Kye Gomez
4 days ago
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# Deep Research Swarm
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!!! abstract "Overview"
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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.
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Key Features:
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- Parallel search processing
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- Multi-agent research coordination
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- Advanced information synthesis
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- Automated query generation
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- Concurrent task execution
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## Getting Started
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!!! tip "Quick Installation"
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```bash
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pip install swarms
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```
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=== "Basic Usage"
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```python
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from swarms.structs import DeepResearchSwarm
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# Initialize the swarm
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swarm = DeepResearchSwarm(
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name="MyResearchSwarm",
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output_type="json",
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max_loops=1
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)
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# Run a single research task
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results = swarm.run("What are the latest developments in quantum computing?")
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```
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=== "Batch Processing"
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```python
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# Run multiple research tasks in parallel
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tasks = [
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"What are the environmental impacts of electric vehicles?",
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"How is AI being used in drug discovery?",
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]
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batch_results = swarm.batched_run(tasks)
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```
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## Configuration
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!!! info "Constructor Arguments"
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| Parameter | Type | Default | Description |
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|-----------|------|---------|-------------|
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| `name` | str | "DeepResearchSwarm" | Name identifier for the swarm |
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| `description` | str | "A swarm that conducts..." | Description of the swarm's purpose |
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| `research_agent` | Agent | research_agent | Custom research agent instance |
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| `max_loops` | int | 1 | Maximum number of research iterations |
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| `nice_print` | bool | True | Enable formatted console output |
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| `output_type` | str | "json" | Output format ("json" or "string") |
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| `max_workers` | int | CPU_COUNT * 2 | Maximum concurrent threads |
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| `token_count` | bool | False | Enable token counting |
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| `research_model_name` | str | "gpt-4o-mini" | Model to use for research |
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## Core Methods
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### Run
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!!! example "Single Task Execution"
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```python
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results = swarm.run("What are the latest breakthroughs in fusion energy?")
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```
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### Batched Run
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!!! example "Parallel Task Execution"
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```python
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tasks = [
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"What are current AI safety initiatives?",
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"How is CRISPR being used in agriculture?",
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]
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results = swarm.batched_run(tasks)
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```
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### Step
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!!! example "Single Step Execution"
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```python
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results = swarm.step("Analyze recent developments in renewable energy storage")
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```
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## Domain-Specific Examples
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=== "Scientific Research"
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```python
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science_swarm = DeepResearchSwarm(
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name="ScienceSwarm",
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output_type="json",
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max_loops=2 # More iterations for thorough research
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)
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results = science_swarm.run(
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"What are the latest experimental results in quantum entanglement?"
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)
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```
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=== "Market Research"
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```python
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market_swarm = DeepResearchSwarm(
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name="MarketSwarm",
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output_type="json"
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)
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results = market_swarm.run(
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"What are the emerging trends in electric vehicle battery technology market?"
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)
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```
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=== "News Analysis"
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```python
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news_swarm = DeepResearchSwarm(
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name="NewsSwarm",
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output_type="string" # Human-readable output
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)
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results = news_swarm.run(
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"What are the global economic impacts of recent geopolitical events?"
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)
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```
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=== "Medical Research"
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```python
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medical_swarm = DeepResearchSwarm(
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name="MedicalSwarm",
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max_loops=2
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)
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results = medical_swarm.run(
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"What are the latest clinical trials for Alzheimer's treatment?"
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)
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```
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## Advanced Features
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??? note "Custom Research Agent"
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```python
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from swarms.agents import Agent
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custom_agent = Agent(
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agent_name="SpecializedResearcher",
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system_prompt="Your specialized prompt here",
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model_name="gpt-4"
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)
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swarm = DeepResearchSwarm(
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research_agent=custom_agent,
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max_loops=2
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)
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```
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??? note "Parallel Processing Control"
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```python
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swarm = DeepResearchSwarm(
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max_workers=8, # Limit to 8 concurrent threads
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nice_print=False # Disable console output for production
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)
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```
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## Best Practices
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!!! success "Recommended Practices"
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1. **Query Formulation**: Be specific and clear in your research queries
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2. **Resource Management**: Adjust `max_workers` based on your system's capabilities
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3. **Output Handling**: Use appropriate `output_type` for your use case
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4. **Error Handling**: Implement try-catch blocks around swarm operations
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5. **Model Selection**: Choose appropriate models based on research complexity
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## Limitations
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!!! warning "Known Limitations"
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- Requires valid API keys for external services
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- Performance depends on system resources
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- Rate limits may apply to external API calls
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- Token limits apply to model responses
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