How to Use AI Inside VSCode: The Best Extensions for Smarter Coding

Written By:
Founder & CTO
July 3, 2025

Visual Studio Code has become the most widely adopted code editor among developers due to its extensibility, active ecosystem, and performance. One of the most significant advances in recent years has been the integration of AI capabilities directly inside the IDE. The combination of AI and VSCode introduces a paradigm shift in how developers approach coding tasks, offering real-time code generation, refactoring support, intelligent test generation, context-aware explanations, and much more. In this blog, we will explore in-depth how to use AI inside VSCode, focusing on the best AI extensions that can help you write cleaner, faster, and more robust code.

Why Integrate AI into VSCode
Enhanced Developer Productivity

The traditional software development cycle often includes repetitive, low-level tasks that consume valuable time. With AI extensions in VSCode, developers can eliminate boilerplate coding, reduce the time spent writing unit tests, auto-generate documentation, and enhance code comprehension. These improvements drastically cut development time while maintaining code quality.

Context-Aware Code Suggestions

Unlike traditional IntelliSense, AI-based suggestions rely on large-scale transformer models trained on massive codebases. These models provide semantically relevant code completions based on surrounding code context, making them useful even when working with unfamiliar APIs or frameworks.

Support for Multi-Modal Tasks

Some AI extensions can generate database schema migrations, UI components, HTTP request handlers, and even deployment scripts, turning the IDE into an intelligent co-pilot that operates across multiple layers of the application stack.

Best AI Extensions for Smarter Coding in VSCode

GitHub Copilot
Overview

GitHub Copilot is an AI-powered code assistant developed by GitHub in collaboration with OpenAI. It is built on top of OpenAI Codex, a variant of the GPT language model trained specifically on publicly available code.

Technical Capabilities

Copilot analyzes your current cursor context, including file contents, function definitions, variable names, and inline comments. It uses this information to provide predictive code completions, entire function implementations, and inline suggestions.

  • Works across many languages including Python, JavaScript, TypeScript, Go, Ruby, and Java
  • Understands file-wide and even limited multi-file context in some workflows
  • Generates code inline or through dedicated prompt blocks
  • Reacts to natural language comments such as "// create a binary search function"
Limitations and Considerations

While Copilot is effective for many common programming tasks, it may occasionally produce syntactically correct but logically incorrect code. It is essential for developers to review the generated code and apply domain-specific knowledge before integrating it into production workflows.

Use Cases
  • Scaffold functions and utility scripts
  • Generate React component templates
  • Write shell scripts and automation logic
  • Generate boilerplate setup for frameworks like Express or Flask

GoCodeo
Overview

GoCodeo is an AI coding agent designed for full-stack application development directly inside VSCode. Unlike traditional autocomplete tools, GoCodeo is a system of task-oriented AI agents that can understand, scaffold, configure, and test complex applications.

Technical Capabilities

GoCodeo operates through modular agents focused on different parts of the software lifecycle. It leverages LLMs to execute structured multi-step instructions, transforming high-level prompts into production-ready code. It also integrates with tools like Supabase, Vercel, and GitHub Actions, allowing developers to build, deploy, and test applications end-to-end within their editor.

  • ASK agent understands user requirements in natural language and formulates code tasks
  • BUILD agent generates full components, backend routes, and schemas
  • MCP agent performs multi-context refactoring and reasoning
  • TEST agent generates and executes test cases using in-editor logic
Deep Integration

GoCodeo is optimized for JavaScript, TypeScript, Next.js, React, Supabase, and Postgres-based stacks. It detects framework conventions and maintains architectural integrity across the generated modules.

Use Cases
  • Build an entire CRUD dashboard from a prompt
  • Connect frontend forms to backend APIs and Supabase databases
  • Auto-configure CI/CD pipelines
  • Refactor large codebases with agent feedback

Continue
Overview

Continue is an open-source VSCode extension that lets you connect to any LLM backend, giving you control over model inference, API costs, and data privacy. Unlike Copilot, it does not rely on a centralized hosted model and can be paired with OpenAI, Anthropic, or local LLMs like LLaMA or Mistral.

Technical Capabilities

Continue wraps around your chosen model backend and supports embedding-based vector search across your codebase. This allows the model to respond contextually to your queries based on real-time indexed files. You can control:

  • The size of the context window
  • The type of embedding models used
  • The precision and latency tradeoffs in local versus cloud inference
Developer Advantage

For teams with specific compliance requirements or researchers experimenting with fine-tuned LLMs, Continue offers the most customizable and transparent solution.

Use Cases
  • Run GPT-4 on your private codebase
  • Integrate Claude 3.5 or Code LLaMA with project-specific tuning
  • Add source code RAG (retrieval augmented generation) for context-aware completions
  • Execute AI-assisted PR reviews or feature planning

Amazon CodeWhisperer
Overview

CodeWhisperer is Amazon’s entry into the AI coding assistant space, with an emphasis on enterprise-grade security and seamless AWS integration. Available as a VSCode extension, it provides real-time code suggestions and static analysis.

Technical Capabilities

Unlike Copilot, CodeWhisperer places strong emphasis on safe coding. It performs real-time vulnerability scanning and flags risky patterns such as hard-coded secrets or dangerous shell commands. It is particularly effective when used inside AWS-based stacks.

  • Suggests IAM policy generation
  • Provides function suggestions for Lambda, DynamoDB, S3 operations
  • Scans for security vulnerabilities using trained ML models
Use Cases
  • Generate AWS-specific infrastructure code
  • Automate configuration of serverless functions
  • Run security scans while coding
  • Produce consistent, compliant scripts in regulated environments

CodeGeeX
Overview

CodeGeeX is a multilingual AI code generation tool designed to support diverse programming languages and maintain high levels of code readability. Built on PanGu-α architecture, it is tailored for developers working across different international teams and codebases.

Technical Capabilities

It includes support for over 15 programming languages, offers human-like commenting patterns, and generates clean code aligned with widely accepted standards. The LLM is optimized for inference performance and cross-language translation.

  • Supports C, Python, PHP, R, JavaScript, Julia, and more
  • Offers multilingual documentation generation
  • Optimized for GPU inference
  • Available in hosted and local configurations
Use Cases
  • Translate legacy code between programming languages
  • Build hybrid apps with multiple language components
  • Generate multi-lingual documentation or user messages
  • Improve accessibility in globally distributed development teams

Cursor IDE Integration via VSCode Bridge
Overview

Cursor is a fork of VSCode specifically optimized for AI-native workflows. While it operates as a separate IDE, many developers use bridge extensions to sync Cursor’s agent workflows into their primary VSCode setup.

Technical Capabilities

Cursor provides advanced refactoring agents, semantic search over your codebase, and AI-powered PR diff summaries. It builds and maintains an internal vector database of your project, allowing LLMs to reason about entire modules or systems.

  • AI agents refactor legacy code and recommend architectural changes
  • Inline documentation agents summarize and annotate complex logic
  • Pull request reviewers suggest code changes using AI diffs
Use Cases
  • Maintain and modernize legacy applications
  • Use AI to understand unfamiliar codebases
  • Pair large team contributions with intelligent PR analysis
  • Integrate Cursor’s workflows into daily VSCode tasks

Decision Matrix for Developers

The evolution of developer tooling is accelerating, and the integration of AI directly into VSCode marks a new chapter in software engineering. Whether you are building a SaaS product, experimenting with AI model APIs, or working on legacy modernization, there is an AI assistant built for your workflow. By carefully selecting and configuring the right AI extensions, you can turn VSCode into a powerful command center that enhances your intelligence as a developer, automates tedious work, and brings you closer to production-ready code faster than ever before.