The definitive list that answers "I want to do X in Python, which tool should I use?"
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A curated list of awesome Machine Learning frameworks, libraries and software.
817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 29 security domains · Apache 2.0
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Convert Machine Learning Code Between Frameworks
Pocket Flow: Codebase to Tutorial
Pocket Flow: 100-line LLM framework. Let Agents build Agents!
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
A friendly library for parsing HTTP request arguments, with built-in support for popular web frameworks, including Flask, Django, Bottle, Tornado, Pyramid, webapp2, Falcon, and aiohttp.
The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.
HTML minifier for Python frameworks (not only Django, despite the name).
Cake Fuzzer is a project that is meant to help automatically and continuously discover vulnerabilities in web applications created based on specific frameworks with very limited false positives.