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Best AI tools for developers in 2024: AI-powered coding
Last updated: October 7, 2024
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Artificial intelligence has forever changed the way software is developed. Automated tasks and improved teamwork, fueled by AI, are fundamentally rewriting the software lifecycle.
This article explores AI tools for developers. We’ll look at their key features, capabilities, benefits, limitations, and areas needing improvement. Understanding these technologies’ current state helps developers and managers decide whether to integrate them into their workflows.
The article covers
- AI agents,
- AI code reviewers,
- AI code assistants.
Our aim is to provide developers, managers, and tech leaders with insights to make informed tool integration decisions for greater efficiency, productivity, and innovation.
| AI developer tool type | Tool name | Usage recommendation |
|---|---|---|
| AI Agent | MetaGPT | Ignore |
| ChatDev | Ignore | |
| Devin AI | Ignore | |
| GPT Pilot | Promising | |
| OpenHands 🆕 | Developing | |
| Aider 🆕 | Recommended | |
| AI Code Reviewer | Korbit | Ignore |
| AI Code Review Action | Ignore | |
| CodeRabbit | Promising | |
| AI Code Assistant | Supermaven | Ignore |
| Gemini Code Assist | Promising | |
| GitHub Copilot | Recommended | |
| Gorilla | Ignore | |
| Cursor 🆕 | Recommended | |
| GitLab Duo 🆕 | Promising |
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Glossary
Article: A scientific publication about a particular tool.
Recommendation:
- Ignore: The tool has been completely discontinued or ceased to be supported or developed or it’s on a super early stage of development.
- Promising: The tool has potential, might be useful in the future.
- Developing: Initial research has been done, the tool is useful, but at present it still has a number of shortcomings that the authors need to improve.
- Recommended: The tool has been tested in various environments and cases and has a proven positive impact on productivity.
Cursor
Website: https://www.cursor.com/
Recommendation: Recommended(especially for VSCode users)
About Cursor
Cursor is an AI-powered code editor designed to enhance developer productivity through advanced features that facilitate coding and collaboration.
Key features
- AI pair programming: Integrates AI to assist developers with predictive code edits and contextual suggestions.
- Natural language editing: Allows writing code using natural language instructions.
- Privacy and security: SOC 2 certified, ensuring user privacy by not storing any code.
- Compatibility: Built on Visual Studio Code, allowing seamless import of existing extensions, themes, and keybindings.
- Chat interface: Provides a conversational AI assistant within the editor for asking questions and getting help.
- Code refactoring: Offers AI-powered suggestions for improving code structure and readability.
- Multi-language support: Works with a wide range of programming languages and frameworks.
Additional information
Cursor has gained popularity among developers due to its intuitive interface and powerful AI capabilities. It uses advanced language models to understand context and provide more accurate suggestions compared to traditional autocomplete features. The tool also includes a feature called “Edit in English,” which allows developers to describe desired code changes in natural language, and the AI attempts to implement those changes.
While Cursor is free to use, it offers a Pro plan with additional features such as GPT-4 integration, longer context windows, and priority support. The tool is regularly updated, with new features and improvements being added based on user feedback and advancements in AI technology.
Opinion
- More intuitive than GitHub Copilot
- Interpreter mode requires a PRO subscription
- Can vectorize any documentation for future reference
- Compatible with VS Code extensions
- “Apply changes” feature doesn’t work with a private API key
- Allows extension of chat context with codebase, docs, web search, etc.
Aider
GitHub: https://github.com/paul-gauthier/aider
Recommendation: Recommended
About Aider
Aider is a command-line tool for AI-assisted pair programming, enabling collaboration with large language models (LLMs) like GPT-3.5 and GPT-4 directly in the terminal.
Key features
- AI collaboration: Facilitates interaction with LLMs for writing, editing, and debugging code.
- Automatic Git integration: AI-made changes are automatically committed with descriptive messages.
- Multi-file support: Allows working with multiple source files simultaneously.
- Real-time updates: Tracks manual edits in the code editor to keep the AI informed.
- Language-agnostic: Works with various programming languages and file types.
- Customizable prompts: Allows users to define custom system prompts for specific project needs.
- Code explanation: Can provide detailed explanations of existing code.
Additional information
Aider stands out for its command-line interface, which appeals to developers who prefer terminal-based workflows. It integrates seamlessly with git, allowing for easy version control of AI-assisted changes. The tool is particularly useful for refactoring tasks, bug fixing, and adding new features to existing codebases.
One of Aider’s strengths is its ability to understand and work with the entire codebase context, not just individual files. This allows it to make more informed suggestions and changes that consider the project as a whole.
Aider requires an OpenAI API key to function, which means users have more control over their data and can use their own API quotas. This can be both an advantage (in terms of privacy and cost control) and a limitation (requiring setup and potential additional costs) depending on the user’s needs.
Opinion
- Excels at modifying multiple files simultaneously.
- Particularly useful for developers comfortable with command-line interfaces.
- The git integration provides a safety net for experimenting with AI-suggested changes
GitLab Duo
Website: https://about.gitlab.com/gitlab-duo/
Recommendation: Promising
About GitLab Duo
GitLab Duo is an advanced suite of AI capabilities integrated into GitLab, designed to enhance productivity throughout the software development lifecycle.
Key features
- Code Suggestions: Predicts and suggests code as developers type.
- Real-time Chat Support: Provides assistance through natural language explanations.
- Vulnerability Management: Offers tools for explaining and resolving security vulnerabilities.
- Merge Request Enhancements: Summarizes code changes and suggests reviewers.
- Organizational Controls: Allows control over AI capability access.
- AI-Assisted Issue Management: Helps with categorizing, prioritizing, and assigning issues.
- Code Quality Checks: Provides AI-powered insights on code quality and potential improvements.
- Documentation Generation: Assists in creating and updating project documentation.
Additional information
GitLab Duo represents GitLab’s comprehensive approach to integrating AI throughout the DevOps lifecycle. Unlike standalone AI coding assistants, Duo is deeply integrated into the GitLab platform, offering AI assistance at various stages of development, from planning to deployment.
The tool uses a combination of proprietary AI models and integrations with leading AI providers to offer its features. This approach allows GitLab to tailor the AI capabilities specifically to the needs of DevOps workflows while also leveraging cutting-edge language models for tasks like code generation and natural language interaction.
GitLab Duo places a strong emphasis on security and compliance. It includes features that help identify and explain potential security vulnerabilities in code, making it easier for teams to maintain secure development practices. The organizational controls allow companies to manage how and where AI is used within their GitLab instances, addressing concerns about data privacy and code confidentiality.
One of the unique aspects of GitLab Duo is its holistic approach to AI assistance in software development. It doesn’t just focus on code writing but also aids in project management, code review, and even DevOps processes, making it a comprehensive tool for development teams using GitLab.
Opinion
- Setup can be challenging
- Less robust than GitHub Copilot
- Chat functionality appears basic
- Code suggestions were difficult to implement
- Limited community resources for tips and tricks
- The integrated approach could be powerful for teams already deeply invested in the GitLab ecosystem
OpenHands (previously OpenDevin)
Website: https://docs.all-hands.dev/modules/usage/intro
GitHub: https://github.com/OpenDevin/OpenDevin
Recommendation: Developing
About OpenDevin
OpenDevin is an autonomous AI software engineer capable of executing complex engineering tasks and collaborating on software development projects.
Opinion
- Most promising autonomous AI software engineer tested
- Successfully handled Gradle building tool
- Built a simple todo app in 30 minutes for $25, though the app didn’t function properly
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MetaGPT
Website: https://docs.deepwisdom.ai/main/en/
Article: https://arxiv.org/abs/2308.00352
Other resources: https://www.1001epochs.ch/blog/metagpt-for-future-of-work
GitHub: https://github.com/geekan/MetaGPT
Recommendation: Ignore
About MetaGPT
MetaGPT is a multi-agent framework based on Large Language Models (LLMs) that aims to redefine the paradigms of task execution, collaboration, and decision-making in the workplace. It consists of two primary layers:
- Foundational components layer: Provides the essential building blocks for individual agent operations, including environment, roles, tools, and actions.
- Collaboration layer: Breaks down complex tasks, assigns them to appropriate agents, and ensures adherence to guidelines while fostering data sharing and a shared knowledge base.
Key features of MetaGPT include role definitions, quick learning, knowledge sharing, and a human-centric approach. It offers benefits such as automation, integration of human SOPs, creative program generation, and enhanced performance through multiple AI agents.
Benefits
Compared to other LLM-based frameworks, MetaGPT stands out in terms of scalability, customizability, and consistent performance across diverse benchmarks. Its development philosophy emphasizes adaptability, user-centricity, and a collaborative ecosystem.
Limitations
However, MetaGPT is still under development and may not be ideal for highly intricate projects. Its capabilities are also restricted to its training data, necessitating frequent updates for accuracy.
Key Points
- Concept of MetaGPT: MetaGPT is designed to address the limitations of existing LLM-based multi-agent systems which often produce inconsistent logic due to cascading errors. It incorporates human-like workflows to streamline and standardize the development process, thus reducing errors and improving efficiency.
- Standardized Operating Procedures (SOPs): The framework utilizes SOPs to guide the interactions and responsibilities among agents. SOPs help in breaking down complex tasks into simpler subtasks and defining clear roles for each agent.
- Role-based system: MetaGPT assigns specific roles and responsibilities to different agents, such as Product Manager, Architect, Engineer, etc. Each role has defined inputs and outputs, which are strictly adhered to, ensuring a coherent workflow.
- Communication protocols: To avoid miscommunications that commonly occur in unstructured natural language interactions, MetaGPT employs structured communication interfaces. Agents communicate through specific, structured outputs like flowcharts, design artifacts, and documented requirements, reducing the risk of information loss or distortion.
- Executable feedback mechanism: An innovative aspect of MetaGPT is its executable feedback mechanism, which allows continuous code verification and debugging during runtime, thereby enhancing the quality of the generated code.
- Empirical validation: The article reports that MetaGPT has been tested against benchmarks like HumanEval and MBPP, showing superior performance in terms of task completion rates and code quality compared to existing systems.
- Collaborative software engineering: MetaGPT has proven particularly effective in collaborative software engineering scenarios, showing its capability to manage complex software development tasks with multiple agents involved.
ChatDev (whitelist access only)
Website: –
Article: https://arxiv.org/pdf/2307.07924.pdf
GitHub: https://github.com/OpenBMB/ChatDev?tab=readme-ov-file
Recommendation: Ignore
About ChatDev
ChatDev that leverages large language models (LLMs) to streamline the entire software development process through natural language communication.
Key points
- ChatDev is a virtual chat-powered software development company that mirrors the waterfall model, dividing the process into four stages: designing, coding, testing, and documenting.
- At each stage, ChatDev recruits “software agents” with different roles, such as programmers, reviewers, and testers, who engage in collaborative dialogue to propose and validate solutions.
- The chat chain breaks down each stage into atomic subtasks, enabling dual roles to discuss and resolve specific issues through context-aware communication.
- To address code hallucination challenges, ChatDev introduces a “thought instruction” mechanism where an instructor explicitly provides guidance to the assistant programmer on code modifications.
- Experiments show ChatDev’s efficiency and cost-effectiveness, with the ability to complete the entire software development process in under 7 minutes and at a cost of less than $1.
- The framework demonstrates the potential of integrating LLMs into software development, streamlining key processes and promoting effective collaboration among diverse roles.
Devin AI (whitelist access only)
Website: https://www.cognition-labs.com/introducing-devin
Article: –
GitHub: –
Recommendation: Ignore
About Devin
Devin is a tireless, skilled teammate, equally ready to build alongside you or independently complete tasks for you to review. With Devin, engineers can focus on more interesting problems and engineering teams can strive for more ambitious goals.
GPT Pilot
Website: –
Article: –
GitHub: https://github.com/Pythagora-io/gpt-pilot
Recommendation: Promising
About GPT Pilot
Here’s how GPT Pilot builds apps, according to a quote from a project’s GitHub README:
- You enter the app name and the description.
- Product Owner agent like in real life, does nothing. 🙂
- Specification Writer agent asks a couple of questions to understand the requirements better if project description is not good enough.
- Architect agent writes up technologies that will be used for the app and checks if all technologies are installed on the machine and installs them if not.
- Tech Lead agent writes up development tasks that the Developer must implement.
- Developer agent takes each task and writes up what needs to be done to implement it. The description is in human-readable form.
- Code Monkey agent takes the Developer’s description and the existing file and implements the changes.
- Reviewer agent reviews every step of the task and if something is done wrong Reviewer sends it back to Code Monkey.
- Troubleshooter agent helps you to give good feedback to GPT Pilot when something is wrong.
- Debugger agent hate to see him, but he is your best friend when things go south.
- Technical Writer agent writes documentation for the project.
Gorilla
Website: https://gorilla.cs.berkeley.edu/
Article: https://arxiv.org/pdf/2305.15334.pdf
GitHub: https://github.com/ShishirPatil/gorilla
Recommendation: Ignore
About Gorilla
Gorilla enables LLMs to use tools by invoking APIs. Given a natural language query, Gorilla comes up with the semantically- and syntactically- correct API to invoke. With Gorilla, we are the first to demonstrate how to use LLMs to invoke 1,600+ (and growing) API calls accurately while reducing hallucination. We also release APIBench, the largest collection of APIs, curated and easy to be trained on! Join us, as we try to expand the largest API store and teach LLMs how to write them! Hop on our Discord, or open a PR, or email us if you would like to have your API incorporated as well.
Korbit
Website: https://www.korbit.ai/
Article: –
GitHub: –
Recommendation: Ignore
About Korbit
Korbit is an AI-powered tool designed for automatic pull request review.
- While it generates a significant number of comments, some are useful, but they can be challenging to identify due to the sheer volume.
- Korbit is capable of handling both small and large code diffs.
AI Code Review Action
Website: –
Article: –
GitHub: https://github.com/marketplace/actions/ai-code-review-action
Recommendation: Ignore
About AI Code Review Action
- This tool is integrated into the GitHub Actions workflow.
- Similar to Korbit, it generates a substantial number of comments, many of which may be considered redundant or unhelpful. (also there are duplications between this tool and Korbit so it seems they use similar prompting strategy)
- However, it struggles with larger code diffs, potentially limiting its effectiveness in complex projects.
- AI Code Review Action on GitHub utilizes the publicly available GPT-3.5 Turbo model.
CodeRabbit
Website: https://coderabbit.ai/
Article: –
GitHub: https://github.com/marketplace/coderabbitai
Recommendation: Promising
About CodeRabbit
Data, privacy, and security: CodeRabbit does not use data collected during code reviews to train or influence the models. Queries to the Large Language Models (LLMs) are ephemeral and there is zero retention on LLMs. Neither we nor the LLMs provider(s) share any data collected during the code review process with third parties.
- CodeRabbit takes a broader approach, focusing not only on code review but also on suggesting best practices.
- In addition to reviewing code changes, it generates comprehensive overviews and patch notes, providing valuable insights for developers.
- CodeRabbit places a strong emphasis on security, ensuring the protection of sensitive information.
Supermaven
Website: https://supermaven.com/
Article: –
GitHub: –
Recommendation: Ignore
About Supermaven
- Supermaven offers code completion suggestions, but they often lack context and relevance to the project at hand.
- It primarily focuses on providing completions for single lines of code.
- While Supermaven boasts fast reaction times, the code completions may contain typos and mistakes.
Gemini Code Assist
Website: https://cloud.google.com/code
Article: –
GitHub: –
Recommendation: Promising
About Gemini Google Cloud Code
SEPTEMBER 2024 UPDATE: Gemini Code Assist has become an integral part of Android Studio, though it currently has limited functionalities, particularly in chat capabilities.
- Positioned as a viable alternative to GitHub Copilot, Google Cloud Code offers similar capabilities.
- It features a Gemini chat interface and generally provides slower code completions compared to GitHub Copilot.
- Seems like Google Cloud Code does not support all programming languages, such as Flutter.
- Limited information is available regarding data privacy measures implemented by this tool.
GitHub Copilot
Website: https://github.com/features/copilot
Article: –
GitHub: –
Recommendation: Recommended
About GitHub Copilot
- Widely regarded as a top choice among code assistant tools, GitHub Copilot stands out for its advanced features.
- It leverages a GPT-based chat interface that incorporates project context, resulting in more relevant and accurate code suggestions.
- GitHub Copilot allows users to configure data privacy settings, addressing potential concerns about sensitive information.
AI developer tools: Conclusions
A tl;dr version of our research.
Automated code review agents: Comparative conclusion
- Usefulness of comments: While both Korbit and AI Code Review Action on GitHub generate numerous comments, the sheer volume can make it challenging to identify genuinely useful feedback. On the other hand, CodeRabbit’s approach of providing comprehensive overviews and patch notes may be more effective in conveying meaningful insights.
- Scope of review: CodeRabbit stands out by not only reviewing code but also suggesting best practices, which can be invaluable for maintaining high-quality code and adhering to industry standards.
- Data privacy: While data privacy is a critical aspect of any code review tool, CodeRabbit explicitly prioritizes the protection of sensitive information, giving it an advantage in security-conscious environments
Honest opinion
I currently see 3 use cases for these tools:
- Private projects
- Projects with a single developer
- Low-commercial-experience developers
AI-powered code assistants: Comparative analysis
- Code completion accuracy: GitHub Copilot and Cloud Code provides accurate and context-aware code completions, thanks to its ability to understand the project’s codebase.
- Data privacy: GitHub Copilot offers configurable data privacy settings, allowing users to control the level of information shared with the tool. On the other hand, Google Cloud Code lacks transparency regarding its data privacy practices.
AI agents: Comparative analysis
MetaGPT incorporates human-like workflows and standardized operating procedures (SOPs) to address the limitations of existing LLM-based approaches. It assigns specific roles and responsibilities to different agents, promoting a coherent and structured development process. MetaGPT’s features include an executable feedback mechanism for continuous code verification and debugging, as well as a focus on knowledge sharing and collaboration.
Similarly, GPT Pilot takes a step-by-step approach, with each agent (e.g., specification writer, architect, developer) playing a distinct role in the software development process. This structured workflow helps to mitigate the risk of cascading errors and inconsistencies.
While these AI agent-based frameworks demonstrate the potential of integrating LLMs into software development, they are still in their early stages of development and not yet ready for widespread production use. In our tests, they were unable to generate a complete and functional TODO application with Ktor, JWT authentication, and serialization, highlighting the need for further refinement and maturation before they can be reliably used for complex software projects.
Contents
AI developer tools FAQ
Frequently asked questions about AI tools used in software development.
What is generative AI and how does it benefit software developers?
Generative AI, such as models developed by OpenAI, can create code snippets, automate repetitive tasks, and assist in debugging, significantly enhancing productivity and efficiency for software developers.
What are AI developer tools?
AI developer tools are a collection of software applications and libraries that assist developers in building, testing, and deploying artificial intelligence functionalities within their software. These tools can streamline workflows and improve the efficiency of AI development.
How are AI models used in coding tools?
AI models are integrated into coding tools and IDEs to provide intelligent code suggestions, automate error detection, and generate documentation, streamlining the development process.
Who can benefit from AI developer tools?
AI developer tools are beneficial for various developers, including those with experience in machine learning, data science, and traditional software development. Even beginners can leverage user-friendly tools to integrate basic AI features.
What are the common use cases for AI developer tools?
- Training and deploying machine learning models for tasks like image or speech recognition, natural language processing, and anomaly detection.
- Automating repetitive coding tasks and generating code snippets based on developer intent.
- Optimizing software performance and identifying potential bugs through AI-powered analysis.
What types of AI developer tools are available?
I developer tools come in many flavors, designed to assist programmers in various stages of the development workflow. Here's a breakdown of some common types:
Code completion and assistants: These tools use AI to predict the next line of code, suggest code snippets, or even generate entire functions. Examples include Tabnine, JetBrains AI assistant, and aiXcoder.
Code review and debugging: Tools in this category can analyze code for errors, suggest improvements, and even help with debugging complex problems. Some examples include Codium, Stepsize AI, and Sourcery.
Documentation generation: These AI-powered tools can automatically generate documentation from your code, saving developers time and effort. Rewind.ai is a popular example.
General AI assistants: Some development environments like Replit include built-in chatbots powered by AI that can answer questions, provide suggestions, and even help with debugging.
UI/UX Design assistants: There are AI tools that can help with designing user interfaces by generating mockups or suggesting layouts based on user data. While these aren't strictly code-focused, they can be valuable for developers involved in the entire application creation process.
How do you use AI in your development process?
At Pragmatic Coders, we use AI tools to generate code, brainstorm, or streamline daily tasks.
Learn more: AI in software development: how we’re saving clients’ time & money
Why do you need to integrate AI developer tools into your product lifecycle ASAP?
Artificial intelligence is crucial to do things faster: experiment, make mistakes, and learn from then.
Joe Justice, ex-Tesla employee and Agile coach shared with us his observations on AI implementation:
- I think companies that aren’t using AI are behind, and those that aren’t using their own AI have missed the opportunity to start training it.
- Once you start training your own AI, you see which types of data and datasets are most useful. This realization starts to change how you gather information and even change how you work to make it easier to gather information. They haven’t even started that learning curve yet.
Most importantly, AI is crucial to innovate, which you most probably want to do if you're building digital products.
Learn more: How Elon Musk’s innovation strategy can fuel your app’s success
How do AI-powered IDEs enhance productivity?
AI-powered IDEs, like Visual Studio Code with Copilot, offer intelligent features that assist developers in writing, optimizing, and debugging code more efficiently.
What is the role of OpenAI in advancing AI tools for developers?
OpenAI has developed powerful AI models like GPT-4 that are integrated into various development tools, providing advanced assistance in coding, debugging, and project management.
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