> For the complete documentation index, see [llms.txt](https://help.zke.com/symbol/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.zke.com/symbol/fet-fetch.ai.md).

# FET(Fetch.ai)

* Issue Time

  \--
* Total Supply

  1,152,997,575 FET
* Circulation

  1,043,462,805 FET
* White paper

  <https://fetch.ai/uploads/technical-introduction.pdf>
* X Twitter

  <https://twitter.com/fetch\\_ai>
* Telegram

  <https://t.me/fetch\\_ai>
* Website

  <https://fetch.ai/>
* Block Explorer

  <https://etherscan.io/token/0xaea46A60368A7bD060eec7DF8CBa43b7EF41Ad85>

Fetch.ai is a platform for swarm learning by connecting Internet of Things (IoT) devices and algorithms. It was launched in 2017 by a team in Cambridge, UK. Fetch.ai is built on a high-throughput sharded ledger and provides smart contract capabilities to deploy machine learning and artificial intelligence solutions to solve problems with decentralization. These open source tools are designed to help users create ecosystem infrastructure and deploy business models.

{% hint style="info" %}
Trade on ZKE Exchange：<https://www.zke.com/&#x20>;

Twitter：<https://twitter.com/ZKE\\_com&#x20>;

Telegram：<https://t.me/ZKEGlobal>

Support: <https://support.zke.com>
{% endhint %}