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examples/demos/apps/m&a_swarm.py
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from typing import List
from loguru import logger
from swarms.structs.agent import Agent
from swarms.structs.conversation import Conversation
from swarms.utils.history_output_formatter import history_output_formatter
from swarms_tools import exa_search
# System prompts for each agent
INTAKE_AGENT_PROMPT = """
You are an M&A Intake Specialist responsible for gathering comprehensive information about a potential transaction.
ROLE:
Engage with the user to understand the full context of the potential M&A deal, extracting critical details that will guide subsequent analyses.
RESPONSIBILITIES:
- Conduct a thorough initial interview to understand:
* Transaction type (acquisition, merger, divestiture)
* Industry and sector specifics
* Target company profile and size
* Strategic objectives
* Buyer/seller perspective
* Timeline and urgency
* Budget constraints
* Specific concerns or focus areas
OUTPUT FORMAT:
Provide a comprehensive Deal Brief that includes:
1. Transaction Overview
- Proposed transaction type
- Key parties involved
- Initial strategic rationale
2. Stakeholder Context
- Buyer's background and motivations
- Target company's current position
- Key decision-makers
3. Initial Assessment
- Preliminary strategic fit
- Potential challenges or red flags
- Recommended focus areas for deeper analysis
4. Information Gaps
- Questions that need further clarification
- Additional data points required
IMPORTANT:
- Be thorough and systematic
- Ask probing questions to uncover nuanced details
- Maintain a neutral, professional tone
- Prepare a foundation for subsequent in-depth analysis
"""
MARKET_ANALYSIS_PROMPT = """
You are an M&A Market Intelligence Analyst tasked with conducting comprehensive market research.
ROLE:
Perform an in-depth analysis of market dynamics, competitive landscape, and strategic implications for the potential transaction.
TOOLS:
You have access to the exa_search tool for gathering real-time market intelligence.
RESPONSIBILITIES:
1. Conduct Market Research
- Use exa_search to gather current market insights
- Analyze industry trends, size, and growth potential
- Identify key players and market share distribution
2. Competitive Landscape Analysis
- Map out competitive ecosystem
- Assess target company's market positioning
- Identify potential competitive advantages or vulnerabilities
3. Strategic Fit Evaluation
- Analyze alignment with buyer's strategic objectives
- Assess potential market entry or expansion opportunities
- Evaluate potential for market disruption
4. External Factor Assessment
- Examine regulatory environment
- Analyze technological disruption potential
- Consider macroeconomic impacts
OUTPUT FORMAT:
Provide a comprehensive Market Analysis Report:
1. Market Overview
- Market size and growth trajectory
- Key industry trends
- Competitive landscape summary
2. Strategic Fit Assessment
- Market attractiveness score (1-10)
- Strategic alignment evaluation
- Potential synergies and opportunities
3. Risk and Opportunity Mapping
- Key market opportunities
- Potential competitive threats
- Regulatory and technological risk factors
4. Recommended Next Steps
- Areas requiring deeper investigation
- Initial strategic recommendations
"""
FINANCIAL_VALUATION_PROMPT = """
You are an M&A Financial Analysis and Risk Expert. Perform comprehensive financial evaluation and risk assessment.
RESPONSIBILITIES:
1. Financial Health Analysis
- Analyze revenue trends and quality
- Evaluate profitability metrics (EBITDA, margins)
- Conduct cash flow analysis
- Assess balance sheet strength
- Review working capital requirements
2. Valuation Analysis
- Perform comparable company analysis
- Conduct precedent transaction analysis
- Develop Discounted Cash Flow (DCF) model
- Assess asset-based valuation
3. Synergy and Risk Assessment
- Quantify potential revenue and cost synergies
- Identify financial and operational risks
- Evaluate integration complexity
- Assess potential deal-breakers
OUTPUT FORMAT:
1. Comprehensive Financial Analysis Report
2. Valuation Range (low, mid, high scenarios)
3. Synergy Potential Breakdown
4. Detailed Risk Matrix
5. Recommended Pricing Strategy
"""
DEAL_STRUCTURING_PROMPT = """
You are an M&A Deal Structuring Advisor. Recommend the optimal transaction structure.
RESPONSIBILITIES:
1. Transaction Structure Design
- Evaluate asset vs stock purchase options
- Analyze cash vs stock consideration
- Design earnout provisions
- Develop contingent payment structures
2. Financing Strategy
- Recommend debt/equity mix
- Identify optimal financing sources
- Assess impact on buyer's capital structure
3. Tax and Legal Optimization
- Design tax-efficient structure
- Consider jurisdictional implications
- Minimize tax liabilities
4. Deal Protection Mechanisms
- Develop escrow arrangements
- Design representations and warranties
- Create indemnification provisions
- Recommend non-compete agreements
OUTPUT FORMAT:
1. Recommended Deal Structure
2. Detailed Payment Terms
3. Key Contractual Protections
4. Tax Optimization Strategy
5. Rationale for Proposed Structure
"""
INTEGRATION_PLANNING_PROMPT = """
You are an M&A Integration Planning Expert. Develop a comprehensive post-merger integration roadmap.
RESPONSIBILITIES:
1. Immediate Integration Priorities
- Define critical day-1 actions
- Develop communication strategy
- Identify quick win opportunities
2. 100-Day Integration Plan
- Design organizational structure alignment
- Establish governance framework
- Create detailed integration milestones
3. Functional Integration Strategy
- Plan operations consolidation
- Design systems and technology integration
- Align sales and marketing approaches
- Develop cultural integration plan
4. Synergy Realization
- Create detailed synergy capture timeline
- Establish performance tracking mechanisms
- Define accountability framework
OUTPUT FORMAT:
1. Comprehensive Integration Roadmap
2. Detailed 100-Day Plan
3. Functional Integration Strategies
4. Synergy Realization Timeline
5. Risk Mitigation Recommendations
"""
FINAL_RECOMMENDATION_PROMPT = """
You are the Senior M&A Advisory Partner. Synthesize all analyses into a comprehensive recommendation.
RESPONSIBILITIES:
1. Executive Summary
- Summarize transaction overview
- Highlight strategic rationale
- Articulate key value drivers
2. Investment Thesis Validation
- Assess strategic benefits
- Evaluate financial attractiveness
- Project long-term potential
3. Comprehensive Risk Assessment
- Summarize top risks
- Provide mitigation strategies
- Identify potential deal-breakers
4. Final Recommendation
- Provide clear GO/NO-GO recommendation
- Specify recommended offer range
- Outline key proceeding conditions
OUTPUT FORMAT:
1. Executive-Level Recommendation Report
2. Decision Framework
3. Risk-Adjusted Strategic Perspective
4. Actionable Next Steps
5. Recommendation Confidence Level
"""
class MAAdvisorySwarm:
def __init__(
self,
name: str = "M&A Advisory Swarm",
description: str = "Comprehensive AI-driven M&A advisory system",
max_loops: int = 1,
user_name: str = "M&A Advisor",
output_type: str = "json",
):
self.max_loops = max_loops
self.name = name
self.description = description
self.user_name = user_name
self.output_type = output_type
self.agents = self._initialize_agents()
self.conversation = Conversation()
self.exa_search_results = []
self.search_queries = []
self.current_iteration = 0
self.max_iterations = 1 # Limiting to 1 iteration for full sequential demo
self.analysis_concluded = False
self.handle_initial_processing()
def handle_initial_processing(self):
self.conversation.add(
role="System",
content=f"Company: {self.name}\n"
f"Description: {self.description}\n"
f"Mission: Provide comprehensive M&A advisory for {self.user_name}"
)
def _initialize_agents(self) -> List[Agent]:
return [
Agent(
agent_name="Emma-Intake-Specialist",
agent_description="Gathers comprehensive initial information about the potential M&A transaction.",
system_prompt=INTAKE_AGENT_PROMPT,
max_loops=self.max_loops,
dynamic_temperature_enabled=True,
output_type="final",
),
Agent(
agent_name="Marcus-Market-Analyst",
agent_description="Conducts in-depth market research and competitive analysis.",
system_prompt=MARKET_ANALYSIS_PROMPT,
max_loops=self.max_loops,
dynamic_temperature_enabled=True,
output_type="final",
),
Agent(
agent_name="Sophia-Financial-Analyst",
agent_description="Performs comprehensive financial valuation and risk assessment.",
system_prompt=FINANCIAL_VALUATION_PROMPT,
max_loops=self.max_loops,
dynamic_temperature_enabled=True,
output_type="final",
),
Agent(
agent_name="David-Deal-Structuring-Advisor",
agent_description="Recommends optimal deal structure and terms.",
system_prompt=DEAL_STRUCTURING_PROMPT,
max_loops=self.max_loops,
dynamic_temperature_enabled=True,
output_type="final",
),
Agent(
agent_name="Nathan-Integration-Planner",
agent_description="Develops comprehensive post-merger integration roadmap.",
system_prompt=INTEGRATION_PLANNING_PROMPT,
max_loops=self.max_loops,
dynamic_temperature_enabled=True,
output_type="final",
),
Agent(
agent_name="Alex-Final-Recommendation-Partner",
agent_description="Synthesizes all analyses into a comprehensive recommendation.",
system_prompt=FINAL_RECOMMENDATION_PROMPT,
max_loops=self.max_loops,
dynamic_temperature_enabled=True,
output_type="final",
)
]
def find_agent_by_name(self, name: str) -> Agent:
for agent in self.agents:
if name in agent.agent_name:
return agent
return None
def intake_and_scoping(self, user_input: str):
"""Phase 1: Intake and initial deal scoping"""
emma_agent = self.find_agent_by_name("Intake-Specialist")
emma_output = emma_agent.run(
f"User Input: {user_input}\n\n"
f"Conversation History: {self.conversation.get_str()}\n\n"
f"Analyze the potential M&A transaction, extract key details, and prepare a comprehensive deal brief. "
f"If information is unclear, ask clarifying questions."
)
self.conversation.add(
role="Intake-Specialist", content=emma_output
)
# Extract potential search queries for market research
self.search_queries = self._extract_search_queries(emma_output)
return emma_output
def _extract_search_queries(self, intake_output: str) -> List[str]:
"""Extract search queries from Intake Specialist output"""
queries = []
lines = intake_output.split('\n')
# Look for lines that could be good search queries
for line in lines:
line = line.strip()
# Simple heuristic: lines with potential research keywords
if any(keyword in line.lower() for keyword in ['market', 'industry', 'trend', 'competitor', 'analysis']):
if len(line) > 20: # Ensure query is substantial
queries.append(line)
# Fallback queries if none found
if not queries:
queries = [
"M&A trends in technology sector",
"Market analysis for potential business acquisition",
"Competitive landscape in enterprise software"
]
return queries[:3] # Limit to 3 queries
def market_research(self):
"""Phase 2: Conduct market research using exa_search"""
# Execute exa_search for each query
self.exa_search_results = []
for query in self.search_queries:
result = exa_search(query)
self.exa_search_results.append({
"query": query,
"exa_result": result
})
# Pass results to Market Analysis agent
marcus_agent = self.find_agent_by_name("Market-Analyst")
# Build exa context
exa_context = "\n\n[Exa Market Research Results]\n"
for item in self.exa_search_results:
exa_context += f"Query: {item['query']}\nResults: {item['exa_result']}\n\n"
marcus_output = marcus_agent.run(
f"Conversation History: {self.conversation.get_str()}\n\n"
f"{exa_context}\n"
f"Analyze these market research results. Provide comprehensive market intelligence and strategic insights."
)
self.conversation.add(
role="Market-Analyst", content=marcus_output
)
return marcus_output
def financial_valuation(self):
"""Phase 3: Perform comprehensive financial valuation and risk assessment"""
sophia_agent = self.find_agent_by_name("Financial-Analyst")
sophia_output = sophia_agent.run(
f"Conversation History: {self.conversation.get_str()}\n\n"
f"Perform comprehensive financial analysis and risk assessment based on previous insights."
)
self.conversation.add(
role="Financial-Analyst", content=sophia_output
)
return sophia_output
def deal_structuring(self):
"""Phase 4: Recommend optimal deal structure"""
david_agent = self.find_agent_by_name("Deal-Structuring-Advisor")
david_output = david_agent.run(
f"Conversation History: {self.conversation.get_str()}\n\n"
f"Recommend the optimal transaction structure and terms based on all prior analyses."
)
self.conversation.add(
role="Deal-Structuring-Advisor", content=david_output
)
return david_output
def integration_planning(self):
"""Phase 5: Develop post-merger integration roadmap"""
nathan_agent = self.find_agent_by_name("Integration-Planner")
nathan_output = nathan_agent.run(
f"Conversation History: {self.conversation.get_str()}\n\n"
f"Create a comprehensive integration plan to realize deal value."
)
self.conversation.add(
role="Integration-Planner", content=nathan_output
)
return nathan_output
def final_recommendation(self):
"""Phase 6: Synthesize all analyses into a comprehensive recommendation"""
alex_agent = self.find_agent_by_name("Final-Recommendation-Partner")
alex_output = alex_agent.run(
f"Conversation History: {self.conversation.get_str()}\n\n"
f"Synthesize all agent analyses into a comprehensive, actionable M&A recommendation."
)
self.conversation.add(
role="Final-Recommendation-Partner", content=alex_output
)
return alex_output
def run(self, initial_user_input: str):
"""
Run the M&A advisory swarm with continuous analysis.
Args:
initial_user_input: User's initial M&A transaction details
"""
self.conversation.add(role=self.user_name, content=initial_user_input)
while not self.analysis_concluded and self.current_iteration < self.max_iterations:
self.current_iteration += 1
logger.info(f"Starting analysis iteration {self.current_iteration}")
# Phase 1: Intake and Scoping
print(f"\n{'='*60}")
print("ITERATION - INTAKE AND SCOPING")
print(f"{'='*60}\n")
self.intake_and_scoping(initial_user_input)
# Phase 2: Market Research (with exa_search)
print(f"\n{'='*60}")
print("ITERATION - MARKET RESEARCH")
print(f"{'='*60}\n")
self.market_research()
# Phase 3: Financial Valuation
print(f"\n{'='*60}")
print("ITERATION - FINANCIAL VALUATION")
print(f"{'='*60}\n")
self.financial_valuation()
# Phase 4: Deal Structuring
print(f"\n{'='*60}")
print("ITERATION - DEAL STRUCTURING")
print(f"{'='*60}\n")
self.deal_structuring()
# Phase 5: Integration Planning
print(f"\n{'='*60}")
print("ITERATION - INTEGRATION PLANNING")
print(f"{'='*60}\n")
self.integration_planning()
# Phase 6: Final Recommendation
print(f"\n{'='*60}")
print("ITERATION - FINAL RECOMMENDATION")
print(f"{'='*60}\n")
self.final_recommendation()
# Conclude analysis after one full sequence for demo purposes
self.analysis_concluded = True
# Return formatted conversation history
return history_output_formatter(
self.conversation, type=self.output_type
)
def main():
"""Main entry point for M&A advisory swarm"""
# Example M&A transaction details
transaction_details = """
We are exploring a potential acquisition of DataPulse Analytics by TechNova Solutions.
Transaction Context:
- Buyer: TechNova Solutions (NASDAQ: TNVA) - $500M annual revenue enterprise software company
- Target: DataPulse Analytics - Series B AI-driven analytics startup based in San Francisco
- Primary Objectives:
* Expand predictive analytics capabilities in healthcare and financial services
* Accelerate AI-powered business intelligence product roadmap
* Acquire top-tier machine learning engineering talent
Key Considerations:
- Deep integration of DataPulse's proprietary AI models into TechNova's existing platform
- Retention of key DataPulse leadership and engineering team
- Projected 3-year ROI and synergy potential
- Regulatory and compliance alignment
- Technology stack compatibility
"""
# Initialize the swarm
ma_advisory_swarm = MAAdvisorySwarm(
name="AI-Powered M&A Advisory System",
description="Comprehensive AI-driven M&A advisory and market intelligence platform",
user_name="Corporate Development Team",
output_type="json",
max_loops=1,
)
# Run the swarm
print("\n" + "="*60)
print("INITIALIZING M&A ADVISORY SWARM")
print("="*60 + "\n")
ma_advisory_swarm.run(initial_user_input=transaction_details)
if __name__ == "__main__":
main()
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