# Sub-Agent Delegation

> Enable parallel task execution by delegating work to multiple sub-agents that run independently and return consolidated results.

## Overview

Agent delegation allows a main agent to spawn multiple sub-agents and delegate tasks to them for parallel processing. Each sub-agent runs independently with its own conversation context and returns results that the main agent can consolidate and process further.

This pattern is useful when:

* Breaking down complex problems into independent subtasks
* Processing multiple related tasks in parallel
* Separating concerns between different specialized sub-agents
* Improving throughput for parallelizable work

## Quick Start

<Note>
  This example is available on GitHub: [examples/01\_standalone\_sdk/25\_agent\_delegation.py](https://github.com/OpenHands/software-agent-sdk/blob/main/examples/01_standalone_sdk/25_agent_delegation.py)
</Note>

```python icon="python" expandable examples/01_standalone_sdk/25_agent_delegation.py theme={null}
"""
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_1 = conversation.conversation_stats.get_combined_metrics().accumulated_cost
print(f"EXAMPLE_COST (simple delegation): {cost_1}")

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_2 = conversation.conversation_stats.get_combined_metrics().accumulated_cost
print(f"EXAMPLE_COST (user-defined agents): {cost_2}")

print("All done!")

# Full example cost report for CI workflow
print(f"EXAMPLE_COST: {cost_1 + cost_2}")
```

```bash Running the Example theme={null}
export LLM_API_KEY="your-api-key"
cd agent-sdk
uv run python examples/01_standalone_sdk/25_agent_delegation.py
```

## How It Works

The delegation system consists of two main operations:

### 1. Spawning Sub-Agents

Before delegating work, the agent must first spawn sub-agents with meaningful identifiers:

```python  theme={null}
# Agent uses the delegate tool to spawn sub-agents
{
    "command": "spawn",
    "ids": ["lodging", "activities"]
}
```

Each spawned sub-agent:

* Gets a unique identifier that the agent specify (e.g., "lodging", "activities")
* Inherits the same LLM configuration as the parent agent
* Operates in the same workspace as the main agent
* Maintains its own independent conversation context

### 2. Delegating Tasks

Once sub-agents are spawned, the agent can delegate tasks to them:

```python  theme={null}
# Agent uses the delegate tool to assign tasks
{
    "command": "delegate",
    "tasks": {
        "lodging": "Find the best budget-friendly areas to stay in London",
        "activities": "List top 5 must-see attractions and hidden gems in London"
    }
}
```

The delegate operation:

* Runs all sub-agent tasks in parallel using threads
* Blocks until all sub-agents complete their work
* Returns a single consolidated observation with all results
* Handles errors gracefully and reports them per sub-agent

## Setting Up the DelegateTool

### 1. Register the Tool

```python  theme={null}
from openhands.sdk.tool import register_tool
from openhands.tools.delegate import DelegateTool

register_tool("DelegateTool", DelegateTool)
```

### 2. Add to Agent Tools

```python  theme={null}
from openhands.sdk import Tool
from openhands.tools.preset.default import get_default_tools

tools = get_default_tools(enable_browser=False)
tools.append(Tool(name="DelegateTool"))

agent = Agent(llm=llm, tools=tools)
```

### 3. Configure Maximum Sub-Agents (Optional)

The user can limit the maximum number of concurrent sub-agents:

```python  theme={null}
from openhands.tools.delegate import DelegateTool

class CustomDelegateTool(DelegateTool):
    @classmethod
    def create(cls, conv_state, max_children: int = 3):
        # Only allow up to 3 sub-agents
        return super().create(conv_state, max_children=max_children)

register_tool("DelegateTool", CustomDelegateTool)
```

## Tool Commands

### spawn

Initialize sub-agents with meaningful identifiers.

**Parameters:**

* `command`: `"spawn"`
* `ids`: List of string identifiers (e.g., `["research", "implementation", "testing"]`)

**Returns:**
A message indicating the sub-agents were successfully spawned.

**Example:**

```python  theme={null}
{
    "command": "spawn",
    "ids": ["research", "implementation", "testing"]
}
```

### delegate

Send tasks to specific sub-agents and wait for results.

**Parameters:**

* `command`: `"delegate"`
* `tasks`: Dictionary mapping sub-agent IDs to task descriptions

**Returns:**
A consolidated message containing all results from the sub-agents.

**Example:**

```python  theme={null}
{
    "command": "delegate",
    "tasks": {
        "research": "Find best practices for async code",
        "implementation": "Refactor the MyClass class",
        "testing": "Write unit tests for the refactored code"
    }
}
```


---

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