"""
Agent Delegation Example
This example demonstrates the agent delegation feature where a main agent
delegates tasks to sub-agents for parallel processing.
Each sub-agent runs independently and returns its results to the main agent,
which then merges both analyses into a single consolidated report.
"""
import os
from pydantic import SecretStr
from openhands.sdk import (
LLM,
Agent,
AgentContext,
Conversation,
Tool,
get_logger,
)
from openhands.sdk.context import Skill
from openhands.sdk.tool import register_tool
from openhands.tools.delegate import (
DelegateTool,
DelegationVisualizer,
register_agent,
)
from openhands.tools.preset.default import get_default_tools
ONLY_RUN_SIMPLE_DELEGATION = False
logger = get_logger(__name__)
# Configure LLM and agent
# You can get an API key from https://app.all-hands.dev/settings/api-keys
api_key = os.getenv("LLM_API_KEY")
assert api_key is not None, "LLM_API_KEY environment variable is not set."
model = os.getenv("LLM_MODEL", "anthropic/claude-sonnet-4-5-20250929")
llm = LLM(
model=model,
api_key=SecretStr(api_key),
base_url=os.environ.get("LLM_BASE_URL", None),
usage_id="agent",
)
cwd = os.getcwd()
register_tool("DelegateTool", DelegateTool)
tools = get_default_tools(enable_browser=False)
tools.append(Tool(name="DelegateTool"))
main_agent = Agent(
llm=llm,
tools=tools,
)
conversation = Conversation(
agent=main_agent,
workspace=cwd,
visualizer=DelegationVisualizer(name="Delegator"),
)
task_message = (
"Forget about coding. Let's switch to travel planning. "
"Let's plan a trip to London. I have two issues I need to solve: "
"Lodging: what are the best areas to stay at while keeping budget in mind? "
"Activities: what are the top 5 must-see attractions and hidden gems? "
"Please use the delegation tools to handle these two tasks in parallel. "
"Make sure the sub-agents use their own knowledge "
"and dont rely on internet access. "
"They should keep it short. After getting the results, merge both analyses "
"into a single consolidated report.\n\n"
)
conversation.send_message(task_message)
conversation.run()
conversation.send_message(
"Ask the lodging sub-agent what it thinks about Covent Garden."
)
conversation.run()
# Report cost for simple delegation example
cost = conversation.conversation_stats.get_combined_metrics().accumulated_cost
print(f"EXAMPLE_COST (simple delegation): {cost}")
print("Simple delegation example done!", "\n" * 20)
# -------- Agent Delegation Second Part: User-Defined Agent Types --------
if ONLY_RUN_SIMPLE_DELEGATION:
exit(0)
def create_lodging_planner(llm: LLM) -> Agent:
"""Create a lodging planner focused on London stays."""
skills = [
Skill(
name="lodging_planning",
content=(
"You specialize in finding great places to stay in London. "
"Provide 3-4 hotel recommendations with neighborhoods, quick "
"pros/cons, "
"and notes on transit convenience. Keep options varied by budget."
),
trigger=None,
)
]
return Agent(
llm=llm,
tools=[],
agent_context=AgentContext(
skills=skills,
system_message_suffix="Focus only on London lodging recommendations.",
),
)
def create_activities_planner(llm: LLM) -> Agent:
"""Create an activities planner focused on London itineraries."""
skills = [
Skill(
name="activities_planning",
content=(
"You design concise London itineraries. Suggest 2-3 daily "
"highlights, grouped by proximity to minimize travel time. "
"Include food/coffee stops "
"and note required tickets/reservations."
),
trigger=None,
)
]
return Agent(
llm=llm,
tools=[],
agent_context=AgentContext(
skills=skills,
system_message_suffix="Plan practical, time-efficient days in London.",
),
)
# Register user-defined agent types (default agent type is always available)
register_agent(
name="lodging_planner",
factory_func=create_lodging_planner,
description="Finds London lodging options with transit-friendly picks.",
)
register_agent(
name="activities_planner",
factory_func=create_activities_planner,
description="Creates time-efficient London activity itineraries.",
)
# Make the delegation tool available to the main agent
register_tool("DelegateTool", DelegateTool)
main_agent = Agent(
llm=llm,
tools=[Tool(name="DelegateTool")],
)
conversation = Conversation(
agent=main_agent,
workspace=cwd,
visualizer=DelegationVisualizer(name="Delegator"),
)
task_message = (
"Plan a 3-day London trip. "
"1) Spawn two sub-agents: lodging_planner (hotel options) and "
"activities_planner (itinerary). "
"2) Ask lodging_planner for 3-4 central London hotel recommendations with "
"neighborhoods, quick pros/cons, and transit notes by budget. "
"3) Ask activities_planner for a concise 3-day itinerary with nearby stops, "
" food/coffee suggestions, and any ticket/reservation notes. "
"4) Share both sub-agent results and propose a combined plan."
)
print("=" * 100)
print("Demonstrating London trip delegation (lodging + activities)...")
print("=" * 100)
conversation.send_message(task_message)
conversation.run()
conversation.send_message(
"Ask the lodging sub-agent what it thinks about Covent Garden."
)
conversation.run()
# Report cost for user-defined agent types example
cost = conversation.conversation_stats.get_combined_metrics().accumulated_cost
print(f"EXAMPLE_COST (user-defined agents): {cost}")
print("All done!")
