Repository History
104 repositories tagged with Machine Learning
AutoResearch: AI/ML Research Agents from Idea to Paper-Ready Evidence
AutoResearch is an open-source agent workflow designed for AI and machine learning research. It automates the entire research process, from generating ideas and planning experiments to execution, analysis, and independent evaluation. This project helps researchers produce paper-ready evidence efficiently and with traceable provenance.
AREX-Skill: A Skill Library for Automated Machine Learning and Auto-Research
AREX-Skill is a powerful skill library designed to advance automated machine learning and auto-research. It distills over 5,000 executable skills from more than 1,000 popular GitHub repositories, making complex ML knowledge directly usable by coding agents. This project significantly enhances agent performance in various research tasks by providing structured, validated operating knowledge.
agent.cpp: Building Local LLM Agents with C++ and llama.cpp
agent.cpp provides essential building blocks for developing local AI agents using C++. It leverages llama.cpp to enable efficient execution of small language models directly on your hardware. This library offers a modular approach with features like agent loops, callbacks, tools, and grammar-constrained output, making it ideal for creating custom, privacy-focused agent solutions.
EnvHarness: Dynamically Adapting Environments for Agent Learning
EnvHarness empowers large language models (LLMs) acting as autonomous agents to learn more effectively from interactive environments. It achieves this by wrapping static environments with plug-in components, making them dynamically controllable without altering their internal code. This innovative approach allows environments to target specific agent weaknesses and continuously teach as agents improve, leading to more effective and efficient learning outcomes.
ADK-Rust: Build AI Agents in Rust with a Powerful Development Kit
ADK-Rust is a robust Agent Development Kit (ADK) for building AI agents in Rust. It offers a flexible framework with modular components for models, tools, memory, and real-time voice capabilities. Designed to be model-agnostic and deployment-agnostic, ADK-Rust empowers developers to create powerful and efficient AI agents.
Awesome Dynamic Agent Skills: A Curated List for LLM Agent Skill Systems
Awesome Dynamic Agent Skills is a comprehensive curated reading list accompanying a TMLR 2026 survey on dynamic, self-evolving skill systems for LLM agents. It provides a unified taxonomy, an eight-stage lifecycle, and a ten-operator vocabulary for understanding how LLM agents acquire and manage skills. The repository audits 124 papers, offering valuable insights into this rapidly evolving field.
AMD Skills: Empowering AI Agents with AMD's Optimized Software Stack
AMD Skills is the official catalog of AI agent skills from AMD, designed to empower AI agents with optimized software for AMD hardware. This repository provides knowledge, scripts, and conventions for working with AMD's stack, enabling seamless integration with major coding agents like Cursor, Claude Code, OpenAI Codex, and Gemini CLI.
awesome-free-models: A Curated List of Free AI Models and Tools
Discover awesome-free-models, a comprehensive GitHub repository featuring a curated list of free AI models, APIs, and tools. This invaluable resource allows users to leverage powerful AI capabilities without incurring any costs, making advanced AI accessible to everyone. It's an essential guide for developers and enthusiasts seeking open-weight models, free API tiers, and local inference solutions.
Ragas: Supercharge Your LLM Application Evaluations
Ragas is an ultimate toolkit for evaluating and optimizing Large Language Model (LLM) applications. It offers objective metrics, intelligent test generation, and data-driven insights to move beyond subjective assessments. This framework helps developers build feedback loops and continuously improve their LLM applications.
awesome-agentic-ai: Your Comprehensive Hub for Agentic AI Resources
awesome-agentic-ai is a curated list of resources for learning, building, and mastering Agentic AI systems. This repository serves as a complete hub, offering a learning roadmap, top frameworks, tools, and real-world examples. It's designed for both beginners and experts looking to explore autonomous AI agents.
Colibri: Run 744B GLM-5.2 MoE on Consumer Machines with Pure C
Colibri is an innovative project that enables running the massive 744B-parameter GLM-5.2 Mixture-of-Experts (MoE) model on consumer-grade machines with as little as 25GB of RAM. It achieves this remarkable feat through a pure C engine with zero dependencies, streaming model experts from disk on demand. This allows users to interact with a frontier-class LLM without requiring expensive GPU hardware.
rag-zero-to-hero-guide: Your Comprehensive Path to Mastering RAG
This repository offers a comprehensive guide to Retrieval-Augmented Generation (RAG), covering everything from fundamental concepts to advanced techniques. It includes detailed courses on RAG basics and evaluation, alongside an extensive toolkit of frameworks, libraries, and research papers. Ideal for AI engineers and LLM enthusiasts, this resource provides a structured learning path for building and optimizing RAG systems.