Discover tools for parsing PDFs, Word documents, spreadsheets, and extracting structured data from unstructured files.
Document Parsing tools are AI-powered software designed to help developers and teams tackle document parsing-related tasks more efficiently. These tools are typically published as open-source projects on GitHub and can be integrated into existing workflows via MCP (Model Context Protocol), Claude Skills, or standalone agent frameworks. On Agent Skills Hub, we index 10 quality-scored document parsing tools across languages including Python, TypeScript.
In 2026, the AI agent ecosystem is maturing rapidly. Document Parsing tools can significantly boost development efficiency by automating repetitive tasks, reducing human error, and providing intelligent suggestions. The top 3 tools — PDFMathTranslate, Skill_Seekers, qiaomu-anything-to-notebooklm — have earned an average of 6,340 GitHub stars, reflecting strong community validation. 10 of the listed tools come with clear open-source licenses, ensuring freedom to use and modify.
When choosing a document parsing tool, consider these factors: 1) Community activity — GitHub stars and recent commit frequency indicate reliability; 2) Integration method — check if it supports MCP, Claude, or your preferred agent framework; 3) Language compatibility — the most common language in this list is Python; 4) Quality score — Agent Skills Hub's composite score evaluates code quality, documentation completeness, and maintenance activity. Our recommendation: start with PDFMathTranslate — it ranks highest in both star count and quality score.
[EMNLP 2025 Demo] PDF scientific paper translation with preserved formats - 基于 AI 完整保留排版的 PDF 文档全文双语翻译，支持 Google/DeepL/Ollama/OpenAI 等服务，提供 CLI/GUI/MCP/Docker/Zotero
Convert documentation websites, GitHub repositories, and PDFs into Claude AI skills with automatic conflict detection
Claude Skill: Multi-source content processor for NotebookLM. Supports WeChat articles, web pages, YouTube, PDF, Markdown, search queries → Podcast/PPT/MindMap/Quiz etc.
모두 파싱해버리겠다 — HWP·HWPX·PDF·Office 문서를 Markdown으로. 양식 자동 채우기와 신구대조를 갖춘 CLI·MCP 서버 | Convert Korean documents (HWP, HWPX, PDF, Office) to Markdown — CLI and MCP server with form filling and diff
Give your AI agent eyes for PDFs — structured text, tables, OCR, visual evidence, and page-level citations via MCP. Native Rust, local-first.
天枢 - 企业级 AI 一站式数据预处理平台 | PDF/Office转Markdown | 支持MCP协议AI助手集成 | Vue3+FastAPI全栈方案 | 文档解析 | 多模态信息提取
Agent-native knowledge engine with MCP tools for document indexing, wiki organization, fast retrieval and deep reading across PDF/DOCX/PPTX/Markdown
Agent skill for Codex, Claude Code, and OpenClaw that translates entire books (PDF/DOCX/EPUB) into any language using parallel subagents.
PDF extraction that audits its own output — and certifies any other extractor's, catching pages they silently dropped. Verify signed manifests offline: free, MIT, no account. 0.903 on opendataloader-bench, #2 of 8 engines. 7-tool MCP server.
ExtractThinker is a Document Intelligence library for LLMs, offering ORM-style interaction for flexible and powerful document workflows.
| Tool | Stars | Language | License | Score |
|---|---|---|---|---|
| PDFMathTranslate | ★ 36.2k | Python | AGPL-3.0 | 75 |
| Skill_Seekers | ★ 14.7k | Python | MIT | 73 |
| qiaomu-anything-to-notebooklm | ★ 5.8k | Python | MIT | 65 |
| kordoc | ★ 1.7k | TypeScript | MIT | 81 |
| pdf-reader-mcp | ★ 894 | TypeScript | MIT | 69 |
| mineru-tianshu | ★ 747 | Python | Apache-2.0 | 64 |
| MinerU-Document-Explorer | ★ 612 | TypeScript | MIT | 53 |
| translate-book | ★ 1.0k | Python | MIT | 68 |
| pdfmux | ★ 79 | Python | MIT | 63 |
| ExtractThinker | ★ 1.6k | Python | Apache-2.0 | 49 |
The top document parsing tools in 2026 are PDFMathTranslate, Skill_Seekers, qiaomu-anything-to-notebooklm. Agent Skills Hub ranks 10 options by GitHub stars, quality score (6 dimensions including completeness, examples, and agent readiness), and recent activity. The list is rebuilt every 8 hours from live GitHub data.
PDFMathTranslate (36.2k stars) is the most adopted choice for general document parsing workflows, written in Python. Skill_Seekers (14.7k stars) is a strong alternative. Pick by your existing stack: match the language and runtime your team already uses to minimize integration cost. If unsure, start with PDFMathTranslate — it has the deepest community and the most examples online.
Avoid pre-built document parsing tools when (1) your use case requires deep customization that the tool's plugin system doesn't support, (2) you have strict compliance requirements that ban third-party dependencies, (3) the tool's maintenance is inactive (last commit >6 months ago), or (4) your data volume is small enough that a 50-line custom script is cheaper than learning the tool. For most production workflows above 100 requests/day, the time savings from a maintained tool outweigh the customization loss.
Document Parsing focuses specifically on discover tools for parsing pdfs, word documents, spreadsheets, and extracting structured data from unstructured files. Content Writing is a related but distinct category — see https://agentskillshub.top/best/content-writing/ for those tools. The two often appear in the same agent pipeline but solve different problems: choose document parsing when your primary goal is the specific task, and content writing when the workflow is broader.
For most teams, yes. PDFMathTranslate has 36.2k stars worth of community testing, handles edge cases you haven't thought of, and ships with documentation. Build your own only when (1) your requirements are deeply non-standard, (2) you have a security/compliance reason to avoid OSS dependencies, or (3) the maintenance burden is small enough (<200 lines of code) that you'll save time long-term. The break-even point is usually around 2-3 weeks of dev time saved.
Most document parsing tools listed are open source under permissive licenses (MIT, Apache 2.0). A handful offer paid managed/cloud versions on top of free self-hosted core. Always check the LICENSE file on each tool's GitHub repository before commercial use — some use AGPL or non-commercial restrictions that may not fit your deployment model.
