Frog API 文档中心

Frog API Guide

统一接入文档 · 教程整理版 Unified integration docs · extracted knowledge base

新手强烈推荐阅读,包含完整注册与配置流程 包含完整接入与配置流程
📢 接入地址 📢 API Endpoint
主站:Main: https://www.frog-api.com
亚太加速:Asia-Pacific Acceleration: https://www.frog-api.com
备用:Backup: https://twob.pp.ua
API 接口1 :API Endpoint: https://www.frog-api.com/v1
API 接口2 :API Endpoint: https://www.frog-api.com/v1

快捷入口

Quick Links

注册账号 Registration 👉 前往主站 👉 Go to Main Site

注册教程

接口示例

API Examples

添加自定义 Provider(关键步骤)

Add Custom Provider (Key Step)

在配置文件中,将以下 JSON 代码片段加入到 models.providers 块中: Add the following JSON to models.providers block:

JSON
"models": {
  "providers": {
    "claude": {
      "baseUrl": "https://www.frog-api.com",
      "apiKey": "sk-xxxxxx你的网站获取的秘钥",
      "api": "anthropic-messages",
      "models": [
        {
          "id": "claude-opus-4-6",
          "name": "claude-opus-4-6",
          "reasoning": false,
          "input": ["text"],
          "cost": {
            "input": 0,
            "output": 0,
            "cacheRead": 0,
            "cacheWrite": 0
          },
          "contextWindow": 200000,
          "maxTokens": 8192
        }
      ]
    }
  }
}

cURL 示例

cURL Example

BASH
curl https://www.frog-api.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxx" \
  -d '{
    "model": "[次]gemini-3-pro-preview",
    "messages": [{"role": "user", "content": "Hi!"}]
  }'

Python 示例

Python Example

PYTHON
from openai import OpenAI

client = OpenAI(
    api_key="sk-xxxxxxxx",
    base_url="https://www.frog-api.com/v1"
)

response = client.chat.completions.create(
    model="[次]gemini-3-pro-preview",
    messages=[{"role": "user", "content": "Hi!"}]
)

获取 API 令牌

Get API Token

🎬 令牌配置教程

点击令牌管理

Click Token Management

显示操作项,选择添加令牌 Show options, select Add Token

令牌管理

输入名称并选择分组

Enter Name and Select Group

名称任意输入,点击下滑栏按需求选择令牌分组 Enter any name, select Token Group from dropdown

默认选auto分组即可!!! Just select "auto" group by default!!!

输入名称
选择分组

设置过期时间和数量

Set Expiration and Quantity

过期时间选择永不过期,新建数量选择一个 Set expiration to Never, quantity to 1

设置过期时间

设置令牌额度

Set Token Quota

令牌额度选择无限额度 Select Unlimited Quota

令牌额度

模型限制(可选)

Model Restrictions (Optional)

模型限制列表默认不做选择 Leave model restrictions empty by default

模型限制

复制密钥

Copy Key

生成的密钥选择小眼睛旁边的点击复制即可 Click Copy button next to the generated key

复制密钥

Claude Code 配置指南

Claude Code Setup

GitHub farion1231/cc-switch v3.11.1(单击跳转!)

安装步骤

Installation Steps

1. 安装 Node.js

1. Install Node.js

访问 Node.js 官网,建议下载左侧的 LTS 版本,它是长期支持版,更加稳定。 Visit Node.js official website, download the LTS version on the left for better stability.

2. 安装 Git

2. Install Git

访问 Git 官网,页面会自动推荐适合你操作系统的版本,点击按钮即可下载。 Visit Git official website, the page will automatically recommend the version for your OS.

3. 安装 Claude Code

3. Install Claude Code

打开 cmd 命令行,通过 npm 安装 claude-code: Open cmd and install claude-code via npm:

BASH
npm install -g claude-code

🎬 Claude Code 配置教程

配置步骤

Configuration Steps

1. 打开 cc switch

1. Open cc switch

启动 cc switch 应用程序。 Launch cc switch application.

打开cc switch

2. 点击右上角加号

2. Click Plus Icon

在界面右上角找到并点击加号按钮,添加新的供应商配置。 Find and click the plus button in the top right corner to add a new provider.

点击加号

3. 填写配置信息

3. Fill Configuration

  • 供应商名称:随意填写(例如:Frog API) Provider Name: Any name (e.g., Frog API)
  • API Key:填入您的站点令牌秘钥 API Key: Enter your site token
  • 请求地址https://www.frog-api.com Request URL: https://www.frog-api.com
  • 主模型claude-opus-4-6 Main Model: claude-opus-4-6
填写配置信息

4. 命令行启动 Claude Code

4. Start Claude Code via CLI

在命令行中启动 Claude Code。 Start Claude Code from command line.

命令行启动

5. 切换模型

5. Switch Model

进入后输入 /model 切换到刚刚设定的模型名称。 Enter /model to switch to the configured model.

切换模型

6. 开始使用

6. Start Using

回车确定选中后即可开始正常使用 Claude Code。 Press Enter to confirm and start using Claude Code.

开始使用

CC Switch 配置教程

CC Switch Configuration

GitHub farion1231/cc-switch v3.11.1(单击跳转下载!)
📌 关于 CC Switch📌 About CC Switch CC Switch 是一个强大的 API 切换工具,支持多种 AI 服务配置。以下是针对不同插件的详细配置教程。 CC Switch is a powerful API switching tool that supports multiple AI service configurations. Below are detailed tutorials for different plugins.

1. Claude 配置

1. Claude Configuration

打开 cc switch 并选择 Claude

Open cc switch and select Claude

启动 cc switch,根据所使用插件进行配置,选择 Claude。 Launch cc switch and select Claude based on your plugin.

打开cc switch
选择Claude插件

点击右上角加号添加配置

Click Plus to Add Configuration

  • 供应商名称:随意填写 Provider Name: Any name
  • API Key:站点令牌秘钥 API Key: Site token
  • 请求地址https://www.frog-api.com Request URL: https://www.frog-api.com
  • API 格式:anthropic messages(原生) API Format: anthropic messages (native)
  • 主模型claude-opus-4-6 Main Model: claude-opus-4-6
点击加号
填入配置信息

2. Gemini 配置

2. Gemini Configuration

打开 cc switch 并选择 Gemini

Open cc switch and select Gemini

在 cc switch 中选择 Gemini 插件。 Select Gemini plugin in cc switch.

选择Gemini
Gemini配置1

点击右上角加号添加配置

Click Plus to Add Configuration

  • 供应商名称:随意填写 Provider Name: Any name
  • API Key:站点令牌秘钥 API Key: Site token
  • 请求地址https://www.frog-api.com Request URL: https://www.frog-api.com
  • 主模型[次]gemini-3.1-pro-preview Main Model: [次]gemini-3.1-pro-preview
Gemini配置2

3. OpenCode 配置

3. OpenCode Configuration

打开 cc switch 并选择 OpenCode

Open cc switch and select OpenCode

在 cc switch 中选择 OpenCode 插件。 Select OpenCode plugin in cc switch.

选择OpenCode
OpenCode配置1

点击右上角加号添加配置

Click Plus to Add Configuration

  • 供应商标识:claude Provider ID: claude
  • 供应商名称:随意填写 Provider Name: Any name
  • API Key:站点令牌秘钥 API Key: Site token
  • 请求地址https://www.frog-api.com Request URL: https://www.frog-api.com
  • 主模型claude-opus-4-6 Main Model: claude-opus-4-6
  • 显示名称claude-opus-4-6 Display Name: claude-opus-4-6
OpenCode配置2

4. OpenClaw 配置

4. OpenClaw Configuration

选择 OpenClaw 界面

Select OpenClaw Interface

在 cc switch 中选择 OpenClaw 插件界面。 Select OpenClaw plugin interface in cc switch.

选择OpenClaw
OpenClaw配置1

点击右上角加号添加配置

Click Plus to Add Configuration

  • 供应商标识:claude Provider ID: claude
  • 供应商名称:Frog API Provider Name: Frog API
  • API 协议:anthropic messages API Protocol: anthropic messages
  • API Key:站点令牌秘钥 API Key: Site token
  • API 端点https://www.frog-api.com API Endpoint: https://www.frog-api.com
  • 模型 IDclaude-opus-4-6 Model ID: claude-opus-4-6
  • 模型名称claude-opus-4-6 Model Name: claude-opus-4-6
OpenClaw配置2
💡 提示💡 Tip 所有配置都使用 auto 分组的令牌。配置完成后即可在对应的插件中使用 Frog API 服务。如需更详细的图文教程,请参考上方视频或在 QQ 群中咨询管理员。 Use tokens from auto group for all configurations. After setup, you can use Frog API service in the corresponding plugins. For more detailed tutorials, please refer to the video above or contact administrators in the QQ group.

Gemini CLI 配置指南

Gemini CLI Setup

🎬 Gemini CLI 配置教程

安装步骤

Installation Steps

1. 安装 Node.js

1. Install Node.js

访问 Node.js 官网,建议下载左侧的 LTS 版本,它是长期支持版,更加稳定。 Visit Node.js official website, download the LTS version on the left for better stability.

2. 安装 Git

2. Install Git

访问 Git 官网,页面会自动推荐适合你操作系统的版本,点击按钮即可下载。 Visit Git official website, the page will automatically recommend the version for your OS.

3. 安装 Gemini CLI

3. Install Gemini CLI

打开 cmd 命令行,通过 npm 安装 gemini-cli: Open cmd and install gemini-cli via npm:

BASH
npm install -g @google/gemini-cli

配置步骤

Configuration Steps

配置 (.env)

Configure (.env)

创建配置文件 (Windows: %USERPROFILE%\.gemini\.env, Mac/Linux: ~/.gemini/.env) Create config file at ~/.gemini/.env

.ENV
GOOGLE_GEMINI_BASE_URL=https://www.frog-api.com
GEMINI_API_KEY=sk-xxxxxxxx
GEMINI_MODEL=gemini-2.5-pro
注意:API Key 请使用 Gemini/Vertex 分组的令牌。 Note: Use token from Gemini/Vertex group.

启动

Start

BASH
gemini

CodeX CLI 配置指南

CodeX CLI Setup

添加 agents.defaults(关键步骤)

Add agents.defaults (Key Step)

还需要告知 OpenClaw 优先使用该节点: Tell OpenClaw to use this provider by default:

JSON
"agents": {
  "defaults": {
    "model": {
      "primary": "claude/claude-opus-4-6"
    },
    "models": {
      "claude/claude-opus-4-6": {
        "alias": "claude-opus-4-6"
      }
    }
  }
}

4. 启动

4. Start

codex

VS Code (Cline)

🎬 Cline 配置教程

配置步骤

Configuration

  • 安装 Cline 插件。
  • API Provider: OpenAI Compatible
  • Base URL: https://www.frog-api.com/v1
  • API Key: 填入您的令牌
  • Model ID: [次]gemini-2.5-pro
image2.png
image3.jpg

OpenClaw 配置指南

OpenClaw Setup

Website https://openclaw.ai/(官网单击跳转!) GitHub openclaw/openclaw(GitHub 仓库)
⚠️ 注意 因为 Clawdbot 改名,有些插件 SDK 安装的时候注意如果安装失败,注意要改成 OpenClaw Due to Clawdbot renaming, if SDK installation fails, try using OpenClaw instead

🎬 OpenClaw 配置教程

配置文件下载

Configuration File Download

openclaw.json(点击下载完整配置文件 - Windows版) openclaw.json(点击下载完整配置文件 - Mac版)

阿里云配置教程

Alibaba Cloud Configuration

📖 官方文档 阿里云官方配置教程: Official Alibaba Cloud tutorial: https://help.aliyun.com/zh/model-studio/openclaw

配置 JSON 文件

Configure JSON File

使用以下 JSON 配置模板,替换教程中的文本为下方文本,并替换自己的密钥: Use the following JSON configuration template, replace the tutorial text with the content below, and replace with your own API key:

JSON
"models": {
  "providers": {
    "claude": {
      "baseUrl": "https://www.frog-api.com",
      "apiKey": "sk-xxxxxxx秘钥",
      "api": "anthropic-messages",
      "models": [
        {
          "id": "claude-opus-4-6",
          "name": "claude-opus-4-6",
          "reasoning": false,
          "input": ["text"],
          "cost": {
            "input": 0,
            "output": 0,
            "cacheRead": 0,
            "cacheWrite": 0
          },
          "contextWindow": 200000,
          "maxTokens": 8192
        }
      ]
    }
  }
},
"agents": {
  "defaults": {
    "model": {
      "primary": "claude/claude-opus-4-6"
    },
    "models": {
      "claude/claude-opus-4-6": {
        "alias": "claude-opus-4-6"
      }
    }
  }
}
配置说明: Configuration Notes:
  • sk-xxxxxxx秘钥 替换为您从 Frog API 网站获取的实际令牌 Replace sk-xxxxxxx秘钥 with your actual token from Frog API
  • baseUrl 使用 https://www.frog-api.com Use https://www.frog-api.com as baseUrl
  • api 协议选择 anthropic-messages Use anthropic-messages as API protocol
  • 模型使用 claude-opus-4-6 Use claude-opus-4-6 model

腾讯云配置教程

Tencent Cloud Configuration

在自定义服务器里面选择自定义模型配置

Select Custom Model Configuration in Custom Server

打开 OpenClaw,进入自定义服务器设置,选择自定义模型配置选项。 Open OpenClaw, go to custom server settings, and select custom model configuration.

选择自定义模型配置

配置自定义 JSON 文件

Configure Custom JSON File

使用以下 JSON 配置模板: Use the following JSON configuration template:

JSON
{
  "provider": "openai",
  "base_url": "https://www.frog-api.com",
  "api": "anthropic-messages",
  "api_key": "sk-xxxxxxxxx",
  "model": {
    "id": "claude-opus-4-6",
    "name": "claude-opus-4-6"
  }
}

通用配置模板

Universal Configuration Template

如果需要其他模型,可以使用以下通用模板接入任何兼容 OpenAI/Anthropic (openai-completions/anthropic-messages) 协议的模型: For other models, use this universal template to connect any OpenAI/Anthropic compatible model:

JSON
{
  "provider": "openai",
  "base_url": "https://www.frog-api.com",
  "api": "anthropic-messages",
  "api_key": "sk-xxxxxxxxx",
  "model": {
    "id": "claude-opus-4-6",
    "name": "claude-opus-4-6"
  }
}
参数说明: Parameter Description:
  • provider: 提供商名称(可自定义) provider: Provider name (customizable)
  • base_url: API 接入点 base_url: API endpoint
  • api: API 协议类型(openai-completions 或 anthropic-messages) api: API protocol type (openai-completions or anthropic-messages)
  • api_key: 您的 Frog API 令牌 api_key: Your Frog API token
  • model.id: 模型标识符 model.id: Model identifier
  • model.name: 模型显示名称 model.name: Model display name

一、本地部署

1. Local Deployment

安装 Node.js

Install Node.js

访问 nodejs.org 下载 LTS 长期支持版(稳定性强,适合开发/生产环境)。页面会自动识别你的系统,直接点下载按钮就行。 Visit nodejs.org to download LTS version.

💡 Node.js 安装包自带 npm(包管理器),不用单独下载。

安装 Git

Install Git

访问 git-scm.com 下载安装。Windows 建议选 64 位版本,安装时默认选项基本够用。 Visit git-scm.com to download and install.

验证安装

Verify Installation

安装完成后,打开命令行(Windows 用 CMD/PowerShell,Mac/Linux 用终端),分别输入以下命令,能显示版本号就是安装成功: After installation, open terminal and run these commands to verify:

BASH
node -v
npm -v

安装 OpenClaw

Install OpenClaw

BASH
npm i -g openclaw

二、核心配置流程

2. Core Configuration

定位配置文件

Locate Config File

OpenClaw 的配置在 openclaw.json 中。请根据您的操作系统找到它: OpenClaw config is in openclaw.json. Find it based on your OS:

操作系统 默认路径
Windows C:\Users\<Your_Username>\.openclaw\openclaw.json
macOS / Linux ~/.openclaw/openclaw.json

添加自定义 Provider(关键步骤)

Add Custom Provider (Key Step)

在配置文件中,将以下 JSON 代码片段加入到 models.providers 块中。根据您的需求选择配置: Add the following JSON to models.providers block. Choose based on your needs:

JSON
"models": {
  "providers": {
    "claude": {
      "baseUrl": "https://www.frog-api.com",
      "apiKey": "sk-xxxxxx你的网站获取的秘钥",
      "api": "anthropic-messages",
      "models": [
        {
          "id": "claude-opus-4-6",
          "name": "claude-opus-4-6",
          "reasoning": false,
          "input": ["text"],
          "cost": {
            "input": 0,
            "output": 0,
            "cacheRead": 0,
            "cacheWrite": 0
          },
          "contextWindow": 200000,
          "maxTokens": 8192
        }
      ]
    }
  }
}

添加 agents.defaults(关键步骤)

Add agents.defaults (Key Step)

还需要告知 OpenClaw 优先使用该节点。根据上面选择的 Provider 配置对应的 defaults: Tell OpenClaw to use this provider by default. Configure based on your chosen provider:

JSON
"agents": {
  "defaults": {
    "model": {
      "primary": "claude/claude-opus-4-6"
    },
    "models": {
      "claude/claude-opus-4-6": {
        "alias": "claude-opus-4-6"
      }
    }
  }
}

配置文件下载

Configuration File Download

在配置文件中,将以下 JSON 代码片段加入到 models.providers 块中: Add the following JSON to models.providers block:

📦 openclaw.json(点击下载完整配置文件 - Windows版) 📦 openclaw.json(点击下载完整配置文件 - Mac版)

三、启动服务

3. Start Service

启动网关服务

Start Gateway Service

BASH
openclaw gateway

打开链接

Open Link

还需要告知 OpenClaw 优先使用该节点: Tell OpenClaw to use this provider by default:

URL http://localhost:18789
💡 提示💡 Tip 请使用 auto 分组的令牌。详细配置步骤请观看上方视频教程。 Use token from auto group. Watch the video tutorial above for detailed configuration steps.

OpenCode 配置指南

OpenCode Setup

Website https://opencode.ai(单击跳转!)

🎬 OpenCode 配置教程

配置步骤

Configuration Steps

创建配置文件

Create Config File

在项目根目录创建 opencode.json 配置文件: Create opencode.json config file in project root:

JSON
{
  "$schema": "https://opencode.ai/config.json",
  "theme": "opencode",
  "provider": {
    "google": {
      "options": {
        "apiKey": "YOUR_API_KEY_HERE",
        "baseURL": "https://www.frog-api.com/v1"
      }
    }
  },
  "model": "google/gemini-3-pro-preview",
  "autoupdate": true
}

配置说明

Configuration Details

  • apiKey:将 YOUR_API_KEY_HERE 替换为您的 Frog API 令牌 apiKey: Replace YOUR_API_KEY_HERE with your Frog API token
  • baseURL:API 接入点 https://www.frog-api.com/v1 baseURL: API endpoint https://www.frog-api.com/v1
  • model:使用 google/gemini-3-pro-preview 模型 model: Use google/gemini-3-pro-preview model
💡 提示💡 Tip 请使用 auto 分组的令牌。配置完成后,OpenCode 将自动使用 Frog API 的 API 服务。 Use token from auto group. After configuration, OpenCode will automatically use Frog API's API service.

Copaw 配置指南

Copaw Setup

Website https://copaw.agentscope.io/docs/quickstart(配置安装教程)

配置步骤

Configuration Steps

1. 选择模型添加供应商

1. Select Model and Add Provider

在 Copaw 中选择模型,然后添加供应商配置。 Select a model in Copaw and add provider configuration.

选择模型添加供应商

2. 新建并添加模型

2. Create and Add Model

创建新的模型配置,填写从 Frog API 网站模型广场复制的模型名称(按量模型)。 Create new model configuration and enter the model name copied from Frog API model marketplace (pay-per-use model).

新建并添加模型

3. 配置 API 信息

3. Configure API Information

  • API 地址: https://www.frog-api.com/v1 API URL: https://www.frog-api.com/v1
  • API Key: 填入您的 Frog API 令牌 API Key: Enter your Frog API token
  • 模型名称: 从模型广场复制的按量模型名称 Model Name: Pay-per-use model name from marketplace
配置API信息

4. 保存设置

4. Save Settings

保存配置后即可开始使用 Copaw 进行聊天。 After saving the configuration, you can start chatting with Copaw.

保存设置
配置完成
配置完成
💡 提示💡 Tip 请使用 auto 分组的令牌。配置完成后,Copaw 将自动使用 Frog API 的 API 服务。建议选择按量计费的模型以获得更好的性价比。 Use token from auto group. After configuration, Copaw will automatically use Frog API's API service. Pay-per-use models are recommended for better cost efficiency.

Dify

URL https://cloud.dify.ai

配置步骤

Configuration

  • 设置 -> 模型供应商 -> OpenAI-API-compatible
  • URL: https://www.frog-api.com/v1
  • API Key: 填入您的令牌
  • Model: [次]gemini-2.5-pro
image2.png
image3.png
image4.png
image5.png
image6.png
image7.png

n8n

🎬 n8n 配置教程

配置步骤

Configuration

  • 添加 OpenAI 模块 -> Message a model
  • Credential: Create New -> Custom
  • URL: https://www.frog-api.com/v1
  • API Key: 填入您的令牌
  • Model: [次]gemini-2.5-pro
image2.png
image3.png
image4.png
image5.png
image6.png
image7.png

Easydict (macOS)

配置步骤

Configuration

  • 服务: OpenAI 翻译
  • API Key: 填入您的令牌
  • 完整地址 (Full URL): https://www.frog-api.com/v1/chat/completions
  • Model: [次]gemini-2.5-pro
image2.png

Python SDK

🎬 Python SDK 配置教程

PYTHON - 基础调用示例
from openai import OpenAI

# 初始化客户端
client = OpenAI(
    api_key="sk-xxxxxxxx",              # 填入您的令牌
    base_url="https://www.frog-api.com/v1" # API 接入点
)

# 发送请求
response = client.chat.completions.create(
    model="[次]gemini-2.5-pro",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"}
    ],
    stream=False
)

print(response.choices[0].message.content)

🎬 Python 分析视频教程

PYTHON - 视频分析代码
import cv2
import base64
import requests
import os
import math

class VideoAnalyzer:
    def __init__(self, video_path):
        self.video_path = video_path
        if not os.path.exists(video_path):
            raise FileNotFoundError(f"❌ 找不到视频文件: {video_path}")
        
    def get_metadata(self):
        """1. 获取视频基础技术参数"""
        cap = cv2.VideoCapture(self.video_path)
        
        if not cap.isOpened():
            return None

        fps = cap.get(cv2.CAP_PROP_FPS)
        frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        duration = frame_count / fps if fps > 0 else 0
        
        cap.release()
        
        return {
            "width": width,
            "height": height,
            "fps": round(fps, 2),
            "frame_count": frame_count,
            "duration_sec": round(duration, 2),
            "file_size_mb": round(os.path.getsize(self.video_path) / (1024 * 1024), 2)
        }

    def extract_keyframes(self, max_frames=5, target_width=512):
        """2. 抽取关键帧用于AI分析"""
        print("📸 正在抽取关键帧...")
        cap = cv2.VideoCapture(self.video_path)
        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        
        if total_frames == 0:
            return []
            
        interval = max(1, total_frames // max_frames)
        base64_frames = []
        
        for i in range(0, total_frames, interval):
            if len(base64_frames) >= max_frames:
                break
                
            cap.set(cv2.CAP_PROP_POS_FRAMES, i)
            ret, frame = cap.read()
            
            if ret:
                h, w, _ = frame.shape
                aspect_ratio = h / w
                new_height = int(target_width * aspect_ratio)
                resized_frame = cv2.resize(frame, (target_width, new_height))
                
                _, buffer = cv2.imencode('.jpg', resized_frame)
                
                base64_str = base64.b64encode(buffer).decode('utf-8')
                base64_frames.append(base64_str)
        
        cap.release()
        print(f"✅ 成功抽取 {len(base64_frames)} 帧关键画面")
        return base64_frames

    def analyze_content_with_ai(self, api_key, base64_frames):
        """3. 调用视觉大模型解析视频内容"""
        print("🧠 正在请求 AI 分析视频内容...")
        
        url = "https://www.frog-api.com/v1/chat/completions"
        
        content_payload = [
            {"type": "text", "text": "这是同一个视频中按时间顺序抽取的几帧画面。请详细描述这个视频里发生了什么?包括场景、人物动作、氛围和主要事件。"}
        ]
        
        for b64 in base64_frames:
            content_payload.append({
                "type": "image_url",
                "image_url": {
                    "url": f"data:image/jpeg;base64,{b64}",
                    "detail": "low"
                }
            })

        payload = {
            "model": "[次]gemini-3-pro-preview", 
            "messages": [{"role": "user", "content": content_payload}],
            "max_tokens": 1000,
            "stream": True
        }

        try:
            response = requests.post(url, headers={"Authorization": f"Bearer {api_key}"}, json=payload, stream=True)
            
            print("\n📝 视频分析报告:\n" + "="*30)
            full_analysis = ""
            
            for line in response.iter_lines():
                if line:
                    decoded = line.decode('utf-8')
                    if decoded.startswith('data: ') and decoded != 'data: [DONE]':
                        try:
                            chunk = decoded[6:]
                            import json
                            delta = json.loads(chunk)['choices'][0]['delta'].get('content', '')
                            print(delta, end='', flush=True)
                            full_analysis += delta
                        except:
                            pass
            
            print("\n" + "="*30)
            return full_analysis
            
        except Exception as e:
            print(f"❌ 分析失败: {e}")
            return None

# 使用示例
if __name__ == "__main__":
    video_file = r"你的视频文件路径.mp4"  # 替换为你的视频文件路径
    my_api_key = "sk-xxxxxxxx"  # 替换为你的API Key
    
    if not os.path.exists(video_file):
        print(f"⚠️ 未找到 {video_file},请先准备一个视频文件。")
    else:
        analyzer = VideoAnalyzer(video_file)
        meta = analyzer.get_metadata()
        print(f"\n📊 视频元数据: {meta}")
        frames = analyzer.extract_keyframes(max_frames=5)
        if frames:
            analyzer.analyze_content_with_ai(my_api_key, frames)

🎬 Python 分析图片教程

PYTHON - 图片分析代码
import requests, json, base64

API_URL = "https://www.frog-api.com/v1/chat/completions"
API_KEY = "Bearer sk-xxxxxxxx"  # 替换为你的API Key

def analyze_image(img_path):
    """分析图片"""
    with open(img_path, "rb") as f:
        img_base64 = base64.b64encode(f.read()).decode()
    
    payload = {
        "model": "[次]gemini-3-pro-preview",  # 模型名称
        "messages": [{
            "role": "user",
            "content": [
                {"type": "text", "text": "请描述这张图片"},
                {
                    "type": "image_url",
                    "image_url": {"url": f"data:image/jpeg;base64,{img_base64}"}
                }
            ]
        }],
        "stream": True
    }
    
    headers = {
        "Content-Type": "application/json",
        "Authorization": API_KEY
    }
    
    response = requests.post(API_URL, json=payload, headers=headers, stream=True)
    for line in response.iter_lines():
        if line:
            line = line.decode('utf-8').replace('data: ', '')
            if line.strip() == '[DONE]': break
            try:
                data = json.loads(line)
                if content := data['choices'][0]['delta'].get('content'):
                    print(content, end="", flush=True)
            except:
                continue
    print()

# 使用示例
analyze_image(r"你的图片路径.jpg")  # 替换为实际图片路径

🎬 Python 视频生成教程

PYTHON - 视频生成代码
import requests
import time
import json
import os

def generate_video_stream_with_retry(prompt, api_key, max_retries=3):
    """带重试机制的流式视频生成指令获取函数"""
    base_url = "https://www.frog-api.com/v1/chat/completions"
    
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }

    payload = {
        "model": "veo_3_1",
        "messages": [
            {
                "role": "user",
                "content": f"请帮我生成一个视频,描述是:{prompt}。请告诉我视频生成的步骤或直接提供视频链接。"
            }
        ],
        "max_tokens": 5000,
        "temperature": 0.7,
        "stream": True
    }

    for attempt in range(max_retries):
        print(f"\n🔄 尝试 {attempt + 1}/{max_retries}...")
        full_content = ""
        
        try:
            response = requests.post(base_url, headers=headers, json=payload, timeout=120, stream=True)
            
            if response.status_code != 200:
                print(f"❌ 请求失败,状态码: {response.status_code}")
                if 500 <= response.status_code < 600:
                    print("⏳ 服务器端错误,等待后重试...")
                    time.sleep(5)
                    continue
                else:
                    return None

            print("✅ 连接成功,开始接收数据流...\n")
            print("-" * 30)

            for line in response.iter_lines():
                if line:
                    decoded_line = line.decode('utf-8')
                    if decoded_line.startswith('data: '):
                        data_str = decoded_line[6:]
                        if data_str.strip() == '[DONE]':
                            print("\n" + "-" * 30)
                            print("\n✅ 流式传输结束")
                            break
                        try:
                            data_json = json.loads(data_str)
                            delta = data_json['choices'][0]['delta'].get('content', '')
                            if delta:
                                print(delta, end='', flush=True)
                                full_content += delta
                        except json.JSONDecodeError:
                            continue
            
            if full_content:
                with open("ai_response.txt", "w", encoding="utf-8") as f:
                    f.write(full_content)
                print(f"📝 完整回复已保存到 ai_response.txt")
                return full_content
            else:
                print("⚠️ 未接收到任何内容")
                return None

        except requests.exceptions.Timeout:
            print("⏰ 连接超时")
            time.sleep(5)
            continue
        except Exception as e:
            print(f"❌ 未知错误: {e}")
            return None

    print(f"😞 经过 {max_retries} 次尝试后仍然失败")
    return None

# 使用示例
if __name__ == "__main__":
    my_api_key = "sk-xxxxxxxx"  # 替换为你的API Key
    
    result = generate_video_stream_with_retry(
        prompt="在海上冲浪的狗",
        api_key=my_api_key,
        max_retries=5
    )
    
    if result:
        print("\n🎬 任务完成")
    else:
        print("\n❌ 任务失败")

🎬 Python 图片生成教程

PYTHON - 图片生成代码
import requests
import json
import os
import re
from datetime import datetime
from pathlib import Path
from typing import Optional, Dict, Any, List
from urllib.parse import urlparse

class ImageGenerator:
    def __init__(self):
        self.api_key = "sk-xxxxxxxx"  # 替换为你的API密钥
        self.api_url = "https://www.frog-api.com/v1/chat/completions"
        self.model = "nano-banana"
        self.headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        }
        
    def generate_image(self, prompt: str, save_dir: str = "./generated_images") -> Dict[str, Any]:
        """生成图像并返回图片链接"""
        Path(save_dir).mkdir(parents=True, exist_ok=True)
        
        payload = {
            "model": self.model,
            "messages": [{"role": "user", "content": f"Generate an image based on this prompt: {prompt}"}],
            "max_tokens": 1000
        }
        
        print(f"正在生成图像...")
        print(f"提示词: {prompt}")
        
        try:
            response = requests.post(self.api_url, headers=self.headers, json=payload, timeout=600)
            
            if response.status_code == 200:
                return self._process_response(response, prompt, save_dir)
            else:
                print(f"API请求失败: {response.status_code}")
                return {"success": False, "error": f"HTTP {response.status_code}", "image_links": []}
                
        except requests.exceptions.RequestException as e:
            print(f"请求异常: {e}")
            return {"success": False, "error": str(e), "image_links": []}
    
    def _process_response(self, response, prompt, save_dir):
        result = {"success": False, "image_links": [], "content": "", "error": None}
        
        try:
            response_data = response.json()
            if "choices" in response_data and response_data["choices"]:
                content = response_data["choices"][0]["message"]["content"]
                result["content"] = content
                print(f"API响应内容: {content}")
                
                # 提取图片链接
                url_patterns = [
                    r'https?://[^\s]+?\.(?:jpg|jpeg|png|gif|bmp|webp)',
                    r'https?://[^\s]+?/image/[^\s]+',
                ]
                
                found_links = []
                for pattern in url_patterns:
                    matches = re.findall(pattern, content, re.IGNORECASE)
                    found_links.extend(matches)
                
                if found_links:
                    result["success"] = True
                    result["image_links"] = found_links
                    print(f"找到图片链接: {found_links}")
                else:
                    result["success"] = True
                    result["note"] = "API返回的是文本描述,未找到图片链接"
                    
        except Exception as e:
            result["error"] = f"处理响应失败: {e}"
        
        return result

def main():
    print("🎨 图像生成脚本")
    print("-" * 50)
    
    generator = ImageGenerator()
    prompt = "一只可爱的小狗在花园里玩耍"  # 修改这里的prompt内容
    
    result = generator.generate_image(prompt=prompt, save_dir="./test_images")
    
    print("\n" + "=" * 50)
    if result.get("success", False):
        print("✅ 请求成功!")
        if result.get("image_links"):
            print(f"\n📷 找到 {len(result['image_links'])} 个图片链接:")
            for i, link in enumerate(result["image_links"], 1):
                print(f"  {i}. {link}")
    else:
        print(f"❌ 生成失败: {result.get('error', '未知错误')}")

if __name__ == "__main__":
    main()
图片识别Vision Support 如果需要发送图片,请使用模型 gemini-2.5-flash-image-preview 并参考标准 OpenAI Vision 格式。

Cursor

Website https://cursor.com/(单击跳转下载!)

🎬 Cursor 配置教程

配置指南

Configuration Guide

打开设置

Open Settings

点击右上角的齿轮图标 ⚙️,选择 Models 选项 Click the gear icon ⚙️ in the top right corner, select Models

配置 API

Configure API

  • OpenAI API Key:输入您的 API 密钥(直接用默认令牌即可) OpenAI API Key: Enter your API key (use default token)
  • Override OpenAI Base URL:勾选并输入 https://www.frog-api.com/v1 Override OpenAI Base URL: Check and enter https://www.frog-api.com/v1
  • 点击 Verify 验证配置 Click Verify to validate configuration
  • 输入模型名称:gemini-2.5-pro Enter model name: gemini-2.5-pro
⚠️ 重要提示⚠️ Important Note Cursor 目前不支持 Agent 模式,只能使用 Chat 聊天对话模式。您可以在对话过程中让 AI 生成代码,然后手动应用到实际代码中。如果您是新手并且非常依赖 Agent 模式进行 Vibe Coding,建议使用 VS Code 的 RooCodeCline 插件(支持 Agent 模式)作为替代方案。 Cursor currently does not support Agent mode, only Chat mode is available. You can ask AI to generate code during conversation and manually apply it. If you're a beginner relying on Agent mode for Vibe Coding, consider using VS Code with RooCode or Cline plugins (which support Agent mode) as alternatives.

Chatbox

Website https://chatboxai.app(单击跳转!)

🎬 Chatbox 电脑端配置教程

🎬 Chatbox 手机端配置教程

配置步骤

Configuration

  • 设置 -> 模型提供方 -> 手动添加配置 (Custom)
  • API 模式:OpenAI API 兼容
  • API Host: https://www.frog-api.com/v1
  • API Key: 填入您的令牌
  • 模型 (Model): [次]gemini-2.5-pro
  • 注意:勾选所有模型能力,开启"改善网络兼容性"。
image5.png
image6.png
image7.png
image8.jpg
image2.png
image3.png
image4.png

Cherry Studio

Website https://cherry-ai.com(单击跳转!)

🎬 Cherry Studio 配置教程

配置步骤

Configuration

  • 设置 -> 手动自定义创建
  • API 地址: https://www.frog-api.com
  • API 密钥: 填入您的令牌
  • 模型名称: [次]gemini-2.5-pro
image2.jpg
image3.jpg
image4.png
image5.png

酒馆 (SillyTavern)

SillyTavern

配置步骤

Configuration

  • API 来源: Custom (OpenAI Compatible)
  • API Endpoint: https://www.frog-api.com/v1
  • API Key: 填入您的令牌
  • Model: [次]gemini-2.5-pro
提示:如果遇到 "socket hang up" 或 "No candidates returned",请尝试更换预设或检查网络。 Tip: Check network or preset if errors occur.
image2.jpg
image3.png
image4.png
image5.png

安卓 SillyTavern 酒馆安装教程

Android SillyTavern Installation

🎬 安卓 SillyTavern 配置教程

安装 Termux

Install Termux

从 F-Droid 或 GitHub 下载并安装 Termux。 Download and install Termux from F-Droid or GitHub.

安装Termux
Termux界面

更新软件包

Update Packages

在 Termux 中更新软件包: Update packages in Termux:

BASH
pkg update && pkg upgrade
更新软件包
更新完成

安装 Git

Install Git

BASH
pkg install git
安装Git

克隆酒馆仓库

Clone SillyTavern

BASH
git clone https://github.com/SillyTavern/SillyTavern -b release
克隆酒馆

进入目录并安装 Node.js

Enter Directory and Install Node.js

进入 SillyTavern 目录并安装 Node.js: Enter SillyTavern directory and install Node.js:

BASH
cd SillyTavern
BASH
pkg install nodejs
进入目录
安装nodejs

安装依赖

Install Dependencies

BASH
npm install
npm install

运行酒馆

Run SillyTavern

如果已在 SillyTavern 文件夹下,直接运行启动脚本: If already in SillyTavern folder, run the start script:

BASH
cd SillyTavern
./start.sh
运行酒馆
酒馆启动
如果无法跳转或乱码,请在浏览器中打开:http://127.0.0.1:8000 If redirect fails, open in browser: http://127.0.0.1:8000

配置 API

Configure API

在 SillyTavern 中配置 Frog API: Configure Frog API in SillyTavern:

  • 点击设置,聊天补全来源选择 自定义(兼容 OpenAI 协议)
  • 自定义端点 URL: https://www.frog-api.com/v1
  • API 密钥: 填入您的令牌
  • 模型名称: [次]gemini-2.5-pro
  • 点击"获取可用模型"验证配置
配置步骤1
配置步骤2
配置步骤3
配置步骤4
⚠️ 注意⚠️ Note 检查配置时不要出现末尾空格! Make sure there are no trailing spaces in the configuration!

Lovemo

Website lovemo.app(单击跳转!)
API URL https://www.frog-api.com
Model [次]gemini-2.5-pro

API 来源: Custom (OpenAI Compatible)

image2.png

Omate

Website omate.org(单击跳转!)
API URL https://www.frog-api.com/v1
Model [次]gemini-2.5-pro

API 来源: Custom (OpenAI Compatible)

image2.png
image3.png

Rikkahub

Website rikka-ai.com(单击跳转!)
API URL https://www.frog-api.com/v1
Model [次]gemini-2.5-pro

API 来源: Custom (OpenAI Compatible)

image2.png
image3.png

Tavo

Website tavoai.dev(单击跳转!)
API URL https://www.frog-api.com/v1
Model [次]gemini-2.5-pro

API 来源: Custom (OpenAI Compatible)

image2.png
image3.png
image4.png

LifeKline

Website lifekline(单击跳转!)
API URL https://www.frog-api.com/v1
Model [次]gemini-3-pro-preview

注意:此应用使用 gemini-3-pro-preview 模型。

image2.png
image3.png

提示词优化器配置教程

Prompt Optimizer Configuration

Website https://prompt.always200.com/(单击跳转!)
📌 关于提示词优化器 📌 About Prompt Optimizer 提示词优化器是一个强大的工具,可以帮助你优化和改进 AI 提示词,提升对话质量。 Prompt Optimizer is a powerful tool that helps you optimize and improve AI prompts for better conversation quality.

配置步骤

Configuration Steps

1. 点击网页右上角的模型管理

1. Click Model Management in Top Right

打开提示词优化器网站后,点击页面右上角的"模型管理"按钮。 After opening the Prompt Optimizer website, click the "Model Management" button in the top right corner.

点击模型管理

2. 配置 API 信息

2. Configure API Information

在模型管理页面中,按照以下信息进行配置: In the Model Management page, configure with the following information:

  • 提供商:选择 openai Provider: Select openai
  • API 密钥:填入您从 Frog API 网站获取的令牌 API Key: Enter your token from Frog API
  • API 地址https://www.frog-api.com/v1 API URL: https://www.frog-api.com/v1
配置API信息

3. 选择模型

3. Select Model

在模型选择中,选择 [次]gemini-2.5-pro 模型。 In model selection, choose [次]gemini-2.5-pro model.

Model [次]gemini-2.5-pro
选择模型

4. 开始使用

4. Start Using

配置完成后,选择刚才配置的模型进行对话即可。提示词优化器将帮助你优化和改进你的 AI 提示词。 After configuration, select the configured model to start chatting. The Prompt Optimizer will help you optimize and improve your AI prompts.

💡 提示💡 Tip 请使用 auto 分组的令牌。配置完成后,你可以使用提示词优化器来改进你的 AI 对话提示词,获得更好的对话效果。 Use token from auto group. After configuration, you can use the Prompt Optimizer to improve your AI conversation prompts for better results.

小懿 (XiaoYi)

Website xiaoyi.ink(单击跳转!)
API URL https://www.frog-api.com/v1
Model [次]gemini-2.5-pro

API 来源: Custom (OpenAI Compatible)

image2.png
image3.png
image4.png

小手机 (SmallPhone)

API URL https://www.frog-api.com/v1
Model [次]gemini-2.5-pro

API 来源: Custom (OpenAI Compatible)

image2.png