Toon is a data serialization library and toolkit designed to convert complex objects into compact, human-readable formats optimized for large language models. By focusing on token efficiency, the library minimizes the context window footprint of structured data through techniques like key folding and tabular layout optimization. It provides a streaming-capable processor that handles the encoding and decoding of hierarchical data while maintaining structural integrity.
The project distinguishes itself through its path-aware transformation pipeline and configurable serialization logic, which allow for precise control over how data is represented. It supports advanced features such as dotted path expansion, custom delimiter styles, and the normalization of complex data types like dates and maps. These capabilities enable developers to adapt serialized output to specific system requirements while ensuring consistent parsing behavior across different environments.
Beyond core serialization, the library includes a suite of developer-facing tools for data format conversion, schema validation, and editor integration. It also provides diagnostic utilities to analyze and compare token counts, helping users measure the efficiency of their data structures. The framework is built to handle large datasets incrementally through event-driven stream processing, ensuring memory efficiency even when working with massive records.
