Automatisation
JamAIBase
The collaborative spreadsheet for building AI apps, cell by cell.
★ 1,1 k étoilesApache-2.0
Voir le guide →PyScrappy est un projet de automatisation open source, créé par mldsveda (mldsveda/PyScrappy sur GitHub). Il compte 260 étoiles sur GitHub et 82 forks, il est publié sous la licence MIT et a été mis à jour pour la dernière fois sur GitHub le 25 septembre 2026.
Self-healing web scraping for AI agents, plus an MCP server.
Decide what you want the project to do before installing anything.
Grab the source and skim the README for setup requirements.
Install dependencies and add any API keys or settings it needs.
Launch it and follow the example workflow from the docs.
Exposes its capabilities to AI agents over the Model Context Protocol.
Inspect and extend every part of the pipeline.
Chain steps together without writing boilerplate.
Produces clean, structured data agents can use.
Run the whole stack on your own server.
Drive it programmatically from your own code.
Download the source and move into the project folder.
git clone https://github.com/mldsveda/PyScrappy.git
cd PyScrappyCreate a virtual environment and install the requirements.
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtStart the app or CLI and follow the README examples.
python main.py --helpIt is an open-source project at mldsveda/PyScrappy with 82 forks. Check the repo’s contributors and recent commits to judge how active maintenance is.
Yes. It is MIT licensed, so you can use it commercially with attribution.
Basic setup is config-driven, but custom scrapers and pipelines benefit from some Python.
It includes stealth techniques, but no tool guarantees success against every site — respect robots.txt and terms of service.
Yes, most setups support cron-style scheduling or webhook triggers.
The collaborative spreadsheet for building AI apps, cell by cell.
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Gratuit et ouvert : récupérez le code source, mettez une étoile au dépôt et commencez à créer.