LangChain C#: How to Build Your First AI Chain

Imagine if you could build AI workflows like Lego blocks. That’s exactly what LangChain enables you to do.

Chains in LangChain let you compose multiple steps involving prompts, language models, tools, and memory into a logical pipeline. In this post—the second of our LangChain for .NET series—we’ll explore how to create and run your first LangChain Chain in C#. Let’s unpack it step by step.

What Is a Chain in LangChain?

A Chain in LangChain is a pipeline that connects prompts, language models, and logic. You use it when you want to:

• Automate sequences of prompt + model calls.
• Structure reusable tasks (e.g., summarizing input, answering questions).
• Introduce modularity and separation of concerns.

In short: use a Chain when your app needs more than a single LLM call.

Using PromptTemplate: Defining Reusable Prompts

In LangChain, prompts can be structured using the PromptTemplate class. This helps you define prompts with placeholders for dynamic input values, keeping your templates clean and reusable:

var prompt = new PromptTemplate(
    template: "Translate the following sentence to French: {sentence}",
    inputVariables: new[] { "sentence" }
);

The template uses {sentence} as a variable placeholder. When you later call the chain, you provide the actual sentence.

Expanded Benefit:

• Maintainability: Update prompt logic in one place.
• Clarity: Makes your code more readable.
• Separation of logic and content: Developers and content writers can collaborate more efficiently.

You can also combine templates with custom validations or metadata for further extensibility.

Introducing LLMChain: Link a Prompt to an LLM

With your PromptTemplate ready, you need to hook it up to an actual LLM. LLMChain allows this by linking the template and model into one cohesive unit:

var llm = new OpenAIModel("your-openai-key");

var chain = new LLMChain(
    prompt: prompt,
    llm: llm
);

var result = await chain.RunAsync(new Dictionary<string, string> {
    ["sentence"] = "I love programming."
});

Console.WriteLine(result);

Output: J’aime programmer.

What’s Happening Behind the Scenes:

• LangChain fills in the prompt using the dictionary values.
• Sends the filled-in prompt to OpenAI.
• Receives the model output.

You can think of LLMChain as a mini-service that transforms structured input into meaningful LLM output. This pattern reduces redundancy and centralizes prompt usage across your application.

Using PromptOutputParser: Extracting Structured Output

Language models can output messy or verbose responses. To structure the response, use PromptOutputParser like this:

var parser = new RegexOutputParser(
    pattern: "Name: (?<name>.*)\nAge: (?<age>\d+)"
);

var parsed = parser.Parse("Name: Alice\nAge: 30");
Console.WriteLine(parsed["name"]); // Alice

Why It’s Important:

• It turns unstructured text into a dictionary of typed values.
• You can validate, format, or manipulate outputs reliably.

You could even feed parsed data into another LLMChain, enabling multi-step data processing pipelines. Regex parsers are simple to start with, but LangChain also supports custom parsers if needed.

Tip: Always test your patterns against sample outputs to avoid runtime issues.

Executing and Debugging Chains in C#

LangChain for .NET comes with powerful debugging hooks that let you trace execution step-by-step. This is critical in production-grade workflows:

chain.OnStep += step => Console.WriteLine($"Step: {step.Description}");

Execution Logging Use Cases:

• Monitor token usage.
• Audit prompts sent to the LLM.
• Capture intermediate outputs for analysis.

You can also perform unit testing using mock models:

var mockLlm = new MockLLM("Expected Output");

This helps you simulate responses in a test environment without making real API calls.

Pro Tips:

• Always validate inputs before passing to a chain.
• Wrap chains in try/catch blocks for production.
• Use dependency injection to switch between real and mock LLMs.
• Leverage structured logging to integrate with observability platforms like Seq or Serilog.

FAQ: Common Questions About Chains in LangChain

Conclusion: Build Chains, Build Possibilities

LangChain isn’t just another C# tool—it’s the start of a new programming mindset. You’ve now seen how to assemble intelligent workflows in C# using just a few building blocks: prompts, LLMs, and structured output. Each chain you build brings your application one step closer to real-world AI integration.

Don’t stop here. Take what you’ve learned and experiment. Tweak the prompts, swap in different models, and introduce logic that reflects your business needs. This is where simple code becomes powerful architecture.

If you’re serious about building smart .NET apps powered by language models, now is the time to get hands-on. Try creating your own chain today—and if you run into questions or ideas worth sharing, drop them in the comments.