{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"Renders and parses the Harmony response format used by OpenAI's gpt-oss models, enabling structured conversation handling, reasoning output, and function calls in Python.","skillfed_tags":["llm-inference","prompt-formatting","rust-binding"],"use_cases":["Format multi-turn conversations with system, developer, and user roles for direct gpt-oss inference.","Parse structured model outputs including reasoning, commentary, and function calls from gpt-oss completions.","Define tool namespaces and function signatures in a format gpt-oss expects for reliable tool calling.","Encode conversations for token counting and prompt optimization before sending to a gpt-oss model.","Build a custom inference pipeline that requires explicit control over prompt and response formatting."],"what_it_does":"OpenAI Harmony is a Python library that encodes and decodes conversations in the Harmony response format, a structured format designed for OpenAI's open-weight gpt-oss models. The format enables models to output to multiple channels (analysis, commentary, final), handle chain-of-thought reasoning, and structure function calls with tool namespaces. The library wraps a Rust core via PyO3 bindings for performance, exposing a pure-Python API with dataclasses and helper functions.\n\nYou use it when building inference solutions that directly invoke gpt-oss models and need to format prompts and parse responses according to the Harmony specification. If you're using gpt-oss through an API provider (HuggingFace, Ollama, vLLM), the provider handles formatting and you don't need this library. The library depends only on Pydantic and aims to keep token sequences loss-free during rendering and parsing.","worth_installing":"Yes, if you are building a custom inference solution directly using gpt-oss models and need to handle the Harmony format yourself. No, if you are using gpt-oss through a managed API or inference provider\u2014they handle formatting transparently. Caution: the package is aging (282 days since last release) and license terms are unclear; verify both before committing to production use."},"id":"openai-harmony","links":{"html":"https://skillfed.io/packages/openai-harmony","md":"https://skillfed.io/packages/openai-harmony.md","pypi":"https://pypi.org/project/openai-harmony/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-11-05","license_spdx":null,"license_treatment":"unclear","name":"openai-harmony","python_support":"supports_current","summary":"OpenAI's response format for its open-weight model series gpt-oss"},"popularity":{"monthly_downloads":7237088,"position":1764,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.0.8"}