Using SRE SDK to Build Agents

The SmythOS SDK is a toolkit for building agents programmatically on top of the SmythOS Runtime Environment.
It offers a clean API for defining agents, adding skills, composing workflows, and connecting to core components.

When to use the SDK

Use the SDK for version-controlled agent projects, advanced logic, backend integration, or automated workflows. Ideal for CI/CD and testing pipelines.

Key Capabilities

Feature	Description
Agent API	Define, prompt, and manage agents in TypeScript
Skills	Add custom functions and API calls
Components	Compose modular workflows (LLMs, APIs, logic, data)
Streaming	Real-time agent responses
Storage/VectorDB	Built-in access to memory and storage layers
State Handling	Save/load agent state and skills

Getting Started

Install the SDK:

npm install @smythos/sdk

Create an agent:

import { Agent } from '@smythos/sdk';

const agent = new Agent({
    name: 'Assistant',
    model: 'gpt-4o',
    behavior: 'You answer user questions.',
});

Add a skill:

agent.addSkill({
    name: 'echo',
    description: 'Repeat the user input.',
    process: async ({ text }) => text,
});

Prompt the agent:

const result = await agent.prompt('Tell me about SmythOS.');
console.log(result);

See more complete examples in the SDK repo.

Building Blocks

Agents

Agents combine a model, behavior, and a set of skills.

const agent = new Agent({
    name: 'DataBot',
    model: 'gpt-4',
    behavior: 'Summarizes and analyzes data.',
});

Skills

Define a skill to extend agent actions or expose tools to the LLM.

agent.addSkill({
    name: 'summarize',
    description: 'Summarizes text.',
    ai_exposed: true,
    process: async ({ input }) => summarizeText(input),
});

Directly call a skill:

const sum = await agent.call('summarize', { input: 'SmythOS is an agent runtime.' });

Components

Compose workflows from building blocks like LLMs, APIs, or logic tools.

import { GenAILLM, Classifier } from '@smythos/sdk';

const llm = GenAILLM({ model: 'gpt-4o' }, agent);
const classifier = Classifier({ classes: ['news', 'sports'] }, agent);

classifier.in({ Input: llm.out.Reply });

Prompting and Streaming

Get a response:

const output = await agent.prompt('How does vector search work?');

Stream agent output for real-time applications:

const stream = await agent.prompt('Stream me this.').stream();
stream.on('data', (chunk) => process.stdout.write(chunk));

Integration Patterns

• Save/load agent state or workflow definitions
• Use built-in connectors for LLMs, storage, or vector DBs
• Run locally for dev; deploy with custom configs in production

More Examples

Explore the SDK examples folder for advanced agent projects.

What’s Next

• Component Reference
• Enterprise Deployment
• Building Agents

Combine with Studio

You can import SDK-built agents into Agent Studio for visualization and collaboration.