5 Best Cursor Alternatives in 2026

## Overview Continue is an **open-source AI coding assistant** that integrates directly into your IDE (VS Code, JetBrains), bringing large language model (LLM) capabilities to your development workflow. It helps developers write, debug, and refactor code more efficiently. ## Key Features - Supports both **local LLMs** and cloud models for flexible deployment. - Integrates Retrieval-Augmented Generation (`RAG`) to provide context-aware suggestions. - Interactive chat and command palette for natural language coding. - Deep integration with `VS Code` and `JetBrains` IDEs. ## Use Cases - Accelerate coding with context-aware suggestions and completions. - Debug complex issues by leveraging LLMs to analyze code and provide solutions. - Refactor existing codebases with AI-powered guidance. ## Community & Contributions Actively maintained with a growing community, welcoming contributions to enhance its capabilities. ## Call to Action Explore Continue on GitHub and supercharge your coding.

Cline is an open-source, model-agnostic AI coding agent for automating software development tasks. It integrates into IDEs to help developers understand, refactor, and automate code. ## Key Features - Integrate directly into VS Code and JetBrains IDEs - Automate coding tasks with terminal-first workflows - Execute multi-step tasks using a dedicated Plan Mode - Connect to different LLMs with a model-agnostic architecture - Understand and refactor complex codebases ## Conclusion This tool is for developers who need a flexible AI assistant for end-to-end coding automation inside their existing development environment.

## Overview Kilocode is an open-source **AI coding assistant** `VS Code` extension that integrates leading large language models directly into your development workflow. It's built for developers seeking to enhance productivity and streamline coding tasks with powerful **AI developer tools**. ## Key Features - Seamless integration with popular LLMs: `ChatGPT`, `Claude`, `Gemini`, and `Sonnet`. - Context-aware code generation, refactoring, and explanation directly within `VS Code`. - Customizable prompts and model settings for tailored AI assistance. - Supports multiple programming languages and frameworks. ## Use Cases - Rapidly generate boilerplate code or complex functions. - Refactor existing code for improved readability and performance. - Understand unfamiliar codebases with AI-powered explanations. ## Community & Contributions Kilocode is an open-source project that welcomes community contributions. ## Call to Action Explore the repository or install the Kilocode `VS Code` extension today!

Roo Code is an autonomous AI coding assistant for Visual Studio Code that executes complex engineering tasks through direct terminal access and file-system manipulation. It operates as a specialized agent capable of managing the full development lifecycle within the IDE environment. * Executes terminal commands to install dependencies, run builds, and manage database migrations. * Edits source code using precise diffs to implement features or resolve bugs across multiple files. * Utilizes the Model Context Protocol (MCP) to integrate custom tools and external server resources. * Performs browser-based testing via Playwright to verify UI changes and debug front-end regressions. * Integrates with Anthropic Claude 3.5 Sonnet, OpenAI-compatible APIs, and local models through Ollama. * Compatible with any VS Code-based environment including GitHub Codespaces and Cursor. * Automating boilerplate generation and complex architectural refactoring in legacy codebases. * Debugging full-stack applications by analyzing runtime logs and applying fixes in a continuous loop. Install the extension via the VS Code Marketplace and configure an LLM provider to begin.

**Goose** is an industrial-strength **open source AI agent** developed by Block (formerly Square). Unlike standard coding assistants (like Copilot or ChatGPT) that simply *suggest* code snippets, Goose acts as a virtual senior engineer sitting at your terminal. **The Core Problem**: Most AI tools are passive. You copy code, paste it into your editor, debug errors, and repeat. This "human-in-the-loop" friction limits productivity for complex refactors or multi-file changes. **The Goose Solution**: Goose connects directly to your shell and IDE. It plans a solution, executes terminal commands, edits files, runs tests to verify its work, and iterates on errors—**autonomously**. It leverages the **Model Context Protocol (MCP)** to plug into any tool, effectively turning natural language into executable engineering work. ## Key Features * **Model Agnostic Intelligence**: Break free from vendor lock-in. Configure Goose to run with **Claude 3.5 Sonnet**, **GPT-4o**, **Gemini**, or local open-source models (via Ollama). You control the brain; Goose provides the hands. * **Autonomous Agentic Loop**: Goose doesn't just write code; it **self-corrects**. If a build fails or a test errors out, Goose reads the output, diagnoses the issue, and applies a fix without your intervention. * **Model Context Protocol (MCP) Support**: Built on Anthropic's open standard, Goose can be extended with "skills." Connect it to Google Drive, Slack, PostgreSQL, or custom internal APIs to give it context beyond your codebase. * **Dual Interface**: Use the robust **CLI** for headless tasks and server environments, or the **Electron-based Desktop App** for a visual, interactive experience with rich diff views. * **Project-Specific "Hints"**: Create a `.goosehints` file to teach Goose your repository's specific architectural patterns, coding standards, and "gotchas," ensuring generated code matches your team's style. * **Secure & Local-First**: Goose runs entirely on your machine. Your code never leaves your local environment unless you explicitly configure a cloud-based LLM provider. ## Architecture & Tech Stack Goose is engineered for performance and safety, utilizing a modular architecture that separates the *brain* (LLM) from the *body* (Execution Engine). * **Core Runtime (Rust)**: The heart of Goose is written in **Rust**, ensuring memory safety and blazing-fast execution. This binary handles the agentic loop, file system operations, and terminal management. * **Extension System (MCP)**: Tools are implemented as **MCP Servers**. When you ask Goose to "check the database," it communicates with a distinct Postgres MCP server via standard JSON-RPC messages. This decouples the core agent from specific tool implementations. * **Context Window Management**: Goose employs intelligent **token management strategies**. It summarizes past interactions and prunes irrelevant context to keep the LLM focused and costs low, preventing "context drift" in long sessions. * **Frontend**: The desktop experience is built with **Electron** and **React**, providing a bridge between the Rust backend and a user-friendly chat interface. ## Pros and Cons **Pros** * **Total Control**: You own the stack. Swap models instantly if a cheaper or smarter one comes out. * **Real Automation**: Can handle multi-step tasks like "migrate this entire module from Python 2 to 3" with minimal supervision. * **Ecosystem Friendly**: Standardizing on MCP means you can use tools built for Claude or other agents. * **Developer Experience**: The "Goose Hints" feature drastically reduces the need for repetitive prompting about coding styles. **Cons** * **Token Costs**: Autonomous loops can burn through API credits quickly if the agent gets stuck in a loop. * **Setup Friction**: Requires more initial configuration (API keys, tool installations) compared to "one-click" paid products like Cursor. * **Maturity**: As a newer project, it may have more edge-case bugs than established enterprise tools. ## Getting Started Installation is simple via a single shell script. You will need an API key from a provider like OpenAI, Anthropic, or OpenRouter. ```bash # 1. Install Goose (Mac/Linux) curl -fsSL https://github.com/block/goose/releases/download/stable/download_cli.sh | bash # 2. Configure your LLM provider # Follow the interactive prompts to set your API key goose configure # 3. Start the agent goose session # 4. Give your first command # "Analyze the current directory and create a README.md describing the project structure." ``` ## Conclusion **Goose** represents the future of the **open source AI agent**. It stops treating AI as a chatbot and starts treating it as a digital employee. By combining the flexibility of the **Model Context Protocol** with a rugged, developer-first CLI, it empowers you to build faster and automate the boring parts of engineering. If you are ready to move beyond auto-complete, it is time to let the Goose loose.