Open-source AI and machine learning tools on GitHub — LLM frameworks, model runtimes, agents and training libraries, ranked by stars and reviewed by TopGit.
Superpowers is an open-source (MIT) skills library and bootstrap instruction set that turns a coding agent's ad-hoc habits into a fixed pipeline: brainstorm a spec, write a plan, build under TDD, review, then close out the branch. Skills trigger automatically once installed, and the same methodology works across eleven different agent harnesses, each requiring its own install step.
TensorFlow is Google's open-source, end-to-end platform for machine learning, hosted at tensorflow/tensorflow under the Apache-2.0 license. It was originally built within Google Brain's Machine Intelligence team for ML and neural network research, and today it ships stable Python and C++ APIs alongside GPU, CPU-only, and Docker install paths. The README positions it as covering both research work and shipping ML-powered applications.
Firecrawl is an API and set of SDKs for turning any URL into Markdown, structured JSON, or a screenshot, handling JS rendering and proxy rotation itself. It's built for feeding AI agents and LLM pipelines clean web data, with dedicated search, crawl, map, and agent endpoints, an MCP server, and SDKs for nine languages.
AutoGPT is an open-source platform designed for building, deploying, and running AI agents that can carry out complete workflows. Users can define tasks in plain English or use a visual builder to shape each step. The project offers two primary paths: a managed, hosted AutoGPT Platform that handles infrastructure and model access for a fee, and a self-hosting option that is free but requires users to provide their own infrastructure and model API keys. Agents can run on demand, on schedules, or from triggers, connecting to over 45 platforms and hundreds of AI models. It's presented as a tool to automate various functions, from executive operations and sales research to marketing campaign drafts and incident triage in engineering.
anthropics/skills is Anthropic's public collection of Agent Skills for Claude — SKILL.md folders spanning creative, technical, enterprise, and document tasks, plus a spec and a template for building your own.
prompts.chat is the largest open-source prompt library for AI, formerly called Awesome ChatGPT Prompts. It hosts curated prompts in CSV and Markdown, available as a public website, Hugging Face dataset, or self-hosted instance. The project supports multiple LLM providers including ChatGPT, Claude, Gemini, Llama, and Mistral. Self-hosting uses a Next.js setup wizard that configures authentication via GitHub, Google, or Azure AD, with PostgreSQL as the recommended database. CLI access, an MCP server, and a Claude Code plugin extend its reach into developer workflows. The codebase is MIT-licensed while prompt data falls under CC0. Its 166k GitHub stars make it an AI resource on the platform with 166k GitHub stars, and it has been cited by Harvard, Columbia, and Forbes.
prompts.chat is an open-source library of prompts written for AI chat assistants, first released in December 2022 under the name Awesome ChatGPT Prompts. The GitHub project has since grown and now distributes prompts through a website, a CSV file, a Markdown file, and a Hugging Face dataset, alongside a self-hosting option, a CLI, an MCP server, and a Claude Code plugin.
Hugging Face Transformers (huggingface/transformers) is a Python library that centralizes model definitions for text, computer vision, audio, video, and multimodal machine learning, covering both inference and training. The README describes it as a pivot point compatible with training frameworks such as Axolotl, DeepSpeed, and PyTorch-Lightning, and inference engines such as vLLM, SGLang, and TGI, with more than 1M+ model checkpoints listed on the Hugging Face Hub.
The Agency is a collection of specialized AI agent personas — markdown prompt files with a defined identity, workflow, and success metrics — for Claude Code, Cursor, and other AI tools, installable through a CLI script or a native desktop app.
JavaGuide là một repository tổng hợp kiến thức phỏng vấn Java và backend được duy trì bởi cộng đồng, bao phủ gần như toàn bộ các chủ đề mà một ứng viên backend cần nắm vững: từ Java core (collections, concurrency, JVM), hệ điều hành và mạng máy tính, cho đến cơ sở dữ liệu (MySQL, Redis), kiến trúc phân tán, và các công cụ devops như Docker, Git. Điểm mạnh rõ rệt là mỗi phần đều đi kèm bài tổng hợp câu hỏi phỏng vấn được đánh dấu "必看" (bắt buộc đọc), kèm theo phân tích source code chi tiết cho các cấu trúc dữ liệu phổ biến như HashMap, ConcurrentHashMap, và các topic nâng cao như AQS, JMM. Repository cũng mở rộng sang AI application development với một repo riêng (AIGuide) về LLM, Agent, RAG và MCP, phản ánh xu hướng hiện tại trong việc tuyển dụng backend. Nhược điểm là nội dung rất dàn trải — hơn 100 file markdown trải dài từ BigDecimal đến Elasticsearch, nên nếu bạn đã có nền tảng vững ở một số phần, việc lọc đúng nội dung cần ôn tập đòi hỏi thêm effort. Cá nhân tôi thấy đây là tài liệu tốt nhất để "làm quen" với toàn cảnh backend Java trước khi đi sâu vào từng chủ đề riêng lẻ, nhưng không nên dùng nó thay cho việc đọc source code thật hoặc tài liệu chính thức.
Dify is an open-source LLM app platform that packages a visual workflow/agent builder, RAG pipeline, model provider management, and app-level APIs into one self-hostable (or cloud) stack, aimed at teams who want to skip stitching those pieces together themselves.
Langflow pairs a drag-and-drop canvas with editable Python components, letting you build an LLM-powered agent flow and ship it as an API, MCP server, or JSON export.
LangChain is a Python framework (MIT-licensed) for assembling LLM-powered apps and agents from standard components: model wrappers, prompts, retrieval, tools, and chain/graph orchestration handed off to LangGraph. It's for developers gluing together model providers and data sources, not for a single prompt-response call.
A GPL-3.0 GitHub repo compiling system prompts, tool definitions, and model names for 25+ AI products, from Cursor and Claude Code to lesser-known agents like Cluely and Qoder.
Shubhamsaboo/awesome-llm-apps is a substantial collection of over 100 open-source AI agents, agent skills, and Retrieval Augmented Generation (RAG) applications. Hand-built, end-to-end tested, and licensed under Apache-2.0, this Python-based repository supports a range of LLMs including Claude, Gemini, GPT, DeepSeek, Llama, and Qwen. It offers a wide array of examples, from single-file starter agents and advanced multi-agent teams to voice AI agents, generative UIs, autonomous game-playing agents, and various RAG implementations. The collection also includes LLM optimization tools, fine-tuning recipes, and crash courses on agent frameworks, providing practical, ready-to-deploy solutions for common LLM development challenges.
gstack is Garry Tan's open-source pack of 23 Claude Code skills and 8 power tools — a CEO, an eng manager, a QA lead, a security officer, and more — each a Markdown slash command that runs one stage of a sprint from idea to a shipped PR. It's MIT-licensed and free, documented as a roughly 30-second install for Claude Code, with setup support for OpenClaw, Cursor, and seven more agents.
MoneyPrinterTurbo is a self-hosted AI video generation pipeline that automates scriptwriting, stock footage selection, text-to-speech narration, subtitle rendering, and ffmpeg-based video assembly into HD MP4s. It ships with four interfaces (WebUI, API, CLI, AI Agent) and supports batch production. The tool is particularly attractive for creators who need volume at low per-video cost, since you supply your own API keys rather than paying per-export fees. Setup involves Python 3.11+, at least one LLM provider (many supported), and ffmpeg — Whisper captioning is optional but demands a multi-gigabyte model download. It's a legitimate workhorse for automated short video workflows, not a toy.
llama.cpp is an open-source project at ggml-org/llama.cpp that implements LLM and VLM inference in plain C/C++ with no external dependencies, built on top of the ggml tensor library. The README documents support for x86 SIMD, Apple Silicon, RISC-V, and GPU backends including CUDA, HIP, MUSA, Vulkan, and SYCL, along with quantization down to 1.5-bit, a CLI, and an OpenAI-compatible server.
Microsoft's introductory course on building generative AI applications walks you through 21 structured lessons created by Microsoft Cloud Advocates. The curriculum alternates between conceptual 'Learn' lessons and hands-on 'Build' lessons, progressing from LLM fundamentals through advanced topics like RAG, AI agents, and fine-tuning. Each lesson includes a written explanation, video introduction, and working code samples in both Python and TypeScript. Basic Python or TypeScript knowledge is expected. The course supports Azure OpenAI, OpenAI API, Microsoft Foundry Models, and Foundry Local for fully offline execution. With over 50 language translations maintained via automated GitHub Actions, it's accessible to a global developer audience. MIT licensed with an active Discord community and developer forum for peer support.
Browser Use provides a framework for AI agents to interact with web browsers, enabling automated tasks like form filling, data extraction, and QA testing. It offers both a Python library for programmatic control and a CLI for agent-driven workflows, supporting various LLMs and offering cloud services for scalability and advanced browser fingerprinting.
Promptfoo is a command-line tool and Node library for testing LLM prompts and running security red-team scans. You describe your test cases in a config file, point it at one or more models, and get a side-by-side matrix of results instead of squinting at chat outputs by hand.
Firecrawl is an API and set of SDKs for turning any URL into Markdown, structured JSON, or a screenshot, handling JS rendering and proxy rotation itself. It's built for feeding AI agents and LLM pipelines clean web data, with dedicated search, crawl, map, and agent endpoints, an MCP server, and SDKs for nine languages.
Supabase wraps Postgres with a Firebase-style developer experience — auto-generating REST and GraphQL APIs, wiring up realtime websocket subscriptions, auth via JWT, file storage backed by S3, and a pgvector extension for AI vector search. Everything runs on open source components (PostgREST, GoTrue, Realtime) under an Apache-2.0 license. You can spin up a project in minutes via the hosted dashboard or run the full stack locally with Docker and the Supabase CLI. The trade-off is that you still need to think in relational terms: schema design, migrations, and query optimization are still your job.
llama.cpp is an open-source project at ggml-org/llama.cpp that implements LLM and VLM inference in plain C/C++ with no external dependencies, built on top of the ggml tensor library. The README documents support for x86 SIMD, Apple Silicon, RISC-V, and GPU backends including CUDA, HIP, MUSA, Vulkan, and SYCL, along with quantization down to 1.5-bit, a CLI, and an OpenAI-compatible server.
Orca is a desktop, mobile, and VPS app written in TypeScript that runs several coding agents at once, each in its own git worktree, with a built-in terminal, a browser-based Design Mode, and a CLI for scripting the same workflow.
Ruflo (ruvnet/ruflo on GitHub) is a meta-harness that wraps around Claude Code and Codex, adding swarm coordination, persistent memory, and cross-machine agent communication on top of the models you already run. It installs either as Claude Code plugins or via a full `npx ruflo init` CLI setup, and the README describes 100+ specialized agents, a hooks-based routing system, and a self-learning memory layer built on AgentDB.
jcode is a Rust CLI and TUI coding agent that logs into Claude, OpenAI, Gemini, Copilot, and OpenAI-compatible endpoints, then adds a semantic memory graph and a multi-agent Swarm mode on top. Its README leans on a PSS benchmark table against seven other agents to make its RAM-efficiency case, rather than a feature list.
Project AIRI is a self-hosted AI VTuber platform inspired by Neuro-sama that combines voice interaction, game-playing agents, and avatar animation into a single system. The project supports Minecraft and Factorio gameplay, real-time voice chat, and multiple LLM providers through its xsai library. Built with TypeScript and designed for Web, desktop (Electron), and mobile deployment, AIRI is MIT-licensed with 47k GitHub stars and a growing ecosystem of sub-projects covering everything from memory systems to MCP integration. It is early-stage software where memory persistence and pure in-browser inference remain works in progress, but the breadth of integration points and multi-platform support make it a serious attempt at the Neuro-sama alternative its creators are openly targeting.
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
🚀Clone a voice in 5 seconds to generate arbitrary speech in real-time
中文分词 词性标注 命名实体识别 依存句法分析 成分句法分析 语义依存分析 语义角色标注 指代消解 风格转换 语义相似度 新词发现 关键词短语提取 自动摘要 文本分类聚类 拼音简繁转换 自然语言处理
OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
💫 Industrial-strength Natural Language Processing (NLP) in Python
Visualizer for neural network, deep learning and machine learning models
《开源大模型食用指南》针对中国宝宝量身打造的基于Linux环境快速微调（全参数/Lora）、部署国内外开源大模型（LLM）/多模态大模型（MLLM）教程
An LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations.
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
A curated list of awesome Deep Learning tutorials, projects and communities.
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
A complete daily plan for studying to become a machine learning engineer.