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

Description and use

**Summary**
xberg‑io/xberg is a polyglot document‑intelligence framework built around a high‑performance Rust core. It can extract text, metadata, images and structured data from PDFs, Office files, images and more than 97 other formats, and it is exposed through a CLI, REST API, MCP server or native SDKs for Rust, Python, Ruby, Java, Go, PHP, Elixir, C#, R, C, and TypeScript (Node/Bun/Wasm/Deno). With 8.5 k GitHub stars and active maintenance, it offers a unified “document‑as‑service” layer that AI agents can tap via the Model Context Protocol (MCP).

**Value proposition**
xberg turns raw, heterogeneous documents into structured knowledge that can be consumed directly by LLM‑powered assistants, RAG pipelines, or any backend that needs reliable content extraction. By providing a single, language‑agnostic API and a standard MCP interface, it eliminates the need to stitch together multiple parsers, reduces latency with its Rust engine, and simplifies compliance and security audits through a single, well‑audited codebase.

**Practical adoption path**

1. **Prototype** – Pull the pre‑built Docker image or run the CLI to extract a sample document and verify output quality.
2. **Integrate** – Choose the SDK that matches your stack (e.g., xberg-py for Python or xberg-js for Node) and replace existing ad‑hoc parsers with calls to the library or the REST endpoint.
3. **Scale** – Deploy the MCP server or the REST service behind a load balancer; configure caching or async job queues as needed.
4. **Extend** – If custom formats are required, contribute a Rust plugin or use the provided extension hooks, then publish the updated SDKs across languages.

**Production readiness**
The project shows strong production signals: recent commits (as of 2026‑06‑25), a vibrant community (8.5 k stars, 500+ forks), and a broad language ecosystem. Its Rust core delivers deterministic performance and memory safety, while the multiple SDKs and MCP server give flexibility for micro‑service or monolith deployments. Pending a final review of licensing and security policies, xberg‑io/xberg is mature enough for a serious pilot or full‑scale production use.

Problem

xberg-io/xberg 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