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CLI-based personal AI that runs 15+ LLMs from your terminal and pairs with Markdown tools like Obsidian and VS Code.
AI coding assistant that gathers project context to plan, generate, test and ship code across the SDLC via IDE and chat integrations.
Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
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.
No public pricing
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No public pricing
- ✦Terminal/CLI interface for prompting LLMs
- ✦Access to 15 models across 7 providers
- ✦Markdown workflow integration (Obsidian, VS Code, MkDocs)
- ✦Situational web-app generation and local run
- ✦Web scraping/content extraction (gather)
- ✦Vision-model support for images
- ✦Configurable prompt templates and intents
- ✦Automatic context-gathering from connected engineering sources
- ✦AI-generated code, tests and pull requests from tickets
- ✦Task planning that breaks complex work into subtasks
- ✦Auto-updating engineering documentation
- ✦Vector search over embedded project data
- ✦Multiple selectable AI models (GPT, Gemini, Claude, Llama, etc.)
- ✦Engineering productivity analytics dashboard
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦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
- →Prompt and compare multiple LLMs from the terminal
- →Generate and run small web apps without setup
- →Automate Markdown document workflows
- →Extract structured content from text and webpages
- →Engineering teams automating ticket-to-PR workflows
- →Developers wanting AI-assisted debugging and test generation
- →Engineering managers tracking AI-driven productivity gains
- →Teams centralizing documentation from scattered sources
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →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