Comparing C# AI Libraries: Which One Boosts Dev Productivity Most?

I’ve been curious about something lately: with so many AI libraries available for C#, which ones actually make a difference in day-to-day development? Not just the ones with the flashiest demos, but the tools that genuinely speed up my workflow and help me ship better code faster.

So I decided to experiment. Over the past few weeks, I’ve been testing different approaches to integrating AI into my C# projects. What I discovered surprised me—there’s no single “best” option. Instead, the right choice depends heavily on what you’re trying to accomplish.

The Question That Started My Exploration

I started with a simple question: What if I could reduce the time I spend on repetitive coding tasks by 30-40%? According to recent research, AI tools in 2025 are delivering exactly that through intelligent code completion, automated refactoring, and bug detection. But which library would work best for my specific needs?

I identified four main categories of needs in my projects:

1. IDE-level productivity (autocomplete, refactoring suggestions)
2. Custom AI agent workflows (building specialized AI assistants)
3. Model training and deployment (when I need full control)
4. Cloud-integrated solutions (for enterprise scenarios)

Let me walk you through what I tested in each category.

IDE-Integrated Tools: The Daily Productivity Boost

I started with the most obvious candidates: GitHub Copilot and JetBrains AI Assistant. These tools live inside your IDE and offer real-time suggestions as you code.

My Copilot Experiment: I spent a week using Copilot exclusively for a REST API project. The autocomplete was impressive—it often predicted entire method bodies correctly. But I noticed something interesting: it excelled at boilerplate code but sometimes suggested outdated patterns for newer .NET features.

JetBrains AI Assistant: When I switched to JetBrains AI Assistant in Rider, I found the refactoring suggestions more context-aware. It understood my project structure better and offered architectural improvements, not just line-by-line completions.

The verdict? For pure coding productivity, these IDE-integrated tools are essential. Recent studies confirm that IDE-integrated tools dominate the developer productivity space in 2025, and I can see why—they’re always there when you need them.

Building Custom AI Agents: When You Need More Control

Here’s where things got interesting. I wanted to build a custom AI assistant that could analyze code repositories and suggest architecture improvements. IDE tools couldn’t handle this—I needed something more flexible.

Installing and Getting Started

I explored three options: Semantic Kernel, LlmTornado, and building directly with OpenAI’s SDK. Let me show you what I learned.

For my experiments with LlmTornado, installation was straightforward:

dotnet add package LlmTornado
dotnet add package LlmTornado.Agents

Testing Multiple Providers

What intrigued me most was testing different AI providers for the same task. Could I easily switch between OpenAI, Anthropic, and Azure OpenAI without rewriting my code?

Here’s a complete example I built to compare responses across providers:

using LlmTornado;
using LlmTornado.Chat;
using LlmTornado.Agents;

// Initialize clients for different providers
var openAiKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY");
var anthropicKey = Environment.GetEnvironmentVariable("ANTHROPIC_API_KEY");

var openAiClient = new TornadoApi(openAiKey);
var anthropicClient = new TornadoApi(anthropicKey, ProviderAuthentication.Anthropic);

// Create agents with identical instructions but different providers
var openAiAgent = new TornadoAgent(
client: openAiClient,
model: ChatModel.OpenAi.Gpt4,
name: "OpenAI_Reviewer",
instructions: "Review C# code for performance issues and suggest improvements."
);

var anthropicAgent = new TornadoAgent(
client: anthropicClient,
model: ChatModel.Anthropic.Claude35Sonnet,
name: "Claude_Reviewer",
instructions: "Review C# code for performance issues and suggest improvements."
);

// Test with the same code sample
string codeToReview = @"
public List<int> ProcessNumbers(List<int> numbers) {
var result = new List<int>();
foreach(var num in numbers) {
if (num % 2 == 0) {
result.Add(num * 2);
}
}
return result;
}";

Console.WriteLine("=== OpenAI GPT-4 Review ===");
await foreach (var chunk in openAiAgent.StreamAsync($"Review this code:\n{codeToReview}"))
{
Console.Write(chunk.Delta);
}

Console.WriteLine("\n\n=== Claude 3.5 Sonnet Review ===");
await foreach (var chunk in anthropicAgent.StreamAsync($"Review this code:\n{codeToReview}"))
{
Console.Write(chunk.Delta);
}

What surprised me was how different the responses were. GPT-4 focused heavily on LINQ optimization, while Claude emphasized memory allocation patterns. Having both perspectives was valuable—and switching between them required changing just two lines of code.

Building a Research Assistant with Tools

I then wondered: could I build an agent that not only chats but actually performs actions? Here’s what I created:

using LlmTornado;
using LlmTornado.Chat;
using LlmTornado.Agents;
using LlmTornado.ChatFunctions;

var api = new TornadoApi(Environment.GetEnvironmentVariable("OPENAI_API_KEY"));

// Create a research assistant with specialized behavior
var agent = new TornadoAgent(
client: api,
model: ChatModel.OpenAi.Gpt4,
name: "ResearchAssistant",
instructions: @"You are a technical research assistant specializing in C# and .NET.
Provide detailed, cited answers with specific examples.
When calculations are needed, use the calculator tool.
Always cite your sources."
);

// Define a calculator tool for the agent
var calculatorTool = new ChatFunction(
"calculate_expression",
"Evaluates mathematical expressions",
new {
expression = new { type = "string", description = "Math expression to evaluate" }
},
(args) => {
var expr = args["expression"].ToString();
// Simple evaluation (in production, use NCalc or similar)
var dataTable = new System.Data.DataTable();
var result = dataTable.Compute(expr, "");
return Task.FromResult(result.ToString());
}
);

agent.AddTool(calculatorTool);

// Test the agent with a research query
var query = "If a C# application processes 10,000 requests per second and each request " +
"allocates an average of 2KB, how much memory is allocated per minute? " +
"Then suggest optimization strategies.";

Console.WriteLine("Query: " + query);
Console.WriteLine("\nAgent Response:");

await foreach (var chunk in agent.StreamAsync(query))
{
Console.Write(chunk.Delta);
}

The agent used its calculator tool automatically when needed, then provided optimization suggestions. This pattern—combining language understanding with concrete actions—felt powerful.

Provider Failover: A Practical Need

What happens when your primary AI provider has downtime? I tested LlmTornado’s automatic failover feature:

using LlmTornado;
using LlmTornado.Chat;

// Configure multiple providers with automatic failover
var config = new TornadoApiConfig
{
Providers = new List<ProviderConfig>
{
new() {
Provider = ProviderAuthentication.OpenAI,
ApiKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY"),
Priority = 1
},
new() {
Provider = ProviderAuthentication.Anthropic,
ApiKey = Environment.GetEnvironmentVariable("ANTHROPIC_API_KEY"),
Priority = 2
},
new() {
Provider = ProviderAuthentication.AzureOpenAI,
ApiKey = Environment.GetEnvironmentVariable("AZURE_API_KEY"),
Endpoint = Environment.GetEnvironmentVariable("AZURE_ENDPOINT"),
Priority = 3
}
},
EnableAutomaticFailover = true,
FailoverRetryAttempts = 2
};

var api = new TornadoApi(config);

// This will automatically try providers in order if one fails
var conversation = api.Chat.CreateConversation();
conversation.AppendSystemMessage("You are a helpful C# coding assistant.");

try
{
var response = await conversation.GetResponseAsync("Explain async/await in C#");
Console.WriteLine($"Response from: {response.Provider}");
Console.WriteLine(response.ToString());
}
catch (Exception ex)
{
Console.WriteLine($"All providers failed: {ex.Message}");
}

During my testing, I deliberately misconfigured the primary provider. The SDK seamlessly switched to the backup without my code needing to handle it. For production systems, this resilience is crucial.

Decision Matrix: Which Tool for Which Need?

After weeks of experimentation, here’s how I think about choosing between these options:

Need	Best Option	Why
Daily coding productivity	JetBrains AI / Copilot	Deep IDE integration, always available
Multi-provider flexibility	LlmTornado	Switch providers without code changes
Custom agent workflows	LlmTornado / Semantic Kernel	Tool integration and orchestration
Full ML control	ML.NET	Train custom models, no API dependencies
Enterprise cloud integration	Azure AI	Native Azure services integration

What Surprised Me Most

Three things stood out during this exploration:

1. The Power of Provider-Agnostic Design As highlighted in recent AI trends, being able to switch between OpenAI, Anthropic, and Azure OpenAI without rewriting code is incredibly valuable. During testing, I encountered rate limits, temporary outages, and cases where one provider simply understood a query better than another.

2. IDE Tools Are Non-Negotiable Despite exploring custom AI solutions, I still rely on JetBrains AI Assistant daily. It’s not either/or—you need both IDE productivity tools and a flexible SDK for custom solutions.

3. Real-World Impact Is Measurable Case studies from 2025 show developers reducing development time by 30-40% and significantly improving code quality. I experienced this firsthand—tasks that used to take hours now take minutes.

Visualizing the Architecture

When building with these tools, I found it helpful to think about the architecture in layers:

┌─────────────────────────────────────┐
│   IDE Layer (Copilot, JetBrains)   │ ← Daily coding productivity
├─────────────────────────────────────┤
│   Agent Layer (LlmTornado/SK)      │ ← Custom workflows & orchestration
├─────────────────────────────────────┤
│   Provider Layer (OpenAI, Claude)   │ ← Actual AI models
├─────────────────────────────────────┤
│   Specialized ML (ML.NET, Azure)    │ ← Custom models when needed
└─────────────────────────────────────┘

The key insight: these tools complement each other rather than compete.

My Current Setup

After all this testing, here’s what I actually use daily:

• JetBrains AI Assistant for IDE productivity (autocomplete, refactoring)
• LlmTornado for custom AI agents and workflows (with OpenAI as primary, Anthropic as backup)
• ML.NET only when I need to train specialized models

For developers just starting with AI in C#, I’d recommend beginning with IDE tools and exploring custom solutions once you hit their limitations. For more examples and patterns, check the LlmTornado repository—the demo projects show realistic usage scenarios beyond simple hello-world examples.

Questions That Remain

My exploration raised new questions I’m still investigating:

• How do these tools perform with very large codebases (100k+ lines)?
• What’s the actual cost difference between providers for production workloads?
• Can we effectively combine multiple AI providers in a single agent?

The AI tooling landscape for C# is maturing rapidly. What matters most isn’t finding the “perfect” tool, but understanding which tool serves which purpose—and having the flexibility to switch when your needs change.