Repository History
107 repositories tagged with LLM
LobeHub: Your Chief Agent Operator for AI Team Orchestration
LobeHub acts as a Chief Agent Operator, streamlining the management of your AI team. It enables hiring, scheduling, and reporting on agents for 24/7 operations, allowing users to maintain control without constant online presence. This platform transforms individual AI tools into a cohesive, productive team.
DeepTutor: Lifelong Personalized Tutoring with AI Agents
DeepTutor is an advanced AI-powered platform designed for lifelong personalized tutoring, integrating various learning modes into a single, extensible system. It leverages large language models and multi-agent systems to offer features like interactive chat, quiz generation, and skill development. This project provides a comprehensive environment for learners and educators seeking intelligent, adaptive educational tools.
Memori: Agent-Native Memory Infrastructure for LLM Production Systems
Memori provides agent-native memory infrastructure, offering an LLM-agnostic layer that transforms agent execution and conversations into structured, persistent state. Designed for enterprise use, it seamlessly integrates with existing data infrastructure and supports various deployment environments, ensuring robust memory management for AI agents.
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.
TencentDB Agent Memory: Enhancing AI Agents with Layered Long-Term Memory
TencentDB Agent Memory provides AI agents with fully local, long-term memory through a 4-tier progressive pipeline, eliminating external API dependencies. It significantly reduces token usage and improves task success rates by employing symbolic short-term memory and layered long-term memory. This innovative approach helps agents learn workflows and retain context more effectively.
Ludwig: Low-Code Declarative Deep Learning for LLMs and AI Models
Ludwig is a powerful, low-code declarative deep learning framework designed for building custom LLMs, neural networks, and other AI models. It simplifies the process of training, fine-tuning, and deploying models, from LLM fine-tuning to tabular classification, using a simple YAML configuration without boilerplate Python code. This makes advanced AI development accessible and efficient for a wide range of applications.
Lamini: The Official Python Client for Generative AI API
Lamini is the official Python client and SDK designed to interact with the Lamini API, enabling developers to create their own Generative AI applications. It provides a straightforward interface for integrating powerful AI capabilities into Python projects. This package simplifies the process of building and deploying generative AI solutions.
xTuring: Build, Personalize, and Control Your Own LLMs
xTuring is an open-source framework designed to simplify the process of building, personalizing, and controlling Large Language Models (LLMs). It provides an easy way to fine-tune open-source LLMs on your own data, offering features from data pre-processing to efficient training and inference. This tool empowers developers to create private, personalized LLMs locally or in their private cloud environments.
torchtune: PyTorch Native Library for LLM Post-Training and Experimentation
torchtune is a PyTorch native library designed for authoring, post-training, and experimenting with Large Language Models (LLMs). It offers hackable training recipes, simple PyTorch implementations of popular LLMs, and best-in-class memory efficiency. Please note: torchtune is no longer actively maintained as of 2025.
RAGChecker: A Fine-grained Framework for Diagnosing RAG Systems
RAGChecker is an advanced automatic evaluation framework developed by Amazon Science, specifically designed to assess and diagnose Retrieval-Augmented Generation (RAG) systems. It offers a comprehensive suite of metrics and tools for in-depth analysis of RAG performance. This framework empowers developers and researchers to thoroughly evaluate and enhance their RAG systems with precision.
rerankers: Unified API for Reranking and Cross-Encoder Models
rerankers is a lightweight, low-dependency Python library that provides a unified API for various reranking and cross-encoder models. It simplifies the integration of different reranking approaches into retrieval architectures, offering a consistent interface for diverse models like cross-encoders, RankGPT, T5, and API-based rerankers. This library aims to make reranking more accessible and easier to implement for developers.