7 Best text-extract-api Alternatives in 2026 (Open Source)
text-extract-api — Local FastAPI for OCR extraction and PII removal from images, PDFs and Office files to Markdown or JSON. vs cloud OCR services: fully on-premise with pluggable OCR strategies (4 engines), built-in PII removal, and distributed Celery scaling — no vendor lock-in
Short answer
- Closest match to text-extract-api: MarkItDown.
- Most actively developed: MinerU (905 commits in the last 90 days).
- Fastest growing: MarkItDown (+15,067 GitHub stars in the last 30 days).
- No commit in 6+ months: olmocr, MegaParse and LLM Sherpa.
These 7 open-source tools do the same job. They are ordered by how closely they match text-extract-api, with live GitHub data so you can see which projects are actively maintained.
By package downloads MarkItDown is the most used here (13.9M in the last 30 days), and it also has the most GitHub stars. See all agent tools by downloads.
| Tool | GitHub stars | Stars / 30d | Last commit | Downloads / 30d |
|---|---|---|---|---|
| text-extract-api(original) | 3.2k | +17 | 2025-12-08 | — |
| MarkItDown | 188.1k | +15,067 | 2026-10-03 | 13.9M |
| unstructured | 15.5k | +187 | 2026-10-02 | 2.5M |
| Docling | 68.3k | +1,850 | 2026-10-03 | 2.6M |
| olmocr | 19.7k | +413 | 2026-03-25 | 17.4K |
| MegaParse | 7.4k | +11 | 2025-02-21 | — |
| LLM Sherpa | 1.8k | 0 | 2024-10-18 | 18.5K |
| MinerU | 81.0k | +3,732 | 2026-09-29 | 96.9K |
1. MarkItDown
Python tool for converting files and office documents to Markdown.
What sets it apart: Microsoft's official document-to-Markdown converter for LLMs — built by the AutoGen team with MCP server support, unlike textract which predates the LLM era
Best for: Converting documents to Markdown for LLM consumption in RAG pipelines; Batch document processing for AI text analysis
2. unstructured
Open-source ETL for converting documents into structured data for language models
What sets it apart: vs LlamaParse: broader format support (20+ types) with open-source core; vs Apache Tika: ML-enhanced extraction with table detection and LLM-optimized output
Best for: RAG pipelines needing document ingestion; Enterprise document processing for AI applications; Converting unstructured documents to structured data for LLMs
3. Docling
Get your documents ready for gen AI
What sets it apart: Unlike LlamaParse (cloud-only, paid) or PyMuPDF (basic extraction), Docling runs fully locally, handles 20+ formats including audio and XML schemas, and produces a unified DoclingDocument representation with advanced PDF layout understanding backed by IBM Research.
Best for: Enterprise document processing pipelines needing high-fidelity PDF parsing with table/formula extraction; RAG applications that need to ingest diverse document formats into structured representations for LLM consumption
4. olmocr
Toolkit for linearizing PDFs for LLM datasets/training
What sets it apart: Open-source VLM-based OCR achieving 82+ on olmOCR-Bench, rivaling commercial solutions like Mistral OCR — vs traditional OCR tools (Tesseract) that struggle with complex layouts
Best for: Batch PDF-to-text conversion at scale with high accuracy; Academic and research document digitization; Building RAG pipelines that need clean text from PDFs
5. MegaParse
File Parser optimised for LLM Ingestion with no loss 🧠 Parse PDFs, Docx, PPTx in a format that is ideal for LLMs.
What sets it apart: vs Unstructured / LLMSherpa / PyPDF: vision-powered multimodal parsing using GPT-4o/Claude for complex layouts — handles tables, images, and visual formatting that rule-based parsers miss
Best for: Complex document digitization preserving layout and structure; RAG pipelines needing high-fidelity document parsing; Mixed-format data extraction and content migration
6. LLM Sherpa
Developer APIs to Accelerate LLM Projects
What sets it apart: vs PyPDF/unstructured/pdfplumber: preserves document hierarchy (sections, subsections, tables-in-context) that other parsers discard — enables semantically optimal chunks for RAG instead of arbitrary line-break splits
Best for: RAG applications needing structure-aware PDF chunking; Table extraction with section context preservation; Document analysis where layout semantics matter for LLM accuracy
7. MinerU
Transforms complex documents like PDFs into LLM-ready markdown/JSON for your Agentic workflows.
What sets it apart: Unlike PyPDF (text-only extraction) or cloud OCR services, MinerU preserves document layout including tables with embedded formulas and achieves 86.2 on OmniDocBench — purpose-built for AI/RAG document pipelines
Best for: RAG pipelines needing high-fidelity PDF/DOCX extraction with tables, formulas, and layouts preserved; Academic and research teams processing scientific papers with complex mathematical notation
FAQ
- What are the best alternatives to text-extract-api?
- The closest open-source alternatives to text-extract-api are MarkItDown, unstructured and Docling, followed by olmocr, MegaParse and LLM Sherpa. They are ranked by how closely they match what text-extract-api does.
- Which text-extract-api alternative is the most popular?
- MarkItDown has the most GitHub stars among text-extract-api alternatives, with 188,114 stars.
- Which text-extract-api alternative is the most actively maintained?
- By recent activity, MinerU (905 commits in the last 90 days) is the most actively developed alternative.
Maintain text-extract-api or one of these alternatives?
Each tool page has a maintainer box: a README badge with your live rank and stars, or a homepage + category feature for $49 / 7 days.
text-extract-api · MarkItDown · unstructured · Docling · olmocr · MegaParse