swarms/docs/examples/ma_swarm.md

Mergers & Acquisition (M&A) Advisory Swarm

The M&A Advisory Swarm is a sophisticated multi-agent system designed to automate and streamline the entire mergers & acquisitions advisory workflow. By orchestrating a series of specialized AI agents, this swarm provides comprehensive analysis from initial intake to final recommendation.

What it Does

The MAAdvisorySwarm operates as a sequential workflow, where each agent's output builds upon previous analyses, ensuring a cohesive and comprehensive advisory process. The swarm consists of the following agents:

Agent Name	Agent (Name)	Key Responsibilities
Intake & Scoping	Emma	Gathers essential information about the potential deal, including deal type, industry, target profile, objectives, timeline, budget, and specific concerns. It generates an initial Deal Brief.
Market & Strategic Analysis	Marcus	Evaluates industry dynamics, competitive landscape, and strategic fit. It leverages the exa_search tool to gather real-time market intelligence on trends, key players, and external factors.
Financial Valuation & Risk Assessment	Sophia	Performs comprehensive financial health analysis, various valuation methodologies (comparable companies, precedent transactions, DCF), synergy assessment, and a detailed risk assessment (financial, operational, legal, market).
Deal Structuring	David	Recommends the optimal transaction structure, considering asset vs. stock purchase, cash vs. stock consideration, earnouts, financing strategies, tax optimization, and deal protection mechanisms.
Integration Planning	Nathan	Develops a comprehensive post-merger integration roadmap, including Day 1 priorities, a 100-day plan, functional integration strategies (operations, systems, sales, HR), and synergy realization timelines.
Final Recommendation	Alex	Synthesizes all prior agent analyses into a comprehensive, executive-ready M&A Advisory Report, including an executive summary, investment thesis, key risks, deal structure, integration approach, and a clear GO/NO-GO/CONDITIONAL recommendation.

How to Set Up

To set up and run the M&A Advisory Swarm, follow these steps:

Prerequisites

• Python 3.8+
• An Exa API Key (for the exa_search tool)

Installation

1. Install dependencies: The ma_advisory.py script relies on several libraries. These can be installed using the requirements.txt file located at the root of your project:

   pip install -r requirements.txt
   

   This will install httpx, python-dotenv, loguru, and other necessary packages.

2. Set up Exa API Key: The Market & Strategic Analysis Agent utilizes the exa_search tool, which requires an EXA_API_KEY. Create a .env file in the root directory of your project (or wherever your application loads environment variables) and add your Exa API key:

   EXA_API_KEY="YOUR_EXA_API_KEY"
   

   Replace "YOUR_EXA_API_KEY" with your actual Exa API key.

How to Run

Navigate to the examples/demos/apps/ directory and run the ma_advisory.py script.

python examples/demos/apps/ma_advisory.py

OR, you can execute the following Python code directly (ensure all dependencies and the .env file are correctly set up):

from typing import List
import os
from dotenv import load_dotenv
from loguru import logger
import httpx
from swarms.structs.agent import Agent
from swarms.structs.conversation import Conversation
from swarms.utils.history_output_formatter import history_output_formatter
from swarms.utils.any_to_str import any_to_str

# --- Exa Search Tool Integration ---
def exa_search(
    query: str,
    characters: int = 1000,  # Increased for more detailed M&A research
    sources: int = 5,  # More sources for comprehensive analysis
) -> str:
    """
    Perform a highly summarized Exa web search for M&A market intelligence.

    Args:
        query (str): Search query for M&A research.
        characters (int): Max characters for summary.
        sources (int): Number of sources.

    Returns:
        str: Condensed summary of search results.
    """
    api_key = os.getenv("EXA_API_KEY")
    if not api_key:
        raise ValueError("EXA_API_KEY environment variable is not set")

    headers = {
        "x-api-key": api_key,
        "content-type": "application/json",
    }

    payload = {
        "query": query,
        "type": "auto",
        "numResults": sources,
        "contents": {
            "text": True,
            "summary": {
                "schema": {
                    "type": "object",
                    "required": ["answer"],
                    "additionalProperties": False,
                    "properties": {
                        "answer": {
                            "type": "string",
                            "description": "Highly condensed summary of the M&A research result",
                        }
                    },
                }
            },
            "context": {"maxCharacters": characters},
        },
    }

    try:
        logger.info(f"[SEARCH] Exa M&A research: {query[:50]}...")
        response = httpx.post(
            "https://api.exa.ai/search",
            json=payload,
            headers=headers,
            timeout=30,
        )
        response.raise_for_status()
        json_data = response.json()
        return any_to_str(json_data)
    except Exception as e:
        logger.error(f"Exa search failed: {e}")
        return f"Search failed: {str(e)}. Please try again."

# Load environment variables
load_dotenv()

# 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()

How it Can Be Used for M&A

The M&A Advisory Swarm can be utilized for a variety of M&A tasks, providing an automated and efficient approach to complex deal workflows:

• Automated Deal Scoping: Quickly gather and structure initial information about a potential transaction.
• Real-time Market Intelligence: Leverage web search capabilities to rapidly research industry trends, competitive landscapes, and strategic fit.
• Comprehensive Financial & Risk Analysis: Perform detailed financial evaluations, valuation modeling, synergy assessments, and identify critical risks.
• Optimized Deal Structuring: Recommend the most advantageous transaction structures, financing strategies, and deal protection mechanisms.
• Proactive Integration Planning: Develop robust integration roadmaps to ensure seamless post-merger transitions and value realization.
• Executive-Ready Recommendations: Synthesize complex analyses into clear, actionable recommendations for decision-makers.

By chaining these specialized agents, the M&A Advisory Swarm provides an end-to-end solution for corporate development teams, investment bankers, and M&A professionals, reducing manual effort and increasing the speed and quality of strategic decision-making.

Contributing to Swarms

Platform	Link	Description
📚 Documentation	docs.swarms.world	Official documentation and guides
📝 Blog	Medium	Latest updates and technical articles
💬 Discord	Join Discord	Live chat and community support
🐦 Twitter	@kyegomez	Latest news and announcements
👥 LinkedIn	The Swarm Corporation	Professional network and updates
📺 YouTube	Swarms Channel	Tutorials and demos
🎫 Events	Sign up here	Join our community events