The project's built-in logging system can record the metadata of each interaction in detail, including: 1) the complete content of prompts and role definitions; 2) a sequence of timestamps down to milliseconds; 3) the number of tokens consumed and their validity period. The log supports both console real-time output and file persistence modes, which can be flexibly configured via the -log parameter.
In the prompt optimization scenario, developers can 1) analyze the token consumption patterns of historical conversations; 2) compare the response quality of different prompt structures; and 3) build conversation datasets for model fine-tuning. Empirical tests show that this feature can improve the efficiency of 35%'s prompt engineering iterations, especially in complex workflow debugging.
This answer comes from the articleGemini-CLI-2-API: Converting the Gemini CLI to an OpenAI-compatible Native API ServiceThe
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