LangChain for .NET: Quickstart with NuGet (2025)

What if I told you that integrating ChatGPT-like models into your .NET app is now just a few lines away? No convoluted APIs. No fragile wrappers. Just pure .NET—and one NuGet package: LangChain.

This post kicks off our deep dive into using the official LangChain NuGet package for .NET developers aiming to embed large language models (LLMs) into their C# applications. Whether you’re playing with OpenAI or running Ollama locally, this post will get you started in minutes.

What Is LangChain and Why It Matters in .NET

LangChain started as a powerful abstraction tool for Python developers to build complex LLM workflows. Think of it as middleware for prompt engineering—it helps you connect models, memory, tools, and logic without reinventing the wheel each time.

Until recently, .NET developers had no native tooling to match LangChain’s flexibility. Now with the official LangChain NuGet package, we can finally bring robust AI chaining into C# projects without the glue code mess.

The Evolution of LangChain

LangChain began as an open-source Python library to help developers create prompt pipelines using large language models (LLMs). Its early popularity stemmed from simplifying the integration of tools, chaining logic, and handling conversational memory in apps like chatbots and agents.

The vision was clear: let developers compose LLM-driven workflows like building with Lego blocks. Python got the first mover advantage, but the .NET community soon demanded the same modularity and power.

Bridging the Gap: LangChain for .NET Developers

The arrival of the LangChain NuGet package bridges the AI tooling gap. Built for C#, it wraps around the same architectural principles as its Python counterpart but conforms to .NET idioms.

No more manually constructing JSON payloads or calling REST APIs for every AI interaction. Now you can:

• Use memory and context persistence.
• Chain together prompts, tools, and LLMs.
• Tap into powerful features like retrieval-augmented generation (RAG).

All while staying in your comfortable .NET environment.

Setting Up the LangChain NuGet Package

Prerequisites and Development Environment

Before jumping in, make sure your setup is ready:

• .NET 7 or later is recommended.
• Visual Studio 2022 or JetBrains Rider are both great IDEs for .NET development.
• Install the LangChain NuGet package from NuGet.org.

# In Package Manager Console:
Install-Package LangChain

# Or via CLI:
dotnet add package LangChain

You might also need:

• OpenAI or Azure OpenAI credentials.
• A basic understanding of dependency injection in .NET.

Installing the NuGet Package

1. Open your project or create a new .NET Console App.
2. Use dotnet add package LangChain or install it via NuGet UI in Visual Studio.
3. Add your API key as an environment variable or via appsettings.json.

{
  "LangChain": {
    "OpenAIApiKey": "your-api-key-here"
  }
}

Then read it via configuration or inject it into services.

First Steps: Your “Hello, AI” App

Here’s how to get started with a basic LangChain setup in a .NET Console app:

var openAi = new OpenAITextCompletion("your-api-key");
var prompt = new PromptTemplate("What is the capital of France?");
var chain = new LLMChain(openAi, prompt);

var response = await chain.RunAsync();
Console.WriteLine(response);

This simple chain connects a prompt to an OpenAI model and gets the response. Think of LLMChain as your AI pipeline container.

Key Features and Use Cases

Chaining LLMs with Memory and Logic

LangChain allows chaining multiple steps with retained memory and logic, useful for:

• Multi-turn conversation.
• Sequential reasoning tasks.
• Conditional prompts (if/else logic).

var memory = new ConversationBufferMemory();
var prompt = new PromptTemplate("User: {input}\nAssistant: ");
var chain = new ConversationChain(openAi, prompt, memory);

await chain.RunAsync("What's the weather like in Berlin?");

Tooling and Plugins Support

LangChain supports integration with tools and plugins:

• Web search tools
• Custom APIs
• SQL databases
• Calculators

This enables your AI agent to retrieve real-time data, run code, or query external systems dynamically.

Real-World Applications in .NET

Here are a few practical use cases:

• AI Chatbots with memory and context.
• Developer Assistants for code generation or documentation.
• Document Summarization using RAG or embedding-based retrieval.
• Customer Service Agents that understand history and respond smartly.

Common Pitfalls and Troubleshooting

Package Compatibility and Dependency Issues

• Ensure all projects reference the same version of LangChain.
• Avoid mixing preview and stable versions of dependencies.
• Use dotnet restore and dotnet clean liberally during version upgrades.

Model Configuration Challenges

• Check your token limits (OpenAI models like GPT-3.5 have input/output caps).
• Fine-tune temperature, max_tokens, and stop_sequences for better control.
• Ensure your API key is valid and correctly scoped for the model used.

FAQ: Your First LangChain Questions Answered

Conclusion: .NET Meets AI Magic

LangChain is no longer just for Pythonistas. With the NuGet package, you now have a powerful toolkit to build smart, stateful, and context-aware AI apps in C#. Whether you’re building a chatbot, a dev assistant, or a summarizer—LangChain can streamline your architecture.

Try it today. And don’t forget to follow the rest of this series where we’ll dive into agents, tool integration, retrieval, and more advanced chains.

What would you build first with LangChain in .NET?