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The [gpt-oss models][gpt-oss] were trained on the [harmony response format][harmony-format] for defining conversation structures, generating reasoning output and structuring function calls. If you are not using gpt-oss directly but through an API or a provider like Ollama, you will not have to be concerned about this as your inference solution will handle the formatting. If you are building your own inference solution, this guide will walk you through the prompt format. The format is designed to mimic the OpenAI Responses API, so if you have used that API before, this format should hopefully feel familiar to you. gpt-oss should not be used without using the harmony format as it will not work correctly.
The [gpt-oss models][gpt-oss] were trained on the [harmony response format][harmony-format] for defining conversation structures, generating reasoning output and structuring function calls. If you are not using gpt-oss directly but through an API or a provider like HuggingFace, Ollama, or vLLM, you will not have to be concerned about this as your inference solution will handle the formatting. If you are building your own inference solution, this guide will walk you through the prompt format. The format is designed to mimic the OpenAI Responses API, so if you have used that API before, this format should hopefully feel familiar to you. gpt-oss should not be used without using the harmony format as it will not work correctly.
The format enables the model to output to multiple different channels for chain of thought, and tool calling premables along with regular responses. It also enables specifying various tool namespaces, and structured outputs along with a clear instruction hierarchy. [Check out the guide][harmony-format] to learn more about the format itself.