17 Best AI Coding Assistant Tools in 2024 That Programmers Around the World Are Using

preamble

As the saying goes, "A good tool for a good job is a good tool for a good job". Choosing a good tool is especially important for programmers.Whether it's used to automate code completion or debugging, AI coding assistants can save you time and improve the quality of your code. In this article, we'll explore some of the 17 most frequently used AI coding tools by programmers worldwide to revolutionize your development process.

What is an AI coding assistant?

AI coding assistants utilize AI to help developers with a variety of coding-related tasks. They can analyze code snippets, offer suggestions, identify errors, provide fixes, and even generate entire code snippets based on prompts.
They are often embedded in integrated development environments (IDEs) or as standalone platforms that support multiple programming languages. Popular AI coding assistants include GitHub Copilot, Tabnine, and Microsoft IntelliCode.

AI coding tools use Large Language Models LLMs), which are trained on large code datasets from various sources LLMs such as open source projects, repositories, programming language documentation and tutorials).
The wider the range of code they are exposed to, the more comprehensive and accurate their understanding becomes. This enables them to recognize patterns, suggest code snippets and complete complex coding tasks.

17 Best AI Coding Tools

AI coding assistants have been getting a lot of attention from programmers, but which one to use specifically is the question. Below, we'll explore some of the best AI coding tools available.

👉 Top AI coding assistant tools include

1 Github Copilot

2 Tabnine

3 Cursor AI

4 Sourcegraph

5 Replit

6 Codiga

7 Sourcery

8 DeepCode AI
9 Hugging Face

10 Amazon SageMaker

11 Amazon Q Developer

12 Codium

13 Mutable AI

14 AskCodi

15 Microsoft IntelliCode

16 CodeGeeX

17 OpenAI Codex

1. GitHub Copilot

GitHub Copilot is an AI code auto-completion tool developed by GitHub in collaboration with OpenAI. It suggests whole lines or blocks of code for developers as they enter them.
GitHub Copilot Integrates directly into code editors such as Visual Studio Code, GitHub Codespaces, JetBrains IDE, and Neovim to provide real-time suggestions based on the context in which the code was written.

★ Key Features:

• Multiple Language Support GitHub Copilot supports a wide range of programming languages, including Python, JavaScript, TypeScript, Ruby, Go, and more. It is especially effective for languages with large amounts of publicly available code.
• Customizable and Interactive: Developers are not tied to the suggestions Copilot provides. They can accept, reject, or modify suggestions, making it a flexible coding aid rather than a substitute for human creativity and supervision.
• GitHub Copilot Chat: The chat feature allows developers to collaborate more interactively with the AI to ask questions, get clarifications and refine their understanding of the code.

2. Tabnine

Tabnine is a privacy-focused AI code completion tool that improves coding speed and code quality and provides flexibility for local and cloud-based AI models. It helps developers by providing intelligent, context-aware code suggestions.
It supports more than 80 programming languages and frameworks and integrates with most major IDEs.

★ Key Features:

• Context-aware suggestions: Tabnine provides code suggestions relevant to the current project.
• Local and Cloud Models: It offers cloud-based models and local models. Cloud-based models utilize AI trained by Tabnine on large datasets, while local models allow developers to keep everything on their own machines, which is great for privacy-sensitive or proprietary projects.
• Team-level AI models: Tabnine provides teams with team-trained models. These models learn from the shared codebase used by the development team, meaning that the recommendations reflect team-specific coding standards, patterns, and frameworks.

3. Cursor AI

Cursor AI is an AI-powered coding assistant designed to improve software development efficiency. Beginners and experienced developers alike will find it useful as it automates repetitive coding tasks, reduces errors and provides refactoring suggestions.
Its main goal is to simplify the coding process and improve the overall code quality.

★ Key Features:

• AI-driven code suggestions: Cursor AI provides intelligent, real-time code suggestions based on the context of what you're writing, helping you avoid common mistakes and speed up coding.
• Auto-completion and Syntax Correction: Cursor AI provides auto-completion to help developers write code faster, and it also checks for syntax errors to ensure that code follows best practices.
• Collaboration Tools: Cursor AI allows multiple developers to collaborate in real-time, sharing code suggestions and debugging solutions. It also integrates well with popular version control systems, including Git.

4. Sourcegraph

Cody. is an AI coding assistant developed by Sourcegraph designed to help developers by providing intelligent code suggestions, automating repetitive tasks, and improving code search and understanding.
It integrates with Sourcegraph's Code Intelligence Platform, allowing developers to search, understand and generate code in their workflow.

It is ideal for organizations and teams that need strong privacy controls and scalable search capabilities.

★ Key Features:

• Code Search Integration: One of the outstanding features of Cody is its deep integration with Sourcegraph's Code Search. It can be used to find, navigate, and reference code in large and complex codebases, even in different versions of the repository.
• Understanding the Codebase: Cody leverages Sourcegraph's indexing and cross-library code intelligence to provide recommendations based on public datasets and your own code.
• Cross repository insights: Developers can use Cody to quickly see how similar features or components are implemented in different parts of a large, multi-version project, which can greatly improve collaboration and consistency across teams.

5. Replit

Replit is a browser-based online integrated development environment (IDE) and collaborative coding platform that allows developers to write, test, and deploy code in multiple programming languages.

Unlike traditional desktop IDEs, Replit's cloud-based platform integrates coding, collaboration and deployment into a single, seamless environment.
The platform integrates AI-driven tools that allow developers to utilize intelligent suggestions and code generation anytime, anywhere, making the development process faster and more intuitive.

★ Key Features:

• Ghostwriter AI assistantGhostwriter AI assistant: Ghostwriter is at the heart of Replit's AI coding capabilities, an AI-powered coding assistant.Ghostwriter analyzes the context of the code and provides intelligent code suggestions, auto-completion and debugging assistance. It runs in real-time, providing developers with contextual code generation and insights as they type.
• Learn from code patterns: Ghostwriter improves over time by learning from developers' codebases. The more you use Replit, the better the AI assistant recognizes your coding style and provides relevant suggestions that match your patterns.
• AI-Assisted Collaborative Coding: Replit's multiplayer mode combined with AI enables teams to collaborate on the same code base in real-time. Multiple developers can work together while benefiting from Ghostwriter's code suggestions.

6. Codiga

Codiga is an AI-driven coding assistant and static code analysis tool designed to help developers write cleaner, safer, and more efficient code.
It integrates into popular IDEs and code bases to provide intelligent code suggestions, automate repetitive coding tasks, and implement best practices with static analysis capabilities.

★ Key Features:

• Static Code Analysis: One of the best features of Codiga is the ability to perform real-time static code analysis. This means that Codiga can continuously check for bugs, code odors, security holes, and inefficiencies as you write your code.
• AI-powered code suggestions: AI-powered code suggestions: Codiga provides intelligent code completion that automatically completes entire lines or blocks of code. These suggestions are context-aware, which means that Codiga understands the structure of the code you're writing and tailors the suggestions to the best practices of the particular language or framework you're using.
• Reusable Code Snippets: This tool allows developers to create and share code snippets (called recipes) that can be reused across projects. These recipes can be customized to specific coding patterns, algorithms, or team coding standards.

7. Sourcery

Sourcery is an AI code review and refactoring tool designed to improve code quality by automatically suggesting and implementing refactorings that follow best practices.
Unlike other code suggestion tools that focus primarily on autocompletion, Sourcery specializes in improving code readability, efficiency, and maintainability by providing real-time refactoring suggestions.

★ Key Features:

• Real-time refactoring suggestions: Sourcery proactively analyzes your code as you enter it and provides suggestions for improvement. This may involve simplifying complex logic, decomposing large functions, removing redundant code, or following better coding standards.
• Implementing Best Practices: The tool incorporates a number of coding best practices to automate the implementation of cleaner, more efficient coding patterns.Sourcery refines the code by following principles such as the DRY (Do Not Repeat Yourself) rule, which makes the code more modular and reduces technical debt over time.
• Automated Code Review: Sourcery integrates with GitHub and GitLab to provide automated code review as part of the CI/CD pipeline. It helps the entire team maintain high code quality by flagging areas for improvement before code merges, reducing the time spent on manual code reviews.

8. DeepCode AI (Snyk)

DeepCode AI (now part of Snyk) is an advanced AI code analysis tool designed to help developers identify and fix security vulnerabilities, code quality issues and bugs in real time.
DeepCode utilizes machine learning and AI to analyze codebases and provide intelligent recommendations to improve code security, performance, and maintainability.

★ Key Features:

• Real-time vulnerability detection Real-time vulnerability detection: DeepCode detects security vulnerabilities in real-time, helping developers find and fix problems as they write code. It detects vulnerabilities such as SQL injection, cross-site scripting (XSS), and other critical issues that can jeopardize an application's security.
• Integration with Snyk: Since being acquired by Snyk, DeepCode is now integrated into the Snyk platform, adding advanced security features such as open source dependency scanning, container security, and infrastructure-as-code security.
• Security-focused analysis: DeepCode provides in-depth analysis of the security aspects of code. It understands coding patterns that can be exploited by attackers and suggests preventive measures, making it an essential tool for improving the security of any application.

9. Hugging Face

Hugging Face is an open source machine learning platform specializing in Natural Language Processing (NLP), providing tools and models for a variety of AI applications.
Hugging Face is known for its state-of-the-art NLP models and its role in open source converter development, but it has also become a great resource for developers working on AI code generation and integrating machine learning into coding projects.

★ Key Features:

• Pre-trained AI models: Hugging Face provides a large library of pre-trained AI models for different applications such as text generation, sentiment analysis, translation and code generation. These models, especially the transducer-based models, can be fine-tuned or used for coding tasks such as generating code based on natural language descriptions or predicting the next line of code in a given context.
• In-browser AI coding environment: Hugging Face also offers the Spaces platform, where developers can create and deploy machine learning applications, including an AI coding environment.
• Code Generation and Completion: Hugging Face's advanced models, such as CodeGen (part of the GPT-3 family, but optimized for code), can generate entire blocks of code or provide completion for unfinished code segments.

10. Amazon SageMaker

Amazon SageMaker is a comprehensive, fully managed service from AWS (Amazon Web Services) that enables data scientists, developers, and machine learning (ML) engineers to build, train, and deploy machine learning models at scale.

Amazon SageMaker is not an AI code completion tool like GitHub Copilot or Tabnine, but it is a powerful environment for developing and deploying custom machine learning models, including models that can be used for AI coding-assisted tasks such as code suggestions, autocompletion, bug fixing, and more advanced AI-driven development tasks.

★ Key Features:

• AutoML capabilities (SageMaker Autopilot): AutoML capabilities (SageMaker Autopilot): SageMaker Autopilot allows users to automate the construction of machine learning models without the need to write extensive code. It is useful for developers who may not be machine learning experts but need help in automatically creating, training, and tuning models for tasks such as code completion or error prediction.
• Hyperparameter Tuning: SageMaker provides automatic hyperparameter tuning to ensure optimal performance of models used for AI coding assistance. By fine-tuning hyperparameters, developers can improve the accuracy and speed of AI models to suggest code completions or identify errors.
• Manageable, scalable infrastructure: Amazon SageMaker handles the underlying infrastructure needed to run machine learning workloads, allowing developers to focus on building models without worrying about server management.

11. Amazon Q Developer

Amazon Q Developer is an AI-powered generative assistant designed by AWS to help developers throughout the software development lifecycle (SDLC).
It builds on Amazon Bedrock to increase productivity by automating repetitive tasks, providing real-time code suggestions, and offering solutions for debugging, planning, and upgrading code.

★ Key Features:

• Conversational AI Assistant: Amazon Q Developer allows developers to ask questions about AWS services, architecture, and best practices in a natural language format.
• AWS Integration: You can generate cloud-native solutions, optimize AWS resource usage, and get help with tasks such as setting up AWS services (e.g., Lambda, S3, API Gateway) and creating efficient data pipelines using natural language commands.
• Cost and resource management: In addition to code generation, Amazon Q helps analyze AWS resource usage and costs and integrates with AWS Cost Explorer to provide cost insights.

12. Codium

Codeium (Windsurf Editor) is an AI-driven tool designed to improve code quality by automatically generating code.

Unit testing, analyzing code behavior and providing actionable insights.
Unlike traditional AI code assistants that focus on code completion, Codium specializes in helping developers write reliable, error-free code by ensuring comprehensive test coverage and early detection of potential issues.

★ Key Features:

• AI-generated Test Cases: Codium's main function is to automatically generate test cases based on the logic of the code. It understands the structure and intent of the code and generates meaningful tests to ensure that the code behaves as expected in various situations.
• Context-aware testing: Codium analyzes the specific context of the code to create targeted tests. Instead of using generic tests, the tool examines the purpose of the code and creates unit tests that match the intended functionality, ensuring that all edge cases and logical paths are covered.
• Test Coverage Insight: Codium helps you gain insight into the test coverage of your code. It can highlight areas that lack adequate testing and recommend additional test cases.

13. MutableAI

MutableAI is an AI-powered code assistant designed to simplify the coding process by providing intelligent code suggestions, refactoring and autocompletion features. It helps developers write, optimize and maintain code more efficiently and accurately.

★ Key Features:

• Code refactoring: Code refactoring: Beyond simple code suggestions, MutableAI helps refactor and optimize code. Developers can use it to simplify complex code structures, reduce technical debt, and improve code readability by automatically identifying areas of code that can be refactored to improve performance or clarity.
• Learn from your codebase: MutableAI can train on your specific project or team's codebase to provide personalized advice that matches your coding style, preferred libraries and frameworks.
• Automated Code Documentation: MutableAI helps write clean, maintainable code by providing suggestions for adding comments and documentation.

14. AskCodi

AskCodi is an AI-powered code assistant designed to improve developer productivity by providing real-time code suggestions, automating repetitive tasks, and offering context-aware solutions.
It supports multiple languages and streamlines the development process with quick access to code snippets, documentation and debugging suggestions.

★ Key Features:

• Natural Language Queries: AskCodi can interpret natural language queries. Developers can ask AskCodi questions in plain English, such as "How do I create a function in Python that reads a CSV file?" , AskCodi will provide the corresponding code.
• Code Snippet Library: AskCodi provides access to a rich library of commonly used code snippets, enabling developers to quickly insert reusable blocks of code.
• Code Documentation: AskCodi provides code documentation features to automatically generate code comments and documentation.

15. Microsoft IntelliCode

Microsoft IntelliCode is an AI-assisted code completion tool designed to improve developer productivity by providing intelligent suggestions and recommendations directly in the development environment.

Built into Visual Studio and Visual Studio Code, IntelliCode utilizes machine learning models to provide context-aware code suggestions based on your code, common patterns, and best practices from thousands of open source software libraries.

★ Key Features:

• Hotspots identification: IntelliCode highlights areas of code that it recognizes as "hotspots" and on which developers typically focus their efforts. These areas typically involve complex logic, frequent modifications, or business-critical functionality.
• Machine Learning Trained on Best Practices: IntelliCode's AI is trained on thousands of high-quality, high-starred open source software sources on GitHub to provide recommendations based on best practices and common patterns.
• Code Formatting: IntelliCode assists with automated code formatting based on best practices, ensuring that code conforms to the standard conventions of the programming language.

16. CodeGeeX

CodeGeeX is an AI code generation tool that helps developers by providing intelligent code suggestions, auto-completion and other productivity-enhancing features.
It is designed to support a wide range of programming languages and integrates seamlessly with popular integrated development environments (IDEs).

★ Key Features:

• AI-Driven Code Generation: CodeGeeX utilizes powerful AI models to analyze developer code and provide intelligent suggestions. These suggestions can include entire lines of code, blocks of code, or function templates, dramatically speeding up the development process and reducing errors.
• Multiple Programming Languages Support: CodeGeeX supports Python, JavaScript, Java, C++, Go and many other programming languages.
• Cloud-based model: CodeGeeX operates through a cloud-based AI model that delivers high-quality code suggestions without the need for extensive local resources.

17. OpenAI Codex

OpenAI Codex is an AI model developed by OpenAI to aid code generation and understanding.
It is the basis for tools like GitHub Copilot, which provides a powerful resource for developers by interpreting natural language instructions and converting them into executable code.

Codex still generates code and assists with programming tasks, but is no longer actively developed or supported as a standalone product.
OpenAI recommends that users switch to the more advanced GPT-3.5 Turbo or GPT-4, both of which can handle programming tasks more efficiently than Codex. These models have proven to be more versatile while providing great coding power.

Advantages and disadvantages of using AI coding tools

AI programming tools are becoming increasingly popular to help and speed up the workflow. Let's take a look at the pros and cons of using an AI coding assistant.

Advantages of using AI coding tools:

Disadvantages of using AI coding tools:

How to choose the right AI coding tool

When choosing an AI coding tool, consider the following factors: your preferred programming language, how well the tool integrates with your development environment, privacy issues (e.g., whether or not you use local models), and whether you are working alone or with a team.
The ideal AI coding assistant should fit seamlessly into your workflow, increase productivity, and meet the specific needs of your project or organization.

summarize

AI coding tools are like a double-edged sword: if you know what you're doing, they can dramatically speed up your development, but if you're inexperienced, the debugging process can become cumbersome.
No AI tool will build exactly what you need without a good hint, and it won't generate code that works, and is error-free, in the first few iterations, so you'll need to keep providing new hints, or fixing problems yourself.

Above is a compilation of 17 AI coding assistant tools, which are introduced in multiple dimensions, including functional description, main features, price, and advantages and disadvantages. I believe that after reading this article which tool to choose has been clear.