Stars
Tools for merging pretrained large language models.
Simple, scalable AI model deployment on GPU clusters
Run your own AI cluster at home with everyday devices 📱💻 🖥️⌚
Universal memory layer for AI Agents; Announcing OpenMemory MCP - local and secure memory management.
Neo4j graph construction from unstructured data using LLMs
Agent driven automation starting with the web. Try it: https://www.emergence.ai/web-automation-api
Get up and running with OpenAI gpt-oss, DeepSeek-R1, Gemma 3 and other models.
Making large AI models cheaper, faster and more accessible
DSPy: The framework for programming—not prompting—language models
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs
Retrieval and Retrieval-augmented LLMs
Medusa: Simple Framework for Accelerating LLM Generation with Multiple Decoding Heads
Fabric is an open-source framework for augmenting humans using AI. It provides a modular system for solving specific problems using a crowdsourced set of AI prompts that can be used anywhere.
SGLang is a fast serving framework for large language models and vision language models.
Real-time face swap for PC streaming or video calls
AppAgent: Multimodal Agents as Smartphone Users, an LLM-based multimodal agent framework designed to operate smartphone apps.
ChatGLM3 series: Open Bilingual Chat LLMs | 开源双语对话语言模型
面向开发者的 LLM 入门教程，吴恩达大模型系列课程中文版
手把手带你实战 Huggingface Transformers 课程视频同步更新在B站与YouTube
AI Native Data App Development framework with AWEL(Agentic Workflow Expression Language) and Agents
GUI for ChatGPT API and many LLMs. Supports agents, file-based QA, GPT finetuning and query with web search. All with a neat UI.
A Next-Generation Training Engine Built for Ultra-Large MoE Models
MS-Agent: Lightweight Framework for Empowering Agents with Autonomous Exploration in Complex Task Scenarios
🤖 AgentVerse 🪐 is designed to facilitate the deployment of multiple LLM-based agents in various applications, which primarily provides two frameworks: task-solving and simulation