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 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 EndpointsExample 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
Need help or a demo? Join Discord | Book a Call
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