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AI-powered code editor with agentic workflows for developers.
IDE coding assistant for VS Code and JetBrains that uses your own API keys across 15+ model providers, with agentic mode and autocomplete.
Design-system platform that packages tokens, code components, and rules into scoped context for AI coding agents.
Google's AI coding assistant for code completion, generation, chat and review across IDEs and GitHub.
AI coding assistant with multi-model chat and developer tools to help write, explain and improve code faster.
No public pricing
No public pricing
No public pricing
- ✦AI-powered code completion and suggestions
- ✦Automated lint fixing
- ✦Cascade agent for advanced coding assistance
- ✦Integrated app building and deployment
- ✦MCP server support for custom tools
- ✦Terminal command integration
- ✦Memory of codebase structure and workflow
- ✦BYOK access to 15+ model providers
- ✦Agentic planning-then-build mode
- ✦AI autocomplete
- ✦MCP connections to external systems
- ✦Custom rules and live context tracking
- ✦Local models via Ollama/LM Studio
- ✦VS Code and JetBrains plugins
- ✦Scoped MCP context distribution to multiple AI coding tools
- ✦Design token and component API management
- ✦Collaborative documentation with analytics
- ✦Figma and Storybook data source integration
- ✦Feedback loop for improving AI context quality
- ✦Skill and exporter management for agent capabilities
- ✦AI code completion and suggestions
- ✦Natural-language code generation
- ✦In-IDE chat assistance
- ✦AI code review
- ✦IDE integrations (VS Code, JetBrains, etc.)
- ✦GitHub integration
- ✦AI code generation
- ✦Multi-model chat
- ✦Developer utility tools
- ✦Code explanation and improvement
- →Accelerating software development by automating repetitive tasks
- →Reducing onboarding time for new developers
- →Improving code quality and reducing tech debt
- →Streamlining the app building and deployment process
- →Enhancing developer productivity by keeping them in a state of flow
- →Code generation, refactoring and debugging
- →Control AI spend with your own keys
- →Switch between frontier models per task
- →Keep code private and data-sovereign
- →Product teams giving AI coding agents accurate design-system context
- →Design system managers publishing a single source of truth
- →Engineering teams reducing token usage by scoping agent context per team
- →Speeding up coding with AI completions
- →Generating code from plain-language prompts
- →Getting in-editor help and explanations
- →Reviewing pull requests with AI
- →Understanding unfamiliar codebases
- →Writing code faster
- →Explaining or debugging code
- →Generating tests and documentation