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Deep Research Swarm

!!! abstract "Overview" The Deep Research Swarm is a powerful, production-grade research system that conducts comprehensive analysis across multiple domains using parallel processing and advanced AI agents.

Key Features:

- Parallel search processing

- Multi-agent research coordination

- Advanced information synthesis

- Automated query generation

- Concurrent task execution

Getting Started

!!! tip "Quick Installation" bash pip install swarms

=== "Basic Usage" ```python from swarms.structs import DeepResearchSwarm

# Initialize the swarm
swarm = DeepResearchSwarm(
    name="MyResearchSwarm",
    output_type="json",
    max_loops=1
)

# Run a single research task
results = swarm.run("What are the latest developments in quantum computing?")
```

=== "Batch Processing" python # Run multiple research tasks in parallel tasks = [ "What are the environmental impacts of electric vehicles?", "How is AI being used in drug discovery?", ] batch_results = swarm.batched_run(tasks)

Configuration

!!! info "Constructor Arguments"

Parameter	Type	Default	Description
`name`	str	"DeepResearchSwarm"	Name identifier for the swarm
`description`	str	"A swarm that conducts..."	Description of the swarm's purpose
`research_agent`	Agent	research_agent	Custom research agent instance
`max_loops`	int	1	Maximum number of research iterations
`nice_print`	bool	True	Enable formatted console output
`output_type`	str	"json"	Output format ("json" or "string")
`max_workers`	int	CPU_COUNT * 2	Maximum concurrent threads
`token_count`	bool	False	Enable token counting
`research_model_name`	str	"gpt-4o-mini"	Model to use for research

Core Methods

Run

!!! example "Single Task Execution" python results = swarm.run("What are the latest breakthroughs in fusion energy?")

Batched Run

!!! example "Parallel Task Execution" python tasks = [ "What are current AI safety initiatives?", "How is CRISPR being used in agriculture?", ] results = swarm.batched_run(tasks)

Step

!!! example "Single Step Execution" python results = swarm.step("Analyze recent developments in renewable energy storage")

Domain-Specific Examples

=== "Scientific Research" ```python science_swarm = DeepResearchSwarm( name="ScienceSwarm", output_type="json", max_loops=2 # More iterations for thorough research )

results = science_swarm.run(
    "What are the latest experimental results in quantum entanglement?"
)
```

=== "Market Research" ```python market_swarm = DeepResearchSwarm( name="MarketSwarm", output_type="json" )

results = market_swarm.run(
    "What are the emerging trends in electric vehicle battery technology market?"
)
```

=== "News Analysis" ```python news_swarm = DeepResearchSwarm( name="NewsSwarm", output_type="string" # Human-readable output )

results = news_swarm.run(
    "What are the global economic impacts of recent geopolitical events?"
)
```

=== "Medical Research" ```python medical_swarm = DeepResearchSwarm( name="MedicalSwarm", max_loops=2 )

results = medical_swarm.run(
    "What are the latest clinical trials for Alzheimer's treatment?"
)
```

Advanced Features

??? note "Custom Research Agent" ```python from swarms import Agent

custom_agent = Agent(
    agent_name="SpecializedResearcher",
    system_prompt="Your specialized prompt here",
    model_name="gpt-4"
)

swarm = DeepResearchSwarm(
    research_agent=custom_agent,
    max_loops=2
)
```

??? note "Parallel Processing Control" python swarm = DeepResearchSwarm( max_workers=8, # Limit to 8 concurrent threads nice_print=False # Disable console output for production )

Best Practices

!!! success "Recommended Practices" 1. Query Formulation: Be specific and clear in your research queries 2. Resource Management: Adjust max_workers based on your system's capabilities 3. Output Handling: Use appropriate output_type for your use case 4. Error Handling: Implement try-catch blocks around swarm operations 5. Model Selection: Choose appropriate models based on research complexity

Limitations

!!! warning "Known Limitations"

- Requires valid API keys for external services

- Performance depends on system resources

- Rate limits may apply to external API calls

- Token limits apply to model responses

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