Best AI Coding Assistants for Mac of 2025 - Page 3

Aider enables collaborative programming with LLMs, allowing you to modify code within your local git repository seamlessly. You can initiate a new project or enhance an existing git repository with ease. Aider is optimized for use with models like GPT-4o and Claude 3.5 Sonnet, and it can interface with nearly any LLM available. Additionally, Aider has achieved impressive results on the SWE Bench, a rigorous software engineering benchmark that evaluates the ability to resolve actual GitHub issues from well-known open-source projects such as Django, Scikit-learn, and Matplotlib, among others. This capability makes Aider a valuable tool for developers looking to improve their coding efficiency and tackle complex challenges in software development.

Unblocked augments code by integrating context from platforms such as Slack, Confluence, and Jira, providing you with useful answers without the need for extensive searches. Utilizing Unblocked doesn't demand any alterations to your team's existing workflows or additional setup efforts, as you simply connect your tools and begin using the service seamlessly. By comprehending your codebase, architecture, and internal processes, Unblocked delivers prompt and precise answers, eliminating the need to disturb or wait for your colleagues. It also highlights relevant documents and previous discussions tied to any open file, ensuring you possess the same insight as those who originally wrote the code. Data security is a priority, with access across network layers being safeguarded through encryption both in transit and at rest, utilizing keys specific to each team. Importantly, customer data is never utilized to train shared models, maintaining confidentiality and trust. Moreover, Unblocked aids new team members in quickly locating the information they require, enabling them to begin contributing to projects almost immediately. This streamlined access not only enhances productivity but also fosters a collaborative environment.

Incorporating questions or generating code within the context of your codebase can yield precise outcomes, enhancing your work with specific folders, online documentation, terminal output, and files. PearAI enables direct coding in your projects while providing visibility into the differences through diffs. For instance, by using CMD+I (or CTRL+I on Windows), you can request PearAI's assistance in implementing error handling and adding comments. Remarkably, without writing any code ourselves, we successfully introduced a new feature to the unfamiliar codebase by creating a documentation page for the PearAI landing site. This approach can significantly accelerate your development process by integrating AI smoothly into your daily tasks. The primary aim of PearAI is to shorten the journey from concept to implementation. As coding remains an essential element of product development, we anticipate that advancements in AI will profoundly transform this landscape in the foreseeable future. Our mission is to cultivate an environment that embraces these transformative changes, addressing both immediate needs and future developments as they arise. As technology evolves, we aspire to keep pace with innovations that will reshape the way developers work and create.

Introducing an all-in-one AI coding companion designed for your preferred code editor, it seamlessly integrates with advanced models such as GPT-4o and Claude 3.5 Sonnet. This innovative tool provides real-time code suggestions tailored to your cursor's position and the comments you write as you code. You can effortlessly select any piece of code and modify it using a straightforward prompt, enabling you to tweak functions or even overhaul entire classes with ease. Engage with your active files or explore a fully indexed codebase by utilizing top-tier models like Claude 3.5 Sonnet or GPT-4o, and if you encounter issues, simply send a screenshot for prompt debugging assistance. Just instruct Monica Code to generate or adjust multiple files, while navigating through various versions of your code seamlessly. By articulating your needs in everyday language, Monica Code can help you produce relevant code snippets or structures in the programming language of your choice, proving invaluable for a range of programming projects, from basic scripts to intricate application frameworks. This powerful tool not only enhances productivity but also fosters a more intuitive coding experience.

Optimize the management of source code context and generate effective prompts efficiently. Ship alongside ChatGPT and Claude, the 16x Prompt tool enables developers to oversee source code context and prompts for tackling intricate coding challenges within existing codebases. By inputting your personal API key, you gain access to APIs from OpenAI, Anthropic, Azure OpenAI, OpenRouter, and other third-party services compatible with the OpenAI API, such as Ollama and OxyAPI. Utilizing these APIs ensures that your code remains secure, preventing it from being exposed to the training datasets of OpenAI or Anthropic. You can also evaluate the code outputs from various LLM models, such as GPT-4o and Claude 3.5 Sonnet, side by side, to determine the most suitable option for your specific requirements. Additionally, you can create and store your most effective prompts as task instructions or custom guidelines to apply across diverse tech stacks like Next.js, Python, and SQL. Enhance your prompting strategy by experimenting with different optimization settings for optimal results. Furthermore, you can organize your source code context through designated workspaces, allowing for the efficient management of multiple repositories and projects, facilitating seamless transitions between them. This comprehensive approach not only streamlines development but also fosters a more collaborative coding environment.

Amazon Q Developer is an advanced AI assistant built for professional developers, combining coding intelligence with deep AWS expertise. It’s designed to handle every stage of development—from writing and refactoring code to performing upgrades and automating documentation. Integrated with major IDEs and the AWS Management Console, it empowers developers to code faster and operate smarter using secure, context-aware assistance. Its agentic automation can autonomously implement features, test applications, and perform large-scale migrations like .NET to Linux or Java 8 to Java 17 in minutes. Developers can chat directly with Amazon Q inside Slack, Microsoft Teams, GitHub, and GitLab, where it provides architectural recommendations and incident resolution guidance. The tool also supports CLI autocompletions and AWS resource management to streamline workflows from the terminal to the cloud. Offering enterprise-grade access controls and IAM integration, it ensures that organizational data and permissions remain protected. Available on the AWS Free Tier, Amazon Q Developer enables up to 50 monthly AI interactions and 1,000 lines of code transformation at no cost, helping teams start building smarter right away.

An autonomous coding assistant integrated directly within your IDE, this tool can create and modify files, execute commands, browse the web, and more, all with your explicit consent throughout the process. Cline is designed to tackle intricate software development challenges methodically. Equipped with capabilities to generate and adjust files, navigate extensive projects, utilize web resources, and perform terminal commands (once you authorize), he offers support that transcends mere code completion or technical assistance. Unlike traditional AI scripts that operate in isolated environments, this extension features a user-friendly interface that requires your approval for every file modification and command execution, ensuring a secure and user-friendly approach to harnessing the capabilities of autonomous AI. By incorporating such a system, developers can explore innovative solutions while maintaining control over their coding environment.

Defang is a developer-focused platform designed to streamline the development, deployment, and debugging of cloud applications. Utilizing AI-driven tools, Defang empowers developers to quickly evolve their concepts into fully operational applications hosted on their selected cloud services. The platform accommodates various programming languages such as Go, JavaScript, and Python, enabling users to kickstart their projects with example templates or create outlines through natural language instructions. With just a single command, Defang automates the building and deployment processes, managing configurations related to computing, storage, load balancing, networking, logging, and security seamlessly. The Defang Command Line Interface (CLI) enhances user interaction with the platform, providing installation methods through shell scripts, Homebrew, Winget, Nix, or direct downloads. Developers can specify their services using compose.yaml files, which Defang leverages to facilitate application deployment in the cloud. Overall, this platform significantly reduces the complexity traditionally associated with cloud application development, making it more accessible for developers at all skill levels.

TRAE redefines the future of software engineering as a fully autonomous AI coding platform that helps developers understand, execute, and deliver projects end-to-end. Acting as a “10x AI Engineer,” TRAE can design architectures, generate complex code, debug in real time, and handle deployment—all within a single environment. The platform’s SOLO mode enables responsive, independent development powered by contextual understanding of repositories, documents, and live browser interactions. With MCP (Model Context Protocol), TRAE agents intelligently pull in external tools and resources when needed, streamlining the workflow without manual configuration. Developers can even create and customize agent teams for specialized tasks, defining unique skill sets and logic for each. TRAE also includes CUE, a predictive feature that anticipates your next edit with a single keystroke for lightning-fast coding. Built with a “local-first” privacy model, it ensures sensitive code stays secure with encrypted data handling and regional deployment compliance. Loved globally by developers for its speed, UI design, and real-time intelligence, TRAE is fast becoming the IDE of choice for modern software builders.

The SWE-agent is a sophisticated AI-driven platform that automates a variety of tasks, including addressing GitHub issues, conducting cybersecurity operations such as Capture The Flag (CTF) challenges, and tackling coding problems. Utilizing advanced language models like GPT-4 or Claude, it operates within isolated computing environments to perform tasks independently, delivering customizable solutions tailored for developers and cybersecurity experts. This versatile tool caters to numerous applications, ranging from enhancing software repositories to detecting vulnerabilities and executing specialized tasks. Crafted by a collaboration of researchers from Princeton and Stanford University, SWE-agent exemplifies the integration of machine learning with effective problem-solving in the realms of software development and cybersecurity. With its innovative features, it represents a significant advancement in automating complex workflows for professionals in these fields.

Devika is an innovative open-source AI software engineer that interprets high-level commands, dissects them into actionable steps, gathers pertinent information, and writes code to achieve specified goals. By leveraging advanced language models, reasoning techniques, and browsing functionalities, Devika effectively aids in software development, handling intricate coding challenges with little human oversight. The platform is compatible with various programming languages and boasts essential features such as sophisticated AI planning, contextual keyword identification, and real-time agent monitoring. With the intention of becoming a formidable competitor to proprietary AI solutions, Devika presents a bold, open-source alternative for developers seeking versatile support in their projects. Ultimately, it seeks to empower programmers by streamlining the coding process and enhancing productivity.

Fynix serves as an AI-driven platform aimed at enhancing software development efficiency by providing smart coding assistance and agent-based code reviews. This tool seamlessly integrates with widely used IDEs such as VS Code, offering features like context-aware autocomplete, natural language inputs for code corrections and translations, along with automatic visualizations of code flow. The Code Assistant feature of Fynix enables developers to produce cleaner and more efficient code at an accelerated pace, and the forthcoming Code Quality Agent promises to streamline bug detection while upholding coding standards. Supporting a variety of programming languages and frameworks, and compatible with tools like Jira, Fynix proves to be a flexible solution for fostering improved coding practices and team collaboration. As developers strive for excellence in their coding endeavors, Fynix stands out as an essential ally in the software development landscape.

Codespell.ai, an AI-powered coding tool, improves code quality and increases productivity. Its main feature, Design Studio automates project set-up by generating starter-kit codebases and structured designs.

CodeNext.ai is an innovative AI-driven coding assistant tailored for Xcode developers, featuring advanced context-aware code completion alongside interactive chat capabilities. It is compatible with numerous top-tier AI models, such as OpenAI, Azure OpenAI, Google AI, Mistral, Anthropic, Deepseek, Ollama, and others, allowing developers the convenience to select and switch models according to their preferences. The tool offers smart, instant code suggestions as you type, significantly boosting productivity and coding effectiveness. Additionally, its chat functionality empowers developers to communicate in natural language for tasks like writing code, debugging, refactoring, and executing various coding operations within or outside the codebase. CodeNext.ai also incorporates custom chat plugins, facilitating the execution of terminal commands and shortcuts right within the chat interface, thereby optimizing the overall development process. Ultimately, this sophisticated assistant not only simplifies coding tasks but also enhances collaboration and streamlines the workflow for developers.

Codex CLI is a powerful open-source AI tool that runs in your command line interface (CLI), offering developers an intuitive way to automate coding tasks and improve code quality. By pairing Codex CLI with your terminal, developers gain access to AI-driven code generation, debugging, and editing capabilities. It enables users to write, modify, and understand their code more efficiently with real-time suggestions, all while working directly in the terminal without switching between tools. Codex CLI supports a seamless coding experience, empowering developers to focus more on building and less on managing tedious coding processes.

Gemini CLI is an open-source command line interface that brings the full power of Gemini’s AI models into developers’ terminals, offering a seamless and direct way to interact with AI. Designed for efficiency and flexibility, it enables coding assistance, content generation, problem solving, and task management all through natural language commands. Developers using Gemini CLI get access to Gemini 2.5 Pro with a generous free tier of 60 requests per minute and 1,000 daily requests, supporting both individual users and professional teams with scalable paid plans. The platform incorporates tools like Google Search integration for dynamic context, Model Context Protocol (MCP) support, and prompt customization to tailor AI behavior. It is fully open source under Apache 2.0, encouraging community input and transparency around security. Gemini CLI can be embedded into existing workflows and automated via non-interactive script invocation. This combination of features elevates the command line from a basic tool to an AI-empowered workspace. Gemini CLI aims to make advanced AI capabilities accessible, customizable, and powerful for developers everywhere.

Kiro is an advanced integrated development environment powered by artificial intelligence, designed to streamline AI-driven programming by transforming natural-language instructions into structured requirements, system architectures, and specific implementation tasks that undergo thorough testing. Created specifically for autonomous workflows, it offers features such as specification-based development, multimodal communication, and "agent hooks" that activate background tasks during events like file saving, alongside an autopilot mode that takes charge of executing lengthy scripts while keeping the user engaged. By effectively managing context, Kiro minimizes repetitive tasks and simplifies the integration of complex functionalities within extensive codebases. Its built-in integrations with MCP allow seamless connections to documentation, databases, and APIs, and users can influence the development process using visual aids like UI designs or architecture blueprints. With a strong focus on enterprise-level security and privacy, Kiro guarantees safe deployment, while compatibility with Claude Sonnet models, Open VSX plugins, and existing VS Code configurations ensures an intuitive and AI-enhanced experience. Additionally, the platform continuously evolves, adapting to user feedback and emerging technologies to maintain its position at the forefront of software development tools.

Elastic Copilot is an extension for VS Code that serves as an AI-driven programming companion, utilizing the complete context windows of top-tier models without limitations to generate production-ready code. Seamlessly integrated into the editor, it allows users to access a terminal for executing commands, installing libraries, running tests, and handling system tasks without needing to exit the workspace. The extension's file system capabilities enable users to create, modify, and organize files and directories with a comprehensive grasp of the project structure. Additionally, an in-editor web browser facilitates real-time testing of web applications, providing instant feedback on any UI modifications. Every action undertaken is meticulously documented in a development history, which permits users to review their workflow, revert to previous stages, and track the evolution of the project. Elastic Copilot stands out in its ability to produce intricate functions, resolve bugs, and refactor existing code, transforming natural language queries into well-structured implementations. By streamlining the development process, it enhances productivity and fosters a more efficient coding environment.

Qoder is a sophisticated coding platform designed specifically for genuine software development, surpassing standard code completion by integrating advanced context engineering with intelligent AI agents that possess a profound understanding of your project. It enables developers to assign intricate, asynchronous tasks via its Quest Mode, wherein agents operate independently to provide complete results, while also allowing for enhanced functionality through Model Context Protocol (MCP) integrations that connect with various external tools and services. Additionally, Qoder’s Memory system captures coding style, project-specific insights, and reusable context to guarantee consistent, project-aware outputs throughout the development process. Developers can engage in chat for advice or code recommendations, maintain a Repo Wiki for consolidating knowledge, and exercise control over behavior through Rules to ensure that AI-generated work remains secure and guided. This combination of context-sensitive automation, agent delegation, and customizable AI behavior not only empowers teams to think more critically and code more effectively but also fosters an environment where innovation and collaboration can thrive. Through these features, Qoder revolutionizes the coding experience, making it more efficient and aligned with the specific needs of each project.

Incerto serves as an AI-driven "Database Co-Pilot" that possesses a profound understanding of your database ecosystem, enabling it to proactively oversee operations, thereby minimizing manual tasks and removing production bottlenecks. It consistently tracks more than 100 established issues, including inefficient queries and cluster malfunctions, and autonomously activates verified solutions through its context-aware AI agents, all before any negative impact on users occurs. By identifying slow queries and refining them using a human-in-the-loop AI workflow designed for specific database management system architectures, it significantly boosts performance. Its intuitive "text-to-task" interface empowers users to articulate tasks in a conversational manner, such as migrating user data, investigating performance issues, or crafting queries, with the system adeptly interpreting and executing these tasks while remaining fully cognizant of the schema, workload, and infrastructure context. Furthermore, a sophisticated SQL editor provides AI support and facilitates a seamless transition from descriptive language to precise SQL commands, ensuring users can work more efficiently and effectively, regardless of their technical expertise. This comprehensive tool ultimately transforms database management into a more streamlined and user-friendly experience.

Verdent is an advanced development platform powered by AI, aimed at assisting developers in managing intricate projects and producing code that is both efficient and reliable for production use. It features two main products: Verdent Deck, a desktop application, and Verdent for VS Code, which serves as an integrated extension. Both tools facilitate the operation of multiple AI agents concurrently, utilize DiffLens for clarifying code changes by highlighting modifications and their reasons, offer code reviews driven by GPT-5 technology, implement a Plan-first Alignment for organized execution, and incorporate a browser tool along with a verifier subagent for accuracy checks. Verdent Deck enables developers to efficiently handle numerous tasks with oversight in isolated environments, allowing for asynchronous completion while maintaining a comprehensive view of the project's status and progress. On the other hand, Verdent for VS Code functions as an AI coding assistant that autonomously plans, develops, and verifies tasks directly within the editor, making it particularly beneficial for engineers who prioritize precision, clarity, and reliable outcomes in environments that demand production-grade quality. This dual approach ensures that developers can tackle even the most complex challenges with confidence and effectiveness.

Kodu is an innovative platform driven by AI that empowers individuals to conceptualize, prototype, and create software applications without requiring extensive coding skills. Its standout feature is Claude Code, an extension for VSCode that integrates agentic coding functionalities directly into the editor: this tool can autonomously generate code, analyze changes it implements, convert pasted images or mockups into operational user interfaces, and execute command-line instructions all within a chat interface. In addition, Kodu provides Kodu Engineer, a voice-activated assistant that allows users to articulate their project ideas in everyday language while observing the real-time development of their application through screen sharing and immediate deployment. The platform operates on its proprietary "Kodu Cloud" inference API, utilizing a "rateless" model connection that enables users to efficiently prototype and enhance applications. Furthermore, Kodu's seamless workflow bridges the gap between ideation, design, and production, streamlining the transition from mockup to fully operational app with minimal obstacles or delays. This comprehensive approach not only simplifies the development process but also encourages creativity and innovation among users.

Codacy is an automated code review tool. It helps identify problems through static code analysis. This allows engineering teams to save time and tackle technical debt. Codacy seamlessly integrates with your existing workflows on Git provider as well as with Slack and JIRA or using Webhooks. Each commit and pull-request includes notifications about security issues, code coverage, duplicate code, and code complexity. Advanced code metrics provide insight into the health of a project as well as team performance and other metrics. The Codacy CLI allows you to run Codacy code analysis locally. This allows teams to see Codacy results without needing to check their Git provider, or the Codacy app. Codacy supports more than 30 programming languages and is available in free open source and enterprise versions (cloud or self-hosted). For more see https://www.codacy.com/

Visual Studio IntelliCode enhances the development experience through AI-driven assistance. By prioritizing suggestions that you are most likely to need, IntelliCode helps streamline your workflow significantly. Its recommendations are informed by a wealth of information derived from numerous open-source projects on GitHub, each boasting over 100 stars. When used alongside the context of your existing code, the completion suggestions are customized to align with established coding practices. Beyond just completing statements, IntelliCode also offers signature assistance, suggesting the most relevant overload options based on the current context. Not only does IntelliCode provide personalized recommendations for your code, but it also facilitates sharing these insights across your team effortlessly. Thanks to this preview feature, you can create a team model that offers tailored suggestions for code outside the open-source realm, including methods from your custom utility classes or specialized libraries. Furthermore, by integrating our build task into your development pipeline, you can ensure that team completions remain current with any changes in the repository, fostering a more collaborative coding environment. This powerful tool ultimately empowers developers to work more efficiently and effectively within their teams.

StarCoder and StarCoderBase represent advanced Large Language Models specifically designed for code, developed using openly licensed data from GitHub, which encompasses over 80 programming languages, Git commits, GitHub issues, and Jupyter notebooks. In a manner akin to LLaMA, we constructed a model with approximately 15 billion parameters trained on a staggering 1 trillion tokens. Furthermore, we tailored the StarCoderBase model with 35 billion Python tokens, leading to the creation of what we now refer to as StarCoder. Our evaluations indicated that StarCoderBase surpasses other existing open Code LLMs when tested against popular programming benchmarks and performs on par with or even exceeds proprietary models like code-cushman-001 from OpenAI, the original Codex model that fueled early iterations of GitHub Copilot. With an impressive context length exceeding 8,000 tokens, the StarCoder models possess the capability to handle more information than any other open LLM, thus paving the way for a variety of innovative applications. This versatility is highlighted by our ability to prompt the StarCoder models through a sequence of dialogues, effectively transforming them into dynamic technical assistants that can provide support in diverse programming tasks.