How Cursor Works Internally?

You might have read the news that OpenAI is buying Windsurf for a whopping 3B! In other news, Anysphere, the parent company of Cursor, is raising 900M at a valuation of 9B$! That’s a lot of money for code-generating applications. However, it seems reasonable when you realize Cursor is currently at 300M of revenue. It is supposedly the fastest growing Software as a Service.

The question that is bugging me is why is Cursor or Windsurf special? How do they work internally? Aren’t they just a VS Code wrapper?

Cursor is an AI-first code editor built to boost developer productivity by writing and editing code. It’s a fork of Visual Studio Code aka VS Code augmented with powerful AI capabilities. Cursor acts like an intelligent pair programmer integrated directly into the IDE, understanding the project and assisting in real-time.

But how? It does so by deeply indexing the codebase and learning the coding style of the user. By indexing the complete codebase as vector embeddings, it can catch errors, suggest improvements, and even refactor code easily.

I am assuming everyone has used Cursor at some point, but here’s a quick rundown of the top features of the app:

AI Chat (Contextual Assistant) – Cursor provides a chat sidebar where you can converse with an AI about your code. Unlike a generic chat, it’s aware of your current file, cursor location, and project context. You can ask questions like “Is there a bug in this function?” and get answers based on your actual code.
Semantic Codebase Search – Cursor can act as a smart search engine for your codebase. Instead of just keyword matching, it uses semantic search to understand the meaning of your query and find relevant code. For example, you might ask in chat, “Where do we configure logging?” and Cursor will retrieve the code snippets or files that likely contain the configuration. Under the hood, Cursor indexes your entire repository by computing embeddings for each file (i.e. numerical vector representations of code semantics). This process allows it to answer codebase-wide questions effectively. It retrieves the most relevant code chunks and feeds them into the AI’s response.
Smart Refactoring and Multi-File Edits – Cursor has the superpower to perform large-scale or logical refactors. These refactors are done through natural language commands. It uses a dedicated (and smaller) edit model, which is different from the main LLM model answering queries.
Inline Code Completions (Tab Completion) – Similar to GitHub Copilot, Cursor offers inline code completion as you type, but with enhanced “intelligence.” The AI in Cursor’s Tab feature predicts not just the next token, but potentially the next several lines or the next logical edit you might make based on semantic similarities in your code.
Additional Productivity Features like Cmd/Ctrl+K – There’s also inline commands like Cmd+K for on-demand code generation or editing – you can select a block of code, press a shortcut, and describe an edit (e.g. “optimize this loop”), and the AI will apply it.

Now let’s dive into the technical architecture that makes these features possible. At a high level, Cursor consists of a client-side application (the VS Code-based editor) and a set of backend AI services. Let’s dive into how the client and server work together to orchestrate language model prompts, code indexing, and application of edits.

Cursor’s desktop application is built on a fork of VS Code, which means it reuses the core editor, UI, and extension ecosystem of VS Code. This gives a lot of IDE features for free (text editing, syntax highlighting, language server support, debugging, etc.) upon which Cursor layers its own AI features. The client includes custom-made UI elements like the chat sidebar, the Composer panel, and special shortcuts (Tab, Cmd+K) to invoke AI actions. Because it’s a true fork (not just a plugin), Cursor can tightly integrate AI into the workflow – for example, the autocompletion is woven into the editor’s suggestion engine, and the chat can directly modify files.

Building Custom Sandbox: Cursor uses language servers (the same ones VS Code uses for languages like Python, TypeScript, Go, etc.) to get real-time information about the code. This provides features like “go to definition,” find references, linting errors, etc. Cursor leverages this in creative ways. Notably, it implements a concept called the “shadow workspace”: essentially a hidden background workspace that the AI can use to safely test changes and get feedback from language servers. For instance, if the AI writes some code, Cursor can spin up a hidden editor window, apply the AI’s changes there (not in your actual open files), and let the language server report any errors or type-check issues. Those diagnostics are fed back to the AI so it can adjust its suggestions before presenting them to you – super cool!

In essence, the client provides the AI with a sandboxed development environment, complete with compiler/linters – to improve the accuracy of its code edits. (This is currently done via an invisible Electron window that mirrors your project, and future plans involve kernel-level file system proxies for even faster isolation)

Beyond the shadow workspace, the client also handles things like the @ symbol context insertion (when you reference @File or @Code in a prompt, the client knows to fetch that file’s content or snippet), and manages the UI for applying AI changes (e.g. the “Play” button for instant apply of chat suggestions). If you use the “instant apply” feature in chat or Composer, the client receives the diff or new code from the AI and applies it to the actual files, possibly showing you a preview or performing a safe merge. We’ll discuss how those AI responses are generated next.

While some lightweight processing (like splitting code into chunks for indexing) happens locally, the heavy AI lifting is done by Cursor’s cloud backend. When you invoke an AI feature, the client assembles the necessary context (your prompt, selected code, etc.) and sends a request to Cursor’s backend. The backend is responsible for building the final prompt for the large language model, interfacing with the model, and returning the results to the editor. In fact, even if you configure Cursor to use your own OpenAI API key, the requests still funnel through Cursor’s backend for prompt construction and orchestration. This allows Cursor to insert system instructions, code context, and tool-specific formatting around your query before it hits the language model.

Large and Small models orchestration: Cursor uses a mix of AI models – both “frontier” large models (like GPT-4 or Claude 3.5) and purpose-built specialized models For example, for natural language chat about code or very complex tasks, it might use a top-tier model (GPT-4) to maximize quality. But for faster autocomplete and routine code edits, Cursor has its own optimized models. In fact, the Cursor team trained a custom code model nicknamed “Copilot++” (inspired by OpenAI’s Codex/Copilot) to better predict the next code edits.

They also developed a specialized “fast apply” model for rapidly applying large code changes. This model was fine-tuned on Cursor-specific data. This includes examples of Cmd+K edit instructions and their corresponding code diffs. The model is used to perform multi-line edits much faster than GPT-4 can. The custom model (built on a 70-billion-parameter Llama base) runs on Cursor’s servers via an inference engine called Fireworks, and it can generate code with extremely high throughput – over 1000 tokens per second – using an advanced technique called speculative decoding. In short, the backend includes an LLM orchestration layer that picks which model to use for a given task, optimizes the prompt, and leverages performance tricks (like parallel token generation) to deliver results with low latency.

Storing embeddings in a Vector DB: The backend services also include the vector database that stores code embeddings for your entire project (more on that below), as well as caching layers and routing logic. All communication is designed with privacy and performance in mind: if Privacy Mode is enabled, the backend won’t retain any of your code or data after fulfilling a request. If Privacy Mode is off, Cursor may log some telemetry or anonymized data to improve its models, but even then the raw code is not persisted in their servers long-term.

Codebase Scanning: One of the core enablers of Cursor’s “project awareness” is its code indexing system. When you first open a project in Cursor, it will scan and index the entire codebase in the background. How does this work: Cursor splits each file into smaller chunks and computes a vector embedding for each chunk. An embedding is essentially a numerical representation that captures the semantic content of the text (in this case, code). Cursor uses either OpenAI’s embedding models or a custom embedding model to generate these vectors. Each chunk’s embedding is stored in a vector database along with metadata – for example, which file and line numbers it came from.

The chunks are typically on the order of a few hundred tokens each. Splitting code is necessary both to stay within model token limits and to increase the granularity of search. Cursor uses intelligent strategies for chunking – it won’t just cut blindly every N lines. Tools like tree-sitter (which parses source code into syntax trees) help break the code at logical boundaries (functions, classes) so that each chunk is a coherent block of code. This way, when a chunk is retrieved, it contains a complete construct or thought, which is more useful for the AI to see.

Using a RAG: Once the codebase is indexed into embeddings, semantic search becomes possible. For example, when you ask the chat “Find all places where we call the authenticateUser function,” Cursor will convert that query into an embedding vector and query the vector database for nearest matches. It might retrieve several code chunks across different files that look related (calls to that function, its definition, doc comments mentioning it, etc.). These relevant snippets are then brought back into the context window for the language model. In practical terms, Cursor’s AI will include those code snippets in the prompt it builds for the LLM, often with some annotation like file names. This approach – Retrieval-Augmented Generation (RAG) – means the AI isn’t limited to the code in the file you’re currently editing; it can draw upon any part of your project as long as the index finds it relevant.

This is how Cursor achieves its “whole-project awareness” in practice.

When you interact with Cursor’s AI (via chat or a command), a lot is happening behind the scenes to construct an effective prompt for the language model. Cursor’s backend takes various context sources and weaves them together in a prompt according to a certain format. These sources include: the user’s query or instruction, the code context (from the open file or retrieved via semantic search), possibly additional context like documentation or examples, and the conversation history (for chat). There are also a system or role prompt with instructions that guides the model. Here’s Cursor’s leaked custom system prompt.

Token length issues: Managing the context window is critical because language models have token limits. Cursor therefore employs strategies to maximize useful information in the prompt and omit or compress less relevant data. One strategy is windowing or chunking for large outputs – if the task is to refactor a 1000-line file, Cursor might break the task into smaller sections, process them individually (maybe with the model planning and applying changes section by section), and then stitch the results.

Cursor’s system also makes use of abstract syntax tree (AST) analysis and static analysis via language servers to enrich context. For example, if you have an error message or a symbol name in your prompt, Cursor could ask the language server for the definition of that symbol or the type information, and include that in the prompt as additional context. The AI might be told, “Here is the definition of function X from file Y,” to better answer a question about X. This kind of integration between traditional tooling (LSP, AST parsing) and LLM is a key part of Cursor’s design to improve accuracy.

We touched on the Shadow Workspace earlier – that’s another form of context management. In an iterative editing scenario, the AI might propose a code change, then the hidden workspace is used to check the result (e.g. does it compile?) before finalizing the answer. If the check fails, the AI can get the compiler or linter feedback (like “variable foo is undefined”) and incorporate that into a follow-up prompt (essentially a self-refinement loop). This loop can repeat a few times in the background within a single user command, so that by the time the AI presents a diff to you, it’s more likely to be correct and apply cleanly. Keep in mind, all of this is invisible to the user!

Applying edits: Another important aspect is how edits are represented and applied. Cursor often has the model produce answers as code edits rather than just plain text explanation. For instance, if you ask it to implement a function, the response might be the full function code block ready to insert. In refactor cases, the AI might output a diff or a list of changes. Cursor’s interface can interpret these and apply them to the project.

Cursor’s team has implemented several optimizations to make the experience feel fast and smooth:

Specialized Model Tuning – As mentioned, Cursor fine-tuned its own large language model for code edits (“Fast Apply” model). This model is designed to handle code modifications and multi-file edits more reliably than general models.
Speculative Decoding for Speed – Cursor leverages an advanced inference technique via Fireworks called speculative decoding. In normal LLM generation, the model generates tokens sequentially, which can be slow. Speculative decoding allows a second “draft” model to guess ahead and generate multiple tokens in parallel, which the main model then quickly verifies.
Caching and Session Optimization – On top of caching file data on the backend, Cursor likely caches embedding results and search results. If you ask two similar questions back to back, the second one can reuse the vector search results from the first if appropriate, instead of hitting the database again.
Memory and Resource Management – Running heavy models and multiple editor instances can be resource-intensive. The “shadow workspace” feature, for instance, doubles some resource usage (since a hidden VSCode window with language servers is running). Cursor mitigates this by only launching the shadow workspace on demand and tearing it down after some idle time.
Extensibility via MCP – As a forward-looking feature, Cursor supports the Model Context Protocol (MCP). This allows external tools or data sources to be hooked into Cursor’s AI. For example, an MCP plugin could let the AI query your database or fetch documentation from an internal wiki when you ask a question.

In conclusion, Cursor’s engineering marries the capabilities of large language models with the practical tooling of an IDE. Indexing the codebase and using RAG gives the AI a working knowledge of your project. By leveraging the VS Code infrastructure, it provides the AI with compiler/linter feedback and a tight loop for applying changes safely. By orchestrating specialized models and caching, it achieves an impressively responsive user experience. All these layers – from the client UI to the backend model servers – work together to improve the developer experience immensely.

Hence, Cursor is used by more than a million developers!

Cursor Documentation and Privacy Policy – Details on codebase indexing (embeddings) and data handling.

Fireworks Blog on Cursor– Cursor’s key features and custom LLM (fast apply) performance stats.

Developer Insights – Overview of Cursor as a VS Code fork with whole-codebase intelligence.

“Semantic Code Search” – Explanation of Cursor’s code chunking, embedding, and RAG approach.