Best AI Coding Assistants for Linux

A state-of-the-art AI that builds better software, cheaper and faster. K.Explorer is an AI powered Code Assistant trained on many millions of private corporate lines of code, for specific domains, and on billions of public and open-source lines of code for general purposes. Its code auto-completion features suggest code completions and entire function bodies as you type or as you search the engine for help. To make development faster and more agile it even supports Natural Language for programmers to get guidance while telling a text story about the code they want to write.

DeepSeek Coder is a cutting-edge software tool designed to revolutionize the landscape of data analysis and coding. By leveraging advanced machine learning algorithms and natural language processing capabilities, it empowers users to seamlessly integrate data querying, analysis, and visualization into their workflow. The intuitive interface of DeepSeek Coder enables both novice and experienced programmers to efficiently write, test, and optimize code. Its robust set of features includes real-time syntax checking, intelligent code completion, and comprehensive debugging tools, all designed to streamline the coding process. Additionally, DeepSeek Coder's ability to understand and interpret complex data sets ensures that users can derive meaningful insights and create sophisticated data-driven applications with ease.

Increase software development and delivery velocity using generative AI assistance, with enterprise security and privacy protection. Gemini Code Assist completes your code as you write, and generates whole code blocks or functions on demand. Code assistance is available in many popular IDEs, such as Visual Studio Code, JetBrains IDEs (IntelliJ, PyCharm, GoLand, WebStorm, and more), Cloud Workstations, Cloud Shell Editor, and supports 20+ programming languages, including Java, JavaScript, Python, C, C++, Go, PHP, and SQL. Through a natural language chat interface, you can quickly chat with Gemini Code Assist to get answers to your coding questions, or receive guidance on coding best practices. Chat is available in all supported IDEs. Enterprises can customize Gemini Code Assist using their organization’s private codebases and knowledge sources so that Gemini Code Assist can offer more tailored assistance. Gemini Code Assist enables large-scale changes to entire codebases.

bolt.diy is an open-source platform that enables developers to easily create, run, edit, and deploy full-stack web applications with a variety of large language models (LLMs). It supports a wide range of models, including OpenAI, Anthropic, Ollama, OpenRouter, Gemini, LMStudio, Mistral, xAI, HuggingFace, DeepSeek, and Groq. The platform offers seamless integration through the Vercel AI SDK, allowing users to customize and extend their applications with the LLMs of their choice. With its intuitive interface, bolt.diy is designed to simplify AI development workflows, making it a great tool for both experimentation and production-ready applications.

​Roo Code, formerly known as Roo Cline, is an AI-powered autonomous coding agent integrated into Visual Studio Code, designed to enhance software development efficiency. It facilitates natural language interactions, enabling users to generate code, refactor existing code, debug, and update documentation seamlessly. It can read and write files directly within the workspace, execute terminal commands, and automate browser actions. It supports integration with any OpenAI-compatible or custom APIs/models and allows customization through various modes, including Code Mode for general coding tasks, Architect Mode for system design, Ask Mode for inquiries, Debug Mode for troubleshooting, and user-defined Custom Modes for specialized tasks. Roo Code also features the Model Context Protocol (MCP), extending its capabilities by integrating with external tools and APIs.

Datalore is a collaborative data science and analytics platform aimed at boosting the whole analytics workflow and making work with data enjoyable for both data scientists and data savvy business teams across the enterprise. Keeping a major focus on data teams workflow, Datalore offers technical-savvy business users the ability to work together with data teams, using no-code or low-code together with the power of Jupyter notebooks. Datalore enables analytical self-service for business users, enabling them to work with data using SQL and no-code cells, build reports and deep dive into data. It offloads the core data team with simple tasks. Datalore enables analysts and data scientists to share results with ML Engineers. You can run your code on powerful CPUs or GPUs and collaborate with your colleagues in real-time.

Easily share shell aliases/functions and help your coworkers save a few keystrokes. Securely sync API keys, ports and other application configurations between developers. Use Fig's autocomplete for your team's internal scripts & CLI tools. Create easy-to-discover shell scripts and sync them across your engineering team. Extend autocomplete with custom suggestions tailored to your team. All cloud features are opt-in and your data is encrypted at rest. For autocomplete, all of your keystrokes are processed locally and never leave your device.

Cody, Sourcegraph’s AI code assistant goes beyond individual dev productivity, helping enterprises achieve consistency and quality at scale with AI. Unlike traditional coding assistants, Cody understands the entire codebase, enabling deeper contextual awareness for smarter autocompletions, refactoring, and AI-driven code suggestions. It integrates with IDEs like VS Code, Visual Studio, Eclipse, and JetBrains, providing inline editing and chat without disrupting workflows. Cody also connects with tools like Notion, Linear, and Prometheus to enhance development context. Powered by advanced LLMs like Claude Sonnet 4 and GPT-4o, it optimizes speed and performance based on enterprise needs, and is always adding the latest AI models. Developers report significant efficiency gains, with some saving up to six hours per week and doubling their coding speed.

We introduce Codestral, our first-ever code model. Codestral is an open-weight generative AI model explicitly designed for code generation tasks. It helps developers write and interact with code through a shared instruction and completion API endpoint. As it masters code and English, it can be used to design advanced AI applications for software developers. Codestral is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash. It also performs well on more specific ones like Swift and Fortran. This broad language base ensures Codestral can assist developers in various coding environments and projects.

Ask questions or generate code with the context of your codebase for accurate results. This also works with choosing specific folders, online docs, terminal content, files, and more. PearAI can directly code in your files, and allow you to see diffs. Try CMD+I (CTRL+I on Windows). Here, we ask PearAI to help us add error handling and comments. Without writing a single line of code, we were able to make a new feature in an unfamiliar codebase: adding a documentation page to the PearAI landing page. Speed up your development process by seamlessly integrating AI into your workflow. PearAI's goal is to reduce the time it takes for an individual to go from idea to creation. Coding itself is a crucial tool in product development, and we believe that with the advancement of AI, it may drastically change over the coming years. We aim to build the environment that will encompass these changes, both in the short-term and long-term.

Manage source code context and generate optimized prompts. Ship with ChatGPT and Claude. 16x Prompt helps developers manage source code context and prompts to complete complex coding tasks on existing codebases. Enter your own API key to use APIs from OpenAI, Anthropic, Azure OpenAI, OpenRouter, or 3rd party services that offer OpenAI API compatibility, such as Ollama and OxyAPI. Using API avoids leaking your code to OpenAI or Anthropic training data. Compare the code output of different LLM models (for example, GPT-4o & Claude 3.5 Sonnet) side-by-side to see which one is the best for your use case. Craft and save your best prompts as task instructions or custom instructions to use across different tech stacks like Next.js, Python, and SQL. Fine-tune your prompt with various optimization settings to get the best results. Organize your source code context using workspaces to manage multiple repositories and projects in one place and switch between them easily.

Defang is a developer-centric platform that simplifies the process of developing, deploying, and debugging cloud applications. By leveraging AI-assisted tooling, Defang enables developers to swiftly transition from an idea to a deployed application on their preferred cloud provider. The platform supports multiple programming languages, including Go, JavaScript, and Python, allowing developers to start with sample projects or generate project outlines using natural language prompts. With a single command, Defang builds and deploys applications, handling configurations for computing, storage, load balancing, networking, logging, and security. The Defang Command Line Interface (CLI) facilitates interactions with the platform, offering installation options via shell scripts, Homebrew, Winget, Nix, or direct download. Developers can define services using compose.yaml files, which Defang utilizes to deploy applications to the cloud.

CodeNext.ai is an AI-powered coding assistant designed specifically for Xcode developers, offering context-aware code completion and agentic chat functionalities. It supports a wide range of leading AI models, including OpenAI, Azure OpenAI, Google AI, Mistral, Anthropic, Deepseek, Ollama, and more, providing developers with the flexibility to choose and switch between models as needed. It delivers intelligent, real-time code suggestions as you type, enhancing productivity and coding efficiency. Its agentic chat feature allows developers to interact in natural language to write code, fix bugs, refactor, and perform various coding tasks within or beyond the codebase. CodeNext.ai includes custom chat plugins that enable the execution of terminal commands and shortcuts directly within the chat interface, streamlining the development workflow.

Kiro is an AI‑powered integrated development environment that brings structure to AI‑driven coding by converting natural‑language prompts into clear requirements, system designs, and discrete implementation tasks validated by robust tests. Built from the ground up for agentic workflows, it features spec‑driven development, multimodal chat, “agent hooks” that trigger background tasks on events like file saves, and an autopilot mode that autonomously runs large scripts while keeping you in control. With smart context management, Kiro reduces repetitive prompts and helps implement complex features across large codebases. Native MCP integrations let you connect to documentation, databases, and APIs, and you can guide development with images of UI designs or architecture diagrams. Enterprise‑grade security and privacy ensure safe deployment, while support for Claude Sonnet models, Open VSX plugins, and existing VS Code settings delivers a familiar yet AI‑supercharged experience.

Elastic Copilot is a VS Code extension that functions as a context‑aware AI pair programmer, harnessing the full context windows of industry‑leading models with no caps to produce production‑ready code. Embedded directly in the editor, it offers integrated terminal access for executing commands, installing packages, running tests, and performing system operations without leaving your workspace. Its file system integration lets you create, modify, and organize files and directories with a deep understanding of your project’s structure, while an in‑editor browser enables real‑time testing of web applications and immediate feedback on UI changes. Every action is captured in a development history, allowing you to review your workflow, revert to any point in time, and audit project evolution. Elastic Copilot excels at generating complex functions, fixing bugs, and refactoring existing code, turning natural prompts into clean implementations.

Kodu is an AI-powered development platform that allows users to ideate, prototype, and build software products without needing deep coding expertise. It features Claude Code, a VSCode extension powered by Claude 3.7 Sonnet, which brings agentic coding capabilities directly into your editor: the tool can generate code, inspect diffs of changes it makes, interpret pasted images or mockups into functional UI, and run CLI commands without leaving the chat interface. Kodu also offers Kodu Engineer, a voice-driven assistant that lets you describe your project in natural language and watch it build your app live with screen sharing and instant deployment. Behind the scenes, Kodu utilizes its own “Kodu Cloud” inference API with a “rateless” model connection, allowing users to prototype and extend apps rapidly. All of this is delivered through a workflow that connects idea, design, and production, from mockup to live app, with minimal friction or handoff overhead.

Build apps faster with ML-powered coding companion. Accelerate application development with automatic code recommendations based on the code and comments in your IDE. Empower developers to use artificial intelligence (AI) responsibly to create syntactically correct and secure applications. Generate entire functions and logical code blocks without having to search and customize code snippets from the web. Stay focused and never leave the IDE, with real-time customized code recommendations for all your Java, Python, and JavaScript projects. Amazon CodeWhisperer is a machine learning (ML)–powered service that helps improve developer productivity by generating code recommendations based on their comments in natural language and code in the integrated development environment (IDE). Accelerate frontend and backend development by empowering developers with automatic code recommendations. Save time and effort by using CodeWhisperer to generate code to build and train your ML models.