部署OpenClaw 百炼大模型 QQ/飞书

系统:CentOS 7 大模型:百炼

网络环境:魔法! 也可以使用国内源

一.安装OpenClaw

环境:node 版本要>=22 pnpm git

如果你的网络环境不好,就先把环境安装了

Linux有一键安装

 curl -fsSL https://openclaw.ai/install.sh | bash

安装失败的话大概率都是网络环境不行

安装完成后 开始运行引导程序

openclaw onboard --install-daemon

引导选项

选项选择
I understand this is personal-by-default and shared/multi-user use requires lock-down. Continue?yes
Onboarding modeManual
Config handling(第一次安装不会有这个)Reset
Reset scope(第一次安装不会有这个)Full reset
What do you want to set up?Local gateway (this machine)
Workspace directory/root/.openclaw/workspace

选择大模型,国内国外的都有部分适配

如果你想免费用一会的话可以选择Qwen

你有这里大模型Api Kay的话就选择对应的

但是调用频繁会限流

如果你的 大模型里面没有就先选择 Skip for now 跳过

我选择的是跳过,我用阿里百炼

Filter models by providerAll providers
Default modelkeep current
Gateway port18789 (默认端口)
Gateway bind根据你自己选择我选择的是LAN(0.0.0.0)
Gateway authtoken
Tailscale exposureServe
Reset Tailscale serve/funnel on exit?No
How do you want to provide the gateway token?Generate/store plaintext token (Default)
Gateway token (blank to generate)空白
Configure chat channels now?yes
Select a channelfinished(我先不选择接入,后面都可以设置)
Search providerSkip for now (也先不选择,后面能设置)
Configure skills now? (recommended)yes
Install missing skill dependenciesgithub(都可以选择)
Show Homebrew install command?yes
Set GOOGLE_PLACES_API_KEY for goplaces?no
Set GEMINI_API_KEY for nano-banana-pro?no
Set NOTION_API_KEY for notion?no
Set OPENAI_API_KEY for openai-image-gen?no
Set ELEVENLABS_API_KEY for sag?no
Enable hooks?全选
Gateway service runtimeNode (recommended)
Gateway service already installed(第一次安装不会有这个)Reinstall
How do you want to hatch your bot?Open the Web UI

选择完就安装完成啦

如果你是在虚拟机部署的

现在看看网关有没有启动

root@localhost:~# openclaw gateway status

🦞 OpenClaw 2026.3.11 (29dc654) — Your config is valid, your assumptions are not.

22:36:33 [agents/model-providers] Failed to discover Ollama models: TypeError: fetch failed
│
◇  
Service: systemd (enabled)
File logs: /tmp/openclaw/openclaw-2026-03-13.log
Command: /usr/bin/node /usr/lib/node_modules/openclaw/dist/index.js gateway --port 18789
Service file: ~/.config/systemd/user/openclaw-gateway.service
Service env: OPENCLAW_GATEWAY_PORT=18789

Config (cli): ~/.openclaw/openclaw.json
Config (service): ~/.openclaw/openclaw.json

Gateway: bind=loopback (127.0.0.1), port=18789 (service args)
Probe target: ws://127.0.0.1:18789
Dashboard: http://127.0.0.1:18789/
Probe note: Loopback-only gateway; only local clients can connect.

Runtime: running (pid 3629, state active, sub running, last exit 0, reason 0)
RPC probe: ok

Listening: 127.0.0.1:18789
Troubles: run openclaw status
Troubleshooting: https://docs.openclaw.ai/troubleshooting

显示systemd (enabled)就是启动了

然后在浏览器输入127.0.0.1:18789就能进入OpenClaw的web页面啦

目前我只是装了OpenClaw(虾壳)还没有给他选择大模型所以是用不了的。

安装百炼大模型

先找到OpenClaw的目录 我这里有设置在/root里面

配置 openclaw.json

root@localhost:~# ls -a
.   anaconda-ks.cfg  .bash_logout   .bashrc  .cshrc    love.txt  .openclaw  .tcshrc
..  .bash_history    .bash_profile  .config  .lesshst  .npm      .ssh       .viminfo
root@localhost:~# cd .openclaw/
root@localhost:~/.openclaw# ls
agents       cron      logs               openclaw.json.bak.1  openclaw.json.bak.4
canvas       devices   openclaw.json      openclaw.json.bak.2  update-check.json
completions  identity  openclaw.json.bak  openclaw.json.bak.3  workspace
root@localhost:~/.openclaw# vi openclaw.json
#openclaw.json 替换成下面的内容
{
  "models": {
    "mode": "merge",
    "providers": {
      "bailian": {
        "baseUrl": "https://coding.dashscope.aliyuncs.com/v1",
        "apiKey": "YOUR_API_KEY", #替换成百炼的API Kay
        "api": "openai-completions",
        "models": [
          {
            "id": "qwen3.5-plus",
            "name": "qwen3.5-plus",
            "reasoning": false,
            "input": ["text", "image"],
            "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 },
            "contextWindow": 1000000,
            "maxTokens": 65536,
            "compat": {
              "thinkingFormat": "qwen"
            }
          },
          {
            "id": "qwen3-max-2026-01-23",
            "name": "qwen3-max-2026-01-23",
            "reasoning": false,
            "input": ["text"],
            "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 },
            "contextWindow": 262144,
            "maxTokens": 65536,
            "compat": {
              "thinkingFormat": "qwen"
            }
          },
          {
            "id": "qwen3-coder-next",
            "name": "qwen3-coder-next",
            "reasoning": false,
            "input": ["text"],
            "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 },
            "contextWindow": 262144,
            "maxTokens": 65536
          },
          {
            "id": "qwen3-coder-plus",
            "name": "qwen3-coder-plus",
            "reasoning": false,
            "input": ["text"],
            "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 },
            "contextWindow": 1000000,
            "maxTokens": 65536
          },
          {
            "id": "MiniMax-M2.5",
            "name": "MiniMax-M2.5",
            "reasoning": false,
            "input": ["text"],
            "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 },
            "contextWindow": 196608,
            "maxTokens": 32768
          },
          {
            "id": "glm-5",
            "name": "glm-5",
            "reasoning": false,
            "input": ["text"],
            "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 },
            "contextWindow": 202752,
            "maxTokens": 16384,
            "compat": {
              "thinkingFormat": "qwen"
            }
          },
          {
            "id": "glm-4.7",
            "name": "glm-4.7",
            "reasoning": false,
            "input": ["text"],
            "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 },
            "contextWindow": 202752,
            "maxTokens": 16384,
            "compat": {
              "thinkingFormat": "qwen"
            }
          },
          {
            "id": "kimi-k2.5",
            "name": "kimi-k2.5",
            "reasoning": false,
            "input": ["text", "image"],
            "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 },
            "contextWindow": 262144,
            "maxTokens": 32768,
            "compat": {
              "thinkingFormat": "qwen"
            }
          }
        ]
      }
    }
  },
  "agents": {
    "defaults": {
      "model": {
        "primary": "bailian/qwen3.5-plus"
      },
      "models": {
        "bailian/qwen3.5-plus": {},
        "bailian/qwen3-max-2026-01-23": {},
        "bailian/qwen3-coder-next": {},
        "bailian/qwen3-coder-plus": {},
        "bailian/MiniMax-M2.5": {},
        "bailian/glm-5": {},
        "bailian/glm-4.7": {},
        "bailian/kimi-k2.5": {}
      }
    }
  },
  "gateway": {
    "mode": "local"
  }
}

配置完成后就能使用啦!

二.连接到QQ/飞书

1.连接到QQ

这个最简单了,腾讯有开专门的接口

先登录QQ开放平台 QQ开放平台|机器人列表

创建一个机器人然后按照他给的代码在ctrl+c/v进终端进行了

2.飞书

登录 开发者后台 – 飞书开放平台

点击创建企业自应用

然后随便填写创建

点击版本管理与发布,创建一个版本

随便填一下创建并发布

开通权限按自己的需求看着给

安装飞书插件 npx -y @larksuite/openclaw-lark-tools install

扫码创建一个机器人,如果你有机器人了,扫码后也可以选择

评论

  1. yawara
    2 月前
    2026-3-15 23:58:59

    好厉害呀小哥哥(≧ω≦)

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