> ## Documentation Index
> Fetch the complete documentation index at: https://docs.openhands.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Sub-Agent Delegation

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

export const path_to_script_0 = "examples/01_standalone_sdk/25_agent_delegation.py"

> A ready-to-run example is available [here](#ready-to-run-example)!

## 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

## 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 icon="python" wrap 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 icon="python" wrap 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

<Steps>
  <Step>
    ### Register the Tool

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

    register_tool("DelegateTool", DelegateTool)
    ```
  </Step>

  <Step>
    ### Add to Agent Tools

    ```python icon="python" wrap 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)
    ```
  </Step>

  <Step>
    ### Configure Maximum Sub-Agents (Optional)

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

    ```python icon="python" wrap 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)
    ```
  </Step>
</Steps>

## 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 icon="python" wrap 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 icon="python" wrap 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"
    }
}
```

## Ready-to-run Example

<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 openhands.sdk import (
    LLM,
    Agent,
    AgentContext,
    Conversation,
    Tool,
    get_logger,
)
from openhands.sdk.context import Skill
from openhands.sdk.subagent import register_agent
from openhands.sdk.tool import register_tool
from openhands.tools.delegate import (
    DelegateTool,
    DelegationVisualizer,
)
from openhands.tools.preset.default import get_default_tools, register_builtins_agents


ONLY_RUN_SIMPLE_DELEGATION = False

logger = get_logger(__name__)

# Configure LLM and agent
llm = LLM(
    model=os.getenv("LLM_MODEL", "anthropic/claude-sonnet-4-5-20250929"),
    api_key=os.getenv("LLM_API_KEY"),
    base_url=os.environ.get("LLM_BASE_URL", None),
    usage_id="agent",
)

cwd = os.getcwd()

tools = get_default_tools(enable_browser=True)
tools.append(Tool(name=DelegateTool.name))
register_builtins_agents()

main_agent = Agent(
    llm=llm,
    tools=tools,
)
conversation = Conversation(
    agent=main_agent,
    workspace=cwd,
    visualizer=DelegationVisualizer(name="Delegator"),
)

conversation.send_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.run()

conversation.send_message(
    "Ask the lodging sub-agent what it thinks about Covent Garden."
)
conversation.run()

# Report cost for simple delegation example
cost_simple = conversation.conversation_stats.get_combined_metrics().accumulated_cost
print(f"EXAMPLE_COST (simple delegation): {cost_simple}")

print("Simple delegation example done!", "\n" * 20)

if ONLY_RUN_SIMPLE_DELEGATION:
    # For CI: always emit the EXAMPLE_COST marker before exiting.
    print(f"EXAMPLE_COST: {cost_simple}")
    exit(0)


# -------- Agent Delegation Second Part: Built-in Agent Types (Explore + Bash) --------

main_agent = Agent(
    llm=llm,
    tools=[Tool(name=DelegateTool.name)],
)
conversation = Conversation(
    agent=main_agent,
    workspace=cwd,
    visualizer=DelegationVisualizer(name="Delegator (builtins)"),
)

builtin_task_message = (
    "Demonstrate SDK built-in sub-agent types. "
    "1) Spawn an 'explore' sub-agent and ask it to list the markdown files in "
    "openhands-sdk/openhands/sdk/subagent/builtins/ and summarize what each "
    "built-in agent type is for (based on the file contents). "
    "2) Spawn a 'bash' sub-agent and ask it to run `python --version` in the "
    "terminal and return the exact output. "
    "3) Merge both results into a short report. "
    "Do not use internet access."
)

print("=" * 100)
print("Demonstrating built-in agent delegation (explore + bash)...")
print("=" * 100)

conversation.send_message(builtin_task_message)
conversation.run()

# Report cost for builtin agent types example
cost_builtin = conversation.conversation_stats.get_combined_metrics().accumulated_cost
print(f"EXAMPLE_COST (builtin agents): {cost_builtin}")

print("Built-in agent delegation example done!", "\n" * 20)


# -------- Agent Delegation Third Part: User-Defined Agent Types --------


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

print("All done!")

# Full example cost report for CI workflow
print(f"EXAMPLE_COST: {cost_simple + cost_builtin + cost_user_defined}")
```

You can run the example code as-is.

<Note>
  The model name should follow the [LiteLLM convention](https://models.litellm.ai/): `provider/model_name` (e.g., `anthropic/claude-sonnet-4-5-20250929`, `openai/gpt-4o`).
  The `LLM_API_KEY` should be the API key for your chosen provider.
</Note>

<CodeGroup>
  <CodeBlock language="bash" filename="Bring-your-own provider key" icon="terminal" wrap>
    {`export LLM_API_KEY="your-api-key"\nexport LLM_MODEL="anthropic/claude-sonnet-4-5-20250929"  # or openai/gpt-4o, etc.\ncd software-agent-sdk\nuv run python ${path_to_script_0}`}
  </CodeBlock>

  <CodeBlock language="bash" filename="OpenHands Cloud" icon="terminal" wrap>
    {`# https://app.all-hands.dev/settings/api-keys\nexport LLM_API_KEY="your-openhands-api-key"\nexport LLM_MODEL="openhands/claude-sonnet-4-5-20250929"\ncd software-agent-sdk\nuv run python ${path_to_script_0}`}
  </CodeBlock>
</CodeGroup>

<Tip>
  **ChatGPT Plus/Pro subscribers**: You can use `LLM.subscription_login()` to authenticate with your ChatGPT account and access Codex models without consuming API credits. See the [LLM Subscriptions guide](/sdk/guides/llm-subscriptions) for details.
</Tip>