How to Build an Open-Source AI Agent Framework Like Cursor
Z
Zack Saadioui
8/11/2025
How to Build an Open-Source Agent Framework That Mimics Cursor
Hey everyone, hope you're doing well. So, I've been diving deep into the world of AI-powered development tools lately, & one that's been making some serious waves is Cursor. If you haven't heard of it, it's essentially a fork of VS Code that's been supercharged with deeply integrated AI features. It’s pretty slick & got me thinking: what would it take to build our own open-source version of something like this?
Turns out, it's more achievable than you might think. We're not talking about building a full-blown IDE from scratch. Instead, we can stand on the shoulders of giants by using existing open-source tools to piece together a framework that gives us many of Cursor's coolest features. This isn't just a fun technical challenge; it's about creating a development environment that's tailored to your specific needs, completely private, & doesn't come with a monthly subscription.
In this guide, I'm going to walk you through the concepts & components you'd need to build an open-source agent framework that mimics the core functionalities of Cursor. We'll cover the architecture, the key technologies, & how they all fit together. Let's get into it.
Understanding the Magic Behind Cursor
First off, let's break down what makes Cursor so special. It's not just another AI chatbot bolted onto an editor. Its power lies in the deep, contextual understanding of your entire codebase. Here are the key features we'll be aiming to replicate:
Intelligent Code Generation & Completion: This is more than just autocompleting a variable name. Cursor's "Copilot++" can suggest multi-line chunks of code, refactor existing code, & even generate entire functions based on a comment or a natural language prompt.
Natural Language Editing: The "Cmd+K" feature is a game-changer. You highlight a block of code, describe the changes you want in plain English, & the AI does the heavy lifting.
Repository-Wide Context: This is the secret sauce. Cursor doesn't just look at the open file; it has an understanding of your entire project, allowing for more accurate & relevant suggestions, refactoring across multiple files, & answering questions about your codebase.
Integrated Chat & Debugging: A conversational interface that's aware of your code, can help you debug issues, explain complex parts of the code, & even help write tests.
Privacy & Local Control: Cursor offers a privacy mode where your code isn't stored on their servers. We're going to take this a step further by building a system that can run entirely on your local machine.
So, our goal is to build a system with these capabilities using open-source components. It's an ambitious project, but the tools available today are incredibly powerful.
The High-Level Architecture: An Agentic Approach
To pull this off, we're going to design a system with two main parts:
The "Brain": An AI Agent Framework. This will be the core of our system, responsible for all the reasoning, planning, & code generation. It will manage the language models & orchestrate the various tasks.
The "Body": A VS Code Extension. This will be our interface, the bridge between the user & the AI "brain." It will handle user input, display AI suggestions, & interact directly with the VS Code editor to read & modify files.
This separation of concerns is key. The agent framework handles the "what" (e.g., "refactor this function to be more efficient"), & the VS Code extension handles the "how" (e.g., "get the text from the active editor, send it to the agent, & then replace the selection with the agent's output").
Part 1: Building the "Brain" with an Open-Source Agent Framework
The "brain" of our operation will be built on an AI agent framework. These frameworks provide the scaffolding for creating complex, multi-step AI workflows. Here are a few of the best open-source options out there:
Microsoft AutoGen: This is a REALLY interesting one for our use case. AutoGen excels at creating "multi-agent" systems. You can imagine setting up a team of specialized AI agents: a "Planner" agent that breaks down a complex task, a "Coder" agent that writes the code, & a "Reviewer" agent that checks the code for errors. This mimics a real development team's workflow & can lead to some seriously robust results. For something like我们的Cursor mimic, you could have an agent dedicated to reading files, another to writing code, & a third to interacting with the terminal.
LangChain: Probably the most well-known framework for building LLM applications. LangChain is incredibly versatile & provides modules for everything from prompt engineering to tool usage & memory. You could build a powerful single agent that uses different "tools" to interact with the file system, the terminal, & a vector database of your codebase (more on that later).
CrewAI: This framework is built on top of LangChain but focuses specifically on orchestrating role-playing agents. It's designed to make it easier to create collaborative agent teams, which, as we discussed with AutoGen, is a powerful paradigm for software development.
For this guide, let's lean into the concepts of AutoGen, as the multi-agent approach is a great fit for the different tasks a coding assistant needs to perform.
Our agent setup might look something like this:
The
1
UserProxyAgent
: This is a built-in AutoGen agent that acts as the user's proxy. It will take our natural language prompts from the VS Code extension & kick off the workflow.
The
1
CodeWriterAgent
: This is our primary coding agent. Its job is to take a prompt & generate or modify code. We'd give it a system message that primes it for being an expert programmer.
The
1
CodeDebuggerAgent
: This agent's specialty is finding & fixing errors. If the
1
CodeWriterAgent
produces code that doesn't work, we can pass the code & the error message to this agent for a fix.
The
1
FileSystemAgent
: This agent would be given tools to read & write files. This is crucial for repository-wide context. For example, when you ask, "How does our authentication system work?", this agent could be tasked with finding & reading the relevant files (
1
auth.ts
,
1
userModel.js
, etc.).
The beauty of these frameworks is that they are model-agnostic. You can plug in models from OpenAI, Anthropic, or, for our goal of a private, local setup, an open-source model running via a tool like Ollama. Models like CodeLlama, DeepSeek Coder, or CodeQwen are specifically trained on code & can produce amazing results, all while running on your own hardware.
Part 2: Building the "Body" with a VS Code Extension
Now that we have our "brain," we need a way to connect it to our editor. This is where the VS Code extension API comes in. VS Code is incredibly extensible, which is a huge part of why it's so popular. We can use its API to do almost anything a user can do manually.
Here's what our extension will need to do:
Create Custom Commands & Keybindings: We'll want to register a command, let's say
1
extension.askAI
, & bind it to a key combination like
1
Cmd+K
. When a user presses this, it will trigger our extension's main logic.
Access Editor Context: The extension needs to know what the user is doing. The VS Code API lets us get the currently selected text, the content of the active file, the user's cursor position, & more. This is the context we'll feed to our agent.
Create a Webview for Chat: For a more interactive, chat-like experience, we can create a webview panel in the VS Code sidebar. This is essentially a sandboxed webpage running inside VS Code, where we can build a chat interface using standard HTML, CSS, & JavaScript. This is how we can replicate the "chat with your code" feature.
Interact with the Editor: After our AI agent framework returns a result (e.g., a block of refactored code), our extension needs to perform an action. The API allows us to insert text, replace a selection, open a new file, or even show diff views so the user can approve the changes.
Communicate with the Agent Framework: The extension will need to communicate with our Python-based agent framework. A simple way to do this is to have the agent framework run as a local web server (using something like Flask or FastAPI) & have the VS Code extension (which is a Node.js process) make HTTP requests to it.
A great real-world example of this in action is the Continue extension. It's an open-source AI code assistant that can connect to local models via Ollama. It's a fantastic project to study to see how all these pieces fit together in a production-ready extension.
Putting It All Together: The Workflow
So, let's walk through a typical use case, our "Cmd+K" natural language editing feature:
The user selects a block of code in VS Code.
They press
1
Cmd+K
, which we've bound to our custom command.
Our VS Code extension grabs the selected text & opens an input box asking, "What do you want to do?"
The user types, "Make this function asynchronous & add error handling."
The extension packages the selected code & the user's prompt into a JSON payload & sends it via an HTTP request to our local agent server.
On the server, our AutoGen
1
UserProxyAgent
receives the request. It then passes the task to the
1
CodeWriterAgent
.
The
1
CodeWriterAgent
, powered by a local CodeLlama model via Ollama, analyzes the code & the prompt & generates the new, refactored code.
The new code is passed back to the
1
UserProxyAgent
, which then returns it in the HTTP response.
Our VS Code extension receives the response & uses the editor API to replace the user's original selection with the new code.
And there you have it! A seamless, AI-powered refactoring, all done on your local machine.
The Secret to Deep Context: Vector Databases
To truly mimic Cursor's ability to understand your entire repository, you need a way to give your agent framework long-term memory of your codebase. The most effective way to do this is with a vector database.
Here's the idea:
When you open a project, a background process in our extension "indexes" the entire codebase.
This process reads every file, chunks the code into meaningful segments (like functions or classes), & uses a language model to convert each chunk into a numerical representation called an "embedding." These embeddings capture the semantic meaning of the code.
These embeddings are stored in a local vector database, like ChromaDB or FAISS.
Now, when you ask a question like, "Where do we handle user payments?", our extension will first convert your question into an embedding. It then queries the vector database to find the code chunks with the most similar embeddings.
These relevant code chunks are then injected into the prompt we send to our
1
CodeWriterAgent
, giving it the exact context it needs to answer your question accurately.
This process, known as Retrieval-Augmented Generation (RAG), is the key to unlocking deep, repository-wide understanding for your AI assistant. It's how you go from a simple autocomplete tool to a true coding partner. And with tools like LangChain, implementing a RAG pipeline has become surprisingly straightforward.
For businesses looking to implement this kind of advanced AI, but maybe without the time to build a whole framework from scratch, this is where a platform like Arsturn can be a HUGE help. Arsturn helps businesses build no-code AI chatbots trained on their own data. You could, for example, feed it your entire technical documentation, API specs, & even code snippets. The result is an AI assistant that can instantly answer developer questions, help with onboarding, & provide personalized support, boosting productivity across the team. It’s a great way to leverage the power of custom-trained AI without the heavy lifting.
Final Thoughts
Building an open-source version of a tool as polished as Cursor is no small feat. But as we've seen, the building blocks are all there, & they're more accessible than ever. By combining a powerful multi-agent framework like AutoGen with the flexibility of a custom VS Code extension & the contextual power of a vector database, you can create an AI coding assistant that is not only incredibly capable but also completely private & tailored to your exact workflow.
It's a testament to the power of the open-source community. Projects like LangChain, AutoGen, Ollama, & Continue are democratizing access to this transformative technology, allowing us to build the future of software development ourselves.
Honestly, it's a pretty exciting time to be a developer. The tools we use are becoming smarter, more collaborative, & more integrated into our creative process. This isn't about replacing developers; it's about augmenting our abilities, automating the tedious stuff, & letting us focus on the complex, creative problem-solving that we do best.
Hope this was helpful & gives you a solid starting point. Let me know what you think, & I'd love to see what you end up building