15 of the best AI coding tools and assistants for developers

Ever find yourself stuck, waiting on front-end code to catch up with your design sprint, or rewriting the same logic just to test another layout idea?

Artificial Intelligence (AI) coding tools are changing that. These tools help you build, test, and iterate faster without breaking flow.

According to the PM field guide for AI, an LLM can help clarify early thinking, working as a “brain dump” according to Darsh Thakkar, product manager at Microsoft. With changing roadmaps and flattened timelines, the right AI tools for coding can sharpen your approach before you move into prototyping.

This guide breaks down what to look for—and which tools to try—if you want to speed up your build process without sacrificing code quality.

Read on to learn:

• 15 of the top AI coding tools
• What to look for in coding tools for AI
• Answers to common questions about AI coding tools

1. Figma Make Local

Ideal for: Designers and developers who want to go from concept to a fully designed project ready for release within hours.

Figma Make Local is a prompt-to-app tool that transforms natural language descriptors into functional designs and interactive prototypes you can release within hours. It goes beyond Figma Make by giving you a base to ship your idea, and it builds fully designed software using conversational AI to reduce handoffs.

What differentiates Make Local from a regular code generator is the creation of a complete product feature and idea. By describing what you want, the lift for creation becomes much easier. Plus, its context-aware system knows how to design the way other tools don’t. With Figma Make Local, the design process becomes the entire build process.

The workflow stays connected through a continuously collaborative environment for developers and product managers. Dev Mode gives developers accurate specs, code snippets, and component mappings as designers build. Through the Figma MCP Catalog, you can connect to the AI tools already in your development environment. This brings context directly into the coding workflow your team already uses.

Whether you’re validating an idea through prototyping or moving a design straight to deployment via Figma Sites, Make Local eliminates the distance between a good idea and an excellent build by combining design, coding, and shipment in a comprehensive package.

Key features

• Collaborative workspace. Uses a collaborative environment that integrates with Figma Sites and Figma Design workflows to build better and faster. Combine design and development through a single collaborative workspace.
• Prompt-based project creation. AI text-to-code prompting for layouts, buttons, animations, and entire Web apps. Develop something you can ship in hours, not days.
• Full code access. Generates production-ready HTML, CSS, or React directly from Figma layers during the design process. No more switching between different tools, not even on Figma.
• Easy component linking. Component linking between design libraries and live website structures through the MCP catalog. Make Local recognizes these automatically, spotting associations you may not have yourself.

2. GitHub Copilot

Ideal for: Repository-integrated coding assistance

GitHub Copilot is an AI pair programmer built natively within VS Code, JetBrains, Xcode, and Neovim. It handles inline code completions, multi-file edits, and pull request (PR) summaries. Its agentic mode handles tasks autonomously, pushing code through processes until the work is done.

Copilot integrates with GitHub repositories, making it easier to refactor code, review pull requests, and generate docstrings. Its chat interface supports interactive troubleshooting and code review within the IDE. Copilot supports multiple programming languages and adapts to your coding patterns, making it useful for developers working across varied projects or full-stack codebases.

Key features

• Agentic coding. Copilot can plan, implement, and open PRs autonomously to delegate routine tasks.
• MCP support. Reviews database schemas, telemetry data, and infrastructure configs to apply context to its work.
• Next edit suggestions. Predicts the location of the next edit you’re likely to make and suggests completions for it.

3. Cursor

Ideal for: Development teams who want deep, whole-codebase AI integration within IDE environments.

Cursor is an AI-native IDE tool that’s baked into the VS Code editor. With high-level goals, Agent Mode can write, edit, test, and run code across files. With the release of Cursor 3, a multi-repo layout was introduced that keeps longer-running tasks running autonomously, even after you close your laptop.

With support for many programming languages, Cursor offers contextual inline suggestions, remembers coding context across sessions, and performs advanced refactoring and codebase searches. The tool’s speed and knowledge can help improve developer workflows and improve code assistance across multiple files.

Key features

• Whole-codebase agent. Cursor allows multi-file refactors, test generation, and bug fixes with an understanding of how your codebase works.
• Parallel agents. Through conversational understanding, the app runs multiple plan parts simultaneously to save time on more complex builds.
• Model routing. Relies on Claude, GPT, Gemini, and DeepSeek to divert tasks to the AI that best handles them.

4. Tabnine

Ideal for: Engineering teams in regulated industries where code privacy protection is non-negotiable.

Tabnine is a privacy-first coding assistant built for organizations that can’t send code to the cloud. It works on local computers using tools like VPC and on-premises Kubernetes, with fully air-gapped deployment options for highly regulated environments. Also, the Tabnine Agentic Platform allows autonomous task execution that uses your codebase’s architecture and coding standards.

Tabine works through real-time suggestions, and the platform has stable performance across IDEs like VS Code, VIM, and JetBrains. It also supports on-premises hosting to help legal, medical, and government teams handle sensitive data.

Key features

• Flexible private deployment. Zero data retention with support for fully air-gapped deployments from teams that can’t accept external data exposure.
• Enterprise Context Engine. Searches symbols, functions, classes, and variables across code repositories to keep suggestions connected to the architecture.
• MCP server integration. Add MCP servers to IDE settings with admin-level governance controls and monthly consumption caps.

5. Lovable.dev

Ideal for: Founders and product managers who want to go from idea to a working full-stack Web app without writing code

Lovable.dev is a conversational AI platform that generates functional apps with React or TypeScript. It generates the front and back ends, and imports directly from Figma via its Import feature to turn a visual spec into a functional starting point. You can then use Lovable’s Visual Edits tool to modify interface elements without writing prompts, syncing data to GitHub to maintain code ownership.

The platform handles authentication, database, and file storage through Lovable Cloud, with built-in payment integrations for Paddle and Stripe. It's structured to take a project from initial idea to functional prototype.

Key features 

• Figma import. Integrates directly with Figma to turn a visual spec into a functional starting point.
• Visual Edits. Change interface styles without writing prompts to reduce credit usage.
• Built-in payments. Supports subscriptions and one-time payments through Paddle and Stripe integrations.

6. Claude Code

Ideal for: Engineering teams working across large, complex codebases

Claude Code by Anthropic handles large repositories, cross-language dependencies, and long-term memory across conversations. It handles tools like the GitHub CLI natively and connects to your MCP servers, including the Figma MCP, to pull design context into development workflows.

Its agentic tools help engineers understand the context behind error causes. Adjustable autonomy lets you choose whether fixes are applied automatically or need human approval.

Key features

• Whole-codebase task handling. Reads directory structures, edits files across a repo, and monitors CI pipelines on GitHub and GitLab.
• Autonomy controls. Settings to control the level of oversight that range from automatic approval of every action to necessary approvals for any action.
• MCP integration and CLI support. Connects to MCP servers and CLI tools without requiring developers to memorize commands.

7. Replit Ghostwriter

Ideal for: Non-technical founders, students, and early-stage teams who want to build and deploy full-stack apps entirely in the browser

Replit Ghostwriter is a browser-based development platform that uses an AI Agent to create full-stack apps from natural language prompts and real-time, inline code improvement suggestions. It supports over 50 languages and saw improvements in speed, autonomous capabilities, and self-testing with the releases of Agent 3 and 4.

The collaborative setup makes it easy to share, get feedback, and deploy without local installations. Authentication, database, and hosting are built-in services, making it possible to take a project from prompt to app from a single URL.

Key features

• Autonomous, self-testing agent. Agent 3, part of Replit, tests your app in a real browser, generates a report, and automatically fixes issues.
• Figma import. Converts Figma designs into working code through a simple integration.
• Security scanning. Automatically checks projects and prepares test patches so apps won’t have to rely on dependency monitoring.

8. JetBrains AI Assistant

Ideal for: Development teams already standardized on JetBrains IDEs who want AI assistance

JetBrains AI Assistant’s AI-powered coding tools are integrated directly into the editor. It features a variety of AI workflows for inline documentation, rename refactoring, and commit messages. A 2025 update added multi-file editing, enabling changes across the entire project in a single chat.

The one-click cross-language converter helps repurpose your code into different programming languages. It can also connect to local AI models or third-party cloud AI models to use them for chats and other features. Because it doesn’t retain data, it's more useful for regulated industries with complex cloud environments.

Key features

• Deep IDE integration. Works directly with IntelliJ refactoring tools, inspections, and code analysis engine.
• Multi-file editing. Use a single chat prompt to make changes across an entire project.
• Custom model support. Context-aware AI chat and multiple AI coding agents like Junie and Claude

9. Sourcegraph Cody

Ideal for: Large enterprises and engineering organizations managing complex, multi-repository codebases

Sourcegraph Cody is an AI coding assistant built on Sourcegraph’s Code Intelligence platform. Cody pulls context from your entire codebase—useful when the same logic runs across different services. It also verifies that any generated code doesn’t match open-source databases, using over 290,000 repositories to support compliance.

Cody supports models that include Claude, ChatGPT, and Google, with the option to bring your own LLM API keys to run self-hosted, local models. Enterprise teams can also set Context Filters that prevent sensitive code from being sent to third-party LLM providers, with deployment options for cloud-hosted and self-hosted setups.

Key features 

• Multi-repository context. Pulls coding context from up to 10 different repositories per chat.
• Guardrail tools. Enterprises can set rules that prevent sensitive code from being sent to third-party LLM providers.
• Model flexibility. Cody works with Claude, GPT, and other self-hosted local models to help teams control costs.

10. Amazon Q Developer

Ideal for: Engineering teams working primarily within AWS

Amazon Q Developer helps teams working inside AWS environments code faster and with fewer errors. It generates infrastructure-as-code snippets, suggests API calls, validates logic, and flags compliance risks in real time. Since 2024, it's expanded into a full agentic platform, adding autonomous agents capable of carrying out multi-step tasks, such as implementing new features.

Prompting uses AWS Console Integration alongside natural language suggestions. For more information, developers can find support through the site's forums and training programs.

Key features

• AWS-native generation. Creates Terraform, CloudFormation, and CDK templates with deep awareness of AWS service APIs.
• Transformation agent. Handles large-scale legacy code transformations with a 4,000 lines of code (LOC) cap, useful for version upgrades or migrations.
• Security and compliance checks. Security policy compliance checks, reference tracking, validation, and documentation

11. Windsurf

Ideal for: Solo developers and small teams who want an AI-native IDE built around agentic workflows

Windsurf is an AI-native IDE built for AI-enhanced team coding. It supports context-aware autocomplete, multi-file memory, and image-to-code prompts. You can write with chat or voice, refactor entire functions with Supercomplete, and sync styles and logic across projects.

More advanced features like Cascade let developers edit across large codebases with AI context retention. The proprietary SWE-1.5 model powers Cascade’s planning and execution, while Codemaps visualizes code structure with grouped sections and execution path traces.

Key features

• Cascade agent. Plans multi-step edits, calls tools, and uses repo context with AI-generated visual code maps that trace execution paths.
• Proprietary model. SWE-1.5 is a coding model that delivers faster speeds than most general-purpose LLMs.
• MCP integration. Works with external services such as Slack, Stripe, and Figma to query databases and review designs.

12. Gemini Code Assist

Ideal for: Enterprise use

Gemini Code Assist integrates Gemini 2.5 models into your IDE. It focuses on enterprise-grade security, compliance, and scalability, making it a good fit for organizations in regulated industries that require stringent compliance, such as finance, healthcare, and government.

Gemini Code Assist supports natural-language prompting for generating cloud infrastructure code, developing APIs, and managing large-scale codebases, with enhanced privacy controls. Its main draw is its integration with Google Cloud services and APIs.

Key features

• Easy-to-use chat interface with natural language
• Model training can be linked to private codebases
• Compliance with industry regulations (HIPAA, SOC 2)
• Integration with Google Cloud services and APIs
• Automatic code completions, generation, and transformation of full functions

13. ChatGPT Advanced Data Analysis

Ideal for: Data analysts, technical product managers, and developers who need quick Python-driven data analysis, visualizations, or one-off scripts without leaving a chat interface

ChatGPT’s Advanced Data Analysis (formerly Code Interpreter) adds real-time code execution capabilities to the language model. It’s useful for developers and analysts who work with Python to clean data, generate plots, run logic tests, or build reports.

You can upload spreadsheets, images, PDFs, or code as files, and the tool will interpret, process, and output structured responses or executable code. It’s conversational enough for quick analysis and technical enough for serious automation work. It’s also capable of debugging, logic flow testing, and statistical analysis.

Key features

• Natural language interpretation. Writes, debugs, and executes Python code on demand using natural language
• Visualization tools. Generates and visualizes reports, tables, and plots directly from structured prompts
• Document review. Instant upload and analysis of CSVs, spreadsheets, images, and other document files

14. Qodo

Ideal for: Engineering teams focused on code quality and test coverage

Qodo (formerly CodiumAI) combines AI-powered code review, automated test generation, and IDE-based feedback to improve code quality. It runs as an IDE plugin and integrates with GitHub and GitLab to create a dedicated review layer across your codebase.

The platform’s multi-agent architecture assigns specialized agents to different review concerns that include bug detection, code quality, security analysis, and text coverage gaps. Test generation runs from within the IDE via the “/test” command in VS Code and JetBrains, with on-premises, VPC, and air-gapped configurations for teams with strict data requirements.

Key features

• Multi-agent code review. Analyzes bugs, code quality, security, and test coverage with automated AI tools.
• Automated test generation. Creates unit tests with coverage gap protection via commands in VS Code and JetBrains.
• Context Engine. Indexes multiple code repositories to understand cross-repo dependences.

15. Figstack

Ideal for: Developers onboarding into unfamiliar codebases, technical learners, and engineers who frequently switch between programming languages

Figstack helps developers understand existing code by translating complex code snippets into plain English. It also converts between programming languages that include Python, JavaScript, Java, Ruby, Go, and C++.

Figstack’s Docstring Writer also auto-generates documentation for code functions. The Time Complexity tool reviews code efficiency and helps identify bottlenecks with suggested changes, which supports developers working across different languages.

Key features

• Explain Code. Translates complex code snippets into simple explanations to help new coders onboard to a project.
• Language Translator. Converts code between programming languages to support polyglot (multi-language) programming.
• Time Complexity analysis. Evaluates code simplicity and efficiency and identifies bottlenecks with suggested ways to improve performance.

What should I look for in a good AI coding tool?

Whether you’re a developer, designer, or product manager, AI-powered workflows are redefining what’s possible in rapid prototyping and cross-functional collaboration.

IDE Integrations

An integrated development environment (IDE) is the software developers use to write, edit, and debug code. Examples include VS Code, JetBrains, and XCode. Good tools meet developers where they work, including GitHub Copilot, Tabnine, and JetBrains AI Assistant.

The best integrations go beyond convenience. For instance, tools that work with the Figma MCP Catalog give the AI more accurate data to work with, while Figma’s Dev Mode helps create design specs, tokens, and component mappings straight into the same editor you use while designing.

Repository-wide context and management

AI coding tools should work across entire repositories when teams manage complex codebases. This makes the difference between a suggestion without context and one that knows your architecture and coding nuances.

This matters most at scale. AI hallucinations can add up quickly across complex codebases. Tools that manage your full codebase include Sourcegraph Cody, Cursor, and Qodo.

Agentic workflows

Agentic workflows, known as “agent mode” in some AI coding tools, can translate high-level goals into an action plan and execute it by editing files across your repo, running tests, and iterating until the work passes. Tools like Claude Code, Cursor, and GitHub Copilot all work this way.

Developers can also “hand off” some of the QA process to these tools, rather than executing each PR manually. But these tools can move fast and break things, so look for tools with adjustable autonomy controls and clear approval steps like Gemini Code Assist and Claude Code.

Code security and compliance

A good tool detects vulnerabilities and protects proprietary data without extra configuration. Amazon Q Developer runs built-in security scans on every suggestion, while Qodo’s review agents act as a dedicated security analysis layer.

For more sensitive industries, such as finance or healthcare, ask whether the tool is SOC 2, HIPAA, or FedRAMP compliant. Tabnine and JetBrains AI Assistant both work with private development options.

Refactoring and testing

Refactoring, or changing code to make it cleaner and easier to maintain without changing its behavior, can take a lot of time when done manually. Good AI tools should handle multi-file refactorings with awareness of the entire codebase and an understanding of how changes can ripple through code dependencies.

Generated code is part of this refactoring, and should be thoroughly tested and validated. Qodo and Tabnine build testing into the review process as new functions are developed. Refactoring without testing creates risk, so the strongest tools can do both.

AI coding tools FAQ

Keep reading for answers to frequently asked questions about the latest AI coding tools and capabilities.

What is an AI coding assistant?

An AI coding assistant is a digital helper that uses machine learning models to suggest, generate, or debug code. It works inside an IDE or a browser to automate repetitive coding tasks.

Why use AI coding tools?

When used correctly, AI coding tools can accelerate development, reduce time spent on syntax or structure, and encourage experimentation. They help teams of varying backgrounds code confidently with AI while staying consistent.

Is there a free AI tool for developers?

Yes, various tools like Figma’s free AI code generator, Replit Ghostwriter’s free tier, or Amazon Q Developer within AWS provide entry-level access for developers who want to explore AI’s capabilities.

Who can benefit from AI developer tools?

AI coding tools benefit developers, designers, engineers, creatives, and UX teams by enabling them to write code faster and prioritize innovation over repetitive tasks.

How do I make sure my AI coding tool is set up securely?

Choose AI coding tools based on model selection, cost, context management, and version support, as these factors will impact how you adapt and maintain your workflows in the future.

What’s the difference between black box and white box testing?

Black-box testing examines a system’s output and external behavior without knowledge of its internal logic, while white-box testing involves more detailed code inspection, logic, and execution path validation. AI can support both kinds of activities through generating test cases, automating input/output validation, inspecting code paths, generating unit and integration tests, and analyzing logic branches more quickly.

Generate front-end code fast with Figma Make

Bugs and small mistakes can steal hours from build time. Fortunately, AI is transforming how modern teams prototype, debug, and ship ideas. Whether you’re optimizing UI components or scaffolding a full website, AI coding tools help you write cleaner code faster.

Figma Make Local transforms natural language prompts into visual designs with functional code. With a few conversations, you can find yourself with a complete product. Figma can help:

• Use Figma Make Local to create high-quality, publish-ready code that’s built alongside your design.
• Browse ready-made templates and publish prototypes with Figma Sites.
• Use plugins like Figma for VS Code to open designs directly in your text editor.
• Hover over elements and instantly see measurements, color specs, and ready-to-export assets in Figma’s Dev Mode.