ИИ-агенты
unstructured
Turn messy documents into clean, structured data for language models.
★ 15,5 тыс. звёздApache-2.0
Смотреть руководство →docling — проект с открытым исходным кодом из категории «ИИ-агенты» от docling-project (docling-project/docling на GitHub). У него 68 521 звезда на GitHub и 5 023 форка, он распространяется по лицензии MIT и последний раз обновлялся на GitHub 8 октября 2026.
Convert PDFs, DOCX, and HTML into clean, AI-ready documents.
Choose one real workload to test the project against.
Clone it locally and read through the getting-started guide.
Install the stack it needs and set any environment variables.
Execute the starter example and check results before going further.
Handles PDF, DOCX, HTML, and more.
Produces clean structured Markdown for downstream AI use.
Built to feed retrieval and agent workflows.
Integrates directly into Python data pipelines.
Permissive licensing.
Pushed in October 2026.
Install docling from PyPI into your Python environment.
pip install doclingConvert a document to structured output.
# see README for the exact CLI/API usage
docling myfile.pdfTake the Markdown output into your RAG or agent ingestion flow.
# integrate the parsed output with your embeddings stepIt is maintained by the docling-project team at docling-project/docling, with 5,023 forks. Check recent releases for format-support updates.
Yes. It is released under the MIT license, which permits commercial use, modification, and redistribution. Read the LICENSE file for the exact terms.
No — it is a local parsing library. You only pay for whatever LLM you pair it with downstream.
Compare on table and layout fidelity plus format coverage for your specific document types.
Run pip install docling and follow the README examples to convert your first file.
Turn messy documents into clean, structured data for language models.
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