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# Board of Directors - Multi-Agent Architecture
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The Board of Directors is a sophisticated multi-agent architecture that implements collective decision-making through democratic processes, voting mechanisms, and role-based leadership. This architecture provides an alternative to single-director patterns by enabling collaborative intelligence through structured governance.
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## 🏛️ Overview
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The Board of Directors architecture follows a democratic workflow pattern:
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1. **Task Reception**: User provides a task to the swarm
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2. **Board Meeting**: Board of Directors convenes to discuss and create a plan
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3. **Voting & Consensus**: Board members vote and reach consensus on task distribution
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4. **Order Distribution**: Board distributes orders to specialized worker agents
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5. **Execution**: Individual agents execute their assigned tasks
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6. **Feedback Loop**: Board evaluates results and issues new orders if needed (up to `max_loops`)
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7. **Context Preservation**: All conversation history and context is maintained throughout the process
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## 🏗️ Architecture Components
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### Core Components
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| Component | Description | Purpose |
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|-----------|-------------|---------|
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| **BoardOfDirectorsSwarm** | Main orchestration class | Manages the entire board workflow and agent coordination |
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| **Board Member Roles** | Role definitions and hierarchy | Defines responsibilities and voting weights for each board member |
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| **Decision Making Process** | Voting and consensus mechanisms | Implements democratic decision-making with weighted voting |
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| **Workflow Management** | Process orchestration | Manages the complete lifecycle from task reception to final delivery |
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### Board Member Interaction Flow
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```mermaid
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sequenceDiagram
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participant User
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participant Chairman
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participant ViceChair
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participant Secretary
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participant Treasurer
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participant ExecDir
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participant Agents
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User->>Chairman: Submit Task
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Chairman->>ViceChair: Notify Board Meeting
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Chairman->>Secretary: Request Meeting Setup
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Chairman->>Treasurer: Resource Assessment
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Chairman->>ExecDir: Strategic Planning
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Note over Chairman,ExecDir: Board Discussion Phase
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Chairman->>ViceChair: Lead Discussion
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ViceChair->>Secretary: Document Decisions
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Secretary->>Treasurer: Budget Considerations
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Treasurer->>ExecDir: Resource Allocation
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ExecDir->>Chairman: Strategic Recommendations
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Note over Chairman,ExecDir: Voting & Consensus
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Chairman->>ViceChair: Call for Vote
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ViceChair->>Secretary: Record Votes
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Secretary->>Treasurer: Financial Approval
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Treasurer->>ExecDir: Resource Approval
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ExecDir->>Chairman: Final Decision
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Note over Chairman,Agents: Execution Phase
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Chairman->>Agents: Distribute Orders
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Agents->>Chairman: Execute Tasks
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Agents->>ViceChair: Progress Reports
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Agents->>Secretary: Documentation
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Agents->>Treasurer: Resource Usage
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Agents->>ExecDir: Strategic Updates
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Note over Chairman,ExecDir: Review & Feedback
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Chairman->>User: Deliver Results
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```
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## 👥 Board Member Roles
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The Board of Directors supports various roles with different responsibilities and voting weights:
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| Role | Description | Voting Weight | Responsibilities |
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|------|-------------|---------------|------------------|
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| `CHAIRMAN` | Primary leader responsible for board meetings and final decisions | 1.5 | Leading meetings, facilitating consensus, making final decisions |
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| `VICE_CHAIRMAN` | Secondary leader who supports the chairman | 1.2 | Supporting chairman, coordinating operations |
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| `SECRETARY` | Responsible for documentation and meeting minutes | 1.0 | Documenting meetings, maintaining records |
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| `TREASURER` | Manages financial aspects and resource allocation | 1.0 | Financial oversight, resource management |
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| `EXECUTIVE_DIRECTOR` | Executive-level board member with operational authority | 1.5 | Strategic planning, operational oversight |
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| `MEMBER` | General board member with specific expertise | 1.0 | Contributing expertise, participating in decisions |
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### Role Hierarchy and Authority
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```python
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# Example: Role hierarchy implementation
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class BoardRoleHierarchy:
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def __init__(self):
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self.roles = {
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"CHAIRMAN": {
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"voting_weight": 1.5,
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"authority_level": "FINAL",
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"supervises": ["VICE_CHAIRMAN", "EXECUTIVE_DIRECTOR", "SECRETARY", "TREASURER", "MEMBER"],
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"responsibilities": ["leadership", "final_decision", "consensus_facilitation"],
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"override_capability": True
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},
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"VICE_CHAIRMAN": {
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"voting_weight": 1.2,
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"authority_level": "SENIOR",
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"supervises": ["MEMBER"],
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"responsibilities": ["operational_support", "coordination", "implementation"],
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"backup_for": "CHAIRMAN"
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},
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"EXECUTIVE_DIRECTOR": {
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"voting_weight": 1.5,
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"authority_level": "SENIOR",
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"supervises": ["MEMBER"],
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"responsibilities": ["strategic_planning", "execution_oversight", "performance_management"],
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"strategic_authority": True
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},
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"SECRETARY": {
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"voting_weight": 1.0,
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"authority_level": "STANDARD",
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"supervises": [],
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"responsibilities": ["documentation", "record_keeping", "communication"],
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"administrative_authority": True
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},
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"TREASURER": {
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"voting_weight": 1.0,
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"authority_level": "STANDARD",
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"supervises": [],
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"responsibilities": ["financial_oversight", "resource_management", "budget_control"],
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"financial_authority": True
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},
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"MEMBER": {
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"voting_weight": 1.0,
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"authority_level": "STANDARD",
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"supervises": [],
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"responsibilities": ["expertise_contribution", "analysis", "voting"],
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"specialized_expertise": True
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}
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}
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```
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## 🚀 Quick Start
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### Basic Setup
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```python
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from swarms import Agent
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from swarms.structs.board_of_directors_swarm import (
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BoardOfDirectorsSwarm,
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BoardMember,
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BoardMemberRole
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)
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from swarms.config.board_config import enable_board_feature
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# Enable the Board of Directors feature
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enable_board_feature()
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# Create board members with specific roles
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chairman = Agent(
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agent_name="Chairman",
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agent_description="Chairman of the Board responsible for leading meetings",
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model_name="gpt-4o-mini",
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system_prompt="You are the Chairman of the Board..."
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)
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vice_chairman = Agent(
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agent_name="Vice-Chairman",
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agent_description="Vice Chairman who supports the Chairman",
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model_name="gpt-4o-mini",
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system_prompt="You are the Vice Chairman..."
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)
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# Create BoardMember objects with roles and expertise
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board_members = [
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BoardMember(chairman, BoardMemberRole.CHAIRMAN, 1.5, ["leadership", "strategy"]),
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BoardMember(vice_chairman, BoardMemberRole.VICE_CHAIRMAN, 1.2, ["operations", "coordination"]),
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]
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# Create worker agents
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research_agent = Agent(
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agent_name="Research-Specialist",
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agent_description="Expert in market research and analysis",
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model_name="gpt-4o",
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)
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financial_agent = Agent(
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agent_name="Financial-Analyst",
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agent_description="Specialist in financial analysis and valuation",
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model_name="gpt-4o",
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)
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# Initialize the Board of Directors swarm
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board_swarm = BoardOfDirectorsSwarm(
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name="Executive_Board_Swarm",
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description="Executive board with specialized roles for strategic decision-making",
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board_members=board_members,
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agents=[research_agent, financial_agent],
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max_loops=2,
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verbose=True,
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decision_threshold=0.6,
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enable_voting=True,
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enable_consensus=True,
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)
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# Execute a complex task with democratic decision-making
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result = board_swarm.run(task="Analyze the market potential for Tesla (TSLA) stock")
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print(result)
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```
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## 📋 Comprehensive Examples
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### 1. Strategic Investment Analysis
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```python
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# Create specialized agents for investment analysis
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market_research_agent = Agent(
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agent_name="Market-Research-Specialist",
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agent_description="Expert in market research, competitive analysis, and industry trends",
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model_name="gpt-4o",
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system_prompt="""You are a Market Research Specialist. Your responsibilities include:
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1. Conducting comprehensive market research and analysis
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2. Identifying market trends, opportunities, and risks
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3. Analyzing competitive landscape and positioning
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4. Providing market size and growth projections
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5. Supporting strategic decision-making with research findings
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You should be thorough, analytical, and objective in your research."""
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)
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financial_analyst_agent = Agent(
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agent_name="Financial-Analyst",
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agent_description="Specialist in financial analysis, valuation, and investment assessment",
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model_name="gpt-4o",
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system_prompt="""You are a Financial Analyst. Your responsibilities include:
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1. Conducting financial analysis and valuation
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2. Assessing investment opportunities and risks
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3. Analyzing financial performance and metrics
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4. Providing financial insights and recommendations
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5. Supporting financial decision-making
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You should be financially astute, analytical, and focused on value creation."""
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)
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technical_assessor_agent = Agent(
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agent_name="Technical-Assessor",
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agent_description="Expert in technical feasibility and implementation assessment",
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model_name="gpt-4o",
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system_prompt="""You are a Technical Assessor. Your responsibilities include:
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1. Evaluating technical feasibility and requirements
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2. Assessing implementation challenges and risks
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3. Analyzing technology stack and architecture
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4. Providing technical insights and recommendations
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5. Supporting technical decision-making
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You should be technically proficient, practical, and solution-oriented."""
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)
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# Create comprehensive board members
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board_members = [
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BoardMember(
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chairman,
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BoardMemberRole.CHAIRMAN,
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1.5,
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["leadership", "strategy", "governance", "decision_making"]
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),
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BoardMember(
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vice_chairman,
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BoardMemberRole.VICE_CHAIRMAN,
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1.2,
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["operations", "coordination", "communication", "implementation"]
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),
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BoardMember(
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secretary,
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BoardMemberRole.SECRETARY,
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1.0,
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["documentation", "compliance", "record_keeping", "communication"]
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),
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BoardMember(
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treasurer,
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BoardMemberRole.TREASURER,
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1.0,
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["finance", "budgeting", "risk_management", "resource_allocation"]
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),
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BoardMember(
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executive_director,
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BoardMemberRole.EXECUTIVE_DIRECTOR,
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1.5,
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["strategy", "operations", "innovation", "performance_management"]
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)
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]
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# Initialize the investment analysis board
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investment_board = BoardOfDirectorsSwarm(
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name="Investment_Analysis_Board",
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description="Specialized board for investment analysis and decision-making",
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board_members=board_members,
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agents=[market_research_agent, financial_analyst_agent, technical_assessor_agent],
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max_loops=3,
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verbose=True,
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decision_threshold=0.75, # Higher threshold for investment decisions
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enable_voting=True,
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enable_consensus=True,
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max_workers=3,
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output_type="dict"
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)
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# Execute investment analysis
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investment_task = """
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Analyze the strategic investment opportunity for a $50M Series B funding round in a
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fintech startup. Consider market conditions, competitive landscape, financial projections,
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technical feasibility, and strategic fit. Provide comprehensive recommendations including:
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1. Investment recommendation (proceed/hold/decline)
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2. Valuation analysis and suggested terms
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3. Risk assessment and mitigation strategies
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4. Strategic value and synergies
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5. Implementation timeline and milestones
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"""
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result = investment_board.run(task=investment_task)
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print("Investment Analysis Results:")
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print(json.dumps(result, indent=2))
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```
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### 2. Technology Strategy Development
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```python
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# Create technology-focused agents
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tech_strategy_agent = Agent(
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agent_name="Tech-Strategy-Specialist",
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agent_description="Expert in technology strategy and digital transformation",
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model_name="gpt-4o",
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system_prompt="""You are a Technology Strategy Specialist. Your responsibilities include:
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1. Developing technology roadmaps and strategies
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2. Assessing digital transformation opportunities
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3. Evaluating emerging technologies and trends
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4. Planning technology investments and priorities
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5. Supporting technology decision-making
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You should be strategic, forward-thinking, and technology-savvy."""
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)
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implementation_planner_agent = Agent(
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agent_name="Implementation-Planner",
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agent_description="Expert in implementation planning and project management",
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model_name="gpt-4o",
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system_prompt="""You are an Implementation Planner. Your responsibilities include:
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1. Creating detailed implementation plans
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2. Assessing resource requirements and timelines
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3. Identifying implementation risks and challenges
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4. Planning change management strategies
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5. Supporting implementation decision-making
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You should be practical, organized, and execution-focused."""
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)
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# Technology strategy board configuration
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tech_board = BoardOfDirectorsSwarm(
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name="Technology_Strategy_Board",
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description="Specialized board for technology strategy and digital transformation",
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board_members=board_members,
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agents=[tech_strategy_agent, implementation_planner_agent, technical_assessor_agent],
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max_loops=4, # More loops for complex technology planning
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verbose=True,
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decision_threshold=0.7,
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enable_voting=True,
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enable_consensus=True,
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max_workers=3,
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output_type="dict"
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)
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# Execute technology strategy development
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tech_strategy_task = """
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Develop a comprehensive technology strategy for a mid-size manufacturing company
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looking to digitize operations and implement Industry 4.0 technologies. Consider:
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1. Current technology assessment and gaps
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2. Technology roadmap and implementation plan
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3. Investment requirements and ROI analysis
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4. Risk assessment and mitigation strategies
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5. Change management and training requirements
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6. Competitive positioning and market advantages
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"""
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result = tech_board.run(task=tech_strategy_task)
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print("Technology Strategy Results:")
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print(json.dumps(result, indent=2))
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```
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### 3. Crisis Management and Response
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```python
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# Create crisis management agents
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crisis_coordinator_agent = Agent(
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agent_name="Crisis-Coordinator",
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agent_description="Expert in crisis management and emergency response",
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model_name="gpt-4o",
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system_prompt="""You are a Crisis Coordinator. Your responsibilities include:
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1. Coordinating crisis response efforts
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2. Assessing crisis severity and impact
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3. Developing immediate response plans
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4. Managing stakeholder communications
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5. Supporting crisis decision-making
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You should be calm, decisive, and action-oriented."""
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)
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communications_specialist_agent = Agent(
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agent_name="Communications-Specialist",
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agent_description="Expert in crisis communications and stakeholder management",
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model_name="gpt-4o",
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system_prompt="""You are a Communications Specialist. Your responsibilities include:
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1. Developing crisis communication strategies
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2. Managing stakeholder communications
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3. Coordinating public relations efforts
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4. Ensuring message consistency and accuracy
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5. Supporting communication decision-making
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You should be clear, empathetic, and strategic in communications."""
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)
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# Crisis management board configuration
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crisis_board = BoardOfDirectorsSwarm(
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name="Crisis_Management_Board",
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description="Specialized board for crisis management and emergency response",
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board_members=board_members,
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agents=[crisis_coordinator_agent, communications_specialist_agent, financial_analyst_agent],
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max_loops=2, # Faster response needed
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verbose=True,
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decision_threshold=0.6, # Lower threshold for urgent decisions
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enable_voting=True,
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enable_consensus=True,
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max_workers=3,
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output_type="dict"
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)
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# Execute crisis management
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crisis_task = """
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Our company is facing a major data breach. Develop an immediate response plan.
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Include:
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1. Immediate containment and mitigation steps
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2. Communication strategy for stakeholders
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3. Legal and regulatory compliance requirements
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4. Financial impact assessment
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5. Long-term recovery and prevention measures
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6. Timeline and resource allocation
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"""
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result = crisis_board.run(task=crisis_task)
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print("Crisis Management Results:")
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print(json.dumps(result, indent=2))
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```
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## ⚙️ Configuration and Parameters
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### BoardOfDirectorsSwarm Parameters
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```python
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# Complete parameter reference
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board_swarm = BoardOfDirectorsSwarm(
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# Basic Configuration
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name="Board_Name", # Name of the board
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description="Board description", # Description of the board's purpose
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# Board Members and Agents
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board_members=board_members, # List of BoardMember objects
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agents=worker_agents, # List of worker Agent objects
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# Execution Control
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max_loops=3, # Maximum number of refinement loops
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max_workers=4, # Maximum parallel workers
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# Decision Making
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decision_threshold=0.7, # Consensus threshold (0.0-1.0)
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enable_voting=True, # Enable voting mechanisms
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enable_consensus=True, # Enable consensus building
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# Advanced Features
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auto_assign_roles=True, # Auto-assign roles based on expertise
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role_mapping={ # Custom role mapping
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"financial_analysis": ["Treasurer", "Financial_Member"],
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"strategic_planning": ["Chairman", "Executive_Director"]
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},
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# Consensus Configuration
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consensus_timeout=300, # Consensus timeout in seconds
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min_participation_rate=0.8, # Minimum participation rate
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auto_fallback_to_chairman=True, # Chairman can make final decisions
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consensus_rounds=3, # Maximum consensus building rounds
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# Output Configuration
|
||||
output_type="dict", # Output format: "dict", "str", "list"
|
||||
verbose=True, # Enable detailed logging
|
||||
|
||||
# Quality Control
|
||||
quality_threshold=0.8, # Quality threshold for outputs
|
||||
enable_quality_gates=True, # Enable quality checkpoints
|
||||
enable_peer_review=True, # Enable peer review mechanisms
|
||||
|
||||
# Performance Optimization
|
||||
parallel_execution=True, # Enable parallel execution
|
||||
enable_agent_pooling=True, # Enable agent pooling
|
||||
timeout_per_agent=300, # Timeout per agent in seconds
|
||||
|
||||
# Monitoring and Logging
|
||||
enable_logging=True, # Enable detailed logging
|
||||
log_level="INFO", # Logging level
|
||||
enable_metrics=True, # Enable performance metrics
|
||||
enable_tracing=True # Enable request tracing
|
||||
)
|
||||
```
|
||||
|
||||
### Voting Configuration
|
||||
|
||||
```python
|
||||
# Voting system configuration
|
||||
voting_config = {
|
||||
"method": "weighted_majority", # Voting method
|
||||
"threshold": 0.75, # Consensus threshold
|
||||
"weights": { # Role-based voting weights
|
||||
"CHAIRMAN": 1.5,
|
||||
"VICE_CHAIRMAN": 1.2,
|
||||
"SECRETARY": 1.0,
|
||||
"TREASURER": 1.0,
|
||||
"EXECUTIVE_DIRECTOR": 1.5
|
||||
},
|
||||
"tie_breaker": "CHAIRMAN", # Tie breaker role
|
||||
"allow_abstention": True, # Allow board members to abstain
|
||||
"secret_ballot": False, # Use secret ballot voting
|
||||
"transparent_process": True # Transparent voting process
|
||||
}
|
||||
```
|
||||
|
||||
### Quality Control Configuration
|
||||
|
||||
```python
|
||||
# Quality control configuration
|
||||
quality_config = {
|
||||
"quality_gates": True, # Enable quality checkpoints
|
||||
"quality_threshold": 0.8, # Quality threshold
|
||||
"enable_peer_review": True, # Enable peer review
|
||||
"review_required": True, # Require peer review
|
||||
"output_validation": True, # Validate outputs
|
||||
"enable_metrics_tracking": True, # Track quality metrics
|
||||
|
||||
# Quality metrics
|
||||
"quality_metrics": {
|
||||
"completeness": {"weight": 0.2, "threshold": 0.8},
|
||||
"accuracy": {"weight": 0.25, "threshold": 0.85},
|
||||
"feasibility": {"weight": 0.2, "threshold": 0.8},
|
||||
"risk": {"weight": 0.15, "threshold": 0.7},
|
||||
"impact": {"weight": 0.2, "threshold": 0.8}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## 📊 Performance Monitoring and Analytics
|
||||
|
||||
### Board Performance Metrics
|
||||
|
||||
```python
|
||||
# Get comprehensive board performance metrics
|
||||
board_summary = board_swarm.get_board_summary()
|
||||
print("Board Summary:")
|
||||
print(f"Board Name: {board_summary['board_name']}")
|
||||
print(f"Total Board Members: {board_summary['total_members']}")
|
||||
print(f"Total Worker Agents: {board_summary['total_agents']}")
|
||||
print(f"Decision Threshold: {board_summary['decision_threshold']}")
|
||||
print(f"Max Loops: {board_summary['max_loops']}")
|
||||
|
||||
# Display board member details
|
||||
print("\nBoard Members:")
|
||||
for member in board_summary['members']:
|
||||
print(f"- {member['name']} (Role: {member['role']}, Weight: {member['voting_weight']})")
|
||||
print(f" Expertise: {', '.join(member['expertise_areas'])}")
|
||||
|
||||
# Display worker agent details
|
||||
print("\nWorker Agents:")
|
||||
for agent in board_summary['agents']:
|
||||
print(f"- {agent['name']}: {agent['description']}")
|
||||
```
|
||||
|
||||
### Decision Analysis
|
||||
|
||||
```python
|
||||
# Analyze decision-making patterns
|
||||
if hasattr(result, 'get') and callable(result.get):
|
||||
conversation_history = result.get('conversation_history', [])
|
||||
|
||||
print(f"\nDecision Analysis:")
|
||||
print(f"Total Messages: {len(conversation_history)}")
|
||||
|
||||
# Count board member contributions
|
||||
board_contributions = {}
|
||||
for msg in conversation_history:
|
||||
if 'Board' in msg.get('role', ''):
|
||||
member_name = msg.get('agent_name', 'Unknown')
|
||||
board_contributions[member_name] = board_contributions.get(member_name, 0) + 1
|
||||
|
||||
print(f"Board Member Contributions:")
|
||||
for member, count in board_contributions.items():
|
||||
print(f"- {member}: {count} contributions")
|
||||
|
||||
# Count agent executions
|
||||
agent_executions = {}
|
||||
for msg in conversation_history:
|
||||
if any(agent.agent_name in msg.get('role', '') for agent in worker_agents):
|
||||
agent_name = msg.get('agent_name', 'Unknown')
|
||||
agent_executions[agent_name] = agent_executions.get(agent_name, 0) + 1
|
||||
|
||||
print(f"\nAgent Executions:")
|
||||
for agent, count in agent_executions.items():
|
||||
print(f"- {agent}: {count} executions")
|
||||
```
|
||||
|
||||
### Performance Monitoring System
|
||||
|
||||
```python
|
||||
# Performance monitoring system
|
||||
class PerformanceMonitor:
|
||||
def __init__(self):
|
||||
self.metrics = {
|
||||
"execution_times": [],
|
||||
"quality_scores": [],
|
||||
"consensus_rounds": [],
|
||||
"error_rates": []
|
||||
}
|
||||
|
||||
def track_execution_time(self, phase, duration):
|
||||
"""Track execution time for different phases"""
|
||||
self.metrics["execution_times"].append({
|
||||
"phase": phase,
|
||||
"duration": duration,
|
||||
"timestamp": datetime.now().isoformat()
|
||||
})
|
||||
|
||||
def track_quality_score(self, score):
|
||||
"""Track quality scores"""
|
||||
self.metrics["quality_scores"].append({
|
||||
"score": score,
|
||||
"timestamp": datetime.now().isoformat()
|
||||
})
|
||||
|
||||
def generate_performance_report(self):
|
||||
"""Generate comprehensive performance report"""
|
||||
return {
|
||||
"average_execution_time": self.calculate_average_execution_time(),
|
||||
"quality_trends": self.analyze_quality_trends(),
|
||||
"consensus_efficiency": self.analyze_consensus_efficiency(),
|
||||
"error_analysis": self.analyze_errors(),
|
||||
"recommendations": self.generate_recommendations()
|
||||
}
|
||||
|
||||
# Usage example
|
||||
monitor = PerformanceMonitor()
|
||||
# ... track metrics during execution ...
|
||||
report = monitor.generate_performance_report()
|
||||
print("Performance Report:")
|
||||
print(json.dumps(report, indent=2))
|
||||
```
|
||||
|
||||
## 🔧 Advanced Features and Customization
|
||||
|
||||
### Custom Board Templates
|
||||
|
||||
```python
|
||||
from swarms.config.board_config import get_default_board_template
|
||||
|
||||
# Get pre-configured board templates
|
||||
financial_board = get_default_board_template("financial_analysis")
|
||||
strategic_board = get_default_board_template("strategic_planning")
|
||||
tech_board = get_default_board_template("technology_assessment")
|
||||
crisis_board = get_default_board_template("crisis_management")
|
||||
|
||||
# Custom board template
|
||||
custom_template = {
|
||||
"name": "Custom_Board",
|
||||
"description": "Custom board for specific use case",
|
||||
"board_members": [
|
||||
{"role": "CHAIRMAN", "expertise": ["leadership", "strategy"]},
|
||||
{"role": "VICE_CHAIRMAN", "expertise": ["operations", "coordination"]},
|
||||
{"role": "SECRETARY", "expertise": ["documentation", "communication"]},
|
||||
{"role": "TREASURER", "expertise": ["finance", "budgeting"]},
|
||||
{"role": "EXECUTIVE_DIRECTOR", "expertise": ["strategy", "operations"]}
|
||||
],
|
||||
"agents": [
|
||||
{"name": "Research_Agent", "expertise": ["research", "analysis"]},
|
||||
{"name": "Technical_Agent", "expertise": ["technical", "implementation"]}
|
||||
],
|
||||
"config": {
|
||||
"max_loops": 3,
|
||||
"decision_threshold": 0.7,
|
||||
"enable_voting": True,
|
||||
"enable_consensus": True
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Dynamic Role Assignment
|
||||
|
||||
```python
|
||||
# Automatically assign roles based on task requirements
|
||||
board_swarm = BoardOfDirectorsSwarm(
|
||||
board_members=board_members,
|
||||
agents=agents,
|
||||
auto_assign_roles=True,
|
||||
role_mapping={
|
||||
"financial_analysis": ["Treasurer", "Financial_Member"],
|
||||
"strategic_planning": ["Chairman", "Executive_Director"],
|
||||
"technical_assessment": ["Technical_Member", "Executive_Director"],
|
||||
"research_analysis": ["Research_Member", "Secretary"],
|
||||
"crisis_management": ["Chairman", "Vice_Chairman", "Communications_Member"]
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
### Consensus Optimization
|
||||
|
||||
```python
|
||||
# Advanced consensus-building mechanisms
|
||||
board_swarm = BoardOfDirectorsSwarm(
|
||||
board_members=board_members,
|
||||
agents=agents,
|
||||
enable_consensus=True,
|
||||
consensus_timeout=300, # 5 minutes timeout
|
||||
min_participation_rate=0.8, # 80% minimum participation
|
||||
auto_fallback_to_chairman=True, # Chairman can make final decisions
|
||||
consensus_rounds=3, # Maximum consensus building rounds
|
||||
consensus_method="weighted_majority", # Consensus method
|
||||
enable_mediation=True, # Enable mediation for conflicts
|
||||
mediation_timeout=120 # Mediation timeout in seconds
|
||||
)
|
||||
```
|
||||
|
||||
## 🛠️ Troubleshooting and Debugging
|
||||
|
||||
### Common Issues and Solutions
|
||||
|
||||
1. **Consensus Failures**
|
||||
- **Issue**: Board cannot reach consensus within loop limit
|
||||
- **Solution**: Lower voting threshold, increase max_loops, or adjust voting weights
|
||||
```python
|
||||
board_swarm = BoardOfDirectorsSwarm(
|
||||
decision_threshold=0.6, # Lower threshold
|
||||
max_loops=5, # More loops
|
||||
consensus_timeout=600 # Longer timeout
|
||||
)
|
||||
```
|
||||
|
||||
2. **Agent Timeout**
|
||||
- **Issue**: Individual agents take too long to respond
|
||||
- **Solution**: Increase timeout settings or optimize agent prompts
|
||||
```python
|
||||
board_swarm = BoardOfDirectorsSwarm(
|
||||
timeout_per_agent=600, # 10 minutes per agent
|
||||
enable_agent_pooling=True # Use agent pooling
|
||||
)
|
||||
```
|
||||
|
||||
3. **Poor Quality Output**
|
||||
- **Issue**: Final output doesn't meet quality standards
|
||||
- **Solution**: Enable quality gates, increase max_loops, or improve agent prompts
|
||||
```python
|
||||
board_swarm = BoardOfDirectorsSwarm(
|
||||
enable_quality_gates=True,
|
||||
quality_threshold=0.8,
|
||||
enable_peer_review=True,
|
||||
max_loops=4
|
||||
)
|
||||
```
|
||||
|
||||
4. **Resource Exhaustion**
|
||||
- **Issue**: System runs out of resources during execution
|
||||
- **Solution**: Implement resource limits, use agent pooling, or optimize parallel execution
|
||||
```python
|
||||
board_swarm = BoardOfDirectorsSwarm(
|
||||
max_workers=2, # Limit parallel workers
|
||||
enable_agent_pooling=True,
|
||||
parallel_execution=False # Disable parallel execution
|
||||
)
|
||||
```
|
||||
|
||||
### Debugging Techniques
|
||||
|
||||
```python
|
||||
# Debugging configuration
|
||||
debug_config = BoardConfig(
|
||||
max_loops=1, # Limit loops for debugging
|
||||
enable_logging=True,
|
||||
log_level="DEBUG",
|
||||
enable_tracing=True,
|
||||
debug_mode=True
|
||||
)
|
||||
|
||||
# Create debug swarm
|
||||
debug_swarm = BoardOfDirectorsSwarm(
|
||||
agents=agents,
|
||||
config=debug_config
|
||||
)
|
||||
|
||||
# Execute with debugging
|
||||
try:
|
||||
result = debug_swarm.run(task)
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
print(f"Debug info: {debug_swarm.get_debug_info()}")
|
||||
|
||||
# Enable detailed logging
|
||||
import logging
|
||||
logging.basicConfig(
|
||||
level=logging.DEBUG,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
|
||||
# Create swarm with logging enabled
|
||||
logging_swarm = BoardOfDirectorsSwarm(
|
||||
agents=agents,
|
||||
config=BoardConfig(
|
||||
enable_logging=True,
|
||||
log_level="DEBUG",
|
||||
enable_metrics=True,
|
||||
enable_tracing=True
|
||||
)
|
||||
)
|
||||
```
|
||||
|
||||
## 📋 Use Cases
|
||||
|
||||
### Corporate Governance
|
||||
- **Strategic Planning**: Long-term business strategy development
|
||||
- **Risk Management**: Comprehensive risk assessment and mitigation
|
||||
- **Resource Allocation**: Optimal distribution of company resources
|
||||
- **Performance Oversight**: Monitoring and evaluating organizational performance
|
||||
|
||||
### Financial Analysis
|
||||
- **Portfolio Management**: Investment portfolio optimization and rebalancing
|
||||
- **Market Analysis**: Comprehensive market research and trend analysis
|
||||
- **Risk Assessment**: Financial risk evaluation and management
|
||||
- **Compliance Monitoring**: Regulatory compliance and audit preparation
|
||||
|
||||
### Research & Development
|
||||
- **Technology Assessment**: Evaluation of emerging technologies
|
||||
- **Product Development**: Strategic product planning and development
|
||||
- **Innovation Management**: Managing innovation pipelines and initiatives
|
||||
- **Quality Assurance**: Ensuring high standards across development processes
|
||||
|
||||
### Project Management
|
||||
- **Complex Project Planning**: Multi-faceted project strategy development
|
||||
- **Resource Optimization**: Efficient allocation of project resources
|
||||
- **Stakeholder Management**: Coordinating diverse stakeholder interests
|
||||
- **Risk Mitigation**: Identifying and addressing project risks
|
||||
|
||||
### Crisis Management
|
||||
- **Emergency Response**: Rapid response to critical situations
|
||||
- **Stakeholder Communication**: Managing communications during crises
|
||||
- **Recovery Planning**: Developing recovery and prevention strategies
|
||||
- **Legal Compliance**: Ensuring compliance during crisis situations
|
||||
|
||||
## 🎯 Success Criteria
|
||||
|
||||
A successful Board of Directors implementation should demonstrate:
|
||||
|
||||
- ✅ **Democratic Decision Making**: All board members contribute to decisions
|
||||
- ✅ **Consensus Achievement**: Decisions reached through collaborative processes
|
||||
- ✅ **Role Effectiveness**: Each board member fulfills their responsibilities
|
||||
- ✅ **Agent Coordination**: Worker agents execute tasks efficiently
|
||||
- ✅ **Quality Output**: High-quality results through collective intelligence
|
||||
- ✅ **Process Transparency**: Clear visibility into decision-making processes
|
||||
- ✅ **Performance Optimization**: Efficient resource utilization and execution
|
||||
- ✅ **Continuous Improvement**: Learning from each execution cycle
|
||||
|
||||
## 📚 Best Practices
|
||||
|
||||
### 1. Role Definition
|
||||
- Clearly define responsibilities for each board member
|
||||
- Ensure expertise areas align with organizational needs
|
||||
- Balance voting weights based on role importance
|
||||
- Document role interactions and communication protocols
|
||||
|
||||
### 2. Task Formulation
|
||||
- Provide clear, specific task descriptions
|
||||
- Include relevant context and constraints
|
||||
- Specify expected outputs and deliverables
|
||||
- Define quality criteria and success metrics
|
||||
|
||||
### 3. Consensus Building
|
||||
- Allow adequate time for discussion and consensus
|
||||
- Encourage diverse perspectives and viewpoints
|
||||
- Use structured decision-making processes
|
||||
- Implement conflict resolution mechanisms
|
||||
|
||||
### 4. Performance Monitoring
|
||||
- Track decision quality and outcomes
|
||||
- Monitor board member participation
|
||||
- Analyze agent utilization and effectiveness
|
||||
- Implement continuous improvement processes
|
||||
|
||||
### 5. Resource Management
|
||||
- Optimize agent allocation and utilization
|
||||
- Implement parallel execution where appropriate
|
||||
- Monitor resource usage and performance
|
||||
- Scale resources based on task complexity
|
||||
|
||||
---
|
||||
|
||||
The Board of Directors architecture represents a sophisticated approach to multi-agent collaboration, enabling organizations to leverage collective intelligence through structured governance and democratic decision-making processes. This comprehensive implementation provides the tools and frameworks needed to build effective, scalable, and intelligent decision-making systems.
|
||||
Loading…
Reference in new issue