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Tools return results using ActionResult or simple strings.

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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"],
    )

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ActionResult Properties

  • extracted_content (default: 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: None) - Task success (only valid with is_done=True)
  • attachments (default: None) - Files to show user
  • metadata (default: None) - Debug/observability data

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Why extracted_content and long_term_memory?

With this you control the context for the LLM.

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1. Include short content always in context

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

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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.

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3. Dont show long content, remember subset in context

return ActionResult(
    extracted_content="[500 lines of product data...]",      # The LLM never sees this because `long_term_memory` overrides it and `include_extracted_content_only_once` is not used
    long_term_memory="Saved user's favorite products",      # This is shown to the LLM in future steps
)

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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 
    )