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

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 [email protected] or contact us via Discord for faster assistance.


Step 2 : Make Your First Request

Example: Token Intelligence

Example Response

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)

Architecture Overview

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


Step 4 : Explore More APIs

DappLooker provides multiple agent-ready endpoints:

API Endpoints

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

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