mrrc 0.7.1

MRRC: A fast MARC library written in Rust with Python bindings

MRRC: MARC Rust Crate

A Rust library for reading, writing, and manipulating MARC bibliographic records, with Python bindings.

Note: This project was developed using agentic coding tools (amp and Claude) and uses beads for agentic issue tracking. The package has not yet had extensive practical testing by humans and should be considered experimental.

Features

• Reads and writes ISO 2709 (MARC21) binary format
• Python bindings with pymarc-compatible API (minor differences documented)
• Multiple serialization formats: JSON, XML, MARCJSON, CSV, Dublin Core, MODS, BIBFRAME
• MARC-8 and UTF-8 character encoding support
• Benchmarked at ~4x pymarc throughput in Python, ~1M records/sec in Rust

Installation

Python (3.10+):

pip install mrrc
# or with uv:
uv add mrrc

Rust:

cargo add mrrc

Example

Python:

from mrrc import MARCReader

# Pass filename directly for best performance (releases GIL)
for record in MARCReader("records.mrc"):
    print(record.title())

File paths use pure Rust I/O, releasing Python's GIL for multi-threaded workloads. See the threading guide for details.

Rust:

use mrrc::MarcReader;
use std::fs::File;

let file = File::open("records.mrc")?;
let mut reader = MarcReader::new(file);
while let Some(record) = reader.read_record()? {
    if let Some(title) = record.title() {
        println!("{}", title);
    }
}

Documentation

• Getting Started
• Python Tutorials
• Rust Tutorials
• API Reference
• Migration from pymarc

Format Support

Format	Read	Write
ISO 2709	Yes	Yes
JSON	Yes	Yes
MARCJSON	Yes	Yes
XML	Yes	Yes
CSV	-	Yes
Dublin Core	-	Yes
MODS	-	Yes
BIBFRAME	Yes	Yes

Full format reference

Platforms

Pre-built Python wheels are available for:

Platform	Architectures
Linux	x86_64, aarch64
macOS	x86_64 (Intel), arm64 (Apple Silicon)
Windows	x64

Status

Experimental. The Python API aims for pymarc compatibility but has some differences; see the migration guide. Rust APIs may change between minor versions.

Roadmap

Version 0.7.0 is suitable for testing but remains experimental. Before a 1.0 release, we plan to complete:

1. Real-world data testing — Validate against large-scale MARC datasets from LOC, Internet Archive, and other sources to discover edge cases
2. Code review — Thorough review of the codebase, particularly the Rust core and PyO3 bindings
3. Performance analysis — Profile with production workloads, optimize bottlenecks, and update benchmark documentation

License

MIT

Links

• Documentation
• PyPI
• crates.io
• GitHub