u/Eastern-Surround7763

Kreuzberg v4.7.0 is here. Kreuzberg is an open-source Rust-core document intelligence library with bindings for Python, TypeScript/Node.js, Go, Ruby, Java, C#, PHP, Elixir, R, C, and WASM. 

We’ve added several features, integrated OpenWEBUI, and made a big improvement in quality across all formats. There is also a new markdown rendering layer and new HTML output, which we now support. And many other fixes and features (find them in our the release notes).

The main highlight is code intelligence and extraction. Kreuzberg now supports 248 formats through our tree-sitter-language-pack library. This is a step toward making Kreuzberg an engine for agents. You can efficiently parse code, allowing direct integration as a library for agents and via MCP. AI agents work with code repositories, review pull requests, index codebases, and analyze source files. Kreuzberg now extracts functions, classes, imports, exports, symbols, and docstrings at the AST level, with code chunking that respects scope boundaries. 

Regarding markdown quality, poor document extraction can lead to further issues down the pipeline. We created a benchmark harness using Structural F1 and Text F1 scoring across over 350 documents and 23 formats, then optimized based on that. LaTeX improved from 0% to 100% SF1. XLSX increased from 30% to 100%. PDF table SF1 went from 15.5% to 53.7%. All 23 formats are now at over 80% SF1. The output pipelines receive is now structurally correct by default. 

Kreuzberg is now available as a document extraction backend for OpenWebUI, with options for docling-serve compatibility or direct connection. This was one of the most requested integrations, and it’s finally here. 

In this release, we’ve added unified architecture where every extractor creates a standard typed document representation. We also included TOON wire format, which is a compact document encoding that reduces LLM prompt token usage by 30 to 50%, semantic chunk labeling, JSON output, strict configuration validation, and improved security. GitHub: https://github.com/kreuzberg-dev/kreuzberg. 

Contributions are always very welcome!

https://kreuzberg.dev/ 

Kreuzberg v4.7.0 is here. Kreuzberg is an open-source Rust-core document intelligence library with bindings for Python, TypeScript/Node.js, Go, Ruby, Java, C#, PHP, Elixir, R, C, and WASM. 

We’ve added several features, integrated OpenWEBUI, and made a big improvement in quality across all formats. There is also a new markdown rendering layer and new HTML output, which we now support. And many other fixes and features (find them in our the release notes).

The main highlight is code intelligence and extraction. Kreuzberg now supports 248 formats through our tree-sitter-language-pack library. This is a step toward making Kreuzberg an engine for agents. You can efficiently parse code, allowing direct integration as a library for agents and via MCP. AI agents work with code repositories, review pull requests, index codebases, and analyze source files. Kreuzberg now extracts functions, classes, imports, exports, symbols, and docstrings at the AST level, with code chunking that respects scope boundaries. 

Regarding markdown quality, poor document extraction can lead to further issues down the pipeline. We created a benchmark harness using Structural F1 and Text F1 scoring across over 350 documents and 23 formats, then optimized based on that. LaTeX improved from 0% to 100% SF1. XLSX increased from 30% to 100%. PDF table SF1 went from 15.5% to 53.7%. All 23 formats are now at over 80% SF1. The output pipelines receive is now structurally correct by default. 

Kreuzberg is now available as a document extraction backend for OpenWebUI, with options for docling-serve compatibility or direct connection. This was one of the most requested integrations, and it’s finally here. 

In this release, we’ve added unified architecture where every extractor creates a standard typed document representation. We also included TOON wire format, which is a compact document encoding that reduces LLM prompt token usage by 30 to 50%, semantic chunk labeling, JSON output, strict configuration validation, and improved security. GitHub: https://github.com/kreuzberg-dev/kreuzberg. 

Contributions are always very welcome!

https://kreuzberg.dev/ 

Kreuzberg v4.7.0 is here. Kreuzberg is an open-source Rust-core document intelligence library with bindings for Python, TypeScript/Node.js, Go, Ruby, Java, C#, PHP, Elixir, R, C, and WASM. 

We’ve added several features, integrated OpenWEBUI, and made a big improvement in quality across all formats. There is also a new markdown rendering layer and new HTML output, which we now support. And many other fixes and features (find them in our the release notes).

The main highlight is code intelligence and extraction. Kreuzberg now supports 248 formats through our tree-sitter-language-pack library. This is a step toward making Kreuzberg an engine for agents. You can efficiently parse code, allowing direct integration as a library for agents and via MCP. AI agents work with code repositories, review pull requests, index codebases, and analyze source files. Kreuzberg now extracts functions, classes, imports, exports, symbols, and docstrings at the AST level, with code chunking that respects scope boundaries. 

Regarding markdown quality, poor document extraction can lead to further issues down the pipeline. We created a benchmark harness using Structural F1 and Text F1 scoring across over 350 documents and 23 formats, then optimized based on that. LaTeX improved from 0% to 100% SF1. XLSX increased from 30% to 100%. PDF table SF1 went from 15.5% to 53.7%. All 23 formats are now at over 80% SF1. The output pipelines receive is now structurally correct by default. 

Kreuzberg is now available as a document extraction backend for OpenWebUI, with options for docling-serve compatibility or direct connection. This was one of the most requested integrations, and it’s finally here. 

In this release, we’ve added unified architecture where every extractor creates a standard typed document representation. We also included TOON wire format, which is a compact document encoding that reduces LLM prompt token usage by 30 to 50%, semantic chunk labeling, JSON output, strict configuration validation, and improved security. GitHub: https://github.com/kreuzberg-dev/kreuzberg. 

Contributions are always very welcome!

https://kreuzberg.dev/ 

Kreuzberg v4.7.0 is here. Kreuzberg is an open-source Rust-core document intelligence library with bindings for Python, TypeScript/Node.js, Go, Ruby, Java, C#, PHP, Elixir, R, C, and WASM. 

We’ve added several features, integrated OpenWEBUI, and made a big improvement in quality across all formats. There is also a new markdown rendering layer and new HTML output, which we now support. And many other fixes and features (find them in our the release notes).

The main highlight is code intelligence and extraction. Kreuzberg now supports 248 formats through our tree-sitter-language-pack library. This is a step toward making Kreuzberg an engine for agents. You can efficiently parse code, allowing direct integration as a library for agents and via MCP. AI agents work with code repositories, review pull requests, index codebases, and analyze source files. Kreuzberg now extracts functions, classes, imports, exports, symbols, and docstrings at the AST level, with code chunking that respects scope boundaries. 

Regarding markdown quality, poor document extraction can lead to further issues down the pipeline. We created a benchmark harness using Structural F1 and Text F1 scoring across over 350 documents and 23 formats, then optimized based on that. LaTeX improved from 0% to 100% SF1. XLSX increased from 30% to 100%. PDF table SF1 went from 15.5% to 53.7%. All 23 formats are now at over 80% SF1. The output pipelines receive is now structurally correct by default. 

Kreuzberg is now available as a document extraction backend for OpenWebUI, with options for docling-serve compatibility or direct connection. This was one of the most requested integrations, and it’s finally here. 

In this release, we’ve added unified architecture where every extractor creates a standard typed document representation. We also included TOON wire format, which is a compact document encoding that reduces LLM prompt token usage by 30 to 50%, semantic chunk labeling, JSON output, strict configuration validation, and improved security. GitHub: https://github.com/kreuzberg-dev/kreuzberg. 

Contributions are always very welcome!

https://kreuzberg.dev/ 

Kreuzberg v4.7.0 is here. Kreuzberg is an open-source Rust-core document intelligence library with bindings for Python, TypeScript/Node.js, Go, Ruby, Java, C#, PHP, Elixir, R, C, and WASM. 

We’ve added several features, integrated OpenWEBUI, and made a big improvement in quality across all formats. There is also a new markdown rendering layer and new HTML output, which we now support. And many other fixes and features (find them in our the release notes).

The main highlight is code intelligence and extraction. Kreuzberg now supports 248 formats through our tree-sitter-language-pack library. This is a step toward making Kreuzberg an engine for agents. You can efficiently parse code, allowing direct integration as a library for agents and via MCP. AI agents work with code repositories, review pull requests, index codebases, and analyze source files. Kreuzberg now extracts functions, classes, imports, exports, symbols, and docstrings at the AST level, with code chunking that respects scope boundaries. 

Regarding markdown quality, poor document extraction can lead to further issues down the pipeline. We created a benchmark harness using Structural F1 and Text F1 scoring across over 350 documents and 23 formats, then optimized based on that. LaTeX improved from 0% to 100% SF1. XLSX increased from 30% to 100%. PDF table SF1 went from 15.5% to 53.7%. All 23 formats are now at over 80% SF1. The output pipelines receive is now structurally correct by default. 

Kreuzberg is now available as a document extraction backend for OpenWebUI, with options for docling-serve compatibility or direct connection. This was one of the most requested integrations, and it’s finally here. 

In this release, we’ve added unified architecture where every extractor creates a standard typed document representation. We also included TOON wire format, which is a compact document encoding that reduces LLM prompt token usage by 30 to 50%, semantic chunk labeling, JSON output, strict configuration validation, and improved security. GitHub: https://github.com/kreuzberg-dev/kreuzberg. 

Contributions are always very welcome!

https://kreuzberg.dev/

Kreuzberg v4.7.0 is here. Kreuzberg is an open-source Rust-core document intelligence library with bindings for Python, TypeScript/Node.js, Go, Ruby, Java, C#, PHP, Elixir, R, C, and WASM. 

We’ve added several features, integrated OpenWEBUI, and made a big improvement in quality across all formats. There is also a new markdown rendering layer and new HTML output, which we now support. And many other fixes and features (find them in our the release notes).

The main highlight is code intelligence and extraction. Kreuzberg now supports 248 formats through our tree-sitter-language-pack library. This is a step toward making Kreuzberg an engine for agents. You can efficiently parse code, allowing direct integration as a library for agents and via MCP. AI agents work with code repositories, review pull requests, index codebases, and analyze source files. Kreuzberg now extracts functions, classes, imports, exports, symbols, and docstrings at the AST level, with code chunking that respects scope boundaries. 

Regarding markdown quality, poor document extraction can lead to further issues down the pipeline. We created a benchmark harness using Structural F1 and Text F1 scoring across over 350 documents and 23 formats, then optimized based on that. LaTeX improved from 0% to 100% SF1. XLSX increased from 30% to 100%. PDF table SF1 went from 15.5% to 53.7%. All 23 formats are now at over 80% SF1. The output pipelines receive is now structurally correct by default. 

Kreuzberg is now available as a document extraction backend for OpenWebUI, with options for docling-serve compatibility or direct connection. This was one of the most requested integrations, and it’s finally here. 

In this release, we’ve added unified architecture where every extractor creates a standard typed document representation. We also included TOON wire format, which is a compact document encoding that reduces LLM prompt token usage by 30 to 50%, semantic chunk labeling, JSON output, strict configuration validation, and improved security. GitHub: https://github.com/kreuzberg-dev/kreuzberg. 

Contributions ar always very welcome!

https://kreuzberg.dev/

Kreuzberg v4.7.0 is here. Kreuzberg is an open-source Rust-core document intelligence library with bindings for Python, TypeScript/Node.js, Go, Ruby, Java, C#, PHP, Elixir, R, C, and WASM. 

We’ve added several features, integrated OpenWEBUI, and made a big improvement in quality across all formats. There is also a new markdown rendering layer and new HTML output, which we now support. And many other fixes and features (find them in our the release notes).

The main highlight is code intelligence and extraction. Kreuzberg now supports 248 formats through our tree-sitter-language-pack library. This is a step toward making Kreuzberg an engine for agents. You can efficiently parse code, allowing direct integration as a library for agents and via MCP. AI agents work with code repositories, review pull requests, index codebases, and analyze source files. Kreuzberg now extracts functions, classes, imports, exports, symbols, and docstrings at the AST level, with code chunking that respects scope boundaries. 

Regarding markdown quality, poor document extraction can lead to further issues down the pipeline. We created a benchmark harness using Structural F1 and Text F1 scoring across over 350 documents and 23 formats, then optimized based on that. LaTeX improved from 0% to 100% SF1. XLSX increased from 30% to 100%. PDF table SF1 went from 15.5% to 53.7%. All 23 formats are now at over 80% SF1. The output pipelines receive is now structurally correct by default. 

Kreuzberg is now available as a document extraction backend for OpenWebUI, with options for docling-serve compatibility or direct connection. This was one of the most requested integrations, and it’s finally here. 

In this release, we’ve added unified architecture where every extractor creates a standard typed document representation. We also included TOON wire format, which is a compact document encoding that reduces LLM prompt token usage by 30 to 50%, semantic chunk labeling, JSON output, strict configuration validation, and improved security. GitHub: https://github.com/kreuzberg-dev/kreuzberg. 

Contributions are always very welcome!

https://kreuzberg.dev/

Kreuzberg v4.7.0 is here. Kreuzberg is a Rust-core document intelligence library with bindings for Python, TypeScript/Node.js, Go, Ruby, Java, C#, PHP, Elixir, R, C, and WASM. 

We’ve added several features, integrated OpenWEBUI, and made a big improvement in quality across all formats. There is also a new markdown rendering layer and new HTML output, which we now support. And many other fixes and features (find them in our the release notes).

The main highlight is code intelligence and extraction. Kreuzberg now supports 248 formats through our tree-sitter-language-pack library. This is a step toward making Kreuzberg an engine for agents. You can efficiently parse code, allowing direct integration as a library for agents and via MCP. AI agents work with code repositories, review pull requests, index codebases, and analyze source files. Kreuzberg now extracts functions, classes, imports, exports, symbols, and docstrings at the AST level, with code chunking that respects scope boundaries. 

Regarding markdown quality, poor document extraction can lead to further issues down the pipeline. We created a benchmark harness using Structural F1 and Text F1 scoring across over 350 documents and 23 formats, then optimized based on that. LaTeX improved from 0% to 100% SF1. XLSX increased from 30% to 100%. PDF table SF1 went from 15.5% to 53.7%. All 23 formats are now at over 80% SF1. The output pipelines receive is now structurally correct by default. 

Kreuzberg is now available as a document extraction backend for OpenWebUI, with options for docling-serve compatibility or direct connection. This was one of the most requested integrations, and it’s finally here. 

In this release, we’ve added unified architecture where every extractor creates a standard typed document representation. We also included TOON wire format, which is a compact document encoding that reduces LLM prompt token usage by 30 to 50%, semantic chunk labeling, JSON output, strict configuration validation, and improved security. GitHub: https://github.com/kreuzberg-dev/kreuzberg. 

https://kreuzberg.dev

Contributions are always very welcome!

Kreuzberg is an open-source Rust-core document intelligence library that works with Python, TypeScript/Node.js, Go, Ruby, Java, C#, PHP, Elixir, R, C, and WASM. 
We’ve added several features, integrated OpenWEBUI, and made a big improvement in quality across all formats. There is also a new markdown rendering layer and new HTML output, which we now support. And much more (which you can find in our the release notes).

The main highlight is code intelligence and extraction. Kreuzberg now supports 248 formats through our tree-sitter-language-pack library. This is a step toward making Kreuzberg an engine for agents too. You can efficiently parse code, allowing direct integration as a library for agents and via MCP. Agents work with code repositories, review pull requests, index codebases, and analyze source files. Kreuzberg now extracts functions, classes, imports, exports, symbols, and docstrings at the AST level, with code chunking that respects scope boundaries. 

Kreuzberg is now available as a document extraction backend for OpenWebUI (by popular request!), with options for docling-serve compatibility or direct connection.

Regarding markdown quality, poor document extraction can lead to further issues down the pipeline. We created a benchmark harness using Structural F1 and Text F1 scoring across over 350 documents and 23 formats, then optimized based on that. LaTeX improved from 0% to 100% SF1. XLSX increased from 30% to 100%. PDF table SF1 went from 15.5% to 53.7%. All 23 formats are now at over 80% SF1. The output pipelines receive is now structurally correct by default. 

In this release, we’ve added unified architecture where every extractor creates a standard typed document representation. We also included TOON wire format, which is a compact document encoding that reduces LLM prompt token usage by 30 to 50%, semantic chunk labeling, JSON output, strict configuration validation, and improved security. GitHub: https://github.com/kreuzberg-dev/kreuzberg. 

Contributions are always very welcome!

Kreuzberg v4.7.0 is here. Kreuzberg is a Rust-core document intelligence library that works with Python, TypeScript/Node.js, Go, Ruby, Java, C#, PHP, Elixir, R, C, and WASM. 

We’ve added several features, integrated OpenWEBUI, and made a big improvement in quality across all formats. There is also a new markdown rendering layer and new HTML output, which we now support. And much more (which you can find in our the release notes).

The main highlight is code intelligence and extraction. Kreuzberg now supports 248 formats through our tree-sitter-language-pack library. This is a step toward making Kreuzberg an engine for agents too. You can efficiently parse code, allowing direct integration as a library for agents and via MCP. Agents work with code repositories, review pull requests, index codebases, and analyze source files. Kreuzberg now extracts functions, classes, imports, exports, symbols, and docstrings at the AST level, with code chunking that respects scope boundaries. 

Regarding markdown quality, poor document extraction can lead to further issues down the pipeline. We created a benchmark harness using Structural F1 and Text F1 scoring across over 350 documents and 23 formats, then optimized based on that. LaTeX improved from 0% to 100% SF1. XLSX increased from 30% to 100%. PDF table SF1 went from 15.5% to 53.7%. All 23 formats are now at over 80% SF1. The output pipelines receive is now structurally correct by default. 

Kreuzberg is now available as a document extraction backend for OpenWebUI (by popular request!), with options for docling-serve compatibility or direct connection.

In this release, we’ve added unified architecture where every extractor creates a standard typed document representation. We also included TOON wire format, which is a compact document encoding that reduces LLM prompt token usage by 30 to 50%, semantic chunk labeling, JSON output, strict configuration validation, and improved security. GitHub: https://github.com/kreuzberg-dev/kreuzberg. 

And- Kreuzberg Cloud out soon, this will be the hosted version is for teams that want the same extraction quality without managing infrastructure. more here: https://kreuzberg.dev

Contributions are always very welcome

Hi folks,

We just released Kreuzberg v4.5, and it's a big one.

Kreuzberg is an open-source (MIT) document intelligence framework supporting 12 programming languages. Written in Rust, with native bindings for Python, TypeScript/Node.js, PHP, Ruby, Java, C#, Go, Elixir, R, C, and WASM. It extracts text, structure, and metadata from 88+ formats, runs OCR, generates embeddings, and is built for AI pipelines and document processing at scale.

What's new in v4.5

A lot! For the full release notes, please visit our changelog: https://github.com/kreuzberg-dev/kreuzberg/releases

The core is this: Kreuzberg now understands document structure (layout/tables), not just text. You'll see that we used Docling's model to do it.

Docling is a great project, and their layout model, RT-DETR v2 (Docling Heron), is excellent. It's also fully open source under a permissive Apache license. We integrated it directly into Kreuzberg, and we want to be upfront about that.

What we've done is embed it into a Rust-native pipeline. The result is document layout extraction that matches Docling's quality and, in some cases, outperforms it. It's 2.8x faster on average, with a fraction of the memory overhead, and without Python as a dependency. If you're already using Docling and happy with the quality, give Kreuzberg a try.

We benchmarked against Docling on 171 PDF documents spanning academic papers, government and legal docs, invoices, OCR scans, and edge cases:

- Structure F1: Kreuzberg 42.1% vs Docling 41.7%
- Text F1: Kreuzberg 88.9% vs Docling 86.7%
- Average processing time: Kreuzberg 1,032 ms/doc vs Docling 2,894 ms/doc

The speed difference comes from Rust's native memory management, pdfium text extraction at the character level, ONNX Runtime inference, and Rayon parallelism across pages.

RT-DETR v2 (Docling Heron) classifies 17 document element types across all 12 language bindings. For pages containing tables, Kreuzberg crops each detected table region from the page image and runs TATR (Table Transformer), a model that predicts the internal structure of tables (rows, columns, headers, and spanning cells). The predicted cell grid is then matched against native PDF text positions to reconstruct accurate markdown tables.

Kreuzberg extracts text directly from the PDF's native text layer using pdfium, preserving exact character positions, font metadata (bold, italic, size), and unicode encoding. Layout detection then classifies and organizes this text according to the document's visual structure. For pages without a native text layer, Kreuzberg automatically detects this and falls back to Tesseract OCR.

When a PDF contains a tagged structure tree (common in PDF/A and accessibility-compliant documents), Kreuzberg uses the author's original paragraph boundaries and heading hierarchy, then applies layout model predictions as classification overrides.

PDFs with broken font CMap tables ("co mputer" → "computer") are now fixed automatically — selective page-level respacing detects affected pages and applies per-character gap analysis, reducing garbled lines from 406 to 0 on test documents with zero performance impact. There's also a new multi-backend OCR pipeline with quality-based fallback, PaddleOCR v2 with a unified 18,000+ character multilingual model, and extraction result caching for all file types.

If you're running Docling in production, benchmark Kreuzberg against it and let us know what you think!

Discord https://discord.gg/rzGzur3kj4

https://kreuzberg.dev/