blackwell-systems/gcf

<p align="center"> <a href="https://gcformat.com/playground.html"><img src="https://img.shields.io/badge/playground-live-2563eb?style=for-the-badge" alt="Playground"></a> <a href="https://gcformat.com/guide/benchmarks.html"><img src="https://img.shields.io/badge/benchmarks-1%2C700%2B%20evals-22c55e?style=for-the-badge" alt="Benchmarks"></a> <a href="https://github.com/blackwell-systems/gcf"><img src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/blackwell-systems/gcf/main/assets/downloads-badge.json&style=for-the-badge" alt="Downloads"></a> <a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-333?style=for-the-badge" alt="License"></a> </p> <p align="center"> <img src="assets/gcf-logo.png" alt="GCF" width="360"> </p> <h3 align="center">The AI-native wire format for structured data.</h3>

[!IMPORTANT] Graph Compact Format (GCF): A Token-Efficient Wire Format for LLM Tool Interactions Dayna Blackwell, 2026. DOI: 10.5281/zenodo.20579817

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100% comprehension on every frontier model tested. 25.5% fewer tokens than TOON, 53% fewer than JSON across 15 datasets. 90.7% on structurally complex code graphs (vs TOON 68.5%, JSON 53.6%). Proven lossless: decode(encode(value)) == value for every structured value, verified across 33,000,000,000+ round-trips in 5 formats (JSON, YAML, TOML, CSV, MessagePack). Zero training required.

Encode any structured data as GCF before sending it to an LLM. JSON, YAML, TOML, CSV, MessagePack: GCF encodes them all. The model reads it natively with zero format instructions. decode() converts back to any format when a human needs to see it. Your existing schemas and validators work on the decoded output unchanged.

pip install gcf-python                    # Python
npm install @blackwell-systems/gcf        # TypeScript
go get github.com/blackwell-systems/gcf-go  # Go