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The SmythOS Runtime Environment (SRE) is an open-source, cloud-native runtime for agentic AI. Secure, modular, and production-ready, it lets developers build, run, and manage intelligent agents across local, cloud, and edge environments. - SmythOS/sre
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SmythOS - The Linux of AI Agents
Reliable Agent Engineering starts with great, open source infrastructure. This repository contains the Smyth Runtime Environment Kernel (SRE), the Software Development Kit (SDK) and Command Line Interface (CLI) for running agents and creating them with code. If you prefer visual drag & drop agent interfaces instead, check out our open source SmythOS Visual Agent Studio! Great community, support, tutorials. Start in minutes!
SmythOS Runtime Environment (SRE)
SRE is an open-source runtime and SDK for production AI agents. It provides OS-level abstractions for AI resources—LLMs, vector databases, storage, and caching—with a unified API that works identically across all providers. Write your agent logic once, scale it anywhere. Built-in security, observability, and 40+ production-ready components included. The operating system layer AI agents have been missing.
Inspired by the architecture of operating system kernels, SmythOS provides a robust and scalable foundation for agent orchestration and lifecycle management, giving every builder the tools to act, not just imagine.
SDK Documentation | SRE Core Documentation | Code Examples | Contributing
Why SmythOS exists
Design Principles
SmythOS provides a complete Operating System for Agentic AI. Just as traditional operating systems manage resources and provide APIs for applications, SmythOS manages AI resources and provides a unified SDK that works from development to production.
Unified Resource Abstraction
SmythOS provides a unified interface for all resources, ensuring consistency and simplicity across your entire AI platform. Whether you're storing a file locally, on S3, or any other storage provider, you don't need to worry about the underlying implementation details. SmythOS offers a powerful abstraction layer where all providers expose the same functions and APIs.
This principle applies to all services - not just storage. Whether you're working with VectorDBs, cache (Redis, RAM), LLMs (OpenAI, Anthropic), or any other resource, the interface remains consistent across providers.
This approach makes your AI platform easy to scale and incredibly flexible. You can seamlessly swap between different providers to test performance, optimize costs, or meet specific requirements without changing a single line of your business logic.
Key Benefits: