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Quickstart

This guide walks you through installing RadarOS and building your first agent in three steps.

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Installation

Install the core package and your preferred LLM provider: 
npm install @radaros/core
npm install openai
For other providers, install anthropic or @google/genai instead of (or in addition to) openai. Set your API key as an environment variable: 
export OPENAI_API_KEY=your-key

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Step 1: Create Your First Agent

Create a simple agent with a few lines of code:
index.ts
import { Agent, openai } from "@radaros/core";

const agent = new Agent({
  name: "assistant",
  model: openai("gpt-4o"),
  instructions: "You are a helpful assistant.",
});

const result = await agent.run("What is TypeScript?");
console.log(result.text);
Run it: 
npx tsx index.ts
You now have a working agent that responds to user input.

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Step 2: Add Tools

Extend your agent with function calling. Define tools with Zod schemas for type-safe parameters:
weather-agent.ts
import { Agent, openai, defineTool } from "@radaros/core";
import { z } from "zod";

const weatherTool = defineTool({
  name: "get_weather",
  description: "Get current weather for a city",
  parameters: z.object({
    city: z.string().describe("City name"),
  }),
  execute: async ({ city }) => `Weather in ${city}: 72°F, sunny`,
});

const agent = new Agent({
  name: "weather-bot",
  model: openai("gpt-4o"),
  instructions: "You help users check the weather.",
  tools: [weatherTool],
});

const result = await agent.run("What's the weather in Tokyo?");
console.log(result.text);
The agent will call get_weather when needed and incorporate the result into its response.

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Step 3: Stream Responses

For real-time output, use the streaming API:
stream-agent.ts
import { Agent, openai } from "@radaros/core";

const agent = new Agent({
  name: "assistant",
  model: openai("gpt-4o"),
  instructions: "You are a helpful assistant.",
});

for await (const chunk of agent.stream("Tell me a joke")) {
  if (chunk.type === "text") {
    process.stdout.write(chunk.text);
  }
}
Streaming supports both text and tool-call chunks. Handle each chunk.type as needed for your use case.

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Next Steps

Agents

Learn about agent configuration, instructions, and advanced options.

Models

Switch providers, use local models with Ollama, or build custom adapters.

Teams

Coordinate multiple agents with route, broadcast, and collaborate modes.

Workflows

Build stateful multi-step workflows with conditions and parallel execution.