langchain-docling
Docling LangChain integration
Decision gist · record as of 2026-08-14
Yes. Active maintenance, no vulnerabilities, low install friction, and permissive MIT license make this a safe choice. Install it if you're building a LangChain application that needs to ingest and process documents—either locally (with the `local` extra) or via a remote Docling endpoint. The base install is lightweight; add the `local` extra only if you need local conversion.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Local document conversion requires the `local` extra (`pip install "langchain-docling[local]"`); without it, you must use a remote Docling Serve or Managed Docling endpoint.
- Low friction install with a pure Python wheel.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions; you may use, modify, and distribute this package freely provided you include the license notice.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 75 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 148,992 downloads/mo, #11,009 on PyPI
Alternatives
Verify before relying
pip install langchain-docling
from langchain_docling import DoclingLoader
loader = DoclingLoader(file_path=["https://arxiv.org/pdf/2408.09869"])
docs = loader.load()- Whether the base install (without `local` extra) can convert documents locally or requires a remote service endpoint.
- Performance characteristics and throughput limits when processing large document batches.
- Supported document formats beyond PDF (e.g., DOCX, images, HTML).
What it is and what it does
langchain-docling bridges Docling, a document conversion and understanding library, with LangChain's document loading and processing ecosystem. It provides a `DoclingLoader` that can ingest documents from URLs or local files and convert them into LangChain-compatible document objects suitable for downstream language model tasks.
You can run document conversion locally (with the `local` extra, which includes AI runtimes like PyTorch) or remotely via Docling Serve, Managed Docling, or IBM watsonx. The loader supports multiple export modes (doc chunks or Markdown), custom chunking strategies, metadata extraction, and backend-specific conversion options. It's designed for developers building RAG pipelines, document analysis workflows, or AI-powered search systems that need reliable document ingestion.
Use it for
- Build a RAG pipeline that ingests PDFs and technical documents into LangChain for semantic search and question-answering.
- Convert scanned documents or images to structured text using OCR, then feed them into language models for analysis.
- Process multiple document formats (PDF, DOCX, images) in bulk and export as Markdown for downstream NLP tasks.
- Integrate document conversion into a LangChain agent that needs to extract and reason over document content.
- Use a remote Docling service to offload heavy document processing while keeping your application lightweight.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Active maintenance, no vulnerabilities, low install friction, and permissive MIT license make this a safe choice. Install it if you're building a LangChain application that needs to ingest and process documents—either locally (with the `local` extra) or via a remote Docling endpoint. The base install is lightweight; add the `local` extra only if you need local conversion.
Install
langchain-docling on PyPI
Before you install
Low friction install with a pure Python wheel. Active maintenance as of 2026-08-14 with no known vulnerabilities. Requires Python 3.10–3.14; the base install pulls only langchain-core and docling-slim, but local document conversion requires the `local` extra with additional dependencies.
Requires Python 3.10 or later. Local document conversion requires the `local` extra (`pip install "langchain-docling[local]"`); without it, you must use a remote Docling Serve or Managed Docling endpoint.
License in practice
MIT license permits commercial and private use with minimal restrictions; you may use, modify, and distribute this package freely provided you include the license notice.
Quickstart
pip install langchain-docling
from langchain_docling import DoclingLoader
loader = DoclingLoader(file_path=["https://arxiv.org/pdf/2408.09869"])
docs = loader.load()Verify before relying
- Whether the base install (without `local` extra) can convert documents locally or requires a remote service endpoint.
- Performance characteristics and throughput limits when processing large document batches.
- Supported document formats beyond PDF (e.g., DOCX, images, HTML).
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packageslangchain-coredocling-slim |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 148,992 / month, #11,009 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: langchain_docling-3.0.0-py3-none-any.whl
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See also langchain-unstructured · docling-core · docling · langchain-aws · docling-slim · langchain-ollama · langchain-openai · langchain-community · langchain-xai · langchain-deepseek