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In the following sections, we’ll look at some common use cases for creating custom communication flows and how to implement them in Agency Swarm.

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1. Adjusting Parameters and Descriptions

Customize parameters by subclassing SendMessage and declaring extra fields via a Pydantic model. Avoid mutating JSON schema directly: 
from pydantic import BaseModel, Field
from agency_swarm.tools.send_message import SendMessage

class SendMessageTask(SendMessage):
    """Use this tool to send tasks to other agents within your agency."""

    class ExtraParams(BaseModel):
        chain_of_thought: str = Field(
            description=(
                "Think step-by-step about how to solve your current task, then break it down into smaller steps for the recipient agent."
            )
        )
        task: str = Field(
            description=(
                "Specify the task required for the recipient agent to complete. Focus on clarifying what the task entails rather than providing exact instructions."
            )
        )

    def __init__(self, sender_agent, recipients=None):
        super().__init__(sender_agent, recipients)
        self.name = "send_message_task"

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2. Adding Additional Fields

You can add extra fields to capture more context, like key moments and decisions: 
from pydantic import BaseModel, Field
from agency_swarm.tools.send_message import SendMessage

class SendMessageWithContext(SendMessage):
    """SendMessage with key moments and decisions tracking."""

    class ExtraParams(BaseModel):
        key_moments: str = Field(
            description=(
                "Document critical moments and decision points from the current conversation "
                "that the recipient agent needs to understand. Include context about what "
                "has been decided or prioritized that will guide the recipient's tool selection "
                "and task execution. For example: 'User decided to prioritize performance over cost', "
                "'Analysis focus shifted to Q4 optimization', etc."
            )
        )
        decisions: str = Field(
            description=(
                "Summarize the specific decisions made that will directly impact which tools "
                "or approaches the recipient agent should use. Be explicit about choices that "
                "narrow down the scope of work. For example: 'Prioritized performance analysis "
                "over cost reduction', 'Selected React over Vue for frontend', etc. This helps "
                "the recipient agent choose the most appropriate tools and approach."
            )
        )

    def __init__(self, sender_agent, recipients=None):
        super().__init__(sender_agent, recipients)
        self.name = "send_message_with_context"
Usage: 
from agency_swarm import Agency, Agent

agent = Agent(
    name="MyAgent",
    instructions="You are a helpful assistant.",
    send_message_tool_class=SendMessageWithContext,  # Use your custom class
)

agency = Agency(agent, communication_flows=[(agent, other_agent)]))
If you have any ideas for new communication flows, please either adjust this page in docs, or add your new send message tool in the agency_swarm/tools/send_message folder and open a PR!