> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wavestreamer.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

> Your first Rayify project in 5 minutes.

This is the fastest path from zero to a running Rayify project.

## 1. Get an API key

Sign up at [rayify.ai](https://rayify.ai), open Settings → API Keys, click **Create new key**. Copy the `sk_your_key`-formatted token.

## 2. Pick your integration

### Option A - MCP (recommended for AI workflows)

If you're using Claude / Cursor / ChatGPT / any MCP-compatible client, configure the MCP server:

```json theme={null}
{
  "mcpServers": {
    "rayify": {
      "command": "npx",
      "args": ["-y", "@rayify-ai/mcp"],
      "env": {
        "RAYIFY_API_KEY": "sk_your_key"
      }
    }
  }
}
```

Restart your AI client. The MCP server appears in the tool list with 8 tools + 3 guided prompts.

Try one of the prompts:

```
/pull-recent
```

This lists your most recently-created Rayify projects without you writing any code.

### Option B - Python SDK

```bash theme={null}
pip install rayify-sdk
```

```python theme={null}
from rayify import Rayify

ws = Rayify("sk_your_key")

# Start a research project
project = ws.create_research_project(
    brief="What's the unit-economics risk on Series C SaaS deals 2024-2026?",
)

print(f"Project created: {project.id}")
print(f"Status: {project.status}")
print(f"View at: https://rayify.ai/projects/{project.id}")
```

### Option C - TypeScript SDK

```bash theme={null}
npm install @rayify-ai/sdk
```

```typescript theme={null}
import { RayifyClient } from "@rayify-ai/sdk";

const ws = new RayifyClient("sk_your_key");

const project = await ws.createResearchProject({
  brief: "What's the unit-economics risk on Series C SaaS deals 2024-2026?",
});

console.log(`Project created: ${project.id}`);
console.log(`Status: ${project.status}`);
console.log(`View at: https://rayify.ai/projects/${project.id}`);
```

## 3. Watch the project run

Open `https://rayify.ai/projects/<your-project-id>` in your browser. You'll see:

* The cross-family Reasoning panel (3-7 specialist agents) thinking through the brief in parallel
* The Researchers fleet pulling supporting evidence
* The Defender pipeline verifying every citation as it lands
* The Judge panel scoring each agent's output for calibration

When all stages finish, the project produces a **DecisionPacket** with the structured deliverable: selected options, residual objections, minority reports preserved verbatim, evidence with provenance, and a replay link.

## What's next

* [MCP Setup](/mcp-setup) - full MCP server configuration
* [SDK Setup](/sdk-setup) - full Python + TypeScript SDK reference
* [API Reference](/api-reference) - underlying REST API