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:

Swarm Type	Description
`AgentRearrange`	Optimizes agent arrangement for task execution
`MixtureOfAgents`	Combines multiple agent types for diverse tasks
`SpreadSheetSwarm`	Uses spreadsheet-like operations for task management
`SequentialWorkflow`	Executes tasks sequentially
`ConcurrentWorkflow`	Executes tasks in parallel
`GroupChat`	Facilitates communication among agents in a group chat format
`MultiAgentRouter`	Routes tasks between multiple agents
`AutoSwarmBuilder`	Automatically builds swarm structure
`HiearchicalSwarm`	Hierarchical organization of agents
`MajorityVoting`	Uses majority voting for decision making
`MALT`	Multi-Agent Language Tasks
`DeepResearchSwarm`	Specialized for deep research tasks
`CouncilAsAJudge`	Council-based judgment system
`InteractiveGroupChat`	Interactive group chat with user participation
`auto`	Automatically selects best swarm type via embedding search

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.

SwarmRouterConfig¶

Configuration model for SwarmRouter.

Attribute	Type	Description
`name`	str	Name identifier for the SwarmRouter instance
`description`	str	Description of the SwarmRouter's purpose
`swarm_type`	SwarmType	Type of swarm to use
`rearrange_flow`	Optional[str]	Flow configuration string
`rules`	Optional[str]	Rules to inject into every agent
`multi_agent_collab_prompt`	bool	Whether to enable multi-agent collaboration prompts
`task`	str	The task to be executed by the swarm

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's purpose
`max_loops`	int	Maximum number of loops to perform
`agents`	List[Union[Agent, Callable]]	List of Agent objects or callable functions
`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", "list", "json", "yaml", "xml")
`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
`multi_agent_collab_prompt`	bool	Whether to enable multi-agent collaboration prompts

Methods:¶

Method	Parameters	Description
`__init__`	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, multi_agent_collab_prompt: bool = True	Initialize the SwarmRouter
`setup`	None	Set up the SwarmRouter by activating APE and handling shared memory and rules
`activate_shared_memory`	None	Activate shared memory with all agents
`handle_rules`	None	Inject rules to every agent
`activate_ape`	None	Activate automatic prompt engineering for agents that support it
`reliability_check`	None	Perform reliability checks on the SwarmRouter configuration
`_create_swarm`	`task: str = None, args, *kwargs`	Create and return the specified swarm type
`update_system_prompt_for_agent_in_swarm`	None	Update system prompts for all agents with collaboration prompts
`_log`	`level: str, message: str, task: str = "", metadata: Dict[str, Any] = None`	Create a log entry
`_run`	`task: str, img: Optional[str] = None, model_response: Optional[str] = None, args, *kwargs`	Run the specified task on the selected swarm type
`run`	`task: str, img: Optional[str] = None, model_response: Optional[str] = None, args, *kwargs`	Execute a task on the selected swarm type
`__call__`	`task: str, args, *kwargs`	Make the SwarmRouter instance callable
`batch_run`	`tasks: List[str], args, *kwargs`	Execute multiple tasks in sequence
`async_run`	`task: str, args, *kwargs`	Execute a task asynchronously
`get_logs`	None	Retrieve all logged entries
`concurrent_run`	`task: str, args, *kwargs`	Execute a task using concurrent execution
`concurrent_batch_run`	`tasks: List[str], args, *kwargs`	Execute multiple tasks concurrently

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")

InteractiveGroupChat¶

Use Case: Interactive group discussions with user participation.

interactive_chat_router = SwarmRouter(
    name="InteractiveGroupChat",
    description="Interactive group chat with user participation",
    max_loops=10,
    agents=[financial_analyst, market_researcher, competitor_analyst],
    swarm_type="InteractiveGroupChat",
    output_type="string"
)

result = interactive_chat_router.run("Discuss the market trends and provide interactive analysis")

The InteractiveGroupChat allows for dynamic interaction between agents and users, enabling real-time participation in group discussions and decision-making processes. This is particularly useful for scenarios requiring human input or validation during the conversation flow.

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"
)