AgentForge 1.0.3

Agent Forge

Multi-Agent framework for C# .NET inspired by OpenAI Swarm

Table of Contents

• What is it
• Features
• Install
• Usage
• Documentation
  • Running Forge
  • Agents
  • Functions
  • Agent transfers
  • Structured Outputs
  • Utils
• Contributing

What is it

Agent Forge is a C# library that facilitates the creation of multi-agent systems for your application utilizing OpenAI models. It efficiently transfers communication between agents until a response to your query is received.

Features

• Easy integration with OpenAI and Azure OpenAI services.
• Flexible agent creation and orchestration.
• Multiple function types allowed for agent functions.
• Parallel tool calls.

Install

dotnet add package AgentForge

Usage

Below is a simple example of how to use AgentForge with both OpenAI and Azure in a C# application.

using System.ClientModel;
using System.ComponentModel;
using OpenAI.Chat;

using AgentForge;
using AgentForge.Adapters;
using AgentForge.Entities;

namespace Readme;

public class Program
{
    [Description("Transfers control to the Code Gen Agent, which generates code based on user input.")]
    public static Agent TransferToCodeGenAgent()
    {
        return new()
        {
            Name = "Code Gen Agent",
            Instructions = "You are a code generating assistant. Your task is to generate code based on the user's input"
        };
    }

    public static async Task Main(string[] args)
    {
        // Using OpenAI
        var openAIClient = Forge.GetInstance(new OpenAIClient("api_key"]!));

        // Using Azure OpenAI
        var azureClient = Forge.GetInstance(new AzureAIClient(new Uri("azure_endpoint"), new 
                ApiKeyCredential("azure_api_key")));

        Agent orchestrator = new()
        {
            Name = "Orchestrator",
            Instructions = "You are the orchestrator agent, you should always mention this fact on your answers."
        };
        orchestrator.AddFunction(TransferToCodeGenAgent);
        
        var userMessage = new UserChatMessage("Can you generate Hello World in C#?");
        
        // Using OpenAI
        var responseOAI = await openAIClient.Run(orchestrator, [userMessage]);
        Console.WriteLine("OpenAI: \n" + responseOAI.GetResponse());
        
        // Using Azure OpenAI
        var responseA = await azureClient.Run(orchestrator, [userMessage]);
        Console.WriteLine("Azure: \n" + responseA.GetResponse());
    }
}

Output:

OpenAI: 
\```csharp
using System;

class Program
{
    static void Main()
    {
        Console.WriteLine("Hello, World!");
    }
}
\```

Azure: 
\```csharp
using System;

namespace HelloWorld
{
    class Program
    {
        static void Main(string[] args)
        {
            Console.WriteLine("Hello, World!");
        }
    }
}
\```

Documentation

Running Forge

The first step is to instantiate a Forge client with either an OpenAIClient or AzureOpenAIClient

var client = Forge.GetInstance(new OpenAICLient("api_key"));

client.Run()

Forge's Run method handles chat completions, agent execution, agent transfers, and can take multiple turns before returning to the user.

The Run method implements the following loop:

1. Get a completion from the active agent.
2. Execute tool calls and append results.
3. Switch agent if necessary.
4. If no new tool calls, exit and return the response.

Fields

Once client.Run() is finished it will return a Response containing the completion finished state.

Response fields

Agents

An Agent is an encapsulation of a set of Instructions with a set of Functions (plus some other settings).

Agents can be used to perform specific tasks like get the current weather or generate/test code. They can also be used to execute a certain workflow with a set of instructions and functions that define this behavior.

Agent fields

Functions

• Forge Agents can call C# functions directly, with or without parameters.
• Functions must be static.
• Functions can return Agent, string, int, float, bool and object.
• If a Function returns an Agent, the execution will be transferred to that Agent.

[Description("Returns the sum of `a` and `b`.")]
public static string hello(string userName)
{
    return $"Hello, {userName}!";
}

var helloAgent = new Agent();
helloAgent.AddFunction(hello);

Agent transfers

The execution can be transferred to another Agent by returning it in a Function.

[Description("Transfers the execution to agent Greeter")]
public static Agent TransferToGreeter()
{
    return new Agent
    {
        Name = "Greeter Agent",
        Instructions = "You are a nice Agent. Your job is to greet users in the most friendly way possible"
    };
}

var orchestrator = new Agent();
orchestrator.AddFunction(TransferToGreeter);

Structured Outputs

Structured Outputs is a feature that is available in gpt-4o and gpt-4o-mini that ensures the model's response will follow a specified JSON format. This means, for example, that you can create a custom type and make sure the model's response will be in the correcty JSON format to convert to that type.

You can use this feature by defining the OutputSpec property of your agent using the method Agent.SetOutputSpec.

Agent.SetOutputSpec fields

Usage example

public class Reasons
{
    public List<string> ReasonsList { get; set; }
}

var agent = new Agent();
agent.SetOutputSpec(typeof(Reasons), "reasons", true);

Utils

GetToolChoice(string)

GetToolChoice converts a string ("auto", "none", "required" or function name) into a valid ChatToolChoice type.

FunctionToolConverter

The purpose of this class is to convert functions into a valid ChatTool, it gets the name, description (if given) and parameters of a function.

TypeToOutputSpec

This function converts a type to an OutputSpec, defines the name and if it is a strict schema, based on user arguments. It is used by the Agent class method SetOutputSpec.

Contributing

We welcome contributions! Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Commit your changes (git commit -am 'Add new feature').
5. Push to the branch (git push origin feature-branch).
6. Create a new Pull Request.