MarkItDown vs text-extract-api

Side-by-side comparison of two AI agent tools

Short answer

  • text-extract-api has had no commit in 9 months; MarkItDown is actively maintained (106 commits in the last 90 days).
  • MarkItDown is growing faster: +15,067 GitHub stars in the last 30 days vs +17 for text-extract-api.
  • Pick MarkItDown for: python tool for converting files and office documents to Markdown. 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.

MarkItDownopen-source

Python tool for converting files and office documents to Markdown.

text-extract-apiopen-source

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

Metrics

MarkItDowntext-extract-api
Stars188.1k3.2k
Star velocity /mo15.1k16.736842105263158
Commits (90d)1060
Releases (6m)50
Overall score0.78056632625133590.20484123380037875

Pros

  • +支持超过 10 种文件格式，包括办公文档、图像 OCR 和音频转录，覆盖面极广
  • +专为 LLM 优化的 Markdown 输出，保留文档结构的同时确保 AI 模型兼容性
  • +提供 MCP 服务器集成，可直接与 Claude Desktop 等 AI 应用协作
  • +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

  • -版本间有重大变更，从 0.0.1 到 0.1.0 的 API 变化可能影响现有代码
  • -需要 Python 3.10 或更高版本，对旧环境支持有限
  • -主要面向机器分析而非人类阅读，可能不适合高保真度的文档转换需求
  • -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

  • •为 LLM 分析准备各类办公文档和 PDF，提取结构化文本内容
  • •构建文档处理管道，将多格式文件批量转换为统一的 Markdown 格式
  • •集成到 AI 工作流中，通过 OCR 和语音转录处理图像和音频内容
  • •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 MarkItDown DetailsView text-extract-api Details

FAQ

Which is more popular, MarkItDown or text-extract-api?
MarkItDown has more GitHub stars (188,114 vs 3,183).
Which is more actively developed, MarkItDown or text-extract-api?
MarkItDown had more commits in the last 90 days (106 vs 0).
Should I use MarkItDown 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.