{"repo": "chilishark27/ga-manager", "files": {"ga_core/assets/configure_mykey.py": {"eaa7b6c1c04eecdcb522f2dd2333ff37c2ccaaa3": {"t": 1785595183.3033319, "n": 54456, "h": "121de44c6cb7c84c", "c": "#!/usr/bin/env python3\n\"\"\"\nGenericAgent — 交互式初始化向导 (configure.py)\n一键配置 LLM 模型 + 消息平台，自动生成 mykey.py\n\n用法:\n    python configure.py\n\"\"\"\n\nimport ast\nimport os\nimport sys\nimport re\nimport shutil\nimport json\nimport urllib.request\nfrom datetime import datetime\n\n# ── ANSI 颜色 ──────────────────────────────────────────────────────────────\nC = {\n    'reset': '\\033[0m', 'bold': '\\033[1m', 'dim': '\\033[2m',\n    'red': '\\033[91m', 'green': '\\033[92m', 'yellow': '\\033[93m',\n    'blue': '\\033[94m', 'magenta': '\\033[95m', 'cyan': '\\033[96m', 'white': '\\033[97m',\n}\n\nPROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nMYKPY_PATH = os.path.join(PROJECT_ROOT, 'mykey.py')\n\n# ── 模型厂商定义 ───────────────────────────────────────────────────────────\n\nLLM_PROVIDERS = [\n    # ═══════════════════════════ 通用协议（官方直连或任意兼容中转）═══════════════════════════\n    {\n        'id': 'oai_chat',\n        'name': 'OpenAI Chat Completions 协议',\n        'desc': '官方直连或任意 OAI 兼容中转/网关，自填 apibase（回车=OpenAI 官方）',\n        'type': 'native_oai',\n        'template': {\n            'name': 'gpt-native', 'apikey': 'sk-<your-key>',\n            'apibase': 'https://api.openai.com/v1', 'model': 'gpt-5.5',\n            'api_mode': 'chat_completions', 'reasoning_effort': 'high',\n            'max_retries': 3, 'connect_timeout': 10, 'read_timeout': 120,\n        },\n        'key_hint': '官方在 https://platform.openai.com/api-keys 获取；中转站填其提供的 Key',\n        'model_choices': ['gpt-5.5', 'gpt-5.4'],\n        'extra_fields': [\n            {'key': 'apibase', 'label': 'API Base（官方或中转地址）', 'default': 'https://api.openai.com/v1'},\n        ],\n    },\n    {\n        'id': 'oai_responses',\n        'name': 'OpenAI Responses 协议',\n        'desc': 'Responses API（o 系列/GPT-5.5 推荐端点），官方或兼容网关，自填 apibase',\n        'type': 'native_oai',\n        'template': {\n            'name': 'gpt-responses', 'apikey': 'sk-<your-key>',\n            'apibase': 'https://api.openai.com/v1', 'model': 'gpt-5.5',\n            'api_mode': 'responses', 'reasoning_effort': 'high',\n            'max_retries': 3, 'connect_timeout': 10, 'read_timeout': 120,\n        },\n        'key_hint': '官方在 https://platform.openai.com/api-keys 获取；中转站填其提供的 Key',\n        'model_choices': ['gpt-5.5', 'gpt-5.4'],\n        'extra_fields': [\n            {'key': 'apibase', 'label': 'API Base（官方或中转地址）', 'default': 'https://api.openai.com/v1'},\n        ],\n    },\n    {\n        'id': 'claude_messages',\n        'name': 'Claude Messages 协议',\n        'desc': 'Anthropic 官方直连或任意 Claude 兼容中转，自填 apibase（回车=官方）',\n        'type': 'native_claude',\n        'template': {\n            'name': 'anthropic-direct', 'apikey': 'sk-ant-<your-key>',\n            'apibase': 'https://api.anthropic.com', 'model': 'claude-opus-4-7',\n            'thinking_type': 'adaptive', 'max_tokens': 32768, 'temperature': 1,\n        },\n        'key_hint': '官方在 https://console.anthropic.com/ 获取；中转站填其提供的 Key',\n        'model_choices': ['claude-opus-4-7', 'claude-sonnet-4-6'],\n        'extra_fields': [\n            {'key': 'apibase', 'label': 'API Base（官方或中转地址）', 'default': 'https://api.anthropic.com'},\n        ],\n    },\n    # ═══════════════════════════ 直连 API（按旗舰能力降序）═══════════════════════════\n    {\n        'id': 'deepseek',\n        'name': 'DeepSeek (v4-Pro / Flash)',\n        'desc': '开源模型，v4-Pro 旗舰 1M 上下文',\n        'type': 'native_oai',\n        'template': {\n            'name': 'deepseek', 'apikey': 'sk-<your-deepseek-key>',\n            'apibase': 'https://api.deepseek.com', 'model': 'deepseek-v4-flash',\n            'api_mode': 'chat_completions', 'reasoning_effort': 'high',\n        },\n        'key_hint': '在 https://platform.deepseek.com/api_keys 获取',\n        'model_choices': ['deepseek-v4-pro', 'deepseek-v4-flash'],\n    },\n    {\n        'id': 'kimi',\n        'name': 'Kimi (k2.6 / k2.5) 双协议',\n        'desc': '月之暗面，支持 Anthropic 和 OAI 双协议',\n        'type': 'native_claude',\n        'template': {\n            'name': 'kimi', 'apikey': 'sk-kimi-<your-key>',\n            'apibase': 'https://api.kimi.com/coding',\n            'model': 'kimi-for-coding', 'fake_cc_system_prompt': True,\n            'thinking_type': 'adaptive',\n        },\n        'key_hint': '在 https://kimi.com/code 或 https://platform.moonshot.cn/ 获取',\n        'model_choices': ['kimi-k2.6', 'kimi-k2.5'],\n        'extra_fields': [\n            {\n                'key': '_protocol', 'label': '选择 API 协议',\n                'type': 'choice',\n                'options': [\n                    {'id': 'native_claude', 'name': 'Anthropic 兼容 (推荐)', 'desc': 'kimi-for-coding 端点，CC 兼容', 'apibase': 'https://api.kimi.com/coding', 'fake_cc_system_prompt': True, 'model': 'kimi-for-coding'},\n                    {'id': 'native_oai', 'name': 'OpenAI 协议', 'desc': 'Moonshot OAI 端点，kimi-k2 系列', 'apibase': 'https://api.moonshot.cn/v1', 'model': 'kimi-k2.6'},\n                ],\n            },\n        ],\n    },\n    {\n        'id': 'qwen',\n        'name': '阿里通义千问 (Qwen3.5 / 百炼)',\n        'desc': '阿里云百炼，Qwen3 系列百万级上下文',\n        'type': 'native_oai',\n        'template': {\n            'name': 'qwen', 'apikey': 'sk-<your-dashscope-key>',\n            'apibase': 'https://dashscope.aliyuncs.com/compatible-mode/v1',\n            'model': 'qwen3.5-plus',\n            'api_mode': 'chat_completions',\n        },\n        'key_hint': '在 https://bailian.console.aliyun.com/ 获取 API Key',\n        'model_choices': ['qwen3.5-plus', 'qwen3-coder-plus'],\n        'extra_fields': [\n            {\n                'key': '_endpoint', 'label': '选择端点',\n                'type': 'choice',\n                'options': [\n                    {'id': 'standard', 'name': '标准按量付费', 'desc': 'dashscope.aliyuncs.com，兼容模式', 'apibase': 'https://dashscope.aliyuncs.com/compatible-mode/v1'},\n                    {'id': 'coding_plan', 'name': '百炼 Coding Plan (订阅)', 'desc': 'coding-intl.dashscope.aliyuncs.com，100万上下文', 'apibase': 'https://coding-intl.dashscope.aliyuncs.com/v1', 'context_win': 1000000},\n                ],\n            },\n        ],\n    },\n    {\n        'id': 'zhipu',\n        'name': '智谱 GLM-5.1 (Coding Plan)',\n        'desc': '智谱 GLM，支持 Coding Plan CN (Anthropic) 和 Global (OAI) 双端点',\n        'type': 'native_claude',\n        'template': {\n            'name': 'zhipu-glm', 'apikey': 'sk-<your-zhipu-key>',\n            'apibase': 'https://open.bigmodel.cn/api/anthropic',\n            'model': 'GLM-5.1-Cloud', 'fake_cc_system_prompt': False,\n            'thinking_type': 'adaptive', 'max_retries': 3,\n            'connect_timeout': 10, 'read_timeout': 180,\n        },\n        'key_hint': 'CN 在 https://open.bigmodel.cn/ 获取；Global 在 https://z.ai/ 获取',\n        'model_choices': ['GLM-5.1-Cloud', 'glm-4.7'],\n        'extra_fields': [\n            {\n                'key': '_plan', 'label': '选择 Coding Plan',\n                'type': 'choice',\n                'options': [\n                    {'id': 'native_claude', 'name': 'Coding Plan CN (Anthropic)', 'desc': 'open.bigmodel.cn，推荐国内用户', 'apibase': 'https://open.bigmodel.cn/api/anthropic', 'fake_cc_system_prompt': False},\n                    {'id': 'native_oai', 'name': 'Coding Plan Global (OAI)', 'desc': 'api.z.ai，OpenAI 协议，全球可用', 'apibase': 'https://api.z.ai/api/paas/v4'},\n                ],\n            },\n        ],\n    },\n    {\n        'id': 'minimax',\n        'name': 'MiniMax M3 (双协议)',\n        'desc': 'MiniMax M3，支持 Anthropic 和 OpenAI 双协议',\n        'type': 'native_claude',\n        'template': {\n            'name': 'minimax', 'apikey': 'eyJh...<your-minimax-key>',\n            'apibase': 'https://api.minimaxi.com/anthropic',\n            'model': 'MiniMax-M3', 'max_retries': 3,\n        },\n        'key_hint': '在 https://platform.minimaxi.com/user-center/basic-information 获取',\n        'model_choices': ['MiniMax-M3', 'MiniMax-M2.7', 'MiniMax-M2.7-highspeed'],\n        'extra_fields': [\n            {\n                'key': '_protocol', 'label': '选择 API 协议',\n                'type': 'choice',\n                'options': [\n                    {'id': 'native_claude', 'name': 'Anthropic 协议 (推荐)', 'desc': '无 <think> 标签，原生 Claude 兼容', 'apibase': 'https://api.minimaxi.com/anthropic'},\n                    {'id': 'native_oai', 'name': 'OpenAI 协议', 'desc': '走 /v1/chat/completions', 'apibase': 'https://api.minimaxi.com/v1', 'context_win': 50000},\n                ],\n            },\n        ],\n    },\n    {\n        'id': 'stepfun',\n        'name': '阶跃星辰 Step-3.5 (推理强)',\n        'desc': '阶跃星辰 Step 系列，支持标准和 Step Plan 双端点',\n        'type': 'native_oai',\n        'template': {\n            'name': 'stepfun', 'apikey': 'sk-<your-stepfun-key>',\n            'apibase': 'https://api.stepfun.com/v1',\n            'model': 'step-3.5-flash',\n            'api_mode': 'chat_completions',\n            'context_win': 262144,\n        },\n        'key_hint': '在 https://platform.stepfun.com/ 获取 API Key',\n        'model_choices': ['step-3.5-flash', 'step-3.5-flash-2603'],\n        'extra_fields': [\n            {\n                'key': '_endpoint', 'label': '选择端点',\n                'type': 'choice',\n                'options': [\n                    {'id': 'standard', 'name': '标准端点', 'desc': 'api.stepfun.com/v1，按量付费', 'apibase': 'https://api.stepfun.com/v1', 'context_win': 262144},\n                    {'id': 'step_plan', 'name': 'Step Plan (订阅)', 'desc': 'api.stepfun.com/step_plan/v1，订阅制', 'apibase': 'https://api.stepfun.com/step_plan/v1', 'context_win': 262144},\n                ],\n            },\n        ],\n    },\n    {\n        'id': 'qianfan',\n        'name': '百度千帆 (ERNIE 5.0 / 第三方)',\n        'desc': '百度智能云千帆，文心一言 ERNIE 5.0 + DeepSeek 等',\n        'type': 'native_oai',\n        'template': {\n            'name': 'baidu-qianfan', 'apikey': '<your-qianfan-key>',\n            'apibase': 'https://qianfan.baidubce.com/v2',\n            'model': 'ernie-5.0-thinking-preview',\n            'api_mode': 'chat_completions',\n        },\n        'key_hint': '在 https://console.bce.baidu.com/qianfan/ 创建应用获取 API Key',\n        'model_choices': ['ernie-5.0-thinking-preview', 'deepseek-v3.2'],\n        'extra_fields': [\n            {'key': 'apibase', 'label': 'API 地址 (apibase)', 'default': 'https://qianfan.baidubce.com/v2'},\n        ],\n    },\n    {\n        'id': 'volcengine',\n        'name': '火山引擎 (豆包 / Ark)',\n        'desc': '字节跳动火山引擎，支持标准 Ark 和 Ark Coding Plan',\n        'type': 'native_oai',\n        'template': {\n            'name': 'volc-ark', 'apikey': '<your-ark-api-key>',\n            'apibase': 'https://ark.cn-beijing.volces.com/api/v3',\n            'model': 'doubao-seed-code-preview-251028',\n            'api_mode': 'chat_completions',\n        },\n        'key_hint': '在 https://console.volcengine.com/ark/ 创建推理接入点后获取 API Key',\n        'model_choices': ['doubao-seed-code-preview-251028', 'doubao-seed-1-8-251228'],\n        'extra_fields': [\n            {\n                'key': '_endpoint', 'label': '选择端点',\n                'type': 'choice',\n                'options': [\n                    {'id': 'standard', 'name': '标准 Ark', 'desc': 'ark.cn-beijing.volces.com/api/v3，按量付费', 'apibase': 'https://ark.cn-beijing.volces.com/api/v3'},\n                    {'id': 'coding_plan', 'name': 'Ark Coding Plan (订阅)', 'desc': 'ark.cn-beijing.volces.com/api/coding/v3', 'apibase': 'https://ark.cn-beijing.volces.com/api/coding/v3'},\n                ],\n            },\n        ],\n    },\n    {\n        'id': 'xiaomi',\n        'name': '小米 MiMo (MiMo 2.5 Pro / TokenPlan)',\n        'desc': '小米 MiMo 系列，超大上下文窗口，支持 TokenPlan 预付费',\n        'type': 'native_oai',\n        'template': {\n            'name': 'xiaomi-mimo', 'apikey': 'sk-<your-xiaomi-key>',\n            'apibase': 'https://api.xiaomimimo.com/v1',\n            'model': 'mimo-v2.5-pro',\n            'api_mode': 'chat_completions',\n        },\n        'key_hint': '在 https://x.xiaomi.com/ 获取 API Key',\n        'model_choices': ['mimo-v2.5-pro', 'mimo-v2-flash'],\n        'extra_fields': [\n            {'key': 'apibase', 'label': 'API 地址 (apibase)', 'default': 'https://api.xiaomimimo.com/v1'},\n        ],\n    },\n    {\n        'id': 'tencent_tokenhub',\n        'name': '腾讯混元 TokenHub (Hy3 / TokenPlan)',\n        'desc': '腾讯云 TokenHub，混元 Hy3 系列，TokenPlan 预付费',\n        'type': 'native_oai',\n        'template': {\n            'name': 'tencent-tokenhub', 'apikey': 'sk-<your-tokenhub-key>',\n            'apibase': 'https://tokenhub.tencentmaas.com/v1',\n            'model': 'hy3-preview',\n            'api_mode': 'chat_completions',\n        },\n        'key_hint': '在 https://console.cloud.tencent.com/tokenhub 获取 API Key',\n        'model_choices': ['hy3-preview'],\n        'extra_fields': [\n            {'key': 'apibase', 'label': 'API 地址 (apibase)', 'default': 'https://tokenhub.tencentmaas.com/v1'},\n        ],\n    },\n    # ═══════════════════════════ 代理 / 中继（支持 Claude/GPT 等顶级模型）══════════\n    {\n        'id': 'cc_relay',\n        'name': 'CC Switch 透传 (社区常用)',\n        'desc': '社区 Claude Code 透传渠道，可接入 Claude Opus',\n        'type': 'native_claude',\n        'template': {\n            'name': 'cc-relay', 'apikey': 'sk-user-<your-relay-key>',\n            'apibase': 'https://<your-cc-switch-host>/claude/office',\n            'model': 'claude-opus-4-7', 'fake_cc_system_prompt': True,\n            'thinking_type': 'adaptive',\n        },\n        'key_hint': '从你的 CC Switch 服务商获取 apikey 和 apibase',\n        'model_choices': ['claude-opus-4-7', 'claude-sonnet-4-6'],\n        'extra_fields': [\n            {'key': 'apibase', 'label': 'API 地址 (apibase)', 'default': 'https://your-host/claude/office'},\n            {'key': 'fake_cc_system_prompt', 'label': 'fake_cc_system_prompt', 'type': 'bool', 'default': True},\n        ],\n    },\n    {\n        'id': 'openrouter',\n        'name': 'OpenRouter (多模型中继)',\n        'desc': '一个 Key 通吃 Claude/GPT/DeepSeek/Qwen 等',\n        'type': 'native_oai',\n        'template': {\n            'name': 'openrouter', 'apikey': 'sk-or-<your-openrouter-key>',\n            'apibase': 'https://openrouter.ai/api/v1',\n            'model': 'anthropic/claude-opus-4-7',\n            'max_retries': 3, 'connect_timeout': 10, 'read_timeout': 120,\n        },\n        'key_hint': '在 https://openrouter.ai/keys 获取',\n        'model_choices': ['anthropic/claude-opus-4-7', 'openai/gpt-5.5'],\n    },\n    {\n        'id': 'commonstack',\n        'name': 'CommonStack (统一网关)',\n        'desc': '一个 Key 通吃 Claude/GPT/Gemini/DeepSeek/MiniMax/Zhipu/xAI 等',\n        'type': 'native_oai',\n        'template': {\n            'name': 'commonstack', 'apikey': 'sk-<your-commonstack-key>',\n            'apibase': 'https://api.commonstack.ai/v1',\n            'model': 'anthropic/claude-opus-4-7',\n            'api_mode': 'chat_completions',\n            'max_retries': 3, 'connect_timeout': 10, 'read_timeout': 120,\n        },\n        'key_hint': '在 https://commonstack.ai 注册后从 Dashboard 获取 API Key',\n        'model_choices': ['anthropic/claude-opus-4-7', 'openai/gpt-5.5'],\n    },\n    {\n        'id': 'crs',\n        'name': 'CRS 反代 (Claude Max 多通道)',\n        'desc': 'CRS 协议的反代服务，支持 Claude Max / Gemini Ultra 通道',\n        'type': 'native_claude',\n        'template': {\n            'name': 'crs', 'apikey': 'cr_<your-crs-key>',\n            'apibase': 'https://<your-crs-host>/api',\n            'model': 'claude-opus-4-7[1m]', 'fake_cc_system_prompt': True,\n            'thinking_type': 'adaptive', 'max_tokens': 32768,\n            'max_retries': 3, 'read_timeout': 180,\n        },\n        'key_hint': '从你的 CRS 服务商获取 key 和 host',\n        'model_choices': ['claude-opus-4-7[1m]', 'claude-sonnet-4-6'],\n        'extra_fields': [\n            {\n                'key': '_channel', 'label': '选择 CRS 通道',\n                'type': 'choice',\n                'options': [\n                    {'id': 'claude_max', 'name': 'Claude Max (默认)', 'desc': '标准 CRS Claude 通道', 'apibase': 'https://<your-crs-host>/api'},\n                    {'id': 'gemini_ultra', 'name': 'Gemini Ultra (Antigravity)', 'desc': 'CRS 包装的 Google Antigravity，不支持 SSE 流式', 'apibase': 'https://<your-crs-gemini-host>/antigravity/api', 'model': 'claude-opus-4-7-thinking', 'stream': False},\n                ],\n            },\n        ],\n    },\n    {\n        'id': 'gmi',\n        'name': 'GMI Serving (通用模型中继)',\n        'desc': 'GMI 通用模型推理服务，支持多种开源/闭源（手动输入模型名）',\n        'type': 'native_oai',\n        'template': {\n            'name': 'gmi', 'apikey': '<your-gmi-key>',\n            'apibase': 'https://api.gmi-serving.com/v1',\n            'model': 'gmi-default',\n            'api_mode': 'chat_completions',\n        },\n        'key_hint': '从 GMI 服务商获取 API Key，探测失败时手动输入模型名',\n        'model_choices': [],  # 中继服务，模型由服务商提供，探测失败时手动输入\n        'extra_fields': [\n            {'key': 'apibase', 'label': 'API 地址 (apibase)', 'default': 'https://api.gmi-serving.com/v1'},\n        ],\n    },\n]\n\n# ── 消息平台定义 ────────────────────────────────────────────────────────────\nPLATFORMS = [\n    {\n        'id': 'none',\n        'name': '不使用消息平台（纯终端 REPL）',\n        'desc': '直接用 python agentmain.py 在终端交互',\n        'deps': [],\n    },\n    {\n        'id': 'telegram',\n        'name': 'Telegram 机器人',\n        'desc': '通过 Telegram Bot 与 Agent 对话',\n        'file': 'frontends/tgapp.py',\n        'deps': ['python-telegram-bot'],\n        'env_vars': [\n            {'key': 'tg_bot_token', 'label': 'Bot Token', 'hint': '从 @BotFather 获取'},\n            {'key': 'tg_allowed_users', 'label': '允许的用户 ID（逗号分隔, 留空=所有人）', 'default': '[]', 'is_list': True},\n        ],\n    },\n    {\n        'id': 'qq',\n        'name': 'QQ 机器人',\n        'desc': '通过 QQ 官方机器人 API 接入',\n        'file': 'frontends/qqapp.py',\n        'deps': ['qq-botpy'],\n        'env_vars': [\n            {'key': 'qq_app_id', 'label': 'App ID', 'hint': 'QQ 开放平台获取'},\n            {'key': 'qq_app_secret', 'label': 'App Secret'},\n            {'key': 'qq_allowed_users', 'label': '允许的用户 OpenID（逗号分隔, 留空=所有人）', 'default': '[]', 'is_list': True},\n        ],\n    },\n    {\n        'id': 'feishu',\n        'name': '飞书机器人',\n        'desc': '通过飞书应用与 Agent 对话',\n        'file': 'frontends/fsapp.py',\n        'deps': ['lark-oapi'],\n        'env_vars': [\n            {'key': 'fs_app_id', 'label': 'App ID', 'hint': '飞书开放平台获取'},\n            {'key': 'fs_app_secret', 'label': 'App Secret'},\n            {'key': 'fs_allowed_users', 'label': '允许的用户（逗号分隔, 留空=所有人）', 'default': '[]', 'is_list': True},\n        ],\n    },\n    {\n        'id': 'wecom',\n        'name': '企业微信机器人',\n        'desc': '通过企业微信 Bot 接入',\n        'file': 'frontends/wecomapp.py',\n        'deps': ['wecombot'],\n        'env_vars': [\n            {'key': 'wecom_bot_id', 'label': 'Bot ID'},\n            {'key': 'wecom_secret', 'label': 'Bot Secret'},\n            {'key': 'wecom_allowed_users', 'label': '允许的用户（逗号分隔, 留空=所有人）', 'default': '[]', 'is_list': True},\n        ],\n    },\n    {\n        'id': 'dingtalk',\n        'name': '钉钉机器人',\n        'desc': '通过钉钉应用接入',\n        'file': 'frontends/dingtalkapp.py',\n        'deps': ['dingtalk-sdk'],\n        'env_vars': [\n            {'key': 'dingtalk_client_id', 'label': 'Client ID (App Key)'},\n            {'key': 'dingtalk_client_secret', 'label': 'Client Secret (App Secret)'},\n            {'key': 'dingtalk_allowed_users', 'label': '允许的用户 StaffID（逗号分隔, 留空=所有人）', 'default': '[]', 'is_list': True},\n        ],\n    },\n    {\n        'id': 'discord',\n        'name': 'Discord 机器人',\n        'desc': '通过 Discord Bot 接入',\n        'file': 'frontends/dcapp.py',\n        'deps': ['discord.py'],\n        'env_vars': [\n            {'key': 'dc_bot_token', 'label': 'Bot Token', 'hint': 'Discord Developer Portal 获取'},\n            {'key': 'dc_allowed_users', 'label': '允许的用户 ID（逗号分隔, 留空=所有人）', 'default': '[]', 'is_list': True},\n        ],\n    },\n    {\n        'id': 'wechat',\n        'name': '微信 (iLink 协议)',\n        'desc': '通过微信个人号与 Agent 对话，扫码自动登录',\n        'file': 'frontends/wechatapp.py',\n        'deps': ['requests', 'qrcode', 'pycryptodome'],\n        'env_vars': [],\n    },\n]\n\n\ndef _masked(v, reveal, tail):\n    \"\"\"生成脱敏字符串：前 reveal 位明文 + * + 后 tail 位明文\"\"\"\n    if len(v) > reveal + tail:\n        return v[:reveal] + '*' * min(len(v) - reveal - tail, 8) + v[-tail:]\n    elif len(v) > reveal:\n        return v[:reveal] + '*' * (len(v) - reveal)\n    return v\n\ndef masked_input(prompt, reveal=6, tail=4):\n    \"\"\"密文输入，支持粘贴：批读取 + 延迟重绘，避免快速键入时丢字符。\n\n    prompt 必须为单行（不含 \\\\n）。\n    \"\"\"\n    sys.stdout.write(prompt)\n    sys.stdout.flush()\n    chars = []\n\n    def _repaint():\n        m = _masked(''.join(chars), reveal, tail)\n        sys.stdout.write(f'\\r{prompt}{m}     \\r{prompt}{m}')\n        sys.stdout.flush()\n\n    def _process(c):\n        \"\"\"处理单个字符，返回 True 表示应退出。\"\"\"\n        if c in ('\\r', '\\n'):\n            return True\n        if c in ('\\x03', '\\x04'):\n            raise KeyboardInterrupt\n        if c in ('\\x08', '\\x7f'):\n            if chars:\n                chars.pop()\n        elif c.isprintable() or c == ' ':\n            chars.append(c)\n        return False\n\n    if os.name == 'nt':\n        import msvcrt\n        while True:\n            c = msvcrt.getwch()\n            if _process(c):\n                break\n            if c in ('\\x08', '\\x7f'):\n                _repaint()          # 退格立即重绘\n                continue\n            if not (c.isprintable() or c == ' '):\n                continue\n            # 批量读取：粘贴时一次取完\n            while msvcrt.kbhit():\n                c2 = msvcrt.getwch()\n                if _process(c2):\n                    value = ''.join(chars)\n                    _repaint()\n                    sys.stdout.write('\\n')\n                    sys.stdout.flush()\n                    return value\n            _repaint()\n    else:\n        import tty, termios, select\n        fd = sys.stdin.fileno()\n        old = termios.tcgetattr(fd)\n        try:\n            tty.setraw(fd)\n            while True:\n                c = sys.stdin.read(1)\n                if _process(c):\n                    break\n                if c in ('\\x08', '\\x7f'):\n                    _repaint()      # 退格立即重绘\n                    continue\n                if not (c.isprintable() or c == ' '):\n                    continue\n                # 批量读取：只要 stdin 有数据就继续读，不重绘\n                while select.select([sys.stdin], [], [], 0) == ([sys.stdin], [], []):\n                    c2 = sys.stdin.read(1)\n                    if _process(c2):\n                        value = ''.join(chars)\n                        _repaint()\n                        termios.tcsetattr(fd, termios.TCSADRAIN, old)\n                        sys.stdout.write('\\n')\n                        sys.stdout.flush()\n                        return value\n                _repaint()\n        finally:\n            termios.tcsetattr(fd, termios.TCSADRAIN, old)\n\n    value = ''.join(chars)\n    _repaint()\n    sys.stdout.write('\\n')\n    sys.stdout.flush()\n    return value\n\n\n# ═══════════════════════════════════════════════════════════════════════════\n#  UI Helpers\n# ═══════════════════════════════════════════════════════════════════════════\n\ndef cprint(text, color=None, bold=False, end='\\n'):\n    parts = []\n    if color: parts.append(C.get(color, ''))\n    if bold: parts.append(C['bold'])\n    parts.append(text)\n    parts.append(C['reset'])\n    print(''.join(parts), end=end)\n\ndef banner():\n    print('\\033[2J\\033[H', end='')  # ANSI 清屏，跨平台\n    print(f\"{C['cyan']}{C['bold']}\")\n    print(\"  ╔═══════════════════════════════════════════════════════════╗\")\n    print(\"  ║        GenericAgent — 交互式初始化向导 v1.2              ║\")\n    print(\"  ║   一键配置 LLM 模型 + 消息平台，自动生成 mykey.py        ║\")\n    print(\"  ╚═══════════════════════════════════════════════════════════╝\")\n    print(f\"{C['reset']}\")\n    print(f\"{C['dim']}  项目目录: {PROJECT_ROOT}{C['reset']}\")\n    print()\n\ndef _check_python():\n    \"\"\"检查 Python 版本，返回 (ok, msg)\"\"\"\n    vi = sys.version_info\n    if vi < (3, 10):\n        return False, f\"Python {vi.major}.{vi.minor} 不满足最低要求 (≥ 3.10)\"\n    if vi[:2] == (3, 12):\n        return True, ''\n    return True, f\"⚠ 当前 Python {vi.major}.{vi.minor}，推荐使用 Python 3.12\"\n\ndef ask_choice(prompt, choices, allow_multi=False, default=None):\n    \"\"\"交互式选择，返回 selected_id 或 [selected_ids]\"\"\"\n    print(f\"\\n{C['bold']}{prompt}{C['reset']}\")\n    if allow_multi:\n        print(f\"{C['dim']}  (可多选，输入序号用逗号分隔，如: 1,3,5；输入 a 全选；回车跳过){C['reset']}\")\n    else:\n        print(f\"{C['dim']}  (输入序号，如: 1){C['reset']}\")\n    for i, c in enumerate(choices, 1):\n        desc = c.get('desc', '')\n        print(f\"  {C['green']}{i}.{C['reset']} {C['bold']}{c['name']}{C['reset']}  {C['dim']}{desc}{C['reset']}\")\n    while True:\n        raw = input(f\"\\n  {C['yellow']}►{C['reset']} \").strip()\n        if not raw and default is not None:\n            return default\n        if allow_multi:\n            if raw.lower() == 'a':\n                return [c['id'] for c in choices]\n            parts = [p.strip() for p in raw.split(',') if p.strip()]\n            selected = []\n            for p in parts:\n                try:\n                    idx = int(p) - 1\n                    if 0 <= idx < len(choices):\n                        selected.append(choices[idx]['id'])\n                except ValueError:\n                    pass\n            if selected:\n                return selected\n        else:\n            try:\n                idx = int(raw) - 1\n                if 0 <= idx < len(choices):\n                    return choices[idx]['id']\n            except ValueError:\n                pass\n        print(f\"  {C['red']}✗ 请输入有效序号{C['reset']}\")\n\ndef ask_input(prompt, default=None, secret=False, hint=None):\n    \"\"\"交互式输入。secret=True 时使用脱敏输入。\"\"\"\n    if hint:\n        cprint(f\"  {hint}\", 'dim')\n    if default is not None:\n        cprint(f\"  [默认: {default}]\", 'dim')\n    prompt_line = f\"  {C['yellow']}►{C['reset']} {prompt}: \"\n    while True:\n        if secret:\n            val = masked_input(prompt_line).strip()\n        else:\n            val = input(prompt_line).strip()\n        if not val and default is not None:\n            return default\n        if val:\n            return val\n        cprint(\"✗ 此项不能为空\", 'red')\n\ndef ask_yesno(prompt, default=True):\n    hint = \"Y/N\"\n    raw = input(f\"\\n  {C['yellow']}►{C['reset']} {prompt} ({hint}): \").strip().lower()\n    if not raw:\n        return default\n    return raw.startswith('y')\n\n\n# ═══════════════════════════════════════════════════════════════════════════\n#  LLM 配置逻辑\n# ═══════════════════════════════════════════════════════════════════════════\n\ndef _get_proxy_handler():\n    \"\"\"从环境变量读取代理配置，返回 ProxyHandler 或 None\"\"\"\n    for var in ('HTTPS_PROXY', 'https_proxy', 'HTTP_PROXY', 'http_proxy'):\n        url = os.environ.get(var)\n        if url:\n            return urllib.request.ProxyHandler({'https': url, 'http': url})\n    return None\n\ndef probe_models(provider, apikey, apibase=None):\n    \"\"\"调用 API 探测可用模型列表，返回模型 ID 列表或 None\"\"\"\n    ptype = provider.get('type', 'native_oai')\n    base = (apibase or provider['template'].get('apibase', '')).rstrip('/')\n\n    if ptype == 'native_claude':\n        url = f\"{base}/v1/models\"\n        headers = {'x-api-key': apikey, 'anthropic-version': '2023-06-01', 'User-Agent': 'GenericAgent/1.0'}\n    else:\n        url = f\"{base}/models\"\n        headers = {'Authorization': f'Bearer {apikey}', 'User-Agent': 'GenericAgent/1.0'}\n\n    print(f\"\\n  {C['dim']}🔍 正在探测可用模型 ({base}/models)...{C['reset']}\", end='', flush=True)\n    if ptype == 'native_claude':\n        print(f\" {C['dim']}(Anthropic 协议，探测可能失败){C['reset']}\", end='', flush=True)\n\n    opener = urllib.request.build_opener()\n    ph = _get_proxy_handler()\n    if ph:\n        opener = urllib.request.build_opener(ph)\n        print(f\" {C['dim']}(via proxy){C['reset']}\", end='', flush=True)\n\n    for attempt in range(2):\n        try:\n            req = urllib.request.Request(url, headers=headers, method='GET')\n            with opener.open(req, timeout=10) as resp:\n                data = json.loads(resp.read().decode())\n                models = data.get('data', [])\n                ids = sorted(set(m['id'] for m in models if isinstance(m, dict) and m.get('id')))\n                if ids:\n                    print(f\" {C['green']}✓ 发现 {len(ids)} 个模型{C['reset']}\")\n                    return ids\n                print(f\" {C['yellow']}⚠ 返回为空{C['reset']}\")\n                return None\n        except Exception as e:\n            if attempt == 0 and 'timeout' in type(e).__name__.lower():\n                print(f\" {C['yellow']}⏱ 超时，重试...{C['reset']}\", end='', flush=True)\n                continue\n            print(f\" {C['yellow']}⚠ 探测失败: {type(e).__name__}（将使用预设列表）{C['reset']}\")\n            return None\n    return None\n\ndef _normalize_model_choices(choices):\n    \"\"\"统一 model_choices 格式为 [{'id': str, 'name': str}]\"\"\"\n    if not choices:\n        return []\n    result = []\n    for item in choices:\n        if isinstance(item, str):\n            result.append({'id': item, 'name': item})\n        elif isinstance(item, dict):\n            result.append(item)\n        elif isinstance(item, (tuple, list)) and len(item) >= 1:\n            result.append({'id': item[0], 'name': item[1] if len(item) > 1 else item[0]})\n    return result\n\ndef _configure_advanced(provider, cfg):\n    \"\"\"配置高级可选字段: proxy, context_win, stream, user_agent, thinking_budget_tokens\"\"\"\n    print(f\"\\n  {C['dim']}── 高级选项（回车跳过，使用默认值）{C['reset']}\")\n    proxy = ask_input(\"HTTP 代理地址 (proxy)\", default='', hint='如 http://127.0.0.1:2082，留空跳过')\n    if proxy:\n        cfg['proxy'] = proxy\n    cw = ask_input(\"上下文窗口阈值 (context_win)\", default='', hint='NativeClaude 默认 28000，其他默认 24000')\n    if cw:\n        cfg['context_win'] = int(cw)\n    if cfg.get('thinking_type') == 'enabled':\n        tbt = ask_input(\"thinking_budget_tokens\", default='', hint='low≈4096, medium≈10240, high≈32768')\n        if tbt:\n            cfg['thinking_budget_tokens'] = int(tbt)\n    if cfg.get('type', provider['type']) == 'native_claude':\n        ua = ask_input(\"User-Agent 版本号\", default='', hint='某些中转按 UA 白名单校验，pin 老版本用')\n        if ua:\n            cfg['user_agent'] = ua\n    stream_default = cfg.get('stream', True)\n    if ask_yesno(\"启用 SSE 流式 (stream)\", default=stream_default):\n        cfg['stream'] = True\n    else:\n        cfg['stream'] = False\n\ndef configure_llm(provider):\n    \"\"\"引导用户配置单个模型\"\"\"\n    print(f\"\\n{C['cyan']}{'─'*60}{C['reset']}\")\n    print(f\"{C['bold']}  配置: {provider['name']}{C['reset']}\")\n    print(f\"  {C['dim']}{provider['desc']}{C['reset']}\")\n    print(f\"{C['cyan']}{'─'*60}{C['reset']}\")\n\n    cfg = dict(provider['template'])\n\n    # API Key（密文输入）\n    cfg['apikey'] = ask_input(\n        f\"API Key\",\n        hint=provider.get('key_hint', ''),\n        secret=True,\n    )\n\n    # 额外字段\n    for field in provider.get('extra_fields', []):\n        if field['key'] == 'apibase':\n            cfg['apibase'] = ask_input(\n                field['label'],\n                default=field.get('default', cfg.get('apibase', '')),\n            )\n        elif field.get('type') == 'bool':\n            cfg[field['key']] = ask_yesno(\n                field['label'],\n                default=field.get('default', True)\n            )\n        elif field.get('type') == 'choice':\n            picked = ask_choice(field['label'], field['options'])\n            chosen = next(o for o in field['options'] if o['id'] == picked)\n            for opt_key, opt_val in chosen.items():\n                if opt_key not in ('id', 'name', 'desc'):\n                    cfg[opt_key] = opt_val\n\n    # 模型选择\n    manual_choice = {'id': '__manual__', 'name': '✏️ 手动输入模型名', 'desc': '自定义模型 ID，不依赖探测结果'}\n    model_list = probe_models(provider, cfg['apikey'], cfg.get('apibase'))\n    if model_list:\n        refresh_choice = {'id': '__refresh__', 'name': '🔄 重新探测'}\n        choices = [refresh_choice, manual_choice] + [{'id': m, 'name': m} for m in model_list]\n        while True:\n            picked = ask_choice(\"API 探测到以下可用模型（或手动输入）:\", choices)\n            if picked == '__refresh__':\n                print(f\"  {C['dim']}再次探测...{C['reset']}\")\n                model_list = probe_models(provider, cfg['apikey'], cfg.get('apibase'))\n                if not model_list:\n                    print(f\"  {C['yellow']}⚠ 再次探测失败{C['reset']}\")\n                    picked = _fallback_model(provider, manual_choice)\n                    break\n                choices = [refresh_choice, manual_choice] + [{'id': m, 'name': m} for m in model_list]\n            elif picked == '__manual__':\n                picked = ask_input(\"请输入模型名\", default=cfg.get('model', ''))\n                break\n            else:\n                break\n        cfg['model'] = picked\n    else:\n        cfg['model'] = _fallback_model(provider, manual_choice)\n\n    # 别名\n    default_name = cfg.get('name', provider['id'])\n    name = ask_input(\"此配置的别名 (name，Mixin 引用用)\", default=default_name)\n    if name:\n        cfg['name'] = name\n\n    # 高级选项\n    if ask_yesno(\"配置高级选项（proxy / context_win / stream 等）？\", default=False):\n        _configure_advanced(provider, cfg)\n\n    return cfg\n\ndef _fallback_model(provider, manual_choice=None):\n    \"\"\"使用预设模型列表让用户选择，始终提供手动输入选项\"\"\"\n    manual_choice = manual_choice or {'id': '__manual__', 'name': '✏️ 手动输入模型名', 'desc': '自定义模型 ID'}\n    normalized = _normalize_model_choices(provider.get('model_choices', []))\n    if normalized:\n        choices = [manual_choice] + normalized\n        picked = ask_choice(\"选择模型（或手动输入）:\", choices)\n        if picked == '__manual__':\n            return ask_input(\"请输入模型名\", default=provider['template'].get('model', ''))\n        return picked\n    return ask_input(\"请输入模型名\", default=provider['template'].get('model', ''))\n\ndef configure_llms():\n    \"\"\"配置 LLM 模型\"\"\"\n    print(f\"\\n{C['bold']}{C['magenta']}╔══════════════════════════════════════╗\")\n    print(f\"║     第一步: 配置 LLM 模型           ║\")\n    print(f\"╚══════════════════════════════════════╝{C['reset']}\")\n    print(f\"\\n{C['dim']}  你可以配置最多 2 个模型组成故障转移 (Mixin) 列表。{C['reset']}\")\n\n    all_cfgs = []\n    provider_id = ask_choice(\"选择模型厂商 (配置第 1 个模型):\", LLM_PROVIDERS)\n    provider = next(p for p in LLM_PROVIDERS if p['id'] == provider_id)\n    cfg = configure_llm(provider)\n    all_cfgs.append(cfg)\n\n    if ask_yesno(\"再添加一个模型做故障转移？\", default=False):\n        providers_ext = [{'id': '__stop__', 'name': '✓ 不需要备选了', 'desc': ''}] + LLM_PROVIDERS\n        provider_id = ask_choice(\n            \"选择模型厂商 (配置第 2 个模型 — 或选「不需要备选了」跳过):\",\n            providers_ext\n        )\n        if provider_id != '__stop__':\n            provider = next(p for p in LLM_PROVIDERS if p['id'] == provider_id)\n            cfg = configure_llm(provider)\n            all_cfgs.append(cfg)\n\n    return all_cfgs\n\n\n# ═══════════════════════════════════════════════════════════════════════════\n#  消息平台配置逻辑\n# ═══════════════════════════════════════════════════════════════════════════\n\ndef configure_platforms():\n    \"\"\"配置消息平台，返回 (platform_configs, pip_hints)\"\"\"\n    print(f\"\\n{C['bold']}{C['magenta']}╔══════════════════════════════════════╗\")\n    print(f\"║     第二步: 配置消息平台             ║\")\n    print(f\"╚══════════════════════════════════════╝{C['reset']}\")\n    print(f\"\\n{C['dim']}  消息平台用于从聊天软件与 Agent 交互。{C['reset']}\")\n    print(f\"{C['dim']}  你也可以跳过此步，直接用终端 REPL。{C['reset']}\")\n\n    platform_ids = ask_choice(\n        \"选择消息平台 (可多选，选 '不使用' 则跳过):\",\n        PLATFORMS,\n        allow_multi=True,\n        default=['none']\n    )\n\n    if 'none' in platform_ids:\n        return [], set()\n\n    selected_platforms = []\n    pip_hints = set()\n\n    for pid in platform_ids:\n        platform = next(p for p in PLATFORMS if p['id'] == pid)\n        pip_hints.update(platform.get('deps', []))\n\n        print(f\"\\n{C['cyan']}{'─'*60}{C['reset']}\")\n        print(f\"{C['bold']}  配置: {platform['name']}{C['reset']}\")\n        print(f\"{C['cyan']}{'─'*60}{C['reset']}\")\n\n        env_vals = {}\n\n        if pid == 'feishu' and ask_yesno(\"使用一键扫码创建应用？（推荐）\", default=True):\n            env_vals = _feishu_scan(platform)\n        if pid == 'wechat' and ask_yesno(\"扫码登录微信 iLink？（推荐）\", default=True):\n            env_vals = _wechat_scan()\n\n        for var in platform['env_vars']:\n            if var['key'] not in env_vals:\n                env_vals.update(_manual_platform_var(var))\n\n        if pid == 'wecom' and ask_yesno(\"设置欢迎消息？\", default=False):\n            env_vals['wecom_welcome_message'] = ask_input(\"欢迎消息内容\", default='你好，我在线上。')\n\n        selected_platforms.append({'platform': platform, 'config': env_vals})\n\n    return selected_platforms, pip_hints\n\ndef _manual_platform_var(var):\n    \"\"\"手动填写单个平台变量\"\"\"\n    val = ask_input(var['label'], hint=var.get('hint', ''), default=var.get('default'))\n    if var.get('is_list'):\n        if val == '[]' or not val:\n            return {var['key']: []}\n        return {var['key']: [x.strip() for x in val.split(',') if x.strip()]}\n    return {var['key']: val}\n\ndef _feishu_scan(platform):\n    \"\"\"飞书一键扫码创建应用，返回 env_vals 或空 dict\"\"\"\n    from io import StringIO\n    try:\n        import lark_oapi as lark\n        import qrcode, threading\n    except ImportError:\n        print(f\"\\n  {C['yellow']}⚠ lark-oapi 未安装，降级为手动配置{C['reset']}\")\n        return {}\n\n    print(f\"\\n  {C['cyan']}📱 正在启动一键创建...{C['reset']}\")\n    print(f\"  {C['dim']}  请用飞书 App 扫描终端二维码，完成授权后自动获取凭据。{C['reset']}\\n\")\n\n    qr_printed = threading.Event()\n    result_holder = {'data': None}\n\n    def handle_qr(info):\n        url = info['url']\n        expire = info['expire_in']\n        qr = qrcode.QRCode(border=1, box_size=1)\n        qr.add_data(url)\n        buf = StringIO()\n        qr.print_ascii(out=buf)\n        qr_art = buf.getvalue()\n        print(f\"\\n  {C['bold']}请用飞书扫描下方二维码，或复制链接在浏览器打开:{C['reset']}\")\n        print(f\"  {C['green']}{qr_art.replace(chr(27), '')}{C['reset']}\")\n        print(f\"  {C['dim']}  链接: {url}{C['reset']}\")\n        print(f\"  {C['dim']}  有效期 {expire} 秒{C['reset']}\")\n        qr_printed.set()\n\n    def handle_status(info):\n        status = info['status']\n        if status == 'polling':\n            print(f\"  {C['yellow']}⏳ 等待扫码...{C['reset']}\")\n        elif status == 'slow_down':\n            print(f\"  {C['yellow']}⏳ 等待中... (间隔 {info.get('interval', '?')}s){C['reset']}\")\n        elif status == 'domain_switched':\n            print(f\"  {C['cyan']}🌐 已切换认证域名{C['reset']}\")\n\n    def run_register():\n        try:\n            result = lark.register_app(\n                on_qr_code=handle_qr,\n                on_status_change=handle_status,\n            )\n            result_holder['data'] = result\n        except Exception as e:\n            print(f\"\\n  {C['red']}✗ 创建失败: {e}{C['reset']}\")\n\n    thread = threading.Thread(target=run_register, daemon=True)\n    thread.start()\n    qr_printed.wait(timeout=15)\n    thread.join(timeout=300)\n\n    if result_holder['data']:\n        result = result_holder['data']\n        print(f\"\\n  {C['green']}✅ 应用创建成功！{C['reset']}\")\n        print(f\"  App ID:     {C['bold']}{result['client_id']}{C['reset']}\")\n        print(f\"  App Secret: {C['bold']}{result['client_secret']}{C['reset']}\")\n        return {\n            'fs_app_id': result['client_id'],\n            'fs_app_secret': result['client_secret'],\n        }\n    else:\n        print(f\"\\n  {C['yellow']}⚠ 扫码创建未完成，降级为手动填写...{C['reset']}\")\n        return {}\n\n\ndef _wechat_scan():\n    \"\"\"微信 iLink 扫码登录，保存 token 到 ~/.wxbot/token.json，返回 env_vals\"\"\"\n    print(f\"\\n  {C['cyan']}📱 正在启动微信 iLink 扫码登录...{C['reset']}\")\n    print(f\"  {C['dim']}  请用微信扫描终端二维码，完成授权后自动获取凭据。{C['reset']}\\n\")\n\n    # 确保项目根在路径中，以便导入 frontends/wechatapp\n    if PROJECT_ROOT not in sys.path:\n        sys.path.insert(0, PROJECT_ROOT)\n    try:\n        from frontends.wechatapp import WxBotClient\n    except ImportError as e:\n        print(f\"\\n  {C['yellow']}⚠ 无法导入 WxBotClient: {e}{C['reset']}\")\n        return {}\n\n    try:\n        bot = WxBotClient()\n        if bot.token:\n            print(f\"  {C['green']}✅ 已有有效 token (bot_id={bot.bot_id}){C['reset']}\")\n            if ask_yesno(\"重新扫码登录？\", default=False):\n                bot.token = ''\n            else:\n                return {}\n        bot.login_qr()\n        print(f\"\\n  {C['green']}✅ 微信 iLink 扫码登录成功！{C['reset']}\")\n        print(f\"  Bot ID: {C['bold']}{bot.bot_id}{C['reset']}\")\n        print(f\"  Token 已保存到: {C['dim']}{bot._tf}{C['reset']}\")\n    except Exception as e:\n        print(f\"\\n  {C['red']}✗ 扫码登录失败: {e}{C['reset']}\")\n        return {}\n\n    return {}\n\n\n\n# ═══════════════════════════════════════════════════════════════════════════\n#  生成 mykey.py\n# ═══════════════════════════════════════════════════════════════════════════\n\ndef _var_type_info(cfg):\n    \"\"\"根据配置类型返回 (var_prefix, session_type)\"\"\"\n    cfg_type = cfg.get('type', 'native_oai')\n    if cfg_type == 'native_claude':\n        return 'native_claude_config', 'NativeClaudeSession'\n    elif cfg_type == 'claude':\n        return 'claude_config', 'ClaudeSession'\n    elif cfg_type == 'oai':\n        return 'oai_config', 'LLMSession'\n    else:\n        return 'native_oai_config', 'NativeOAISession'\n\n\ndef generate_mykey(llm_cfgs, platform_configs):\n    \"\"\"生成 mykey.py 内容\"\"\"\n    lines = []\n    lines.append(\"# ══════════════════════════════════════════════════════════════════════════════\")\n    lines.append(f\"#  GenericAgent — mykey.py (由 configure.py 自动生成 @ {datetime.now().strftime('%Y-%m-%d %H:%M')})\")\n    lines.append(\"# ══════════════════════════════════════════════════════════════════════════════\")\n    lines.append(\"\")\n    lines.append(\"# ── 停止符 ──────────────────────────────────────────────────────────────────\")\n    lines.append(\"_SETUP_DONE = 'configure.py'  # 删除此行可重新触发配置向导\")\n    lines.append(\"\")\n\n    # Mixin 配置\n    names = [c['name'] for c in llm_cfgs]\n    lines.append(\"# ── Mixin 故障转移 ──────────────────────────────────────────────────────────\")\n    lines.append(\"mixin_config = {\")\n    lines.append(f\"    'llm_nos': {names},\")\n    lines.append(\"    'max_retries': 10,\")\n    lines.append(\"    'base_delay': 0.5,\")\n    lines.append(\"}\")\n    lines.append(\"\")\n\n    # 各模型配置\n    type_counts = {}\n    for cfg in llm_cfgs:\n        cfg_type = cfg.get('type', 'native_oai')\n        type_counts[cfg_type] = type_counts.get(cfg_type, 0) + 1\n\n    type_indices = {}\n    for i, cfg in enumerate(llm_cfgs):\n        cfg_type = cfg.get('type', 'native_oai')\n        var_prefix, session_type = _var_type_info(cfg)\n        idx = type_indices.get(cfg_type, 0)\n        type_indices[cfg_type] = idx + 1\n\n        if type_counts[cfg_type] > 1:\n            var_name = f\"{var_prefix}_{idx}\"\n        else:\n            var_name = var_prefix\n\n        lines.append(f\"# ── {cfg['name']} ({session_type}) ─────────────────────────────────────────────\")\n        lines.append(f\"{var_name} = {{\")\n        _write_config_fields(lines, cfg)\n        lines.append(\"}\")\n        lines.append(\"\")\n\n    # 平台配置\n    if platform_configs:\n        lines.append(\"# ══════════════════════════════════════════════════════════════════════════════\")\n        lines.append(\"#  聊天平台集成\")\n        lines.append(\"# ══════════════════════════════════════════════════════════════════════════════\")\n        lines.append(\"\")\n        for pc in platform_configs:\n            for key, val in pc['config'].items():\n                _write_platform_value(lines, key, val)\n            lines.append(\"\")\n\n    # 尾部\n    lines.append(\"# ══════════════════════════════════════════════════════════════════════════════\")\n    lines.append(\"#  配置完毕！运行: python agentmain.py  (终端 REPL)\")\n    if platform_configs:\n        for pc in platform_configs:\n            p = pc['platform']\n            lines.append(f\"#  或: python {p['file']}  ({p['name']})\")\n    lines.append(\"# ══════════════════════════════════════════════════════════════════════════════\")\n\n    return '\\n'.join(lines)\n\ndef _write_config_fields(lines, cfg):\n    \"\"\"写入配置字典的键值对（缩进的 'key': value, 格式）\"\"\"\n    for key in ['name', 'type', 'apikey', 'apibase', 'model', 'api_mode',\n                'fake_cc_system_prompt', 'thinking_type', 'thinking_budget_tokens',\n                'reasoning_effort', 'max_tokens', 'max_retries', 'connect_timeout',\n                'read_timeout', 'temperature', 'context_win',\n                'proxy', 'user_agent', 'stream']:\n        if key not in cfg:\n            continue\n        val = cfg[key]\n        if isinstance(val, bool):\n            lines.append(f\"    '{key}': {str(val)},\")\n        elif isinstance(val, (int, float)):\n            lines.append(f\"    '{key}': {val},\")\n        elif isinstance(val, str):\n            lines.append(f\"    '{key}': '{val}',\")\n        else:\n            lines.append(f\"    '{key}': {repr(val)},\")\n\ndef _write_platform_value(lines, key, val):\n    \"\"\"写入顶级变量（平台配置等）\"\"\"\n    if isinstance(val, list):\n        if val:\n            lines.append(f\"{key} = {repr(val)}\")\n        else:\n            lines.append(f\"{key} = []  # 允许所有用户\")\n    elif isinstance(val, str):\n        lines.append(f\"{key} = '{val}'\")\n    else:\n        lines.append(f\"{key} = {repr(val)}\")\n\n\ndef _parse_existing_mykey():\n    \"\"\"解析已有 mykey.py，返回 (model_names, platform_infos)\n\n    model_names: [str]  — 模型名列表\n    platform_infos: [{'id': str, 'vars': [{'key': str, 'val': ...}]}]  — 平台信息\n    解析失败时返回 ([], [])\n    \"\"\"\n    if not os.path.exists(MYKPY_PATH):\n        return [], []\n\n    with open(MYKPY_PATH, 'r', encoding='utf-8') as f:\n        content = f.read()\n\n    # 解析模型名\n    model_names = []\n    m = re.search(r\"'llm_nos':\\s*\\[([^\\]]*)\\]\", content)\n    if m:\n        model_names = re.findall(r\"'([^']+)'\", m.group(1))\n\n    # 先收集所有已知平台 env var key → 判断值类型\n    all_env_var_keys = {}\n    platform_env_keys = {}  # pid -> [var_key]\n    for p in PLATFORMS:\n        pid = p['id']\n        platform_env_keys.setdefault(pid, [])\n        for var in p.get('env_vars', []):\n            vkey = var['key']\n            all_env_var_keys[vkey] = var\n            platform_env_keys[pid].append(vkey)\n\n    # 逐平台解析所有已知变量\n    platform_infos = []\n    for pid, env_keys in platform_env_keys.items():\n        vars_found = []\n        for vkey in env_keys:\n            var_def = all_env_var_keys[vkey]\n            val = None\n            if var_def.get('is_list'):\n                # 匹配 `xxx = [...]`\n                m_var = re.search(rf\"^{vkey}\\s*=\\s*(\\[[^\\]]*\\])\", content, re.MULTILINE)\n                if m_var:\n                    try:\n                        val = ast.literal_eval(m_var.group(1))\n                    except (ValueError, SyntaxError):\n                        pass\n            else:\n                # 匹配 `xxx = '...'`\n                m_var = re.search(rf\"^{vkey}\\s*=\\s*'([^']*)'\", content, re.MULTILINE)\n                if m_var:\n                    val = m_var.group(1)\n            if val is not None:\n                vars_found.append({'key': vkey, 'val': val})\n        if vars_found:\n            platform_infos.append({'id': pid, 'vars': vars_found})\n\n    return model_names, platform_infos\n\n\ndef _parse_existing_llm_cfgs():\n    \"\"\"解析已有 mykey.py，返回完整 LLM 配置字典列表 [{name, apikey, ...}]\n    解析失败时返回 []\n    \"\"\"\n    if not os.path.exists(MYKPY_PATH):\n        return []\n\n    with open(MYKPY_PATH, 'r', encoding='utf-8') as f:\n        content = f.read()\n\n    cfgs = []\n    # 匹配所有 `xxx = {  ...  }` 顶层字典赋值\n    # 用简单状态机: 找 `\\w+ = {` 然后匹配花括号\n    pattern = re.compile(r'^(\\w+)\\s*=\\s*\\{', re.MULTILINE)\n    for m in pattern.finditer(content):\n        brace_start = m.end() - 1  # '{' 的位置\n        depth = 1\n        i = brace_start + 1\n        while i < len(content) and depth > 0:\n            if content[i] == '{':\n                depth += 1\n            elif content[i] == '}':\n                depth -= 1\n            i += 1\n        if depth == 0:\n            dict_text = content[m.end():i - 1]\n            try:\n                d = ast.literal_eval('{' + dict_text + '}')\n                if isinstance(d, dict) and 'name' in d:\n                    cfgs.append(d)\n            except (ValueError, SyntaxError):\n                continue\n\n    return cfgs\n\n\ndef _backup_with_name(model_names, platform_ids):\n    \"\"\"按 mykey+模型名+机器人名 格式备份旧 mykey.py\"\"\"\n    parts = ['mykey']\n    for m in model_names[:3]:\n        parts.append(m.replace('/', '-').replace('\\\\', '-'))\n    for pid in platform_ids:\n        pid_clean = pid.replace('_', '')\n        if pid_clean not in parts:\n            parts.append(pid_clean)\n    safe_name = '_'.join(parts)\n    if safe_name == 'mykey':\n        safe_name = 'mykey_backup'  # 避免和源文件同名\n    if len(safe_name) > 100:\n        safe_name = safe_name[:100]\n    backup_path = os.path.join(PROJECT_ROOT, f'{safe_name}.py')\n    shutil.copy2(MYKPY_PATH, backup_path)\n    return backup_path\n\n\n# ═══════════════════════════════════════════════════════════════════════════\n#  Main\n# ═══════════════════════════════════════════════════════════════════════════\n\ndef main():\n    banner()\n\n    # Python 版本检查\n    ok, msg = _check_python()\n    if not ok:\n        print(f\"  {C['red']}✗ {msg}{C['reset']}\")\n        sys.exit(1)\n    if msg:\n        color = 'yellow' if '⚠' in msg else 'green'\n        print(f\"  {C[color]}{msg}{C['reset']}\\n\")\n\n    # ── 决策流程 ──\n    llm_cfgs = []\n    platform_configs = []\n    platform_deps = set()\n    is_modify = False\n    is_new = False\n\n    if os.path.exists(MYKPY_PATH):\n        model_names, platform_infos = _parse_existing_mykey()\n        cur_models = ', '.join(model_names) if model_names else '(未知)'\n        cur_platforms = ', '.join(p['id'] for p in platform_infos) if platform_infos else '(无)'\n        print(f\"  {C['dim']}  当前: 模型=[{cur_models}], 平台=[{cur_platforms}]{C['reset']}\")\n\n        mode = ask_choice(\n            \"检测到已有 mykey.py，请选择操作\",\n            [\n                {'id': 'modify', 'name': '修改现有配置', 'desc': '保留未改部分，只重新配置选定项'},\n                {'id': 'new', 'name': '新建配置（备份旧文件）', 'desc': '备份为 mykey+模型+平台.py，然后全新配置'},\n            ],\n            default=None,\n        )\n\n        if mode == 'new':\n            backup_path = _backup_with_name(model_names, [p['id'] for p in platform_infos])\n            print(f\"  {C['green']}✓ 旧配置已备份至:{C['reset']} {C['dim']}{backup_path}{C['reset']}\")\n            is_new = True\n        else:\n            is_modify = True\n            scope = ask_choice(\n                \"你要修改什么？\",\n                [\n                    {'id': 'both', 'name': '两项都重新配置', 'desc': 'LLM + 平台全部更新'},\n                    {'id': 'llm', 'name': '重新配置 LLM 模型', 'desc': f'当前: {cur_models}'},\n                    {'id': 'platform', 'name': '重新配置消息平台', 'desc': f'当前: {cur_platforms}'},\n                ],\n            )\n            if scope in ('llm', 'both'):\n                llm_cfgs = _do_llm()\n            if scope in ('platform', 'both'):\n                platform_configs, platform_deps = configure_platforms()\n            if scope == 'llm' and platform_infos:\n                for pi in platform_infos:\n                    p = next((x for x in PLATFORMS if x['id'] == pi['id']), None)\n                    if p:\n                        config_dict = {v['key']: v['val'] for v in pi['vars']}\n                        platform_configs.append({'platform': p, 'config': config_dict})\n            elif scope == 'platform' and model_names:\n                old_cfgs = _parse_existing_llm_cfgs()\n                if old_cfgs:\n                    llm_cfgs = old_cfgs\n                    print(f\"\\n  {C['green']}✓ 已保留现有 LLM 配置: {', '.join(c['name'] for c in old_cfgs)}{C['reset']}\")\n                else:\n                    print(f\"\\n  {C['yellow']}⚠ 保留 LLM 配置失败，将生成空配置。建议两项都重新配置。{C['reset']}\")\n\n    if not is_modify:\n        if is_new:\n            hint = \"已备份旧配置，请完成全新设置\"\n        else:\n            hint = \"首次配置，建议同时设置模型和消息平台\"\n        print(f\"  {C['dim']}  {hint}。{C['reset']}\")\n\n        scope = ask_choice(\n            \"你想配置什么？\",\n            [\n                {'id': 'both', 'name': '两项都配置 (推荐)', 'desc': 'LLM 模型 + 消息平台，完整初始化'},\n                {'id': 'llm', 'name': '仅 LLM 模型', 'desc': '只配置模型，稍后再配平台'},\n                {'id': 'platform', 'name': '仅消息平台', 'desc': '只配平台，稍后再配模型'},\n            ],\n            default='both',\n        )\n\n        if scope in ('llm', 'both'):\n            llm_cfgs = _do_llm()\n            if scope == 'llm':\n                if ask_yesno(\"是否继续配置消息平台？\", default=True):\n                    platform_configs, platform_deps = configure_platforms()\n\n        if scope == 'both':\n            platform_configs, platform_deps = configure_platforms()\n\n        if scope == 'platform':\n            platform_configs, platform_deps = configure_platforms()\n            if ask_yesno(\"是否继续配置 LLM 模型？\", default=True):\n                llm_cfgs = _do_llm()\n            elif os.path.exists(MYKPY_PATH):\n                # 新建+仅平台：从备份保留旧 LLM 配置\n                old_cfgs = _parse_existing_llm_cfgs()\n                if old_cfgs:\n                    llm_cfgs = old_cfgs\n                    print(f\"\\n  {C['green']}✓ 已保留备份中的 LLM 配置: {', '.join(c['name'] for c in old_cfgs)}{C['reset']}\")\n\n    # ── 生成 mykey.py ──\n    if not llm_cfgs and not platform_configs:\n        print(f\"\\n  {C['yellow']}⚠ 没有配置任何内容，退出。{C['reset']}\")\n        sys.exit(0)\n\n    content = generate_mykey(llm_cfgs, platform_configs)\n\n    # 备份旧文件（修改模式不备份，直接在原文件修改）\n    if os.path.exists(MYKPY_PATH) and not is_modify and not is_new:\n        backup = _backup_with_name(model_names, [p['id'] for p in platform_infos])\n        print(f\"\\n  {C['green']}✓ 旧配置已备份至:{C['reset']} {C['dim']}{backup}{C['reset']}\")\n\n    # 写入\n    with open(MYKPY_PATH, 'w', encoding='utf-8') as f:\n        f.write(content)\n    print(f\"\\n  {C['green']}✓ mykey.py 已生成!{C['reset']}\")\n\n    # ── 完成提示 ──\n    print(f\"\\n{C['bold']}{C['green']}╔══════════════════════════════════════╗\")\n    print(f\"║      配置完成!                      ║\")\n    print(f\"╚══════════════════════════════════════╝{C['reset']}\")\n    print()\n    if llm_cfgs:\n        print(f\"  {C['cyan']}  终端 REPL:{C['reset']}  python agentmain.py\")\n    if platform_configs:\n        for i, pc in enumerate(platform_configs, 1):\n            p = pc['platform']\n            print(f\"  {C['cyan']}  平台 {i} ({p['name']}):{C['reset']}  python {p['file']}\")\n    print()\n\n    # pip 依赖提示\n    all_deps = sorted(platform_deps)\n    if all_deps:\n        print(f\"  {C['yellow']}💡 提示：你需要安装以下依赖以使消息平台正常工作:{C['reset']}\")\n        print(f\"     {C['cyan']}pip install {' '.join(all_deps)}{C['reset']}\")\n        print()\n\n    # ── 入门示例 ──\n    print(f\"  {C['bold']}试试这些命令:{C['reset']}\")\n    examples = [\n        \"帮我在桌面创建一个 hello.txt，内容是 Hello World\",\n        \"请查看你的代码，安装所有用得上的 python 依赖\",\n        \"执行 web setup sop，解锁 web 工具\",\n        \"打开淘宝，搜索 iPhone 16，按价格排序\",\n        \"用rapidocr配置你的ocr能力并存入记忆\",\n        \"git 更新你的代码，然后看看 commit 有什么新功能\",\n        \"把这个记到你的记忆里\",\n    ]\n    for ex in examples:\n        print(f\"    {C['dim']}{ex}{C['reset']}\")\n    print()\n\n    print(f\"  {C['green']}{C['bold']}合抱之木，生于毫末{C['reset']}\\n\")\n\n\ndef _do_llm():\n    \"\"\"配置 LLM 模型，失败则 exit。\"\"\"\n    cfgs = configure_llms()\n    if not cfgs:\n        print(f\"\\n  {C['red']}✗ 至少需要配置一个模型才能使用。退出。{C['reset']}\")\n        sys.exit(1)\n    return cfgs\n\n\nif __name__ == '__main__':\n    try:\n        main()\n    except KeyboardInterrupt:\n        print(f\"\\n\\n  {C['yellow']}⚠ 用户中断{C['reset']}\")\n        sys.exit(0)\n"}, "00fd5f81c09ee25c80e0c87099f993c9019032ee": {"t": 1785763790.7598093, "n": 54456, "h": "121de44c6cb7c84c", "c": "#!/usr/bin/env python3\n\"\"\"\nGenericAgent — 交互式初始化向导 (configure.py)\n一键配置 LLM 模型 + 消息平台，自动生成 mykey.py\n\n用法:\n    python configure.py\n\"\"\"\n\nimport ast\nimport os\nimport sys\nimport re\nimport shutil\nimport json\nimport urllib.request\nfrom datetime import datetime\n\n# ── ANSI 颜色 ──────────────────────────────────────────────────────────────\nC = {\n    'reset': '\\033[0m', 'bold': '\\033[1m', 'dim': '\\033[2m',\n    'red': '\\033[91m', 'green': '\\033[92m', 'yellow': '\\033[93m',\n    'blue': '\\033[94m', 'magenta': '\\033[95m', 'cyan': '\\033[96m', 'white': '\\033[97m',\n}\n\nPROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nMYKPY_PATH = os.path.join(PROJECT_ROOT, 'mykey.py')\n\n# ── 模型厂商定义 ───────────────────────────────────────────────────────────\n\nLLM_PROVIDERS = [\n    # ═══════════════════════════ 通用协议（官方直连或任意兼容中转）═══════════════════════════\n    {\n        'id': 'oai_chat',\n        'name': 'OpenAI Chat Completions 协议',\n        'desc': '官方直连或任意 OAI 兼容中转/网关，自填 apibase（回车=OpenAI 官方）',\n        'type': 'native_oai',\n        'template': {\n            'name': 'gpt-native', 'apikey': 'sk-<your-key>',\n            'apibase': 'https://api.openai.com/v1', 'model': 'gpt-5.5',\n            'api_mode': 'chat_completions', 'reasoning_effort': 'high',\n            'max_retries': 3, 'connect_timeout': 10, 'read_timeout': 120,\n        },\n        'key_hint': '官方在 https://platform.openai.com/api-keys 获取；中转站填其提供的 Key',\n        'model_choices': ['gpt-5.5', 'gpt-5.4'],\n        'extra_fields': [\n            {'key': 'apibase', 'label': 'API Base（官方或中转地址）', 'default': 'https://api.openai.com/v1'},\n        ],\n    },\n    {\n        'id': 'oai_responses',\n        'name': 'OpenAI Responses 协议',\n        'desc': 'Responses API（o 系列/GPT-5.5 推荐端点），官方或兼容网关，自填 apibase',\n        'type': 'native_oai',\n        'template': {\n            'name': 'gpt-responses', 'apikey': 'sk-<your-key>',\n            'apibase': 'https://api.openai.com/v1', 'model': 'gpt-5.5',\n            'api_mode': 'responses', 'reasoning_effort': 'high',\n            'max_retries': 3, 'connect_timeout': 10, 'read_timeout': 120,\n        },\n        'key_hint': '官方在 https://platform.openai.com/api-keys 获取；中转站填其提供的 Key',\n        'model_choices': ['gpt-5.5', 'gpt-5.4'],\n        'extra_fields': [\n            {'key': 'apibase', 'label': 'API Base（官方或中转地址）', 'default': 'https://api.openai.com/v1'},\n        ],\n    },\n    {\n        'id': 'claude_messages',\n        'name': 'Claude Messages 协议',\n        'desc': 'Anthropic 官方直连或任意 Claude 兼容中转，自填 apibase（回车=官方）',\n        'type': 'native_claude',\n        'template': {\n            'name': 'anthropic-direct', 'apikey': 'sk-ant-<your-key>',\n            'apibase': 'https://api.anthropic.com', 'model': 'claude-opus-4-7',\n            'thinking_type': 'adaptive', 'max_tokens': 32768, 'temperature': 1,\n        },\n        'key_hint': '官方在 https://console.anthropic.com/ 获取；中转站填其提供的 Key',\n        'model_choices': ['claude-opus-4-7', 'claude-sonnet-4-6'],\n        'extra_fields': [\n            {'key': 'apibase', 'label': 'API Base（官方或中转地址）', 'default': 'https://api.anthropic.com'},\n        ],\n    },\n    # ═══════════════════════════ 直连 API（按旗舰能力降序）═══════════════════════════\n    {\n        'id': 'deepseek',\n        'name': 'DeepSeek (v4-Pro / Flash)',\n        'desc': '开源模型，v4-Pro 旗舰 1M 上下文',\n        'type': 'native_oai',\n        'template': {\n            'name': 'deepseek', 'apikey': 'sk-<your-deepseek-key>',\n            'apibase': 'https://api.deepseek.com', 'model': 'deepseek-v4-flash',\n            'api_mode': 'chat_completions', 'reasoning_effort': 'high',\n        },\n        'key_hint': '在 https://platform.deepseek.com/api_keys 获取',\n        'model_choices': ['deepseek-v4-pro', 'deepseek-v4-flash'],\n    },\n    {\n        'id': 'kimi',\n        'name': 'Kimi (k2.6 / k2.5) 双协议',\n        'desc': '月之暗面，支持 Anthropic 和 OAI 双协议',\n        'type': 'native_claude',\n        'template': {\n            'name': 'kimi', 'apikey': 'sk-kimi-<your-key>',\n            'apibase': 'https://api.kimi.com/coding',\n            'model': 'kimi-for-coding', 'fake_cc_system_prompt': True,\n            'thinking_type': 'adaptive',\n        },\n        'key_hint': '在 https://kimi.com/code 或 https://platform.moonshot.cn/ 获取',\n        'model_choices': ['kimi-k2.6', 'kimi-k2.5'],\n        'extra_fields': [\n            {\n                'key': '_protocol', 'label': '选择 API 协议',\n                'type': 'choice',\n                'options': [\n                    {'id': 'native_claude', 'name': 'Anthropic 兼容 (推荐)', 'desc': 'kimi-for-coding 端点，CC 兼容', 'apibase': 'https://api.kimi.com/coding', 'fake_cc_system_prompt': True, 'model': 'kimi-for-coding'},\n                    {'id': 'native_oai', 'name': 'OpenAI 协议', 'desc': 'Moonshot OAI 端点，kimi-k2 系列', 'apibase': 'https://api.moonshot.cn/v1', 'model': 'kimi-k2.6'},\n                ],\n            },\n        ],\n    },\n    {\n        'id': 'qwen',\n        'name': '阿里通义千问 (Qwen3.5 / 百炼)',\n        'desc': '阿里云百炼，Qwen3 系列百万级上下文',\n        'type': 'native_oai',\n        'template': {\n            'name': 'qwen', 'apikey': 'sk-<your-dashscope-key>',\n            'apibase': 'https://dashscope.aliyuncs.com/compatible-mode/v1',\n            'model': 'qwen3.5-plus',\n            'api_mode': 'chat_completions',\n        },\n        'key_hint': '在 https://bailian.console.aliyun.com/ 获取 API Key',\n        'model_choices': ['qwen3.5-plus', 'qwen3-coder-plus'],\n        'extra_fields': [\n            {\n                'key': '_endpoint', 'label': '选择端点',\n                'type': 'choice',\n                'options': [\n                    {'id': 'standard', 'name': '标准按量付费', 'desc': 'dashscope.aliyuncs.com，兼容模式', 'apibase': 'https://dashscope.aliyuncs.com/compatible-mode/v1'},\n                    {'id': 'coding_plan', 'name': '百炼 Coding Plan (订阅)', 'desc': 'coding-intl.dashscope.aliyuncs.com，100万上下文', 'apibase': 'https://coding-intl.dashscope.aliyuncs.com/v1', 'context_win': 1000000},\n                ],\n            },\n        ],\n    },\n    {\n        'id': 'zhipu',\n        'name': '智谱 GLM-5.1 (Coding Plan)',\n        'desc': '智谱 GLM，支持 Coding Plan CN (Anthropic) 和 Global (OAI) 双端点',\n        'type': 'native_claude',\n        'template': {\n            'name': 'zhipu-glm', 'apikey': 'sk-<your-zhipu-key>',\n            'apibase': 'https://open.bigmodel.cn/api/anthropic',\n            'model': 'GLM-5.1-Cloud', 'fake_cc_system_prompt': False,\n            'thinking_type': 'adaptive', 'max_retries': 3,\n            'connect_timeout': 10, 'read_timeout': 180,\n        },\n        'key_hint': 'CN 在 https://open.bigmodel.cn/ 获取；Global 在 https://z.ai/ 获取',\n        'model_choices': ['GLM-5.1-Cloud', 'glm-4.7'],\n        'extra_fields': [\n            {\n                'key': '_plan', 'label': '选择 Coding Plan',\n                'type': 'choice',\n                'options': [\n                    {'id': 'native_claude', 'name': 'Coding Plan CN (Anthropic)', 'desc': 'open.bigmodel.cn，推荐国内用户', 'apibase': 'https://open.bigmodel.cn/api/anthropic', 'fake_cc_system_prompt': False},\n                    {'id': 'native_oai', 'name': 'Coding Plan Global (OAI)', 'desc': 'api.z.ai，OpenAI 协议，全球可用', 'apibase': 'https://api.z.ai/api/paas/v4'},\n                ],\n            },\n        ],\n    },\n    {\n        'id': 'minimax',\n        'name': 'MiniMax M3 (双协议)',\n        'desc': 'MiniMax M3，支持 Anthropic 和 OpenAI 双协议',\n        'type': 'native_claude',\n        'template': {\n            'name': 'minimax', 'apikey': 'eyJh...<your-minimax-key>',\n            'apibase': 'https://api.minimaxi.com/anthropic',\n            'model': 'MiniMax-M3', 'max_retries': 3,\n        },\n        'key_hint': '在 https://platform.minimaxi.com/user-center/basic-information 获取',\n        'model_choices': ['MiniMax-M3', 'MiniMax-M2.7', 'MiniMax-M2.7-highspeed'],\n        'extra_fields': [\n            {\n                'key': '_protocol', 'label': '选择 API 协议',\n                'type': 'choice',\n                'options': [\n                    {'id': 'native_claude', 'name': 'Anthropic 协议 (推荐)', 'desc': '无 <think> 标签，原生 Claude 兼容', 'apibase': 'https://api.minimaxi.com/anthropic'},\n                    {'id': 'native_oai', 'name': 'OpenAI 协议', 'desc': '走 /v1/chat/completions', 'apibase': 'https://api.minimaxi.com/v1', 'context_win': 50000},\n                ],\n            },\n        ],\n    },\n    {\n        'id': 'stepfun',\n        'name': '阶跃星辰 Step-3.5 (推理强)',\n        'desc': '阶跃星辰 Step 系列，支持标准和 Step Plan 双端点',\n        'type': 'native_oai',\n        'template': {\n            'name': 'stepfun', 'apikey': 'sk-<your-stepfun-key>',\n            'apibase': 'https://api.stepfun.com/v1',\n            'model': 'step-3.5-flash',\n            'api_mode': 'chat_completions',\n            'context_win': 262144,\n        },\n        'key_hint': '在 https://platform.stepfun.com/ 获取 API Key',\n        'model_choices': ['step-3.5-flash', 'step-3.5-flash-2603'],\n        'extra_fields': [\n            {\n                'key': '_endpoint', 'label': '选择端点',\n                'type': 'choice',\n                'options': [\n                    {'id': 'standard', 'name': '标准端点', 'desc': 'api.stepfun.com/v1，按量付费', 'apibase': 'https://api.stepfun.com/v1', 'context_win': 262144},\n                    {'id': 'step_plan', 'name': 'Step Plan (订阅)', 'desc': 'api.stepfun.com/step_plan/v1，订阅制', 'apibase': 'https://api.stepfun.com/step_plan/v1', 'context_win': 262144},\n                ],\n            },\n        ],\n    },\n    {\n        'id': 'qianfan',\n        'name': '百度千帆 (ERNIE 5.0 / 第三方)',\n        'desc': '百度智能云千帆，文心一言 ERNIE 5.0 + DeepSeek 等',\n        'type': 'native_oai',\n        'template': {\n            'name': 'baidu-qianfan', 'apikey': '<your-qianfan-key>',\n            'apibase': 'https://qianfan.baidubce.com/v2',\n            'model': 'ernie-5.0-thinking-preview',\n            'api_mode': 'chat_completions',\n        },\n        'key_hint': '在 https://console.bce.baidu.com/qianfan/ 创建应用获取 API Key',\n        'model_choices': ['ernie-5.0-thinking-preview', 'deepseek-v3.2'],\n        'extra_fields': [\n            {'key': 'apibase', 'label': 'API 地址 (apibase)', 'default': 'https://qianfan.baidubce.com/v2'},\n        ],\n    },\n    {\n        'id': 'volcengine',\n        'name': '火山引擎 (豆包 / Ark)',\n        'desc': '字节跳动火山引擎，支持标准 Ark 和 Ark Coding Plan',\n        'type': 'native_oai',\n        'template': {\n            'name': 'volc-ark', 'apikey': '<your-ark-api-key>',\n            'apibase': 'https://ark.cn-beijing.volces.com/api/v3',\n            'model': 'doubao-seed-code-preview-251028',\n            'api_mode': 'chat_completions',\n        },\n        'key_hint': '在 https://console.volcengine.com/ark/ 创建推理接入点后获取 API Key',\n        'model_choices': ['doubao-seed-code-preview-251028', 'doubao-seed-1-8-251228'],\n        'extra_fields': [\n            {\n                'key': '_endpoint', 'label': '选择端点',\n                'type': 'choice',\n                'options': [\n                    {'id': 'standard', 'name': '标准 Ark', 'desc': 'ark.cn-beijing.volces.com/api/v3，按量付费', 'apibase': 'https://ark.cn-beijing.volces.com/api/v3'},\n                    {'id': 'coding_plan', 'name': 'Ark Coding Plan (订阅)', 'desc': 'ark.cn-beijing.volces.com/api/coding/v3', 'apibase': 'https://ark.cn-beijing.volces.com/api/coding/v3'},\n                ],\n            },\n        ],\n    },\n    {\n        'id': 'xiaomi',\n        'name': '小米 MiMo (MiMo 2.5 Pro / TokenPlan)',\n        'desc': '小米 MiMo 系列，超大上下文窗口，支持 TokenPlan 预付费',\n        'type': 'native_oai',\n        'template': {\n            'name': 'xiaomi-mimo', 'apikey': 'sk-<your-xiaomi-key>',\n            'apibase': 'https://api.xiaomimimo.com/v1',\n            'model': 'mimo-v2.5-pro',\n            'api_mode': 'chat_completions',\n        },\n        'key_hint': '在 https://x.xiaomi.com/ 获取 API Key',\n        'model_choices': ['mimo-v2.5-pro', 'mimo-v2-flash'],\n        'extra_fields': [\n            {'key': 'apibase', 'label': 'API 地址 (apibase)', 'default': 'https://api.xiaomimimo.com/v1'},\n        ],\n    },\n    {\n        'id': 'tencent_tokenhub',\n        'name': '腾讯混元 TokenHub (Hy3 / TokenPlan)',\n        'desc': '腾讯云 TokenHub，混元 Hy3 系列，TokenPlan 预付费',\n        'type': 'native_oai',\n        'template': {\n            'name': 'tencent-tokenhub', 'apikey': 'sk-<your-tokenhub-key>',\n            'apibase': 'https://tokenhub.tencentmaas.com/v1',\n            'model': 'hy3-preview',\n            'api_mode': 'chat_completions',\n        },\n        'key_hint': '在 https://console.cloud.tencent.com/tokenhub 获取 API Key',\n        'model_choices': ['hy3-preview'],\n        'extra_fields': [\n            {'key': 'apibase', 'label': 'API 地址 (apibase)', 'default': 'https://tokenhub.tencentmaas.com/v1'},\n        ],\n    },\n    # ═══════════════════════════ 代理 / 中继（支持 Claude/GPT 等顶级模型）══════════\n    {\n        'id': 'cc_relay',\n        'name': 'CC Switch 透传 (社区常用)',\n        'desc': '社区 Claude Code 透传渠道，可接入 Claude Opus',\n        'type': 'native_claude',\n        'template': {\n            'name': 'cc-relay', 'apikey': 'sk-user-<your-relay-key>',\n            'apibase': 'https://<your-cc-switch-host>/claude/office',\n            'model': 'claude-opus-4-7', 'fake_cc_system_prompt': True,\n            'thinking_type': 'adaptive',\n        },\n        'key_hint': '从你的 CC Switch 服务商获取 apikey 和 apibase',\n        'model_choices': ['claude-opus-4-7', 'claude-sonnet-4-6'],\n        'extra_fields': [\n            {'key': 'apibase', 'label': 'API 地址 (apibase)', 'default': 'https://your-host/claude/office'},\n            {'key': 'fake_cc_system_prompt', 'label': 'fake_cc_system_prompt', 'type': 'bool', 'default': True},\n        ],\n    },\n    {\n        'id': 'openrouter',\n        'name': 'OpenRouter (多模型中继)',\n        'desc': '一个 Key 通吃 Claude/GPT/DeepSeek/Qwen 等',\n        'type': 'native_oai',\n        'template': {\n            'name': 'openrouter', 'apikey': 'sk-or-<your-openrouter-key>',\n            'apibase': 'https://openrouter.ai/api/v1',\n            'model': 'anthropic/claude-opus-4-7',\n            'max_retries': 3, 'connect_timeout': 10, 'read_timeout': 120,\n        },\n        'key_hint': '在 https://openrouter.ai/keys 获取',\n        'model_choices': ['anthropic/claude-opus-4-7', 'openai/gpt-5.5'],\n    },\n    {\n        'id': 'commonstack',\n        'name': 'CommonStack (统一网关)',\n        'desc': '一个 Key 通吃 Claude/GPT/Gemini/DeepSeek/MiniMax/Zhipu/xAI 等',\n        'type': 'native_oai',\n        'template': {\n            'name': 'commonstack', 'apikey': 'sk-<your-commonstack-key>',\n            'apibase': 'https://api.commonstack.ai/v1',\n            'model': 'anthropic/claude-opus-4-7',\n            'api_mode': 'chat_completions',\n            'max_retries': 3, 'connect_timeout': 10, 'read_timeout': 120,\n        },\n        'key_hint': '在 https://commonstack.ai 注册后从 Dashboard 获取 API Key',\n        'model_choices': ['anthropic/claude-opus-4-7', 'openai/gpt-5.5'],\n    },\n    {\n        'id': 'crs',\n        'name': 'CRS 反代 (Claude Max 多通道)',\n        'desc': 'CRS 协议的反代服务，支持 Claude Max / Gemini Ultra 通道',\n        'type': 'native_claude',\n        'template': {\n            'name': 'crs', 'apikey': 'cr_<your-crs-key>',\n            'apibase': 'https://<your-crs-host>/api',\n            'model': 'claude-opus-4-7[1m]', 'fake_cc_system_prompt': True,\n            'thinking_type': 'adaptive', 'max_tokens': 32768,\n            'max_retries': 3, 'read_timeout': 180,\n        },\n        'key_hint': '从你的 CRS 服务商获取 key 和 host',\n        'model_choices': ['claude-opus-4-7[1m]', 'claude-sonnet-4-6'],\n        'extra_fields': [\n            {\n                'key': '_channel', 'label': '选择 CRS 通道',\n                'type': 'choice',\n                'options': [\n                    {'id': 'claude_max', 'name': 'Claude Max (默认)', 'desc': '标准 CRS Claude 通道', 'apibase': 'https://<your-crs-host>/api'},\n                    {'id': 'gemini_ultra', 'name': 'Gemini Ultra (Antigravity)', 'desc': 'CRS 包装的 Google Antigravity，不支持 SSE 流式', 'apibase': 'https://<your-crs-gemini-host>/antigravity/api', 'model': 'claude-opus-4-7-thinking', 'stream': False},\n                ],\n            },\n        ],\n    },\n    {\n        'id': 'gmi',\n        'name': 'GMI Serving (通用模型中继)',\n        'desc': 'GMI 通用模型推理服务，支持多种开源/闭源（手动输入模型名）',\n        'type': 'native_oai',\n        'template': {\n            'name': 'gmi', 'apikey': '<your-gmi-key>',\n            'apibase': 'https://api.gmi-serving.com/v1',\n            'model': 'gmi-default',\n            'api_mode': 'chat_completions',\n        },\n        'key_hint': '从 GMI 服务商获取 API Key，探测失败时手动输入模型名',\n        'model_choices': [],  # 中继服务，模型由服务商提供，探测失败时手动输入\n        'extra_fields': [\n            {'key': 'apibase', 'label': 'API 地址 (apibase)', 'default': 'https://api.gmi-serving.com/v1'},\n        ],\n    },\n]\n\n# ── 消息平台定义 ────────────────────────────────────────────────────────────\nPLATFORMS = [\n    {\n        'id': 'none',\n        'name': '不使用消息平台（纯终端 REPL）',\n        'desc': '直接用 python agentmain.py 在终端交互',\n        'deps': [],\n    },\n    {\n        'id': 'telegram',\n        'name': 'Telegram 机器人',\n        'desc': '通过 Telegram Bot 与 Agent 对话',\n        'file': 'frontends/tgapp.py',\n        'deps': ['python-telegram-bot'],\n        'env_vars': [\n            {'key': 'tg_bot_token', 'label': 'Bot Token', 'hint': '从 @BotFather 获取'},\n            {'key': 'tg_allowed_users', 'label': '允许的用户 ID（逗号分隔, 留空=所有人）', 'default': '[]', 'is_list': True},\n        ],\n    },\n    {\n        'id': 'qq',\n        'name': 'QQ 机器人',\n        'desc': '通过 QQ 官方机器人 API 接入',\n        'file': 'frontends/qqapp.py',\n        'deps': ['qq-botpy'],\n        'env_vars': [\n            {'key': 'qq_app_id', 'label': 'App ID', 'hint': 'QQ 开放平台获取'},\n            {'key': 'qq_app_secret', 'label': 'App Secret'},\n            {'key': 'qq_allowed_users', 'label': '允许的用户 OpenID（逗号分隔, 留空=所有人）', 'default': '[]', 'is_list': True},\n        ],\n    },\n    {\n        'id': 'feishu',\n        'name': '飞书机器人',\n        'desc': '通过飞书应用与 Agent 对话',\n        'file': 'frontends/fsapp.py',\n        'deps': ['lark-oapi'],\n        'env_vars': [\n            {'key': 'fs_app_id', 'label': 'App ID', 'hint': '飞书开放平台获取'},\n            {'key': 'fs_app_secret', 'label': 'App Secret'},\n            {'key': 'fs_allowed_users', 'label': '允许的用户（逗号分隔, 留空=所有人）', 'default': '[]', 'is_list': True},\n        ],\n    },\n    {\n        'id': 'wecom',\n        'name': '企业微信机器人',\n        'desc': '通过企业微信 Bot 接入',\n        'file': 'frontends/wecomapp.py',\n        'deps': ['wecombot'],\n        'env_vars': [\n            {'key': 'wecom_bot_id', 'label': 'Bot ID'},\n            {'key': 'wecom_secret', 'label': 'Bot Secret'},\n            {'key': 'wecom_allowed_users', 'label': '允许的用户（逗号分隔, 留空=所有人）', 'default': '[]', 'is_list': True},\n        ],\n    },\n    {\n        'id': 'dingtalk',\n        'name': '钉钉机器人',\n        'desc': '通过钉钉应用接入',\n        'file': 'frontends/dingtalkapp.py',\n        'deps': ['dingtalk-sdk'],\n        'env_vars': [\n            {'key': 'dingtalk_client_id', 'label': 'Client ID (App Key)'},\n            {'key': 'dingtalk_client_secret', 'label': 'Client Secret (App Secret)'},\n            {'key': 'dingtalk_allowed_users', 'label': '允许的用户 StaffID（逗号分隔, 留空=所有人）', 'default': '[]', 'is_list': True},\n        ],\n    },\n    {\n        'id': 'discord',\n        'name': 'Discord 机器人',\n        'desc': '通过 Discord Bot 接入',\n        'file': 'frontends/dcapp.py',\n        'deps': ['discord.py'],\n        'env_vars': [\n            {'key': 'dc_bot_token', 'label': 'Bot Token', 'hint': 'Discord Developer Portal 获取'},\n            {'key': 'dc_allowed_users', 'label': '允许的用户 ID（逗号分隔, 留空=所有人）', 'default': '[]', 'is_list': True},\n        ],\n    },\n    {\n        'id': 'wechat',\n        'name': '微信 (iLink 协议)',\n        'desc': '通过微信个人号与 Agent 对话，扫码自动登录',\n        'file': 'frontends/wechatapp.py',\n        'deps': ['requests', 'qrcode', 'pycryptodome'],\n        'env_vars': [],\n    },\n]\n\n\ndef _masked(v, reveal, tail):\n    \"\"\"生成脱敏字符串：前 reveal 位明文 + * + 后 tail 位明文\"\"\"\n    if len(v) > reveal + tail:\n        return v[:reveal] + '*' * min(len(v) - reveal - tail, 8) + v[-tail:]\n    elif len(v) > reveal:\n        return v[:reveal] + '*' * (len(v) - reveal)\n    return v\n\ndef masked_input(prompt, reveal=6, tail=4):\n    \"\"\"密文输入，支持粘贴：批读取 + 延迟重绘，避免快速键入时丢字符。\n\n    prompt 必须为单行（不含 \\\\n）。\n    \"\"\"\n    sys.stdout.write(prompt)\n    sys.stdout.flush()\n    chars = []\n\n    def _repaint():\n        m = _masked(''.join(chars), reveal, tail)\n        sys.stdout.write(f'\\r{prompt}{m}     \\r{prompt}{m}')\n        sys.stdout.flush()\n\n    def _process(c):\n        \"\"\"处理单个字符，返回 True 表示应退出。\"\"\"\n        if c in ('\\r', '\\n'):\n            return True\n        if c in ('\\x03', '\\x04'):\n            raise KeyboardInterrupt\n        if c in ('\\x08', '\\x7f'):\n            if chars:\n                chars.pop()\n        elif c.isprintable() or c == ' ':\n            chars.append(c)\n        return False\n\n    if os.name == 'nt':\n        import msvcrt\n        while True:\n            c = msvcrt.getwch()\n            if _process(c):\n                break\n            if c in ('\\x08', '\\x7f'):\n                _repaint()          # 退格立即重绘\n                continue\n            if not (c.isprintable() or c == ' '):\n                continue\n            # 批量读取：粘贴时一次取完\n            while msvcrt.kbhit():\n                c2 = msvcrt.getwch()\n                if _process(c2):\n                    value = ''.join(chars)\n                    _repaint()\n                    sys.stdout.write('\\n')\n                    sys.stdout.flush()\n                    return value\n            _repaint()\n    else:\n        import tty, termios, select\n        fd = sys.stdin.fileno()\n        old = termios.tcgetattr(fd)\n        try:\n            tty.setraw(fd)\n            while True:\n                c = sys.stdin.read(1)\n                if _process(c):\n                    break\n                if c in ('\\x08', '\\x7f'):\n                    _repaint()      # 退格立即重绘\n                    continue\n                if not (c.isprintable() or c == ' '):\n                    continue\n                # 批量读取：只要 stdin 有数据就继续读，不重绘\n                while select.select([sys.stdin], [], [], 0) == ([sys.stdin], [], []):\n                    c2 = sys.stdin.read(1)\n                    if _process(c2):\n                        value = ''.join(chars)\n                        _repaint()\n                        termios.tcsetattr(fd, termios.TCSADRAIN, old)\n                        sys.stdout.write('\\n')\n                        sys.stdout.flush()\n                        return value\n                _repaint()\n        finally:\n            termios.tcsetattr(fd, termios.TCSADRAIN, old)\n\n    value = ''.join(chars)\n    _repaint()\n    sys.stdout.write('\\n')\n    sys.stdout.flush()\n    return value\n\n\n# ═══════════════════════════════════════════════════════════════════════════\n#  UI Helpers\n# ═══════════════════════════════════════════════════════════════════════════\n\ndef cprint(text, color=None, bold=False, end='\\n'):\n    parts = []\n    if color: parts.append(C.get(color, ''))\n    if bold: parts.append(C['bold'])\n    parts.append(text)\n    parts.append(C['reset'])\n    print(''.join(parts), end=end)\n\ndef banner():\n    print('\\033[2J\\033[H', end='')  # ANSI 清屏，跨平台\n    print(f\"{C['cyan']}{C['bold']}\")\n    print(\"  ╔═══════════════════════════════════════════════════════════╗\")\n    print(\"  ║        GenericAgent — 交互式初始化向导 v1.2              ║\")\n    print(\"  ║   一键配置 LLM 模型 + 消息平台，自动生成 mykey.py        ║\")\n    print(\"  ╚═══════════════════════════════════════════════════════════╝\")\n    print(f\"{C['reset']}\")\n    print(f\"{C['dim']}  项目目录: {PROJECT_ROOT}{C['reset']}\")\n    print()\n\ndef _check_python():\n    \"\"\"检查 Python 版本，返回 (ok, msg)\"\"\"\n    vi = sys.version_info\n    if vi < (3, 10):\n        return False, f\"Python {vi.major}.{vi.minor} 不满足最低要求 (≥ 3.10)\"\n    if vi[:2] == (3, 12):\n        return True, ''\n    return True, f\"⚠ 当前 Python {vi.major}.{vi.minor}，推荐使用 Python 3.12\"\n\ndef ask_choice(prompt, choices, allow_multi=False, default=None):\n    \"\"\"交互式选择，返回 selected_id 或 [selected_ids]\"\"\"\n    print(f\"\\n{C['bold']}{prompt}{C['reset']}\")\n    if allow_multi:\n        print(f\"{C['dim']}  (可多选，输入序号用逗号分隔，如: 1,3,5；输入 a 全选；回车跳过){C['reset']}\")\n    else:\n        print(f\"{C['dim']}  (输入序号，如: 1){C['reset']}\")\n    for i, c in enumerate(choices, 1):\n        desc = c.get('desc', '')\n        print(f\"  {C['green']}{i}.{C['reset']} {C['bold']}{c['name']}{C['reset']}  {C['dim']}{desc}{C['reset']}\")\n    while True:\n        raw = input(f\"\\n  {C['yellow']}►{C['reset']} \").strip()\n        if not raw and default is not None:\n            return default\n        if allow_multi:\n            if raw.lower() == 'a':\n                return [c['id'] for c in choices]\n            parts = [p.strip() for p in raw.split(',') if p.strip()]\n            selected = []\n            for p in parts:\n                try:\n                    idx = int(p) - 1\n                    if 0 <= idx < len(choices):\n                        selected.append(choices[idx]['id'])\n                except ValueError:\n                    pass\n            if selected:\n                return selected\n        else:\n            try:\n                idx = int(raw) - 1\n                if 0 <= idx < len(choices):\n                    return choices[idx]['id']\n            except ValueError:\n                pass\n        print(f\"  {C['red']}✗ 请输入有效序号{C['reset']}\")\n\ndef ask_input(prompt, default=None, secret=False, hint=None):\n    \"\"\"交互式输入。secret=True 时使用脱敏输入。\"\"\"\n    if hint:\n        cprint(f\"  {hint}\", 'dim')\n    if default is not None:\n        cprint(f\"  [默认: {default}]\", 'dim')\n    prompt_line = f\"  {C['yellow']}►{C['reset']} {prompt}: \"\n    while True:\n        if secret:\n            val = masked_input(prompt_line).strip()\n        else:\n            val = input(prompt_line).strip()\n        if not val and default is not None:\n            return default\n        if val:\n            return val\n        cprint(\"✗ 此项不能为空\", 'red')\n\ndef ask_yesno(prompt, default=True):\n    hint = \"Y/N\"\n    raw = input(f\"\\n  {C['yellow']}►{C['reset']} {prompt} ({hint}): \").strip().lower()\n    if not raw:\n        return default\n    return raw.startswith('y')\n\n\n# ═══════════════════════════════════════════════════════════════════════════\n#  LLM 配置逻辑\n# ═══════════════════════════════════════════════════════════════════════════\n\ndef _get_proxy_handler():\n    \"\"\"从环境变量读取代理配置，返回 ProxyHandler 或 None\"\"\"\n    for var in ('HTTPS_PROXY', 'https_proxy', 'HTTP_PROXY', 'http_proxy'):\n        url = os.environ.get(var)\n        if url:\n            return urllib.request.ProxyHandler({'https': url, 'http': url})\n    return None\n\ndef probe_models(provider, apikey, apibase=None):\n    \"\"\"调用 API 探测可用模型列表，返回模型 ID 列表或 None\"\"\"\n    ptype = provider.get('type', 'native_oai')\n    base = (apibase or provider['template'].get('apibase', '')).rstrip('/')\n\n    if ptype == 'native_claude':\n        url = f\"{base}/v1/models\"\n        headers = {'x-api-key': apikey, 'anthropic-version': '2023-06-01', 'User-Agent': 'GenericAgent/1.0'}\n    else:\n        url = f\"{base}/models\"\n        headers = {'Authorization': f'Bearer {apikey}', 'User-Agent': 'GenericAgent/1.0'}\n\n    print(f\"\\n  {C['dim']}🔍 正在探测可用模型 ({base}/models)...{C['reset']}\", end='', flush=True)\n    if ptype == 'native_claude':\n        print(f\" {C['dim']}(Anthropic 协议，探测可能失败){C['reset']}\", end='', flush=True)\n\n    opener = urllib.request.build_opener()\n    ph = _get_proxy_handler()\n    if ph:\n        opener = urllib.request.build_opener(ph)\n        print(f\" {C['dim']}(via proxy){C['reset']}\", end='', flush=True)\n\n    for attempt in range(2):\n        try:\n            req = urllib.request.Request(url, headers=headers, method='GET')\n            with opener.open(req, timeout=10) as resp:\n                data = json.loads(resp.read().decode())\n                models = data.get('data', [])\n                ids = sorted(set(m['id'] for m in models if isinstance(m, dict) and m.get('id')))\n                if ids:\n                    print(f\" {C['green']}✓ 发现 {len(ids)} 个模型{C['reset']}\")\n                    return ids\n                print(f\" {C['yellow']}⚠ 返回为空{C['reset']}\")\n                return None\n        except Exception as e:\n            if attempt == 0 and 'timeout' in type(e).__name__.lower():\n                print(f\" {C['yellow']}⏱ 超时，重试...{C['reset']}\", end='', flush=True)\n                continue\n            print(f\" {C['yellow']}⚠ 探测失败: {type(e).__name__}（将使用预设列表）{C['reset']}\")\n            return None\n    return None\n\ndef _normalize_model_choices(choices):\n    \"\"\"统一 model_choices 格式为 [{'id': str, 'name': str}]\"\"\"\n    if not choices:\n        return []\n    result = []\n    for item in choices:\n        if isinstance(item, str):\n            result.append({'id': item, 'name': item})\n        elif isinstance(item, dict):\n            result.append(item)\n        elif isinstance(item, (tuple, list)) and len(item) >= 1:\n            result.append({'id': item[0], 'name': item[1] if len(item) > 1 else item[0]})\n    return result\n\ndef _configure_advanced(provider, cfg):\n    \"\"\"配置高级可选字段: proxy, context_win, stream, user_agent, thinking_budget_tokens\"\"\"\n    print(f\"\\n  {C['dim']}── 高级选项（回车跳过，使用默认值）{C['reset']}\")\n    proxy = ask_input(\"HTTP 代理地址 (proxy)\", default='', hint='如 http://127.0.0.1:2082，留空跳过')\n    if proxy:\n        cfg['proxy'] = proxy\n    cw = ask_input(\"上下文窗口阈值 (context_win)\", default='', hint='NativeClaude 默认 28000，其他默认 24000')\n    if cw:\n        cfg['context_win'] = int(cw)\n    if cfg.get('thinking_type') == 'enabled':\n        tbt = ask_input(\"thinking_budget_tokens\", default='', hint='low≈4096, medium≈10240, high≈32768')\n        if tbt:\n            cfg['thinking_budget_tokens'] = int(tbt)\n    if cfg.get('type', provider['type']) == 'native_claude':\n        ua = ask_input(\"User-Agent 版本号\", default='', hint='某些中转按 UA 白名单校验，pin 老版本用')\n        if ua:\n            cfg['user_agent'] = ua\n    stream_default = cfg.get('stream', True)\n    if ask_yesno(\"启用 SSE 流式 (stream)\", default=stream_default):\n        cfg['stream'] = True\n    else:\n        cfg['stream'] = False\n\ndef configure_llm(provider):\n    \"\"\"引导用户配置单个模型\"\"\"\n    print(f\"\\n{C['cyan']}{'─'*60}{C['reset']}\")\n    print(f\"{C['bold']}  配置: {provider['name']}{C['reset']}\")\n    print(f\"  {C['dim']}{provider['desc']}{C['reset']}\")\n    print(f\"{C['cyan']}{'─'*60}{C['reset']}\")\n\n    cfg = dict(provider['template'])\n\n    # API Key（密文输入）\n    cfg['apikey'] = ask_input(\n        f\"API Key\",\n        hint=provider.get('key_hint', ''),\n        secret=True,\n    )\n\n    # 额外字段\n    for field in provider.get('extra_fields', []):\n        if field['key'] == 'apibase':\n            cfg['apibase'] = ask_input(\n                field['label'],\n                default=field.get('default', cfg.get('apibase', '')),\n            )\n        elif field.get('type') == 'bool':\n            cfg[field['key']] = ask_yesno(\n                field['label'],\n                default=field.get('default', True)\n            )\n        elif field.get('type') == 'choice':\n            picked = ask_choice(field['label'], field['options'])\n            chosen = next(o for o in field['options'] if o['id'] == picked)\n            for opt_key, opt_val in chosen.items():\n                if opt_key not in ('id', 'name', 'desc'):\n                    cfg[opt_key] = opt_val\n\n    # 模型选择\n    manual_choice = {'id': '__manual__', 'name': '✏️ 手动输入模型名', 'desc': '自定义模型 ID，不依赖探测结果'}\n    model_list = probe_models(provider, cfg['apikey'], cfg.get('apibase'))\n    if model_list:\n        refresh_choice = {'id': '__refresh__', 'name': '🔄 重新探测'}\n        choices = [refresh_choice, manual_choice] + [{'id': m, 'name': m} for m in model_list]\n        while True:\n            picked = ask_choice(\"API 探测到以下可用模型（或手动输入）:\", choices)\n            if picked == '__refresh__':\n                print(f\"  {C['dim']}再次探测...{C['reset']}\")\n                model_list = probe_models(provider, cfg['apikey'], cfg.get('apibase'))\n                if not model_list:\n                    print(f\"  {C['yellow']}⚠ 再次探测失败{C['reset']}\")\n                    picked = _fallback_model(provider, manual_choice)\n                    break\n                choices = [refresh_choice, manual_choice] + [{'id': m, 'name': m} for m in model_list]\n            elif picked == '__manual__':\n                picked = ask_input(\"请输入模型名\", default=cfg.get('model', ''))\n                break\n            else:\n                break\n        cfg['model'] = picked\n    else:\n        cfg['model'] = _fallback_model(provider, manual_choice)\n\n    # 别名\n    default_name = cfg.get('name', provider['id'])\n    name = ask_input(\"此配置的别名 (name，Mixin 引用用)\", default=default_name)\n    if name:\n        cfg['name'] = name\n\n    # 高级选项\n    if ask_yesno(\"配置高级选项（proxy / context_win / stream 等）？\", default=False):\n        _configure_advanced(provider, cfg)\n\n    return cfg\n\ndef _fallback_model(provider, manual_choice=None):\n    \"\"\"使用预设模型列表让用户选择，始终提供手动输入选项\"\"\"\n    manual_choice = manual_choice or {'id': '__manual__', 'name': '✏️ 手动输入模型名', 'desc': '自定义模型 ID'}\n    normalized = _normalize_model_choices(provider.get('model_choices', []))\n    if normalized:\n        choices = [manual_choice] + normalized\n        picked = ask_choice(\"选择模型（或手动输入）:\", choices)\n        if picked == '__manual__':\n            return ask_input(\"请输入模型名\", default=provider['template'].get('model', ''))\n        return picked\n    return ask_input(\"请输入模型名\", default=provider['template'].get('model', ''))\n\ndef configure_llms():\n    \"\"\"配置 LLM 模型\"\"\"\n    print(f\"\\n{C['bold']}{C['magenta']}╔══════════════════════════════════════╗\")\n    print(f\"║     第一步: 配置 LLM 模型           ║\")\n    print(f\"╚══════════════════════════════════════╝{C['reset']}\")\n    print(f\"\\n{C['dim']}  你可以配置最多 2 个模型组成故障转移 (Mixin) 列表。{C['reset']}\")\n\n    all_cfgs = []\n    provider_id = ask_choice(\"选择模型厂商 (配置第 1 个模型):\", LLM_PROVIDERS)\n    provider = next(p for p in LLM_PROVIDERS if p['id'] == provider_id)\n    cfg = configure_llm(provider)\n    all_cfgs.append(cfg)\n\n    if ask_yesno(\"再添加一个模型做故障转移？\", default=False):\n        providers_ext = [{'id': '__stop__', 'name': '✓ 不需要备选了', 'desc': ''}] + LLM_PROVIDERS\n        provider_id = ask_choice(\n            \"选择模型厂商 (配置第 2 个模型 — 或选「不需要备选了」跳过):\",\n            providers_ext\n        )\n        if provider_id != '__stop__':\n            provider = next(p for p in LLM_PROVIDERS if p['id'] == provider_id)\n            cfg = configure_llm(provider)\n            all_cfgs.append(cfg)\n\n    return all_cfgs\n\n\n# ═══════════════════════════════════════════════════════════════════════════\n#  消息平台配置逻辑\n# ═══════════════════════════════════════════════════════════════════════════\n\ndef configure_platforms():\n    \"\"\"配置消息平台，返回 (platform_configs, pip_hints)\"\"\"\n    print(f\"\\n{C['bold']}{C['magenta']}╔══════════════════════════════════════╗\")\n    print(f\"║     第二步: 配置消息平台             ║\")\n    print(f\"╚══════════════════════════════════════╝{C['reset']}\")\n    print(f\"\\n{C['dim']}  消息平台用于从聊天软件与 Agent 交互。{C['reset']}\")\n    print(f\"{C['dim']}  你也可以跳过此步，直接用终端 REPL。{C['reset']}\")\n\n    platform_ids = ask_choice(\n        \"选择消息平台 (可多选，选 '不使用' 则跳过):\",\n        PLATFORMS,\n        allow_multi=True,\n        default=['none']\n    )\n\n    if 'none' in platform_ids:\n        return [], set()\n\n    selected_platforms = []\n    pip_hints = set()\n\n    for pid in platform_ids:\n        platform = next(p for p in PLATFORMS if p['id'] == pid)\n        pip_hints.update(platform.get('deps', []))\n\n        print(f\"\\n{C['cyan']}{'─'*60}{C['reset']}\")\n        print(f\"{C['bold']}  配置: {platform['name']}{C['reset']}\")\n        print(f\"{C['cyan']}{'─'*60}{C['reset']}\")\n\n        env_vals = {}\n\n        if pid == 'feishu' and ask_yesno(\"使用一键扫码创建应用？（推荐）\", default=True):\n            env_vals = _feishu_scan(platform)\n        if pid == 'wechat' and ask_yesno(\"扫码登录微信 iLink？（推荐）\", default=True):\n            env_vals = _wechat_scan()\n\n        for var in platform['env_vars']:\n            if var['key'] not in env_vals:\n                env_vals.update(_manual_platform_var(var))\n\n        if pid == 'wecom' and ask_yesno(\"设置欢迎消息？\", default=False):\n            env_vals['wecom_welcome_message'] = ask_input(\"欢迎消息内容\", default='你好，我在线上。')\n\n        selected_platforms.append({'platform': platform, 'config': env_vals})\n\n    return selected_platforms, pip_hints\n\ndef _manual_platform_var(var):\n    \"\"\"手动填写单个平台变量\"\"\"\n    val = ask_input(var['label'], hint=var.get('hint', ''), default=var.get('default'))\n    if var.get('is_list'):\n        if val == '[]' or not val:\n            return {var['key']: []}\n        return {var['key']: [x.strip() for x in val.split(',') if x.strip()]}\n    return {var['key']: val}\n\ndef _feishu_scan(platform):\n    \"\"\"飞书一键扫码创建应用，返回 env_vals 或空 dict\"\"\"\n    from io import StringIO\n    try:\n        import lark_oapi as lark\n        import qrcode, threading\n    except ImportError:\n        print(f\"\\n  {C['yellow']}⚠ lark-oapi 未安装，降级为手动配置{C['reset']}\")\n        return {}\n\n    print(f\"\\n  {C['cyan']}📱 正在启动一键创建...{C['reset']}\")\n    print(f\"  {C['dim']}  请用飞书 App 扫描终端二维码，完成授权后自动获取凭据。{C['reset']}\\n\")\n\n    qr_printed = threading.Event()\n    result_holder = {'data': None}\n\n    def handle_qr(info):\n        url = info['url']\n        expire = info['expire_in']\n        qr = qrcode.QRCode(border=1, box_size=1)\n        qr.add_data(url)\n        buf = StringIO()\n        qr.print_ascii(out=buf)\n        qr_art = buf.getvalue()\n        print(f\"\\n  {C['bold']}请用飞书扫描下方二维码，或复制链接在浏览器打开:{C['reset']}\")\n        print(f\"  {C['green']}{qr_art.replace(chr(27), '')}{C['reset']}\")\n        print(f\"  {C['dim']}  链接: {url}{C['reset']}\")\n        print(f\"  {C['dim']}  有效期 {expire} 秒{C['reset']}\")\n        qr_printed.set()\n\n    def handle_status(info):\n        status = info['status']\n        if status == 'polling':\n            print(f\"  {C['yellow']}⏳ 等待扫码...{C['reset']}\")\n        elif status == 'slow_down':\n            print(f\"  {C['yellow']}⏳ 等待中... (间隔 {info.get('interval', '?')}s){C['reset']}\")\n        elif status == 'domain_switched':\n            print(f\"  {C['cyan']}🌐 已切换认证域名{C['reset']}\")\n\n    def run_register():\n        try:\n            result = lark.register_app(\n                on_qr_code=handle_qr,\n                on_status_change=handle_status,\n            )\n            result_holder['data'] = result\n        except Exception as e:\n            print(f\"\\n  {C['red']}✗ 创建失败: {e}{C['reset']}\")\n\n    thread = threading.Thread(target=run_register, daemon=True)\n    thread.start()\n    qr_printed.wait(timeout=15)\n    thread.join(timeout=300)\n\n    if result_holder['data']:\n        result = result_holder['data']\n        print(f\"\\n  {C['green']}✅ 应用创建成功！{C['reset']}\")\n        print(f\"  App ID:     {C['bold']}{result['client_id']}{C['reset']}\")\n        print(f\"  App Secret: {C['bold']}{result['client_secret']}{C['reset']}\")\n        return {\n            'fs_app_id': result['client_id'],\n            'fs_app_secret': result['client_secret'],\n        }\n    else:\n        print(f\"\\n  {C['yellow']}⚠ 扫码创建未完成，降级为手动填写...{C['reset']}\")\n        return {}\n\n\ndef _wechat_scan():\n    \"\"\"微信 iLink 扫码登录，保存 token 到 ~/.wxbot/token.json，返回 env_vals\"\"\"\n    print(f\"\\n  {C['cyan']}📱 正在启动微信 iLink 扫码登录...{C['reset']}\")\n    print(f\"  {C['dim']}  请用微信扫描终端二维码，完成授权后自动获取凭据。{C['reset']}\\n\")\n\n    # 确保项目根在路径中，以便导入 frontends/wechatapp\n    if PROJECT_ROOT not in sys.path:\n        sys.path.insert(0, PROJECT_ROOT)\n    try:\n        from frontends.wechatapp import WxBotClient\n    except ImportError as e:\n        print(f\"\\n  {C['yellow']}⚠ 无法导入 WxBotClient: {e}{C['reset']}\")\n        return {}\n\n    try:\n        bot = WxBotClient()\n        if bot.token:\n            print(f\"  {C['green']}✅ 已有有效 token (bot_id={bot.bot_id}){C['reset']}\")\n            if ask_yesno(\"重新扫码登录？\", default=False):\n                bot.token = ''\n            else:\n                return {}\n        bot.login_qr()\n        print(f\"\\n  {C['green']}✅ 微信 iLink 扫码登录成功！{C['reset']}\")\n        print(f\"  Bot ID: {C['bold']}{bot.bot_id}{C['reset']}\")\n        print(f\"  Token 已保存到: {C['dim']}{bot._tf}{C['reset']}\")\n    except Exception as e:\n        print(f\"\\n  {C['red']}✗ 扫码登录失败: {e}{C['reset']}\")\n        return {}\n\n    return {}\n\n\n\n# ═══════════════════════════════════════════════════════════════════════════\n#  生成 mykey.py\n# ═══════════════════════════════════════════════════════════════════════════\n\ndef _var_type_info(cfg):\n    \"\"\"根据配置类型返回 (var_prefix, session_type)\"\"\"\n    cfg_type = cfg.get('type', 'native_oai')\n    if cfg_type == 'native_claude':\n        return 'native_claude_config', 'NativeClaudeSession'\n    elif cfg_type == 'claude':\n        return 'claude_config', 'ClaudeSession'\n    elif cfg_type == 'oai':\n        return 'oai_config', 'LLMSession'\n    else:\n        return 'native_oai_config', 'NativeOAISession'\n\n\ndef generate_mykey(llm_cfgs, platform_configs):\n    \"\"\"生成 mykey.py 内容\"\"\"\n    lines = []\n    lines.append(\"# ══════════════════════════════════════════════════════════════════════════════\")\n    lines.append(f\"#  GenericAgent — mykey.py (由 configure.py 自动生成 @ {datetime.now().strftime('%Y-%m-%d %H:%M')})\")\n    lines.append(\"# ══════════════════════════════════════════════════════════════════════════════\")\n    lines.append(\"\")\n    lines.append(\"# ── 停止符 ──────────────────────────────────────────────────────────────────\")\n    lines.append(\"_SETUP_DONE = 'configure.py'  # 删除此行可重新触发配置向导\")\n    lines.append(\"\")\n\n    # Mixin 配置\n    names = [c['name'] for c in llm_cfgs]\n    lines.append(\"# ── Mixin 故障转移 ──────────────────────────────────────────────────────────\")\n    lines.append(\"mixin_config = {\")\n    lines.append(f\"    'llm_nos': {names},\")\n    lines.append(\"    'max_retries': 10,\")\n    lines.append(\"    'base_delay': 0.5,\")\n    lines.append(\"}\")\n    lines.append(\"\")\n\n    # 各模型配置\n    type_counts = {}\n    for cfg in llm_cfgs:\n        cfg_type = cfg.get('type', 'native_oai')\n        type_counts[cfg_type] = type_counts.get(cfg_type, 0) + 1\n\n    type_indices = {}\n    for i, cfg in enumerate(llm_cfgs):\n        cfg_type = cfg.get('type', 'native_oai')\n        var_prefix, session_type = _var_type_info(cfg)\n        idx = type_indices.get(cfg_type, 0)\n        type_indices[cfg_type] = idx + 1\n\n        if type_counts[cfg_type] > 1:\n            var_name = f\"{var_prefix}_{idx}\"\n        else:\n            var_name = var_prefix\n\n        lines.append(f\"# ── {cfg['name']} ({session_type}) ─────────────────────────────────────────────\")\n        lines.append(f\"{var_name} = {{\")\n        _write_config_fields(lines, cfg)\n        lines.append(\"}\")\n        lines.append(\"\")\n\n    # 平台配置\n    if platform_configs:\n        lines.append(\"# ══════════════════════════════════════════════════════════════════════════════\")\n        lines.append(\"#  聊天平台集成\")\n        lines.append(\"# ══════════════════════════════════════════════════════════════════════════════\")\n        lines.append(\"\")\n        for pc in platform_configs:\n            for key, val in pc['config'].items():\n                _write_platform_value(lines, key, val)\n            lines.append(\"\")\n\n    # 尾部\n    lines.append(\"# ══════════════════════════════════════════════════════════════════════════════\")\n    lines.append(\"#  配置完毕！运行: python agentmain.py  (终端 REPL)\")\n    if platform_configs:\n        for pc in platform_configs:\n            p = pc['platform']\n            lines.append(f\"#  或: python {p['file']}  ({p['name']})\")\n    lines.append(\"# ══════════════════════════════════════════════════════════════════════════════\")\n\n    return '\\n'.join(lines)\n\ndef _write_config_fields(lines, cfg):\n    \"\"\"写入配置字典的键值对（缩进的 'key': value, 格式）\"\"\"\n    for key in ['name', 'type', 'apikey', 'apibase', 'model', 'api_mode',\n                'fake_cc_system_prompt', 'thinking_type', 'thinking_budget_tokens',\n                'reasoning_effort', 'max_tokens', 'max_retries', 'connect_timeout',\n                'read_timeout', 'temperature', 'context_win',\n                'proxy', 'user_agent', 'stream']:\n        if key not in cfg:\n            continue\n        val = cfg[key]\n        if isinstance(val, bool):\n            lines.append(f\"    '{key}': {str(val)},\")\n        elif isinstance(val, (int, float)):\n            lines.append(f\"    '{key}': {val},\")\n        elif isinstance(val, str):\n            lines.append(f\"    '{key}': '{val}',\")\n        else:\n            lines.append(f\"    '{key}': {repr(val)},\")\n\ndef _write_platform_value(lines, key, val):\n    \"\"\"写入顶级变量（平台配置等）\"\"\"\n    if isinstance(val, list):\n        if val:\n            lines.append(f\"{key} = {repr(val)}\")\n        else:\n            lines.append(f\"{key} = []  # 允许所有用户\")\n    elif isinstance(val, str):\n        lines.append(f\"{key} = '{val}'\")\n    else:\n        lines.append(f\"{key} = {repr(val)}\")\n\n\ndef _parse_existing_mykey():\n    \"\"\"解析已有 mykey.py，返回 (model_names, platform_infos)\n\n    model_names: [str]  — 模型名列表\n    platform_infos: [{'id': str, 'vars': [{'key': str, 'val': ...}]}]  — 平台信息\n    解析失败时返回 ([], [])\n    \"\"\"\n    if not os.path.exists(MYKPY_PATH):\n        return [], []\n\n    with open(MYKPY_PATH, 'r', encoding='utf-8') as f:\n        content = f.read()\n\n    # 解析模型名\n    model_names = []\n    m = re.search(r\"'llm_nos':\\s*\\[([^\\]]*)\\]\", content)\n    if m:\n        model_names = re.findall(r\"'([^']+)'\", m.group(1))\n\n    # 先收集所有已知平台 env var key → 判断值类型\n    all_env_var_keys = {}\n    platform_env_keys = {}  # pid -> [var_key]\n    for p in PLATFORMS:\n        pid = p['id']\n        platform_env_keys.setdefault(pid, [])\n        for var in p.get('env_vars', []):\n            vkey = var['key']\n            all_env_var_keys[vkey] = var\n            platform_env_keys[pid].append(vkey)\n\n    # 逐平台解析所有已知变量\n    platform_infos = []\n    for pid, env_keys in platform_env_keys.items():\n        vars_found = []\n        for vkey in env_keys:\n            var_def = all_env_var_keys[vkey]\n            val = None\n            if var_def.get('is_list'):\n                # 匹配 `xxx = [...]`\n                m_var = re.search(rf\"^{vkey}\\s*=\\s*(\\[[^\\]]*\\])\", content, re.MULTILINE)\n                if m_var:\n                    try:\n                        val = ast.literal_eval(m_var.group(1))\n                    except (ValueError, SyntaxError):\n                        pass\n            else:\n                # 匹配 `xxx = '...'`\n                m_var = re.search(rf\"^{vkey}\\s*=\\s*'([^']*)'\", content, re.MULTILINE)\n                if m_var:\n                    val = m_var.group(1)\n            if val is not None:\n                vars_found.append({'key': vkey, 'val': val})\n        if vars_found:\n            platform_infos.append({'id': pid, 'vars': vars_found})\n\n    return model_names, platform_infos\n\n\ndef _parse_existing_llm_cfgs():\n    \"\"\"解析已有 mykey.py，返回完整 LLM 配置字典列表 [{name, apikey, ...}]\n    解析失败时返回 []\n    \"\"\"\n    if not os.path.exists(MYKPY_PATH):\n        return []\n\n    with open(MYKPY_PATH, 'r', encoding='utf-8') as f:\n        content = f.read()\n\n    cfgs = []\n    # 匹配所有 `xxx = {  ...  }` 顶层字典赋值\n    # 用简单状态机: 找 `\\w+ = {` 然后匹配花括号\n    pattern = re.compile(r'^(\\w+)\\s*=\\s*\\{', re.MULTILINE)\n    for m in pattern.finditer(content):\n        brace_start = m.end() - 1  # '{' 的位置\n        depth = 1\n        i = brace_start + 1\n        while i < len(content) and depth > 0:\n            if content[i] == '{':\n                depth += 1\n            elif content[i] == '}':\n                depth -= 1\n            i += 1\n        if depth == 0:\n            dict_text = content[m.end():i - 1]\n            try:\n                d = ast.literal_eval('{' + dict_text + '}')\n                if isinstance(d, dict) and 'name' in d:\n                    cfgs.append(d)\n            except (ValueError, SyntaxError):\n                continue\n\n    return cfgs\n\n\ndef _backup_with_name(model_names, platform_ids):\n    \"\"\"按 mykey+模型名+机器人名 格式备份旧 mykey.py\"\"\"\n    parts = ['mykey']\n    for m in model_names[:3]:\n        parts.append(m.replace('/', '-').replace('\\\\', '-'))\n    for pid in platform_ids:\n        pid_clean = pid.replace('_', '')\n        if pid_clean not in parts:\n            parts.append(pid_clean)\n    safe_name = '_'.join(parts)\n    if safe_name == 'mykey':\n        safe_name = 'mykey_backup'  # 避免和源文件同名\n    if len(safe_name) > 100:\n        safe_name = safe_name[:100]\n    backup_path = os.path.join(PROJECT_ROOT, f'{safe_name}.py')\n    shutil.copy2(MYKPY_PATH, backup_path)\n    return backup_path\n\n\n# ═══════════════════════════════════════════════════════════════════════════\n#  Main\n# ═══════════════════════════════════════════════════════════════════════════\n\ndef main():\n    banner()\n\n    # Python 版本检查\n    ok, msg = _check_python()\n    if not ok:\n        print(f\"  {C['red']}✗ {msg}{C['reset']}\")\n        sys.exit(1)\n    if msg:\n        color = 'yellow' if '⚠' in msg else 'green'\n        print(f\"  {C[color]}{msg}{C['reset']}\\n\")\n\n    # ── 决策流程 ──\n    llm_cfgs = []\n    platform_configs = []\n    platform_deps = set()\n    is_modify = False\n    is_new = False\n\n    if os.path.exists(MYKPY_PATH):\n        model_names, platform_infos = _parse_existing_mykey()\n        cur_models = ', '.join(model_names) if model_names else '(未知)'\n        cur_platforms = ', '.join(p['id'] for p in platform_infos) if platform_infos else '(无)'\n        print(f\"  {C['dim']}  当前: 模型=[{cur_models}], 平台=[{cur_platforms}]{C['reset']}\")\n\n        mode = ask_choice(\n            \"检测到已有 mykey.py，请选择操作\",\n            [\n                {'id': 'modify', 'name': '修改现有配置', 'desc': '保留未改部分，只重新配置选定项'},\n                {'id': 'new', 'name': '新建配置（备份旧文件）', 'desc': '备份为 mykey+模型+平台.py，然后全新配置'},\n            ],\n            default=None,\n        )\n\n        if mode == 'new':\n            backup_path = _backup_with_name(model_names, [p['id'] for p in platform_infos])\n            print(f\"  {C['green']}✓ 旧配置已备份至:{C['reset']} {C['dim']}{backup_path}{C['reset']}\")\n            is_new = True\n        else:\n            is_modify = True\n            scope = ask_choice(\n                \"你要修改什么？\",\n                [\n                    {'id': 'both', 'name': '两项都重新配置', 'desc': 'LLM + 平台全部更新'},\n                    {'id': 'llm', 'name': '重新配置 LLM 模型', 'desc': f'当前: {cur_models}'},\n                    {'id': 'platform', 'name': '重新配置消息平台', 'desc': f'当前: {cur_platforms}'},\n                ],\n            )\n            if scope in ('llm', 'both'):\n                llm_cfgs = _do_llm()\n            if scope in ('platform', 'both'):\n                platform_configs, platform_deps = configure_platforms()\n            if scope == 'llm' and platform_infos:\n                for pi in platform_infos:\n                    p = next((x for x in PLATFORMS if x['id'] == pi['id']), None)\n                    if p:\n                        config_dict = {v['key']: v['val'] for v in pi['vars']}\n                        platform_configs.append({'platform': p, 'config': config_dict})\n            elif scope == 'platform' and model_names:\n                old_cfgs = _parse_existing_llm_cfgs()\n                if old_cfgs:\n                    llm_cfgs = old_cfgs\n                    print(f\"\\n  {C['green']}✓ 已保留现有 LLM 配置: {', '.join(c['name'] for c in old_cfgs)}{C['reset']}\")\n                else:\n                    print(f\"\\n  {C['yellow']}⚠ 保留 LLM 配置失败，将生成空配置。建议两项都重新配置。{C['reset']}\")\n\n    if not is_modify:\n        if is_new:\n            hint = \"已备份旧配置，请完成全新设置\"\n        else:\n            hint = \"首次配置，建议同时设置模型和消息平台\"\n        print(f\"  {C['dim']}  {hint}。{C['reset']}\")\n\n        scope = ask_choice(\n            \"你想配置什么？\",\n            [\n                {'id': 'both', 'name': '两项都配置 (推荐)', 'desc': 'LLM 模型 + 消息平台，完整初始化'},\n                {'id': 'llm', 'name': '仅 LLM 模型', 'desc': '只配置模型，稍后再配平台'},\n                {'id': 'platform', 'name': '仅消息平台', 'desc': '只配平台，稍后再配模型'},\n            ],\n            default='both',\n        )\n\n        if scope in ('llm', 'both'):\n            llm_cfgs = _do_llm()\n            if scope == 'llm':\n                if ask_yesno(\"是否继续配置消息平台？\", default=True):\n                    platform_configs, platform_deps = configure_platforms()\n\n        if scope == 'both':\n            platform_configs, platform_deps = configure_platforms()\n\n        if scope == 'platform':\n            platform_configs, platform_deps = configure_platforms()\n            if ask_yesno(\"是否继续配置 LLM 模型？\", default=True):\n                llm_cfgs = _do_llm()\n            elif os.path.exists(MYKPY_PATH):\n                # 新建+仅平台：从备份保留旧 LLM 配置\n                old_cfgs = _parse_existing_llm_cfgs()\n                if old_cfgs:\n                    llm_cfgs = old_cfgs\n                    print(f\"\\n  {C['green']}✓ 已保留备份中的 LLM 配置: {', '.join(c['name'] for c in old_cfgs)}{C['reset']}\")\n\n    # ── 生成 mykey.py ──\n    if not llm_cfgs and not platform_configs:\n        print(f\"\\n  {C['yellow']}⚠ 没有配置任何内容，退出。{C['reset']}\")\n        sys.exit(0)\n\n    content = generate_mykey(llm_cfgs, platform_configs)\n\n    # 备份旧文件（修改模式不备份，直接在原文件修改）\n    if os.path.exists(MYKPY_PATH) and not is_modify and not is_new:\n        backup = _backup_with_name(model_names, [p['id'] for p in platform_infos])\n        print(f\"\\n  {C['green']}✓ 旧配置已备份至:{C['reset']} {C['dim']}{backup}{C['reset']}\")\n\n    # 写入\n    with open(MYKPY_PATH, 'w', encoding='utf-8') as f:\n        f.write(content)\n    print(f\"\\n  {C['green']}✓ mykey.py 已生成!{C['reset']}\")\n\n    # ── 完成提示 ──\n    print(f\"\\n{C['bold']}{C['green']}╔══════════════════════════════════════╗\")\n    print(f\"║      配置完成!                      ║\")\n    print(f\"╚══════════════════════════════════════╝{C['reset']}\")\n    print()\n    if llm_cfgs:\n        print(f\"  {C['cyan']}  终端 REPL:{C['reset']}  python agentmain.py\")\n    if platform_configs:\n        for i, pc in enumerate(platform_configs, 1):\n            p = pc['platform']\n            print(f\"  {C['cyan']}  平台 {i} ({p['name']}):{C['reset']}  python {p['file']}\")\n    print()\n\n    # pip 依赖提示\n    all_deps = sorted(platform_deps)\n    if all_deps:\n        print(f\"  {C['yellow']}💡 提示：你需要安装以下依赖以使消息平台正常工作:{C['reset']}\")\n        print(f\"     {C['cyan']}pip install {' '.join(all_deps)}{C['reset']}\")\n        print()\n\n    # ── 入门示例 ──\n    print(f\"  {C['bold']}试试这些命令:{C['reset']}\")\n    examples = [\n        \"帮我在桌面创建一个 hello.txt，内容是 Hello World\",\n        \"请查看你的代码，安装所有用得上的 python 依赖\",\n        \"执行 web setup sop，解锁 web 工具\",\n        \"打开淘宝，搜索 iPhone 16，按价格排序\",\n        \"用rapidocr配置你的ocr能力并存入记忆\",\n        \"git 更新你的代码，然后看看 commit 有什么新功能\",\n        \"把这个记到你的记忆里\",\n    ]\n    for ex in examples:\n        print(f\"    {C['dim']}{ex}{C['reset']}\")\n    print()\n\n    print(f\"  {C['green']}{C['bold']}合抱之木，生于毫末{C['reset']}\\n\")\n\n\ndef _do_llm():\n    \"\"\"配置 LLM 模型，失败则 exit。\"\"\"\n    cfgs = configure_llms()\n    if not cfgs:\n        print(f\"\\n  {C['red']}✗ 至少需要配置一个模型才能使用。退出。{C['reset']}\")\n        sys.exit(1)\n    return cfgs\n\n\nif __name__ == '__main__':\n    try:\n        main()\n    except KeyboardInterrupt:\n        print(f\"\\n\\n  {C['yellow']}⚠ 用户中断{C['reset']}\")\n        sys.exit(0)\n"}}, "ga_core/mykey_template_en.py": {"eaa7b6c1c04eecdcb522f2dd2333ff37c2ccaaa3": {"t": 1785674428.20537, "n": 3419, "h": "c02311707403c367", "c": "# ══════════════════════════════════════════════════════════════════════════════\n#  GenericAgent — mykey.py configuration template (copy to mykey.py and fill in)\n# ══════════════════════════════════════════════════════════════════════════════\n#\n#  Quick start:\n#    1. Copy this file to mykey.py\n#    2. Uncomment one of the configs below and fill in your apikey\n#    3. Run `python agentmain.py` or `python launch.pyw`\n#\n#  GA auto-detects any variable whose name contains 'api'/'config'/'cookie'\n#  and picks the session class by keyword:\n#      name contains 'native' + 'claude'  → NativeClaudeSession  (Anthropic API)\n#      name contains 'native' + 'oai'     → NativeOAISession     (OpenAI API)\n#      name contains 'mixin'              → MixinSession         (failover)\n#\n#  Native = tools go in the API's native `tool` field (function calling), same\n#  way Claude Code and Codex do it. Recommended for GPT / Claude / Gemini.\n#\n#  Tip: runtime overrides via `/session.<attr>=<val>` in the REPL, e.g.\n#      /session.reasoning_effort=high\n#      /session.thinking_type=adaptive\n#      /session.temperature=0.3\n#\n# ══════════════════════════════════════════════════════════════════════════════\n\n\n# ── 1. NativeClaudeSession — Anthropic direct ────────────────────────────────\n#  Official Anthropic endpoint. apikey starting with 'sk-ant-' is auto-sent\n#  as x-api-key; any other prefix uses Authorization: Bearer.\n#  Model suffix '[1m]' triggers the 1M-context beta (stripped before sending).\nnative_claude_config = {\n    'name': 'claude',                         # display name & mixin reference\n    'apikey': 'sk-ant-<your-anthropic-key>',\n    'apibase': 'https://api.anthropic.com',\n    'model': 'claude-opus-4-7[1m]',           # or 'claude-sonnet-4-6'\n    'thinking_type': 'adaptive',              # 'adaptive' | 'enabled' | 'disabled'\n    # 'thinking_budget_tokens': 32768,        # required if thinking_type='enabled'\n    # 'max_retries': 3,\n    # 'read_timeout': 180,\n}\n\n\n# ── 2. NativeOAISession — OpenAI direct ──────────────────────────────────────\n#  Standard OpenAI chat/completions endpoint. Also works for any OpenAI-\n#  compatible provider that supports native function-calling tool fields.\nnative_oai_config = {\n    'name': 'gpt',                            # display name & mixin reference\n    'apikey': 'sk-<your-openai-key>',\n    'apibase': 'https://api.openai.com/v1',\n    'model': 'gpt-5.4',                       # or 'o4', 'gpt-5.3-codex', etc.\n    'api_mode': 'chat_completions',           # or 'responses' for /v1/responses\n    # 'reasoning_effort': 'high',             # none|minimal|low|medium|high|xhigh\n    # 'max_retries': 3,\n    # 'read_timeout': 120,\n}\n\n\n# ── 3. Mixin failover (optional) ─────────────────────────────────────────────\n#  List sessions by 'name'; if one fails, the next is tried automatically.\n#  Constraint: all referenced sessions must be Native (mixing Native Claude\n#  and Native OAI is fine; mixing Native with non-Native is not).\n# mixin_config = {\n#     'llm_nos': ['claude', 'gpt'],\n#     'max_retries': 5,\n#     'base_delay': 0.5,\n# }\n\n\n# ── 4. Global HTTP proxy (optional) ──────────────────────────────────────────\n#  Applies to every session that doesn't set its own 'proxy' field.\n# proxy = 'http://127.0.0.1:7890'\n\n\n# ── 5. Chat platform integrations (optional) ─────────────────────────────────\n# tg_bot_token = '...'\n# tg_allowed_users = [123456789]\n"}}, "ga_core/llmcore.py": {"eaa7b6c1c04eecdcb522f2dd2333ff37c2ccaaa3": {"t": 1785674432.0525408, "n": 60964, "h": "21f4af4820d745fd", "c": "import os, json, re, time, requests, sys, threading, urllib3, base64, importlib, uuid, pathlib\nfrom datetime import datetime\nurllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n_RESP_CACHE_KEY = str(uuid.uuid4()); _RESP_CODEX_KEY = str(uuid.uuid4())\n_ROOT = os.path.dirname(os.path.abspath(__file__))\nif _ROOT not in sys.path: sys.path.append(_ROOT)\n\ndef _load_mykeys():\n    global _mykey_path\n    try:\n        import mykey; importlib.reload(mykey); _mykey_path = mykey.__file__\n        return {k: v for k, v in vars(mykey).items() if not k.startswith('_')}\n    except ImportError as e:\n        if getattr(e, 'name', None) != 'mykey':\n            raise Exception(f'[ERROR] mykey.py found but failed to import: {e}') from e\n    except SyntaxError as e:\n        raise Exception(f'[ERROR] mykey.py has syntax error: {e}') from e\n    p = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'mykey.json')\n    if not os.path.exists(p): raise Exception('[ERROR] mykey.py not found in sys.path and mykey.json not found. Run \"python configure_mykey.py\" or copy mykey_template.py to mykey.py and fill in your keys.')\n    with open(_mykey_path := p, encoding='utf-8') as f: mk = json.load(f)\n    if isinstance(mk, dict) and 'remote_url' in mk: return requests.get(mk['remote_url'], timeout=10).json()\n    return mk\n\n_mykey_path = _mykey_mtime = None\ndef reload_mykeys():\n    global _mykey_mtime\n    try:\n        mt = os.stat(_mykey_path).st_mtime_ns if _mykey_path else -1\n        if mt == _mykey_mtime: return globals().get('mykeys', {}), False\n        mk = _load_mykeys(); _mykey_mtime = os.stat(_mykey_path).st_mtime_ns\n        print(f'[Info] Load mykeys from {_mykey_path}')\n        globals().update(mykeys=mk)\n        return mk, True\n    except: return globals().get('mykeys', {}), False\n\ndef __getattr__(name):  # once guard in PEP 562\n    if name == 'mykeys': return reload_mykeys()[0]\n    raise AttributeError(f\"module 'llmcore' has no attribute {name}\")\n\ndef compress_history_tags(messages, keep_recent=10, max_len=800, force=False, interval=5):\n    \"\"\"Compress <thinking>/<tool_use>/<tool_result> tags in older messages to save tokens.\"\"\"\n    compress_history_tags._cd = getattr(compress_history_tags, '_cd', 0) + 1\n    if force: compress_history_tags._cd = 0\n    if compress_history_tags._cd % interval != 0: return messages\n    _before = sum(len(json.dumps(m, ensure_ascii=False)) for m in messages)\n    _pats = {tag: re.compile(rf'(<{tag}>)([\\s\\S]*?)(</{tag}>)') for tag in ('thinking', 'think', 'tool_use', 'tool_result')}\n    _hist_pat = re.compile(r'<(history|key_info|earlier_context)>[\\s\\S]*?</\\1>')\n    def _trunc_str(s): return s[:max_len//2] + '\\n...[Truncated]...\\n' + s[-max_len//2:] if isinstance(s, str) and len(s) > max_len else s\n    def _trunc(text):\n        text = _hist_pat.sub(lambda m: f'<{m.group(1)}>[...]</{m.group(1)}>', text)\n        for pat in _pats.values(): text = pat.sub(lambda m: m.group(1) + _trunc_str(m.group(2)) + m.group(3), text)\n        return text\n    for i, msg in enumerate(messages):\n        if i >= len(messages) - keep_recent: break\n        c = msg['content']\n        if isinstance(c, str): msg['content'] = _trunc(c)\n        elif isinstance(c, list):\n            for b in c:\n                if not isinstance(b, dict): continue\n                t = b.get('type')\n                if t == 'text' and isinstance(b.get('text'), str): b['text'] = _trunc(b['text'])\n                elif t == 'tool_result':\n                    tc = b.get('content')\n                    if isinstance(tc, str): b['content'] = _trunc_str(tc)\n                    elif isinstance(tc, list):\n                        for sub in tc:\n                            if isinstance(sub, dict) and sub.get('type') == 'text': sub['text'] = _trunc_str(sub.get('text'))\n                elif t == 'tool_use' and isinstance(b.get('input'), dict):\n                    for k, v in b['input'].items(): b['input'][k] = _trunc_str(v)\n    print(f\"[Cut] {_before} -> {sum(len(json.dumps(m, ensure_ascii=False)) for m in messages)}\")\n    return messages\n\ndef _sanitize_leading_user_msg(msg):\n    \"\"\"把 user 消息里的 tool_result 块改写成纯文本，避免孤立引用。\n    history 统一使用 Claude content-block 格式：content 是 list of blocks。\"\"\"\n    msg = dict(msg)  # 浅拷贝外层 dict\n    content = msg.get('content')\n    if not isinstance(content, list): return msg\n    texts = []\n    for block in content:\n        if not isinstance(block, dict): continue\n        if block.get('type') == 'tool_result':\n            c = block.get('content', '')\n            if isinstance(c, list):  # content 本身也可能是 list[{type:text,text:...}]\n                texts.extend(b.get('text', '') for b in c if isinstance(b, dict))\n            else: texts.append(str(c))\n        elif block.get('type') == 'text': texts.append(block.get('text', ''))\n    msg['content'] = [{\"type\": \"text\", \"text\": '\\n'.join(t for t in texts if t)}]\n    return msg\n\n_oldprint = print\ndef safeprint(*argv):\n    try: _oldprint(*argv)\n    except OSError: pass\nprint = safeprint\n\ndef trim_messages_history(history, sess):\n    cap = sess.context_win * 3\n    target = int(cap * getattr(sess, 'trim_keep_rate', 0.6))\n    def cost(): return sum(len(json.dumps(m, ensure_ascii=False)) for m in history)\n    compress_history_tags(history, interval=getattr(sess, 'cut_msg_interval', 5))\n    print(f'[Debug] Current context: {cost()} chars, {len(history)} messages.')\n    if cost() <= cap: return\n    compress_history_tags(history, keep_recent=4, force=True)\n    if cost() <= target: return\n    while len(history) > 9 and cost() > target:\n        history.pop(0)\n        while history and history[0].get('role') != 'user': history.pop(0)\n        if history and history[0].get('role') == 'user': history[0] = _sanitize_leading_user_msg(history[0])\n    print(f'[Debug] Trimmed context, current: {cost()} chars, {len(history)} messages.')\n\ndef auto_make_url(base, path):\n    b, p = base.rstrip('/'), path.strip('/')\n    if b.endswith('$'): return b[:-1].rstrip('/')\n    if b.endswith(p): return b\n    return f\"{b}/{p}\" if re.search(r'/v\\d+(/|$)', b) else f\"{b}/v1/{p}\"\n\ndef _parse_claude_json(data):\n    content_blocks = data.get(\"content\", [])\n    _record_usage(data.get(\"usage\", {}), \"messages\")\n    for b in content_blocks:\n        if b.get(\"type\") == \"text\": yield b.get(\"text\", \"\")\n        elif b.get(\"type\") == \"thinking\": yield \"\"\n    return content_blocks\n\ndef _parse_claude_sse(resp_lines):\n    \"\"\"Parse Anthropic SSE stream. Yields text chunks, returns list[content_block].\"\"\"\n    content_blocks = []; current_block = None; tool_json_buf = \"\"\n    stop_reason = None; got_message_stop = False; warn = None\n    for line in resp_lines:\n        if not line: continue\n        line = line.decode('utf-8') if isinstance(line, bytes) else line\n        if not line.startswith(\"data:\"): continue\n        data_str = line[5:].lstrip()\n        if data_str == \"[DONE]\": break\n        try: evt = json.loads(data_str)\n        except Exception as e:\n            print(f\"[SSE] JSON parse error: {e}, line: {data_str[:200]}\")\n            continue\n        evt_type = evt.get(\"type\", \"\")\n        if evt_type == \"message_start\":\n            usage = evt.get(\"message\", {}).get(\"usage\", {})\n            _record_usage(usage, \"messages\")\n        elif evt_type == \"content_block_start\":\n            block = evt.get(\"content_block\", {})\n            if block.get(\"type\") == \"text\": current_block = {\"type\": \"text\", \"text\": \"\"}\n            elif block.get(\"type\") == \"thinking\": current_block = {\"type\": \"thinking\", \"thinking\": \"\", \"signature\": \"\"}\n            elif block.get(\"type\") == \"tool_use\":\n                current_block = {\"type\": \"tool_use\", \"id\": block.get(\"id\", \"\"), \"name\": block.get(\"name\", \"\"), \"input\": {}}\n                tool_json_buf = \"\"\n        elif evt_type == \"content_block_delta\":\n            delta = evt.get(\"delta\", {})\n            if delta.get(\"type\") == \"text_delta\":\n                text = delta.get(\"text\", \"\")\n                if current_block and current_block.get(\"type\") == \"text\": current_block[\"text\"] += text\n                if text: yield text\n            elif delta.get(\"type\") == \"thinking_delta\":\n                if current_block and current_block.get(\"type\") == \"thinking\": current_block[\"thinking\"] += delta.get(\"thinking\", \"\")\n            elif delta.get(\"type\") == \"signature_delta\":\n                if current_block and current_block.get(\"type\") == \"thinking\":\n                    current_block[\"signature\"] = current_block.get(\"signature\", \"\") + delta.get(\"signature\", \"\")\n            elif delta.get(\"type\") == \"input_json_delta\": tool_json_buf += delta.get(\"partial_json\", \"\")\n        elif evt_type == \"content_block_stop\":\n            if current_block:\n                if current_block[\"type\"] == \"tool_use\":\n                    try: current_block[\"input\"] = json.loads(tool_json_buf) if tool_json_buf else {}\n                    except: current_block[\"input\"] = {\"_raw\": tool_json_buf}\n                content_blocks.append(current_block)\n                current_block = None\n        elif evt_type == \"message_delta\":\n            delta = evt.get(\"delta\", {})\n            stop_reason = delta.get(\"stop_reason\", stop_reason)\n            out_usage = evt.get(\"usage\", {})\n            out_tokens = out_usage.get(\"output_tokens\", 0)\n            if out_tokens: print(f\"[Output] tokens={out_tokens} stop_reason={stop_reason}\")\n        elif evt_type == \"message_stop\": got_message_stop = True\n        elif evt_type == \"error\":\n            err = evt.get(\"error\", {})\n            emsg = err.get(\"message\", str(err)) if isinstance(err, dict) else str(err)\n            warn = f\"\\n\\n!!!Error: SSE {emsg}\"; break\n    if not warn:\n        if not got_message_stop and not stop_reason: warn = \"\\n\\n[!!! 流异常中断，未收到完整响应 !!!]\"\n        elif stop_reason == \"max_tokens\": warn = \"\\n\\n[!!! Response truncated: max_tokens !!!]\"\n    if current_block:\n        if current_block[\"type\"] == \"tool_use\":\n            try: current_block[\"input\"] = json.loads(tool_json_buf) if tool_json_buf else {}\n            except: current_block[\"input\"] = {\"_raw\": tool_json_buf}\n        content_blocks.append(current_block); current_block = None\n    if warn:\n        print(f\"[WARN] {warn.strip()}\")\n        insert_at = next((i for i,b in enumerate(content_blocks) if b.get(\"type\") == \"tool_use\"), len(content_blocks))\n        content_blocks.insert(insert_at, {\"type\": \"text\", \"text\": warn}); yield warn\n    return content_blocks\n\ndef _try_parse_tool_args(raw):\n    \"\"\"Parse tool args string; split concatenated JSON objects like {..}{..} if needed.\n    Returns list of parsed dicts.\"\"\"\n    if not raw: return [{}]\n    try: return [json.loads(raw)]\n    except: pass\n    parts = re.split(r'(?<=\\})(?=\\{)', raw)\n    if len(parts) > 1:\n        parsed = []\n        for p in parts:\n            try: parsed.append(json.loads(p))\n            except: return [{\"_raw\": raw}]\n        return parsed\n    return [{\"_raw\": raw}]\n\ndef _parse_openai_sse(resp_lines, api_mode=\"chat_completions\"):\n    \"\"\"Parse OpenAI SSE stream (chat_completions or responses API).\n    Yields text chunks, returns list[content_block].\n    content_block: {type:'text', text:str} | {type:'tool_use', id:str, name:str, input:dict}\n    \"\"\"\n    content_text = \"\"\n    if api_mode == \"responses\":\n        seen_delta = False; fc_buf = {}; current_fc_idx = None\n        for line in resp_lines:\n            if not line: continue\n            line = line.decode('utf-8', errors='replace') if isinstance(line, bytes) else line\n            if not line.startswith(\"data:\"): continue\n            data_str = line[5:].lstrip()\n            if data_str == \"[DONE]\": break\n            try: evt = json.loads(data_str)\n            except: continue\n            etype = evt.get(\"type\", \"\")\n            if etype == \"response.output_text.delta\":\n                delta = evt.get(\"delta\", \"\")\n                if delta: seen_delta = True; content_text += delta; yield delta\n            elif etype == \"response.output_text.done\" and not seen_delta:\n                text = evt.get(\"text\", \"\")\n                if text: content_text += text; yield text\n            elif etype == \"response.output_item.added\":\n                item = evt.get(\"item\", {})\n                if item.get(\"type\") == \"function_call\":\n                    idx = evt.get(\"output_index\", 0)\n                    fc_buf[idx] = {\"id\": item.get(\"call_id\", item.get(\"id\", \"\")), \"name\": item.get(\"name\", \"\"), \"args\": \"\"}\n                    current_fc_idx = idx\n            elif etype == \"response.function_call_arguments.delta\":\n                idx = evt.get(\"output_index\", current_fc_idx or 0)\n                if idx in fc_buf: fc_buf[idx][\"args\"] += evt.get(\"delta\", \"\")\n            elif etype == \"response.function_call_arguments.done\":\n                idx = evt.get(\"output_index\", current_fc_idx or 0)\n                if idx in fc_buf: fc_buf[idx][\"args\"] = evt.get(\"arguments\", fc_buf[idx][\"args\"])\n            elif etype == \"error\":\n                err = evt.get(\"error\", {})\n                emsg = err.get(\"message\", str(err)) if isinstance(err, dict) else str(err)\n                if emsg: content_text += f\"!!!Error: {emsg}\"; yield f\"!!!Error: {emsg}\"\n                break\n            elif etype == \"response.completed\":\n                usage = evt.get(\"response\", {}).get(\"usage\", {})\n                _record_usage(usage, api_mode)\n                break\n        blocks = []\n        if content_text: blocks.append({\"type\": \"text\", \"text\": content_text})\n        for idx in sorted(fc_buf):\n            fc = fc_buf[idx]\n            inps = _try_parse_tool_args(fc[\"args\"])\n            for i, inp in enumerate(inps):\n                bid = fc[\"id\"] or ''\n                if len(inps) > 1: bid = f\"{bid}_{i}\" if bid else f\"split_{i}\"\n                blocks.append({\"type\": \"tool_use\", \"id\": bid, \"name\": fc[\"name\"], \"input\": inp})\n        return blocks\n    else:\n        tc_buf = {}  # index -> {id, name, args}\n        reasoning_text = \"\"\n        for line in resp_lines:\n            if not line: continue\n            line = line.decode('utf-8', errors='replace') if isinstance(line, bytes) else line\n            if not line.startswith(\"data:\"): continue\n            data_str = line[5:].lstrip()\n            if data_str == \"[DONE]\": break\n            try: evt = json.loads(data_str)\n            except: continue\n            ch = (evt.get(\"choices\") or [{}])[0]\n            delta = ch.get(\"delta\") or {}\n            if delta.get(\"reasoning_content\"):\n                reasoning_text += delta[\"reasoning_content\"]\n            if delta.get(\"content\"):\n                text = delta[\"content\"]; content_text += text; yield text\n            for tc in (delta.get(\"tool_calls\") or []):\n                idx = tc.get(\"index\", 0)\n                has_name = bool(tc.get(\"function\", {}).get(\"name\"))\n                if idx not in tc_buf:\n                    if has_name or not tc_buf: tc_buf[idx] = {\"id\": tc.get(\"id\") or '', \"name\": \"\", \"args\": \"\"}\n                    else: idx = max(tc_buf)\n                if has_name: tc_buf[idx][\"name\"] = tc[\"function\"][\"name\"]\n                if tc.get(\"function\", {}).get(\"arguments\"): tc_buf[idx][\"args\"] += tc[\"function\"][\"arguments\"]\n                if tc.get(\"id\") and not tc_buf[idx][\"id\"]: tc_buf[idx][\"id\"] = tc[\"id\"]\n            usage = evt.get(\"usage\")\n            if usage: _record_usage(usage, api_mode)\n        blocks = []\n        if reasoning_text: blocks.append({\"type\": \"thinking\", \"thinking\": reasoning_text})\n        if content_text: blocks.append({\"type\": \"text\", \"text\": content_text})\n        for idx in sorted(tc_buf):\n            tc = tc_buf[idx]\n            inps = _try_parse_tool_args(tc[\"args\"])\n            for i, inp in enumerate(inps):\n                bid = tc[\"id\"] or ''\n                if len(inps) > 1: bid = f\"{bid}_{i}\" if bid else f\"split_{i}\"\n                blocks.append({\"type\": \"tool_use\", \"id\": bid, \"name\": tc[\"name\"], \"input\": inp})\n        return blocks\n\ndef _record_usage(usage, api_mode):\n    if not usage: return\n    if api_mode == 'responses':\n        cached = (usage.get(\"input_tokens_details\") or {}).get(\"cached_tokens\", 0)\n        inp = usage.get(\"input_tokens\", 0); out = usage.get(\"output_tokens\", 0)\n        print(f\"[Cache] input={inp} cached={cached}\")\n        if out: print(f\"[Output] tokens={out}\")\n    elif api_mode == 'chat_completions':\n        cached = (usage.get(\"prompt_tokens_details\") or {}).get(\"cached_tokens\", 0)\n        inp = usage.get(\"prompt_tokens\", 0); out = usage.get(\"completion_tokens\", 0)\n        print(f\"[Cache] input={inp} cached={cached}\")\n        if out: print(f\"[Output] tokens={out}\")\n    elif api_mode == 'messages':\n        ci, cr, inp = usage.get(\"cache_creation_input_tokens\", 0), usage.get(\"cache_read_input_tokens\", 0), usage.get(\"input_tokens\", 0)\n        print(f\"[Cache] input={inp} creation={ci} read={cr}\")\n    \ndef _parse_openai_json(data, api_mode=\"chat_completions\"):\n    blocks = []\n    if api_mode == \"responses\":\n        _record_usage(data.get(\"usage\") or {}, api_mode)\n        for item in (data.get(\"output\") or []):\n            if item.get(\"type\") == \"message\":\n                for p in (item.get(\"content\") or []):\n                    if p.get(\"type\") in (\"output_text\", \"text\") and p.get(\"text\"):\n                        blocks.append({\"type\": \"text\", \"text\": p[\"text\"]}); yield p[\"text\"]\n            elif item.get(\"type\") == \"function_call\":\n                try: args = json.loads(item.get(\"arguments\", \"\")) if item.get(\"arguments\") else {}\n                except: args = {\"_raw\": item.get(\"arguments\", \"\")}\n                blocks.append({\"type\": \"tool_use\", \"id\": item.get(\"call_id\", item.get(\"id\", \"\")),\n                               \"name\": item.get(\"name\", \"\"), \"input\": args})\n    else:\n        _record_usage(data.get(\"usage\") or {}, api_mode)\n        msg = (data.get(\"choices\") or [{}])[0].get(\"message\", {})\n        reasoning = msg.get(\"reasoning_content\", \"\")\n        if reasoning:\n            blocks.append({\"type\": \"thinking\", \"thinking\": reasoning})\n        content = msg.get(\"content\", \"\")\n        if content:\n            blocks.append({\"type\": \"text\", \"text\": content}); yield content\n        for tc in (msg.get(\"tool_calls\") or []):\n            fn = tc.get(\"function\", {})\n            try: args = json.loads(fn.get(\"arguments\", \"\")) if fn.get(\"arguments\") else {}\n            except: args = {\"_raw\": fn.get(\"arguments\", \"\")}\n            blocks.append({\"type\": \"tool_use\", \"id\": tc.get(\"id\", \"\"), \"name\": fn.get(\"name\", \"\"), \"input\": args})\n    return blocks\n\ndef _stamp_oai_cache_markers(messages, model):\n    \"\"\"Add cache_control to last 2 user messages for Anthropic models via OAI-compatible relay.\"\"\"\n    ml = model.lower()\n    if not any(k in ml for k in ('claude', 'anthropic')): return\n    user_idxs = [i for i, m in enumerate(messages) if m.get('role') == 'user']\n    for idx in user_idxs[-2:]:\n        c = messages[idx].get('content')\n        if isinstance(c, str):\n            messages[idx] = {**messages[idx], 'content': [{'type': 'text', 'text': c, 'cache_control': {'type': 'ephemeral'}}]}\n        elif isinstance(c, list) and c:\n            c = list(c); c[-1] = dict(c[-1], cache_control={'type': 'ephemeral'})\n            messages[idx] = {**messages[idx], 'content': c}\n\ndef _stream_with_retry(sess, url, headers, payload, parse_fn):\n    _RETRYABLE = {408, 409, 425, 429, 500, 502, 503, 504, 520, 521, 522, 523, 524, 525, 526, 527, 529}\n    def _delay(resp, attempt):\n        try: ra = float((resp.headers or {}).get(\"retry-after\"))\n        except: ra = None\n        return max(0.5, ra if ra is not None else min(30.0, 1.5 * (2 ** attempt)))\n    for attempt in range(sess.max_retries + 1):\n        streamed = False\n        try:\n            with requests.post(url, headers=headers, json=payload, stream=sess.stream, \n                               timeout=(sess.connect_timeout, sess.read_timeout), proxies=sess.proxies, verify=sess.verify) as r:\n                if r.status_code >= 400:\n                    if r.status_code in _RETRYABLE and attempt < sess.max_retries:\n                        d = _delay(r, attempt)\n                        print(f\"[LLM Retry] HTTP {r.status_code}, retry in {d:.1f}s ({attempt+1}/{sess.max_retries+1})\")\n                        time.sleep(d); continue\n                    try: body = r.text.strip()[:500]\n                    except: body = \"\"\n                    err = f\"!!!Error: HTTP {r.status_code}\" + (f\": {body}\" if body else \"\")\n                    yield err; return [{\"type\": \"text\", \"text\": err}]\n                gen = parse_fn(r)\n                try:\n                    while True: chunk = next(gen); streamed = True; yield chunk\n                except StopIteration as e:\n                    if not e.value and not streamed: raise requests.ConnectionError(\"empty response\")\n                    return e.value or []\n        except (requests.Timeout, requests.ConnectionError) as e:\n            #pathlib.Path(__file__).parent.joinpath('temp','bad_requests.json').write_text(json.dumps({\"url\":url,\"headers\":headers,\"payload\":payload,\"err\":str(e),\"t\":time.time()},ensure_ascii=False),encoding='utf-8')\n            err = f\"!!!Error: {type(e).__name__}: {e}\" if str(e) else f\"!!!Error: {type(e).__name__}\"\n            if attempt < sess.max_retries:\n                d = _delay(None, attempt)\n                print(f\"[LLM Retry] {type(e).__name__}, retry in {d:.1f}s ({attempt+1}/{sess.max_retries+1})\")\n                yield err; time.sleep(d); continue\n            yield err; return [{\"type\": \"text\", \"text\": err}]\n        except Exception as e:\n            err = f\"\\n\\n[!!! 流异常中断 {type(e).__name__}: {e} !!!]\" if streamed else f\"!!!Error: {type(e).__name__}: {e}\"\n            yield err; return [{\"type\": \"text\", \"text\": err}]\n\ndef _openai_stream(sess, messages):\n    model, api_mode = sess.model, sess.api_mode\n    ml = model.lower()\n    temperature = sess.temperature\n    if 'kimi' in ml or 'moonshot' in ml: temperature = 1\n    elif 'minimax' in ml: temperature = max(0.01, min(temperature, 1.0))  # MiniMax requires temp in (0, 1]\n    headers = {\"Authorization\": f\"Bearer {sess.api_key}\", \"Content-Type\": \"application/json\", \"Accept\": \"text/event-stream\", 'originator': 'codex_exec'}\n    headers[\"User-Agent\"] = sess.user_agent\n    if api_mode == \"responses\":\n        url = auto_make_url(sess.api_base, \"responses\")\n        payload = {\"model\": model, \"input\": _to_responses_input(messages), \"stream\": sess.stream, \n                   \"prompt_cache_key\": _RESP_CACHE_KEY, \"instructions\": sess.system or \"You are an Omnipotent Executor.\",\n                   \"client_metadata\": {\"x-codex-window-id\": f\"{_RESP_CACHE_KEY}:0\",\"x-codex-installation-id\": _RESP_CODEX_KEY},\n                   'include': ['reasoning.encrypted_content']}\n        if sess.reasoning_effort: payload[\"reasoning\"] = {\"effort\": sess.reasoning_effort}\n        if sess.max_tokens: payload[\"max_output_tokens\"] = sess.max_tokens\n    else:\n        url = auto_make_url(sess.api_base, \"chat/completions\")\n        if sess.system: messages = [{\"role\": \"system\", \"content\": sess.system}] + messages\n        _stamp_oai_cache_markers(messages, model)\n        payload = {\"model\": model, \"messages\": messages, \"stream\": sess.stream}\n        if sess.stream: payload[\"stream_options\"] = {\"include_usage\": True}\n        if temperature != 1: payload[\"temperature\"] = temperature\n        if sess.max_tokens: payload[\"max_completion_tokens\" if ml.startswith((\"gpt-5\", \"o1\", \"o2\", \"o3\", \"o4\")) else \"max_tokens\"] = sess.max_tokens\n        if sess.reasoning_effort: payload[\"reasoning_effort\"] = sess.reasoning_effort\n    tools = getattr(sess, 'tools', None)\n    if tools: payload[\"tools\"] = _prepare_oai_tools(tools, api_mode)\n    if sess.service_tier: payload[\"service_tier\"] = sess.service_tier\n    parse_fn = (lambda r: _parse_openai_sse(r.iter_lines(), api_mode)) if sess.stream else (lambda r: _parse_openai_json(r.json(), api_mode))\n    return (yield from _stream_with_retry(sess, url, headers, payload, parse_fn))\n        \ndef _prepare_oai_tools(tools, api_mode=\"chat_completions\"):\n    if api_mode == \"responses\":\n        resp_tools = []\n        for t in tools:\n            if t.get(\"type\") == \"function\" and \"function\" in t:\n                rt = {\"type\": \"function\"}; rt.update(t[\"function\"])\n                resp_tools.append(rt)\n            else: resp_tools.append(t)\n        return resp_tools\n    return tools\n\ndef _to_responses_input(messages):\n    result, pending = [], []\n    for msg in messages:\n        role = str(msg.get(\"role\", \"user\")).lower()\n        if role == \"tool\":\n            cid = msg.get(\"tool_call_id\") or (pending.pop(0) if pending else f\"call_{uuid.uuid4().hex[:8]}\")\n            result.append({\"type\": \"function_call_output\", \"call_id\": cid, \"output\": msg.get(\"content\", \"\")})\n            continue\n        if role not in [\"user\", \"assistant\", \"system\", \"developer\"]: role = \"user\"\n        if role == \"system\": role = \"developer\"  # Responses API uses 'developer' instead of 'system'\n        content = msg.get(\"content\", \"\")\n        text_type = \"output_text\" if role == \"assistant\" else \"input_text\"\n        parts = []\n        if isinstance(content, str):\n            if content: parts.append({\"type\": text_type, \"text\": content})\n        elif isinstance(content, list):\n            for part in content:\n                if not isinstance(part, dict): continue\n                ptype = part.get(\"type\")\n                if ptype == \"text\":\n                    text = part.get(\"text\", \"\")\n                    if text: parts.append({\"type\": text_type, \"text\": text})\n                elif ptype == \"image_url\":\n                    url = (part.get(\"image_url\") or {}).get(\"url\", \"\")\n                    if url and role != \"assistant\": parts.append({\"type\": \"input_image\", \"image_url\": url})\n        if len(parts) == 0: parts = [{\"type\": text_type, \"text\": str(content) if not isinstance(content, list) else '[empty]'}]\n        result.append({\"role\": role, \"content\": parts})\n        pending = []\n        for tc in (msg.get(\"tool_calls\") or []):\n            f = tc.get(\"function\", {})\n            cid = tc.get(\"id\") or f\"call_{uuid.uuid4().hex[:8]}\"\n            pending.append(cid)\n            result.append({\"type\": \"function_call\", \"call_id\": cid, \"name\": f.get(\"name\", \"\"), \"arguments\": f.get(\"arguments\", \"\")})\n    return result\n\n\ndef _msgs_claude2oai(messages):\n    result = []\n    for msg in messages:\n        role = msg.get(\"role\", \"user\")\n        content = msg.get(\"content\", \"\")\n        blocks = content if isinstance(content, list) else [{\"type\": \"text\", \"text\": str(content)}]\n        if role == \"assistant\":\n            text_parts, tool_calls, reasoning = [], [], \"\"\n            for b in blocks:\n                if not isinstance(b, dict): continue\n                if b.get(\"type\") == \"thinking\" and b.get(\"thinking\"): reasoning = b[\"thinking\"]\n                elif b.get(\"type\") == \"text\" and b.get(\"text\"): text_parts.append({\"type\": \"text\", \"text\": b.get(\"text\", \"\")})\n                elif b.get(\"type\") == \"tool_use\":\n                    tool_calls.append({\n                        \"id\": b.get(\"id\") or '', \"type\": \"function\",\n                        \"function\": {\"name\": b.get(\"name\", \"\"), \"arguments\": json.dumps(b.get(\"input\", {}), ensure_ascii=False)}\n                    })\n            m = {\"role\": \"assistant\"}\n            if reasoning: m[\"reasoning_content\"] = reasoning\n            if text_parts: m[\"content\"] = text_parts\n            elif not tool_calls: m[\"content\"] = \".\"\n            if tool_calls: m[\"tool_calls\"] = tool_calls\n            result.append(m)\n        elif role == \"user\":\n            text_parts = []\n            for b in blocks:\n                if not isinstance(b, dict): continue\n                if b.get(\"type\") == \"tool_result\":\n                    if text_parts:\n                        result.append({\"role\": \"user\", \"content\": text_parts})\n                        text_parts = []\n                    tr = b.get(\"content\", \"\")\n                    if isinstance(tr, list):\n                        tr = \"\\n\".join(x.get(\"text\", \"\") for x in tr if isinstance(x, dict) and x.get(\"type\") == \"text\")\n                    result.append({\"role\": \"tool\", \"tool_call_id\": b.get(\"tool_use_id\") or '', \"content\": tr if isinstance(tr, str) else str(tr)})\n                elif b.get(\"type\") == \"image\":\n                    src = b.get(\"source\") or {}\n                    if src.get(\"type\") == \"base64\" and src.get(\"data\"):\n                        text_parts.append({\"type\": \"image_url\", \"image_url\": {\"url\": f\"data:{src.get('media_type', 'image/png')};base64,{src.get('data', '')}\"}})\n                elif b.get(\"type\") == \"image_url\": text_parts.append(b)\n                elif b.get(\"type\") == \"text\" and b.get(\"text\"): text_parts.append({\"type\": \"text\", \"text\": b.get(\"text\", \"\")})\n            if text_parts: result.append({\"role\": \"user\", \"content\": text_parts})\n        else: result.append(msg)\n    return result\n\n\nclass BaseSession:\n    def __init__(self, cfg):\n        self.api_key = cfg['apikey']\n        self.api_base = cfg['apibase'].rstrip('/')\n        self.model = cfg.get('model', '')\n        default_context_win = 30000\n        if 'deepseek' in self.model.lower():\n            default_context_win = 70000; self.cut_msg_interval = 25; self.trim_keep_rate = 0.3\n        self.context_win = cfg.get('context_win', default_context_win)\n        self.history = []; self.lock = threading.Lock(); self.system = \"\"\n        self.name = cfg.get('name', self.model)\n        proxy = cfg.get('proxy'); \n        self.proxies = {\"http\": proxy, \"https\": proxy} if proxy else None\n        self.max_retries = max(0, int(cfg.get('max_retries', 4)))\n        self.verify = cfg.get('verify', True)\n        self.stream = cfg.get('stream', True)\n        default_ct, default_rt = (5, 40) if self.stream else (10, 240)\n        self.connect_timeout = max(1, int(cfg.get('timeout', default_ct)))\n        self.read_timeout = max(5, int(cfg.get('read_timeout', default_rt)))\n        def _enum(key, valid):\n            v = cfg.get(key); v = None if v is None else str(v).strip().lower()\n            return v if not v or v in valid else print(f\"[WARN] Invalid {key} {v!r}, ignored.\")\n        self.reasoning_effort = _enum('reasoning_effort', {'none', 'minimal', 'low', 'medium', 'high', 'xhigh'})\n        self.service_tier = _enum('service_tier', {'auto', 'default', 'priority', 'flex'})\n        self.thinking_type = _enum('thinking_type', {'adaptive', 'enabled', 'disabled'})\n        self.thinking_budget_tokens = cfg.get('thinking_budget_tokens')\n        mode = str(cfg.get('api_mode', 'chat_completions')).strip().lower().replace('-', '_')\n        self.api_mode = 'responses' if mode in ('responses', 'response') else 'chat_completions'\n        self.temperature = cfg.get('temperature', 1)\n        self.max_tokens = cfg.get('max_tokens')\n        self.default_ua = \"claude-cli/2.1.152 (external, cli)\"\n        self.user_agent = cfg.get(\"user_agent\", self.default_ua)\n    def _apply_claude_thinking(self, payload):\n        if self.thinking_type:\n            thinking = {\"type\": self.thinking_type}\n            if self.thinking_type == 'enabled':\n                if self.thinking_budget_tokens is None: print(\"[WARN] thinking_type='enabled' requires thinking_budget_tokens, ignored.\")\n                else:\n                    thinking[\"budget_tokens\"] = self.thinking_budget_tokens; payload[\"thinking\"] = thinking\n            else: payload[\"thinking\"] = thinking\n        if self.reasoning_effort:\n            effort = {'low': 'low', 'medium': 'medium', 'high': 'high', 'xhigh': 'max'}.get(self.reasoning_effort)\n            if effort: payload[\"output_config\"] = {\"effort\": effort}\n            else: print(f\"[WARN] reasoning_effort {self.reasoning_effort!r} is unsupported for Claude output_config.effort, ignored.\")\n    def ask(self, prompt):\n        def _ask_gen():\n            with self.lock:\n                self.history.append({\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": prompt}]})\n                trim_messages_history(self.history, self)\n                messages = self.make_messages(self.history)\n            content_blocks = None; content = ''\n            gen = self.raw_ask(messages)\n            try:\n                while True: chunk = next(gen); content += chunk; yield chunk\n            except StopIteration as e: content_blocks = e.value or []\n            if len(content_blocks) > 1: print(f\"[DEBUG BaseSession.ask] content_blocks: {content_blocks}\")\n            for block in (content_blocks or []):\n                if block.get('type', '') == 'tool_use':\n                    tu = {'name': block.get('name', ''), 'arguments': block.get('input', {})}\n                    yield f'<tool_use>{json.dumps(tu, ensure_ascii=False)}</tool_use>'\n            if content.strip() and not content.startswith(\"!!!Error:\"): self.history.append({\"role\": \"assistant\", \"content\": [{\"type\": \"text\", \"text\": content}]})\n        return _ask_gen()\n\ndef _keep_claude_block(b): return not isinstance(b, dict) or b.get(\"type\") != \"thinking\" or b.get(\"signature\")\ndef _drop_unsigned_thinking(messages):\n    for m in messages:\n        c = m.get(\"content\")\n        if isinstance(c, list): m[\"content\"] = [b for b in c if _keep_claude_block(b)]\n    return messages\n\ndef _ensure_thinking_blocks(messages, model):\n    \"\"\"deepseek needs thinking in history!\"\"\"\n    if 'deepseek' not in model.lower(): return messages\n    for m in messages:\n        if m.get(\"role\") != \"assistant\": continue\n        c = m.get(\"content\")\n        if not isinstance(c, list): continue\n        has_thinking = any(isinstance(b, dict) and b.get(\"type\") == \"thinking\" for b in c)\n        if not has_thinking: m[\"content\"] = [{\"type\": \"thinking\", \"thinking\": \"...\", \"signature\": \"placeholder\"}, *c]\n    return messages\n\nclass ClaudeSession(BaseSession):\n    def raw_ask(self, messages):\n        messages = _fix_messages(messages)\n        if self.max_tokens is None: self.max_tokens = 8192\n        headers = {\"x-api-key\": self.api_key, \"Content-Type\": \"application/json\", \"anthropic-version\": \"2023-06-01\", \"anthropic-beta\": \"prompt-caching-2024-07-31\"}\n        payload = {\"model\": self.model, \"messages\": messages, \"max_tokens\": self.max_tokens, \"stream\": self.stream}\n        if self.temperature != 1: payload[\"temperature\"] = self.temperature\n        self._apply_claude_thinking(payload)\n        if self.system: payload[\"system\"] = [{\"type\": \"text\", \"text\": self.system, \"cache_control\": {\"type\": \"persistent\"}}]\n        url = auto_make_url(self.api_base, \"messages\")\n        parse_fn = (lambda r: _parse_claude_sse(r.iter_lines())) if self.stream else (lambda r: _parse_claude_json(r.json()))\n        return (yield from _stream_with_retry(self, url, headers, payload, parse_fn))\n    def make_messages(self, raw_list):\n        msgs = _drop_unsigned_thinking([{\"role\": m['role'], \"content\": list(m['content'])} for m in raw_list])\n        user_idxs = [i for i, m in enumerate(msgs) if m['role'] == 'user']\n        for idx in user_idxs[-2:]:\n            msgs[idx][\"content\"][-1] = dict(msgs[idx][\"content\"][-1], cache_control={\"type\": \"ephemeral\"})\n        return msgs\n\nclass LLMSession(BaseSession):\n    def raw_ask(self, messages): return (yield from _openai_stream(self, messages))\n    def make_messages(self, raw_list): return _msgs_claude2oai(_fix_messages(raw_list))\n\ndef _fix_messages(messages):\n    if not messages: return messages\n    W = lambda c: c if isinstance(c, list) else [{\"type\": \"text\", \"text\": str(c)}]\n    merged = []\n    for m in messages:\n        if m.get('role') not in ('user', 'assistant'): continue\n        blocks = W(m.get('content', []))\n        if merged and m['role'] == merged[-1]['role']:\n            merged[-1]['content'] += [{\"type\": \"text\", \"text\": \"\\n\"}] + blocks\n        else:\n            merged.append({\"role\": m['role'], \"content\": list(blocks)})\n    while merged and merged[0]['role'] != 'user': merged.pop(0)\n    if not merged: return []\n    prev_uses = []\n    for m in merged:\n        c = m['content']\n        if m['role'] == 'assistant':\n            seen, out = set(), []\n            for b in c:\n                uid = b.get('id') if isinstance(b, dict) and b.get('type') == 'tool_use' else None\n                if uid and uid in seen: continue\n                if uid: seen.add(uid)\n                out.append(b)\n            m['content'] = out\n            prev_uses = [b.get('id') for b in out if isinstance(b, dict) and b.get('type') == 'tool_use']\n        else:\n            got, rest = {}, []\n            for b in c:\n                tid = b.get('tool_use_id') if isinstance(b, dict) and b.get('type') == 'tool_result' else None\n                if tid and tid in prev_uses and tid not in got: got[tid] = b\n                elif isinstance(b, dict) and b.get('type') == 'tool_result': rest.append({\"type\": \"text\", \"text\": str(b.get('content', ''))})\n                else: rest.append(b)\n            m['content'] = [got.get(u) or {\"type\": \"tool_result\", \"tool_use_id\": u, \"content\": \"(error)\"} for u in prev_uses] + rest\n            prev_uses = []\n    return merged\n\nclass NativeClaudeSession(BaseSession):\n    native_ua = \"claude-cli/2.1.152 (native, cli)\"\n    def __init__(self, cfg):\n        super().__init__(cfg)\n        self.fake_cc_system_prompt = cfg.get(\"fake_cc_system_prompt\", False)\n        self._session_id = str(uuid.uuid4())\n        self._account_uuid = str(uuid.uuid4())\n        self._device_id = uuid.uuid4().hex + uuid.uuid4().hex[:32]\n        self.tools = None\n        if self.user_agent == self.default_ua: self.user_agent = self.native_ua\n    def raw_ask(self, messages):\n        if self.max_tokens is None: self.max_tokens = 8192\n        model = self.model\n        messages = _fix_messages(messages)\n        if 'claude' in model.lower(): messages = _drop_unsigned_thinking(messages)\n        messages = _ensure_thinking_blocks(messages, self.model)\n        beta_parts = [\"claude-code-20250219\", \"interleaved-thinking-2025-05-14\", \"redact-thinking-2026-02-12\", \"context-management-2025-06-27\", \"prompt-caching-scope-2026-01-05\", \"effort-2025-11-24\"]\n        if \"[1m]\" in model.lower():\n            beta_parts.insert(1, \"context-1m-2025-08-07\"); model = model.replace(\"[1m]\", \"\").replace(\"[1M]\", \"\")\n        headers = {\"Content-Type\": \"application/json\", \"anthropic-version\": \"2023-06-01\",\n            \"anthropic-beta\": \",\".join(beta_parts), \"anthropic-dangerous-direct-browser-access\": \"true\",\n            \"user-agent\": self.user_agent, \"x-app\": \"cli\"}\n        headers.update({\"Accept\": \"application/json\", \"X-Claude-Code-Session-Id\": self._session_id, \"X-Stainless-Arch\": \"x64\", \"X-Stainless-Lang\": \"js\", \"X-Stainless-OS\": \"Windows\", \"X-Stainless-Package-Version\": \"0.94.0\", \"X-Stainless-Retry-Count\": \"0\", \"X-Stainless-Runtime\": \"node\", \"X-Stainless-Runtime-Version\": \"v24.3.0\", \"X-Stainless-Timeout\": \"600\"})\n        if self.api_key.startswith(\"sk-ant-\"): headers[\"x-api-key\"] = self.api_key\n        else: headers[\"authorization\"] = f\"Bearer {self.api_key}\"\n        payload = {\"model\": model, \"messages\": messages, \"max_tokens\": self.max_tokens, \"stream\": self.stream}\n        #if self.fake_cc_system_prompt: payload[\"max_tokens\"] = 64000\n        if self.temperature != 1: payload[\"temperature\"] = self.temperature\n        self._apply_claude_thinking(payload)\n        payload[\"context_management\"] = {\"edits\": [{\"type\": \"clear_thinking_20251015\", \"keep\": \"all\"}]}; \n        if self.fake_cc_system_prompt:\n            if 'thinking' not in payload: payload[\"thinking\"] = {\"type\": \"adaptive\"}\n            if 'output_config' not in payload: payload[\"output_config\"] = {\"effort\": \"medium\"}\n        payload[\"metadata\"] = {\"user_id\": json.dumps({\"device_id\": self._device_id, \"account_uuid\": \"\", \"session_id\": self._session_id}, separators=(',', ':'))}\n        if self.tools:\n            claude_tools = openai_tools_to_claude(self.tools)\n            tools = [dict(t) for t in claude_tools]; tools[-1][\"cache_control\"] = {\"type\": \"ephemeral\"}\n            payload[\"tools\"] = tools\n        else: print(\"[ERROR] No tools provided for this session.\")\n        payload['system'] = [{\"type\": \"text\", \"text\": \"You are Claude Code, Anthropic's official CLI for Claude.\", \"cache_control\": {\"type\": \"ephemeral\"}}]\n        if self.system:\n            if self.fake_cc_system_prompt: payload[\"system\"].append({\"type\": \"text\", \"text\": self.system})\n            else: payload[\"system\"] = [{\"type\": \"text\", \"text\": self.system}]\n        user_idxs = [i for i, m in enumerate(messages) if m['role'] == 'user']\n        for idx in user_idxs[-2:]:\n            messages[idx] = {**messages[idx], \"content\": list(messages[idx][\"content\"])}\n            messages[idx][\"content\"][-1] = dict(messages[idx][\"content\"][-1], cache_control={\"type\": \"ephemeral\"})\n        url = auto_make_url(self.api_base, \"messages\") + '?beta=true'\n        parse_fn = (lambda r: _parse_claude_sse(r.iter_lines())) if self.stream else (lambda r: _parse_claude_json(r.json()))\n        return (yield from _stream_with_retry(self, url, headers, payload, parse_fn))\n\n    def ask(self, msg):\n        assert type(msg) is dict\n        with self.lock:\n            self.history.append(msg)\n            trim_messages_history(self.history, self)\n            messages = [{\"role\": m[\"role\"], \"content\": list(m[\"content\"])} for m in self.history]\n        content_blocks = None\n        gen = self.raw_ask(messages)\n        try:\n            while True: yield next(gen)\n        except StopIteration as e: content_blocks = e.value or []\n        if content_blocks and (_injected := _ensure_text_block(content_blocks)): yield _injected\n        if content_blocks and not (len(content_blocks) == 1 and content_blocks[0].get(\"text\", \"\").startswith(\"!!!Error:\")):\n            self.history.append({\"role\": \"assistant\", \"content\": content_blocks})\n        text_parts = [b[\"text\"] for b in content_blocks if b.get(\"type\") == \"text\"]\n        content = \"\\n\".join(text_parts).strip()\n        tool_calls = [MockToolCall(b[\"name\"], b.get(\"input\", {}), id=b.get(\"id\", \"\")) for b in content_blocks if b.get(\"type\") == \"tool_use\"]\n        if not tool_calls: tool_calls, content = _parse_text_tool_calls(content)\n        thinking_parts = [b[\"thinking\"] for b in content_blocks if b.get(\"type\") == \"thinking\"]\n        thinking = \"\\n\".join(thinking_parts).strip()\n        if not thinking:\n            think_pattern = r\"<think(?:ing)?>(.*?)</think(?:ing)?>\"\n            think_match = re.search(think_pattern, content, re.DOTALL)\n            if think_match:\n                thinking = think_match.group(1).strip()\n                content = re.sub(think_pattern, \"\", content, flags=re.DOTALL)\n        return MockResponse(thinking, content, tool_calls, str(content_blocks))\n\nclass NativeOAISession(NativeClaudeSession):\n    native_ua = \"codex_exec/0.139.0 (Windows 10.0.26200; x86_64) unknown (codex_exec; 0.139.0)\"\n    def raw_ask(self, messages):\n        messages = _fix_messages(messages)\n        messages = _ensure_thinking_blocks(messages, self.model)\n        return (yield from _openai_stream(self, _msgs_claude2oai(messages)))\n\ndef openai_tools_to_claude(tools):\n    \"\"\"[{type:'function', function:{name,description,parameters}}] → [{name,description,input_schema}].\"\"\"\n    result = []\n    for t in tools:\n        if 'input_schema' in t: result.append(t); continue  # 已是claude格式\n        fn = t.get('function', t)\n        result.append({'name': fn['name'], 'description': fn.get('description', ''),\n            'input_schema': fn.get('parameters', {'type': 'object', 'properties': {}})})\n    return result\n\nclass MockFunction:\n    def __init__(self, name, arguments): self.name, self.arguments = name, arguments  \n         \nclass MockToolCall:\n    def __init__(self, name, args, id=''):\n        arg_str = json.dumps(args, ensure_ascii=False) if isinstance(args, (dict, list)) else (args or '{}')\n        self.function = MockFunction(name, arg_str); self.id = id\n\nclass MockResponse:\n    def __init__(self, thinking, content, tool_calls, raw, stop_reason='end_turn'):\n        self.thinking = thinking; self.content = content          \n        self.tool_calls = tool_calls; self.raw = raw\n        self.stop_reason = 'tool_use' if tool_calls else stop_reason\n    def __repr__(self):    \n        return f\"<MockResponse thinking={bool(self.thinking)}, content='{self.content}', tools={bool(self.tool_calls)}>\"\n\nclass ToolClient:\n    def __init__(self, backend, auto_save_tokens=True):\n        self.backend = backend\n        self.auto_save_tokens = auto_save_tokens\n        self.last_tools = ''\n        self.name = self.backend.name\n        self.total_cd_tokens = 0\n        self.log_path = None\n\n    def chat(self, messages, tools=None):\n        tools = json.loads(json.dumps(tools, ensure_ascii=False)) if tools else tools\n        for t in tools or []:\n            f = t.get('function', {})\n            if f.get('name') == 'file_write':\n                props = f.get('parameters', {}).get('properties', {})\n                props.pop('content', None)\n                extra = '. Content must be placed in <file_content> tags in reply body, not in args'\n                if extra not in f.get('description', ''): f['description'] = f.get('description', '') + extra\n                break\n        full_prompt = self._build_protocol_prompt(messages, tools)\n        print(\"Full prompt length:\", len(full_prompt), 'chars')\n        gen = self.backend.ask(full_prompt)\n        _write_llm_log('Prompt', full_prompt, self.log_path)\n        raw_text = ''\n        for chunk in gen:\n            raw_text += chunk; yield chunk\n        _write_llm_log('Response', raw_text, self.log_path)\n        return self._parse_mixed_response(raw_text)\n\n    def _prepare_tool_instruction(self, tools):\n        tool_instruction = \"\"\n        if not tools: return tool_instruction\n        tools_json = json.dumps(tools, ensure_ascii=False, separators=(',', ':'))\n        _en = os.environ.get('GA_LANG') == 'en'\n        if _en:\n            tool_instruction = f\"\"\"\n### Interaction Protocol (must follow strictly, always in effect)\nFollow these steps to think and act:\n1. **Think**: Analyze the current situation and strategy inside `<thinking>` tags.\n2. **Summarize**: Output a minimal one-line (<30 words) physical snapshot in `<summary>`: new info from last tool result + current tool call intent. This goes into long-term working memory. Must contain real information, no filler.\n3. **Act**: If you need to call tools, output one or more **<tool_use> blocks** after your reply, then stop.\n\"\"\"\n        else:\n            tool_instruction = f\"\"\"\n### 交互协议 (必须严格遵守，持续有效)\n请按照以下步骤思考并行动：\n1. **思考**: 在 `<thinking>` 标签中先进行思考，分析现状和策略。\n2. **总结**: 在 `<summary>` 中输出*极为简短*的高度概括的单行（<30字）物理快照，包括上次工具调用结果产生的新信息+本次工具调用意图。此内容将进入长期工作记忆，记录关键信息，严禁输出无实际信息增量的描述。\n3. **行动**: 如需调用工具，请在回复正文之后输出一个（或多个）**<tool_use>块**，然后结束。\n\"\"\"\n        tool_instruction += f'\\nFormat: ```<tool_use>{{\"name\": \"tool_name\", \"arguments\": {{...}}}}</tool_use>```\\n\\n### Tools (mounted, always in effect):\\n{tools_json}\\n'\n        if self.auto_save_tokens and self.last_tools == tools_json:\n            tool_instruction = \"\\n### Tools: still active, **ready to call**. Protocol unchanged.\\n\" if _en else \"\\n### 工具库状态：持续有效（code_run/file_read等），**可正常调用**。调用协议沿用。\\n\"\n        else: self.total_cd_tokens = 0\n        self.last_tools = tools_json\n        return tool_instruction\n\n    def _build_protocol_prompt(self, messages, tools):\n        system_content = next((m['content'] for m in messages if m['role'].lower() == 'system'), \"\")\n        history_msgs = [m for m in messages if m['role'].lower() != 'system']\n        tool_instruction = self._prepare_tool_instruction(tools)\n        system = \"\"; user = \"\"\n        if system_content: system += f\"{system_content}\\n\"\n        system += f\"{tool_instruction}\"\n        for m in history_msgs:\n            role = \"USER\" if m['role'] == 'user' else \"ASSISTANT\"\n            user += f\"=== {role} ===\\n\"\n            for tr in m.get('tool_results', []): user += f'<tool_result>{tr[\"content\"]}</tool_result>\\n'\n            user += str(m['content']) + \"\\n\"\n            self.total_cd_tokens += len(user) // 3\n        if self.total_cd_tokens > 9000: self.last_tools = ''\n        user += \"=== ASSISTANT ===\\n\" \n        return system + user\n\n    def _parse_mixed_response(self, text):\n        remaining_text = text; thinking = ''\n        think_match = re.search(r\"<think(?:ing)?>(.*?)</think(?:ing)?>\", text, re.DOTALL)\n        if think_match:\n            thinking = think_match.group(1).strip()\n            remaining_text = re.sub(r\"<think(?:ing)?>(.*?)</think(?:ing)?>\", \"\", remaining_text, flags=re.DOTALL)\n        tool_calls, remaining_text = _parse_text_tool_calls(remaining_text)\n        if not tool_calls:\n            json_strs = []; errors = []\n            if '<tool_use>' in remaining_text:\n                weaktoolstr = remaining_text.split('<tool_use>')[-1].strip().strip('><')\n                json_str = weaktoolstr if weaktoolstr.endswith('}') else ''\n                if json_str == '' and '```' in weaktoolstr and weaktoolstr.split('```')[0].strip().endswith('}'):\n                    json_str = weaktoolstr.split('```')[0].strip()\n                if json_str: json_strs.append(json_str)\n                remaining_text = remaining_text.replace('<tool_use>'+weaktoolstr, \"\")\n            elif '\"name\":' in remaining_text and '\"arguments\":' in remaining_text:\n                json_match = re.search(r'\\{.*\"name\":.*\\}', remaining_text, re.DOTALL)\n                if json_match:\n                    json_strs.append(json_match.group(0).strip())\n                    remaining_text = remaining_text.replace(json_match.group(0), \"\").strip()\n            for json_str in json_strs:\n                try:\n                    data = tryparse(json_str)\n                    func_name = data.get('name') or data.get('function') or data.get('tool')\n                    args = data.get('arguments') or data.get('args') or data.get('params') or data.get('parameters')\n                    if args is None: args = data\n                    if func_name: tool_calls.append(MockToolCall(func_name, args))\n                except json.JSONDecodeError:\n                    errors.append(f'Failed to parse tool_use JSON: {json_str[:200]}')\n                    self.last_tools = ''\n                except: pass\n            if not tool_calls:\n                for e in errors:\n                    print(f\"[Warn] {e}\"); tool_calls.append(MockToolCall('bad_json', {'msg': e}))\n        return MockResponse(thinking, remaining_text.strip(), tool_calls, text)\n\ndef _parse_text_tool_calls(content):\n    \"\"\"Fallback: extract tool calls from text when model doesn't use native tool_use blocks.\"\"\"\n    tcs = []\n    # try JSON array: [{\"type\":\"tool_use\", \"name\":..., \"input\":...}]\n    _jp = next((p for p in ['[{\"type\":\"tool_use\"', '[{\"type\": \"tool_use\"'] if p in content), None)\n    if _jp and content.endswith('}]'):\n        try:\n            idx = content.index(_jp); raw = json.loads(content[idx:])\n            tcs = [MockToolCall(b[\"name\"], b.get(\"input\", {}), id=b.get(\"id\", \"\")) for b in raw if b.get(\"type\") == \"tool_use\"]\n            return tcs, content[:idx].strip()\n        except: pass\n    # try XML tags: <tool_call>{\"name\":..., \"arguments\":...}</tool_call>\n    _xp = r\"<(?:tool_use|tool_call)>((?:(?!<(?:tool_use|tool_call)>).){15,}?)</(?:tool_use|tool_call)>\"\n    for s in re.findall(_xp, content, re.DOTALL):\n        try:\n            d = tryparse(s.strip()); name = d.get('name')\n            args = d.get('arguments') or d.get('args') or d.get('input') or {}\n            if name: tcs.append(MockToolCall(name, args))\n        except: pass\n    if tcs: content = re.sub(_xp, \"\", content, flags=re.DOTALL).strip()\n    return tcs, content\n\ndef _ensure_text_block(blocks):\n    \"\"\"If response has thinking but no text block, inject a synthetic summary from thinking's first line.\"\"\"\n    if any(b.get(\"type\") == \"text\" for b in blocks): return None\n    th = next((b.get(\"thinking\", \"\") for b in blocks if b.get(\"type\") == \"thinking\"), \"\")\n    if not th: return None\n    line = th.strip().split('\\n', 1)[0]\n    txt = \"<summary>\" + (line[:60] + '...' if len(line) > 60 else line) + \"</summary>\"\n    blocks.insert(1, {\"type\": \"text\", \"text\": txt})\n    return txt\n\ndef _write_llm_log(label, content, log_path=None):\n    if not log_path:\n        log_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), f'temp/model_responses/model_responses_{os.getpid()}.txt')\n    os.makedirs(os.path.dirname(os.path.abspath(log_path)), exist_ok=True)\n    ts = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n    with open(log_path, 'a', encoding='utf-8', errors='replace') as f:\n        f.write(f\"=== {label} === {ts}\\n{content}\\n\\n\")\n\ndef tryparse(json_str):\n    try: return json.loads(json_str)\n    except: pass\n    json_str = json_str.strip().strip('`').replace('json\\n', '', 1).strip()\n    try: return json.loads(json_str)\n    except: pass\n    try: return json.loads(json_str[:-1])\n    except: pass\n    if '}' in json_str: json_str = json_str[:json_str.rfind('}') + 1]\n    return json.loads(json_str)\n\nclass MixinSession:\n    \"\"\"Multi-session fallback with spring-back to primary.\"\"\"\n    def __init__(self, all_sessions, cfg):\n        self._retries, self._base_delay = cfg.get('max_retries', 3), cfg.get('base_delay', 1.5)\n        self._spring_sec = cfg.get('spring_back', 300)\n        self._sessions = [all_sessions[i].backend if isinstance(i, int) else \n                          next(s.backend for s in all_sessions if type(s) is not dict and s.backend.name == i) for i in cfg.get('llm_nos', [])]\n        is_native = lambda s: 'Native' in s.__class__.__name__\n        groups = {is_native(s) for s in self._sessions}\n        assert len(groups) == 1, f\"MixinSession: sessions must be in same group (Native or non-Native), got {[type(s).__name__ for s in self._sessions]}\"\n        self.name = '|'.join(s.name for s in self._sessions)\n        import copy; self._sessions = [copy.copy(s) for s in self._sessions]\n        for s in self._sessions: s.max_retries = 0\n        self._orig_raw_asks = [s.raw_ask for s in self._sessions]\n        self._sessions[0].raw_ask = self._raw_ask\n        self._cur_idx, self._switched_at = 0, 0.0\n    def __getattr__(self, name): return getattr(self._sessions[0], name)\n    _BROADCAST_ATTRS = frozenset({'system', 'tools', 'temperature', 'max_tokens', 'reasoning_effort', 'history', 'stream', 'read_timeout'})\n    def __setattr__(self, name, value):\n        if name in self._BROADCAST_ATTRS:\n            for s in self._sessions:\n                v = openai_tools_to_claude(value) if name == 'tools' and type(s) is NativeClaudeSession else value\n                setattr(s, name, v)\n        else: object.__setattr__(self, name, value)\n    @property\n    def primary(self): return self._sessions[0]\n    @property\n    def model(self): return getattr(self._sessions[self._cur_idx], 'model', None)\n    def _pick(self):\n        if self._cur_idx and time.time() - self._switched_at > self._spring_sec: self._cur_idx = 0\n        return self._cur_idx\n    def _raw_ask(self, *args, **kwargs):\n        base, n = self._pick(), len(self._sessions)\n        test_error = lambda x: isinstance(x, str) and x.lstrip().startswith(('!!!Error:', '[Error:'))\n        for attempt in range(self._retries + 1):\n            idx = (base + attempt) % n\n            gen = self._orig_raw_asks[idx](*args, **kwargs)\n            print(f'[MixinSession] Using session ({self._sessions[idx].name})')\n            last_chunk, return_val, yielded = None, [], False\n            try:\n                while True:\n                    chunk = next(gen); last_chunk = chunk\n                    if not yielded and test_error(chunk): continue\n                    yield chunk; yielded = True\n            except StopIteration as e: return_val = e.value or []\n            is_err = test_error(last_chunk)\n            if not is_err:\n                if attempt > 0: self._cur_idx = idx; self._switched_at = time.time()\n                elif isinstance(last_chunk, str) and '[!!! 流异常中断' in last_chunk and n > 1:\n                    self._cur_idx = (idx + 1) % n; self._switched_at = time.time()\n                    print(f'[MixinSession] Partial failure, next call → s{self._cur_idx} ({self._sessions[self._cur_idx].name})')\n                return return_val\n            if attempt >= self._retries:\n                yield last_chunk; return return_val\n            nxt = (base + attempt + 1) % n\n            if nxt == base:  # full round failed, delay before next\n                rnd = (attempt + 1) // n\n                delay = min(30, self._base_delay * (1.5 ** rnd))\n                print(f'[MixinSession] {last_chunk[:80]}, round {rnd} exhausted, retry in {delay:.1f}s')\n                time.sleep(delay)\n            else: print(f'[MixinSession] {last_chunk[:80]}, retry {attempt+1}/{self._retries} (s{idx}→s{nxt})')\n\nTHINKING_PROMPT_ZH = \"\"\"\n### 行动规范（持续有效）\n每次回复（含工具调用轮）都先在回复文字中包含一个<summary></summary> 中输出极简单行（<30字）物理快照：上次结果新信息+本次意图。此内容进入长期工作记忆。\n\\n**若用户需求未完成，必须进行工具调用！**\n\"\"\".strip()\nTHINKING_PROMPT_EN = \"\"\"\n### Action Protocol (always in effect)\nThe reply body should first include a minimal one-line (<30 words) physical snapshot in <summary></summary>: new info from last result + current intent. This goes into long-term working memory.\n\\n**If the user's request is not yet complete, tool calls are required!**\n\"\"\".strip()\n\nclass NativeToolClient:\n    @staticmethod\n    def _thinking_prompt(): return THINKING_PROMPT_EN if os.environ.get('GA_LANG') == 'en' else THINKING_PROMPT_ZH\n    def __init__(self, backend):\n        self.backend = backend\n        self.backend.system = self._thinking_prompt()\n        self.name = self.backend.name\n        self._pending_tool_ids = []\n        self.log_path = None\n    def set_system(self, extra_system):\n        combined = f\"{extra_system}\\n\\n{self._thinking_prompt()}\" if extra_system else self._thinking_prompt()\n        if combined != self.backend.system: print(f\"[Debug] Updated system prompt, length {len(combined)} chars.\")\n        self.backend.system = combined\n    def chat(self, messages, tools=None):\n        if tools: self.backend.tools = tools\n        if not self.backend.history: self._pending_tool_ids = []\n        combined_content = []; resp = None; tool_results = []\n        for msg in messages:\n            c = msg.get('content', '')\n            if msg['role'] == 'system': \n                self.set_system(c); continue\n            if isinstance(c, str): combined_content.append({\"type\": \"text\", \"text\": c})\n            elif isinstance(c, list): combined_content.extend(c)\n            if msg['role'] == 'user' and msg.get('tool_results'): tool_results.extend(msg['tool_results'])\n        tr_id_set = set();  tool_result_blocks = []\n        for tr in tool_results:\n            tool_use_id, content = tr.get(\"tool_use_id\", \"\"), tr.get(\"content\", \"\")\n            tr_id_set.add(tool_use_id)\n            if tool_use_id: tool_result_blocks.append({\"type\": \"tool_result\", \"tool_use_id\": tool_use_id, \"content\": tr.get(\"content\", \"\")})\n            else: combined_content = [{\"type\": \"text\", \"text\": f'<tool_result>{content}</tool_result>'}] + combined_content\n        for tid in self._pending_tool_ids:\n            if tid not in tr_id_set: tool_result_blocks.append({\"type\": \"tool_result\", \"tool_use_id\": tid, \"content\": \"\"})\n        self._pending_tool_ids = []\n        # Filter whitespace-only text blocks that cause 400 on strict API proxies\n        filtered_content = [c for c in combined_content if c.get(\"text\", \"\").strip()]\n        final_content = tool_result_blocks + filtered_content\n        if not final_content: final_content = [{\"type\": \"text\", \"text\": \".\"}]\n        merged = {\"role\": \"user\", \"content\": final_content}\n        _write_llm_log('Prompt', json.dumps(merged, ensure_ascii=False, indent=2), self.log_path)\n        gen = self.backend.ask(merged)\n        try:\n            while True: \n                chunk = next(gen); yield chunk\n        except StopIteration as e: resp = e.value\n        if resp: _write_llm_log('Response', resp.raw, self.log_path)\n        if resp and hasattr(resp, 'tool_calls') and resp.tool_calls: self._pending_tool_ids = [tc.id for tc in resp.tool_calls]\n        return resp\n\ndef resolve_session(cfg_name):\n    cfg = reload_mykeys()[0].get(cfg_name)\n    if not cfg: raise ValueError(f\"Config '{cfg_name}' not in mykey\")\n    if 'native' in cfg_name: return (NativeClaudeSession if 'claude' in cfg_name else NativeOAISession)(cfg=cfg)\n    if 'claude' in cfg_name: return ClaudeSession(cfg=cfg)\n    return LLMSession(cfg=cfg) if 'oai' in cfg_name else None\n\ndef resolve_client(cfg_name):\n    s = resolve_session(cfg_name)\n    return (NativeToolClient(s) if isinstance(s, (NativeClaudeSession, NativeOAISession)) else ToolClient(s)) if s else None\n\ndef fast_ask(prompt, cfg_name):\n    sess = resolve_session(cfg_name)\n    if not sess: raise ValueError(f\"fast_ask: '{cfg_name}' unsupported\")\n    return \"\".join(sess.raw_ask([{\"role\": \"user\", \"content\": prompt}]))\n"}}}}