A large language model (LLM) is a type of machine learning model designed for understanding, generating, and interacting with human language. These models are trained on extensive datasets containing text from books, articles, websites, and other sources to learn patterns, context, and semantics in language. LLMs are widely used in applications like chatbots, code generation, translation, summarization, and more. They are often built using transformer architectures and are central to the field of generative AI.
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
A Simple and Universal Swarm Intelligence Engine, Predicting Anything. 简洁通用的群体智能引擎，预测万物
Local UI to run and train LLMs and diffusion models. Supports GGUF, MLX, Qwen3.8, DeepSeek-V4, MiniMax-H3, Gemma 4, FLUX and more.
Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
Ultra-lightweight, open-source, self-hosted personal AI agent framework in Python with WebUI, tools, memory, MCP, multi-agent workflows, automation, and chat apps
微舆：人人可用的多Agent舆情分析助手，打破信息茧房，还原舆情原貌，预测未来走向，辅助决策！从0实现，不依赖任何框架。
An autonomous agent that conducts deep research on any data using any LLM providers
🚀 The fast, Pythonic way to build MCP servers and clients.
An open-source RAG-based tool for chatting with your documents.
An open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
Structured Outputs
20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale.
Pocket Flow: Codebase to Tutorial
YuE2: frontier music generation with symbolic planning, zero-shot covers, and agentic music editing.
The official GitHub page for the survey paper "A Survey of Large Language Models".
AI Observability & Evaluation