Merge 7d68229fee into 75049e82a3
Aksh Parekh
1 day ago
committed by
GitHub
commit
f7101c100a
@ -0,0 +1,559 @@
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from typing import List
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from loguru import logger
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from swarms.structs.agent import Agent
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from swarms.structs.conversation import Conversation
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from swarms.utils.history_output_formatter import history_output_formatter
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from swarms_tools import exa_search
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# System prompts for each agent
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INTAKE_AGENT_PROMPT = """
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You are an M&A Intake Specialist responsible for gathering comprehensive information about a potential transaction.
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ROLE:
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Engage with the user to understand the full context of the potential M&A deal, extracting critical details that will guide subsequent analyses.
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RESPONSIBILITIES:
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- Conduct a thorough initial interview to understand:
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* Transaction type (acquisition, merger, divestiture)
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* Industry and sector specifics
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* Target company profile and size
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* Strategic objectives
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* Buyer/seller perspective
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* Timeline and urgency
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* Budget constraints
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* Specific concerns or focus areas
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OUTPUT FORMAT:
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Provide a comprehensive Deal Brief that includes:
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1. Transaction Overview
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- Proposed transaction type
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- Key parties involved
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- Initial strategic rationale
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2. Stakeholder Context
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- Buyer's background and motivations
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- Target company's current position
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- Key decision-makers
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3. Initial Assessment
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- Preliminary strategic fit
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- Potential challenges or red flags
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- Recommended focus areas for deeper analysis
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4. Information Gaps
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- Questions that need further clarification
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- Additional data points required
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IMPORTANT:
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- Be thorough and systematic
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- Ask probing questions to uncover nuanced details
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- Maintain a neutral, professional tone
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- Prepare a foundation for subsequent in-depth analysis
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"""
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MARKET_ANALYSIS_PROMPT = """
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You are an M&A Market Intelligence Analyst tasked with conducting comprehensive market research.
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ROLE:
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Perform an in-depth analysis of market dynamics, competitive landscape, and strategic implications for the potential transaction.
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TOOLS:
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You have access to the exa_search tool for gathering real-time market intelligence.
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RESPONSIBILITIES:
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1. Conduct Market Research
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- Use exa_search to gather current market insights
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- Analyze industry trends, size, and growth potential
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- Identify key players and market share distribution
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2. Competitive Landscape Analysis
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- Map out competitive ecosystem
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- Assess target company's market positioning
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- Identify potential competitive advantages or vulnerabilities
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3. Strategic Fit Evaluation
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- Analyze alignment with buyer's strategic objectives
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- Assess potential market entry or expansion opportunities
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- Evaluate potential for market disruption
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4. External Factor Assessment
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- Examine regulatory environment
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- Analyze technological disruption potential
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- Consider macroeconomic impacts
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OUTPUT FORMAT:
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Provide a comprehensive Market Analysis Report:
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1. Market Overview
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- Market size and growth trajectory
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- Key industry trends
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- Competitive landscape summary
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2. Strategic Fit Assessment
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- Market attractiveness score (1-10)
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- Strategic alignment evaluation
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- Potential synergies and opportunities
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3. Risk and Opportunity Mapping
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- Key market opportunities
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- Potential competitive threats
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- Regulatory and technological risk factors
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4. Recommended Next Steps
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- Areas requiring deeper investigation
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- Initial strategic recommendations
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"""
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FINANCIAL_VALUATION_PROMPT = """
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You are an M&A Financial Analysis and Risk Expert. Perform comprehensive financial evaluation and risk assessment.
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RESPONSIBILITIES:
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1. Financial Health Analysis
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- Analyze revenue trends and quality
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- Evaluate profitability metrics (EBITDA, margins)
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- Conduct cash flow analysis
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- Assess balance sheet strength
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- Review working capital requirements
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2. Valuation Analysis
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- Perform comparable company analysis
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- Conduct precedent transaction analysis
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- Develop Discounted Cash Flow (DCF) model
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- Assess asset-based valuation
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3. Synergy and Risk Assessment
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- Quantify potential revenue and cost synergies
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- Identify financial and operational risks
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- Evaluate integration complexity
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- Assess potential deal-breakers
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OUTPUT FORMAT:
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1. Comprehensive Financial Analysis Report
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2. Valuation Range (low, mid, high scenarios)
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3. Synergy Potential Breakdown
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4. Detailed Risk Matrix
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5. Recommended Pricing Strategy
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"""
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DEAL_STRUCTURING_PROMPT = """
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You are an M&A Deal Structuring Advisor. Recommend the optimal transaction structure.
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RESPONSIBILITIES:
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1. Transaction Structure Design
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- Evaluate asset vs stock purchase options
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- Analyze cash vs stock consideration
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- Design earnout provisions
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- Develop contingent payment structures
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2. Financing Strategy
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- Recommend debt/equity mix
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- Identify optimal financing sources
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- Assess impact on buyer's capital structure
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3. Tax and Legal Optimization
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- Design tax-efficient structure
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- Consider jurisdictional implications
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- Minimize tax liabilities
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4. Deal Protection Mechanisms
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- Develop escrow arrangements
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- Design representations and warranties
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- Create indemnification provisions
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- Recommend non-compete agreements
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OUTPUT FORMAT:
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1. Recommended Deal Structure
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2. Detailed Payment Terms
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3. Key Contractual Protections
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4. Tax Optimization Strategy
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5. Rationale for Proposed Structure
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"""
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INTEGRATION_PLANNING_PROMPT = """
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You are an M&A Integration Planning Expert. Develop a comprehensive post-merger integration roadmap.
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RESPONSIBILITIES:
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1. Immediate Integration Priorities
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- Define critical day-1 actions
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- Develop communication strategy
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- Identify quick win opportunities
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2. 100-Day Integration Plan
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- Design organizational structure alignment
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- Establish governance framework
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- Create detailed integration milestones
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3. Functional Integration Strategy
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- Plan operations consolidation
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- Design systems and technology integration
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- Align sales and marketing approaches
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- Develop cultural integration plan
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4. Synergy Realization
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- Create detailed synergy capture timeline
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- Establish performance tracking mechanisms
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- Define accountability framework
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OUTPUT FORMAT:
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1. Comprehensive Integration Roadmap
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2. Detailed 100-Day Plan
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3. Functional Integration Strategies
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4. Synergy Realization Timeline
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5. Risk Mitigation Recommendations
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"""
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FINAL_RECOMMENDATION_PROMPT = """
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You are the Senior M&A Advisory Partner. Synthesize all analyses into a comprehensive recommendation.
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RESPONSIBILITIES:
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1. Executive Summary
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- Summarize transaction overview
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- Highlight strategic rationale
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- Articulate key value drivers
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2. Investment Thesis Validation
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- Assess strategic benefits
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- Evaluate financial attractiveness
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- Project long-term potential
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3. Comprehensive Risk Assessment
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- Summarize top risks
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- Provide mitigation strategies
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- Identify potential deal-breakers
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4. Final Recommendation
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- Provide clear GO/NO-GO recommendation
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- Specify recommended offer range
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- Outline key proceeding conditions
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OUTPUT FORMAT:
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1. Executive-Level Recommendation Report
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2. Decision Framework
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3. Risk-Adjusted Strategic Perspective
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4. Actionable Next Steps
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5. Recommendation Confidence Level
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"""
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class MAAdvisorySwarm:
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def __init__(
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self,
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name: str = "M&A Advisory Swarm",
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description: str = "Comprehensive AI-driven M&A advisory system",
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max_loops: int = 1,
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user_name: str = "M&A Advisor",
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output_type: str = "json",
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):
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self.max_loops = max_loops
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self.name = name
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self.description = description
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self.user_name = user_name
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self.output_type = output_type
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self.agents = self._initialize_agents()
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self.conversation = Conversation()
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self.exa_search_results = []
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self.search_queries = []
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self.current_iteration = 0
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self.max_iterations = 1 # Limiting to 1 iteration for full sequential demo
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self.analysis_concluded = False
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self.handle_initial_processing()
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def handle_initial_processing(self):
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self.conversation.add(
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role="System",
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content=f"Company: {self.name}\n"
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f"Description: {self.description}\n"
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f"Mission: Provide comprehensive M&A advisory for {self.user_name}"
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)
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def _initialize_agents(self) -> List[Agent]:
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return [
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Agent(
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agent_name="Emma-Intake-Specialist",
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agent_description="Gathers comprehensive initial information about the potential M&A transaction.",
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system_prompt=INTAKE_AGENT_PROMPT,
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max_loops=self.max_loops,
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dynamic_temperature_enabled=True,
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output_type="final",
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),
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Agent(
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agent_name="Marcus-Market-Analyst",
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agent_description="Conducts in-depth market research and competitive analysis.",
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system_prompt=MARKET_ANALYSIS_PROMPT,
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max_loops=self.max_loops,
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dynamic_temperature_enabled=True,
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output_type="final",
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),
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Agent(
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agent_name="Sophia-Financial-Analyst",
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agent_description="Performs comprehensive financial valuation and risk assessment.",
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system_prompt=FINANCIAL_VALUATION_PROMPT,
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max_loops=self.max_loops,
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dynamic_temperature_enabled=True,
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output_type="final",
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),
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Agent(
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agent_name="David-Deal-Structuring-Advisor",
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agent_description="Recommends optimal deal structure and terms.",
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system_prompt=DEAL_STRUCTURING_PROMPT,
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max_loops=self.max_loops,
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dynamic_temperature_enabled=True,
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output_type="final",
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),
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Agent(
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agent_name="Nathan-Integration-Planner",
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agent_description="Develops comprehensive post-merger integration roadmap.",
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system_prompt=INTEGRATION_PLANNING_PROMPT,
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max_loops=self.max_loops,
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dynamic_temperature_enabled=True,
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output_type="final",
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),
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Agent(
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agent_name="Alex-Final-Recommendation-Partner",
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agent_description="Synthesizes all analyses into a comprehensive recommendation.",
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system_prompt=FINAL_RECOMMENDATION_PROMPT,
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max_loops=self.max_loops,
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dynamic_temperature_enabled=True,
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output_type="final",
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)
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]
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def find_agent_by_name(self, name: str) -> Agent:
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for agent in self.agents:
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if name in agent.agent_name:
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return agent
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return None
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def intake_and_scoping(self, user_input: str):
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"""Phase 1: Intake and initial deal scoping"""
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emma_agent = self.find_agent_by_name("Intake-Specialist")
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emma_output = emma_agent.run(
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f"User Input: {user_input}\n\n"
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f"Conversation History: {self.conversation.get_str()}\n\n"
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f"Analyze the potential M&A transaction, extract key details, and prepare a comprehensive deal brief. "
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f"If information is unclear, ask clarifying questions."
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)
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self.conversation.add(
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role="Intake-Specialist", content=emma_output
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)
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# Extract potential search queries for market research
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self.search_queries = self._extract_search_queries(emma_output)
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return emma_output
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def _extract_search_queries(self, intake_output: str) -> List[str]:
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"""Extract search queries from Intake Specialist output"""
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queries = []
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lines = intake_output.split('\n')
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# Look for lines that could be good search queries
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for line in lines:
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line = line.strip()
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# Simple heuristic: lines with potential research keywords
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if any(keyword in line.lower() for keyword in ['market', 'industry', 'trend', 'competitor', 'analysis']):
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if len(line) > 20: # Ensure query is substantial
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queries.append(line)
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# Fallback queries if none found
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if not queries:
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queries = [
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"M&A trends in technology sector",
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"Market analysis for potential business acquisition",
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"Competitive landscape in enterprise software"
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]
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return queries[:3] # Limit to 3 queries
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def market_research(self):
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"""Phase 2: Conduct market research using exa_search"""
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# Execute exa_search for each query
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self.exa_search_results = []
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for query in self.search_queries:
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result = exa_search(query)
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self.exa_search_results.append({
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"query": query,
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"exa_result": result
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})
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# Pass results to Market Analysis agent
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marcus_agent = self.find_agent_by_name("Market-Analyst")
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# Build exa context
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exa_context = "\n\n[Exa Market Research Results]\n"
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for item in self.exa_search_results:
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exa_context += f"Query: {item['query']}\nResults: {item['exa_result']}\n\n"
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marcus_output = marcus_agent.run(
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f"Conversation History: {self.conversation.get_str()}\n\n"
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f"{exa_context}\n"
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f"Analyze these market research results. Provide comprehensive market intelligence and strategic insights."
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)
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self.conversation.add(
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role="Market-Analyst", content=marcus_output
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)
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return marcus_output
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def financial_valuation(self):
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"""Phase 3: Perform comprehensive financial valuation and risk assessment"""
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sophia_agent = self.find_agent_by_name("Financial-Analyst")
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sophia_output = sophia_agent.run(
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f"Conversation History: {self.conversation.get_str()}\n\n"
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f"Perform comprehensive financial analysis and risk assessment based on previous insights."
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)
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self.conversation.add(
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role="Financial-Analyst", content=sophia_output
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)
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return sophia_output
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def deal_structuring(self):
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"""Phase 4: Recommend optimal deal structure"""
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david_agent = self.find_agent_by_name("Deal-Structuring-Advisor")
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david_output = david_agent.run(
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f"Conversation History: {self.conversation.get_str()}\n\n"
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f"Recommend the optimal transaction structure and terms based on all prior analyses."
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)
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self.conversation.add(
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role="Deal-Structuring-Advisor", content=david_output
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)
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return david_output
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def integration_planning(self):
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"""Phase 5: Develop post-merger integration roadmap"""
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nathan_agent = self.find_agent_by_name("Integration-Planner")
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nathan_output = nathan_agent.run(
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f"Conversation History: {self.conversation.get_str()}\n\n"
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f"Create a comprehensive integration plan to realize deal value."
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)
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self.conversation.add(
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role="Integration-Planner", content=nathan_output
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)
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return nathan_output
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def final_recommendation(self):
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"""Phase 6: Synthesize all analyses into a comprehensive recommendation"""
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alex_agent = self.find_agent_by_name("Final-Recommendation-Partner")
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alex_output = alex_agent.run(
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f"Conversation History: {self.conversation.get_str()}\n\n"
|
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f"Synthesize all agent analyses into a comprehensive, actionable M&A recommendation."
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)
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||||
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self.conversation.add(
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role="Final-Recommendation-Partner", content=alex_output
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)
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return alex_output
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def run(self, initial_user_input: str):
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"""
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Run the M&A advisory swarm with continuous analysis.
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Args:
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initial_user_input: User's initial M&A transaction details
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"""
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self.conversation.add(role=self.user_name, content=initial_user_input)
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while not self.analysis_concluded and self.current_iteration < self.max_iterations:
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self.current_iteration += 1
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logger.info(f"Starting analysis iteration {self.current_iteration}")
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# Phase 1: Intake and Scoping
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print(f"\n{'='*60}")
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print("ITERATION - INTAKE AND SCOPING")
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print(f"{'='*60}\n")
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self.intake_and_scoping(initial_user_input)
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# Phase 2: Market Research (with exa_search)
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print(f"\n{'='*60}")
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print("ITERATION - MARKET RESEARCH")
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print(f"{'='*60}\n")
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self.market_research()
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||||
# Phase 3: Financial Valuation
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print(f"\n{'='*60}")
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print("ITERATION - FINANCIAL VALUATION")
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print(f"{'='*60}\n")
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self.financial_valuation()
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# Phase 4: Deal Structuring
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print(f"\n{'='*60}")
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print("ITERATION - DEAL STRUCTURING")
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print(f"{'='*60}\n")
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self.deal_structuring()
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|
||||
# Phase 5: Integration Planning
|
||||
print(f"\n{'='*60}")
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print("ITERATION - INTEGRATION PLANNING")
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print(f"{'='*60}\n")
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self.integration_planning()
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|
||||
# Phase 6: Final Recommendation
|
||||
print(f"\n{'='*60}")
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print("ITERATION - FINAL RECOMMENDATION")
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print(f"{'='*60}\n")
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self.final_recommendation()
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||||
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||||
# 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()
|
||||
Loading…
Reference in new issue