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feishu-multiagent飞书多 Agent

Agent Skill

feishu-multiagent 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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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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:feishu-multiagent(飞书多 Agent)
来源仓库:https://github.com/tinywatermonster/feishu-multiagent
安装命令:
openclaw skills install feishu-multiagent
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 feishu-multiagent

简介

为飞书多账户设置配置 OpenClaw 多代理路由,支持不同appId/appSecret的机器人运行。

  • 适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。
  • 当用户需要两个或多个飞书机器人在同一环境中运行时使用。
  • 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。
  • 适用于多账户管理和代理路由场景。

SKILL.md

name
openclaw-feishu-multi-agent-setup
description
Configure OpenClaw multi-agent routing for Feishu multi-account setups. Use when users need two or more Feishu bots (different appId/appSecret) to run on isolated agents/workspaces with distinct system prompts and independent MEMORY bootstrap, or when troubleshooting wrong-agent routing, shared persona, open_id cross-app errors, pairing/account mixups, memory provider failures, contact scope warnings (99991672), or card streaming permission errors.

Openclaw Feishu Multi Agent Setup

Overview

Set up and verify that each Feishu account is routed to the intended OpenClaw agent with isolated workspace and prompt files. Apply deterministic checks so routing, persona, and channel health are provable from logs. Also ensure each new agent has a valid MEMORY.md and working memory embeddings.

Required Inputs

  • Target agent ids (example: main, assistant)
  • Target workspace paths (example: ~/.openclaw/workspace, ~/.openclaw/workspace-assistant)
  • Feishu account ids (example: default, backup)
  • App credentials per account (appId, appSecret)
  • Memory embedding plan per agent (auto or explicit provider/endpoint)

Workflow

1. Inspect current state first

Run:

openclaw agents list --bindings
openclaw agents bindings --json
openclaw config get channels.feishu

Detect these issues before changing anything:

  • backup/default both mapped to same agent
  • missing agent in agents.list
  • missing account in channels.feishu.accounts
  • duplicated agentDir across agents

2. Back up config before edits

cp ~/.openclaw/openclaw.json ~/.openclaw/openclaw.json.bak.$(date +%Y%m%d-%H%M%S)

3. Ensure agents are isolated

Prefer non-interactive creation when adding a new agent:

openclaw agents add assistant \
  --non-interactive \
  --workspace ~/.openclaw/workspace-assistant \
  --model minimax-portal/MiniMax-M2.5

Ensure agents.list[] has unique workspace and agentDir per agent.

4. Bootstrap required workspace files for new agents

For every new non-main agent workspace, verify these files exist and are customized:

  • AGENTS.md
  • SOUL.md
  • IDENTITY.md
  • USER.md
  • TOOLS.md
  • MEMORY.md (or memory.md)

Why this is mandatory:

  • MEMORY.md is injected into system prompt when present.
  • Without agent-specific MEMORY bootstrap, global long-term context will be missing from prompt.

Also create daily memory directory (used by memory indexing):

mkdir -p ~/.openclaw/workspace-assistant/memory

5. Configure Feishu multi-account block

Ensure this shape exists in ~/.openclaw/openclaw.json:

{
  "channels": {
    "feishu": {
      "enabled": true,
      "defaultAccount": "default",
      "accounts": {
        "default": { "appId": "cli_xxx", "appSecret": "xxx", "enabled": true },
        "backup": { "appId": "cli_yyy", "appSecret": "yyy", "enabled": true }
      }
    }
  }
}

Never echo secrets back to users in plaintext.

6. Bind account to agent deterministically

Use one binding per account:

{
  "bindings": [
    { "agentId": "main", "match": { "channel": "feishu", "accountId": "default" } },
    { "agentId": "assistant", "match": { "channel": "feishu", "accountId": "backup" } }
  ]
}

Or use CLI for simple cases:

openclaw agents bind --agent main --bind feishu:default
openclaw agents bind --agent assistant --bind feishu:backup

7. Ensure prompts are truly different

Update the non-main workspace files so system prompts diverge:

  • AGENTS.md
  • SOUL.md
  • IDENTITY.md
  • USER.md

Remove stale bootstrap template when persona setup is complete:

  • BOOTSTRAP.md

8. Harden memory embeddings per new agent

Probe memory first:

openclaw memory status --agent assistant --deep --json

If provider is none, embeddingProbe.ok=false, or logs show provider errors, pin a working provider explicitly.

Example: route memory embeddings through OpenRouter OpenAI-compatible endpoint for work-agent:

AGENT_ID="work-agent"
IDX=$(openclaw agents list --bindings --json | jq -r --arg id "$AGENT_ID" 'to_entries[] | select(.value.id==$id) | .key')
KEY=$(jq -r '.profiles["openrouter:default"].key' ~/.openclaw/agents/$AGENT_ID/agent/auth-profiles.json)

openclaw config set "agents.list[$IDX].memorySearch.provider" '"openai"' --strict-json
openclaw config set "agents.list[$IDX].memorySearch.model" '"text-embedding-3-small"' --strict-json
openclaw config set "agents.list[$IDX].memorySearch.remote.baseUrl" '"https://openrouter.ai/api/v1"' --strict-json
openclaw config set "agents.list[$IDX].memorySearch.remote.apiKey" "\"$KEY\"" --strict-json
openclaw config set "agents.list[$IDX].memorySearch.fallback" '"none"' --strict-json

Then force index once:

openclaw memory index --agent work-agent --force --verbose
openclaw memory status --agent work-agent --deep --json

Expect:

  • provider is not none
  • embeddingProbe.ok=true
  • files/chunks increase after indexing

9. Validate and restart

openclaw config validate
openclaw gateway restart
openclaw gateway status
openclaw channels status --probe

10. Prove routing with log evidence

Ask user to send route-check to each bot. Then inspect logs:

openclaw channels logs --lines 300 | rg "feishu\\[(default|backup)\\].*dispatching to agent"

Expect:

  • feishu[default] -> session=agent:main:...
  • feishu[backup] -> session=agent:assistant:...

11. Approve pairing with explicit account

Always approve Feishu pairing with account scope in multi-account setups:

openclaw pairing list feishu --json
openclaw pairing approve feishu <code> --account <accountId> --notify

This prevents approving the same sender into the wrong account allowlist.

12. Handle common Feishu errors

open_id cross app:

  • Cause: target open_id belongs to a different app account.
  • Fix: send via matching --account, keep allowFrom scoped per account.
  • Queue cleanup: archive stale cross-app deliveries in ~/.openclaw/delivery-queue/ before restart if they keep retrying.

gemini embeddings failed: ... User location is not supported:

  • Cause: memory embedding provider resolves to Gemini in unsupported region.
  • Fix: set per-agent memorySearch.provider to a supported provider/endpoint (example above).

providerUnavailableReason / provider=none in openclaw memory status --deep:

  • Cause: no usable memory embedding credentials for that agent.
  • Fix: configure agent-specific memorySearch provider + key, then run openclaw memory index --agent <id> --force.

99991672 contact scope warning:

  • Platform fix: grant one contact read scope in Feishu app.
  • Config fallback: set channels.feishu.resolveSenderNames=false.

cardkit:card:write streaming error:

  • Platform fix: grant cardkit:card:write.
  • Config fallback: set channels.feishu.streaming=false and channels.feishu.blockStreaming=false.

Output Contract

When finishing, report:

  1. What changed (files + key fields).
  2. Validation outputs (config validate, gateway status, channels status --probe).
  3. Routing evidence lines for both accounts.
  4. Memory evidence (memory status --agent <id> --deep, index result, provider/embedding probe state).
  5. Any residual warnings and whether they are blocking.

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

92.67%
按下载量换算3,811

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

未展示

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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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