Table of Contents

1. Agents Overview
2. Creating Custom Agents

Agents Overview

Raif also provides Raif::Agents::ReActAgent, which implements a ReAct-style agent loop using tool calls:

# Create a new agent
agent = Raif::Agents::ReActAgent.new(
  task: "Research the history of the Eiffel Tower",
  available_model_tools: [Raif::ModelTools::WikipediaSearch, Raif::ModelTools::FetchUrl],
  creator: current_user
)

# Run the agent and get the final answer
final_answer = agent.run!

# Or run the agent and monitor its progress
agent.run! do |conversation_history_entry|
  Turbo::StreamsChannel.broadcast_append_to(
    :my_agent_channel,
    target: "agent-progress",
    partial: "my_partial_displaying_agent_progress",
    locals: { agent: agent, conversation_history_entry: conversation_history_entry }
  )
end

On each step of the agent loop, an entry will be added to the Raif::Agent#conversation_history and, if you pass a block to the run! method, the block will be called with the conversation_history_entry as an argument. You can use this to monitor and display the agent’s progress in real-time.

The conversation_history_entry will be a hash with “role” and “content” keys:

{
  "role" => "assistant",
  "content" => "a message here"
}

Creating Custom Agents

You can create custom agents using the generator:

rails generate raif:agent WikipediaResearchAgent

This will create a new agent in app/models/raif/agents/wikipedia_research_agent.rb:

module Raif
  module Agents
    class WikipediaResearchAgent < Raif::Agent
      # If you want to always include a certain set of model tools with this agent type,
      # uncomment this callback to populate the available_model_tools attribute with your desired model tools.
      # before_create -> {
      #   self.available_model_tools ||= [
      #     Raif::ModelTools::WikipediaSearchTool,
      #     Raif::ModelTools::FetchUrlTool
      #   ]
      # }

      # Enter your agent's system prompt here. Alternatively, you can change your agent's superclass
      # to an existing agent types (like Raif::Agents::ReActAgent) to utilize an existing system prompt.
      def build_system_prompt
        # TODO: Implement your system prompt here
      end

      # Each iteration of the agent loop will generate a new Raif::ModelCompletion record and
      # then call this method with it as an argument.
      def process_iteration_model_completion(model_completion)
        # TODO: Implement your iteration processing here
      end
    end
  end
end

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Read next: Model Tools