---
title: "sre"
type: "tool"
slug: "smythos-sre"
canonical_url: "https://www.graphcanon.com/tools/smythos-sre"
github_url: "https://github.com/SmythOS/sre"
homepage_url: "https://smythos.com"
stars: 1286
forks: 198
primary_language: "TypeScript"
license: "MIT"
categories: ["ai-agents"]
tags: ["llmops", "agents", "agent-framework", "retrieval-augmented-generation", "autonomous-agents", "multi-agent-systems", "agi", "ai-agents"]
updated_at: "2026-07-07T18:47:16.337175+00:00"
---

# sre

> Smyth Runtime Environment (SRE): An open-source runtime and SDK for production AI agents

The SmythOS SRE is an advanced, cloud-native framework designed to streamline the development, deployment, and scaling of intelligent agentic systems. It focuses on security, modularity, ease-of-use, and offers a broad set of features such as unified resource APIs and built-in observability tools for robust agent lifecycle management.

## Facts

- Repository: https://github.com/SmythOS/sre
- Homepage: https://smythos.com
- Stars: 1,286 · Forks: 198 · Open issues: 33 · Watchers: 13
- Primary language: TypeScript
- License: MIT
- Last pushed: 2026-04-03T05:31:50+00:00

## Categories

- [AI Agents](/categories/ai-agents.md)

## Tags

llmops, agents, agent-framework, retrieval-augmented-generation, autonomous-agents, multi-agent-systems, agi, ai-agents

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## README (excerpt)

```text
# 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](https://github.com/SmythOS/smythos-studio)! Great community, support, tutorials. Start in minutes!



<br>

<div align="center">

&nbsp;
&nbsp;


</div>

<br>

<div align="left">

### 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.

<br>

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.

</div>

<br>

<div align="center">

[SDK Documentation](https://smythos.github.io/sre/sdk/) &nbsp;|&nbsp; [SRE Core Documentation](https://smythos.github.io/sre/core/) &nbsp;|&nbsp; [Code Examples](examples) &nbsp;|&nbsp; [Contributing](CONTRIBUTING.md)

</div>



<div align="center">

</div>
    
<br>

## Why SmythOS exists

1. Shipping production-ready AI agents shouldn’t feel like rocket science.
2. Autonomy and control can, and must, coexist.
3. Security isn’t an add-on; it’s built-in.
4. The coming Internet of Agents must stay open and accessible to everyone.

## 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:**

- **Agent-First Design**: Built specifically for AI agent workloads
- **Developer-Friendly**: Simple SDK that scales from development to production
- **Modular Architecture**: Extensible connector system for any infrastructure
- **Production-Ready**: Scalable, observable, and battle-tested
- **Enterprise Security**: Built-in access control and secure credential management

## Quick Start

We made a great tutorial that's really worth watching:



### Method 1: Using the CLI (Recommended)

Install the [CLI](packages/cli/) globally and create a new project:

```bash
npm i -g @smythos/cli
sre create
```

The CLI will guide you step-by-step to create your SDK project with the right configuration for your needs.

### Method 2: Direct SDK Installation

Add the SDK directly to your existing project:

```bash
npm install @smythos/sdk
```

Check the [Examples](examples), [documentation](https://smythos.github.io/sre/sdk/) and [Code Templates](https://github.com/Smyth
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/smythos-sre`](/api/graphcanon/tools/smythos-sre)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_