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

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:

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:

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:

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:

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:

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:

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:

# 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:

# 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