PDF and Document Parsers for RAG

LightRAG combines knowledge graphs with vector search to answer questions across a document collection. It's a self-hosted Python framework for developers building document assistants, particularly where answers depend on relationships between facts in different files, such as legal or financial material.

RAGFlow is an Apache 2.0 licensed RAG engine for teams building AI agents that need to answer questions from their own documents. It can run on a self-hosted server through Docker on Windows, macOS or Linux. A separate hosted cloud service is available.

Docling is an MIT-licensed, open source document parser for developers turning files into structured content for search and AI applications. It runs locally on macOS, Linux, and Windows, including in air-gapped environments. Its PDF processing identifies page layout and reading order, extracts tables, code, and formulas, and classifies images.

MinerU parses documents locally into structured text for AI agents, RAG systems and knowledge bases. It's for people working with scanned PDFs, academic papers and Office files whose tables, formulas or page layouts need more care than plain text extraction.

Bisheng is an open source, self-hosted platform for teams building AI applications around business documents and processes. Its visual workflow editor combines automated tasks with human feedback, including intervention during multi-turn conversations. It's suited to document review, support ticket assistance and report generation that need more control than a single chatbot exchange.

Nougat is a local AI PDF parser for researchers and developers who need scientific papers as usable text, including their equations and tables. It converts academic PDFs into Markdown-style documents with LaTeX notation, so the output retains structure that plain text extraction can lose.

QAnything is a self-hosted knowledge base for people and teams who want to ask questions about their own documents, including collections that mix Chinese and English. It can answer in either language regardless of the document's language, and runs locally through Docker on Windows, macOS and Linux.

Unstructured is a local document processing library for developers building LLM applications and document ingestion pipelines. It turns PDFs, Word documents, HTML, emails and images into document elements that applications can use. The Python library is open source under Apache 2.0 and runs on your own hardware, including through Docker images for x86_64 and Apple Silicon.

Morphik Core is a self-hosted multimodal retrieval engine for developers building AI applications around visually rich documents. It searches diagrams, schematics, charts, and datasheets alongside text, so applications can retrieve information that text extraction alone can miss. You can run it on your own server, including through Docker, or use Morphik's hosted service.

R2R is a self-hosted AI retrieval system for developers building applications that answer questions using their own documents. It combines search, retrieval-augmented generation (RAG) and a reasoning agent behind a REST API. The project is open source under the MIT license and runs as a Python service or in Docker.

docTR is an open-source Python OCR library for developers building document processing tools and researchers comparing text recognition models. It reads PDFs and images on your own hardware, locating words and recognizing their text. The library uses PyTorch and carries the Apache 2.0 license.

xberg, formerly Kreuzberg, is a local document extraction engine for developers building AI search, document processing, and retrieval-augmented generation applications. It reads PDFs, Office files, scanned images, email, and nested archives, extracting text, tables, images, and metadata through one shared engine. It's open source under MIT.

Zerox is an MIT-licensed OCR library for developers preparing documents for AI applications. Its Node.js and Python packages run on your own machine or server, while cloud vision models read the document pages and produce Markdown. Document conversion happens locally, but page images go to the selected model provider, so this workflow needs internet access and provider credentials.

DeepSeek-OCR is an open-source OCR model for developers building document processing tools and researchers studying how AI reads text through images. It runs on your own hardware with NVIDIA CUDA GPUs. Its distinctive focus is visual text compression: representing document images with compact sets of vision tokens for a language model to read.

PDF-Extract-Kit is a local AI model toolbox for developers and researchers building document processing applications. It extracts text, tables and mathematical formulas from PDFs, with separate models for identifying page elements and recognizing their contents. It's open source under AGPL-3.0, written in Python, and supports CPU or GPU execution on your own hardware.

MonkeyOCR is a local AI document parser for developers and researchers working with English and Chinese PDFs or images. It extracts text, formulas and tables while identifying page structure and relationships between blocks. That makes it useful for documents where plain text extraction loses reading order or separates content from its layout.

Data-Juicer is a Python framework for preparing AI datasets on your own machine or a distributed Ray cluster. It's for researchers and teams curating model training data, agent interaction records or documents for retrieval. The project is open source under Apache 2.0.

LayoutParser is an open-source Python library for developers and researchers who need to detect page structure in document images and turn OCR output into structured data. Its pretrained deep learning models share a common interface, so you can work with models trained on different document datasets without rewriting the surrounding pipeline.

Gitingest turns a Git repository or local directory into a text digest that developers can give to an LLM as code context. It combines the directory tree and file contents in one extract, so you don't have to assemble context file by file. It's open source under the MIT license.

Crawl4AI is a self-hosted web crawler and scraper for developers building AI agents, retrieval-augmented generation (RAG) systems and data pipelines. It turns web pages into Markdown or structured JSON and runs as a Python library or a Docker server on your own hardware. The open-source code uses the Apache 2.0 license.

Jina Reader turns web pages and documents into text that LLMs can use, with Markdown or JSON output. It's for developers building AI agents, search tools and systems that answer questions using retrieved documents. You can self-host the Apache 2.0 service code in Docker or use Jina's hosted API.

Marker is a local document converter for developers and teams turning PDFs, scans and Office files into structured text. It preserves tables, equations and page structure for document processing and AI workflows. Its pipeline reads embedded PDF text and uses Surya OCR where text is missing or damaged, rather than reading every page through a vision model.

LlamaIndex is an MIT-licensed Python framework for developers building AI agents and apps that answer questions using their own data. It connects documents and other sources to language models, then helps an app find the relevant material when a user asks something. Its open source framework can work with models served through Ollama.

olmOCR is an open-source OCR toolkit for turning PDFs and image documents into text for LLM datasets and training. It suits researchers and developers who need readable document content, including pages where columns, figures, or mathematical notation make text extraction difficult. You can run it on your own GPU, including through Docker, or use a remote inference server.

dots.ocr is a self-hosted document parser that combines multilingual text recognition and page layout analysis in one vision-language model. It's for developers and teams converting PDFs or document images into structured text while running inference on their own hardware. The Python project is open source under the MIT license.

Surya is a local OCR toolkit for developers extracting text and structure from PDFs and document images. It combines text recognition, layout analysis and table recognition in one vision-language model, so results retain page structure and reading order rather than just the words.

PaddleOCR is an open source OCR and document parsing toolkit for developers building document search, RAG systems and AI agents. It runs on your own hardware or a self-hosted server and turns PDFs and images into structured Markdown or JSON. The Python toolkit uses PaddlePaddle and carries the Apache 2.0 license.