Agent Multi-Agent Communication Methods

The Agent class provides powerful built-in methods for facilitating communication and collaboration between multiple agents. These methods enable agents to talk to each other, pass information, and coordinate complex multi-agent workflows seamlessly.

Overview

Multi-agent communication is essential for building sophisticated AI systems where different agents need to collaborate, share information, and coordinate their actions. The Agent class provides several methods to facilitate this communication:

Method	Purpose	Use Case
talk_to	Direct communication between two agents	Agent handoffs, expert consultation
talk_to_multiple_agents	Concurrent communication with multiple agents	Broadcasting, consensus building
receive_message	Process incoming messages from other agents	Message handling, task delegation
send_agent_message	Send formatted messages to other agents	Direct messaging, notifications

Features

Feature	Description
Direct Agent Communication	Enable one-to-one conversations between agents
Concurrent Multi-Agent Communication	Broadcast messages to multiple agents simultaneously
Message Processing	Handle incoming messages with contextual formatting
Error Handling	Robust error handling for failed communications
Threading Support	Efficient concurrent processing using ThreadPoolExecutor
Flexible Parameters	Support for images, custom arguments, and kwargs

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Core Methods

talk_to(agent, task, img=None, *args, **kwargs)

Enables direct communication between the current agent and another agent. The method processes the task, generates a response, and then passes that response to the target agent.

Parameters:

Parameter	Type	Default	Description
agent	Any	Required	The target agent instance to communicate with
task	str	Required	The task or message to send to the agent
img	str	None	Optional image path for multimodal communication
*args	Any	-	Additional positional arguments
**kwargs	Any	-	Additional keyword arguments

Returns: Any - The response from the target agent

Usage Example:

from swarms import Agent

# Create two specialized agents
researcher = Agent(
    agent_name="Research-Agent",
    system_prompt="You are a research specialist focused on gathering and analyzing information.",
    max_loops=1,
)

analyst = Agent(
    agent_name="Analysis-Agent", 
    system_prompt="You are an analytical specialist focused on interpreting research data.",
    max_loops=1,
)

# Agent communication
research_result = researcher.talk_to(
    agent=analyst,
    task="Analyze the market trends for renewable energy stocks"
)

print(research_result)

talk_to_multiple_agents(agents, task, *args, **kwargs)

Enables concurrent communication with multiple agents using ThreadPoolExecutor for efficient parallel processing.

Parameters:

Parameter	Type	Default	Description
agents	List[Union[Any, Callable]]	Required	List of agent instances to communicate with
task	str	Required	The task or message to send to all agents
*args	Any	-	Additional positional arguments
**kwargs	Any	-	Additional keyword arguments

Returns: List[Any] - List of responses from all agents (or None for failed communications)

Usage Example:

from swarms import Agent

# Create multiple specialized agents
agents = [
    Agent(
        agent_name="Financial-Analyst",
        system_prompt="You are a financial analysis expert.",
        max_loops=1,
    ),
    Agent(
        agent_name="Risk-Assessor", 
        system_prompt="You are a risk assessment specialist.",
        max_loops=1,
    ),
    Agent(
        agent_name="Market-Researcher",
        system_prompt="You are a market research expert.",
        max_loops=1,
    )
]

coordinator = Agent(
    agent_name="Coordinator-Agent",
    system_prompt="You coordinate multi-agent analysis.",
    max_loops=1,
)

# Broadcast to multiple agents
responses = coordinator.talk_to_multiple_agents(
    agents=agents,
    task="Evaluate the investment potential of Tesla stock"
)

# Process responses
for i, response in enumerate(responses):
    if response:
        print(f"Agent {i+1} Response: {response}")
    else:
        print(f"Agent {i+1} failed to respond")

receive_message(agent_name, task, *args, **kwargs)

Processes incoming messages from other agents with proper context formatting.

Parameters:

Parameter	Type	Default	Description
agent_name	str	Required	Name of the sending agent
task	str	Required	The message content
*args	Any	-	Additional positional arguments
**kwargs	Any	-	Additional keyword arguments

Returns: Any - The agent's response to the received message

Usage Example:

from swarms import Agent

# Create an agent that can receive messages
recipient_agent = Agent(
    agent_name="Support-Agent",
    system_prompt="You provide helpful support and assistance.",
    max_loops=1,
)

# Simulate receiving a message from another agent
response = recipient_agent.receive_message(
    agent_name="Customer-Service-Agent",
    task="A customer is asking about refund policies. Can you help?"
)

print(response)

send_agent_message(agent_name, message, *args, **kwargs)

Sends a formatted message from the current agent to a specified target agent.

Parameters:

Parameter	Type	Default	Description
agent_name	str	Required	Name of the target agent
message	str	Required	The message to send
*args	Any	-	Additional positional arguments
**kwargs	Any	-	Additional keyword arguments

Returns: Any - The result of sending the message

Usage Example:

from swarms import Agent

sender_agent = Agent(
    agent_name="Notification-Agent",
    system_prompt="You send notifications and updates.",
    max_loops=1,
)

# Send a message to another agent
result = sender_agent.send_agent_message(
    agent_name="Task-Manager-Agent",
    message="Task XYZ has been completed successfully"
)

print(result)

This comprehensive guide covers all aspects of multi-agent communication using the Agent class methods. These methods provide the foundation for building sophisticated multi-agent systems with robust communication capabilities.