> For the complete documentation index, see [llms.txt](https://traceonai.gitbook.io/traceonai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://traceonai.gitbook.io/traceonai/getting-started/quickstart.md).

# Core Features

#### AI Agents for Blockchain Security

TraceonAI employs specialized AI agents to ensure the security and integrity of blockchain interactions:

* **CodeReveal**: An advanced smart contract scanner that performs deep bytecode analysis to detect vulnerabilities, rug-pull patterns, and cloned contracts. It utilizes on-chain forensics and machine learning to identify hidden risks and malicious logic.
* **RugTrace**: A decentralized machine learning tool that conducts on-chain predictive analytics, decoding high-risk token ecosystems through multivariate transaction forensics and employing wallet clustering and neural behavioral modeling to identify malicious actors.

#### AI-Powered Bots for Advanced Crypto Trading

TraceonAI's suite of bots enhances trading strategies by providing real-time data and insights:

* **Narratron**: Analyzes Twitter discussions in real-time to identify trending coins and narrative patterns, assigning scores from 0 to 100 to quantify market sentiment. Users receive actionable alerts when significant narrative shifts occur.
* **Influx:** Monitors up to three crypto influencers simultaneously, notifying users instantly when they tweet about new Ethereum tokens. Users can set price and market cap targets for up to five tokens, receiving alerts when profit goals are reached.
* **AlphaTracer**: Provides immediate analysis of any wallet address, detailing holdings, token distribution, and historical performance across multiple blockchains. It offers comprehensive profit and loss calculations, entry/exit point detection, and benchmarking against market indices and whale wallets.


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# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## 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, and the optional `goal` query parameter:

```
GET https://traceonai.gitbook.io/traceonai/getting-started/quickstart.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

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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.