Kumeo Architecture

Overview

Kumeo is a declarative language and platform for orchestrating distributed agent workflows. The architecture is designed around a core principle: simplify the complex task of making heterogeneous agents collaborate while ensuring enterprise-grade performance and scalability.

This document outlines the architectural components and their interactions within the Kumeo ecosystem.

System Architecture

Kumeo Architecture Diagram

Core Components

  1. Kumeo DSL - The declarative language used to define agent workflows
  2. Compiler - Transforms DSL code into executable Rust
  3. Runtime - Executes compiled workflows and manages agent communication
  4. Monitoring System - Tracks performance, resource usage, and workflow status
  5. UI - Visual editor and dashboard for workflow design and monitoring

Data Flow 

┌──────────┐     ┌──────────┐     ┌─────────────┐     ┌──────────────┐
│ Kumeo DSL│────▶│ Compiler │────▶│ Rust Code + │────▶│ Kubernetes   │
│ (.kumeo) │     │          │     │ K8s Manifests│     │ Deployment   │
└──────────┘     └──────────┘     └─────────────┘     └──────────────┘
                                                              │
                                                              ▼
┌──────────┐     ┌──────────┐     ┌─────────────┐     ┌──────────────┐
│ Monitoring│◀───│ NATS     │◀───│ Agent       │◀───│ Kumeo Runtime │
│ Dashboard │     │ Events   │     │ Interactions │     │ (Rust)       │
└──────────┘     └──────────┘     └─────────────┘     └──────────────┘

Component Details

1. Kumeo DSL

The Kumeo Domain-Specific Language provides a declarative syntax for defining:

  • Workflow Structure - The overall flow definition
  • Agents - Individual processing units (LLMs, ML models, etc.)
  • Events - Communication channels between agents
  • Context - Shared data and knowledge bases
  • Deployment Configuration - Resource requirements and scaling parameters

Example:

workflow FraudDetection {
  source: NATS("transactions")
  target: NATS("alerts")
  context: BayesianNetwork("risk.bn")

  agents: [
    LLM("ollama/llama3", prompt="Classify  as fraud? Context: "),
    MLModel("isolation_forest.pkl", input=LLM.output),
    DecisionMatrix("policy.dmx", input=MLModel.output)
  ]
}

2. Compiler

The compiler is built in Rust and performs:

  • Lexical and Syntactic Analysis - Parses .kumeo files into an Abstract Syntax Tree
  • Semantic Validation - Ensures workflow integrity and agent compatibility
  • Multi-Language Code Generation - Produces optimized code in the most appropriate language for each component
  • Kubernetes Manifest Generation - Creates deployment configurations
  • Automated Deployment Process - Handles the complete deployment lifecycle

The compiler enforces type safety and correct agent interaction patterns, catching errors at compile-time rather than runtime.

Multi-Language Code Generation

Kumeo uses a multi-backstage approach to code generation, selecting the most appropriate language for each agent type:

Agent Type Generated Language Rationale
LLM Rust Performance, native Ollama integration
Basic Transformations Rust Performance, memory safety
Routing & I/O Rust Low latency, NATS performance
ML Models Python Ecosystem compatibility (scikit-learn, TensorFlow)
Bayesian Networks Python Rich libraries (PyMC3, pomegranate)
Custom Logic User’s choice Flexibility for specific requirements

This approach enables Kumeo to leverage the strengths of each language:

  • Rust for performance-critical components and system infrastructure
  • Python for data science and machine learning capabilities
  • Inter-language communication via NATS for seamless integration

Template-Based Generation

To minimize errors in generated code, Kumeo employs a template-based approach:

  1. Language-specific templates for each agent type and function
  2. Parameter substitution with validation to prevent injection issues
  3. Consistent interfaces across languages to ensure compatibility
  4. Generated tests to verify correct operation

3. Runtime

The Kumeo runtime is a multi-language execution environment with the following capabilities:

Runtime Components

Core Runtime (Rust)

  • High-Performance Foundation - Built in Rust for maximum performance
  • Agent Lifecycle Management - Initializes, monitors, and terminates agents
  • Event Routing - Efficient message passing between agents via NATS
  • Resilience - Built-in retry logic and failure handling

Python Runtime

  • Rapid Development - Easy agent development with Python
  • ML/Data Science - Native support for ML frameworks (scikit-learn, PyTorch, etc.)
  • Async/Await - First-class support for asynchronous operations
  • Type Hints - Full type annotation support for better development experience

Language Interoperability

  • Transparent Communication - Agents in different languages can communicate seamlessly
  • Unified API - Consistent programming model across languages
  • Automatic Serialization - Built-in serialization of common data types
  • Provides Observability - Exposes metrics and logs for monitoring

The runtime is designed to be lightweight, with minimal overhead and maximum throughput. It communicates with the NATS messaging system for event distribution.

Agent Types and Integration

Agent Type Implementation Integration Method
LLM Rust API clients Direct API calls (OpenAI, Ollama, etc.)
ML Model ONNX Runtime Direct inference with serialized models
Bayesian Network Custom Rust implementation In-memory inference
Human-in-the-loop Web interface Webhook notifications and UI integration
Custom Plugin system Dynamic loading via WebAssembly modules

4. NATS Integration

NATS serves as the messaging backbone of Kumeo:

  • Subject-based Messaging - Maps directly to Kumeo event sources and targets
  • Publish-Subscribe Pattern - Allows multiple consumers for the same event stream
  • JetStream - Provides persistence and replay capabilities for critical workflows
  • Observability - Enables monitoring of message flow and system health

5. Kubernetes Integration

Kumeo deploys as a set of Kubernetes resources:

  • StatefulSets - For the runtime and persistent components
  • Deployments - For agent instances that can scale horizontally
  • Services - For internal communication between components
  • ConfigMaps - For workflow definitions and configuration
  • Custom Resource Definitions - For workflow-specific extensions

6. UI Layer

The UI is built with Svelte and provides:

  • Visual Workflow Editor - Drag-and-drop interface for workflow creation
  • Code Editor - For direct .kumeo file editing with syntax highlighting
  • Monitoring Dashboard - Real-time visualization of workflow execution
  • Agent Inspector - Detailed view of agent behavior and performance

Security Architecture

Security is a first-class concern in Kumeo:

  • Authentication - Integration with OIDC providers (Auth0, Keycloak, etc.)
  • Authorization - Role-Based Access Control for workflows and agents
  • Encryption - TLS for all communications, at-rest encryption for sensitive data
  • Isolation - Agent execution in separate containers with resource limits
  • Audit Logging - Comprehensive tracking of all system operations

Scalability and Performance

Kumeo is designed for enterprise-scale workloads:

  • Horizontal Scaling - Add more nodes to handle increased load
  • Vertical Scaling - Configure resource requirements per agent
  • Partitioning - Split workflows across multiple instances
  • Caching - Reduce redundant computations and API calls
  • Batching - Combine multiple events for efficient processing

Performance benchmarks show Kumeo can handle:

  • Up to 10,000 events per second per node
  • Latency under 50ms for simple agent interactions
  • Support for workflows with hundreds of interconnected agents

Development and Extension

Kumeo can be extended in several ways:

  1. Custom Agent Types - Implement new agent interfaces in Rust
  2. Language Extensions - Add new DSL features via compiler plugins
  3. UI Widgets - Create custom visualizations and controls
  4. Integrations - Connect with existing tools and platforms

Deployment Models

Kumeo supports multiple deployment scenarios:

  • On-Premises - Full installation on private Kubernetes clusters
  • Cloud - Deployment to any cloud provider with Kubernetes support
  • Hybrid - Mix of cloud and on-premises components
  • Edge - Lightweight deployments for edge computing scenarios

NoOps Philosophy

Kumeo embodies a true NoOps (No Operations) philosophy, where the platform handles all operational aspects automatically:

Complete Deployment Automation

  • One-Command Deployment - A single command takes workflows from definition to running in production
  • Zero Configuration Required - Sensible defaults are provided for all components
  • Infrastructure as Code - The workflow definition itself contains all necessary deployment information
  • Self-Healing System - The platform automatically handles failures and restarts

End-to-End Responsibility

The Kumeo platform takes responsibility for the entire lifecycle:

  1. Code Generation - Creating optimized, language-specific implementations
  2. Dependency Management - Resolving and packaging all required libraries
  3. Container Creation - Building optimized containers for each component
  4. Deployment - Applying manifests to Kubernetes and verifying successful deployment
  5. Monitoring - Tracking system health and performance
  6. Scaling - Automatically adjusting resources based on workload
  7. Updates - Seamlessly deploying changes when workflows are modified

Developer Experience

This NoOps approach provides a superior developer experience:

  • Focus on Business Logic - Developers only need to define what they want, not how to deploy it
  • Immediate Feedback - Fast deployment cycle allows rapid iteration
  • Reduced Complexity - No need to understand underlying infrastructure details
  • Consistent Environments - Development and production environments behave identically

Future Architecture Directions

Planned architectural enhancements include:

  • Federated Workflows - Cross-organization workflow execution
  • AI-Powered Optimization - Automatic tuning of workflow parameters
  • Multi-language Support - Runtime components in languages beyond Rust
  • Serverless Integration - Support for event-driven serverless execution models

References