Kodosumi is a production-oriented distributed runtime environment for managing and executing enterprise-scale agent services. Built on Ray, it is designed to help users deploy, orchestrate, and scale AI agent services, covering agent execution, pipelines, and observability.
[Purpose] The core purpose of Kodosumi is to enable AI agents to run at scale, reliably, and quickly. It is positioned for enterprise-grade scenarios and is designed for production from day one, making it suitable for teams that need to put agent services into actual operation and scale them horizontally.
[Main Capabilities]
- Scalability: Implemented through Ray, it supports local deployment, parallel pipeline execution, and fine-tuning, and can scale with demand.
- Real-time monitoring: It provides built-in observability through the Ray dashboard, allowing users to view agent operation and resource usage.
- No vendor lock-in: Kodosumi is open source, framework-agnostic, and cross-platform portable, so users can use any preferred AI framework.
- Minimal configuration: Only one YAML configuration file is needed to define dependencies, environment variables, and perform deployment.
[Core Concepts] Agent: An autonomous object capable of executing a specific task or service, and the basic building block in Kodosumi. Each Agent encapsulates specialized capabilities and can be orchestrated into larger workflows. Flow: An automated process, workflow, or interconnected task system that defines how multiple Agents coordinate and pass data between steps to accomplish complex goals. Agentic Service: A self-contained, deployable unit that integrates one or more Flows. It is the entity to be deployed to Kodosumi, packaging Agents, Flows, and configuration into a single runtime.
[Usage] The installation command is pip install kodosumi. According to the official example, you can import Agent and Flow from kodosumi, define an Agent class (such as name and model), create a Flow and pass in a list of agents, then call flow.deploy() to deploy. The example comments explain that it runs on Ray.
[Applicable Scenarios] Scenarios that require running AI agent services at enterprise scale, including multi-agent orchestration, parallel pipeline execution, real-time monitoring of agent operation and resource usage, and teams that want to smoothly scale from local deployment to a larger scale while avoiding vendor lock-in.
[Limitations] The user profile did not provide information about pricing, platform support scope, specific performance metrics, or further limitations, and none has been provided for now.
