MarkItDown 把文件轉成可讀 Markdown 的邊界
使用 Python 將檔案和 Office 文件轉換為 Markdown。
秒懂
- 它是什麼？
- Convert files and office documents to Markdown with Python.
- 適合誰用？
- 適合需要在 Python 流程中處理 PDF、Office、HTML、圖片或音訊等來源的人；不適合未設權限隔離便處理不可信檔案。先用 `convert_local()` 或 `convert_stream()` 指向窄範圍輸入，記錄轉換文字與外部資源存取。
- 可以商用嗎？
- 可以。MIT 是寬鬆授權：你可以使用、修改並販售以它為基礎的軟體，只需保留著作權與授權聲明。
- 還在維護嗎？
- 有在維護。儲存庫最近一次提交在 1 天前。
- 用什麼語言寫的？
- 主要是 Python（依據 GitHub 的語言統計）。
以上回答依據專案的 GitHub 資料（最近同步於 2026年9月15日）與我們的分析，不構成法律意見。
開源專案深度解析
markitdown，定位與使用邊界
README 將 microsoft/markitdown 描述為「Convert files and office documents to Markdown with Python.」。這是專案自述的定位，不能延伸成未載明的相容性或效能承諾。本文把可追溯的功能、命令、設定和限制分開整理，讓讀者能以同一個專案記號回到來源。 README 將 microsoft/markitdown 描述為「Convert files and office documents to Markdown with Python.」。這是專案自述的定位，不能延伸成未載明的相容性或效能承諾。本文把可追溯的功能、命令、設定和限制分開整理，讓讀者能以同一個專案記號回到來源。 README 將 microsoft/markitdown 描述為「Convert files and office documents to Markdown with Python.」。這是專案自述的定位，不能延伸成未載明的相容性或效能承諾。本文把可追溯的功能、命令、設定和限制分開整理，讓讀者能以同一個專案記號回到來源。 README 將 microsoft/markitdown 描述為「Convert files and office documents to Markdown with Python.」。這是專案自述的定位，不能延伸成未載明的相容性或效能承諾。本文把可追溯的功能、命令、設定和限制分開整理，讓讀者能以同一個專案記號回到來源。
markitdown，README 揭示的核心路徑
從 README 可見的實作線索包括： MarkItDown []( []( > [!IMPORTANT] > MarkItDown performs I/O with the privileges of the current process. Like open() or requests.get(), it will access resources that the process itself can access. Sanitize your inputs in untrusted environments, and call the narrowest convert function needed for your use case (e.g., convertstream(), or convertlocal()). See the [Security Considerations](security-considerations) section of the documentation for more information. MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines. To this end, it is most comparable to [textract]( but with a focus on preserving important document structure and content as Markdown (including: headings, lists, tables, links, etc.) While the output is often reasonably presentable and human-friendly, it is meant to be consumed by text analysis tools -- and may not be the best option for high-fidelity document conversions for human consumption. MarkItDown currently supports the conversion from: - PDF - PowerPoint - Word - Excel - Images (EXIF metadata and OCR) - Audio (EXIF metadata and speech transcription) - HTML - Text-based formats (CSV, JSON, XML) - ZIP files (iterates over contents) - YouTube URLs - EPubs - ... and more! Why Markdown? Markdown is extremely close to plain text, with minimal markup or formatting, but still provides a way to represent important document structure. Mainstream LLMs, such as OpenAI's GPT-4o, natively "speak" Markdown, and often incorporate Markdown into their responses unprompted. This suggests that they have been trained on vast amounts of Markdown-formatted text, and understand it well. As a side benefit, Markdown conventions are also highly token-efficient. Prerequisites MarkItDown requires 。這些內容說明作者希望使用者如何理解專案；沒有出現的作業系統、輸入格式、部署拓撲或錯誤處理，不在本文替它補上假設。 從 README 可見的實作線索包括： MarkItDown []( []( > [!IMPORTANT] > MarkItDown performs I/O with the privileges of the current process. Like open() or requests.get(), it will access resources that the process itself can access. Sanitize your inputs in untrusted environments, and call the narrowest convert function needed for your use case (e.g., convertstream(), or convertlocal()). See the [Security Considerations](security-considerations) section of the documentation for more information. MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines. To this end, it is most comparable to [textract]( but with a focus on preserving important document structure and content as Markdown (including: headings, lists, tables, links, etc.) While the output is often reasonably presentable and human-friendly, it is meant to be consumed by text analysis tools -- and may not be the best option for high-fidelity document conversions for human consumption. MarkItDown currently supports the conversion from: - PDF - PowerPoint - Word - Excel - Images (EXIF metadata and OCR) - Audio (EXIF metadata and speech transcription) - HTML - Text-based formats (CSV, JSON, XML) - ZIP files (iterates over contents) - YouTube URLs - EPubs - ... and more! Why Markdown? Markdown is extremely close to plain text, with minimal markup or formatting, but still provides a way to represent important document structure. Mainstream LLMs, such as OpenAI's GPT-4o, natively "speak" Markdown, and often incorporate Markdown into their responses unprompted. This suggests that they have been trained on vast amounts of Markdown-formatted text, and understand it well. As a side benefit, Markdown conventions are also highly token-efficient. Prerequisites MarkItDown requires 。這些內容說明作者希望使用者如何理解專案；沒有出現的作業系統、輸入格式、部署拓撲或錯誤處理，不在本文替它補上假設。 從 README 可見的實作線索包括： MarkItDown []( []( > [!IMPORTANT] > MarkItDown performs I/O with the privileges of the current process. Like open() or requests.get(), it will access resources that the process itself can access. Sanitize your inputs in untrusted environments, and call the narrowest convert function needed for your use case (e.g., convertstream(), or convertlocal()). See the [Security Considerations](security-considerations) section of the documentation for more information. MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines. To this end, it is most comparable to [textract]( but with a focus on preserving important document structure and content as Markdown (including: headings, lists, tables, links, etc.) While the output is often reasonably presentable and human-friendly, it is meant to be consumed by text analysis tools -- and may not be the best option for high-fidelity document conversions for human consumption. MarkItDown currently supports the conversion from: - PDF - PowerPoint - Word - Excel - Images (EXIF metadata and OCR) - Audio (EXIF metadata and speech transcription) - HTML - Text-based formats (CSV, JSON, XML) - ZIP files (iterates over contents) - YouTube URLs - EPubs - ... and more! Why Markdown? Markdown is extremely close to plain text, with minimal markup or formatting, but still provides a way to represent important document structure. Mainstream LLMs, such as OpenAI's GPT-4o, natively "speak" Markdown, and often incorporate Markdown into their responses unprompted. This suggests that they have been trained on vast amounts of Markdown-formatted text, and understand it well. As a side benefit, Markdown conventions are also highly token-efficient. Prerequisites MarkItDown requires 。這些內容說明作者希望使用者如何理解專案；沒有出現的作業系統、輸入格式、部署拓撲或錯誤處理，不在本文替它補上假設。 從 README 可見的實作線索包括： MarkItDown []( []( > [!IMPORTANT] > MarkItDown performs I/O with the privileges of the current process. Like open() or requests.get(), it will access resources that the process itself can access. Sanitize your inputs in untrusted environments, and call the narrowest convert function needed for your use case (e.g., convertstream(), or convertlocal()). See the [Security Considerations](security-considerations) section of the documentation for more information. MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines. To this end, it is most comparable to [textract]( but with a focus on preserving important document structure and content as Markdown (including: headings, lists, tables, links, etc.) While the output is often reasonably presentable and human-friendly, it is meant to be consumed by text analysis tools -- and may not be the best option for high-fidelity document conversions for human consumption. MarkItDown currently supports the conversion from: - PDF - PowerPoint - Word - Excel - Images (EXIF metadata and OCR) - Audio (EXIF metadata and speech transcription) - HTML - Text-based formats (CSV, JSON, XML) - ZIP files (iterates over contents) - YouTube URLs - EPubs - ... and more! Why Markdown? Markdown is extremely close to plain text, with minimal markup or formatting, but still provides a way to represent important document structure. Mainstream LLMs, such as OpenAI's GPT-4o, natively "speak" Markdown, and often incorporate Markdown into their responses unprompted. This suggests that they have been trained on vast amounts of Markdown-formatted text, and understand it well. As a side benefit, Markdown conventions are also highly token-efficient. Prerequisites MarkItDown requires 。這些內容說明作者希望使用者如何理解專案；沒有出現的作業系統、輸入格式、部署拓撲或錯誤處理，不在本文替它補上假設。
markitdown，輸入、輸出與可觀察結果
採用時應先對照 microsoft/markitdown 的實際入口。若 README 提供命令，應以原命令建立最小測試；若只提供圖形介面或文件入口，就記錄版本與畫面結果。針對本專案，檢查重點是輸入是否被接受、輸出是否符合 README 所描述的形式，以及失敗時是否留下可追查訊息。 採用時應先對照 microsoft/markitdown 的實際入口。若 README 提供命令，應以原命令建立最小測試；若只提供圖形介面或文件入口，就記錄版本與畫面結果。針對本專案，檢查重點是輸入是否被接受、輸出是否符合 README 所描述的形式，以及失敗時是否留下可追查訊息。 採用時應先對照 microsoft/markitdown 的實際入口。若 README 提供命令，應以原命令建立最小測試；若只提供圖形介面或文件入口，就記錄版本與畫面結果。針對本專案，檢查重點是輸入是否被接受、輸出是否符合 README 所描述的形式，以及失敗時是否留下可追查訊息。 採用時應先對照 microsoft/markitdown 的實際入口。若 README 提供命令，應以原命令建立最小測試；若只提供圖形介面或文件入口，就記錄版本與畫面結果。針對本專案，檢查重點是輸入是否被接受、輸出是否符合 README 所描述的形式，以及失敗時是否留下可追查訊息。
markitdown，設定檔、依賴與環境條件
維護判斷要看專案自己的檔案和名稱。請把 README 中提到的設定、目錄或套件納入變更檢查，並將結果與 markitdown 的版本或分支一起保存。素材未說明的更新策略、相容矩陣與服務承諾，應保留為未知，而不是用社群統計替代。 維護判斷要看專案自己的檔案和名稱。請把 README 中提到的設定、目錄或套件納入變更檢查，並將結果與 markitdown 的版本或分支一起保存。素材未說明的更新策略、相容矩陣與服務承諾，應保留為未知，而不是用社群統計替代。 維護判斷要看專案自己的檔案和名稱。請把 README 中提到的設定、目錄或套件納入變更檢查，並將結果與 markitdown 的版本或分支一起保存。素材未說明的更新策略、相容矩陣與服務承諾，應保留為未知，而不是用社群統計替代。 維護判斷要看專案自己的檔案和名稱。請把 README 中提到的設定、目錄或套件納入變更檢查，並將結果與 markitdown 的版本或分支一起保存。素材未說明的更新策略、相容矩陣與服務承諾，應保留為未知，而不是用社群統計替代。
markitdown，版本、授權與維護風險
授權資料顯示 MIT。對 microsoft/markitdown 而言，分發、修改或整合時仍要查看倉庫的 LICENSE 文字，確認衍生物、通知與再分發條件；授權名稱本身不代表程式碼已通過安全或品質審查。 授權資料顯示 MIT。對 microsoft/markitdown 而言，分發、修改或整合時仍要查看倉庫的 LICENSE 文字，確認衍生物、通知與再分發條件；授權名稱本身不代表程式碼已通過安全或品質審查。 授權資料顯示 MIT。對 microsoft/markitdown 而言，分發、修改或整合時仍要查看倉庫的 LICENSE 文字，確認衍生物、通知與再分發條件；授權名稱本身不代表程式碼已通過安全或品質審查。 授權資料顯示 MIT。對 microsoft/markitdown 而言，分發、修改或整合時仍要查看倉庫的 LICENSE 文字，確認衍生物、通知與再分發條件；授權名稱本身不代表程式碼已通過安全或品質審查。
markitdown，採用前的專案專屬核驗
核驗 markitdown 時，先選一個 README 明確涵蓋的流程，再用專案專屬命令或檔案觀察結果。若是文件工具，保存原始檔和轉換輸出；若是韌體或模擬器，先在隔離硬體或 debugger 中測試。這樣才能把 markitdown 的明確能力與未說明部分分開。 核驗 markitdown 時，先選一個 README 明確涵蓋的流程，再用專案專屬命令或檔案觀察結果。若是文件工具，保存原始檔和轉換輸出；若是韌體或模擬器，先在隔離硬體或 debugger 中測試。這樣才能把 markitdown 的明確能力與未說明部分分開。 核驗 markitdown 時，先選一個 README 明確涵蓋的流程，再用專案專屬命令或檔案觀察結果。若是文件工具，保存原始檔和轉換輸出；若是韌體或模擬器，先在隔離硬體或 debugger 中測試。這樣才能把 markitdown 的明確能力與未說明部分分開。 核驗 markitdown 時，先選一個 README 明確涵蓋的流程，再用專案專屬命令或檔案觀察結果。若是文件工具，保存原始檔和轉換輸出；若是韌體或模擬器，先在隔離硬體或 debugger 中測試。這樣才能把 markitdown 的明確能力與未說明部分分開。
編輯結論
適合需要在 Python 流程中處理 PDF、Office、HTML、圖片或音訊等來源的人；不適合未設權限隔離便處理不可信檔案。先用 `convert_local()` 或 `convert_stream()` 指向窄範圍輸入，記錄轉換文字與外部資源存取。
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