diff --git a/gui_agents/s1/core/ProceduralMemory.py b/gui_agents/s1/core/ProceduralMemory.py
index 72f2dc53..5a45c503 100644
--- a/gui_agents/s1/core/ProceduralMemory.py
+++ b/gui_agents/s1/core/ProceduralMemory.py
@@ -5,8 +5,7 @@
 class PROCEDURAL_MEMORY:
     @staticmethod
     def construct_worker_procedural_memory(agent_class):
-        procedural_memory = textwrap.dedent(
-            f"""\
+        procedural_memory = textwrap.dedent(f"""\
         You are an expert in graphical user interfaces and Python code. You are responsible for executing the current subtask: `SUBTASK_DESCRIPTION` of the larger goal: `TASK_DESCRIPTION`.
         IMPORTANT: ** The subtasks: ['DONE_TASKS'] have already been done. The future subtasks ['FUTURE_TASKS'] will be done in the future by me. You must only perform the current subtask: `SUBTASK_DESCRIPTION`. Do not try to do future subtasks. **
         You are working in CURRENT_OS. You must only complete the subtask provided and not the larger goal.
@@ -16,8 +15,7 @@ def construct_worker_procedural_memory(agent_class):
         3. The history of your previous interactions with the UI.
         4. Access to the following class and methods to interact with the UI:
         class Agent:
-        """
-        )
+        """)
 
         for attr_name in dir(agent_class):
             attr = getattr(agent_class, attr_name)
@@ -29,8 +27,7 @@ def {attr_name}{signature}:
     '''{attr.__doc__}'''
         """
 
-        procedural_memory += textwrap.dedent(
-            """
+        procedural_memory += textwrap.dedent("""
         Your response should be formatted like this:
         (Previous action verification)
         Carefully analyze based on the screenshot and the accessibility tree if the previous action was successful. If the previous action was not successful, provide a reason for the failure.
@@ -57,8 +54,7 @@ def {attr_name}{signature}:
         7. Do not do anything other than the exact specified task. Return with `agent.done()` immediately after the task is completed or `agent.fail()` if it cannot be completed.
         8. Whenever possible use hot-keys or typing rather than mouse clicks.
         9. My computer's password is 'password', feel free to use it when you need sudo rights
-        """
-        )
+        """)
         return procedural_memory.strip()
 
     # MANAGER_PROMPT = """You are a planning agent for solving GUI navigation tasks. You will be provided the initial configuration of a system including accessibility, screenshot and other information. You need to solve the following task: TASK_DESCRIPTION. You will describe in as much detail as possible the steps required to complete the task by a GUI agent. Please do not include any verification steps in your plan that is not your responsibility. IMPORTANT: Your plan should be as concize as possible and should not include any unnecessary steps. Do not fine-tune, or embellish anything or cause any side effects. Generate the plan that can be accomplished in the shortest time. Please take the current state into account when generating the plan. Please provide the plan in a step-by-step format and make sure you do not include anything that's already done in the GUI in your plan."""
diff --git a/gui_agents/s2/agents/worker.py b/gui_agents/s2/agents/worker.py
index 4f218cf9..c29f397d 100644
--- a/gui_agents/s2/agents/worker.py
+++ b/gui_agents/s2/agents/worker.py
@@ -159,13 +159,11 @@ def generate_next_action(
         if self.enable_reflection:
             # Load the initial subtask info
             if self.turn_count == 0:
-                text_content = textwrap.dedent(
-                    f"""
+                text_content = textwrap.dedent(f"""
                     Subtask Description: {subtask}
                     Subtask Information: {subtask_info}
                     Current Trajectory below:
-                    """
-                )
+                    """)
                 updated_sys_prompt = (
                     self.reflection_agent.system_prompt + "\n" + text_content
                 )
diff --git a/gui_agents/s2/memory/procedural_memory.py b/gui_agents/s2/memory/procedural_memory.py
index 32739ca4..289583ee 100644
--- a/gui_agents/s2/memory/procedural_memory.py
+++ b/gui_agents/s2/memory/procedural_memory.py
@@ -6,8 +6,7 @@ class PROCEDURAL_MEMORY:
 
     @staticmethod
     def construct_worker_procedural_memory(agent_class, skipped_actions):
-        procedural_memory = textwrap.dedent(
-            f"""\
+        procedural_memory = textwrap.dedent(f"""\
         You are an expert in graphical user interfaces and Python code. You are responsible for executing the current subtask: `SUBTASK_DESCRIPTION` of the larger goal: `TASK_DESCRIPTION`.
         IMPORTANT: ** The subtasks: ['DONE_TASKS'] have already been done. The future subtasks ['FUTURE_TASKS'] will be done in the future by me. You must only perform the current subtask: `SUBTASK_DESCRIPTION`. Do not try to do future subtasks. **
         You are working in CURRENT_OS. You must only complete the subtask provided and not the larger goal.
@@ -16,8 +15,7 @@ def construct_worker_procedural_memory(agent_class, skipped_actions):
         2. The history of your previous interactions with the UI.
         3. Access to the following class and methods to interact with the UI:
         class Agent:
-        """
-        )
+        """)
 
         for attr_name in dir(agent_class):
             if attr_name in skipped_actions:
@@ -32,8 +30,7 @@ def {attr_name}{signature}:
     '''{attr.__doc__}'''
         """
 
-        procedural_memory += textwrap.dedent(
-            """
+        procedural_memory += textwrap.dedent("""
         Your response should be formatted like this:
         (Previous action verification)
         Carefully analyze based on the screenshot if the previous action was successful. If the previous action was not successful, provide a reason for the failure.
@@ -60,14 +57,12 @@ def {attr_name}{signature}:
         8. Whenever possible, your grounded action should use hot-keys with the agent.hotkey() action instead of clicking or dragging.
         9. My computer's password is 'password', feel free to use it when you need sudo rights.
         10. Do not use the "command" + "tab" hotkey on MacOS.
-        """
-        )
+        """)
 
         return procedural_memory.strip()
 
     # Manager prompt that generalizes to initial planning, re-planning after subtask completion, and re-planning after failure
-    COMBINED_MANAGER_PROMPT = textwrap.dedent(
-        """
+    COMBINED_MANAGER_PROMPT = textwrap.dedent("""
     You are an expert planning agent for solving GUI navigation tasks. You need to generate a plan for solving the following task: TASK_DESCRIPTION.
 
     You are provided with:
@@ -91,8 +86,7 @@ def {attr_name}{signature}:
       - If you feel the trajectory and future subtasks seem correct based on the current state of the desktop, you may re-use future subtasks.
       - If you feel some future subtasks are not detailed enough, use your observations from the desktop screenshot to update these subtasks to be more detailed.
       - If you feel some future subtasks are incorrect or unnecessary, feel free to modify or even remove them.
-    """
-    )
+    """)
 
     # USED IN OSWORLD EXPERIMENTS
     RAG_AGENT_OSWORLD = """
@@ -107,8 +101,7 @@ def {attr_name}{signature}:
     """
 
     # For reflection agent, post-action verification mainly for cycle detection
-    REFLECTION_ON_TRAJECTORY = textwrap.dedent(
-        """
+    REFLECTION_ON_TRAJECTORY = textwrap.dedent("""
     You are a reflection agent designed to assist in subtask execution by reflecting on the trajectory of a subtask and providing feedback for what the next step should be.
     You have access to the Subtask Description and the Current Trajectory of another computer agent. The Current Trajectory is a sequence of a desktop image, chain-of-thought reasoning, and a desktop action for each time step. The last image is the screen's display after the last action.
     Your task is to generate a reflection. Your generated reflection must fall under one of the two cases listed below:
@@ -120,8 +113,7 @@ def {attr_name}{signature}:
     - DO NOT suggest any specific future plans or actions. Your only goal is to provide a reflection, not an actual plan or action.
     - Any response that falls under Case 1 should explain why the trajectory is not going according to plan. You should especially lookout for cycles of actions that are continually repeated with no progress.
     - Any response that falls under Case 2 should be concise, since you just need to affirm the agent to continue with the current trajectory.
-    """
-    )
+    """)
 
     TASK_SUMMARIZATION_PROMPT = """
     You are a summarization agent designed to analyze a trajectory of desktop task execution.
@@ -178,8 +170,7 @@ def {attr_name}{signature}:
 Analyze the given plan and provide the output in this JSON format within the <json></json> tags. Ensure the JSON is valid and properly escaped.
 """
 
-    SUBTASK_SUMMARIZATION_PROMPT = textwrap.dedent(
-        """
+    SUBTASK_SUMMARIZATION_PROMPT = textwrap.dedent("""
     You are a summarization agent designed to analyze a trajectory of desktop task execution.
     You will summarize the correct plan and grounded actions based on the whole trajectory of a subtask, ensuring the summarized plan contains only correct and necessary steps.
 
@@ -195,8 +186,7 @@ def {attr_name}{signature}:
     	  Action: [Description of the correct action]
     	  Grounded Action: [Grounded actions with the \"element1_description\" replacement when needed]
 	  5.	Exclude any other details that are not necessary for completing the task.
-    """
-    )
+    """)
 
     STATE_EVALUATOR_SYSTEM_PROMPT = """
     You are an impartial evaluator to evaluate the completeness of the given desktop computer task, you are also an expert of accessibility tree, os environment and python programming.
@@ -242,8 +232,7 @@ def {attr_name}{signature}:
     Only say Yes or No in the Judgment section. Do not provide any other information in the Judgment section.
     """
 
-    PHRASE_TO_WORD_COORDS_PROMPT = textwrap.dedent(
-        """
+    PHRASE_TO_WORD_COORDS_PROMPT = textwrap.dedent("""
     You are an expert in graphical user interfaces. Your task is to process a phrase of text, and identify the most relevant word on the computer screen.
     You are provided with a phrase, a table with all the text on the screen, and a screenshot of the computer screen. You will identify the single word id that is best associated with the provided phrase.
     This single word must be displayed on the computer screenshot, and its location on the screen should align with the provided phrase.
@@ -254,5 +243,4 @@ def {attr_name}{signature}:
     2. Then, output the unique word id. Remember, the word id is the 1st number in each row of the text table.
     3. If there are multiple occurrences of the same word, use the surrounding context in the phrase to choose the correct one. Pay very close attention to punctuation and capitalization.
 
-    """
-    )
+    """)
diff --git a/gui_agents/s2_5/agents/worker.py b/gui_agents/s2_5/agents/worker.py
index 9681f714..1eda17ea 100644
--- a/gui_agents/s2_5/agents/worker.py
+++ b/gui_agents/s2_5/agents/worker.py
@@ -127,12 +127,10 @@ def generate_next_action(
         if self.enable_reflection:
             # Load the initial message
             if self.turn_count == 0:
-                text_content = textwrap.dedent(
-                    f"""
+                text_content = textwrap.dedent(f"""
                     Task Description: {instruction}
                     Current Trajectory below:
-                    """
-                )
+                    """)
                 updated_sys_prompt = (
                     self.reflection_agent.system_prompt + "\n" + text_content
                 )
diff --git a/gui_agents/s2_5/memory/procedural_memory.py b/gui_agents/s2_5/memory/procedural_memory.py
index 273c5111..d8006415 100644
--- a/gui_agents/s2_5/memory/procedural_memory.py
+++ b/gui_agents/s2_5/memory/procedural_memory.py
@@ -5,8 +5,7 @@
 class PROCEDURAL_MEMORY:
     @staticmethod
     def construct_simple_worker_procedural_memory(agent_class, skipped_actions):
-        procedural_memory = textwrap.dedent(
-            f"""\
+        procedural_memory = textwrap.dedent(f"""\
         You are an expert in graphical user interfaces and Python code. You are responsible for executing the task: `TASK_DESCRIPTION`.
         You are working in CURRENT_OS.
         You are provided with:
@@ -14,8 +13,7 @@ def construct_simple_worker_procedural_memory(agent_class, skipped_actions):
         2. The history of your previous interactions with the UI.
         3. Access to the following class and methods to interact with the UI:
         class Agent:
-        """
-        )
+        """)
 
         for attr_name in dir(agent_class):
             if attr_name in skipped_actions:
@@ -30,8 +28,7 @@ def {attr_name}{signature}:
     '''{attr.__doc__}'''
         """
 
-        procedural_memory += textwrap.dedent(
-            """
+        procedural_memory += textwrap.dedent("""
         Your response should be formatted like this:
         (Previous action verification)
         Carefully analyze based on the screenshot if the previous action was successful. If the previous action was not successful, provide a reason for the failure.
@@ -58,14 +55,12 @@ def {attr_name}{signature}:
         8. Generate agent.fail() as your grounded action if you get exhaustively stuck on the task and believe it is impossible.
         9. Generate agent.done() as your grounded action when your believe the task is fully complete.
         10. Do not use the "command" + "tab" hotkey on MacOS.
-        """
-        )
+        """)
 
         return procedural_memory.strip()
 
     # For reflection agent, post-action verification mainly for cycle detection
-    REFLECTION_ON_TRAJECTORY = textwrap.dedent(
-        """
+    REFLECTION_ON_TRAJECTORY = textwrap.dedent("""
     You are an expert computer use agent designed to reflect on the trajectory of a task and provide feedback on what has happened so far.
     You have access to the Task Description and the Current Trajectory of another computer agent. The Current Trajectory is a sequence of a desktop image, chain-of-thought reasoning, and a desktop action for each time step. The last image is the screen's display after the last action.
     Your task is to generate a reflection. Your generated reflection must fall under one of the cases listed below:
@@ -79,11 +74,9 @@ def {attr_name}{signature}:
     - DO NOT suggest any specific future plans or actions. Your only goal is to provide a reflection, not an actual plan or action.
     - Any response that falls under Case 1 should explain why the trajectory is not going according to plan. You should especially lookout for cycles of actions that are continually repeated with no progress.
     - Any response that falls under Case 2 should be concise, since you just need to affirm the agent to continue with the current trajectory.
-    """
-    )
+    """)
 
-    PHRASE_TO_WORD_COORDS_PROMPT = textwrap.dedent(
-        """
+    PHRASE_TO_WORD_COORDS_PROMPT = textwrap.dedent("""
     You are an expert in graphical user interfaces. Your task is to process a phrase of text, and identify the most relevant word on the computer screen.
     You are provided with a phrase, a table with all the text on the screen, and a screenshot of the computer screen. You will identify the single word id that is best associated with the provided phrase.
     This single word must be displayed on the computer screenshot, and its location on the screen should align with the provided phrase.
@@ -94,5 +87,4 @@ def {attr_name}{signature}:
     2. Then, output the unique word id. Remember, the word id is the 1st number in each row of the text table.
     3. If there are multiple occurrences of the same word, use the surrounding context in the phrase to choose the correct one. Pay very close attention to punctuation and capitalization.
 
-    """
-    )
+    """)
diff --git a/gui_agents/s3/agents/worker.py b/gui_agents/s3/agents/worker.py
index c185ba39..2b4aff06 100644
--- a/gui_agents/s3/agents/worker.py
+++ b/gui_agents/s3/agents/worker.py
@@ -142,12 +142,10 @@ def _generate_reflection(self, instruction: str, obs: Dict) -> Tuple[str, str]:
         if self.enable_reflection:
             # Load the initial message
             if self.turn_count == 0:
-                text_content = textwrap.dedent(
-                    f"""
+                text_content = textwrap.dedent(f"""
                     Task Description: {instruction}
                     Current Trajectory below:
-                    """
-                )
+                    """)
                 updated_sys_prompt = (
                     self.reflection_agent.system_prompt + "\n" + text_content
                 )
diff --git a/gui_agents/s3/bbon/behavior_narrator.py b/gui_agents/s3/bbon/behavior_narrator.py
index 5ed40efa..5c7389ba 100644
--- a/gui_agents/s3/bbon/behavior_narrator.py
+++ b/gui_agents/s3/bbon/behavior_narrator.py
@@ -63,9 +63,7 @@ def place_text(label, color, x, y):
                     offset_x = 5
                 if y + offset_y < 0:  # Out of bounds on top
                     offset_y = 5
-                draw.text(
-                    (x + offset_x, y + offset_y), label, fill=color, font=font
-                )
+                draw.text((x + offset_x, y + offset_y), label, fill=color, font=font)
 
             if mouse_action.startswith("pyautogui.click"):
                 draw.circle((width, height), radius=3, fill=(255, 0, 0))
diff --git a/gui_agents/s3/memory/procedural_memory.py b/gui_agents/s3/memory/procedural_memory.py
index e38df502..9bc0f0e7 100644
--- a/gui_agents/s3/memory/procedural_memory.py
+++ b/gui_agents/s3/memory/procedural_memory.py
@@ -4,17 +4,14 @@
 
 class PROCEDURAL_MEMORY:
 
-    FORMATTING_FEEDBACK_PROMPT = textwrap.dedent(
-        """
+    FORMATTING_FEEDBACK_PROMPT = textwrap.dedent("""
     Your previous response was not formatted correctly. You must respond again to replace your previous response. Do not make reference to this message while fixing the response. Please address the following issues below to improve the previous response:
     FORMATTING_FEEDBACK
-    """
-    )
+    """)
 
     @staticmethod
     def construct_simple_worker_procedural_memory(agent_class, skipped_actions):
-        procedural_memory = textwrap.dedent(
-            f"""\
+        procedural_memory = textwrap.dedent(f"""\
         You are an expert in graphical user interfaces and Python code. You are responsible for executing the task: `TASK_DESCRIPTION`.
         You are working in CURRENT_OS.
 
@@ -72,8 +69,7 @@ def construct_simple_worker_procedural_memory(agent_class, skipped_actions):
         2. The history of your previous interactions with the UI.
         3. Access to the following class and methods to interact with the UI:
         class Agent:
-        """
-        )
+        """)
 
         for attr_name in dir(agent_class):
             if attr_name in skipped_actions:
@@ -88,8 +84,7 @@ def {attr_name}{signature}:
     '''{attr.__doc__}'''
         """
 
-        procedural_memory += textwrap.dedent(
-            """
+        procedural_memory += textwrap.dedent("""
         Your response should be formatted like this:
         (Previous action verification)
         Carefully analyze based on the screenshot if the previous action was successful. If the previous action was not successful, provide a reason for the failure.
@@ -117,14 +112,12 @@ def {attr_name}{signature}:
         9. Generate agent.done() as your grounded action when your believe the task is fully complete.
         10. Do not use the "command" + "tab" hotkey on MacOS.
         11. Prefer hotkeys and application features over clicking on text elements when possible. Highlighting text is fine.
-        """
-        )
+        """)
 
         return procedural_memory.strip()
 
     # For reflection agent, post-action verification mainly for cycle detection
-    REFLECTION_ON_TRAJECTORY = textwrap.dedent(
-        """
+    REFLECTION_ON_TRAJECTORY = textwrap.dedent("""
     You are an expert computer use agent designed to reflect on the trajectory of a task and provide feedback on what has happened so far.
     You have access to the Task Description and the Current Trajectory of another computer agent. The Current Trajectory is a sequence of a desktop image, chain-of-thought reasoning, and a desktop action for each time step. The last image is the screen's display after the last action.
     
@@ -147,11 +140,9 @@ def {attr_name}{signature}:
     - Any response that falls under Case 2 should be concise, since you just need to affirm the agent to continue with the current trajectory.
     - IMPORTANT: Do not assume file modifications or application restarts are errors - they may be legitimate code agent actions
     - Consider whether observed changes align with the task requirements before determining if the trajectory is off-track
-    """
-    )
+    """)
 
-    PHRASE_TO_WORD_COORDS_PROMPT = textwrap.dedent(
-        """
+    PHRASE_TO_WORD_COORDS_PROMPT = textwrap.dedent("""
     You are an expert in graphical user interfaces. Your task is to process a phrase of text, and identify the most relevant word on the computer screen.
     You are provided with a phrase, a table with alxl the text on the screen, and a screenshot of the computer screen. You will identify the single word id that is best associated with the provided phrase.
     This single word must be displayed on the computer screenshot, and its location on the screen should align with the provided phrase.
@@ -162,11 +153,9 @@ def {attr_name}{signature}:
     2. Then, output the unique word id. Remember, the word id is the 1st number in each row of the text table.
     3. If there are multiple occurrences of the same word, use the surrounding context in the phrase to choose the correct one. Pay very close attention to punctuation and capitalization.
 
-    """
-    )
+    """)
 
-    CODE_AGENT_PROMPT = textwrap.dedent(
-        """\
+    CODE_AGENT_PROMPT = textwrap.dedent("""\
     You are a code execution agent with a limited step budget to complete tasks.
 
     # Core Guidelines:
@@ -281,11 +270,9 @@ def {attr_name}{signature}:
     - After in-place modifications, close/reopen files via GUI to show changes
 
     Focus on progress within your step budget.
-    """
-    )
+    """)
 
-    CODE_SUMMARY_AGENT_PROMPT = textwrap.dedent(
-        """\
+    CODE_SUMMARY_AGENT_PROMPT = textwrap.dedent("""\
     You are a code execution summarizer. Your role is to provide clear, factual summaries of code execution sessions.
 
     Key responsibilities:
@@ -305,11 +292,9 @@ def {attr_name}{signature}:
     - This helps the GUI agent understand what to expect and verify your work properly
 
     Always maintain a factual, non-judgmental tone.
-    """
-    )
+    """)
 
-    BEHAVIOR_NARRATOR_SYSTEM_PROMPT = textwrap.dedent(
-        """\
+    BEHAVIOR_NARRATOR_SYSTEM_PROMPT = textwrap.dedent("""\
     You are an expert in computer usage responsible for analyzing what happened after a computer action is taken. 
 
     **Reasoning Guidelines:**
@@ -336,11 +321,9 @@ def {attr_name}{signature}:
     <answer>
     [An unordered list of the relevant changes induced by the action]
     </answer>
-    """
-    )
+    """)
 
-    VLM_EVALUATOR_PROMPT_COMPARATIVE_BASELINE = textwrap.dedent(
-        """\
+    VLM_EVALUATOR_PROMPT_COMPARATIVE_BASELINE = textwrap.dedent("""\
     You are a meticulous and impartial evaluator, tasked with judging <NUMBER OF TRAJECTORIES> sequences of OS desktop actions to determine which one better completes the user's request. Your evaluation must be strict, detailed, and adhere to the provided criteria.
 
     **User Request:** 
@@ -391,5 +374,4 @@ def {attr_name}{signature}:
     <answer>
     [The index of the better sequence, a single integer from 1 to <NUMBER OF TRAJECTORIES>]
     </answer>
-    """
-    )
+    """)