ai-agent-book/chapter9/browser-use-rpa/browser-use/docs/customize/tools/response.mdx

---
title: "Tool Response"
description: ""
icon: "arrow-turn-down-left"
mode: "wide"
---

Tools return results using `ActionResult` or simple strings.

## Return Types

```python
@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: `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

## Why `extracted_content` and `long_term_memory`?
With this you control the context for the LLM.

### 1. Include short content always in context
```python
def simple_tool() -> str:
    return "Hello, world!"  # Keep in context for all future steps 
```

### 2. Show long content once, remember subset in context
```python
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. Dont show long content, remember subset in context
```python
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
)
```

## Terminating the Agent

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

```python
@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 
    )
```