# Datasource Guide

Datasources are integral to the functioning of many applications. They serve as a bridge between the raw data and the agents that consume this data. Within this guide, you'll find an explanation of how to utilize datasources effectively and how they interplay with agents and teams of agents.

## What is a Datasource?

A **Datasource** is a repository or a location from which data can be extracted, queried, or imported. This data can come from various sources such as databases, applications, files, or even web pages that can be crawled. Agents can utilize these datasources to fetch, modify, or interact with the data as needed.

## How are Datasources related to Agents and Teams of Agents?

- **Agents**: An agent is essentially a worker or an operative in the system. It fetches data from a datasource, processes this data based on the logic defined, and then possibly stores the results back or performs actions based on the data.

- **Teams of Agents**: In scenarios where data processing is complex and requires multi-step operations, a team of agents is used. Here, each agent in the team might be responsible for a specific task, and they might rely on multiple datasources to accomplish their goals.

## Datasource Categories and Types

Datasources can be categorized based on where the data is coming from. Here are the types of categories:

1. **Database**: Traditional storage systems where structured data resides. Examples include relational databases such as MySQL, Postgres, or cloud-based databases like Firebase.

2. **File**: This category pertains to file-based data sources. Here, the data can be extracted or ingested from a file that's uploaded to the system.

3. **Crawler**: Web crawlers are designed to fetch data from the internet. If a specific webpage's data needs to be extracted and processed, a crawler is employed.

4. **Application**: Modern applications offer APIs that allow third-party integrations. These applications become datasources when agents need to interact with the data they offer. Examples include Notion, Google Analytics, etc.

### Types of Datasources:

- **Postgres**: A popular open-source relational database. Agents can generate queries to fetch or modify data within a Postgres DB.
  
- **MySQL**: Another widespread relational database. Similar to Postgres, agents can use this datasource to query data as needed.
  
- **File Upload**: If the data is in the form of a file, it can be uploaded to the system. The agents can then process this file-based data.
  
- **Web Page Crawler**: A tool designed to navigate the web and extract data from specific webpages.
  
- **Notion**: A versatile application that offers notes, databases, and more. With the right integrations, Notion can serve as a datasource, providing agents with the necessary data.
  
- **Google Analytics**: A tool by Google designed to offer web analytics. The data from Google Analytics can be fetched and used by agents for further analysis or action.
  
- **Firebase**: A cloud-based platform by Google that offers a NoSQL database. Agents can query Firebase to fetch or modify the data as required.

## Conclusion

Datasources are foundational to data-driven operations. By understanding the available datasources and their categories, one can design agents effectively, ensuring that they fetch the correct data and process it as needed. Whether it's querying a database, crawling a web page, or interfacing with an application's API, knowing your datasources is the first step in the data processing journey.