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openclaw-dual-agentOpenClaw dual Agent 效率

Agent Skill

openclaw-dual-agent 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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下载量

1,904
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:openclaw-dual-agent(OpenClaw dual Agent 效率)
来源仓库:https://github.com/djc00p/openclaw-dual-agent
安装命令:
openclaw skills install openclaw-dual-agent
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install openclaw-dual-agent

简介

同时运行付费 Anthropic 模型与免费 OpenRouter/Ollama 模型的混合代理。

  • 适用于成本敏感场景下的多模型 fallback 与负载均衡需求。
  • 通过触发短语激活双代理协同工作模式,自动分配推理任务。
  • 需配置多个 API 密钥并管理配额,注意不同模型输出的格式差异。
  • 建议监控调用频率与费用,避免因并发导致超额支出。

SKILL.md

name
openclaw-dual-agent
description
Run two OpenClaw agents simultaneously — a paid Anthropic agent and a free agent using either OpenRouter or local Ollama models. Trigger phrases: multi-agent setup, add a second agent, free agent openclaw, run two agents, openrouter openclaw, ollama agent, local model openclaw, parallel agents, cost optimization agent.
metadata
{"clawdbot": {"emoji": "🤖", "requires": {"bins": ["jq"]}, "env": ["ANTHROPIC_API_KEY"], "os": ["darwin", "linux", "win32"]}, "homepage": "https://clawhub.com/djc00p/openclaw-dual-agent"}

Multi-Agent OpenClaw Setup

Run a paid Anthropic agent and free OpenRouter agent side by side with separate Telegram bots.

Quick Start

  1. Create two Telegram bots via @BotFather and extract chat IDs:
   curl https://api.telegram.org/bot{TOKEN}/getUpdates | jq '.result[0].message.chat.id'
  1. Authenticate agents:
   # Run interactively — avoids exposing keys in shell history
   openclaw onboard

> ⚠️ Never pass API keys directly on the CLI (e.g. --anthropic-api-key ...) — it exposes them in shell history. Always use openclaw onboard interactively. Credential files (auth-profiles.json, openclaw.json) should be chmod 600.

  1. Configure openclaw.json with two agents, separate bindings, and Telegram accounts.
  1. Verify setup:
   openclaw doctor
   openclaw sessions cleanup \
     --store /Users/YOUR_USERNAME/.openclaw/agents/main/store \
     --enforce --fix-missing
   openclaw restart

Key Concepts

  • Agent isolation: Each agent has its own agentDir, workspace, and model config.
  • Binding routing: accountId in bindings directs Telegram messages to the correct agent.
  • Model refs: Use provider/modelid format (e.g., anthropic/claude-sonnet-4-6).
  • Per-agent auth: OpenRouter requires auth-profiles.json in each agent's directory.

Common Usage

Adding a free agent:

  • Create agentDir at /Users/YOUR_USERNAME/.openclaw/agents/free-agent/agent
  • Add agent entry to openclaw.json with model.primary: "openrouter/..."
  • Create auth-profiles.json with OpenRouter API key in agent's directory
  • Add binding with unique accountId (e.g., "tg2")
  • Restart: openclaw restart

Switching models: Edit openclaw.json agent's model.primary and fallbacks with valid provider/id strings.

Masking secrets for logs:

cat ~/.openclaw/openclaw.json | \
  jq '.channels.telegram.accounts |= map_values(.botToken = "[REDACTED]")'

Option B: Local Ollama Agent (Free + Private)

Instead of OpenRouter, run your second agent on a local Ollama model — completely free, fully private, and offline-capable.

Install & Configure Ollama

macOS:

# Install via Homebrew
brew install ollama

# Or download from https://ollama.ai

Start Ollama:

# In a dedicated terminal, keep it running
ollama serve

Pull a model (choose one based on your needs):

# Google Gemma 4 26B — good balance of capability and speed (17GB)
ollama pull gemma4:26b

# Meta Llama 3.3 70B — very capable, excellent reasoning (43GB)
ollama pull llama3.3:70b

# Qwen 2.5 32B — strong coding and multilingual (20GB)
ollama pull qwen2.5:32b

# Mistral 7B — fast and lightweight, good for quick responses (4GB)
ollama pull mistral:7b

Configure OpenClaw with Ollama Agent

Add the agent entry to openclaw.json (e.g., id: "ayo"):

{
  "id": "ayo",
  "name": "Ayo",
  "workspace": "/Users/YOUR_USERNAME/.openclaw/workspace-ayo",
  "agentDir": "/Users/YOUR_USERNAME/.openclaw/agents/ayo/agent",
  "model": {
    "primary": "ollama/gemma4:26b",
    "fallbacks": [
      "openrouter/free"
    ]
  },
  "heartbeat": {
    "every": "1h",
    "model": "openrouter/free"
  }
}

Key points:

  • Model format: Always use ollama/modelname:tag (e.g., ollama/gemma4:26b, ollama/llama3.3:70b)
  • No API key needed: Ollama runs entirely locally. No auth-profiles.json required.
  • Ollama must be running: Start ollama serve in a terminal before the gateway starts
  • Pull first: Run ollama pull modelname:tag before configuring (the model must exist locally)
  • Heartbeat fallback: The example uses openrouter/free as a fallback since Ollama models may be slower for heartbeats. You can also use the same Ollama model (ollama/gemma4:26b) if you prefer fully local operation
  • Add Telegram binding: Include a separate binding with a unique accountId (e.g., "tg_ollama") to route messages to Ayo

After config change:

# Verify no errors
openclaw doctor

# Restart the gateway
openclaw gateway restart

Common Gotchas

❌ Wrong✅ CorrectIssue
gemma4:26b:localollama/gemma4:26bInvalid format; always use provider/model:tag
gemma4:26bollama/gemma4:26bWithout prefix, OpenClaw won't route to Ollama
ollama/kimi-k2.5:cloudopenrouter/kimi-k2.5:cloudCloud models don't belong in Ollama fallbacks
Model not pulledollama pull gemma4:26bGateway fails silently if model doesn't exist locally

If you see "Invalid input" errors in openclaw doctor, check the model.primary format — it must start with ollama/.

References

  • references/config-reference.md — Full openclaw.json, bindings, and auth-profiles.json examples
  • references/troubleshooting.md — Common errors, fixes, and Node.js compatibility notes

适合场景

01

调用多模型

02

代码和文本生成

03

Agent 推理流程

04

OpenRouter 模型接入

能力概览

能力 1

统一调用多种 LLM

能力 2

支持 Claude、Gemini、Kimi 等模型

能力 3

适合聊天、代码和推理任务

能力 4

可作为 Agent 模型调用入口

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

73.1%
按下载量换算1,392

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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