據 AlphaMoat 統計,截至 2026-09,Agent Network 累計下載 2,997 次、獲 3 個星標,在全部 5,078 個 AI 技能中排名第 3,732 位,在「AI 智慧體」分類(969 個)中排名第 753 位。
| 平台 | 累計安裝 | 累計下載 | 星標 | 評論 |
|---|---|---|---|---|
| ClawHub ↗ | 88 | 2968 | 3 | - |
| SkillHub ↗ | 124 | 2997 | 3 | — |
各平台的計數口徑不同,統計時間不一致。
它提供訊息歷史、@提醒、任務建立與分配、決策投票、收件箱通知和線上狀態,並使用 SQLite 儲存協調記錄。當前缺少訪問控制,能夠訪問軟體包的呼叫方可能冒充 agent 或讀取、修改、刪除共享記錄;僅適用於可信的本地或單營運者流程。
資料釋出於 2026-10-29
以下是根據技能文件生成的示例,不代表實際執行結果。
2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。
| 月份 | 累計下載 | 環比 | 星標 |
|---|---|---|---|
| 2026-09 | 2997 | — | 3 |
| 2026-08 | 2820 | +5.3% | 3 |
| 2026-07 | 2678 | +11.6% | 3 |
| 2026-06 | 2399 | +11.2% | 3 |
| 2026-05 | 2158 | — | 3 |
| 2026-03 | 1539 | — | 3 |
以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 2026-10-23。
--- name: agent-network description: Multi-Agent group chat collaboration system inspired by DingTalk/Lark. Enables AI agents to chat in groups, @mention each other, assign tasks, make decisions via voting, and collaborate. Use when building multi-agent systems that need structured communication, task delegation, decision making, or group coordination. --- # Agent Network - Multi-Agent Collaboration System A complete multi-agent group chat and collaboration platform that allows AI agents to communicate, coordinate, and collaborate in a structured environment similar to enterprise chat platforms like DingTalk or Lark. ## What This Skill Provides
- **Group Chat System** - Multiple agents can chat in groups with message history
- **@Mentions** - Agents can @mention each other to trigger notifications
- **Task Management** - Create, assign, track, and complete tasks
- **Decision Voting** - Propose decisions and vote (for/against/abstain)
- **Inbox Notifications** - Unread message tracking and notification center
- **Online Status** - Real-time agent online/offline status
- **Central Coordinator** - Message routing and agent lifecycle management
## Quick Start
```python
from agent_network import AgentManager, GroupManager, MessageManager, TaskManager, DecisionManager, get_coordinator
# Initialize default agents
from agent_network import init_default_agents
init_default_agents()
# Get the coordinator
coordinator = get_coordinator()
# Register agents
coordinator.register_agent(agent_id=1)
coordinator.register_agent(agent_id=2)
# Create a group
group = GroupManager.create("Dev Team", owner_id=1, description="Development team chat")
GroupManager.add_member(group.id, agent_id=2)
# Send a message with @mention
MessageManager.send_message(
from_agent_id=1,
content="@小邢 Please check the server status",
group_id=group.id
)
# Assign a task
task = TaskManager.create(
title="Fix login bug",
assigner_id=1,
assignee_id=2,
description="Users can't login with SSO",
priority="high"
)
# Create a decision
decision = DecisionManager.create(
title="Adopt new database?",
description="Should we migrate to distributed database?",
proposer_id=1,
group_id=group.id
)
# Vote on decision
DecisionManager.vote(decision.id, agent_id=2, vote="for", comment="Agreed, better performance")
```
## Core Components
### 1. Agent Management (`agent_manager.py`)
Register and manage agents with online/offline status:
```python
from agent_network import AgentManager
# Register new agent
agent = AgentManager.register("NewAgent", "Developer", "Backend specialist")
# Set status
AgentManager.go_online(agent.id)
AgentManager.go_offline(agent.id)
# Get online agents
online = AgentManager.get_online_agents()
```
### 2. Group Management (`group_manager.py`)
Create groups and manage membership:
```python
from agent_network import GroupManager
# Create group
group = GroupManager.create("Project Alpha", owner_id=1)
# Add members
GroupManager.add_member(group.id, agent_id=2)
GroupManager.add_member(group.id, agent_id=3)
# List members
members = GroupManager.get_members(group.id)
online_members = GroupManager.list_online_members(group.id)
```
### 3. Message System (`message_manager.py`)
Send messages with @mention support:
```python
from agent_network import MessageManager
# Send message
msg = MessageManager.send_message(
from_agent_id=1,
content="Hello team!",
group_id=1
)
# @mention automatically detected
msg = MessageManager.send_message(
from_agent_id=1,
content="@Alice @Bob Please review this",
group_id=1
)
# Get message history
messages = MessageManager.get_group_messages(group_id=1, limit=50)
# Search messages
results = MessageManager.search_messages("keyword", group_id=1)
# Get unread count
unread = MessageManager.get_unread_count(agent_id=1)
inbox = MessageManager.get_agent_inbox(agent_id=1, only_unread=True)
```
### 4. Task Management (`task_manager.py`)
Full task lifecycle:
```python
from agent_network import TaskManager
# Create task
task = TaskManager.create(
title="Implement API",
assigner_id=1,
assignee_id=2,
description="Build REST endpoints",
priority="high", # low/normal/high/urgent
due_date="2026-02-15"
)
# Update status
TaskManager.start_task(task.id, agent_id=2)
TaskManager.complete_task(task.id, agent_id=2, result="All tests passed")
# Add comments
TaskManager.add_comment(task.id, agent_id=2, "50% complete")
# List tasks
all_tasks = TaskManager.get_all()
my_tasks = TaskManager.get_agent_tasks(agent_id=2, status="pending")
```
### 5. Decision Voting (`decision_manager.py`)
Collaborative decision making:
```python
from agent_network import DecisionManager
# Create proposal
decision = DecisionManager.create(
title="Use microservices?",
description="Should we refactor to microservices?",
proposer_id=1,
group_id=1
)
# Vote
DecisionManager.vote(decision.id, agent_id=2, vote="for", comment="Better scalability")
DecisionManager.vote(decision.id, agent_id=3, vote="against")
# Update status
DecisionManager.update_status(decision.id, "approved", updater_id=1)
# Check results
decision = DecisionManager.get_by_id(decision.id)
print(f"Pass rate: {decision.pass_rate}%")
```
### 6. Central Coordinator (`coordinator.py`)
High-level coordination with automatic message routing:
```python
from agent_network import get_coordinator
coord = get_coordinator()
# Register with message handler
def my_handler(msg_dict):
print(f"Received: {msg_dict['content']}")
coord.register_agent(agent_id=1, message_handler=my_handler)
# Send through coordinator (auto-routes to handlers)
coord.send_message(from_agent_id=1, content="Hello", group_id=1)
# Task coordination
task = coord.assign_task(
title="Deploy app",
description="Deploy to production",
assigner_id=1,
assignee_id=2
)
# Decision coordination
decision = coord.propose_decision(
title="Release v2.0?",
description="Ready for release?",
proposer_id=1
)
coord.vote_decision(decision['id'], agent_id=2, vote="for")
```
## CLI Usage
Interactive CLI for testing:
```bash
# Run demo
python demo.py
# Interactive CLI
python cli.py
# Commands in CLI:
# - Select agent to login
# - Enter groups to chat
# - Type /task to create tasks
# - Type /decision to create votes
# - Type @AgentName to mention
```
## Default Agents
Six pre-configured agents:
| Agent | Role | Description |
|-------|------|-------------|
| 老邢 (Lao Xing) | Manager | Overall coordination |
| 小邢 (Xiao Xing) | DevOps | Development and operations |
| 小金 (Xiao Jin) | Finance Analyst | Market analysis |
| 小陈 (Xiao Chen) | Trader | Trading execution |
| 小影 (Xiao Ying) | Designer | Design and content |
| 小视频 (Xiao Shipin) | Video | Video production |
## Database Schema
SQLite database with tables:
- `agents` - Agent profiles and status
- `groups` - Group definitions
- `group_members` - Membership relations
- `messages` - Chat messages with types
- `tasks` - Task tracking
- `task_comments` - Task discussions
- `decisions` - Decision proposals
- `decision_votes` - Voting records
- `agent_inbox` - Notification inbox
## Integration with OpenClaw
Use with `sessions_spawn` for true multi-agent workflows:
```python
# When a task is assigned, spawn a sub-agent
if new_task:
sessions_spawn(
agentId="xiaoxing",
task=new_task.description,
label=f"task-{new_task.task_id}"
)
```
## Files Reference
- `scripts/agent_network/` - Python modules
- `__init__.py` - Package exports
- `database.py` - SQLite management
- `agent_manager.py` - Agent CRUD
- `group_manager.py` - Group management
- `message_manager.py` - Messaging system
- `task_manager.py` - Task management
- `decision_manager.py` - Voting system
- `coordinator.py` - Central coordinator
- `scripts/cli.py` - Interactive CLI
- `scripts/demo.py` - Demo script
- `references/schema.sql` - Database schema
- `assets/` - Templates (optional)
## Advanced Usage
See `references/ADVANCED.md` for:
- Custom agent handlers
- Webhook integrations
- Message filtering
- Custom workflows
將開發過程中的經驗、錯誤與修正記錄為 Markdown 檔案,供日後重用並提升為工作區記憶。
用自然語言描述需求,在本機與遠端技能市場尋找技能並取得推薦。
給 AI agent 用的瀏覽器自動化命令列技能。
將使用者明確糾正與自我檢視整理成分層記憶,並依使用情況升級或封存。
在安裝第三方技能之前,按清單審查來源、許可權與危險模式的技能。
將代理記憶與跨技能狀態整理成具型別與約束的知識圖譜。
規劃 AI 代理主動跟進、工作記憶與回顧流程的工作區設定指南。
指導如何建立與更新有效 Claude 技能的寫作指南。
資料月份:2026-09 · 下載/星標/安裝數聚合自公開技能註冊庫(ClawHub、SkillHub),按月更新。口徑說明見資料方法。