A collective list of free APIs

FastAPI framework, high performance, easy to learn, fast to code, ready for production

A high-throughput and memory-efficient inference and serving engine for LLMs

You like pytorch? You like micrograd? You love tinygrad! ❤️

AGI's query engine - Platform for building AI that can learn and answer questions over federated data.

aider is AI pair programming in your terminal

AI orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data…

Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

Turns Data and AI algorithms into production-ready web applications in no time.

Typer, build great CLIs. Easy to code. Based on Python type hints.

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

🦉 Data Versioning and ML Experiments

PyGWalker: Turn your pandas dataframe into an interactive UI for visual analysis

Parallel computing with task scheduling

20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale.

A reactive notebook for Python — run reproducible experiments, execute as a script, deploy as an app, and version with git.

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

Bayesian Modeling and Probabilistic Programming in Python

Unified framework for building enterprise RAG pipelines with small, specialized models

Python bindings for llama.cpp

A python library for user-friendly forecasting and anomaly detection on time series.

An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)

A Library for Advanced Deep Time Series Models.

Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts

High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

📚 Parameterize, execute, and analyze notebooks

Implementation of the LLaMA language model based on nanoGPT. Supports flash attention, Int8 and GPTQ 4bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.

HACS gives you a powerful UI to handle downloads of all your custom needs.

Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristi…