Research Team Swarm

from swarms import Agent, HierarchicalSwarm
import os

# Configure your LLM - using OpenAI as example
api_key = os.getenv("OPENAI_API_KEY")

# Define specialized research agents
researcher_agent = Agent(
    agent_name="Primary-Researcher",
    system_prompt="""
    You are an expert researcher specializing in gathering comprehensive information.
    Your role is to:
    - Identify key information sources and data points
    - Gather relevant facts, statistics, and expert opinions
    - Cite sources accurately
    - Flag areas requiring deeper investigation
    
    Provide thorough, well-sourced research findings.
    """,
    model_name="claude-sonnet-4-6",
    max_loops=1,
    dynamic_temperature_enabled=True,
)

data_analyst = Agent(
    agent_name="Data-Analyst",
    system_prompt="""
    You are a data analyst who transforms raw research into insights.
    Your role is to:
    - Identify patterns and trends in research data
    - Perform statistical analysis where applicable
    - Highlight key findings and anomalies
    - Present data in clear, logical structures
    
    Focus on extracting actionable insights from research.
    """,
    model_name="claude-sonnet-4-6",
    max_loops=1,
    dynamic_temperature_enabled=True,
)

critical_analyst = Agent(
    agent_name="Critical-Analyst",
    system_prompt="""
    You are a critical analyst who evaluates research quality.
    Your role is to:
    - Assess the validity and reliability of findings
    - Identify gaps, biases, or logical inconsistencies
    - Challenge assumptions and verify conclusions
    - Suggest areas for additional investigation
    
    Provide rigorous quality assurance for research outputs.
    """,
    model_name="claude-sonnet-4-6",
    max_loops=1,
    dynamic_temperature_enabled=True,
)

report_writer = Agent(
    agent_name="Report-Writer",
    system_prompt="""
    You are an expert technical writer who synthesizes research into reports.
    Your role is to:
    - Combine research, analysis, and critical feedback into coherent narratives
    - Structure information logically with clear sections
    - Write in clear, professional language
    - Include executive summaries and key recommendations
    
    Create publication-ready research reports.
    """,
    model_name="claude-sonnet-4-6",
    max_loops=1,
    dynamic_temperature_enabled=True,
)

# Create the hierarchical research team
research_team = HierarchicalSwarm(
    name="Research-Team-Swarm",
    description="Autonomous research team for comprehensive analysis",
    agents=[researcher_agent, data_analyst, critical_analyst, report_writer],
    max_loops=2,  # the second loop can hand the first loop's findings to the report writer
    output_type="list",
)

# Execute research project
if __name__ == "__main__":
    research_query = """
    Conduct a comprehensive research report on the current state of 
    artificial intelligence in healthcare, focusing on:
    - Current applications and use cases
    - Regulatory challenges and compliance
    - Market size and growth projections
    - Key players and competitive landscape
    - Future trends and opportunities
    
    Target audience: Healthcare executives and investors
    """
    
    # Run the swarm
    history = research_team.run(research_query)

    # The last message is a loop marker, so take the report writer's latest answer.
    report = next(
        message["content"]
        for message in reversed(history)
        if message["role"] == "Report-Writer"
    )

    # Save the report
    with open("healthcare_ai_report.md", "w") as f:
        f.write(report)
    
    print("Research report completed and saved to healthcare_ai_report.md")

medical_expert = Agent(
    agent_name="Medical-Expert",
    system_prompt="You are a medical doctor who validates healthcare claims...",
    model_name="claude-sonnet-4-6",
    max_loops=1,
)

# Add to the team; the director decides which agents get which orders
research_team = HierarchicalSwarm(
    agents=[researcher_agent, data_analyst, medical_expert, critical_analyst, report_writer],
    # ... other params
)

researcher_agent = Agent(
    agent_name="Primary-Researcher",
    # ... other params
    max_loops=3,  # Allow multiple research iterations
    stopping_condition=lambda response: "research is comprehensive" in response.lower(),
)

report_writer = Agent(
    agent_name="Report-Writer",
    system_prompt="""
    Create reports in the following format:
    1. Executive Summary (1 page)
    2. Methodology
    3. Key Findings (with data visualizations)
    4. Detailed Analysis
    5. Recommendations
    6. Appendices
    
    Use markdown formatting with tables and bullet points.
    """,
    # ... other params
)

researcher_agent = Agent(
    agent_name="Primary-Researcher",
    persistent_memory=True,   # off by default; opt in explicitly
    context_compression=True, # summarize when nearing the context limit
    context_length=32000,
    # ... other params
)

from swarms import Agent, batched_grid_agent_execution

# Research multiple topics in parallel
topics = [
    "AI in diagnostics",
    "AI in drug discovery",
    "AI in patient care",
]

# One researcher per topic, so no agent instance is shared between threads.
researchers = [
    Agent(
        agent_name=f"Primary-Researcher-{i}",
        system_prompt=researcher_agent.system_prompt,
        model_name="claude-sonnet-4-6",
        max_loops=1,
    )
    for i in range(len(topics))
]

# Results come back in topic order.
results = batched_grid_agent_execution(agents=researchers, tasks=topics)

researcher_agent = Agent(
    agent_name="Primary-Researcher",
    # ... other params
    prompt_caching=True,  # Enable provider-side prompt caching
)