> ## Documentation Index
> Fetch the complete documentation index at: https://docs.swarms.world/llms.txt
> Use this file to discover all available pages before exploring further.

# Research Team Swarm

> Build an autonomous research team that collaborates to produce comprehensive research reports

## Overview

This example demonstrates how to build a research team swarm that collaborates to produce comprehensive research reports. The team consists of specialized agents working in a hierarchical structure to gather information, analyze data, and synthesize findings into actionable insights.

## Business Value

* **Accelerated Research**: Complete comprehensive research in hours instead of days
* **Diverse Perspectives**: Multiple specialized agents provide different analytical viewpoints
* **Quality Assurance**: Built-in review and synthesis steps ensure accuracy
* **Scalability**: Handle multiple research projects simultaneously
* **Cost Efficiency**: Reduce manual research hours by 70-80%

## Architecture Choice

We use **HierarchicalSwarm** because:

* Research flows naturally from data gathering → analysis → synthesis
* A director agent can coordinate specialist researchers
* Each layer builds upon the previous layer's output
* Clear separation of concerns (research vs analysis vs writing)

## Complete Implementation

```python theme={null}
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=1,
)

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

## How It Works

1. **Primary Researcher** gathers initial information and sources
2. **Data Analyst** processes the research into structured insights
3. **Critical Analyst** reviews findings for quality and completeness
4. **Report Writer** synthesizes everything into a final report

Each agent receives the output of previous agents, building upon their work to create a comprehensive final product.

## Customization Tips

### Add Domain Experts

```python theme={null}
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,
)

# Insert between data_analyst and critical_analyst
research_team = HierarchicalSwarm(
    agents=[researcher_agent, data_analyst, medical_expert, critical_analyst, report_writer],
    # ... other params
)
```

### Add Multi-Loop Research

For iterative research that digs deeper:

```python theme={null}
researcher_agent = Agent(
    agent_name="Primary-Researcher",
    # ... other params
    max_loops=3,  # Allow multiple research iterations
    stopping_condition="research is comprehensive",
)
```

### Configure Output Format

```python theme={null}
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
)
```

### Add Memory for Long Projects

```python theme={null}
from swarms.memory import ChromaDB

memory = ChromaDB(
    output_dir="research_memory",
    n_results=5,
)

researcher_agent = Agent(
    agent_name="Primary-Researcher",
    long_term_memory=memory,
    # ... other params
)
```

## Real-World Applications

* **Market Research**: Competitive intelligence and market analysis
* **Due Diligence**: Investment and M\&A research
* **Academic Research**: Literature reviews and meta-analysis
* **Policy Analysis**: Regulatory impact assessments
* **Technology Evaluation**: Vendor selection and technology assessments

## Performance Optimization

### Parallel Research Tasks

For independent research streams:

```python theme={null}
from swarms import ConcurrentWorkflow

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

parallel_research = ConcurrentWorkflow(
    agents=[researcher_agent],
    max_loops=1,
)

results = [parallel_research.run(topic) for topic in topics]
```

### Cache Common Research

```python theme={null}
researcher_agent = Agent(
    agent_name="Primary-Researcher",
    # ... other params
    cache_enabled=True,  # Reuse similar research queries
)
```

## Next Steps

* Explore [Content Creation Pipeline](/examples/use-cases/content-creation) for publishing research
* See [Data Analysis Swarm](/examples/use-cases/data-analysis) for quantitative research
* Review [Agent Configuration](/concepts/agents) for advanced agent customization