# Quick Start

## What You’ll Build

In this guide, you'll fetch real-time on-chain intelligence and integrate it into an AI agent within minutes.

By the end, you will be able to:

* Query token data (price, indicators, smart money flow)
* Use the data inside an AI workflow
* Build a foundation for trading, risk or analytics agents

***

## Setup Guide

### Prerequisites

* Basic understanding of APIs
* curl, Node.js, or Python
* Optional: an AI agent or LLM setup

### Step 1 : Get Access

#### Option A - x402 (Recommended)

Use x402 with USDC for per-request payments - no API key required.\
Fast setup, instant access, and ready-to-use integration.\
Ideal for autonomous agents.

#### Option B - API Key

To get access to your API key, please send a request to <help@dapplooker.com> or contact us via [Discord](https://discord.gg/EW9eDXTS) for faster assistance.

***

### Step 2 : Make Your First Request

#### Example: Token Intelligence

```sh
curl https://api.dapplooker.com/v1/crypto-market?token_tickers=VIRTUAL&chain=base
```

#### Example Response

{% code expandable="true" %}

```json
{
  "success": true,
  "data": [
    {
      "id": "virtual-protocol",
      "token_info": {
        "id": "virtual-protocol",
        "symbol": "VIRTUAL",
        "name": "Virtuals Protocol",
        "handle": "virtuals_io",
        "description": "An AI x Metaverse Protocol that is building the future of virtual interactions.",
        "ca": "0x0b3e328455c4059eeb9e3f84b5543f74e24e7e1b",
        "chain": "base"
      },
      "technical_indicators": {
        "support": "0.6681064012",
        "resistance": "0.7237519807",
        "rsi": "35.38",
        "sma": "0.6934899312"
      },
      "token_metrics": {
        "usd_price": "0.6718",
        "mcap": "440980066.00",
        "fdv": "671787004.00",
        "volume_24h": "64892107.00",
        "total_liquidity": null,
        "price_change_percentage_1h": "-1.84",
        "price_change_percentage_24h": "-2.79",
        "price_change_percentage_7d": "-4.93",
        "price_change_percentage_30d": "2.15",
        "volume_change_percentage_7d": "-9.40",
        "volume_change_percentage_30d": "42.30",
        "mcap_change_percentage_7d": "-3.50",
        "mcap_change_percentage_30d": "4.90",
        "price_high_24h": "0.7238",
        "price_ath": "5.0700",
        "circulating_supply": "656428396.70",
        "total_supply": "1000000000.00"
      },
      "x_social_metrics": {
        "mindshare_3d": 9.8124259049597,
        "mindshare_change_percentage_3d": 2.7,
        "impression_count_3d": 322510,
        "impression_count_change_percentage_3d": 104.55,
        "engagement_count_3d": 8286,
        "engagement_count_change_percentage_3d": 232.77,
        "follower_count_3d": 273141,
        "smart_follower_count_3d": 4338,
        "mindshare_7d": 7.91001915457965,
        "mindshare_change_percentage_7d": 54.52,
        "impression_count_7d": 969438,
        "impression_count_change_percentage_7d": -42.56,
        "engagement_count_7d": 11851,
        "engagement_count_change_percentage_7d": -1.65,
        "follower_count_7d": 279107,
        "smart_follower_count_7d": 4800
      },
      "last_updated_at": "2026-04-29T18:45:20.520Z"
    }
  ],
  "meta": {
    "pagination": {
      "page": 1,
      "pageSize": 30,
      "pageCount": 1,
      "total": 1
    }
  }
}
```

{% endcode %}

#### What this provides

* Price and technical indicators
* Smart money flow insights
* Confidence score for AI-driven decisions

***

### Step 3 : Use in an AI Agent

DappLooker provides the data layer. Your AI model provides the reasoning layer.

#### Quick Example (Node.js)

```typescript
const query = "Is ETH bullish right now?";

const response = await fetch(
  "https://api.dapplooker.com/v1/crypto-market?token_tickers=ETH"
);

const data = await response.json();

// Pass `data` into your LLM for reasoning
```

#### Architecture Overview

```
User → AI Agent → DappLooker API → LLM → Action
```

DappLooker acts as the real-time data engine powering your agent.

***

### Step 4 : Explore More APIs

DappLooker provides multiple agent-ready endpoints:

{% content-ref url="/pages/FF7zwFXkzDKksLlG6vfB" %}
[API Endpoints](/products/api-endpoints.md)
{% endcontent-ref %}

***

## Example Use Cases

* AI agents executing trades based on real-time signals
* Quant bots capturing arbitrage and funding opportunities
* Smart alerts for wallets and exchanges
* Treasury agents optimizing yield and allocations
* Copilots powering trading apps and terminals
* Analytics tools for token insights and research
* Enterprise feeds for trading desks and funds

***

## Next Steps

* Connect your AI agent
* Dive into the API [documentation](/get-started/introduction.md)
* Need help or a demo? Join [Discord](https://discord.gg/VQMWhkzF) | [Book a Call](https://calendly.com/prakhhaar/dapplooker-ai-request)


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.dapplooker.com/get-started/quick-start.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.