sre MCP工具

Method 2: Direct SDK Installation

Add the SDK directly to your existing project:

npm install @smythos/sdk

Check the Examples, documentation and Code Templates to get started.

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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.

Quick Start

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

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

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>

    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>

    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>

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>

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.

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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.

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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

Method 1: Using the CLI (Recommended)

Install the CLI globally and create a new project:

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.

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.

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