Agent Recipes

The SmythOS Runtime Environment (SRE) gives you reusable components and connectors.
This page shows common recipes for combining them into working agents, using real examples from the SRE repo.

How to use these

• Copy and adapt directly into your project
• Mix and match with Studio exports by importing .smyth files
• Extend each recipe with your own skills and data sources

Recipe 1: RAG (Retrieval-Augmented Generation)

Index documents, then answer queries with context.

{`import { Agent, Model } from '@smythos/sdk';

// Create an agent with GPT-4
const agent = new Agent({ name: 'RAG Demo', model: 'gpt-4o' });

// Pinecone connector with OpenAI embeddings
const pinecone = agent.vectorDB.Pinecone('my-namespace', {
  indexName: 'my-index',
  apiKey: process.env.PINECONE_API_KEY,
  embeddings: Model.OpenAI('text-embedding-3-large'),
});

// Insert a document
await pinecone.insertDoc('doc-1', 'The Eiffel Tower is in Paris, France.');

// Query with retrieval
const results = await pinecone.search('Where is the Eiffel Tower located?');
console.log(results);
`}

When to use

Use RAG for factual Q&A, knowledge base bots, and contextual assistants.

Recipe 2: API Call → Classify → Summarize

Fetch external data, route it, and produce a concise answer.

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

const agent = new Agent({ name: 'News Summarizer', model: 'gpt-4o' });

// Fetch data from an API
const api = agent.component.APICall({ url: 'https://newsapi.org/v2/top-headlines' });

// Classify articles by topic
const classifier = agent.component.Classifier({ classes: ['tech', 'sports', 'politics'] });

// Summarize with the LLM
classifier.out.Topic.connect(agent.llm.OpenAI('gpt-4o').in.Input);

// Example run
const response = await agent.run();
console.log(response);
`}

Recipe 3: Parallel Workflows (Fan-Out / Fan-In)

Run multiple LLM prompts in parallel, then join results.

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

const agent = new Agent({ name: 'Parallel Demo', model: 'gpt-4o' });

// Branch into async tasks
const asyncBlock = agent.component.Async();

// Two tasks in parallel
asyncBlock.add(agent.llm.OpenAI('gpt-4o').prompt('Summarize AI news'));
asyncBlock.add(agent.llm.OpenAI('gpt-4o').prompt('Summarize sports news'));

// Join results
const awaitBlock = agent.component.Await(asyncBlock);

const output = await agent.run();
console.log(output);
`}

Why it matters

Parallel execution improves speed and lets agents tackle multiple subtasks simultaneously.

Recipe 4: Stream Responses for a Chat UI

Stream tokens for a live user interface instead of waiting for full completion.

{`import { Agent, TLLMEvent } from '@smythos/sdk';

const agent = new Agent({ name: 'ChatBot', model: 'gpt-4o' });

const stream = await agent.llm.OpenAI('gpt-4o').prompt('Tell me a joke.').stream();

stream.on(TLLMEvent.Content, (chunk) => process.stdout.write(chunk));
stream.on(TLLMEvent.End, () => console.log('\\n-- done --'));
`}

Recipe 5: Import and Extend a Studio Workflow

Mix no-code (Studio) with code (SDK).

{`import path from 'node:path';
import { Agent, Model } from '@smythos/sdk';

async function main() {
  const agentPath = path.resolve(__dirname, 'my-agent.smyth');

  // Import Studio workflow
  const agent = Agent.import(agentPath, {
    model: Model.OpenAI('gpt-4o'),
  });

  // Extend with custom code skill
  agent.addSkill({
    name: 'reverse',
    description: 'Reverse text',
    process: async ({ input }) => input.split('').reverse().join(''),
  });

  // Run
  const result = await agent.prompt('Reverse "SmythOS".');
  console.log(result);
}
main().catch(console.error);
`}

Why it’s powerful

This workflow lets non-coders design in Studio, then developers extend with code for custom logic or integrations.

What’s Next?

• Explore more in the SRE repo examples
• Learn about Building Agents
• Dive into Hybrid Workflows for mixing approaches