Best AI Coding Agents for Windows - Page 2

Codex CLI is an open-source, lightweight coding agent that integrates directly into your terminal, designed to help developers write, edit, and understand code efficiently. By pairing with Codex CLI, developers can leverage the power of AI to streamline their workflow, get real-time code suggestions, and improve their coding accuracy, all from within their command line interface. It provides a seamless, accessible way to enhance coding productivity while staying in the environment developers are already comfortable with.

Gemini CLI is a free, open-source AI agent that integrates Gemini’s powerful AI capabilities directly into developers’ command line terminals. It offers fast, lightweight access to Gemini 3 Pro, enabling developers to generate code, solve problems, and manage tasks using natural language prompts. The CLI supports up to 60 model requests per minute and 1,000 requests per day at no cost, with additional paid options for professionals requiring higher usage. Gemini CLI includes advanced features like Google Search grounding for real-time web context, prompt customization, and automation within scripts. It is fully extensible and open source, welcoming community contributions via GitHub. Designed to enhance workflow efficiency, Gemini CLI brings AI-powered coding assistance to the terminal environment.

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.

Qwen3‑Coder is an agentic code model available in multiple sizes, led by the 480B‑parameter Mixture‑of‑Experts variant (35B active) that natively supports 256K‑token contexts (extendable to 1M) and achieves state‑of‑the‑art results on Agentic Coding, Browser‑Use, and Tool‑Use tasks comparable to Claude Sonnet 4. Pre‑training on 7.5T tokens (70 % code) and synthetic data cleaned via Qwen2.5‑Coder optimized both coding proficiency and general abilities, while post‑training employs large‑scale, execution‑driven reinforcement learning and long‑horizon RL across 20,000 parallel environments to excel on multi‑turn software‑engineering benchmarks like SWE‑Bench Verified without test‑time scaling. Alongside the model, the open source Qwen Code CLI (forked from Gemini Code) unleashes Qwen3‑Coder in agentic workflows with customized prompts, function calling protocols, and seamless integration with Node.js, OpenAI SDKs, and more.

Qoder is an agentic coding platform engineered for real software development, designed to go far beyond typical code completion by combining enhanced context engineering with intelligent AI agents that deeply understand your project. It allows developers to delegate complex, asynchronous tasks using its Quest Mode, where agents work autonomously and return finished results, and to extend capabilities through Model Context Protocol (MCP) integrations with external tools and services. Qoder’s Memory system preserves coding style, project-specific guidance, and reusable context to ensure consistent, project-aware outputs over time. Developers can also interact via chat for guidance or code suggestions, maintain a Repo Wiki for knowledge consolidation, and control behavior through Rules to keep AI-generated work safe and guided. This blend of context-aware automation, agent delegation, and customizable AI behavior empowers teams to think deeper, code smarter, and build better.

Crush is a glamorous AI coding agent that lives right in your terminal, seamlessly connecting your tools, code, and workflows with any Large Language Model (LLM) of your choice. It offers multi-model flexibility, letting you choose from a variety of LLMs or add your own using OpenAI or Anthropic-compatible APIs, and supports mid-session switching between them while preserving context. Crush is session-based, enabling multiple project-specific contexts to coexist. Powered by Language Server Protocol (LSP) enhancements, it incorporates coding-aware context just like a developer’s editor. It's highly extensible via Model Context Protocol (MCP) plugins using HTTP, stdio, or SSE for added capabilities. Crush runs anywhere, leveraging Charm’s sleek Bubble Tea-based TUI for a polished terminal user experience. Written in Go and MIT-licensed (with FSL-1.1 for trademarks), enabling developers to stay in their terminal while taking advantage of expressive AI coding assistance.

GitHub Copilot CLI brings the core capabilities of the Copilot coding assistant into your terminal, enabling you to write, debug, refactor, and understand code via natural language directly in the command line. It works locally and in sync with your GitHub workflow, granting the ability to access repositories, issues, and pull requests through conversational commands while staying authenticated with your GitHub account. The tool operates as an agent in your terminal; you can ask it to autonomously create or modify files, execute commands, implement new features, fix bugs, prototype, and adjust codebases based on your specifications. Deep GitHub integration ensures context awareness (e.g., code history, branches, project layout), and the CLI experience is optimized to reduce context switching between your editor and terminal. The system supports iterative collaboration, allowing you to fine-tune or reissue commands as the project evolves.

Verdent is an AI-powered development platform designed to help developers manage large-scale complexity and deliver production-ready code with efficiency and reliability. It consists of two products, Verdent Deck, a desktop app, and Verdent for VS Code, an integrated extension. Both support running multiple AI agents in parallel, provide DiffLens to explain code diffs by highlighting what changed and why, offer GPT-5-based code review, implement Plan-first Alignment for structured execution, and include a browser tool and verifier subagent to check accuracy. Verdent Deck allows developers to run multiple tasks with oversight in isolated contexts, completing them asynchronously while providing a clear project overview and progress tracking. Verdent for VS Code operates as an AI coding agent that autonomously plans, codes, and verifies tasks within the editor, optimized for engineers who need precision, transparency, and dependable results in production-grade environments.

Nora is described as a “deep reasoning agent” built for software development with a special focus on Web3 stacks. The platform supports major smart-contract languages like Solidity, Move, Cairo, and Rust and adapts to their execution models and semantics. It is compiler- and VM-aware by design: it understands bytecode generation, control flow, instruction-level transformations, and custom runtime environments (EVM, WASM, etc.). Its debugging and validation capabilities are context-aware, enabling it to identify subtle bugs, unintended state behaviors, and architectural bottlenecks across complex codebases. Nora also aims to accelerate the path from idea to product by assisting teams with core module development, interface wiring, integration testing, deployment logic, and maintaining architectural integrity, helping reduce context-switching and speed up Web3 productization.

Emdash is an orchestration layer that lets you run multiple coding agents in parallel, each in its own isolated Git worktree, so you can simultaneously spin up different agents to tackle independent subtasks or experiments without interference. It’s provider-agnostic, meaning you can pick from various AI models and CLIs (for example, Claude Code, Codex, and others) to fit your workflow. With Emdash, you can assign issues or tickets (from Linear, GitHub, or Jira) directly to a chosen agent, then watch multiple agents operate side by side in real time. The UI shows live agent status and activity, and once agents generate code, you can review diffs, comment, and open pull requests, all without leaving Emdash. Because every agent runs in a separate worktree, changes stay sandboxed and comparable, enabling you to test different implementations or strategies side-by-side safely.

Mistral Vibe CLI is a command-line interface built for “vibe-coding,” enabling developers to interact with their codebases through natural-language commands rather than manual edits or rigid IDE workflows. It hooks into version control (e.g., Git repositories), inspects project files, directory structure, and Git status to build context, and uses that context along with backend AI coding models (such as Devstral 2/Devstral Small) to execute operations like multi-file edits, refactoring, code generation, search, and file manipulation, all triggered via plain-English instructions. Because it maintains project awareness (dependencies, file structure, history), it can perform coordinated, cross-file changes (e.g., renaming a function and updating all references across the repo), generate boilerplate across modules, or even scaffold new features from a high-level prompt.

GPT Pilot is an open-source AI tool that acts as a full AI developer, capable of generating production-ready applications with minimal human input. Unlike simple code autocompletion tools, GPT Pilot can write complete features, debug code, communicate about issues, and even request code reviews. This tool aims to push the boundaries of AI-assisted software development by handling up to 95% of coding tasks, while leaving the final 5% to developers. It’s built to integrate with platforms like VS Code, enabling developers to collaborate seamlessly with AI in real-time.

gpt-engineer is an open-source platform designed to automate software development by enabling users to specify software requirements in natural language. The tool utilizes AI to generate and execute code based on user specifications, making the development process faster and more intuitive. gpt-engineer can also implement requested improvements and handle iterative development, allowing users to focus on high-level tasks rather than manual coding. Built with a terminal-based interface, it is ideal for developers seeking a customizable, hackable solution for experimenting with AI-assisted programming.

smol-developer is an open-source library that enables developers to integrate a powerful AI-powered "junior developer" agent into their applications. This agent uses natural language processing to generate, scaffold, and assist with the development of code. Unlike conventional approaches, smol-developer allows for a more interactive development process, where the AI agent iterates and refines the code based on feedback, making it ideal for building project-specific scaffolds and automating repetitive tasks. Developers can leverage this tool to speed up the development cycle, create customized codebases, and collaborate with AI on development tasks in real-time.

OpenCode is the AI coding agent purpose-built for the terminal. It delivers a responsive, themeable terminal UI that feels native while streamlining your workflow. With LSP auto-loading, it ensures the right language servers are always available for accurate, context-aware coding support. Developers can spin up multiple AI agents in parallel sessions on the same project, maximizing productivity. Shareable links make it easy to reference, debug, or collaborate across sessions. Supporting Claude Pro and 75+ LLM providers via Models.dev, OpenCode gives you full freedom to choose your coding companion.

An open source, terminal-based AI coding engine that helps you complete large tasks, work around bad output, and maximize productivity. Plandex uses long-running agents to complete tasks that span multiple files and require many steps. It breaks up large tasks into smaller subtasks, then implements each one, continuing until it finishes the job. It helps you churn through your backlog, work with unfamiliar technologies, get unstuck, and spend less time on the boring stuff. Changes are accumulated in a protected sandbox so that you can review them before automatically applying them to your project files. Built-in version control allows you to easily go backwards and try a different approach. Branches allow you to try multiple approaches and compare the results.

Auggie CLI brings Augment’s intelligent coding agent directly into your terminal by leveraging its powerful context engine to analyze code, make edits, and execute tools both interactively and within automated workflows. Developers can install it via npm (requiring Node.js 22+ and a compatible shell), then launch a full-screen interactive session using auggie, complete with real-time streaming, visual progress, and conversational tooling, for debugging, feature development, PR review, or triaging alerts. For automation, Auggie offers streamlined modes ideal for CI/CD pipelines and background tasks. The CLI also supports custom slash commands for repeatable workflows, integrates with external tools and systems via native integrations and Model Context Protocol (MCP) servers, and can be scripted in pipelines or GitHub Actions for tasks like auto-generating PR descriptions.