diff --git a/libs/python/agent/agent/__init__.py b/libs/python/agent/agent/__init__.py
index 6797dab6..08d782d3 100644
--- a/libs/python/agent/agent/__init__.py
+++ b/libs/python/agent/agent/__init__.py
@@ -5,7 +5,7 @@ agent - Decorator-based Computer Use Agent with liteLLM integration
 import logging
 import sys
 
-from .decorators import agent_loop
+from .decorators import register_agent
 from .agent import ComputerAgent
 from .types import Messages, AgentResponse
 
@@ -13,7 +13,7 @@ from .types import Messages, AgentResponse
 from . import loops
 
 __all__ = [
-    "agent_loop",
+    "register_agent",
     "ComputerAgent",
     "Messages",
     "AgentResponse"
diff --git a/libs/python/agent/agent/agent.py b/libs/python/agent/agent/agent.py
index f117fe8b..efacea45 100644
--- a/libs/python/agent/agent/agent.py
+++ b/libs/python/agent/agent/agent.py
@@ -616,9 +616,9 @@ class ComputerAgent:
         if "click" not in capabilities:
             raise ValueError(f"Agent loop {self.agent_loop.__name__} does not support click predictions")
         if hasattr(self.agent_loop, 'predict_click'):
-            if not self.computer_handler:
-                raise ValueError("Computer tool is required for predict_click")
             if not image_b64:
+                if not self.computer_handler:
+                    raise ValueError("Computer tool or image_b64 is required for predict_click")
                 image_b64 = await self.computer_handler.screenshot()
             return await self.agent_loop.predict_click(
                 model=self.model,
diff --git a/libs/python/agent/agent/decorators.py b/libs/python/agent/agent/decorators.py
index 7305b702..7fba0443 100644
--- a/libs/python/agent/agent/decorators.py
+++ b/libs/python/agent/agent/decorators.py
@@ -2,13 +2,8 @@
 Decorators for agent - agent_loop decorator
 """
 
-import asyncio
-import inspect
-from typing import Dict, List, Any, Callable, Optional
-from functools import wraps
-
+from typing import List, Optional
 from .types import AgentConfigInfo
-from .loops.base import AsyncAgentConfig
 
 # Global registry
 _agent_configs: List[AgentConfigInfo] = []
diff --git a/libs/python/agent/agent/loops/base.py b/libs/python/agent/agent/loops/base.py
new file mode 100644
index 00000000..887605b1
--- /dev/null
+++ b/libs/python/agent/agent/loops/base.py
@@ -0,0 +1,76 @@
+"""
+Base protocol for async agent configurations
+"""
+
+from typing import Protocol, List, Dict, Any, Optional, Tuple, Union
+from abc import abstractmethod
+from ..types import AgentCapability
+
+class AsyncAgentConfig(Protocol):
+    """Protocol defining the interface for async agent configurations."""
+    
+    @abstractmethod
+    async def predict_step(
+        self,
+        messages: List[Dict[str, Any]],
+        model: str,
+        tools: Optional[List[Dict[str, Any]]] = None,
+        max_retries: Optional[int] = None,
+        stream: bool = False,
+        computer_handler=None,
+        _on_api_start=None,
+        _on_api_end=None,
+        _on_usage=None,
+        _on_screenshot=None,
+        **kwargs
+    ) -> Dict[str, Any]:
+        """
+        Predict the next step based on input items.
+        
+        Args:
+            messages: Input items following Responses format (message, function_call, computer_call)
+            model: Model name to use
+            tools: Optional list of tool schemas
+            max_retries: Maximum number of retries for failed API calls
+            stream: Whether to stream responses
+            computer_handler: Computer handler instance
+            _on_api_start: Callback for API start
+            _on_api_end: Callback for API end
+            _on_usage: Callback for usage tracking
+            _on_screenshot: Callback for screenshot events
+            **kwargs: Additional arguments
+            
+        Returns:
+            Dictionary with "output" (output items) and "usage" array
+        """
+        ...
+    
+    @abstractmethod
+    async def predict_click(
+        self,
+        model: str,
+        image_b64: str,
+        instruction: str
+    ) -> Optional[Tuple[int, int]]:
+        """
+        Predict click coordinates based on image and instruction.
+        
+        Args:
+            model: Model name to use
+            image_b64: Base64 encoded image
+            instruction: Instruction for where to click
+            
+        Returns:
+            None or tuple with (x, y) coordinates
+        """
+        ...
+    
+    @abstractmethod
+    def get_capabilities(self) -> List[AgentCapability]:
+        """
+        Get list of capabilities supported by this agent config.
+        
+        Returns:
+            List of capability strings (e.g., ["step", "click"])
+        """
+        ...
diff --git a/libs/python/agent/agent/loops/gta1.py b/libs/python/agent/agent/loops/gta1.py
new file mode 100644
index 00000000..4d0d3349
--- /dev/null
+++ b/libs/python/agent/agent/loops/gta1.py
@@ -0,0 +1,178 @@
+"""
+GTA1 agent loop implementation for click prediction using litellm.acompletion
+"""
+
+import asyncio
+import json
+import re
+import base64
+from typing import Dict, List, Any, AsyncGenerator, Union, Optional, Tuple
+from io import BytesIO
+from PIL import Image
+import litellm
+
+from ..decorators import register_agent
+from ..types import Messages, AgentResponse, Tools, AgentCapability
+from ..loops.base import AsyncAgentConfig
+
+SYSTEM_PROMPT = '''
+You are an expert UI element locator. Given a GUI image and a user's element description, provide the coordinates of the specified element as a single (x,y) point. The image resolution is height {height} and width {width}. For elements with area, return the center point.
+
+Output the coordinate pair exactly:
+(x,y)
+'''
+
+def extract_coordinates(raw_string: str) -> Tuple[float, float]:
+    """Extract coordinates from model output."""
+    try:
+        matches = re.findall(r"\((-?\d*\.?\d+),\s*(-?\d*\.?\d+)\)", raw_string)
+        return tuple(map(float, matches[0])) # type: ignore
+    except:
+        return (0.0, 0.0)
+
+def smart_resize(height: int, width: int, factor: int = 28, min_pixels: int = 3136, max_pixels: int = 8847360) -> Tuple[int, int]:
+    """Smart resize function similar to qwen_vl_utils."""
+    # Calculate the total pixels
+    total_pixels = height * width
+    
+    # If already within bounds, return original dimensions
+    if min_pixels <= total_pixels <= max_pixels:
+        # Round to nearest factor
+        new_height = (height // factor) * factor
+        new_width = (width // factor) * factor
+        return new_height, new_width
+    
+    # Calculate scaling factor
+    if total_pixels > max_pixels:
+        scale = (max_pixels / total_pixels) ** 0.5
+    else:
+        scale = (min_pixels / total_pixels) ** 0.5
+    
+    # Apply scaling
+    new_height = int(height * scale)
+    new_width = int(width * scale)
+    
+    # Round to nearest factor
+    new_height = (new_height // factor) * factor
+    new_width = (new_width // factor) * factor
+    
+    # Ensure minimum size
+    new_height = max(new_height, factor)
+    new_width = max(new_width, factor)
+    
+    return new_height, new_width
+
+@register_agent(models=r".*GTA1-.*", priority=10)
+class GTA1Config(AsyncAgentConfig):
+    """GTA1 agent configuration implementing AsyncAgentConfig protocol for click prediction."""
+    
+    async def predict_step(
+        self,
+        messages: Messages,
+        model: str,
+        tools: Optional[List[Dict[str, Any]]] = None,
+        max_retries: Optional[int] = None,
+        stream: bool = False,
+        computer_handler=None,
+        use_prompt_caching: Optional[bool] = False,
+        _on_api_start=None,
+        _on_api_end=None,
+        _on_usage=None,
+        _on_screenshot=None,
+        **kwargs
+    ) -> Dict[str, Any]:
+        """
+        GTA1 does not support step prediction - only click prediction.
+        """
+        raise NotImplementedError("GTA1 agent only supports click prediction via predict_click method")
+    
+    async def predict_click(
+        self,
+        model: str,
+        image_b64: str,
+        instruction: str,
+        **kwargs
+    ) -> Optional[Tuple[float, float]]:
+        """
+        Predict click coordinates using GTA1 model via litellm.acompletion.
+        
+        Args:
+            model: The GTA1 model name
+            image_b64: Base64 encoded image
+            instruction: Instruction for where to click
+            
+        Returns:
+            Tuple of (x, y) coordinates or None if prediction fails
+        """
+        try:
+            # Decode base64 image
+            image_data = base64.b64decode(image_b64)
+            image = Image.open(BytesIO(image_data))
+            width, height = image.width, image.height
+            
+            # Smart resize the image (similar to qwen_vl_utils)
+            resized_height, resized_width = smart_resize(
+                height, width, 
+                factor=28,  # Default factor for Qwen models
+                min_pixels=3136,
+                max_pixels=4096 * 2160
+            )
+            resized_image = image.resize((resized_width, resized_height))
+            scale_x, scale_y = width / resized_width, height / resized_height
+            
+            # Convert resized image back to base64
+            buffered = BytesIO()
+            resized_image.save(buffered, format="PNG")
+            resized_image_b64 = base64.b64encode(buffered.getvalue()).decode()
+            
+            # Prepare system and user messages
+            system_message = {
+                "role": "system",
+                "content": SYSTEM_PROMPT.format(height=resized_height, width=resized_width)
+            }
+            
+            user_message = {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image_url",
+                        "image_url": {
+                            "url": f"data:image/png;base64,{resized_image_b64}"
+                        }
+                    },
+                    {
+                        "type": "text",
+                        "text": instruction
+                    }
+                ]
+            }
+            
+            # Prepare API call kwargs
+            api_kwargs = {
+                "model": model,
+                "messages": [system_message, user_message],
+                "max_tokens": 32,
+                "temperature": 0.0,
+                **kwargs
+            }
+            
+            # Use liteLLM acompletion
+            response = await litellm.acompletion(**api_kwargs)
+            
+            # Extract response text
+            output_text = response.choices[0].message.content
+            
+            # Extract and rescale coordinates
+            pred_x, pred_y = extract_coordinates(output_text)
+            pred_x *= scale_x
+            pred_y *= scale_y
+            
+            return (pred_x, pred_y)
+            
+        except Exception as e:
+            print(f"GTA1 click prediction failed: {e}")
+            return None
+    
+    def get_capabilities(self) -> List[AgentCapability]:
+        """Return the capabilities supported by this agent."""
+        return ["click"]