请问能否集成 codebuddy 的积分统计了？或者是成本统计这块

[easonjim]

请问能否集成 codebuddy 的积分统计了？或者是成本统计这块
但是 opencode 可能存在多个会话一个问题上，所以这块能否采用累加的形式来统计计算进去成本里面，或者说是积分消耗了？

[kuops]

那你应该是去找个 tui 插件 https://github.com/Hotakus/opencode-visual-cache 而不是认证插件做这个功能

[easonjim]

@kuops 明白了，感谢提供思路
另外，我还发现一个问题，使用了认证插件后，这里显示不能推理：
请问是模型返回的字段问题吗？

[kuops]

@kuops 明白了，感谢提供思路 另外，我还发现一个问题，使用了认证插件后，这里显示不能推理： 请问是模型返回的字段问题吗？

这个我看了一下，开发这个好像没啥必要，主要是有个几个问题，第一个 codebuddy 里面这几个东西貌似是写死的，对应界面上没有选择，但是后台会传，另外就是会出来一堆 Thought

你自己如果需要就 opencode debug config |jq '.provider.codebuddy' 把配置 dump出来手动改一下

{
  "npm": "@ai-sdk/openai-compatible",
  "name": "CodeBuddy",
  "options": {
    "baseURL": "https://copilot.tencent.com/v2",
    "setCacheKey": true
  },
  "models": {
    "auto": {
      "name": "Auto",
      "limit": {
        "context": 168000,
        "output": 32000
      },
      "tool_call": true,
      "attachment": true,
      "reasoning": true,
      "interleaved": {
        "field": "reasoning_content"
      },
      "options": {
        "reasoningEffort": "high",
        "reasoningSummary": "auto"
      }
    },
    "hy3": {
      "name": "Hy3 (Free)",
      "limit": {
        "context": 192000,
        "output": 64000
      },
      "tool_call": true,
      "attachment": true,
      "reasoning": true,
      "interleaved": {
        "field": "reasoning_content"
      },
      "options": {
        "reasoningEffort": "high",
        "reasoningSummary": "auto"
      }
    },
    "glm-5.2": {
      "name": "GLM-5.2 (x0.79)",
      "limit": {
        "context": 1000000,
        "output": 48000
      },
      "tool_call": true,
      "attachment": true,
      "reasoning": true,
      "interleaved": {
        "field": "reasoning_content"
      },
      "options": {
        "reasoningEffort": "high",
        "reasoningSummary": "auto"
      },
      "variants": {
        "high": {
          "reasoningEffort": "high",
          "reasoningSummary": "auto"
        },
        "xhigh": {
          "reasoningEffort": "xhigh",
          "reasoningSummary": "auto"
        }
      }
    },
    "glm-5.1": {
      "name": "GLM-5.1 (x0.79)",
      "limit": {
        "context": 200000,
        "output": 48000
      },
      "tool_call": true,
      "attachment": true,
      "reasoning": true,
      "interleaved": {
        "field": "reasoning_content"
      },
      "options": {
        "reasoningEffort": "medium",
        "reasoningSummary": "auto"
      }
    },
    "glm-5v-turbo": {
      "name": "GLM-5v-Turbo (x0.71)",
      "limit": {
        "context": 200000,
        "output": 64000
      },
      "tool_call": true,
      "attachment": true,
      "reasoning": true,
      "interleaved": {
        "field": "reasoning_content"
      },
      "options": {
        "reasoningEffort": "medium",
        "reasoningSummary": "auto"
      }
    },
    "kimi-k3-1": {
      "name": "Kimi-K3 (x1.62)",
      "limit": {
        "context": 1000000,
        "output": 32000
      },
      "tool_call": true,
      "attachment": true,
      "reasoning": true,
      "interleaved": {
        "field": "reasoning_content"
      },
      "options": {
        "reasoningEffort": "medium",
        "reasoningSummary": "auto"
      }
    },
    "kimi-k2.7": {
      "name": "Kimi-K2.7-Code (x0.57)",
      "limit": {
        "context": 256000,
        "output": 32000
      },
      "tool_call": true,
      "attachment": true,
      "reasoning": true,
      "interleaved": {
        "field": "reasoning_content"
      },
      "options": {
        "reasoningEffort": "medium",
        "reasoningSummary": "auto"
      }
    },
    "kimi-k2.6": {
      "name": "Kimi-K2.6 (x0.52)",
      "limit": {
        "context": 256000,
        "output": 32000
      },
      "tool_call": true,
      "attachment": true,
      "reasoning": true,
      "interleaved": {
        "field": "reasoning_content"
      },
      "options": {
        "reasoningEffort": "medium",
        "reasoningSummary": "auto"
      }
    },
    "minimax-m3": {
      "name": "MiniMax-M3 (x0.25)",
      "limit": {
        "context": 512000,
        "output": 128000
      },
      "tool_call": true,
      "attachment": true,
      "reasoning": true,
      "interleaved": {
        "field": "reasoning_content"
      },
      "options": {
        "reasoningEffort": "medium",
        "reasoningSummary": "auto"
      }
    },
    "deepseek-v4-pro": {
      "name": "Deepseek-V4-Pro (x0.13)",
      "limit": {
        "context": 1000000,
        "output": 128000
      },
      "tool_call": true,
      "attachment": true,
      "reasoning": true,
      "interleaved": {
        "field": "reasoning_content"
      },
      "options": {
        "reasoningEffort": "high",
        "reasoningSummary": "auto"
      },
      "variants": {
        "high": {
          "reasoningEffort": "high",
          "reasoningSummary": "auto"
        },
        "xhigh": {
          "reasoningEffort": "xhigh",
          "reasoningSummary": "auto"
        }
      }
    },
    "deepseek-v4-flash": {
      "name": "Deepseek-V4-Flash (x0.05)",
      "limit": {
        "context": 1000000,
        "output": 128000
      },
      "tool_call": true,
      "attachment": true,
      "reasoning": true,
      "interleaved": {
        "field": "reasoning_content"
      },
      "options": {
        "reasoningEffort": "high",
        "reasoningSummary": "auto"
      },
      "variants": {
        "high": {
          "reasoningEffort": "high",
          "reasoningSummary": "auto"
        },
        "xhigh": {
          "reasoningEffort": "xhigh",
          "reasoningSummary": "auto"
        }
      }
    }
  }
}

后面 opencode 修复了 Thought 我再加个把， anomalyco/opencode#31999 , 你自己加个 reasoningEffort