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Tool Response Format

Tools return results using ActionResult or simple strings.

Return Types

@tools.action('My tool')
def my_tool() -> str:
    return "Task completed successfully"

@tools.action('Advanced tool')
def advanced_tool() -> ActionResult:
    return ActionResult(
        extracted_content="Main result",
        long_term_memory="Remember this info",
        error="Something went wrong",
        is_done=True,
        success=True,
        attachments=["file.pdf"],
    )

ActionResult Properties

• extracted_content (default: str or None) - Main result passed to LLM, this is equivalent to returning a string.
• include_extracted_content_only_once (default: False) - Set to True for large content to include it only once in the LLM input.
• long_term_memory (default: None) - This is always included in the LLM input for all future steps.
• error (default: None) - Error message, we catch exceptions and set this automatically. This is always included in the LLM input.
• is_done (default: False) - Tool completes entire task.
• success (default: True) - Task success (only valid with is_done=True).
• attachments (default: None) - Files to show user.
• metadata (default: None) - Debug/observability data.

Why extracted_content and long_term_memory?

With this you control the context for the LLM.

1. Include short content always in context

def simple_tool() -> str:
    return "Hello, world!"  # Keep in context for all future steps

2. Show long content once, remember subset in context

return ActionResult(
    extracted_content="[500 lines of product data...]",  # Shows to LLM once
    include_extracted_content_only_once=True,  # Never show full output again
    long_term_memory="Found 50 products"  # Only this in future steps
)

We save the full extracted_content to files which the LLM can read in future steps.

3. Don't show long content, remember subset in context

return ActionResult(
    extracted_content="[500 lines of product data...]",  # The LLM never sees this
    long_term_memory="Saved user's favorite products",  # This is shown to the LLM in future steps
)

Terminating the Agent

Set is_done=True to stop the agent completely. Use when your tool finishes the entire task:

@tools.action(description='Complete the task')
def finish_task() -> ActionResult:
    return ActionResult(
        extracted_content="Task completed!",
        is_done=True,  # Stops the agent
        success=True  # Task succeeded
    )

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