by emanueleielo
An open-source AI-powered frontend development platform built on DeepAgents and LangGraph. Generate complete React applications through natural language conversations.
# Add to your Claude Code skills
git clone https://github.com/emanueleielo/deepagents-open-lovableGuides for using ai agents skills like deepagents-open-lovable.
Last scanned: 6/5/2026
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}deepagents-open-lovable is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by emanueleielo. An open-source AI-powered frontend development platform built on DeepAgents and LangGraph. Generate complete React applications through natural language conversations. It has 106 GitHub stars.
Yes. deepagents-open-lovable passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/emanueleielo/deepagents-open-lovable" and add it to your Claude Code skills directory (see the Installation section above).
deepagents-open-lovable is primarily written in TypeScript. It is open-source under emanueleielo on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh deepagents-open-lovable against similar tools.
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An open-source AI-powered frontend development platform built on DeepAgents and LangGraph. Generate complete React applications through natural language conversations.
This platform enables you to build frontend applications by simply describing what you want. The AI agent understands modern web development best practices and generates production-ready code using React, Next.js, Tailwind CSS, and shadcn/ui.

Begin by describing your application in natural language. The agent understands your requirements and starts planning the implementation.

Watch as the agent works on your project. This example shows the "CodeCraft" website being built with real-time task tracking and progress updates.

Specialized sub-agents handle specific tasks. Here, the designer sub-agent is creating files while you can see the filesystem structure being built in real-time.

The completed application preview. From concept to working website through simple conversation.
deepagents-open-lovable/
├── agent/ # Python backend (LangGraph)
│ ├── src/
│ │ ├── agent.py # Main agent configuration
│ │ ├── prompts.py # System prompts
│ │ ├── tools.py # Custom tools
│ │ ├── subagents.py # Designer & image researcher
│ │ └── skills/ # Skill definitions
│ └── langgraph.json # LangGraph configuration
└── gui/ # React/Vite frontend
└── src/
├── components/ # UI components
├── hooks/ # Custom React hooks
├── pages/ # Route components
└── api/ # Backend API client
git clone https://github.com/emanueleielo/deepagents-open-lovable.git
cd deepagents-open-lovable
cd agent
# Create and activate virtual environment
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Configure environment
cp .env.example .env
# Edit .env and add your ANTHROPIC_API_KEY
cd ../gui
# Install dependencies
npm install
# Configure environment (optional)
cp gui.example .env
Terminal 1 - Backend:
cd agent
langgraph dev
# Runs at http://localhost:2024
Terminal 2 - Frontend:
cd gui
npm run dev
# Runs at http://localhost:5173
Open your browser to http://localhost:5173 and start building!
| Variable | Required | Description |
|---|---|---|
ANTHROPIC_API_KEY |
Yes | Your Anthropic API key |
MODEL |
No | Model to use (default: anthropic:claude-sonnet-4-5-20250929) |
TAVILY_API_KEY |
No | Tavily API key for web search |
| Variable | Required | Description |
|---|---|---|
VITE_API_URL |
No | LangGraph API URL (default: http://localhost:2024) |
VERCEL_API_TOKEN |
No | Vercel token for preview deployments |
Backend:
Frontend:
Create new tools in agent/src/tools.py:
from langchain_core.tools import tool
@tool
def my_custom_tool(param: str) -> str:
"""Tool description for the agent."""
return f"Result: {param}"
Define sub-agents in agent/src/subagents.py:
subagents = [
{
"name": "my-agent",
"description": "What this agent does",
"prompt": "System prompt for the agent",
}
]
Create skill files in agent/src/skills/<skill-name>/SKILL.md:
# Skill Name
Description of what this skill does.
## Instructions
Step-by-step instructions for the agent.
Contributions are welcome! Please feel free to submit a Pull Request.
This project is open source. See the individual components for their respective licenses.