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Understanding the core concepts of Xorafin will help you quickly learn how the platform works and how to build AI agents effectively. Xorafin is built around a few key ideas: agents, inputs, outputs, models, and agent configuration files.

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Agent

An agent is the core unit in Xorafin. An agent is an AI-powered entity that receives an input, processes it using a language model (and optionally tools), and returns an output. Agents can perform tasks such as:
  • generating content
  • summarizing information
  • answering questions
  • researching topics
  • automating workflows
Each agent is defined by a configuration file that describes how the agent behaves.

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Agent File

Every agent in Xorafin is defined through an agent configuration file. This file specifies:
  • the agent name
  • the model used
  • the inputs the agent accepts
  • the outputs the agent produces
  • optional tools or settings
Xorafin supports multiple formats for defining agents:
  • JSON
  • YAML
  • TOML
Example: 
{
  "name": "research-agent",
  "description": "Research a topic and generate a summary",
  "model": "gpt-4",
  "input": "topic",
  "output": "summary"
}