MegaParse vs text-extract-api

Side-by-side comparison of two AI agent tools

Short answer

  • text-extract-api is growing faster: +17 GitHub stars in the last 30 days vs +11 for MegaParse.
  • Pick MegaParse for: file Parser optimised for LLM Ingestion with no loss Parse PDFs, Docx, PPTx in a format that is ideal for LLMs. Pick text-extract-api for: local FastAPI for OCR extraction and PII removal from images, PDFs and Office files to Markdown or JSON.

From GitHub data refreshed daily.

MegaParseopen-source

File Parser optimised for LLM Ingestion with no loss 🧠 Parse PDFs, Docx, PPTx in a format that is ideal for LLMs.

text-extract-apiopen-source

Local FastAPI for OCR extraction and PII removal from images, PDFs and Office files to Markdown or JSON

Metrics

MegaParsetext-extract-api
Stars7.4k3.2k
Star velocity /mo10.89473684210526416.736842105263158
Commits (90d)00
Releases (6m)00
Overall score0.192865505002783630.20484123380037875

Pros

  • +Zero information loss during parsing with specific focus on preserving complex document elements like tables, headers, and images
  • +Superior performance with 0.87 similarity ratio in benchmarks, significantly outperforming competing parsers
  • +Dual parsing modes including MegaParse Vision that leverages advanced multimodal AI models for enhanced document understanding
  • +完全本地化处理，无外部依赖，确保数据隐私和安全性
  • +支持多种先进OCR策略（LLaMA Vision、EasyOCR等），识别精度极高
  • +集成分布式队列和缓存机制，支持大规模文档批量处理

Cons

  • -Requires multiple external dependencies (poppler, tesseract, libmagic on Mac) which can complicate installation
  • -Needs OpenAI or Anthropic API keys for operation, adding ongoing costs for usage
  • -Minimum Python 3.11 requirement may limit compatibility with older environments
  • -需要安装多个依赖组件（Docker、Ollama），初始设置较为复杂
  • -本地运行PyTorch模型需要较大计算资源和存储空间

Use Cases

  • •Preparing documents for RAG (Retrieval-Augmented Generation) systems where preserving all context and formatting is critical
  • •Converting complex academic or business documents with tables and images into LLM-ready format for analysis
  • •Building document processing pipelines that need to maintain fidelity across diverse file formats (PDF, Word, PowerPoint)
  • •医疗机构将MRI报告、病历等医疗文档转换为结构化数据
  • •企业财务部门处理发票、合同等文档并自动移除敏感信息
  • •法律机构批量数字化和分析大量合规文档或法律条文
View MegaParse DetailsView text-extract-api Details

FAQ

Which is more popular, MegaParse or text-extract-api?
MegaParse has more GitHub stars (7,413 vs 3,183).
Which is more actively developed, MegaParse or text-extract-api?
MegaParse had more commits in the last 90 days (0 vs 0).
Should I use MegaParse or text-extract-api?
Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.