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AgentFrameworkToolkit.OpenAI 1.10.0

Agent Framework Toolkit @ OpenAI

This package is aimed at OpenAI as an LLM Provider. Check out the General README.md for other providers and shared features in Agent Framework Toolkit.

What is Agent Framework Toolkit?

Agent Framework Toolkit is an opinionated C# wrapper on top of the Microsoft Agent Framework that makes various things easier to work with:

• Easier to set advanced Agent Options (often only needing half or fewer lines of code to do the same things) that normally would need the Breaking Glass approach.
• Easier Tools / MCP Tools Definition
• Easier Structured Output calling with .RunAsync<>(...) even on AIAgents using Tool Calling Middleware.

FAQ

Q: If I use the Agent Framework Toolkit, does it limit or hinder what I can do with Microsoft Agent Framework?

A: No, everything you can do with Microsoft Agent Framework can still be done with Agent Framework Toolkit. It is just a wrapper that enables options that are hard to use otherwise

Q: What is the release frequency of Agent Framework Toolkit (can I always use the latest Microsoft Agent Framework release)?

A: This NuGet package is released as often (or more) than the Microsoft Agent Framework. At a minimum, it will be bumped to the latest Microsoft Agent Framework Release within a day of official release. It follows the same versioning scheme as AF, so the same or higher version number will always be compatible with the latest release.

Q: Why are the agents not AIAgent / ChatClientAgents? Are they compatible with the rest of the Microsoft Agent Framework?

A: The specialized agents in Agent Framework Toolkit are all 100% compatible with AF as they simply inherit from AIAgent

Getting Started

1. Install the 'AgentFrameworkToolkit.OpenAI' NuGet Package (dotnet add package AgentFrameworkToolkit.OpenAI)
2. Get your OpenAI API Key
3. Create an OpenAIAgentFactory instance (Namespace: AgentFrameworkToolkit.OpenAI)
4. Use instance to create your OpenAIAgent (which is a regular Microsoft Agent Framework AIAgent behind the scenes)

Minimal Code Example

//Create your AgentFactory
OpenAIAgentFactory agentFactory = new OpenAIAgentFactory("<API Key>");

//Create your Agent
OpenAIAgent agent = agentFactory.CreateAgent("gpt-5");
AgentResponse response = await agent.RunAsync("Hello World");
Console.WriteLine(response);

Normal Code Example

//Create your AgentFactory
OpenAIAgentFactory agentFactory = new OpenAIAgentFactory("<API Key>");

//Create your Agent
OpenAIAgent agent = agentFactory.CreateAgent(new AgentOptions //Use AgentOptions overload to access more options
{
 Model = "gpt-5",
 ReasoningEffort = OpenAIReasoningEffort.Low, //Set reasoning effort
 Instructions = "You are a nice AI", //The System Prompt
 Tools = [], //Add your tools here
});

AgentResponse response = await agent.RunAsync("Hello World");
Console.WriteLine(response);

Full Code example with ALL options

//Create your AgentFactory (using a connection object for more options)
OpenAIAgentFactory agentFactory = new OpenAIAgentFactory(new OpenAIConnection
{
 //Endpoint = "<endpoint>", //Optional: if targeting non-OpenAI provider
 ApiKey = "<apiKey>",
 NetworkTimeout = TimeSpan.FromMinutes(5), //Set call timeout
 DefaultClientType = ClientType.ResponsesApi, //Set default Client Type for each agent (ChatClient or ResponsesAPI)
 AdditionalOpenAIClientOptions = options =>
 {
 //Set additional properties if needed
 }
});

//Create your Agent
OpenAIAgent agent = agentFactory.CreateAgent(new AgentOptions
{
 //Mandatory
 Model = "gpt-5", //Model to use
 
 //Optional (Common)
 ClientType = ClientType.ChatClient, //Choose ClientType (ChatClient or Responses API)
 Name = "MyAgent", //Agent Name
 Temperature = 0, //The Temperature of the LLM Call (1 = Normal; 0 = Less creativity) [ONLY NON-REASONING MODELS]
 ReasoningEffort = OpenAIReasoningEffort.Low, //Set Reasoning Effort [ONLY REASONING MODELS]
 ReasoningSummaryVerbosity = OpenAIReasoningSummaryVerbosity.Detailed, //Only used in Responses API [ONLY REASONING MODELS]
 StoredOutputEnabled = false, //Set OpenAI's "store" setting
 ServiceTier = OpenAIServiceTier.Default, //Set OpenAI service tier
 Instructions = "You are a nice AI", //The System Prompt for the Agent to Follow
 Tools = [], //Add your tools for Tool Calling here
 ToolCallingMiddleware = async (callingAgent, context, next, token) => //Tool Calling Middleware to Inspect, change, and cancel tool-calling
 {
 AIFunctionArguments arguments = context.Arguments; //Details on the tool-call that is about to happen
 return await next(context, token);
 },
 OpenTelemetryMiddleware = new OpenTelemetryMiddleware(source: "MyOpenTelemetrySource", telemetryAgent => telemetryAgent.EnableSensitiveData = true), //Configure OpenTelemetry Middleware

 //Optional (Rarely used)
 MaxOutputTokens = 2000, //Max allow token
 Id = "1234", //Set the ID of Agent (else a random GUID is assigned as ID)
 Description = "My Description", //Description of the Agent (not used by the LLM)
 LoggingMiddleware = new LoggingMiddleware( /* Configure custom logging */),
 Services = null, //Setup Tool Calling Service Injection (See https://youtu.be/EGs-Myf5MB4 for more details)
 LoggerFactory = null, //Setup logger Factory (Alternative to Middleware)
 ChatHistoryProvider = new MyChatMessageStore(), //Set a custom message store
 AIContextProviders = [new MyAIContextProvider()], //Set custom AI context providers
 AdditionalChatClientAgentOptions = options =>
 {
 //Option to set even more options if not covered by AgentFrameworkToolkit
 },
 RawToolCallDetails = Console.WriteLine, //Raw Tool calling Middleware (if you just wish to log what tools are being called. ToolCallingMiddleware is a more advanced version of this)
 RawHttpCallDetails = details => //Intercept the raw HTTP Call to the LLM (great for advanced debugging sessions)
 {
 Console.WriteLine(details.RequestUrl);
 Console.WriteLine(details.RequestData);
 Console.WriteLine(details.ResponseData);
 },
 ClientFactory = client =>
 {
 //Interact with the underlying Client-factory
 return client;
 }
});

AgentResponse response = await agent.RunAsync("Hello World");
Console.WriteLine(response);

Batch Runner

OpenAIBatchRunner batchRunner = new("<apiKey>");

ChatBatchRun run = await batchRunner.RunChatBatchAsync(
 new ChatBatchOptions
 {
 Model = "gpt-5-mini"
 },
 [
 ChatBatchRequest.Create("Summarize this text.")
 ]);

EmbeddingBatchRun embeddingRun = await batchRunner.RunEmbeddingBatchAsync(
 new EmbeddingBatchOptions
 {
 Model = "text-embedding-3-small"
 },
 [
 EmbeddingBatchRequest.Create("Embed this text.")
 ]);

Note: Batch runner APIs are marked experimental with AFT999.