ИИ-агенты
firecrawl
Turn any website into clean, agent-ready data with one API.
★ 189,6 тыс. звёздAGPL-3.0
Смотреть руководство →unstructured — проект с открытым исходным кодом из категории «ИИ-агенты» от Unstructured-IO (Unstructured-IO/unstructured на GitHub). У него 15 546 звёзд на GitHub и 1 350 форков, он распространяется по лицензии Apache-2.0 и последний раз обновлялся на GitHub 8 октября 2026.
Turn messy documents into clean, structured data for language models.
Write down the outcome you want in one sentence.
Clone it locally and check the README.
Get dependencies and tooling in place per the docs.
Follow the minimal example, then adapt it to your needs.
PDFs, Word, emails, images, HTML, and more.
Splits documents into meaningful elements.
Prepares text for embeddings and retrieval.
Adds metadata that improves downstream use.
Structured formats models can consume.
A hosted platform for production scale.
Download the source and move into the project folder.
git clone https://github.com/Unstructured-IO/unstructured.git
cd unstructuredIsolate dependencies, then install requirements.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtLaunch using the entry point shown in the README.
python -m unstructured # check README for the exact commandIt is an open-source project at Unstructured-IO/unstructured with 1350 forks. Check the contributors tab and recent commits to judge how active maintenance is.
Yes — Apache 2.0 allows commercial use and modification. Keep the license and notice files, and note it includes a patent grant.
PDFs, Word, emails, HTML, images, and more — partitioning complex documents is its core job.
Both: the Apache-2.0 library is the starting point; Unstructured also sells a hosted platform for production.
Clone the repo and follow the README quickstart — the docs at https://www.unstructured.io/ walk through setup.
Turn any website into clean, agent-ready data with one API.
Shrimp Task Manager: an MCP server that turns natural language into structured, dependent dev tasks.
Convert PDFs, DOCX, and HTML into clean, AI-ready documents.
Over a thousand tool integrations so agents can turn intent into action.
Physical Intelligence's open vision-language-action models (pi0, pi0-FAST, pi0.5) for robot policies.
Turn papers, text, or topics into editable research figures and slides.
Другие проекты из категории «ИИ-агенты» с наибольшим числом звёзд в каталоге LibreAI — реалистичный список альтернатив, если unstructured вам не подходит.
A personal AI that really does things — on any OS, with your data staying yours.
An agentic skills framework & software development methodology that works.
Agent harness optimization system with skills, memory and security for Claude Code, Codex and more
The agent that grows with you — a Python framework for adaptive AI agents.
DeepSeek's plugin-based agent harness where everything is a plugin.
Соседние проекты из категории «ИИ-агенты» в каталоге, ближайшие по числу звёзд к unstructured — продолжайте изучать полку по обе стороны от него.
A secure, fast sandbox runtime where AI agents can execute safely.
Open-source hub of integrations, SDK and CLI for building and deploying GPT/LLM chatbots and agents.
Automated penetration-testing agent framework powered by LLMs, published at USENIX Security 2024.
Microsoft's R&D-Agent: LLM agents that automate data-driven research, Kaggle modelling and LLM fine-tuning.
Supercharge LLM app evaluations with objective metrics, synthetic test data and feedback loops.
Bridge AI assistants to the Unity Editor via MCP: manage assets, scenes, scripts and tasks.
Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.