- Frontend & Backend Developers: Prompt Bolt.diy to scaffold components, APIs, UI, and run terminal commands.
- Full Stack Teams: Collaborate across services—multi file editing, version history, and sync project folders.
- AI Enthusiasts & Tinkerers: Test code automation, prompt engineering, and local or external LLM models.
- QA & Test Engineers: Use terminal outputs and browser previews to iterate and validate code.
- Educators & Learners: Experiment with live code, prompts, and real world development workflows.
- Open Source Contributors: Extend model bindings, tooling, and integrate new LLMs using the extensible architecture.
How to Use Bolt.diy?
- Install or Run: Clone the repo (git clone https://github.com/stackblitz labs/bolt.diy -b stable) and follow README or use Docker.
- Configure LLMs: Add API keys for OpenAI, Claude, Gemini, HuggingFace, or run models locally via Ollama or LMStudio.
- Prompt & Scaffold: Describe your project or desired component in natural language, optionally specifying frameworks (e.g., Astro, Tailwind). Bolt will generate code across multiple files.
- Review & Approve: Review diffs, accept or refine changes, and watch it install dependencies, run builds, or open browser previews.
- Iterate & Deploy: Continue prompting for features, edits, or debugging. Export code as ZIP, push to GitHub, or deploy via Docker.
- Multi Model, Model Agnostic: Seamlessly switch between LLM providers (OpenAI, Anthropic, Gemini, local) with full support.
- Full Project Context: Reads your whole codebase to apply coherent multi file edits—no isolated snippets.
- Integrated Terminal & Browser: View command output and in app previews for immediate feedback.
- Open Source Flexibility: MIT licensed, community driven, easily extendable—bolt.diy even integrates Docker and custom prompts.
- Version Revert & Export: Revert changes, download code via ZIP, or publish directly to GitHub/Vercel.
- Browser First Development: Runs in-browser using WebAssembly containers—no local IDE required.
- Agentic multi step edits: Prompt plan apply across many files in one flow.
- Model flexibility: Choose any supported LLM, including local options.
- Live dev environment: Terminal and browser previews inside the editor.
- Open source collaboration: Community contributions, transparent roadmap.
- Initial setup is complex: Requires cloning, Docker or local model setup.
- Rough edges: Open source version can be less polished and harder to deploy than Bolt.new.
- Requires dev environment: Needs Docker or command line, not ideal for beginners.
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Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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