The backend agents build with - Multimodal database, orchestration, and serving in one file
Top MACHINE-LEARNING GitHub Repositories & Tools (2026)
Discover the most starred and trending open source tools tagged with #machine-learning.
Deep Learning for humans
Ultralytics YOLO27, YOLO26, YOLO11, YOLOv8 β object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking
PennyLane is an open-source quantum software platform for quantum computing, quantum machine learning, and quantum chemistry. Create meaningful quantum algorithms, from inspiration to implementation.
Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. π³Docker-friendly.β‘Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more.
Streamlit β A faster way to build and share data apps.
Build and share delightful machine learning apps, all in Python. π Star to support our work!
Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python
NLTK Source
A toolkit for making real world machine learning and data analysis applications in C++
Open Machine Learning Compiler Framework
π Geometric Computer Vision Library for Spatial AI
AI powered open source recommender system engine supports classical/LLM rankers and multimodal content via embedding
β‘ TabPFN: Foundation Model for Tabular Data β‘
The Enterprise-Grade Multi-Agent Orchestration Framework. Website: https://swarms.ai
A flexible, high-performance serving system for machine learning models
This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc
A machine learning software for extracting information from scholarly documents
High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.