persistence to microservices communication via the Model Context Protocol. The platform includes three specialized services for content processing, knowledge graph management, and real-time communication, all coordinated through a sophisticated service bootstrap system with dependency injection and service discovery. Beyond its technical excellence, the platform serves as a blueprint for building enterprise AI systems, with comprehensive monitoring through Prometheus and Grafana, multi-tenant support, and production-tested performance handling 15,000+ requests per second. The inclusion of WebAssembly plugins, comprehensive testing infrastructure, and detailed documentation makes it both a powerful tool and an educational resource for the Rust and AI communities.
Technical Stack
Core Platform
- ▸Rust 1.75+
- ▸Actix Web
- ▸Tokio
- ▸PostgreSQL 15+
- ▸Redis 7+
AI Integration
- ▸OpenAI GPT-4
- ▸Anthropic Claude
- ▸AWS Bedrock
- ▸Token Management
- ▸Template Engine
Architecture
- ▸Event Sourcing
- ▸CQRS
- ▸MCP Protocol
- ▸Service Bootstrap
- ▸Circuit Breakers
Microservices
- ▸Content Processing
- ▸Knowledge Graph (Dgraph)
- ▸WebSocket Server
- ▸WASM Plugins
- ▸Actor Model
Infrastructure
- ▸Docker
- ▸Kubernetes
- ▸Prometheus
- ▸Grafana
- ▸Distributed Tracing
Key Features
Native AI provider integration with OpenAI, Anthropic, and AWS Bedrock
Event-driven architecture with PostgreSQL event sourcing and replay capabilities
Complete MCP implementation with HTTP, WebSocket, and stdio transports
Three specialized microservices for content, knowledge graphs, and real-time communication
WebAssembly plugin system for extensible content processing
Advanced service bootstrap with dependency injection and service discovery
Production monitoring with Prometheus metrics and Grafana dashboards
Multi-tenant architecture with per-tenant event streams and data isolation
10,000+ concurrent WebSocket connections with actor-based isolation
Comprehensive testing infrastructure including chaos engineering
Code Examples
Technical Challenges
Implementing reliable event sourcing with high-throughput write performance
Building a type-safe dependency injection system in Rust
Creating efficient WebAssembly sandboxing for untrusted plugins
Designing a scalable actor model for WebSocket connection management
Ensuring zero-downtime deployments with event replay capabilities