Mixture of Agents Example

import requests
import os

API_BASE_URL = "https://api.swarms.world"
API_KEY = os.environ.get("SWARMS_API_KEY", "your_api_key_here")

headers = {
    "x-api-key": API_KEY,
    "Content-Type": "application/json"
}

def run_due_diligence(startup_info: str) -> dict:
    """Run multi-expert due diligence on a startup."""

    swarm_config = {
        "name": "Startup Due Diligence",
        "description": "Multi-expert investment analysis",
        "swarm_type": "MixtureOfAgents",
        "task": f"""Conduct due diligence analysis for investment consideration:

{startup_info}

Provide your specialized analysis and a score from 1-10.""",
        "agents": [
            {
                "agent_name": "Technical Expert",
                "description": "Evaluates technology and engineering",
                "system_prompt": """You are a Technical Due Diligence Expert. Analyze:
1. Tech stack (scalability, modernity)
2. Engineering team strength
3. IP and technical moat
4. Technical risks

Provide a TECHNICAL SCORE (1-10) with justification.""",
                "model_name": "gpt-4o",
                "max_loops": 1,
                "temperature": 0.3
            },
            {
                "agent_name": "Financial Analyst",
                "description": "Assesses financials and valuation",
                "system_prompt": """You are a Financial Due Diligence Expert. Analyze:
1. Revenue and growth trajectory
2. Unit economics (CAC, LTV, margins)
3. Burn rate and runway
4. Valuation reasonableness

Provide a FINANCIAL SCORE (1-10) with justification.""",
                "model_name": "gpt-4o",
                "max_loops": 1,
                "temperature": 0.3
            },
            {
                "agent_name": "Market Strategist",
                "description": "Evaluates market opportunity",
                "system_prompt": """You are a Market Strategy Expert. Analyze:
1. TAM/SAM and market growth
2. Competitive landscape
3. Competitive moat
4. Go-to-market strategy

Provide a MARKET SCORE (1-10) with justification.""",
                "model_name": "gpt-4o",
                "max_loops": 1,
                "temperature": 0.3
            },
            {
                "agent_name": "Legal Advisor",
                "description": "Reviews legal and compliance",
                "system_prompt": """You are a Legal Due Diligence Expert. Analyze:
1. Corporate structure
2. Regulatory compliance
3. IP ownership
4. Legal risks

Provide a LEGAL SCORE (1-10) with justification.""",
                "model_name": "gpt-4o",
                "max_loops": 1,
                "temperature": 0.3
            }
        ],
        "max_loops": 1
    }

    response = requests.post(
        f"{API_BASE_URL}/v1/swarm/completions",
        headers=headers,
        json=swarm_config,
        timeout=180
    )

    return response.json()

import re

# Startup to analyze
startup = """
COMPANY: DataFlow AI
INDUSTRY: Enterprise SaaS / AI
DESCRIPTION: AI-powered data integration platform that automates ETL pipelines using natural language.

DETAILS:
- Founded: 2023
- Stage: Seed
- Funding Sought: $4M
- Team Size: 12
- Revenue: $180K ARR, growing 25% MoM
- Tech Stack: Python, Kubernetes, PostgreSQL, GPT-4 API
- Metrics: 45 paying customers, 92% retention, $4K ACV
- Competitors: Fivetran, Airbyte, dbt, Matillion
"""

# Run analysis
result = run_due_diligence(startup)

# Extract scores and display
def extract_score(content):
    match = re.search(r'score[:\s]+(\d+)/10', content.lower())
    return int(match.group(1)) if match else None

scores = {}
for output in result.get("output", []):
    expert = output["role"]
    content = output["content"]
    score = extract_score(content)
    scores[expert] = score

    print(f"\n{'='*50}")
    print(f"{expert.upper()}" + (f" - Score: {score}/10" if score else ""))
    print(f"{'='*50}")
    print(content[:600] + "...")

# Aggregate
if scores:
    valid_scores = [s for s in scores.values() if s]
    avg = sum(valid_scores) / len(valid_scores)

    print(f"\n{'='*50}")
    print("AGGREGATE ASSESSMENT")
    print(f"{'='*50}")
    for expert, score in scores.items():
        bar = "#" * (score or 0) + "-" * (10 - (score or 0))
        print(f"{expert:20} [{bar}] {score}/10")

    print(f"\nOVERALL SCORE: {avg:.1f}/10")

    if avg >= 7:
        print("RECOMMENDATION: Proceed with investment")
    elif avg >= 5:
        print("RECOMMENDATION: Address concerns before proceeding")
    else:
        print("RECOMMENDATION: Pass")

print(f"\nTotal cost: ${result['usage']['billing_info']['total_cost']:.4f}")

==================================================
TECHNICAL EXPERT - Score: 7/10
==================================================
TECHNICAL ANALYSIS

Tech Stack Assessment:
- Python + Kubernetes is a solid, scalable foundation
- PostgreSQL appropriate for structured data
- GPT-4 API dependency is a risk factor

Strengths:
- Modern cloud-native architecture
- AI-first approach differentiates from legacy ETL tools

Concerns:
- Heavy reliance on OpenAI API (vendor lock-in, cost)...

==================================================
FINANCIAL ANALYST - Score: 8/10
==================================================
FINANCIAL ANALYSIS

Revenue: $180K ARR with 25% MoM growth is strong for seed stage...

==================================================
AGGREGATE ASSESSMENT
==================================================
Technical Expert      [#######---] 7/10
Financial Analyst     [########--] 8/10
Market Strategist     [#######---] 7/10
Legal Advisor         [######----] 6/10

OVERALL SCORE: 7.0/10
RECOMMENDATION: Proceed with investment

Total cost: $0.1523