# Welcome

## **The Intelligence Layer for Autonomous Agents**

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Welcome to the official documentation for <mark style="color:orange;">**DappLooker AI**</mark> — the data marketplace powering the next generation of crypto-native, autonomous AI agents.

Whether you're building DeFi trading bots, auditing agents, risk analyzers, AI wallets, or multi-agent systems, this is your command center for accessing high-quality, real-time on-chain data at scale.

### What is DappLooker AI?

**DappLooker AI** provides structured, composable, and LLM-friendly APIs designed specifically for autonomous agents operating in Web3. Think of us as the *Jarvis* for on-chain AI delivering the data your agents need to sense, decide, and act without human intervention.

#### We offer:

🧠 Agent-Native APIs for trading, staking, and risk detection

📊 Real-Time Market + Technical Analysis

🦈 Smart Money & Whale Wallet Flows

🛡️ Rug Detection, Risk Alerts & LP Analysis

🧬 Token Mindshare & Social Metrics

📈 Holder Activity, Wallet Clustering, Developer Patterns

🤖 Natural Language Query (NLQ) APIs for LLM Agents

### Why Use DappLooker?

Traditional APIs weren’t built for agents — they’re slow, fragmented, and human-first.

DappLooker AI is built differently:

* **Fast**: Real-time, low-latency data with 99.99% uptime
* **Unified**: One integration to access 15+ networks
* **Affordable**: $999/month for $10k+ worth of crypto intelligence
* **Scalable**: Already powering 500+ agents, 10M+ queries/month
* **Trusted**: Used by top projects like Loky, Sovra AI, H1DR4, Kosher Capital & more

### Built for Agents, Not Humans

Your agents don’t need dashboards. They need data.

DappLooker gives your AI agents direct, programmatic access to on-chain intelligence so they can operate autonomously, intelligently, and efficiently.


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# 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/welcome.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.