LinkedOut

Automate your LinkedIn networking using an intelligent browser agent. Upload your resume, craft example messages, and let the agent handle the networking while you focus on what matters.

Features

• Resume-Based Networking: Upload your resume and let the agent craft personalized connection messages
• Flexible Search: Find potential connections using LinkedIn search URLs or custom filters
• Multiple Modes:
  • Full automation: Connect with personalized messages
  • Lite mode: Connect without messages
  • Observer mode: Just collect profile information without connecting

Project Structure

• frontend/: React-based UI code (TypeScript)
• mimicflow/agents/: LinkedIn automation agent code
• mimicflow/app/: FastAPI backend
• browser-use/: Browser automation framework powered by browser-use, using LangChain and Playwright. We've modified it for our LinkedIn use case.

Setup Instructions

1. Install uv (choose one method):

# Using curl (macOS/Linux)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Using pip
pipx install uv

# Using Homebrew
brew install uv

2. Create and activate virtual environment:

uv venv --python 3.11
source .venv/bin/activate

3. Install dependencies:

uv sync
uv pip install -e ./browser-use
uv pip install playwright
playwright install

4. Set up frontend:

cd frontend
npm install
npm run dev

If you encounter frontend setup issues:

rm -rf node_modules
rm package-lock.json
npm install
npm run dev

5. Start the backend (from root directory):

uv run uvicorn mimicflow.app.main:app --reload --host 0.0.0.0 --port 8000

6. Set environment variables:

export OPENAI_API_KEY=your_key_here
export GEMINI_API_KEY=your_key_here

Usage

1. Make sure you're logged into LinkedIn on Chrome
2. Completely quit Chrome browser (ensure it's not running in background)
3. Open localhost:5173 in a different browser (e.g., Safari)
4. Configure your automation parameters:
   • Upload your resume
   • Set your API keys
   • Choose connection mode
   • Enter LinkedIn search URL or filters
   • Start automation

The frontend runs on port 5173 and the backend on port 8000.

Development

To contribute to this project:

1. Fork the repository
2. Create a new branch for your feature
3. Submit a pull request

License

Apache 2.0