sre

# SmythOS - The Operating System for Agentic AI [](https://smythos.com) [](https://discord.gg/smythos) [](https://github.com/SmythOS/sre/blob/main/LICENSE) [](https://www.youtube.com/watch?v=KDFu9s0Nm38) Everything you need to build, deploy, and manage intelligent AI agents at scale. SmythOS is designed with a philosophy inspired by operating system kernels, ensuring a robust and scalable foundation for AI agents. [SDK Documentation](https://smythos.github.io/sre/sdk/) | [SRE Core Documentation](https://smythos.github.io/sre/core/) | [Code Examples](examples) ## 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: [](https://www.youtube.com/watch?v=TRETl7AG-Gc) ### 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/SmythOS/sre-project-templates) to get started. --- **Note:** If you face an issue with the CLI or with your code, set environment variable LOG_LEVEL="debug" and run your code again. Then share the logs with us, it will help diagnose the problem. ## Repository Structure This monorepo contains three main packages: ### SRE (Smyth Runtime Environment) - `packages/core` The **SRE** is the core runtime environment that powers SmythOS. Think of it as the kernel of the AI agent operating system. **Features:** - **Modular Architecture**: Pluggable connectors for every service (Storage, LLM, VectorDB, Cache, etc.) - **Security-First**: Built-in Candidate/ACL system for secure resource access - **Resource Management**: Intelligent memory, storage, and compute management - **Agent Orchestration**: Complete agent lifecycle management - **40+ Components**: Production-ready components for AI, data processing, and integrations **Supported Connectors:** - **Storage**: Local, S3, Google Cloud, Azure - **LLM**: OpenAI, Anthropic, Google AI, AWS Bedrock, Groq, Perplexity - **VectorDB**: Pinecone, Milvus, RAMVec - **Cache**: RAM, Redis - **Vault**: JSON File, AWS Secrets Manager, HashiCorp ### SDK - `packages/sdk` The **SDK** provides a clean, developer-friendly abstraction layer over the SRE runtime. It's designed for simplicity without sacrificing power. **Why Use the SDK:** - **Simple API**: Clean, intuitive interface that's easy to learn - **Type-Safe**: Full TypeScript support with IntelliSense - **Production-Ready**: Same code works in development and production - **Configuration-Independent**: Business logic stays unchanged as infrastructure scales ### CLI - `packages/cli` The **SRE CLI** helps you get started quickly with scaffolding and project management. ## Code examples The SDK allows you to build agents with code or load and run a .smyth file. .smyth is the extension of agents built with our SmythOS builder. ## Example 1 : load and run an agent from .smyth file ```typescript async function main() { const agentPath = path.resolve(__dirname, 'my-agent.smyth'); //Importing the agent workflow const agent = Agent.import(agentPath, { model: Model.OpenAI('gpt-4o'), }); //query the agent and get the full response const result = await agent.prompt('Hello, how are you ?'); console.log(result); } ``` Want stream mode ? easy <details> <summary><strong>Click to expand:</strong> Stream Mode Example - Real-time response streaming with events</summary> ```typescript const events = await agent.prompt('Hello, how are you ?').stream(); events.on('content', (text) => { console.log('content'); }); events.on('end', /*... handle end ... */) events.on('usage', /*... collect agent usage data ... */) events.on('toolCall', /*... ... */) events.on('toolResult', /*... ... */) ... ``` </details> Want chat mode ? easy <details> <summary><strong>Click to expand:</strong> Chat Mode Example - Conversational agent with memory</summary> ```typescript const chat = agent.chat(); //from there you can use the prompt or prompt.stream to handle it let result = await chat.prompt("Hello, I'm Smyth") console.log(result); result = await chat.prompt('Do you remember my name ?"); console.log(result); //the difference between agent.prompt() and chat.prompt() is that the later remembers the conversation ``` </details> ## Example 2 : Article Writer Agent In this example we are coding the agent logic with the help of the SDK elements. <details> <summary><strong>Click to expand:</strong> Complete Article Writer Agent - Full example using LLM + VectorDB + Storage</summary> ```typescript import { Agent, Model } from '@smythos/sdk'; async function main() { // Create an intelligent agent const agent = new Agent({ name: 'Article Writer', model: 'gpt-4o', behavior: 'You are a copy writing assistant. The user will provide a topic and you have to write an article about it and store it.', }); // Add a custom skill that combines multiple AI capabilities agent.addSkill({ id: 'AgentWriter_001', name: 'WriteAndStoreArticle', description: 'Writes an article about a given topic and stores it', process: async ({ topic }) => { // VectorDB - Search for relevant context const vec = agent.vectordb.Pinecone({ namespace: 'myNameSpace', indexName: 'demo-vec', pineconeApiKey: process.env.PINECONE_API_KEY, embeddings: Model.OpenAI('text-embedding-3-large'), }); const searchResult = await vec.search(topic, { topK: 10, includeMetadata: true, }); const context = searchResult.map((e) => e?.metadata?.text).join('\n'); // LLM - Generate the article const llm = agent.llm.OpenAI('gpt-4o-mini'); const result = await llm.prompt(`Write an article about ${topic} using the following context: ${context}`); // Storage - Save the article const storage = agent.storage.S3({ /*... S3 Config ...*/ }); const uri = await storage.write('article.txt', result); return `The article has been generated and stored. Internal URI: ${uri}`; }, }); // Use the agent const result = await agent.prompt('Write an article about Sakura trees'); console.log(result); } main().catch(console.error); ``` </details> ## Architecture Highlights ### Built-in Security Security is a core tenant of SRE. Every operation requires proper authorization through the **Candidate/ACL system**, ensuring that agents only access resources they are permitted to. ```typescript const candidate = AccessCandidate.agent(agentId); const storage = ConnectorService.getStorageConnector().user(candidate); await storage.write('data.json', content); ``` ### Development to Production Evolution Your business logic stays identical while infrastructure scales: When you use the SDK, SmythOS Runtime Environment will be implicitly initialized with general connectors that covers standard agent use cases. <details> <summary><strong>Click to expand:</strong> Basic SRE Setup - Default development configuration</summary> ```typescript // you don't need to explicitly initialize SRE // we are just showing you how it is initialized internally // const sre = SRE.init({ // Cache: { Connector: 'RAM' }, // Storage: { Connector: 'Local' }, // Log: { Connector: 'ConsoleLog' }, // }); async function main() { // your agent logic goes here } main(); ``` </details> But you can explicitly initialize SRE with other built-in connectors, or make your own Use cases : - You want to use a custom agents store - You want to store your API keys and other credentials in a more secure vault - You need enterprise grade security and data isolation - ... <details> <summary><strong>Click to expand:</strong> Production SRE Setup - Enterprise-grade configuration with custom connectors</summary> ```typescript const sre = SRE.init({ Account: { Connector: 'EnterpriseAccountConnector', Settings: { ... } }, Vault: { Connector: 'Hashicorp', Settings: { url: 'https://vault.company.com' } }, Cache: { Connector: 'Redis', Settings: { url: 'redis://prod-cluster' } }, Storage: { Connector: 'S3', Settings: { bucket: 'company-ai-agents' } }, VectorDB: { Connector: 'Pinecone', Settings: { indexName: 'company-ai-agents' } }, Log: { Connector: 'CustomLogStore'}, }); async function main() { // your agent logic goes here } main(); ``` </details> ### Component System 40+ production-ready components for every AI use case. These components can be invoked programmatically or through the symbolic representation of the agent workflow (the .smyth file). - **AI/LLM**: `GenAILLM`, `ImageGen`, `LLMAssistant` - **External**: `APICall`, `WebSearch`, `WebScrape`, `HuggingFace` - **Data**: `DataSourceIndexer`, `DataSourceLookup` `JSONFilter` - **Logic**: `LogicAND`, `LogicOR`, `Classifier`, `ForEach` - **Storage**: `LocalStorage`, `S3` - **Code**: `ECMAScript`, `ServerlessCode` ## Key Features | Feature | Description | | --------------------- | ------------------------------------------------------ | | **Agent-Centric** | Built specifically for AI agent workloads and patterns | | **Secure by Default** | Enterprise-grade security with data isolation | | **High Performance** | Optimized for high-throughput AI operations | | **Modular** | Swap any component without breaking your system | | **Observable** | Built-in monitoring, logging, and debugging tools | | **Cloud-Native** | Runs anywhere - local, cloud, edge, or hybrid | | **Scalable** | From development to enterprise production | ## Contributing We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) and [Code of Conduct](CODE_OF_CONDUCT.md). ## License This project is licensed under the [MIT License](LICENSE). ## What's Next? - We will release an open source visual agent IDE later this year. - Support us at [SmythOS](https://smythos.com) - Join our [community](https://discord.gg/smythos) to stay updated on new features, connectors, and capabilities. --- /smɪθ oʊ ɛs/ Ride the llama. Skip the drama.