Kodosumi
Kodosumi is an open-source distributed runtime environment that manages and executes AI agents at enterprise scale, offering seamless scalability, real-time monitoring, and framework-agnostic integration.
https://kodosumi.io/?ref=producthunt
Product Information
Updated:Jun 13, 2025
What is Kodosumi
Kodosumi is a pre-configured runtime environment built specifically for developers to deploy and scale AI agents efficiently. Built on trusted technologies like Ray, Litestar, and FastAPI, it provides a robust infrastructure for managing complex AI workflows. As a free and open-source solution, Kodosumi allows teams to run their AI agents locally, on-premises, or in any cloud environment while maintaining full control over their deployment and integration choices.
Key Features of Kodosumi
Kodosumi is an open-source distributed runtime environment designed specifically for managing and executing AI agents at enterprise scale. It provides seamless integration with existing LLM frameworks, real-time monitoring capabilities, and efficient handling of long-running agent workflows through Ray infrastructure. The platform offers framework-agnostic deployment options, built-in observability tools, and minimal configuration requirements, making it easier for developers to build, deploy, and scale their AI agents without vendor lock-in.
Distributed Scaling: Leverages Ray infrastructure to handle bursty agent traffic and automatically scale horizontally across clusters for consistent performance
Real-time Monitoring: Built-in dashboard provides comprehensive observability with real-time insights and detailed logging for debugging complex agent workflows
Framework Agnostic Integration: Seamlessly integrates with any existing LLMs (including self-hosted), agent frameworks, and tools without enforcing specific vendor requirements
Simplified Deployment: Requires only a single YAML configuration file to deploy agents, with consistent deployment options across Kubernetes, Docker, or bare metal
Use Cases of Kodosumi
Long-running AI Workflows: Managing complex AI agent tasks that run for extended periods with unpredictable duration, ensuring reliable execution and monitoring
Enterprise AI Deployment: Scaling AI agents across organization infrastructure while maintaining performance and observability for business applications
AI Agent Marketplace: Deploying and monetizing AI agents through integration with Sokosumi Marketplace, allowing developers to earn from their agent services
Pros
Open-source and free to use
No vendor lock-in with framework-agnostic design
Built on proven enterprise-scale technologies (Ray, FastAPI, Litestar)
Cons
Still in early development phase
Requires basic Python knowledge for implementation
Some concepts may be subject to change as the framework evolves
How to Use Kodosumi
Install Kodosumi: Install Kodosumi using pip: 'pip install kodosumi'
Create directory structure: Create a directory for your agentic apps: 'mkdir ./home' and copy example apps: 'cp -r ./kodosumi/apps/hymn ./home/'
Configure environment: Create config.yaml file to define Python package requirements and environment variables. Include application name, route prefix, import path, and runtime environment settings including required pip packages and environment variables
Start Ray cluster: Change to the home directory and start Ray cluster: 'cd home' followed by 'ray start --head'
Set up environment variables: Copy example environment file and configure variables: 'cp .env.example .env' and edit as needed using 'nano .env'
Deploy applications: Deploy your applications using Ray Serve: 'serve deploy ./hymn/config.yaml'. Monitor deployment progress at http://localhost:8265/#/serve
Start Kodosumi services: Launch Kodosumi and register Ray endpoints: 'koco start --register http://localhost:8001/-/routes'
Monitor and manage: Access Ray dashboard at http://localhost:8265 for real-time monitoring and debugging of your agentic services
Kodosumi FAQs
Kodosumi is a pre-configured runtime environment to build, deploy, and scale AI agents using Ray, Litestar and FastAPI. It's free and open-source.
Kodosumi Video
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