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@ -727,6 +727,7 @@ _Libraries for Machine Learning. Also see [awesome-machine-learning](https://git
- [gym](https://github.com/openai/gym) - A toolkit for developing and comparing reinforcement learning algorithms.
- [Feature-engine](https://github.com/feature-engine/feature_engine) - sklearn compatible API with the widest toolset for feature engineering and selection.
- [H2O](https://github.com/h2oai/h2o-3) - Open Source Fast Scalable Machine Learning Platform.
- [LangChain](https://github.com/langchain-ai/langchain) - Building applications with LLMs through composability.
- [LlamaIndex](https://github.com/run-llama/llama_index) - A data framework for your LLM application.
- [Metrics](https://github.com/benhamner/Metrics) - Machine learning evaluation metrics.
- [MindsDB](https://github.com/mindsdb/mindsdb) - MindsDB is an open source AI layer for existing databases that allows you to effortlessly develop, train and deploy state-of-the-art machine learning models using standard queries.
@ -1149,6 +1150,7 @@ _Traditional full stack web frameworks. Also see [RESTful API](https://github.co
- [awesome-fasthtml](https://github.com/amosgyamfi/awesome-fasthtml)
- [masonite](https://github.com/MasoniteFramework/masonite) - The modern and developer centric Python web framework.
- Asynchronous
- [Litestar](https://github.com/litestar-org/litestar) - Production-ready, capable and extensible ASGI Web framework.
- [microdot](https://github.com/miguelgrinberg/microdot) - The impossibly small web framework for Python and MicroPython.
- [reflex](https://github.com/reflex-dev/reflex) – A framework for building reactive, full-stack web applications entirely with python .
- [tornado](https://github.com/tornadoweb/tornado) - A web framework and asynchronous networking library.