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

# What is Calvin?

**Calvin** is an AI-powered multi-agent platform designed for eCommerce.\
It brings together specialized agents that handle the full lifecycle of your digital operations — from **development, testing, and maintenance**, to **personalization, marketing, analytics, and customer support**.

Calvin includes a library of **predefined agents**, each optimized for a specific area of eCommerce, such as:

* **Development agents** that help build and deploy new features.
* **Testing agents** that automate QA and ensure reliability.
* **Maintenance agents** that monitor uptime and performance.
* **Marketing agents** that personalize campaigns and content.
* **Personalization agents** that tailor recommendations, experiences, and interactions.
* **Analytics agents** that uncover insights and trends.
* **Support agents** that enhance post-purchase experiences.

In addition, Calvin allows the creation of **custom agents** that can be fully tailored to unique business needs, processes, and tools — enabling every team to extend and adapt the platform to their workflow.


---

# 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:

```
GET https://gopersonal.gitbook.io/calvin/getting-started/what-is-calvin.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.