This is the canonical implementation of OpenAI's Harmony response format, the structured prompt format used by the gpt-oss open-weight model series. The repository provides a high-performance Rust core for rendering conversations into token sequences and parsing model outputs back into structured messages. It supports multi-channel reasoning (analysis, commentary, final), tool/function calling with JSON Schema-defined parameters, structured outputs, and streaming incremental parsing. The library is packaged with first-class Python bindings via PyO3 and a JavaScript/WASM build for browser use. A Next.js demo visualizes the token-level rendering. The codebase demonstrates strong engineering: a custom BPE tokenizer (tiktoken) implementation in Rust, a streaming parser that gracefully handles partial UTF-8 sequences, and a JSON Schema-to-TypeScript converter for tool definitions. Tests are comprehensive with parity between Rust and Python test suites.
nfelib is a Python library providing data bindings, serialization, and SOAP client support for Brazilian electronic fiscal documents (NF-e, CT-e, MDF-e, NFSe, BP-e). It uses xsdata to auto-generate Python dataclasses from official XSD schemas (versions v4.0, v3.0, v1.0). The library includes a CommonMixin base class for common XML operations (to_xml, from_xml, sign_xml, to_pdf, validate_xml), SOAP service bindings generated from WSDLs, and server endpoint configurations per Brazilian state (UF). It also provides code generation tools (generate_bindings.py) and server endpoint scraping utilities (servers_scraper.py). The architecture mixes auto-generated binding code with handcrafted integration patterns and testing utilities. The project has strong test coverage, including round-trip XML serialization tests, protocol buffer validation, and fingerprint-based detection of external schema changes. It is designed for ERP integration (e.g., Odoo) and fiscal compliance software.