31 Must-Know AI Code Generation Tools & Top Strategies for Developers

Do you want to make coding faster and more efficient? Thanks to recent advancements in AI code generation, the development process becomes easier and faster. AI Copilots have revolutionized the coding landscape, providing developers with advanced tools that can significantly improve their productivity. The process is a real game-changer for professionals who want to write better code faster. AI code generation can help you stay ahead of the competition by automating coding tasks and reducing the likelihood of errors. Successful coders emphasize the advantages of AI Copilots, which are becoming more prevalent in the tech world.

What Is AI Code Generation?

AI code generation involves using software tools, powered by Artificial Intelligence (AI) and Machine Learning (ML), to write computer code. Instead of manually typing out every line of code, a person gives the AI tool a description of what they want the code to do. The AI tool then employs advanced Language Models (LLMs) and Generative AI techniques to automatically suggest or produce code based on user-provided input specifying the desired functionality. 

While the code it comes up with might not be perfect, it gives developers a solid starting point, making it faster and easier to write code. This nifty technology not only helps experienced developers but also makes coding more accessible to everyone, regardless of skill level. It's like having a helpful coding assistant at your fingertips!

How Does AI Code Generation Work?

AI code generation operates on ML algorithms trained with existing source code, often sourced from open-source projects. This works through three primary methods

Autocomplete Feature

Developers initiate code writing, and the AI tool attempts to autocomplete the code based on patterns learned from the training dataset.

Natural Language Input

Developers articulate intentions through natural language input, prompting the AI tool to generate code suggestions aligned with their objectives

Direct Interaction

Developers engage in direct conversation with the AI using chat interface, issuing specific requests or bug-fixing commands, demonstrating the conversational capability of the technology.

Advancements in Generative AI for Coding

Generative AI for coding owes its feasibility to recent advancements in Large Language Model (LLM) technologies and Natural Language Processing (NLP). Employing Deep Learning (DL) algorithms and extensive Neural Networks, these models undergo training on diverse datasets extracted from publicly accessible code repositories maintained by open-source projects.

Streamlining Coding Workflow with Generative AI

Programmers input plain text prompts outlining the desired code functionality. Generative AI tools respond by suggesting code snippets or generating complete functions, streamlining the coding workflow by automating repetitive tasks and minimizing manual coding efforts. This technology further exhibits versatility by facilitating code writing in different programming languages, thereby expediting code creation. 

Considerations and Challenges in Using Generative AI for Coding

While the accuracy of code produced by Generative AI and LLM technologies continues to improve, it is imperative to acknowledge the potential for flaws. Consequently, code generated by these tools should undergo thorough review and refinement by human developers. 

AI Code Generation in Action: Examples and Case Studies

One prominent example is GitHub Copilot, which leverages the Codex AI developed by OpenAI, trained on a massive dataset to generate code snippets in response to plain language prompts. Similarly, GPT-3, another LLM model, can propose code snippets based on user inputs.

Another notable case is the AI development tool TabNine, which acts as an AI-powered autocomplete tool that generates code suggestions as developers type. This tool, too, has been trained on vast codebases, enabling it to predict and suggest appropriate snippets.

Revolutionizing MacOS Productivity with AI Copilot

Imagine an AI that can type anywhere on macOS, with full context of what's on your screen. Imagine if, your email wrote itself, your terminal was fluent in Bash, your team updates became effortless. You don’t have to imagine any of this anymore. You can download Omnipilot’s MacOS AI Copilot for free right now and use AI anywhere on MacOS! 

Try our free AI copilot today — just head to and enter your email, and you’ll be able to download our MacOS app in seconds! Let Omnipilot make your life easier.

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Top 31 AI Code Generation Tools

1. Omnipilot

Imagine an AI that can type anywhere on macOS, with full context of what's on your screen. Imagine if, your email wrote itself, your terminal was fluent in Bash, your team updates became effortless. You don’t have to imagine any of this anymore. You can download Omnipilot’s MacOS AI Copilot for free right now and use AI anywhere on MacOS! 

Try our free AI copilot today — just head to and enter your email, and you’ll be able to download our MacOS app in seconds! Let Omnipilot make your life easier.

2. OpenAI Codex

OpenAI Codex is the most prominent AI code tool available today. It’s based on OpenAI's LLMs (GPT-3 and GPT-4) and is trained on billions of lines of code. The tool is proficient in more than a dozen programming languages. (As of March 2023, the Codex models are now deprecated. Check out the new Chat models, which are capable of handling many coding tasks with similar capabilities.)

3. Copilot

Copilot uses publicly available code from GitHub repositories so that users can access large datasets and quickly develop accurate code. The tool detects errors in code and recommends changes to it. You can start using GitHub Copilot by installing one of the extensions in your preferred environment.

4. ChatGPT (GPT-3/4)

Created by OpenAI, the AI chatbot ChatGPT has become hugely popular, and finding different ways to use it has become a major trend. By taking advantage of transformers, ChatGPT can broaden its contextual awareness and sound very human-like. It has a wider scope of responses and can even generate code. But just like with any other AI tool, ChatGPT code might include bugs, be difficult to maintain, include security vulnerabilities, and so on. As already mentioned, AI code tools are still no replacement for human developers, even if they can be incredibly helpful in speeding up the process.

5. AlphaCode

Another AI-based code generator is Google-backed DeepMind’s AlphaCode, which gives developers access to source code from various language libraries. With AlphaCode, developers can leverage thousands of pre-made libraries, helping them connect and use third-party APIs quickly and easily. AlphaCode is not yet available to the public.

6. Tabnine

Tabnine is an AI code completion tool that utilizes deep learning algorithms to provide the user with intelligent code completion capabilities. Tabnine supports several programming languages such as Java, Python, C++, and more. This tool is open-source and is used by leading tech companies like Facebook and Google.

7. CodeT5

CodeT5 is an open AI code generator that helps developers to create reliable and bug-free code quickly and easily. It is also open-source and provides support for various programming languages such as Java, Python, and JavaScript. CodeT5 also has an online version as well as an offline version for data security.

8. Polycoder

Polycoder is an open-source alternative to OpenAI Codex. It is trained on a 249 GB codebase written in 12 programming languages. With Polycoder, users can generate code for web applications, machine learning, natural language processing and more. It is well-regarded amongst programmers because of its capability of generating code quickly.

9. Deepcode

DeepCode is a cloud-based AI code analysis tool that automatically scans the codebase of a project and identifies potential bugs and vulnerabilities. It offers support for multiple languages such as Java, Python, and JavaScript. DeepCode is well-regarded for its accurate bug detection.

10. WPCode

WPCode is an AI-driven WordPress code generator created by Isotropic. It supports both developers and non-technical WordPress creators, allowing them to quickly generate high-quality code snippets. CodeWP supports not only HTML and CSS but also languages such as Java and Python. It even includes AI assistants to suggest improvements to code snippets.

11. AskCodi

AskCodi is a code generator that offers a full suite of development tools to help developers build and ship projects faster. With its AI-based code generation, it helps developers write better code and shorter code blocks, with fewer mistakes. AskCodi can be used to develop both web and mobile applications.

12. Codiga

Codiga is a static analysis tool that ensures code is secure and efficient. It supports popular languages like JavaScript, Python, Ruby, Kotlin, and more. With Codiga, you can test your code for vulnerabilities and security issues in real time. It also includes an auto-fixer to quickly address any issues in the code.

13. Visual Studio IntelliCode

Visual Studio IntelliCode is an extension of the Visual Studio Code editor created by Microsoft that provides AI-assisted development experiences to improve developer productivity. It offers smarter IntelliSense completions and helps reduce the amount of time developers spend navigating and debugging code.

14. PyCharm

PyCharm is an AI code completion tool from JetBrains that provides developers with intelligent code completion capabilities. This tool supports various programming languages such as Java, Python, and JavaScript. PyCharm is well regarded for its accuracy and can help developers reduce the amount of time spent on coding tasks.

15. AIXcoder

AIXcoder is an AI-powered programming pair designed to aid development teams in writing code. It supports languages such as Java, Python, and JavaScript. This tool also offers a range of features such as automated routine tasks, AI-powered code completion, real-time code analysis and error checks while typing.

16. Ponicode

Ponicode is an AI-powered code assistant designed to help developers optimize their coding workflow. It uses natural language processing and machine learning to generate code from user-defined descriptions. The tool is maintained by CircleCI.

17. Jedi

Jedi is an open-source option for code completion in AI. It mostly functions as a plugin for editors and IDEs that use Python static analysis tools.

18. Wing Python IDE Pro

Created by Wingware, Wing IDE is a Python-specific software setup that combines the code editing, code navigation, and debugging mechanisms required to Code and Test Software applications. It offers various features such as an intelligent auto-completing Editor, Refactoring, Multi-Selection, and Code Snippets, which make coding much easier and more efficient.

19. Smol Developer

Smol is an open-source artificial intelligence agent designed to function as a personal junior developer, capable of generating an entire codebase from your specific product specifications. Unlike traditional, rigid starter templates, Smol can create any kind of application based on your unique requirements. Boasting a codebase that is simple, safe, and small, it offers the perfect blend of ease of understanding, customization, and a helpful, harmless, and honest approach to AI development.

20. Cody (Sourcegraph)

Cody (not to be confused with AskCodi), Sourcegraph's AI tool, is a comprehensive coding assistant. It understands your entire codebase, answers queries, and writes code. Beyond guidance, Cody provides detailed code explanations, locates specific components, and identifies potential issues with suggested fixes. Cody works directly in VS code with an extension.

21. CodeWhisperer (Amazon)

CodeWhisperer is a tool developed by Amazon. It offers real-time, AI-driven code suggestions and identifies potential open-source code matches for easier review. It even scans for security vulnerabilities, suggesting immediate patches. An added bonus is its commitment to code safety, always aligning with best security practices such as OWASP guidelines.

22. Google Bard

Bard can help with programming and software development tasks, including code generation, debugging and code explanation. These capabilities are supported in more than 20 programming languages including C++, Go, Java, Javascript, Python and Typescript. And you can easily export Python code to Google Colab — no copy and paste required. Bard can also assist with writing functions for Google Sheets.

23. Code Llama (Meta)

Code Llama is a set of large language models specialized for coding, built on the Llama 2 platform. It includes different models for various needs: the general-purpose Code Llama, Code Llama - Python for Python-specific tasks, and Code Llama - Instruct for instruction-based coding. These models vary in size (7B, 13B, and 34B parameters) and can handle up to 16k token inputs, with some improvements on up to 100k tokens. The 7B and 13B models also offer content-based infilling.

24. Claude 2 & 3 (Anthropic)

Claude 2 is the latest natural language AI model introduced by Anthropic, a firm established by Dario Amodei, formerly of OpenAI. This new iteration is engineered for enhanced input and output lengths and boasts superior performance relative to its earlier version. Users can input up to 100K tokens in each prompt, which means that Claude can work over hundreds of pages of technical documentation. Claude 2 scored 71.2% up from 56.0% on the Codex HumanEval, a Python coding test.

25. Stable Code 3B

Stability AI's Stable Code 3B, a new 3 billion parameter Large Language Model specialized in code completion, which is 60% smaller yet performs similarly to the larger CodeLLaMA 7b. This model, trained on diverse programming languages and software engineering-specific data, can run in real-time on modern laptops without a GPU. Stable Code 3B is part of Stability AI's Membership program and offers advanced features like Fill in the Middle capabilities and expanded context size, demonstrating state-of-the-art performance in multi-language coding tasks.

26. Replit

Replit AI is an innovative code completion tool designed to streamline your coding experience by offering tailored suggestions that align with the context of your current file. As you delve into coding, the tool intuitively presents inline suggestions, enhancing your efficiency and accuracy. Replit AI offers advanced features such as the ability to refine suggestions through code comments, the application of prompt engineering for more relevant results, and the flexibility to toggle the code completion feature on or off within the editor settings, ensuring a customized coding environment tailored to your preferences.

27. Plandex

Plandex employs persistent agents that tackle extensive tasks spanning numerous files and involving multiple steps. It segments sizable tasks into manageable subtasks, executing each in sequence until the entire task is accomplished. This tool aids in clearing your backlog, navigating new technologies, overcoming obstacles, and reducing the time spent on mundane activities.

28. Meta AI (Meta Lama 3)

Meta has launched Meta AI, powered by the Llama 3 model with 70 billion parameters. The model positions itself as a powerful asset for improving application functionalities, but it does not match the customization and transparency of more advanced models like GPT-4 Turbo and Claude Opus. The benefits of Meta's approach to open-source AI are multifaceted, including attracting top talent, leveraging community contributions, fostering standardization and lower costs, building goodwill, and aligning with business models that do not rely solely on AI products.  

While it is described as "open weight," providing access to the model's weights, it does not include the full toolkit necessary for reproduction. They also co-developed Llama 3 with Torchtune, the new PyTorch-native library for easily authoring, fine-tuning, and experimenting with LLMs.

29. Meta GPT

Not to be confused with Meta AI, MetaGPT is a tool that automates the generation of software development outputs such as user stories, competitive analysis, requirements, data structures, APIs, and documents from a single line of input. It integrates roles typically found in a software company—product managers, architects, project managers, and engineers—into its workflow. 

These roles are executed by large language models (LLMs) following detailed Standard Operating Procedures (SOPs). The core philosophy behind MetaGPT is "Code = SOP(Team)," emphasizing the application of SOPs to organize and direct the work of its LLM teams. This structure aims to mimic the entire process of a software company, simplifying and automating complex tasks.

30. AutoRegex

AutoRegex is an AI-powered code assistant and developer tool designed to automate the process of generating Regular Expressions (Regex) and translating them into English. This tool simplifies the complex task of writing Regex by offering features such as automated Regex generation, English translation of Regex, and a user-friendly interface. It is ideal for developers, data scientists, and individuals working with Regex in various fields like web development, data analysis, and software development. 

AutoRegex streamlines workflows, saves time, and enhances productivity by providing quick and accurate Regex solutions tailored to specific needs.It serves as an educational resource for understanding and mastering Regex patterns, making it a valuable tool for developers of all skill levels.

31. Llama.cpp

Llama.cpp is designed to facilitate LLM inference with optimal performance and minimal initial setup across various hardware, both locally and in the cloud. It is implemented in plain C/C++ without dependencies and features extensive support for Apple silicon through ARM NEON, Accelerate, and Metal frameworks. 

It also supports AVX, AVX2, and AVX512 for x86 architectures and offers integer quantization from 1.5 to 8 bits to enhance inference speed and reduce memory consumption. For NVIDIA GPUs, llama.cpp includes custom CUDA kernels, with AMD GPU support through HIP.It supports Vulkan, SYCL, and partial OpenCL backends and can perform hybrid CPU+GPU inference to manage models that exceed VRAM capacity.

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5 Crucial Components to Look for in AI Code Generation Tools

1. Compliance

When selecting an AI code generation tool, it is crucial to verify its compliance with relevant regulations in your industry. Look for certifications that prove the tool's compliance with standards and regulations. This will ensure that the tool aligns with legal requirements and industry-specific guidelines.

2. Security

Data encryption is key to safeguarding sensitive information. Ensure that the tool encrypts data transmission and storage to protect your data. Access controls should be robust, allowing you to implement strong authentication measures to prevent unauthorized access. Verify if the tool complies with industry security standards and undergoes regular security audits and updates to address vulnerabilities.

3. Privacy

User data handling policies should be clear and adhere to privacy regulations such as GDPR, CCPA, and more. AI tools should also support data anonymization techniques to protect user privacy. Prioritize tools that prioritize user data privacy and implement measures to safeguard sensitive information.

4. Permissioning

Functional access control is vital for managing permissions based on user roles. Look for tools that offer granular permissions, enabling you to control access to various features based on user responsibilities. Opt-in and opt-out mechanisms should be in place, allowing users to control the use of their data and opt out if needed.

5. Pricing

Understand the pricing model of the AI code generation tool. Determine if it is based on usage, number of users, features, or other metrics. Transparent pricing structures with no hidden costs are ideal. Scalable pricing that aligns with your usage and business growth is essential. Look for tools that offer good customer support, ease of integration with existing systems, performance, and user-friendly interfaces. Assess how well the tool aligns with your unique organizational requirements and priorities in each of these areas.

3 Key Benefits of AI Code Generation

1. Faster Development, More Productivity

The key advantage of using AI-generated code is the significant speed boost it provides in the development process. Developers can automate repetitive tasks, allowing them to focus on high-level architecture and problem-solving. An impressive 88% of developers reported feeling more productive when using GitHub Copilot.

2. Mental Load Alleviated

AI code generation is not just about speeding up development but also about reducing mental effort on repetitive tasks. For example, debugging often involves a lot of reverse engineering to find bugs, which can be a tedious and repetitive task. With the help of GitHub Copilot, developers can spend less mental effort on repetitive tasks, as 87% of developers reported.

3. Less Context Switching

Context switching in software development can be disruptive and decrease productivity. Developers often have to switch between different tasks, projects, or environments, leading to stress and decreased efficiency. With GitHub Copilot, developers can minimize context switching by staying within their Integrated Development Environment (IDE) instead of searching on Google or external documentation.

Common Challenges & Solutions with AI Code Generation 

Code Quality and Reliability

While AI-generated code is generally reliable, it can sometimes produce buggy or insecure code. To ensure trustworthy outcomes, rigorous code reviews must be conducted. Studies show that AI assistants like Copilot may solve only a portion of problems correctly, emphasizing the need for human oversight. By closely monitoring and validating AI-generated code, developers can maintain high standards of quality and reliability, mitigating potential risks.


AI-generated code may sometimes overcomplicate solutions, resulting in unnecessary complexity. This can impede future maintenance, debugging, and collaboration. To address this challenge, developers should prioritize clean, concise code that is easy to understand and maintain. By simplifying code structures and minimizing unnecessary abstractions, AI can be leveraged effectively to enhance productivity and streamline development workflows.

Risk of Technical Debt

AI models may prioritize syntactic correctness over code quality, leading to technical debt accumulation. To mitigate this risk, developers should actively review and optimize AI-generated code for efficiency and maintainability. By focusing on best practices and adopting coding standards, technical debt can be minimized, ensuring sustainable and scalable software development processes.

Loss of Control

Relying too heavily on AI assistants may lead some developers to fear skill erosion. Viewing AI as a supporting tool rather than a replacement is crucial. By integrating AI responsibly and emphasizing human judgment and critical thinking, developers can leverage AI's capabilities without compromising their expertise. Education and continuous learning are essential components for successfully integrating AI into development workflows.

Practical Solutions and Workarounds

To navigate obstacles in AI Code Generation, implementing practical solutions and workarounds is vital

Start small

By gradually introducing AI assistants for specific tasks, developers can evaluate their efficiency and identify potential challenges before widespread implementation.

Prioritize Quality

Implement strict testing and validation processes to ensure AI-generated code meets high standards of accuracy, security, and reliability

Upskill Workforce

Educating developers on AI assistant strengths and limitations encourages them to use these tools as aids, enhancing their expertise and fostering a collaborative development environment.

Related Reading

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  • Best AI coding Assistant
  • Tabnine Alternative
  • Copilot Vs Code
  • Codegpt Vscode
  • Replit Ghostwriter Vs Copilot
  • Sourcegraph Alternatives
  • Best AI code Generator
  • Openai Codex Alternative

Try Our MacOS AI Copilot for Free Today

Omnipilot is an innovative MacOS AI Copilot that revolutionizes the way code is generated and written on Apple devices. This advanced technology allows users to type anywhere on MacOS with full context of what's on their screen, making tasks such as writing emails, working in the terminal, and collaborating with team members more efficient and effortless. With Omnipilot, the process of generating code becomes more streamlined and intuitive, providing users with a powerful tool that simplifies their workflow and boosts productivity.

AI Code Generation: Redefining How Code is Created

AI Code Generation is changing the game when it comes to writing and generating code. By incorporating artificial intelligence into the code creation process, developers can benefit from enhanced automation, intelligent suggestions, and contextual awareness. 

This advanced technology can analyze existing code, understand the context of a project, and provide insightful recommendations to improve code quality and efficiency. With AI Code Generation tools like Omnipilot, developers can now work smarter, not harder, and enjoy a more seamless coding experience.

Boosting Productivity with Omnipilot’s MacOS AI Copilot

By incorporating Omnipilot’s MacOS AI Copilot into your workflow, you can significantly boost your productivity and streamline your coding tasks. This innovative tool leverages artificial intelligence to understand the context of your work, provide intelligent suggestions, and help you write code more efficiently. With Omnipilot, coding becomes a more intuitive and collaborative experience, allowing you to focus on what matters most: creating high-quality code that meets your project requirements. 

Don’t miss out on the opportunity to try Omnipilot’s MacOS AI Copilot for free and experience the future of AI Code Generation today.