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Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
Connects Git repos to answer plain-English questions about your code with file references and dependency context.
AI-powered IDE with code completion, generation, explanation and debugging, plus a cloud dev environment, for developers.
Local desktop app that assembles code-context prompts for LLMs, with API integrations, token tracking, and prompt saving.
AI prototyping tool that generates UI matching your design system, letting product teams test features fast.
No public pricing
No public pricing
No public pricing
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦Natural-language search across a codebase
- ✦Architecture explanations and dependency graphs
- ✦Bug hunter that traces issues across files
- ✦AI code review before opening a PR
- ✦Automatic documentation generation
- ✦Multi-repo support via OAuth
- ✦AI code completion and snippet generation
- ✦Natural-language code generation
- ✦Code explanation and AI Q&A
- ✦Automated bug detection and fixes
- ✦Zero-config cloud development environment
- ✦Project creation from templates or Git
- ✦Source-code context and prompt management
- ✦Custom and formatting instructions
- ✦BYOK API integrations (OpenAI, Claude, Gemini, etc.)
- ✦Token-limit tracking
- ✦Code-edit feature with visual diffs and backups
- ✦Local, offline prompt generation
- ✦AI UI generation from prompts
- ✦Match existing styling and design systems
- ✦Rapid, high-fidelity prototyping
- ✦Live team editing and sharing
- ✦Enterprise security and compliance
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →Onboarding new engineers faster
- →Answering questions about a codebase
- →Understanding how components connect
- →Finding and diagnosing bugs
- →Generating documentation from code
- →Writing and completing code faster with AI
- →Onboarding to unfamiliar codebases
- →Debugging and optimizing code
- →Spinning up dev environments in the browser
- →Building context-rich prompts for AI coding
- →Comparing model outputs on the same task
- →Reusing saved prompts across tech stacks
- →Keeping code private during prompt creation
- →Prototype new product features
- →Test designs with customers
- →Build design-system-consistent mockups