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715 lines
27 KiB
715 lines
27 KiB
# Mergers & Acquisition (M&A) Advisory Swarm
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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.
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## What it Does
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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:
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| Agent Name | Agent (Name) | Key Responsibilities |
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|-----------|--------------|---------------------|
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| 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. |
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| 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. |
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| 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). |
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| 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. |
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| 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. |
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| 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. |
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## How to Set Up
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To set up and run the M&A Advisory Swarm, follow these steps:
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### Prerequisites
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* Python 3.8+
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* An Exa API Key (for the `exa_search` tool)
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### Installation
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1. **Clone the Swarms repository:**
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```bash
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git clone https://github.com/kyegomez/swarms.git
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cd swarms
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```
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*(Note: The `ma_advisory.py` file is assumed to be in `examples/demos/apps/`)*
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2. **Install dependencies:**
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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:
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```bash
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pip install -r requirements.txt
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```
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This will install `httpx`, `python-dotenv`, `loguru`, and other necessary packages.
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3. **Set up Exa API Key:**
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The `Market & Strategic Analysis Agent` utilizes the `exa_search` tool, which requires an `EXA_API_KEY`.
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Create a `.env` file in the root directory of your project (or wherever your application loads environment variables) and add your Exa API key:
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```
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EXA_API_KEY="YOUR_EXA_API_KEY"
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```
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Replace `"YOUR_EXA_API_KEY"` with your actual Exa API key.
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## How to Run
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Navigate to the `examples/demos/apps/` directory and run the `ma_advisory.py` script.
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```bash
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python examples/demos/apps/ma_advisory.py
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```
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OR, you can execute the following Python code directly (ensure all dependencies and the `.env` file are correctly set up):
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```python
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from typing import List
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import os
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from dotenv import load_dotenv
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from loguru import logger
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import httpx
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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.utils.any_to_str import any_to_str
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# --- Exa Search Tool Integration ---
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def exa_search(
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query: str,
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characters: int = 1000, # Increased for more detailed M&A research
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sources: int = 5, # More sources for comprehensive analysis
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) -> str:
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"""
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Perform a highly summarized Exa web search for M&A market intelligence.
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Args:
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query (str): Search query for M&A research.
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characters (int): Max characters for summary.
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sources (int): Number of sources.
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Returns:
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str: Condensed summary of search results.
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"""
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api_key = os.getenv("EXA_API_KEY")
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if not api_key:
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raise ValueError("EXA_API_KEY environment variable is not set")
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headers = {
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"x-api-key": api_key,
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"content-type": "application/json",
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}
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payload = {
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"query": query,
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"type": "auto",
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"numResults": sources,
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"contents": {
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"text": True,
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"summary": {
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"schema": {
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"type": "object",
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"required": ["answer"],
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"additionalProperties": False,
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"properties": {
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"answer": {
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"type": "string",
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"description": "Highly condensed summary of the M&A research result",
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}
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},
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}
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},
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"context": {"maxCharacters": characters},
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},
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}
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try:
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logger.info(f"[SEARCH] Exa M&A research: {query[:50]}...")
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response = httpx.post(
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"https://api.exa.ai/search",
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json=payload,
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headers=headers,
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timeout=30,
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)
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response.raise_for_status()
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json_data = response.json()
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return any_to_str(json_data)
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except Exception as e:
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logger.error(f"Exa search failed: {e}")
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return f"Search failed: {str(e)}. Please try again."
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# Load environment variables
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load_dotenv()
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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"
|
|
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 | |