kreuzberg MCP工具
kreuzberg MCP工具 是 AI Skill Hub 本期精选AI工具之一。已获得 8.3k 颗 GitHub Star，综合评分 8.2 分，整体质量较高。我们强烈推荐将其纳入你的 AI 工具库，帮助提升工作效率。
📚 深度解析
**为什么要使用开源工具而非商业 SaaS？**
对于个人开发者和有隐私需求的用户，本地部署的开源工具意味着数据不离本机，不受第三方服务商的数据政策约束。同时，开源工具通常没有使用次数限制和月度费用，一次安装即可长期使用，对于高频使用场景的总拥有成本（TCO）远低于订阅制商业工具。
**安装与环境准备**
kreuzberg MCP工具 依赖 Rust 运行环境。建议通过 pyenv（Python）或 nvm（Node.js）管理 Rust 版本，避免全局环境污染。对于新手用户，推荐先创建虚拟环境（python -m venv venv && source venv/bin/activate），再安装依赖，这样即使出现问题也可以随时删除虚拟环境重新开始，不影响系统稳定性。
**社区与维护**
GitHub Issue 和 Discussion 是获取帮助的最快渠道。在提问前建议先检查 Closed Issues（已关闭的问题），大多数常见问题都已有解答。遇到 Bug 时，提供 pip list 的输出、完整错误堆栈和最小可复现示例，能显著提高开发者响应速度。AI Skill Hub 将持续追踪 kreuzberg MCP工具 的版本更新，及时通知重要功能变化。
📋 工具概览
基于Rust核心的多语言文档智能框架，支持文本提取、元数据识别等功能。通过FFI与C#、Elixir等语言集成，适合需要高性能文档处理的开发者和企业应用。
kreuzberg MCP工具 是一款基于 Rust 开发的开源工具，专注于 文档处理、Rust核心、多语言FFI 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。
📖 中文文档
基于Rust核心的多语言文档智能框架，支持文本提取、元数据识别等功能。通过FFI与C#、Elixir等语言集成，适合需要高性能文档处理的开发者和企业应用。
kreuzberg MCP工具 是一款基于 Rust 开发的开源工具，专注于 文档处理、Rust核心、多语言FFI 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。
- 开源免费，支持本地部署，数据完全自主可控
- 活跃的 GitHub 开源社区，持续迭代更新
- 提供详细文档和使用示例，新手友好
- 支持自定义配置，灵活适配不同使用环境
- 可作为基础组件集成进现有技术栈或进行二次开发
- 本地部署运行，保护数据隐私，满足合规要求
- 自定义集成到现有系统，扩展技术栈能力
- 作为开源基础组件进行商业化二次开发
# 方式一：cargo install（推荐） cargo install kreuzberg # 方式二：从源码编译 git clone https://github.com/kreuzberg-dev/kreuzberg cd kreuzberg cargo build --release # 二进制在 ./target/release/kreuzberg
- 访问 GitHub 仓库页面
- 按照 README 文档完成依赖安装
- 根据系统环境完成初始化配置
- 参考官方示例或文档开始使用
- 遇到问题可在 GitHub Issues 中查找解答
# 查看帮助 kreuzberg --help # 基本运行 kreuzberg [options] <input> # 详细使用说明请查阅文档 # https://github.com/kreuzberg-dev/kreuzberg
# kreuzberg 配置说明 # 查看配置选项 kreuzberg --config-example > config.yml # 常见配置项 # output_dir: ./output # log_level: info # workers: 4 # 环境变量（覆盖配置文件） export KREUZBERG_CONFIG="/path/to/config.yml"
Kreuzberg
<a href="https://github.com/kreuzberg-dev/kreuzberg/blob/main/LICENSE"> <img src="https://img.shields.io/badge/License-Elastic--2.0-007ec6" alt="License"> </a> <a href="https://docs.kreuzberg.dev"> <img src="https://img.shields.io/badge/Docs-kreuzberg-007ec6" alt="Documentation"> </a> <a href="https://huggingface.co/Kreuzberg"> <img src="https://img.shields.io/badge/Hugging%20Face-Kreuzberg-007ec6" alt="Hugging Face"> </a> </div>
Extract text, metadata, and code intelligence from 90+ file formats and 300+ programming languages at native speeds without needing a GPU.
Key Features
- Code intelligence – Extract functions, classes, imports, symbols, and docstrings from 300+ programming languages via tree-sitter. Results in
ExtractionResult.code_intelligencewith semantic chunking - Extensible architecture – Plugin system for custom OCR backends, validators, post-processors, document extractors, and renderers
- Polyglot – Native bindings for Rust, Python, TypeScript/Node.js, Ruby, Go, Java, Kotlin, C#, PHP, Elixir, R, Dart, Swift, Zig, and C
- 90+ file formats – PDF, Office documents, images, HTML, XML, emails, archives, academic formats across 8 categories
- LLM intelligence – VLM OCR (GPT-4o, Claude, Gemini, Ollama), structured JSON extraction with schema constraints, and provider-hosted embeddings via 143 LLM providers (including local engines: Ollama, LM Studio, vLLM, llama.cpp) through liter-llm
- OCR support – Tesseract (all bindings, including Tesseract-WASM for browsers), PaddleOCR (all native bindings), EasyOCR (Python), VLM OCR (143 vision model providers including local engines), extensible via plugin API
- High performance – Rust core with pure-Rust PDF, SIMD optimizations and full parallelism
- Flexible deployment – Use as library, CLI tool, REST API server, or MCP server
- TOON wire format – Token-efficient serialization for LLM/RAG pipelines, ~30-50% fewer tokens than JSON
- GFM-quality output – Comrak-based rendering with proper fenced code blocks, table nodes, bracket escaping, and cross-format parity (Markdown, HTML, Djot, Plain)
- HTML passthrough – HTML-to-Markdown conversion uses html-to-markdown output directly, bypassing lossy intermediate round-trips
- Memory efficient – Streaming parsers for multi-GB files
Key Features
<details> <summary><strong>OCR with Table Extraction</strong></summary>
Multiple OCR backends (Tesseract, EasyOCR, PaddleOCR) with intelligent table detection and reconstruction. Extract structured data from scanned documents and images with configurable accuracy thresholds.
</details>
<details> <summary><strong>Batch Processing</strong></summary>
Process multiple documents concurrently with configurable parallelism. Optimize throughput for large-scale document processing workloads with automatic resource management.
</details>
<details> <summary><strong>Password-Protected PDFs</strong></summary>
Handle encrypted PDFs with single or multiple password attempts. Supports both RC4 and AES encryption with automatic fallback strategies.
</details>
<details> <summary><strong>Language Detection</strong></summary>
Automatic language detection in extracted text using fast-langdetect. Configure confidence thresholds and access per-language statistics.
</details>
<details> <summary><strong>Metadata Extraction</strong></summary>
Extract comprehensive metadata from all supported formats: authors, titles, creation dates, page counts, EXIF data, and format-specific properties.
</details>
What are Kreuzberg's key features?
- Code intelligence — Extract functions, classes, imports, symbols, docstrings from 300+ languages via tree-sitter
- Extensible architecture — Plugin system for custom OCR backends, validators, post-processors, document extractors, renderers
- Polyglot bindings — Native bindings for 14+ languages (Rust, Python, Node.js, Ruby, Go, Java, Kotlin, C#, PHP, Elixir, R, Dart, Swift, Zig, C)
- 90+ file formats — PDF, Office documents, images, HTML, XML, emails, archives, academic formats across 8 categories
- LLM intelligence — VLM OCR (GPT-4o, Claude, Gemini, Ollama), structured JSON extraction, embeddings via 143 LLM providers
- OCR support — Tesseract (all bindings including WASM for browsers), PaddleOCR, EasyOCR, VLM OCR, extensible via plugin API
- High performance — Rust core with pure-Rust PDF, SIMD optimizations, full parallelism
- Flexible deployment — Library, CLI tool, REST API server, or MCP server
- TOON wire format — Token-efficient serialization for LLM/RAG pipelines, ~30-50% fewer tokens than JSON
- GFM-quality output — Comrak-based Markdown rendering with proper fenced code blocks, table nodes
- Memory efficient — Streaming parsers for multi-GB files
Installation
Each language binding provides comprehensive documentation with examples and best practices. Choose your platform to get started:
Scripting Languages:
- Python – PyPI package, async/sync APIs, OCR backends (Tesseract, PaddleOCR, EasyOCR)
- Ruby – RubyGems package, idiomatic Ruby API, native bindings
- PHP – Composer package, modern PHP 8.2+ support, type-safe API, async extraction
- Elixir – Hex package, OTP integration, concurrent processing
- R – r-universe package, idiomatic R API, extendr bindings
- Dart / Flutter – pub.dev package, flutter_rust_bridge runtime, native bindings for macOS/iOS/Android/Linux/Windows
JavaScript/TypeScript:
- @kreuzberg/node – Native NAPI-RS bindings for Node.js/Bun, fastest performance
- @kreuzberg/wasm – WebAssembly for browsers/Deno/Cloudflare Workers, comprehensive format and OCR support (PDF, Excel, archives, all office formats, real Tesseract via the WASI build) — only ORT-dependent features (paddle-ocr, layout detection, embeddings, auto-rotate) and server modes (api/mcp/cli) are excluded
Compiled Languages:
- Go – Go module with FFI bindings, context-aware async
- Java – Maven Central, Foreign Function & Memory API
- Kotlin – Maven Central, Kotlin/JVM with idiomatic data classes, sealed enums, and coroutine-based async
- C# – NuGet package, .NET 6.0+, full async/await support
- Swift – Swift Package Manager, macOS 13+/iOS 16+, native Swift types and async/await
Native:
- Rust – Core library, flexible feature flags, zero-copy APIs
- Zig –
zig fetch+build.zig.zon, idiomatic error sets, optional types, slice-based memory - C (FFI) – C header + shared library, pkg-config/CMake support, cross-platform
Containers:
- Docker – Official images with API, CLI, and MCP server modes (Core: ~1.0-1.3GB, Full: ~1.0-1.3GB with OCR + legacy format support)
Command-Line:
- CLI – Cross-platform binary, batch processing, MCP server mode
All language bindings include precompiled binaries for both x86_64 and aarch64 architectures on Linux and macOS.
What deployment options are available?
- Library — Use as a dependency in your application
- CLI — Cross-platform binary for batch processing
- REST API server — HTTP endpoint for document extraction
- MCP server — Model Context Protocol server for AI assistants
- Docker — Official images with API, CLI, and MCP modes
Embeddings Support (Optional)
To use embeddings functionality:
1. Install ONNX Runtime 1.24+: - Linux: Download from ONNX Runtime releases (Debian packages may have older versions) - MacOS: brew install onnxruntime - Windows: Download from ONNX Runtime releases
- Use embeddings in your code - see Embeddings Guide
Note: Kreuzberg requires ONNX Runtime version 1.24+ for embeddings. All other Kreuzberg features work without ONNX Runtime.
FAQ
成熟的���档智能框架，Rust核心保证性能，多语言支持扩展性强。Star数8.3k表明社区认可度高，维护活跃。
- 需要让 Claude / Cursor 操作本地工具的 AI 工程师
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- MCP 配置路径拼错或权限不足，重启 Claude Desktop 才生效
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
⚡ 核心功能
- 开源免费，支持本地部署，数据完全自主可控
- 活跃的 GitHub 开源社区，持续迭代更新
- 提供详细文档和使用示例，新手友好
- 支持自定义配置，灵活适配不同使用环境
- 可作为基础组件集成进现有技术栈或进行二次开发
- 需要让 Claude / Cursor 操作本地工具的 AI 工程师
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- MCP 配置路径拼错或权限不足，重启 Claude Desktop 才生效
👥 适合人群
🎯 使用场景
- 本地部署运行，保护数据隐私，满足合规要求
- 自定义集成到现有系统，扩展技术栈能力
- 作为开源基础组件进行商业化二次开发
⚖️ 优点与不足
- +GitHub 8.3k Star，社区高度认可
- +完全开源免费，无授权费用
- +本地部署，数据完全自主可控
- +开发者社区支持，遇问题可查可问
- −安装和初始配置可能需要一定技术基础
- −功能完整性通常不如成熟商业产品
- −技术支持主要依赖开源社区，响应速度不稳定
该工具使用 NOASSERTION 协议，商用场景请仔细阅读协议条款，必要时咨询法律意见。
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
📄 NOASSERTION — 请查阅原始协议条款了解具体使用限制。
🔗 相关工具推荐
❓ 常见问题 FAQ
经综合评估，kreuzberg MCP工具 在AI工具赛道中表现稳健，质量优秀。如果你已有明确的使用需求，可以直接上手体验；如果还在评估阶段，建议对比同类工具后再做决策。
| 原始名称 | kreuzberg |
| 原始描述 | 开源MCP工具：A polyglot document intelligence framework with a Rust core. Extract text, metad。⭐8.3k · Rust |
| Topics | 文档处理Rust核心多语言FFIMCP工具高性能 |
| GitHub | https://github.com/kreuzberg-dev/kreuzberg |
| License | NOASSERTION |
| 语言 | Rust |
收录时间：2026-05-14 · 更新时间：2026-05-16 · License：NOASSERTION · AI Skill Hub 不对第三方内容的准确性作法律背书。