diff --git a/mm_agents/prompts.py b/mm_agents/prompts.py
index 692f546..37609c4 100644
--- a/mm_agents/prompts.py
+++ b/mm_agents/prompts.py
@@ -1193,3 +1193,67 @@ Instruction: {instruction}
 
 AGUVIS_GROUNDING_APPEND_PROMPT = """<|recipient|>os
 pyautogui.{function_name}"""
+
+UITARS_ACTION_SPACE = """
+click(start_box='[x1, y1, x2, y2]')
+left_double(start_box='[x1, y1, x2, y2]')
+right_single(start_box='[x1, y1, x2, y2]')
+drag(start_box='[x1, y1, x2, y2]', end_box='[x3, y3, x4, y4]')
+hotkey(key='')
+type(content='') #If you want to submit your input, use "\\n" at the end of `content`.
+scroll(start_box='[x1, y1, x2, y2]', direction='down or up or right or left')
+wait() #Sleep for 5s and take a screenshot to check for any changes.
+finished()
+"""
+
+UITARS_CALL_USR_ACTION_SPACE = """
+click(start_box='[x1, y1, x2, y2]')
+left_double(start_box='[x1, y1, x2, y2]')
+right_single(start_box='[x1, y1, x2, y2]')
+drag(start_box='[x1, y1, x2, y2]', end_box='[x3, y3, x4, y4]')
+hotkey(key='')
+type(content='') #If you want to submit your input, use "\\n" at the end of `content`.
+scroll(start_box='[x1, y1, x2, y2]', direction='down or up or right or left')
+wait() #Sleep for 5s and take a screenshot to check for any changes.
+finished()
+call_user() # Submit the task and call the user when the task is unsolvable, or when you need the user's help.
+"""
+
+UITARS_USR_PROMPT_NOTHOUGHT = """You are a GUI agent. You are given a task and your action history, with screenshots. You need to perform the next action to complete the task. 
+## Output Format
+```
+Action: ...
+```
+## Action Space
+click(start_box='[x1, y1, x2, y2]')
+left_double(start_box='[x1, y1, x2, y2]')
+right_single(start_box='[x1, y1, x2, y2]')
+drag(start_box='[x1, y1, x2, y2]', end_box='[x3, y3, x4, y4]')
+hotkey(key='')
+type(content='') #If you want to submit your input, use "\\n" at the end of `content`.
+scroll(start_box='[x1, y1, x2, y2]', direction='down or up or right or left')
+wait() #Sleep for 5s and take a screenshot to check for any changes.
+finished()
+call_user() # Submit the task and call the user when the task is unsolvable, or when you need the user's help.
+## User Instruction
+{instruction}
+"""
+
+UITARS_USR_PROMPT_THOUGHT = """You are a GUI agent. You are given a task and your action history, with screenshots. You need to perform the next action to complete the task. 
+
+## Output Format
+```
+Thought: ...
+Action: ...
+```
+
+## Action Space
+{action_space}
+
+## Note
+- Use {language} in `Thought` part.
+- Write a small plan and finally summarize your next action (with its target element) in one sentence in `Thought` part.
+
+## User Instruction
+{instruction}
+"""
\ No newline at end of file
diff --git a/mm_agents/uitars_agent.py b/mm_agents/uitars_agent.py
new file mode 100644
index 0000000..2c96776
--- /dev/null
+++ b/mm_agents/uitars_agent.py
@@ -0,0 +1,772 @@
+import ast
+import base64
+import logging
+import math
+import re
+import xml.etree.ElementTree as ET
+from io import BytesIO
+from typing import Dict, List
+
+import backoff
+import numpy as np
+from PIL import Image
+from requests.exceptions import SSLError
+import openai
+from openai import OpenAI
+from google.api_core.exceptions import (
+    BadRequest,
+    InternalServerError,
+    InvalidArgument,
+    ResourceExhausted,
+)
+
+from mm_agents.accessibility_tree_wrap.heuristic_retrieve import (
+    filter_nodes,
+)
+from mm_agents.prompts import (
+    UITARS_ACTION_SPACE,
+    UITARS_CALL_USR_ACTION_SPACE,
+    UITARS_USR_PROMPT_NOTHOUGHT,
+    UITARS_USR_PROMPT_THOUGHT,
+)
+
+
+logger = logging.getLogger("desktopenv.agent")
+
+FINISH_WORD = "finished"
+WAIT_WORD = "wait"
+ENV_FAIL_WORD = "error_env"
+CALL_USER = "call_user"
+
+pure_text_settings = ["a11y_tree"]
+
+attributes_ns_ubuntu = "https://accessibility.windows.example.org/ns/attributes"
+attributes_ns_windows = "https://accessibility.windows.example.org/ns/attributes"
+state_ns_ubuntu = "https://accessibility.ubuntu.example.org/ns/state"
+state_ns_windows = "https://accessibility.windows.example.org/ns/state"
+component_ns_ubuntu = "https://accessibility.ubuntu.example.org/ns/component"
+component_ns_windows = "https://accessibility.windows.example.org/ns/component"
+value_ns_ubuntu = "https://accessibility.ubuntu.example.org/ns/value"
+value_ns_windows = "https://accessibility.windows.example.org/ns/value"
+class_ns_windows = "https://accessibility.windows.example.org/ns/class"
+# More namespaces defined in OSWorld, please check desktop_env/server/main.py
+
+# 定义一个函数来解析每个 action
+def parse_action(action_str):
+    try:
+        # 解析字符串为 AST 节点
+        node = ast.parse(action_str, mode='eval')
+
+        # 确保节点是一个表达式
+        if not isinstance(node, ast.Expression):
+            raise ValueError("Not an expression")
+
+        # 获取表达式的主体
+        call = node.body
+
+        # 确保主体是一个函数调用
+        if not isinstance(call, ast.Call):
+            raise ValueError("Not a function call")
+
+        # 获取函数名
+        if isinstance(call.func, ast.Name):
+            func_name = call.func.id
+        elif isinstance(call.func, ast.Attribute):
+            func_name = call.func.attr
+        else:
+            func_name = None
+
+        # 获取关键字参数
+        kwargs = {}
+        for kw in call.keywords:
+            key = kw.arg
+            # 处理不同类型的值，这里假设都是常量
+            if isinstance(kw.value, ast.Constant):
+                value = kw.value.value
+            elif isinstance(kw.value, ast.Str):  # 兼容旧版本 Python
+                value = kw.value.s
+            else:
+                value = None
+            kwargs[key] = value
+
+        return {
+            'function': func_name,
+            'args': kwargs
+        }
+
+    except Exception as e:
+        print(f"Failed to parse action '{action_str}': {e}")
+        return None
+    
+def escape_single_quotes(text):
+    # 匹配未转义的单引号（不匹配 \\'）
+    pattern = r"(?<!\\)'"
+    return re.sub(pattern, r"\\'", text)
+
+def parse_action_qwen2vl(text, factor, image_height, image_width):
+    text = text.strip()
+    # 正则表达式匹配 Action 字符串
+    if text.startswith("Thought:"):
+        thought_pattern = r"Thought: (.+?)(?=\s*Action:|$)"
+        thought_hint = "Thought: "
+    elif text.startswith("Reflection:"):
+        thought_pattern = r"Reflection: (.+?)Action_Summary: (.+?)(?=\s*Action:|$)"
+        thought_hint = "Reflection: "
+    elif text.startswith("Action_Summary:"):
+        thought_pattern = r"Action_Summary: (.+?)(?=\s*Action:|$)"
+        thought_hint = "Action_Summary: "
+    else:
+        thought_pattern = r"Thought: (.+?)(?=\s*Action:|$)"
+        thought_hint = "Thought: "
+    reflection, thought = None, None
+    thought_match = re.search(thought_pattern, text, re.DOTALL)
+    if thought_match:
+        if len(thought_match.groups()) == 1:
+            thought = thought_match.group(1).strip()
+        elif len(thought_match.groups()) == 2:
+            thought = thought_match.group(2).strip()
+            reflection = thought_match.group(1).strip()
+    assert "Action:" in text
+    action_str = text.split("Action:")[-1]
+
+    tmp_all_action = action_str.split("\n\n")
+    all_action = []
+    for action_str in tmp_all_action:
+        if "type(content" in action_str:
+            # 正则表达式匹配 content 中的字符串并转义单引号
+            def escape_quotes(match):
+                content = match.group(1)  # 获取 content 的值
+                return content
+
+            # 使用正则表达式进行替换
+            pattern = r"type\(content='(.*?)'\)"  # 匹配 type(content='...')
+            content = re.sub(pattern, escape_quotes, action_str)
+
+            # 处理字符串
+            action_str = escape_single_quotes(content)
+            action_str = "type(content='" + action_str + "')"
+        all_action.append(action_str)
+
+    parsed_actions = [parse_action(action.replace("\n","\\n").lstrip()) for action in all_action]
+    actions = []
+    for action_instance, raw_str in zip(parsed_actions, all_action):
+        if action_instance == None:
+            print(f"Action can't parse: {raw_str}")
+            continue
+        action_type = action_instance["function"]
+        params = action_instance["args"]
+
+        # import pdb; pdb.set_trace()
+        action_inputs = {}
+        for param_name, param in params.items():
+            if param == "": continue
+            param = param.lstrip()  # 去掉引号和多余的空格
+            # 处理start_box或者end_box参数格式 '<bbox>x1 y1 x2 y2</bbox>'
+            action_inputs[param_name.strip()] = param
+            
+            if "start_box" in param_name or "end_box" in param_name:
+                ori_box = param
+                # Remove parentheses and split the string by commas
+                numbers = ori_box.replace("(", "").replace(")", "").split(",")
+
+                # Convert to float and scale by 1000
+                float_numbers = [float(num) / factor for num in numbers]
+                if len(float_numbers) == 2:
+                    float_numbers = [float_numbers[0], float_numbers[1], float_numbers[0], float_numbers[1]]
+                action_inputs[param_name.strip()] = str(float_numbers)
+
+        # import pdb; pdb.set_trace()
+        actions.append({
+            "reflection": reflection,
+            "thought": thought,
+            "action_type": action_type,
+            "action_inputs": action_inputs,
+            "text": text
+        })
+    return actions
+
+def parsing_response_to_pyautogui_code(responses, image_height: int, image_width:int, input_swap:bool=True) -> str:
+    '''
+    将M模型的输出解析为OSWorld中的action，生成pyautogui代码字符串
+    参数:
+        response: 包含模型输出的字典，结构类似于：
+        {
+            "action_type": "hotkey",
+            "action_inputs": {
+                "hotkey": "v ctrl",
+                "start_box": None,
+                "end_box": None
+            }
+        }
+    返回:
+        生成的pyautogui代码字符串
+    '''
+
+    pyautogui_code = f"import pyautogui\nimport time\n"
+    if isinstance(responses, dict):
+        responses = [responses]
+    for response_id, response in enumerate(responses):
+        if "observation" in response:
+            observation = response["observation"]
+        else:
+            observation = ""
+
+        if "thought" in response:
+            thought = response["thought"]
+        else:
+            thought = ""
+        
+        if response_id == 0:
+            pyautogui_code += f"'''\nObservation:\n{observation}\n\nThought:\n{thought}\n'''\n"
+        else:
+            pyautogui_code += f"\ntime.sleep(3)\n"
+
+        action_dict = response
+        action_type = action_dict.get("action_type")
+        action_inputs = action_dict.get("action_inputs", {})
+        
+        if action_type == "hotkey":
+            # Parsing hotkey action
+            if "key" in action_inputs:
+                hotkey = action_inputs.get("key", "")
+            else:
+                hotkey = action_inputs.get("hotkey", "")
+
+            if hotkey:
+                # Handle other hotkeys
+                keys = hotkey.split()  # Split the keys by space
+                pyautogui_code += f"\npyautogui.hotkey({', '.join([repr(k) for k in keys])})"
+        
+        elif action_type == "type":
+            # Parsing typing action using clipboard
+            content = action_inputs.get("content", "")
+            content = escape_single_quotes(content)
+            if content:
+                if input_swap:
+                    pyautogui_code += f"\nimport pyperclip"
+                    pyautogui_code += f"\npyperclip.copy('{content.strip()}')"
+                    pyautogui_code += f"\npyautogui.hotkey('ctrl', 'v')"
+                    pyautogui_code += f"\ntime.sleep(0.5)\n"
+                    if content.endswith("\n") or content.endswith("\\n"):
+                        pyautogui_code += f"\npyautogui.press('enter')"
+                else:
+                    pyautogui_code += f"\npyautogui.write('{content.strip()}', interval=0.1)"
+                    pyautogui_code += f"\ntime.sleep(0.5)\n"
+                    if content.endswith("\n") or content.endswith("\\n"):
+                        pyautogui_code += f"\npyautogui.press('enter')"
+
+        
+        elif action_type in ["drag", "select"]:
+            # Parsing drag or select action based on start and end_boxes
+            start_box = action_inputs.get("start_box")
+            end_box = action_inputs.get("end_box")
+            if start_box and end_box:
+                x1, y1, x2, y2 = eval(start_box)  # Assuming box is in [x1, y1, x2, y2]
+                sx = round(float((x1 + x2) / 2) * image_width, 3)
+                sy = round(float((y1 + y2) / 2) * image_height, 3)
+                x1, y1, x2, y2 = eval(end_box)  # Assuming box is in [x1, y1, x2, y2]
+                ex = round(float((x1 + x2) / 2) * image_width, 3)
+                ey = round(float((y1 + y2) / 2) * image_height, 3)
+                pyautogui_code += (
+                    f"\npyautogui.moveTo({sx}, {sy})\n"
+                    f"\npyautogui.dragTo({ex}, {ey}, duration=1.0)\n"
+                )
+
+        elif action_type == "scroll":
+            # Parsing scroll action
+            start_box = action_inputs.get("start_box")
+            if start_box:
+                x1, y1, x2, y2 = eval(start_box)  # Assuming box is in [x1, y1, x2, y2]
+                x = round(float((x1 + x2) / 2) * image_width, 3)
+                y = round(float((y1 + y2) / 2) * image_height, 3)
+                
+                # # 先点对应区域，再滚动
+                # pyautogui_code += f"\npyautogui.click({x}, {y}, button='left')"
+            else:
+                x = None
+                y = None
+            direction = action_inputs.get("direction", "")
+            
+            if x == None:
+                if "up" in direction.lower():
+                    pyautogui_code += f"\npyautogui.scroll(5)"
+                elif "down" in direction.lower():
+                    pyautogui_code += f"\npyautogui.scroll(-5)"
+            else:
+                if "up" in direction.lower():
+                    pyautogui_code += f"\npyautogui.scroll(5, x={x}, y={y})"
+                elif "down" in direction.lower():
+                    pyautogui_code += f"\npyautogui.scroll(-5, x={x}, y={y})"
+
+        elif action_type in ["click", "left_single", "left_double", "right_single", "hover"]:
+            # Parsing mouse click actions
+            start_box = action_inputs.get("start_box")
+            start_box = str(start_box)
+            if start_box:
+                start_box = eval(start_box)
+                if len(start_box) == 4:
+                    x1, y1, x2, y2 = start_box  # Assuming box is in [x1, y1, x2, y2]
+                elif len(start_box) == 2:
+                    x1, y1 = start_box
+                    x2 = x1
+                    y2 = y1
+                x = round(float((x1 + x2) / 2) * image_width, 3)
+                y = round(float((y1 + y2) / 2) * image_height, 3)
+                if action_type == "left_single" or action_type == "click":
+                    pyautogui_code += f"\npyautogui.click({x}, {y}, button='left')"
+                elif action_type == "left_double":
+                    pyautogui_code += f"\npyautogui.doubleClick({x}, {y}, button='left')"
+                elif action_type == "right_single":
+                    pyautogui_code += f"\npyautogui.click({x}, {y}, button='right')"
+                elif action_type == "hover":
+                    pyautogui_code += f"\npyautogui.moveTo({x}, {y})"
+        
+        elif action_type in ["finished"]:
+            pyautogui_code = f"DONE"
+        
+        else:
+            pyautogui_code += f"\n# Unrecognized action type: {action_type}"
+
+    return pyautogui_code
+
+def pil_to_base64(image):
+    buffer = BytesIO()
+    image.save(buffer, format="PNG")  # 你可以改成 "JPEG" 等格式
+    return base64.b64encode(buffer.getvalue()).decode("utf-8")
+
+def linearize_accessibility_tree(accessibility_tree, platform="ubuntu"):
+
+    if platform == "ubuntu":
+        _attributes_ns = attributes_ns_ubuntu
+        _state_ns = state_ns_ubuntu
+        _component_ns = component_ns_ubuntu
+        _value_ns = value_ns_ubuntu
+    elif platform == "windows":
+        _attributes_ns = attributes_ns_windows
+        _state_ns = state_ns_windows
+        _component_ns = component_ns_windows
+        _value_ns = value_ns_windows
+    else:
+        raise ValueError("Invalid platform, must be 'ubuntu' or 'windows'")
+
+    filtered_nodes = filter_nodes(ET.fromstring(accessibility_tree), platform)
+    linearized_accessibility_tree = [
+        "tag\tname\ttext\tclass\tdescription\tposition (top-left x&y)\tsize (w&h)"
+    ]
+
+    # Linearize the accessibility tree nodes into a table format
+    for node in filtered_nodes:
+        if node.text:
+            text = (
+                node.text
+                if '"' not in node.text
+                else '"{:}"'.format(node.text.replace('"', '""'))
+            )
+
+        elif node.get("{{{:}}}class".format(class_ns_windows), "").endswith(
+            "EditWrapper"
+        ) and node.get("{{{:}}}value".format(_value_ns)):
+            node_text = node.get("{{{:}}}value".format(_value_ns), "")
+            text = (
+                node_text
+                if '"' not in node_text
+                else '"{:}"'.format(node_text.replace('"', '""'))
+            )
+        else:
+            text = '""'
+
+        linearized_accessibility_tree.append(
+            "{:}\t{:}\t{:}\t{:}\t{:}\t{:}\t{:}".format(
+                node.tag,
+                node.get("name", ""),
+                text,
+                (
+                    node.get("{{{:}}}class".format(_attributes_ns), "")
+                    if platform == "ubuntu"
+                    else node.get("{{{:}}}class".format(class_ns_windows), "")
+                ),
+                node.get("{{{:}}}description".format(_attributes_ns), ""),
+                node.get("{{{:}}}screencoord".format(_component_ns), ""),
+                node.get("{{{:}}}size".format(_component_ns), ""),
+            )
+        )
+
+    return "\n".join(linearized_accessibility_tree)
+
+def trim_accessibility_tree(linearized_accessibility_tree, max_tokens):
+    # enc = tiktoken.encoding_for_model("gpt-4")
+    # tokens = enc.encode(linearized_accessibility_tree)
+    # if len(tokens) > max_tokens:
+    #     linearized_accessibility_tree = enc.decode(tokens[:max_tokens])
+    #     linearized_accessibility_tree += "[...]\n"
+    return linearized_accessibility_tree
+
+class UITARSAgent:
+    def __init__(
+        self,
+        platform="ubuntu",
+        max_tokens=1000,
+        top_p=0.9,
+        top_k=1.0,
+        temperature=0.0,
+        action_space="pyautogui",
+        observation_type="screenshot_a11y_tree",
+        # observation_type can be in ["screenshot", "a11y_tree", "screenshot_a11y_tree", "som"]
+        max_trajectory_length=50,
+        a11y_tree_max_tokens=10000,
+        runtime_conf: dict = {
+            "infer_mode": "qwen2vl_user",
+            "prompt_style": "qwen2vl_user",
+            "input_swap": True,
+            "language": "Chinese",
+            "max_steps": 50,
+            "history_n": 5,
+            "screen_height": 1080,
+            "screen_width": 1920
+        }
+    ):
+        self.platform = platform
+        self.max_tokens = max_tokens
+        self.top_p = top_p
+        self.top_k = top_k
+        self.temperature = temperature
+        self.action_space = action_space
+        self.observation_type = observation_type
+        self.max_trajectory_length = max_trajectory_length
+        self.a11y_tree_max_tokens = a11y_tree_max_tokens
+        self.runtime_conf = runtime_conf
+        self.vlm = OpenAI(
+            base_url="http://127.0.0.1:8000/v1",
+            api_key="empty",
+        ) # should replace with your UI-TARS server api
+        self.infer_mode = self.runtime_conf["infer_mode"]
+        self.prompt_style = self.runtime_conf["prompt_style"]
+        self.input_swap = self.runtime_conf["input_swap"]
+        self.language = self.runtime_conf["language"]
+        self.max_steps = self.runtime_conf["max_steps"]
+
+        self.thoughts = []
+        self.actions = []
+        self.observations = []
+        self.history_images = []
+        self.history_responses = []
+        
+        self.prompt_action_space = UITARS_ACTION_SPACE
+        self.customize_action_parser = parse_action_qwen2vl
+        self.action_parse_res_factor = 1000
+        if self.infer_mode == "qwen2vl_user":
+            self.prompt_action_space = UITARS_CALL_USR_ACTION_SPACE
+    
+        self.prompt_template = UITARS_USR_PROMPT_THOUGHT
+        
+        if self.prompt_style == "qwen2vl_user":
+            self.prompt_template = UITARS_USR_PROMPT_THOUGHT
+
+        elif self.prompt_style == "qwen2vl_no_thought":
+            self.prompt_template = UITARS_USR_PROMPT_NOTHOUGHT
+
+        
+        if "history_n" in self.runtime_conf:
+            self.history_n = self.runtime_conf["history_n"]
+        else:
+            self.history_n = 5
+
+    def predict(
+        self, instruction: str, obs: Dict, last_action_after_obs: Dict = None
+    ) -> List:
+        """
+        Predict the next action(s) based on the current observation.
+        """
+
+        # Append trajectory
+        # print(len(self.observations), len(self.actions), len(self.actions))
+        assert len(self.observations) == len(self.actions) and len(self.actions) == len(
+            self.thoughts
+        ), "The number of observations and actions should be the same."
+
+        if len(self.observations) > self.max_trajectory_length:
+            if self.max_trajectory_length == 0:
+                _observations = []
+                _actions = []
+                _thoughts = []
+            else:
+                _observations = self.observations[-self.max_trajectory_length :]
+                _actions = self.actions[-self.max_trajectory_length :]
+                _thoughts = self.thoughts[-self.max_trajectory_length :]
+        else:
+            _observations = self.observations
+            _actions = self.actions
+            _thoughts = self.thoughts
+
+        for previous_obs, previous_action, previous_thought in zip(
+            _observations, _actions, _thoughts
+        ):
+            # {{{1
+            if self.observation_type == "screenshot_a11y_tree":
+                _screenshot = previous_obs["screenshot"]
+                _linearized_accessibility_tree = previous_obs["accessibility_tree"]
+
+            else:
+                raise ValueError(
+                    "Invalid observation_type type: " + self.observation_type
+                )  # 1}}}
+
+        if last_action_after_obs is not None and self.infer_mode == "double_image":
+            self.history_images.append(last_action_after_obs["screenshot"])
+
+        self.history_images.append(obs["screenshot"])
+
+        if self.observation_type in ["screenshot", "screenshot_a11y_tree"]:
+            base64_image = obs["screenshot"]
+            try:
+                linearized_accessibility_tree = (
+                    linearize_accessibility_tree(
+                        accessibility_tree=obs["accessibility_tree"],
+                        platform=self.platform,
+                    )
+                    if self.observation_type == "screenshot_a11y_tree"
+                    else None
+                )
+            except:
+                linearized_accessibility_tree = None
+            # logger.debug("LINEAR AT: %s", linearized_accessibility_tree)
+
+            if linearized_accessibility_tree:
+                linearized_accessibility_tree = trim_accessibility_tree(
+                    linearized_accessibility_tree, self.a11y_tree_max_tokens
+                )
+
+            if self.observation_type == "screenshot_a11y_tree":
+                self.observations.append(
+                    {
+                        "screenshot": base64_image,
+                        "accessibility_tree": linearized_accessibility_tree,
+                    }
+                )
+            else:
+                self.observations.append(
+                    {"screenshot": base64_image, "accessibility_tree": None}
+                )
+
+        else:
+            raise ValueError(
+                "Invalid observation_type type: " + self.observation_type
+            )  # 1}}}
+        
+        if self.infer_mode == "qwen2vl_user":
+            user_prompt = self.prompt_template.format(
+                instruction=instruction,
+                action_space=self.prompt_action_space,
+                language=self.language
+            )
+        elif self.infer_mode == "qwen2vl_no_thought":
+            user_prompt = self.prompt_template.format(
+                instruction=instruction
+            )
+
+        if len(self.history_images) > self.history_n:
+            self.history_images = self.history_images[-self.history_n:]
+
+        max_pixels = 1350 * 28 * 28
+        min_pixels = 100 * 28 * 28
+        messages, images = [], []
+        if isinstance(self.history_images, bytes):
+            self.history_images = [self.history_images]
+        elif isinstance(self.history_images, np.ndarray):
+            self.history_images = list(self.history_images)
+        elif isinstance(self.history_images, list):
+            pass
+        else:
+            raise TypeError(f"Unidentified images type: {type(self.history_images)}")
+        max_image_nums_under_32k = int(32768*0.75/max_pixels*28*28)
+        if len(self.history_images) > max_image_nums_under_32k:
+            num_of_images = min(5, len(self.history_images))
+            max_pixels = int(32768*0.75) // num_of_images
+
+        for turn, image in enumerate(self.history_images):
+            if len(images) >= 5:
+                break
+            try:
+                image = Image.open(BytesIO(image))
+            except Exception as e:
+                raise RuntimeError(f"Error opening image: {e}")
+
+            if image.width * image.height > max_pixels:
+                """
+                如果图片超过/低于像素限制，则计算一个缩放因子resize_factor，使图片的像素数缩小到等于或小于max_pixels。这个缩放因子是通过开平方根计算的，确保纵横比保持不变,这样原始的相对坐标可以不经转换直接复用
+                """
+                resize_factor = math.sqrt(max_pixels / (image.width * image.height))
+                width, height = int(image.width * resize_factor), int(image.height * resize_factor)
+                image = image.resize((width, height))
+            if image.width * image.height < min_pixels:
+                resize_factor = math.sqrt(min_pixels / (image.width * image.height))
+                width, height = math.ceil(image.width * resize_factor), math.ceil(image.height * resize_factor)
+                image = image.resize((width, height))
+
+            if image.mode != "RGB":
+                image = image.convert("RGB")
+
+            images.append(image)
+
+        messages = [
+            {
+                "role": "system",
+                "content": [{"type": "text", "text": "You are a helpful assistant."}]
+            },
+            {
+                "role": "user",
+                "content": [{"type": "text", "text": user_prompt}]
+            }
+        ]
+        
+        image_num = 0
+        if len(self.history_responses) > 0:
+            for history_idx, history_response in enumerate(self.history_responses):
+                # send at most history_n images to the model
+                if history_idx + self.history_n > len(self.history_responses):
+
+                    cur_image = images[image_num]
+                    encoded_string = pil_to_base64(cur_image)
+                    messages.append({
+                        "role": "user",
+                        "content": [{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{encoded_string}"}}]
+                    })
+                    image_num += 1
+                    
+                messages.append({
+                    "role": "assistant",
+                    "content": [history_response]
+                })
+
+            cur_image = images[image_num]
+            encoded_string = pil_to_base64(cur_image)
+            messages.append({
+                "role": "user",
+                "content": [{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{encoded_string}"}}]
+            })
+            image_num += 1
+        
+        else:
+            cur_image = images[image_num]
+            encoded_string = pil_to_base64(cur_image)
+            messages.append({
+                "role": "user",
+                "content": [{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{encoded_string}"}}]
+            })
+            image_num += 1
+
+        try_times = 3
+        while True:
+            if try_times <= 0:
+                print(f"Reach max retry times to fetch response from client, as error flag.")
+                return "client error", ["DONE"], []
+            try:
+                
+                response = self.vlm.chat.completions.create(
+                    model="ui-tars",
+                    messages=messages,
+                    frequency_penalty=1,
+                    max_tokens=self.max_tokens,
+                    temperature=self.temperature,
+                    top_k=self.top_k,
+                    top_p=self.top_p
+                )
+                # print(response.choices[0].message.content)
+                prediction = response.choices[0].message.content.strip()
+                
+                prediction = response[0]["prediction"].strip()
+                parsed_responses = self.customize_action_parser(
+                    prediction,
+                    self.action_parse_res_factor,
+                    self.runtime_conf["screen_height"],
+                    self.runtime_conf["screen_width"]
+                )
+                break
+            except Exception as e:
+                print(f"Error when fetching response from client, with response: {response}")
+                prediction = None
+                try_times -= 1
+                
+        if prediction is None:
+            return "client error", ["DONE"], []
+        
+        self.history_responses.append(prediction)
+        self.thoughts.append(prediction)
+
+        try:
+            parsed_responses = self.customize_action_parser(
+                prediction,
+                self.action_parse_res_factor,
+                self.runtime_conf["screen_height"],
+                self.runtime_conf["screen_width"]
+            )
+        except Exception as e:
+            print(f"Parsing action error: {prediction}, with error:\n{e}")
+            return f"Parsing action error: {prediction}, with error:\n{e}", ["DONE"], []
+
+        actions = []
+        for parsed_response in parsed_responses:
+            if "action_type" in parsed_response:
+
+                if parsed_response["action_type"] == FINISH_WORD:
+                    self.actions.append(actions)
+                    return prediction, ["DONE"], parsed_responses
+                
+                elif parsed_response["action_type"] == WAIT_WORD:
+                    self.actions.append(actions)
+                    return prediction, ["WAIT"], parsed_responses
+                
+                elif parsed_response["action_type"] == ENV_FAIL_WORD:
+                    self.actions.append(actions)
+                    return prediction, ["FAIL"], parsed_responses 
+
+                elif parsed_response["action_type"] == CALL_USER:
+                    self.actions.append(actions)
+                    return prediction, ["FAIL"], parsed_responses 
+            
+            pyautogui_code = parsing_response_to_pyautogui_code(
+                parsed_response,
+                self.runtime_conf["screen_height"],
+                self.runtime_conf["screen_width"],
+                self.input_swap
+            )
+            actions.append(pyautogui_code)
+
+        self.actions.append(actions)
+
+        if len(self.history_responses) >= self.max_trajectory_length:
+            # Default to FAIL if exceed max steps
+            actions = ["FAIL"]
+
+        return prediction, actions, parsed_responses
+
+    @backoff.on_exception(
+        backoff.constant,
+        # here you should add more model exceptions as you want,
+        # but you are forbidden to add "Exception", that is, a common type of exception
+        # because we want to catch this kind of Exception in the outside to ensure each example won't exceed the time limit
+        (
+            # General exceptions
+            SSLError,
+            # OpenAI exceptions
+            openai.RateLimitError,
+            openai.BadRequestError,
+            openai.InternalServerError,
+            # Google exceptions
+            InvalidArgument,
+            ResourceExhausted,
+            InternalServerError,
+            BadRequest,
+            # Groq exceptions
+            # todo: check
+        ),
+        interval=30,
+        max_tries=10,
+    )
+    
+    def reset(self, runtime_logger):
+        self.thoughts = []
+        self.actions = []
+        self.observations = []
+        self.history_images = []
+        self.history_responses = []
diff --git a/run_uitars.py b/run_uitars.py
new file mode 100644
index 0000000..aad12c1
--- /dev/null
+++ b/run_uitars.py
@@ -0,0 +1,319 @@
+"""Script to run end-to-end evaluation on the benchmark.
+Utils and basic architecture credit to https://github.com/web-arena-x/webarena/blob/main/run.py.
+"""
+
+import argparse
+import datetime
+import json
+import logging
+import os
+import sys
+
+from tqdm import tqdm
+
+import lib_run_single
+from desktop_env.desktop_env import DesktopEnv
+from mm_agents.uitars_agent import UITARSAgent
+
+# import wandb
+
+
+#  Logger Configs {{{ #
+logger = logging.getLogger()
+logger.setLevel(logging.DEBUG)
+
+datetime_str: str = datetime.datetime.now().strftime("%Y%m%d@%H%M%S")
+
+file_handler = logging.FileHandler(
+    os.path.join("logs", "normal-{:}.log".format(datetime_str)), encoding="utf-8"
+)
+debug_handler = logging.FileHandler(
+    os.path.join("logs", "debug-{:}.log".format(datetime_str)), encoding="utf-8"
+)
+stdout_handler = logging.StreamHandler(sys.stdout)
+sdebug_handler = logging.FileHandler(
+    os.path.join("logs", "sdebug-{:}.log".format(datetime_str)), encoding="utf-8"
+)
+
+file_handler.setLevel(logging.INFO)
+debug_handler.setLevel(logging.DEBUG)
+stdout_handler.setLevel(logging.INFO)
+sdebug_handler.setLevel(logging.DEBUG)
+
+formatter = logging.Formatter(
+    fmt="\x1b[1;33m[%(asctime)s \x1b[31m%(levelname)s \x1b[32m%(module)s/%(lineno)d-%(processName)s\x1b[1;33m] \x1b[0m%(message)s"
+)
+file_handler.setFormatter(formatter)
+debug_handler.setFormatter(formatter)
+stdout_handler.setFormatter(formatter)
+sdebug_handler.setFormatter(formatter)
+
+stdout_handler.addFilter(logging.Filter("desktopenv"))
+sdebug_handler.addFilter(logging.Filter("desktopenv"))
+
+logger.addHandler(file_handler)
+logger.addHandler(debug_handler)
+logger.addHandler(stdout_handler)
+logger.addHandler(sdebug_handler)
+#  }}} Logger Configs #
+
+logger = logging.getLogger("desktopenv.experiment")
+
+
+def config() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Run end-to-end evaluation on the benchmark"
+    )
+
+    # environment config
+    parser.add_argument("--path_to_vm", type=str, default=None)
+    parser.add_argument(
+        "--headless", action="store_true", help="Run in headless machine"
+    )
+    parser.add_argument(
+        "--action_space", type=str, default="pyautogui", help="Action type"
+    )
+    parser.add_argument(
+        "--observation_type",
+        choices=["screenshot", "a11y_tree", "screenshot_a11y_tree", "som"],
+        default="a11y_tree",
+        help="Observation type",
+    )
+    parser.add_argument("--screen_width", type=int, default=1920)
+    parser.add_argument("--screen_height", type=int, default=1080)
+    parser.add_argument("--sleep_after_execution", type=float, default=0.0)
+    parser.add_argument("--max_steps", type=int, default=15)
+
+    # agent config
+    parser.add_argument("--max_trajectory_length", type=int, default=3)
+    parser.add_argument(
+        "--test_config_base_dir", type=str, default="evaluation_examples"
+    )
+
+    # lm config
+    parser.add_argument("--model", type=str, default="gpt-4o")
+    parser.add_argument("--temperature", type=float, default=1.0)
+    parser.add_argument("--top_p", type=float, default=0.9)
+    parser.add_argument("--max_tokens", type=int, default=1500)
+    parser.add_argument("--stop_token", type=str, default=None)
+
+    # example config
+    parser.add_argument("--domain", type=str, default="all")
+    parser.add_argument(
+        "--test_all_meta_path", type=str, default="evaluation_examples/test_all.json"
+    )
+
+    # logging related
+    parser.add_argument("--result_dir", type=str, default="./results")
+    args = parser.parse_args()
+
+    return args
+
+
+def test(args: argparse.Namespace, test_all_meta: dict) -> None:
+    scores = []
+    max_steps = args.max_steps
+
+    # log args
+    logger.info("Args: %s", args)
+    # set wandb project
+    cfg_args = {
+        "path_to_vm": args.path_to_vm,
+        "headless": args.headless,
+        "action_space": args.action_space,
+        "observation_type": args.observation_type,
+        "screen_width": args.screen_width,
+        "screen_height": args.screen_height,
+        "sleep_after_execution": args.sleep_after_execution,
+        "max_steps": args.max_steps,
+        "max_trajectory_length": args.max_trajectory_length,
+        "model": args.model,
+        "temperature": args.temperature,
+        "top_p": args.top_p,
+        "max_tokens": args.max_tokens,
+        "stop_token": args.stop_token,
+        "result_dir": args.result_dir,
+    }
+
+    agent = UITARSAgent(
+        model=args.model,
+        max_tokens=args.max_tokens,
+        top_p=args.top_p,
+        temperature=args.temperature,
+        action_space=args.action_space,
+        observation_type=args.observation_type,
+        max_trajectory_length=args.max_trajectory_length,
+    )
+
+    env = DesktopEnv(
+        path_to_vm=args.path_to_vm,
+        action_space=agent.action_space,
+        screen_size=(args.screen_width, args.screen_height),
+        headless=args.headless,
+        os_type = "Ubuntu",
+        require_a11y_tree=args.observation_type
+        in ["a11y_tree", "screenshot_a11y_tree", "som"],
+    )
+
+    for domain in tqdm(test_all_meta, desc="Domain"):
+        for example_id in tqdm(test_all_meta[domain], desc="Example", leave=False):
+            config_file = os.path.join(
+                args.test_config_base_dir, f"examples/{domain}/{example_id}.json"
+            )
+            with open(config_file, "r", encoding="utf-8") as f:
+                example = json.load(f)
+
+            logger.info(f"[Domain]: {domain}")
+            logger.info(f"[Example ID]: {example_id}")
+
+            instruction = example["instruction"]
+
+            logger.info(f"[Instruction]: {instruction}")
+            # wandb each example config settings
+            cfg_args["instruction"] = instruction
+            cfg_args["start_time"] = datetime.datetime.now().strftime(
+                "%Y:%m:%d-%H:%M:%S"
+            )
+            # run.config.update(cfg_args)
+
+            example_result_dir = os.path.join(
+                args.result_dir,
+                args.action_space,
+                args.observation_type,
+                args.model,
+                domain,
+                example_id,
+            )
+            os.makedirs(example_result_dir, exist_ok=True)
+            # example start running
+            try:
+                lib_run_single.run_single_example(
+                    agent,
+                    env,
+                    example,
+                    max_steps,
+                    instruction,
+                    args,
+                    example_result_dir,
+                    scores,
+                )
+            except Exception as e:
+                logger.error(f"Exception in {domain}/{example_id}: {e}")
+                env.controller.end_recording(
+                    os.path.join(example_result_dir, "recording.mp4")
+                )
+                with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f:
+                    f.write(
+                        json.dumps(
+                            {"Error": f"Time limit exceeded in {domain}/{example_id}"}
+                        )
+                    )
+                    f.write("\n")
+
+    env.close()
+    logger.info(f"Average score: {sum(scores) / len(scores)}")
+
+
+def get_unfinished(
+    action_space, use_model, observation_type, result_dir, total_file_json
+):
+    target_dir = os.path.join(result_dir, action_space, observation_type, use_model)
+
+    if not os.path.exists(target_dir):
+        return total_file_json
+
+    finished = {}
+    for domain in os.listdir(target_dir):
+        finished[domain] = []
+        domain_path = os.path.join(target_dir, domain)
+        if os.path.isdir(domain_path):
+            for example_id in os.listdir(domain_path):
+                if example_id == "onboard":
+                    continue
+                example_path = os.path.join(domain_path, example_id)
+                if os.path.isdir(example_path):
+                    if "result.txt" not in os.listdir(example_path):
+                        # empty all files under example_id
+                        for file in os.listdir(example_path):
+                            os.remove(os.path.join(example_path, file))
+                    else:
+                        finished[domain].append(example_id)
+
+    if not finished:
+        return total_file_json
+
+    for domain, examples in finished.items():
+        if domain in total_file_json:
+            total_file_json[domain] = [
+                x for x in total_file_json[domain] if x not in examples
+            ]
+
+    return total_file_json
+
+
+def get_result(action_space, use_model, observation_type, result_dir, total_file_json):
+    target_dir = os.path.join(result_dir, action_space, observation_type, use_model)
+    if not os.path.exists(target_dir):
+        print("New experiment, no result yet.")
+        return None
+
+    all_result = []
+
+    for domain in os.listdir(target_dir):
+        domain_path = os.path.join(target_dir, domain)
+        if os.path.isdir(domain_path):
+            for example_id in os.listdir(domain_path):
+                example_path = os.path.join(domain_path, example_id)
+                if os.path.isdir(example_path):
+                    if "result.txt" in os.listdir(example_path):
+                        # empty all files under example_id
+                        try:
+                            all_result.append(
+                                float(
+                                    open(
+                                        os.path.join(example_path, "result.txt"), "r"
+                                    ).read()
+                                )
+                            )
+                        except:
+                            all_result.append(0.0)
+
+    if not all_result:
+        print("New experiment, no result yet.")
+        return None
+    else:
+        print("Current Success Rate:", sum(all_result) / len(all_result) * 100, "%")
+        return all_result
+
+
+if __name__ == "__main__":
+    ####### The complete version of the list of examples #######
+    os.environ["TOKENIZERS_PARALLELISM"] = "false"
+    args = config()
+
+    with open(args.test_all_meta_path, "r", encoding="utf-8") as f:
+        test_all_meta = json.load(f)
+
+    if args.domain != "all":
+        test_all_meta = {args.domain: test_all_meta[args.domain]}
+
+    test_file_list = get_unfinished(
+        args.action_space,
+        args.model,
+        args.observation_type,
+        args.result_dir,
+        test_all_meta,
+    )
+    left_info = ""
+    for domain in test_file_list:
+        left_info += f"{domain}: {len(test_file_list[domain])}\n"
+    logger.info(f"Left tasks:\n{left_info}")
+
+    get_result(
+        args.action_space,
+        args.model,
+        args.observation_type,
+        args.result_dir,
+        test_all_meta,
+    )
+    test(args, test_file_list)