開発ツール
AnyCrawl
A Node.js crawler that turns websites into LLM-ready data and extracts structured SERP results.
★ 3.5k スターMIT
ガイドを見る →llm-courseは、mlabonneが公開しているオープンソースの開発ツールプロジェクトです（GitHub: mlabonne/llm-course）。GitHubスターは83,336、フォークは9,697で、Apache-2.0ライセンスの下で公開されています。GitHubでの最終更新は2026年2月5日です。
A free course to get into large language models, with roadmaps and runnable Colab notebooks
Decide what you want from llm-course and check the README and requirements to confirm it fits your setup and multiple languages environment.
Clone the repository from GitHub (github.com/mlabonne/llm-course) or use the documented install channel if you do not need the source.
Follow the README quickstart to install dependencies and set any required configuration or credentials.
Work through the README's first example end to end, then adapt it to your own project or data.
Structured roadmaps that sequence LLM topics from fundamentals to advanced work.
Hands-on Colab notebooks accompany the lessons so you can run code in the browser.
Covers mathematics, Python, neural networks and NLP foundations first.
Architecture, pre-training and post-training datasets and methods.
Includes fine-tuning and quantization sections with tooling guidance.
The whole course is published openly on GitHub under Apache-2.0.
Start by getting the source and entering the project directory.
git clone https://github.com/mlabonne/llm-course.git
cd llm-courseGet the notebooks and materials locally.
git clone https://github.com/mlabonne/llm-course.git
cd llm-courseMost lessons run directly in Google Colab from the links in the README.
# open the Colab link for a lesson from the READMEIt is maintained by mlabonne and contributors on GitHub, where the repository github.com/mlabonne/llm-course is the canonical home for code, issues and releases.
Yes — it is open source under the Apache-2.0 license. Read the license in the repository for what is permitted, including any conditions on redistribution or commercial use.
No. The course is free; Colab notebooks run on Google Colab, which has a free tier.
Compared with paid LLM courses: This course is free and notebook-driven, covering similar ground to commercial deep-learning specializations.
Clone github.com/mlabonne/llm-course, follow the README quickstart to install it, and run the documented first example before adapting it to your own work.
A Node.js crawler that turns websites into LLM-ready data and extracts structured SERP results.
Turn domain documents into high-quality LLM fine-tuning, RAG and evaluation datasets through a visual UI.
ARTEMIS turns natural-language instructions into reliable Android automation. It automates end-to-end workflows, captures logs, and integrates seamlessly wit…
Anthropic's official collection of notebooks and recipes for using Claude effectively
An AI compute engine: a distributed runtime plus libraries for scaling ML and LLM workloads
Hugging Face's free course on AI agents — smolagents, LangGraph and LlamaIndex, with a final benchmark
LibreAIカタログに掲載された開発ツールプロジェクトの中で最もスターの多いもの — llm-courseが合わなかった場合に検討したい、現実的な候補リストです。
The open source coding agent
The model-definition framework behind modern AI — thousands of pretrained transformers for text, vision, and audio.
Anthropic's agentic coding tool that lives in your terminal and works across your whole codebase
Cross-platform desktop all-in-one manager for Claude Code, Codex, OpenCode and other coding agents
OpenAI's lightweight coding agent that runs locally in your terminal
カタログの中でllm-courseとスター数が近い開発ツールプロジェクト — 前後の棚もそのまま見て回れます。
Adaptive web scraping framework in Python — from a single request to a full-scale crawl
CLI proxy that cuts LLM token consumption 60-90% on common dev commands — one Rust binary
The agent development environment for running a fleet of parallel coding agents in worktrees
Unified, efficient fine-tuning of 100+ LLMs and VLMs with a CLI and Web UI — ACL 2024
Production-grade engineering skills for AI coding agents, by Addy Osmani — 25 skills in one pack
Compress tool outputs, logs and RAG chunks before they reach the LLM — same answers, fewer tokens