Power Up Your C++ Workflow: Integrating Claude Sonnet 4 with a Custom Language Server
Z
Zack Saadioui
8/12/2025
Power Up Your C++ Workflow: Integrating Claude Sonnet 4 with a Custom Language Server
Hey everyone! Let's talk about something that's been on my mind a lot lately: leveling up our C++ development environment. If you're like me, you're always looking for ways to get that extra edge, to code faster, smarter, & with fewer headaches. We've seen AI coding assistants pop up everywhere, but honestly, getting them to play nice with a compiled language like C++ in a really deep, integrated way can be a bit of a journey.
Turns out, with a bit of work, we can build something pretty powerful ourselves. I'm talking about creating a custom C++ Language Server that's supercharged by Anthropic's Claude Sonnet 4. Imagine having an AI that not only understands your C++ code but can also generate new code, refactor complex sections, & offer intelligent suggestions right inside your editor. It's not science fiction; it's totally doable.
This is going to be a deep dive, but stick with me. We'll go through the whole process, from understanding the moving parts to sketching out how to connect them.
So, What's the Big Idea Here?
Alright, let's break down the core concept. We're essentially building a bridge between our C++ code editor (like VS Code) & the powerful brain of Claude Sonnet 4. The bridge itself is the Language Server Protocol (LSP).
Here’s the thing about LSP: it's a game-changer. Before LSP, if you wanted to add features like autocomplete or go-to-definition to an editor, you had to write a separate extension for every single editor (VS Code, Vim, Eclipse, etc.). It was a nightmare. LSP standardizes the communication. You build ONE language server for your language, & as long as an editor "speaks" LSP, it can use your server. This reduces the problem from M editors x N languages to M + N. Pretty cool, right?
Our plan is to create a C++ application that acts as a language server. This server will listen for requests from our code editor (like "the user is asking for code completion at this line"). Instead of just looking at the local code for suggestions, our server will do something special: it will package up the context, send it over to the Claude Sonnet 4 API, & then format Claude's intelligent response into something the editor can understand.
Why Claude Sonnet 4? Because Anthropic has been making HUGE strides in code generation. Sonnet 4, in particular, hits a sweet spot between performance & efficiency, making it great for these kinds of real-time development tasks. It's been shown to be incredibly good at coding tasks, sometimes even outperforming other top models in benchmarks.
So, the stack looks like this:
VS Code (The Client): Our code editor, with a lightweight extension to talk to our server.
The C++ Language Server (The Brains): A C++ program we'll build that implements the LSP.
Claude Sonnet 4 API (The Super-Brain): Provides the AI-powered code generation & analysis.
Let's get into how we can actually build this thing.
Step 1: Setting Up Your C++ Project - The Foundation
First things first, we need a solid C++ project setup. This isn't just a simple "Hello, World!" application. Our language server needs to handle a few key things: speaking the Language Server Protocol & making web requests.
Choosing a C++ LSP Framework
You could implement the entire LSP from scratch, parsing the JSON-RPC messages yourself... but why would you do that to yourself? There are some fantastic open-source libraries that handle the heavy lifting. A couple of good options I've found are:
lsp-framework: This is a modern C++20 implementation that looks really promising. It's designed to be type-safe & easy to use, which is exactly what we want. It abstracts away the messy details of JSON serialization.
LspCpp: Another solid choice, this one uses Boost & has been around for a bit. It has examples to get you started & is used in a few real-world projects.
For this guide, let's imagine we're using something like
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lsp-framework
. The core idea is that these libraries let you register handlers for specific LSP messages. For example, you'd write a C++ lambda function that gets called whenever the editor sends a
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textDocument/completion
request.
Handling HTTP Requests to Claude
Next, our C++ server needs to talk to the Claude API, which is a standard REST API. This means we need a good C++ library for making HTTP requests. Gone are the days of wrestling with raw sockets. Here are a few popular choices:
cpr (C++ Requests): If you've ever used Python's
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requests
library, you'll feel right at home with cpr. Its design is heavily inspired by it, making it super intuitive.
restc-cpp: This is a more modern, coroutine-based library. It's built for asynchronous operations, which is PERFECT for our use case since we don't want our entire language server to freeze while waiting for a response from Claude.
cpp-httplib: A simple, header-only library that's easy to drop into a project. It's great for getting started without a lot of setup.
We'll need to make POST requests to the Anthropic API endpoint, sending our code context as a JSON payload & receiving the AI-generated suggestions back.
So, your initial
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CMakeLists.txt
or build setup would involve pulling in one of these LSP frameworks & one of these HTTP client libraries.
Step 2: Building the Core of the C++ Language Server
Alright, now for the fun part. We're going to sketch out the logic of our language server. Using a library like
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lsp-framework
, the structure would look something like this.
You'd start by setting up a connection & a message handler. This part of the code is responsible for the main loop, listening for messages from the editor.