SwarmRouter Documentation¶

The SwarmRouter class is a flexible routing system designed to manage different types of swarms for task execution. It provides a unified interface to interact with various swarm types, including AgentRearrange, MixtureOfAgents, SpreadSheetSwarm, SequentialWorkflow, ConcurrentWorkflow, GroupChat, MultiAgentRouter, HierarchicalSwarm, MajorityVoting, and auto which will dynamically select the most appropriate swarm for you by analyzing your task, name, and description. We will be continuously adding more swarm architectures as we progress with new developments.

Classes¶

Document¶

A Pydantic model for representing document data.

Attribute	Type	Description
`file_path`	str	Path to the document file.
`data`	str	Content of the document.

SwarmLog¶

A Pydantic model for capturing log entries.

Attribute	Type	Description
`id`	str	Unique identifier for the log entry.
`timestamp`	datetime	Time of log creation.
`level`	str	Log level (e.g., "info", "error").
`message`	str	Log message content.
`swarm_type`	SwarmType	Type of swarm associated with the log.
`task`	str	Task being performed (optional).
`metadata`	Dict[str, Any]	Additional metadata (optional).
`documents`	List[Document]	List of documents associated with the log.

SwarmRouter¶

Main class for routing tasks to different swarm types.

Attribute	Type	Description
`name`	str	Name of the SwarmRouter instance.
`description`	str	Description of the SwarmRouter instance.
`max_loops`	int	Maximum number of loops to perform.
`agents`	List[Union[Agent, Callable]]	List of Agent objects or callable functions to be used in the swarm.
`swarm_type`	SwarmType	Type of swarm to be used.
`autosave`	bool	Flag to enable/disable autosave.
`rearrange_flow`	str	The flow for the AgentRearrange swarm type.
`return_json`	bool	Flag to enable/disable returning the result in JSON format.
`auto_generate_prompts`	bool	Flag to enable/disable auto generation of prompts.
`shared_memory_system`	Any	Shared memory system for agents.
`rules`	str	Rules to inject into every agent.
`documents`	List[str]	List of document file paths.
`output_type`	OutputType	Output format type (e.g., "string", "dict").
`no_cluster_ops`	bool	Flag to disable cluster operations.
`speaker_fn`	callable	Speaker function for GroupChat swarm type.
`load_agents_from_csv`	bool	Flag to enable/disable loading agents from CSV.
`csv_file_path`	str	Path to the CSV file for loading agents.
`return_entire_history`	bool	Flag to enable/disable returning the entire conversation history.
`swarm`	Union[AgentRearrange, MixtureOfAgents, SpreadSheetSwarm, SequentialWorkflow, ConcurrentWorkflow, GroupChat, MultiAgentRouter, HierarchicalSwarm, MajorityVoting]	Instantiated swarm object.
`logs`	List[SwarmLog]	List of log entries captured during operations.

Methods:¶

Method	Parameters	Description
`__init__`	self, name: str = "swarm-router", description: str = "Routes your task to the desired swarm", max_loops: int = 1, agents: List[Union[Agent, Callable]] = [], swarm_type: SwarmType = "SequentialWorkflow", autosave: bool = False, rearrange_flow: str = None, return_json: bool = False, auto_generate_prompts: bool = False, shared_memory_system: Any = None, rules: str = None, documents: List[str] = [], output_type: OutputType = "dict", no_cluster_ops: bool = False, speaker_fn: callable = None, load_agents_from_csv: bool = False, csv_file_path: str = None, return_entire_history: bool = True, args, *kwargs	Initialize the SwarmRouter.
`activate_shared_memory`	`self`	Activate shared memory with all agents.
`handle_rules`	`self`	Inject rules to every agent.
`activate_ape`	`self`	Activate automatic prompt engineering for agents that support it.
`reliability_check`	`self`	Perform reliability checks on the SwarmRouter configuration.
`_create_swarm`	`self, task: str = None, args, *kwargs`	Create and return the specified swarm type or automatically match the best swarm type for a given task.
`_log`	`self, level: str, message: str, task: str = "", metadata: Dict[str, Any] = None`	Create a log entry and add it to the logs list.
`_run`	`self, task: str, img: str, args, *kwargs`	Dynamically run the specified task on the selected or matched swarm type.
`run`	`self, task: str, img: str = None, args, *kwargs`	Execute a task on the selected swarm type.
`__call__`	`self, task: str, args, *kwargs`	Make the SwarmRouter instance callable.
`batch_run`	`self, tasks: List[str], args, *kwargs`	Execute a batch of tasks on the selected or matched swarm type.
`async_run`	`self, task: str, args, *kwargs`	Execute a task on the selected or matched swarm type asynchronously.
`get_logs`	`self`	Retrieve all logged entries.
`concurrent_run`	`self, task: str, args, *kwargs`	Execute a task on the selected or matched swarm type concurrently.
`concurrent_batch_run`	`self, tasks: List[str], args, *kwargs`	Execute a batch of tasks on the selected or matched swarm type concurrently.

Function: swarm_router¶

A convenience function to create and run a SwarmRouter instance.

Parameter	Type	Default	Description
`name`	str	"swarm-router"	Name of the swarm router.
`description`	str	"Routes your task to the desired swarm"	Description of the router.
`max_loops`	int	1	Maximum number of execution loops.
`agents`	List[Union[Agent, Callable]]	[]	List of agents or callables.
`swarm_type`	SwarmType	"SequentialWorkflow"	Type of swarm to use.
`autosave`	bool	False	Whether to autosave results.
`flow`	str	None	Flow configuration.
`return_json`	bool	True	Whether to return results as JSON.
`auto_generate_prompts`	bool	False	Whether to auto-generate prompts.
`task`	str	None	Task to execute.
`rules`	str	None	Rules to inject into every agent.
`*args`			Additional positional arguments passed to SwarmRouter.run()
`**kwargs`			Additional keyword arguments passed to SwarmRouter.run()

Installation¶

To use the SwarmRouter, first install the required dependencies:

pip install swarms swarm_models

Basic Usage¶

import os
from dotenv import load_dotenv
from swarms import Agent, SwarmRouter, SwarmType
from swarm_models import OpenAIChat

load_dotenv()

# Get the OpenAI API key from the environment variable
api_key = os.getenv("GROQ_API_KEY")

# Model
model = OpenAIChat(
    openai_api_base="https://api.groq.com/openai/v1",
    openai_api_key=api_key,
    model_name="llama-3.1-70b-versatile",
    temperature=0.1,
)

# Define specialized system prompts for each agent
DATA_EXTRACTOR_PROMPT = """You are a highly specialized private equity agent focused on data extraction from various documents. Your expertise includes:
1. Extracting key financial metrics (revenue, EBITDA, growth rates, etc.) from financial statements and reports
2. Identifying and extracting important contract terms from legal documents
3. Pulling out relevant market data from industry reports and analyses
4. Extracting operational KPIs from management presentations and internal reports
5. Identifying and extracting key personnel information from organizational charts and bios
Provide accurate, structured data extracted from various document types to support investment analysis."""

SUMMARIZER_PROMPT = """You are an expert private equity agent specializing in summarizing complex documents. Your core competencies include:
1. Distilling lengthy financial reports into concise executive summaries
2. Summarizing legal documents, highlighting key terms and potential risks
3. Condensing industry reports to capture essential market trends and competitive dynamics
4. Summarizing management presentations to highlight key strategic initiatives and projections
5. Creating brief overviews of technical documents, emphasizing critical points for non-technical stakeholders
Deliver clear, concise summaries that capture the essence of various documents while highlighting information crucial for investment decisions."""

# Initialize specialized agents
data_extractor_agent = Agent(
    agent_name="Data-Extractor",
    system_prompt=DATA_EXTRACTOR_PROMPT,
    llm=model,
    max_loops=1,
    autosave=True,
    verbose=True,
    dynamic_temperature_enabled=True,
    saved_state_path="data_extractor_agent.json",
    user_name="pe_firm",
    retry_attempts=1,
    context_length=200000,
    output_type="string",
)

summarizer_agent = Agent(
    agent_name="Document-Summarizer",
    system_prompt=SUMMARIZER_PROMPT,
    llm=model,
    max_loops=1,
    autosave=True,
    verbose=True,
    dynamic_temperature_enabled=True,
    saved_state_path="summarizer_agent.json",
    user_name="pe_firm",
    retry_attempts=1,
    context_length=200000,
    output_type="string",
)

# Initialize the SwarmRouter
router = SwarmRouter(
    name="pe-document-analysis-swarm",
    description="Analyze documents for private equity due diligence and investment decision-making",
    max_loops=1,
    agents=[data_extractor_agent, summarizer_agent],
    swarm_type="ConcurrentWorkflow",
    autosave=True,
    return_json=True,
)

# Example usage
if __name__ == "__main__":
    # Run a comprehensive private equity document analysis task
    result = router.run(
        "Where is the best place to find template term sheets for series A startups? Provide links and references"
    )
    print(result)

    # Retrieve and print logs
    for log in router.get_logs():
        print(f"{log.timestamp} - {log.level}: {log.message}")

Advanced Usage¶

Changing Swarm Types¶

You can create multiple SwarmRouter instances with different swarm types:

sequential_router = SwarmRouter(
    name="SequentialRouter",
    agents=[agent1, agent2],
    swarm_type="SequentialWorkflow"
)

concurrent_router = SwarmRouter(
    name="ConcurrentRouter",
    agents=[agent1, agent2],
    swarm_type="ConcurrentWorkflow"
)

Automatic Swarm Type Selection¶

You can let the SwarmRouter automatically select the best swarm type for a given task:

auto_router = SwarmRouter(
    name="AutoRouter",
    agents=[agent1, agent2],
    swarm_type="auto"
)

result = auto_router.run("Analyze and summarize the quarterly financial report")

Loading Agents from CSV¶

To load agents from a CSV file:

csv_router = SwarmRouter(
    name="CSVAgentRouter",
    load_agents_from_csv=True,
    csv_file_path="agents.csv",
    swarm_type="SequentialWorkflow"
)

result = csv_router.run("Process the client data")

Using Shared Memory System¶

To enable shared memory across agents:

from swarms.memory import SemanticMemory

memory_system = SemanticMemory()

memory_router = SwarmRouter(
    name="MemoryRouter",
    agents=[agent1, agent2],
    shared_memory_system=memory_system,
    swarm_type="SequentialWorkflow"
)

result = memory_router.run("Analyze historical data and make predictions")

Injecting Rules to All Agents¶

To inject common rules into all agents:

rules = """
1. Always provide sources for your information
2. Check your calculations twice
3. Explain your reasoning clearly
4. Highlight uncertainties and assumptions
"""

rules_router = SwarmRouter(
    name="RulesRouter",
    agents=[agent1, agent2],
    rules=rules,
    swarm_type="SequentialWorkflow"
)

result = rules_router.run("Analyze the investment opportunity")

Use Cases¶

AgentRearrange¶

Use Case: Optimizing agent order for complex multi-step tasks.

rearrange_router = SwarmRouter(
    name="TaskOptimizer",
    description="Optimize agent order for multi-step tasks",
    max_loops=3,
    agents=[data_extractor, analyzer, summarizer],
    swarm_type="AgentRearrange",
    rearrange_flow=f"{data_extractor.name} -> {analyzer.name} -> {summarizer.name}"
)

result = rearrange_router.run("Analyze and summarize the quarterly financial report")

MixtureOfAgents¶

Use Case: Combining diverse expert agents for comprehensive analysis.

mixture_router = SwarmRouter(
    name="ExpertPanel",
    description="Combine insights from various expert agents",
    max_loops=1,
    agents=[financial_expert, market_analyst, tech_specialist, aggregator],
    swarm_type="MixtureOfAgents"
)

result = mixture_router.run("Evaluate the potential acquisition of TechStartup Inc.")

SpreadSheetSwarm¶

Use Case: Collaborative data processing and analysis.

spreadsheet_router = SwarmRouter(
    name="DataProcessor",
    description="Collaborative data processing and analysis",
    max_loops=1,
    agents=[data_cleaner, statistical_analyzer, visualizer],
    swarm_type="SpreadSheetSwarm"
)

result = spreadsheet_router.run("Process and visualize customer churn data")

SequentialWorkflow¶

Use Case: Step-by-step document analysis and report generation.

sequential_router = SwarmRouter(
    name="ReportGenerator",
    description="Generate comprehensive reports sequentially",
    max_loops=1,
    agents=[data_extractor, analyzer, writer, reviewer],
    swarm_type="SequentialWorkflow",
    return_entire_history=True
)

result = sequential_router.run("Create a due diligence report for Project Alpha")

ConcurrentWorkflow¶

Use Case: Parallel processing of multiple data sources.

concurrent_router = SwarmRouter(
    name="MultiSourceAnalyzer",
    description="Analyze multiple data sources concurrently",
    max_loops=1,
    agents=[financial_analyst, market_researcher, competitor_analyst],
    swarm_type="ConcurrentWorkflow",
    output_type="string"
)

result = concurrent_router.run("Conduct a comprehensive market analysis for Product X")

GroupChat¶

Use Case: Simulating a group discussion with multiple agents.

group_chat_router = SwarmRouter(
    name="GroupChat",
    description="Simulate a group discussion with multiple agents",
    max_loops=10,
    agents=[financial_analyst, market_researcher, competitor_analyst],
    swarm_type="GroupChat",
    speaker_fn=custom_speaker_function
)

result = group_chat_router.run("Discuss the pros and cons of expanding into the Asian market")

MultiAgentRouter¶

Use Case: Routing tasks to the most appropriate agent.

multi_agent_router = SwarmRouter(
    name="MultiAgentRouter",
    description="Route tasks to specialized agents",
    max_loops=1,
    agents=[financial_analyst, market_researcher, competitor_analyst],
    swarm_type="MultiAgentRouter",
    shared_memory_system=memory_system
)

result = multi_agent_router.run("Analyze the competitive landscape for our new product")

HierarchicalSwarm¶

Use Case: Creating a hierarchical structure of agents with a director.

hierarchical_router = SwarmRouter(
    name="HierarchicalSwarm",
    description="Hierarchical organization of agents with a director",
    max_loops=3,
    agents=[director, analyst1, analyst2, researcher],
    swarm_type="HiearchicalSwarm",
    return_all_history=True
)

result = hierarchical_router.run("Develop a comprehensive market entry strategy")

MajorityVoting¶

Use Case: Using consensus among multiple agents for decision-making.

voting_router = SwarmRouter(
    name="MajorityVoting",
    description="Make decisions using consensus among agents",
    max_loops=1,
    agents=[analyst1, analyst2, analyst3, consensus_agent],
    swarm_type="MajorityVoting"
)

result = voting_router.run("Should we invest in Company X based on the available data?")

Auto Select (Experimental)¶

Autonomously selects the right swarm by conducting vector search on your input task or name or description or all 3.

auto_router = SwarmRouter(
    name="MultiSourceAnalyzer",
    description="Analyze multiple data sources concurrently",
    max_loops=1,
    agents=[financial_analyst, market_researcher, competitor_analyst],
    swarm_type="auto" # Set this to 'auto' for it to auto select your swarm. It's match words like concurrently multiple -> "ConcurrentWorkflow"
)

result = auto_router.run("Conduct a comprehensive market analysis for Product X")

Advanced Features¶

Processing Documents¶

To process documents with the SwarmRouter:

document_router = SwarmRouter(
    name="DocumentProcessor",
    agents=[document_analyzer, summarizer],
    documents=["report.pdf", "contract.docx", "data.csv"],
    swarm_type="SequentialWorkflow"
)

result = document_router.run("Extract key information from the provided documents")

Batch Processing¶

To process multiple tasks in a batch:

tasks = ["Analyze Q1 report", "Summarize competitor landscape", "Evaluate market trends"]
results = router.batch_run(tasks)

Asynchronous Execution¶

For asynchronous task execution:

result = await router.async_run("Generate financial projections")

Concurrent Execution¶

To run a single task concurrently:

result = router.concurrent_run("Analyze multiple data streams")

Concurrent Batch Processing¶

To process multiple tasks concurrently:

tasks = ["Task 1", "Task 2", "Task 3"]
results = router.concurrent_batch_run(tasks)

Using the SwarmRouter as a Callable¶

You can use the SwarmRouter instance directly as a callable:

router = SwarmRouter(
    name="CallableRouter",
    agents=[agent1, agent2],
    swarm_type="SequentialWorkflow"
)

result = router("Analyze the market data")  # Equivalent to router.run("Analyze the market data")

Using the swarm_router Function¶

For quick one-off tasks, you can use the swarm_router function:

from swarms import swarm_router

result = swarm_router(
    name="QuickRouter",
    agents=[agent1, agent2],
    swarm_type="ConcurrentWorkflow",
    task="Analyze the quarterly report"
)

Best Practices¶

Choose the appropriate swarm type based on your task requirements.
Provide clear and specific tasks to the swarm for optimal results.
Regularly review logs to monitor performance and identify potential issues.
Use descriptive names and descriptions for your SwarmRouter and agents.
Implement proper error handling in your application code.
Consider the nature of your tasks when choosing a swarm type (e.g., use ConcurrentWorkflow for tasks that can be parallelized).
Optimize your agents' prompts and configurations for best performance within the swarm.
Utilize the automatic swarm type selection feature for tasks where the optimal swarm type is not immediately clear.
Take advantage of batch processing and concurrent execution for handling multiple tasks efficiently.
Use the reliability check feature to ensure your SwarmRouter is properly configured before running tasks.
Consider using shared memory systems when agents need to share context or knowledge.
Inject common rules when you want consistent behavior across all agents.
Use the appropriate output type (string, dict, etc.) based on how you plan to process the results.
When working with large documents, provide file paths instead of loading content into memory.
For complex agent interactions, use the GroupChat or HierarchicalSwarm types to facilitate structured communication.