agentscope-gemini-usage-fix

Work around a Gemini usage normalization bug in AgentScope where cached_content_token_count=None causes Usage validation to fail. Use this whenever the user uses GeminiChatModel and sees ValidationError on cache_input_tokens.

This skill documents a known bug and a runtime workaround for GeminiChatModel usage extraction.

Bug

GeminiChatModel._extract_usage uses getattr(..., "cached_content_token_count", 0), which returns None when the attribute exists but is None. ChatUsage validates cache_input_tokens as int, so None raises a pydantic ValidationError.

Workaround

Apply this patch before using Gemini models:

python

from agentscope.model._gemini._model import GeminiChatModel

_original = GeminiChatModel._extract_usage

def _patched_extract_usage(self, usage_metadata, start_datetime):
    if not usage_metadata:
        return None
    usage = _original(self, usage_metadata, start_datetime)
    if usage and getattr(usage, "cache_input_tokens", None) is None:
        usage.cache_input_tokens = 0
    return usage

GeminiChatModel._extract_usage = _patched_extract_usage

Upstream Fix

The upstream fix is a one-line change:

python

cache_input_tokens=getattr(
    usage_metadata,
    "cached_content_token_count",
    0,
) or 0,

Reference: https://github.com/agentscope-ai/agentscope/issues/3077