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Module praisonaiagents.handoff

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Classes

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Handoff

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Parameters

• agent: 'Agent' - The target agent to hand off to
• tool_name_override: Optional[str] = None - Custom tool name (defaults to transfer_to_<agent_name>)
• tool_description_override: Optional[str] = None - Custom tool description for the handoff
• on_handoff: Optional[Callable] = None - Callback function executed during handoff
• input_type: Optional[type] = None - Type annotation for structured input data
• input_filter: Optional[Callable[[HandoffInputData], HandoffInputData]] = None - Function to transform input before passing to target

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Methods

• to_tool_function(self, source_agent: 'Agent') → Callable - Converts the handoff configuration into a callable tool function

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HandoffInputData

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Fields

• messages: List[Message] - List of conversation history
• context: Dict[str, Any] - Dictionary containing additional context (e.g., source agent name)

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Functions

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handoff

def handoff(
    agent: 'Agent',
    tool_name_override: Optional[str] = None,
    tool_description_override: Optional[str] = None,
    on_handoff: Optional[Callable] = None,
    input_type: Optional[type] = None,
    input_filter: Optional[Callable[[HandoffInputData], HandoffInputData]] = None
) -> Handoff

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Usage Examples

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Basic Handoff

from praisonaiagents import Agent

# Create specialised agents
billing_agent = Agent(
    name="Billing Agent",
    role="Handle billing inquiries",
    goal="Resolve billing issues"
)

refund_agent = Agent(
    name="Refund Agent",
    role="Process refunds",
    goal="Handle refund requests"
)

# Create triage agent with handoffs
triage_agent = Agent(
    name="Triage Agent",
    role="Route customer inquiries",
    goal="Direct customers to the right specialist",
    handoffs=[billing_agent, refund_agent]
)

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Advanced Handoff with Callbacks

from praisonaiagents import Agent, handoff

def log_handoff(source_agent: Agent, input_data: HandoffInputData):
    print(f"Handoff from {source_agent.name} with {len(input_data.messages)} messages")

support_agent = Agent(
    name="Support Agent",
    handoffs=[
        handoff(
            escalation_agent,
            tool_name_override="escalate_to_manager",
            tool_description_override="Escalate complex issues to a manager",
            on_handoff=log_handoff
        )
    ]
)

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Handoff with Input Filtering

from praisonaiagents import handoff, handoff_filters

# Remove tool calls from conversation history
handoff_with_filter = handoff(
    technical_agent,
    input_filter=handoff_filters.remove_all_tools
)

# Keep only last 5 messages
handoff_with_limit = handoff(
    summary_agent,
    input_filter=handoff_filters.keep_last_n_messages(5)
)

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Structured Input Handoff

from pydantic import BaseModel
from praisonaiagents import Agent, handoff

class EscalationData(BaseModel):
    priority: str
    reason: str
    customer_id: str

escalation_agent = Agent(
    name="Escalation Handler",
    handoffs=[
        handoff(
            manager_agent,
            input_type=EscalationData,
            tool_description_override="Escalate with structured priority data"
        )
    ]
)

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Callback Patterns

1. No parameters - Simple notification
   def notify_handoff():
       logging.info("Handoff occurred")
   
2. One parameter - Receives source agent
   def log_source(source_agent: Agent):
       print(f"Handoff from: {source_agent.name}")
   
3. Two parameters - Receives source agent and input data
   def process_handoff(source_agent: Agent, input_data: HandoffInputData):
       # Access full context and messages
       print(f"Agent: {source_agent.name}")
       print(f"Messages: {len(input_data.messages)}")
   

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Built-in Input Filters

• remove_all_tools - Removes all tool calls from message history
• keep_last_n_messages(n) - Keeps only the last n messages
• remove_system_messages - Removes system messages from history

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How It Works

1. Configuration Phase:
   • Agents are created with a handoffs parameter containing target agents
   • The handoff module converts these into callable tool functions
   • Tools are registered with the agent’s LLM for function calling
2. Runtime Delegation:
   • When an agent determines delegation is needed, it calls the handoff tool
   • The handoff executes any configured callbacks
   • Conversation history is collected and filtered
   • The target agent receives the context and continues the conversation
   • The response is returned to the original caller
3. Context Preservation:
   • Full conversation history is maintained
   • Source agent identification is preserved
   • Custom context can be passed via structured inputs

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Best Practices

1. Design Clear Agent Boundaries - Each agent should have a specific expertise
2. Use Descriptive Names - Tool names should clearly indicate the target agent’s role
3. Implement Callbacks - Use callbacks for logging, monitoring, or custom logic
4. Filter Appropriately - Remove unnecessary context to stay within token limits
5. Test Handoff Chains - Ensure agents don’t create infinite delegation loops