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Google's AI coding assistant for code completion, generation, chat and review across IDEs and GitHub.
IDE coding assistant for VS Code and JetBrains that uses your own API keys across 15+ model providers, with agentic mode and autocomplete.
AI code-review and context tool that maps a codebase into a knowledge graph so coding agents and PR reviews stay grounded in real context.
Design-system platform that packages tokens, code components, and rules into scoped context for AI coding agents.
AI-powered IDE with code completion, generation, explanation and debugging, plus a cloud dev environment, for developers.
No public pricing
No public pricing
- ✦AI code completion and suggestions
- ✦Natural-language code generation
- ✦In-IDE chat assistance
- ✦AI code review
- ✦IDE integrations (VS Code, JetBrains, etc.)
- ✦GitHub integration
- ✦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
- ✦AI Architect knowledge graph of the codebase
- ✦Codebase-aware AI code reviews
- ✦Grounded coding and technical design docs
- ✦Impact and feasibility assessment
- ✦Integrations: Cursor, Claude Code, Copilot, Codex, Jira, Slack
- ✦Self-hosted deployment option
- ✦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 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
- →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
- →Code generation, refactoring and debugging
- →Control AI spend with your own keys
- →Switch between frontier models per task
- →Keep code private and data-sovereign
- →Automated code review
- →Context-grounded AI coding
- →Technical design and scoping
- →Cutting agent token costs
- →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
- →Writing and completing code faster with AI
- →Onboarding to unfamiliar codebases
- →Debugging and optimizing code
- →Spinning up dev environments in the browser