xberg-io/kreuzberg — A polyglot document intelligence framework with a Rust core.

Description and use

**Brief Summary**
Kreuzberg (xberg‑io/kreuzberg) is a polyglot document‑intelligence framework built around a high‑performance Rust core. It can extract raw text, metadata, images and structured data from PDFs, Office files, images and more than 97 other formats, and it is exposed through a CLI, a REST‑API, an MCP server, or native SDKs for Rust, Python, Ruby, Java, Go, PHP, Elixir, C#, R, C, and TypeScript (Node/Bun/Wasm/Deno).

**Value**
Kreuzberg gives AI assistants a reliable, language‑agnostic bridge to the real‑world data they need to reason over, turning unstructured documents into machine‑readable knowledge that can be fed into Retrieval‑Augmented Generation (RAG) pipelines, knowledge graphs, or downstream analytics. By standardising the interaction via the Model‑Context‑Protocol (MCP), it eliminates the need for custom parsers and ad‑hoc integrations, dramatically reducing development effort and error‑prone glue code.

**Practical Adoption Path**

1. **Prototype** – Install the pre‑built CLI or run the Dockerised MCP server to quickly test extraction on sample files in any language you already use.
2. **Integrate** – Replace ad‑hoc parsers with the language‑specific SDK (e.g., kreuzberg-py for Python or kreuzberg-rs for Rust) or call the REST endpoint from existing services.
3. **Scale** – Deploy the MCP server behind a load balancer or run multiple instances in a Kubernetes cluster; the Rust core’s low latency and modest memory footprint make horizontal scaling inexpensive.
4. **Connect to AI** – Feed the extracted JSON/structured payloads into your LLM‑based agents, RAG pipelines, or knowledge bases using the same MCP contract, ensuring consistent data handling across all downstream components.

**Production Readiness**

• **Activity & Community**: 8.5 k GitHub stars, 500+ forks, recent commits (last update 2026‑06‑24) and a healthy mix of contributors indicate strong momentum.
• **Multi‑language SDKs & API**: Full coverage of major backend and data‑science languages plus a CLI/REST/MCP interface makes it easy to adopt in heterogeneous stacks.
• **Performance & Reliability**: The Rust core delivers low‑latency, memory‑efficient processing, suitable for high‑throughput document pipelines.
• **Ecosystem Fit**: Explicit support for MCP aligns it with emerging standards for AI‑agent tooling, simplifying integration with other Model‑Context‑Protocol services.

Overall, Kreuzberg appears production‑ready for pilots and can be rolled out to full‑scale environments after a standard security/license audit and verification of maintainers’ responsiveness.

Problem

xberg-io/kreuzberg helps connect AI assistants to real tools and data through a standard protocol.

Outcome

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

Workflow

• connect AI agents to tools
• ship Model Context Protocol servers
• standardize integrations

Evidence

A polyglot document intelligence framework with a Rust core. Extract text, metadata, images, and structured information from PDFs, Office documents, images, and 97+ formats. Available for Rust, Python, Ruby, Java, Go, PHP, Elixir, C#, R, C, TypeScript (Node/Bun/Wasm/Deno)- or use via CLI, REST API, or MCP server.

Source