glm-free-api

🚀 智谱清言 ChatGLM4大模型逆向API白嫖测试【特长：超强智能体】，支持高速流式输出、支持智能体对话、支持多轮对话、支持AI绘图、支持联网搜索、支持长文档解读、支持代码调用、支持图像解析，零配置部署，多路token支持，自动清理会话痕迹。

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GLM AI Free 服务 provides high-speed streaming output, multi-turn dialogue support, intelligent agent dialogue support, AI drawing support, online search support, long document interpretation support, image parsing support. It offers zero-configuration deployment, multi-token support, and automatic session trace cleaning. It is fully compatible with the ChatGPT interface. The repository also includes six other free APIs for various services like Moonshot AI, StepChat, Qwen, Metaso, Spark, and Emohaa. The tool supports tasks such as chat completions, AI drawing, document interpretation, image parsing, and refresh token survival check.

README:

GLM AI Free 服务

支持高速流式输出、支持多轮对话、支持智能体对话、支持AI绘图、支持联网搜索、支持长文档解读、支持图像解析，零配置部署，多路token支持，自动清理会话痕迹。

与ChatGPT接口完全兼容。

还有以下七个free-api欢迎关注：

Moonshot AI（Kimi.ai）接口转API kimi-free-api

阶跃星辰 (跃问StepChat) 接口转API step-free-api

阿里通义 (Qwen) 接口转API qwen-free-api

秘塔AI (Metaso) 接口转API metaso-free-api

讯飞星火（Spark）接口转API spark-free-api

MiniMax（海螺AI）接口转API hailuo-free-api

聆心智能 (Emohaa) 接口转API emohaa-free-api

免责声明

逆向API是不稳定的，建议前往智谱AI官方 https://open.bigmodel.cn/ 付费使用API，避免封禁的风险。

本组织和个人不接受任何资金捐助和交易，此项目是纯粹研究交流学习性质！

仅限自用，禁止对外提供服务或商用，避免对官方造成服务压力，否则风险自担！

在线体验

此链接仅临时测试功能，只有一路并发，如果遇到异常请稍后重试，建议自行部署使用。

https://udify.app/chat/Pe89TtaX3rKXM8NS

效果示例

验明正身Demo

智能体对话Demo

对应智能体链接：网抑云评论生成器

结合Dify工作流Demo

体验地址：https://udify.app/chat/m46YgeVLNzFh4zRs

多轮对话Demo

AI绘图Demo

联网搜索Demo

长文档解读Demo

代码调用Demo

图像解析Demo

接入准备

从智谱清言获取refresh_token

进入智谱清言随便发起一个对话，然后F12打开开发者工具，从Application > Cookies中找到chatglm_refresh_token的值，这将作为Authorization的Bearer Token值：Authorization: Bearer TOKEN

智能体接入

打开智能体的聊天界面，地址栏的一串ID就是智能体的ID，复制下来备用，这个值将用作调用时的 model 参数值。

多账号接入

目前似乎限制同个账号同时只能有一路输出，你可以通过提供多个账号的chatglm_refresh_token并使用,拼接提供：

Authorization: Bearer TOKEN1,TOKEN2,TOKEN3

每次请求服务会从中挑选一个。

Docker部署

请准备一台具有公网IP的服务器并将8000端口开放。

拉取镜像并启动服务

docker run -it -d --init --name glm-free-api -p 8000:8000 -e TZ=Asia/Shanghai vinlic/glm-free-api:latest

查看服务实时日志

docker logs -f glm-free-api

重启服务

docker restart glm-free-api

停止服务

docker stop glm-free-api

Docker-compose部署

version: '3'

services:
  glm-free-api:
    container_name: glm-free-api
    image: vinlic/glm-free-api:latest
    restart: always
    ports:
      - "8000:8000"
    environment:
      - TZ=Asia/Shanghai

Render部署

注意：部分部署区域可能无法连接glm，如容器日志出现请求超时或无法连接，请切换其他区域部署！ 注意：免费账户的容器实例将在一段时间不活动时自动停止运行，这会导致下次请求时遇到50秒或更长的延迟，建议查看Render容器保活

fork本项目到你的github账号下。
访问 Render 并登录你的github账号。
构建你的 Web Service（New+ -> Build and deploy from a Git repository -> Connect你fork的项目 -> 选择部署区域 -> 选择实例类型为Free -> Create Web Service）。
等待构建完成后，复制分配的域名并拼接URL访问即可。

Vercel部署

注意：Vercel免费账户的请求响应超时时间为10秒，但接口响应通常较久，可能会遇到Vercel返回的504超时错误！

请先确保安装了Node.js环境。

npm i -g vercel --registry http://registry.npmmirror.com
vercel login
git clone https://github.com/LLM-Red-Team/glm-free-api
cd glm-free-api
vercel --prod

原生部署

请准备一台具有公网IP的服务器并将8000端口开放。

请先安装好Node.js环境并且配置好环境变量，确认node命令可用。

安装依赖

npm i

安装PM2进行进程守护

npm i -g pm2

编译构建，看到dist目录就是构建完成

npm run build

启动服务

pm2 start dist/index.js --name "glm-free-api"

查看服务实时日志

pm2 logs glm-free-api

重启服务

pm2 reload glm-free-api

停止服务

pm2 stop glm-free-api

接口列表

目前支持与openai兼容的 /v1/chat/completions 接口，可自行使用与openai或其他兼容的客户端接入接口，或者使用 dify 等线上服务接入使用。

对话补全

对话补全接口，与openai的 chat-completions-api 兼容。

POST /v1/chat/completions

header 需要设置 Authorization 头部：

Authorization: Bearer [refresh_token]

请求数据：

{
    // 如果使用智能体请填写智能体ID到此处，否则可以乱填
    "model": "glm4",
    // 目前多轮对话基于消息合并实现，某些场景可能导致能力下降且token最高为4096
    // 如果您想获得原生的多轮对话体验，可以传入首轮消息获得的id，来接续上下文
    // "conversation_id": "65f6c28546bae1f0fbb532de",
    "messages": [
        {
            "role": "user",
            "content": "你叫什么？"
        }
    ],
    // 如果使用SSE流请设置为true，默认false
    "stream": false
}

响应数据：

{
    // 如果想获得原生多轮对话体验，此id，你可以传入到下一轮对话的conversation_id来接续上下文
    "id": "65f6c28546bae1f0fbb532de",
    "model": "glm4",
    "object": "chat.completion",
    "choices": [
        {
            "index": 0,
            "message": {
                "role": "assistant",
                "content": "我叫智谱清言，是基于智谱 AI 公司于 2023 年训练的 ChatGLM 开发的。我的任务是针对用户的问题和要求提供适当的答复和支持。"
            },
            "finish_reason": "stop"
        }
    ],
    "usage": {
        "prompt_tokens": 1,
        "completion_tokens": 1,
        "total_tokens": 2
    },
    "created": 1710152062
}

AI绘图

对话补全接口，与openai的 images-create-api 兼容。

POST /v1/images/generations

header 需要设置 Authorization 头部：

Authorization: Bearer [refresh_token]

请求数据：

{
    // 如果使用智能体请填写智能体ID到此处，否则可以乱填
    "model": "cogview-3",
    "prompt": "一只可爱的猫"
}

响应数据：

{
    "created": 1711507449,
    "data": [
        {
            "url": "https://sfile.chatglm.cn/testpath/5e56234b-34ae-593c-ba4e-3f7ba77b5768_0.png"
        }
    ]
}

文档解读

提供一个可访问的文件URL或者BASE64_URL进行解析。

POST /v1/chat/completions

header 需要设置 Authorization 头部：

Authorization: Bearer [refresh_token]

请求数据：

{
    // 如果使用智能体请填写智能体ID到此处，否则可以乱填
    "model": "glm4",
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "file",
                    "file_url": {
                        "url": "https://mj101-1317487292.cos.ap-shanghai.myqcloud.com/ai/test.pdf"
                    }
                },
                {
                    "type": "text",
                    "text": "文档里说了什么？"
                }
            ]
        }
    ],
    // 如果使用SSE流请设置为true，默认false
    "stream": false
}

响应数据：

{
    "id": "cnmuo7mcp7f9hjcmihn0",
    "model": "glm4",
    "object": "chat.completion",
    "choices": [
        {
            "index": 0,
            "message": {
                "role": "assistant",
                "content": "根据文档内容，我总结如下：\n\n这是一份关于希腊罗马时期的魔法咒语和仪式的文本，包含几个魔法仪式：\n\n1. 一个涉及面包、仪式场所和特定咒语的仪式，用于使某人爱上你。\n\n2. 一个针对女神赫卡忒的召唤仪式，用来折磨某人直到她自愿来到你身边。\n\n3. 一个通过念诵爱神阿芙罗狄蒂的秘密名字，连续七天进行仪式，来赢得一个美丽女子的心。\n\n4. 一个通过燃烧没药并念诵咒语，让一个女子对你产生强烈欲望的仪式。\n\n这些仪式都带有魔法和迷信色彩，使用各种咒语和象征性行为来影响人的感情和意愿。"
            },
            "finish_reason": "stop"
        }
    ],
    "usage": {
        "prompt_tokens": 1,
        "completion_tokens": 1,
        "total_tokens": 2
    },
    "created": 100920
}

图像解析

提供一个可访问的图像URL或者BASE64_URL进行解析。

此格式兼容 gpt-4-vision-preview API格式，您也可以用这个格式传送文档进行解析。

POST /v1/chat/completions

header 需要设置 Authorization 头部：

Authorization: Bearer [refresh_token]

请求数据：

{
    "model": "65c046a531d3fcb034918abe",
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "http://1255881664.vod2.myqcloud.com/6a0cd388vodbj1255881664/7b97ce1d3270835009240537095/uSfDwh6ZpB0A.png"
                    }
                },
                {
                    "type": "text",
                    "text": "图像描述了什么？"
                }
            ]
        }
    ],
    "stream": false
}

响应数据：

{
    "id": "65f6c28546bae1f0fbb532de",
    "model": "glm",
    "object": "chat.completion",
    "choices": [
        {
            "index": 0,
            "message": {
                "role": "assistant",
                "content": "图片中展示的是一个蓝色背景下的logo，具体地，左边是一个由多个蓝色的圆点组成的圆形图案，右边是“智谱·AI”四个字，字体颜色为蓝色。"
            },
            "finish_reason": "stop"
        }
    ],
    "usage": {
        "prompt_tokens": 1,
        "completion_tokens": 1,
        "total_tokens": 2
    },
    "created": 1710670469
}

refresh_token存活检测

检测refresh_token是否存活，如果存活live未true，否则为false，请不要频繁（小于10分钟）调用此接口。

POST /token/check

请求数据：

{
    "token": "eyJhbGciOiJIUzUxMiIsInR5cCI6IkpXVCJ9..."
}

响应数据：

{
    "live": true
}

注意事项

Nginx反代优化

如果您正在使用Nginx反向代理glm-free-api，请添加以下配置项优化流的输出效果，优化体验感。

# 关闭代理缓冲。当设置为off时，Nginx会立即将客户端请求发送到后端服务器，并立即将从后端服务器接收到的响应发送回客户端。
proxy_buffering off;
# 启用分块传输编码。分块传输编码允许服务器为动态生成的内容分块发送数据，而不需要预先知道内容的大小。
chunked_transfer_encoding on;
# 开启TCP_NOPUSH，这告诉Nginx在数据包发送到客户端之前，尽可能地发送数据。这通常在sendfile使用时配合使用，可以提高网络效率。
tcp_nopush on;
# 开启TCP_NODELAY，这告诉Nginx不延迟发送数据，立即发送小数据包。在某些情况下，这可以减少网络的延迟。
tcp_nodelay on;
# 设置保持连接的超时时间，这里设置为120秒。如果在这段时间内，客户端和服务器之间没有进一步的通信，连接将被关闭。
keepalive_timeout 120;

Token统计

由于推理侧不在glm-free-api，因此token不可统计，将以固定数字返回。

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BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

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uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

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griptape

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glm-free-api

README:

GLM AI Free 服务

目录

免责声明

在线体验

效果示例

验明正身Demo

智能体对话Demo

结合Dify工作流Demo

多轮对话Demo

AI绘图Demo

联网搜索Demo

长文档解读Demo

代码调用Demo

图像解析Demo

接入准备

智能体接入

多账号接入

Docker部署

Docker-compose部署

Render部署

Vercel部署

原生部署

推荐使用客户端

接口列表

对话补全

AI绘图

文档解读

图像解析

refresh_token存活检测

注意事项

Nginx反代优化

Token统计

Star History

For Tasks:

For Jobs:

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