diff --git a/docs/examples/ma_swarm.md b/docs/examples/ma_swarm.md deleted file mode 100644 index 3eb40777..00000000 --- a/docs/examples/ma_swarm.md +++ /dev/null @@ -1,638 +0,0 @@ -# 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: - -## Step 1: Setup and Installation - -### Prerequisites - -| Requirement | -|-----------------------| -| Python 3.8 or higher | -| pip package manager | - -1. **Install dependencies:** - Use the following command to download all dependencies. - ```bash - # Install Swarms framework - pip install swarms - - # Install environment and logging dependencies - pip install python-dotenv loguru - - # Install HTTP client and tools - pip install httpx swarms_tools - ``` -2. **Set up API Keys:** - The `Property Research 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 API keys: - ``` - EXA_API_KEY="YOUR_EXA_API_KEY" - OPENAI_API_KEY="OPENAI_API_KEY" - ``` - Replace `"YOUR_EXA_API_KEY"` & `"OPENAI_API_KEY"` with your actual API keys. - -## Step 2: Running the Mergers & Acquisitions Advisory Swarm - -```python -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() -``` - -## 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](https://docs.swarms.world) | Official documentation and guides | -| 📝 Blog | [Medium](https://medium.com/@kyeg) | Latest updates and technical articles | -| 💬 Discord | [Join Discord](https://discord.gg/EamjgSaEQf) | Live chat and community support | -| 🐦 Twitter | [@kyegomez](https://twitter.com/kyegomez) | Latest news and announcements | -| 👥 LinkedIn | [The Swarm Corporation](https://www.linkedin.com/company/the-swarm-corporation) | Professional network and updates | -| 📺 YouTube | [Swarms Channel](https://www.youtube.com/channel/UC9yXyitkbU_WSy7bd_41SqQ) | Tutorials and demos | -| 🎫 Events | [Sign up here](https://lu.ma/5p2jnc2v) | Join our community events |