diff --git a/src/agent/custom_agent.py b/src/agent/custom_agent.py
index ed4ceb6..4b0eff3 100644
--- a/src/agent/custom_agent.py
+++ b/src/agent/custom_agent.py
@@ -208,8 +208,9 @@ class CustomAgent(Agent):
     @time_execution_async("--get_next_action")
     async def get_next_action(self, input_messages: list[BaseMessage]) -> AgentOutput:
         """Get next action from LLM based on current state"""
-
-        ai_message = self.llm.invoke(input_messages)
+        fixed_input_messages = self._convert_input_messages(input_messages)
+        ai_message = self.llm.invoke(fixed_input_messages)
+        self.message_manager._add_message_with_tokens(ai_message)
 
         if hasattr(ai_message, "reasoning_content"):
             logger.info("🤯 Start Deep Thinking: ")
@@ -221,10 +222,16 @@ class CustomAgent(Agent):
         else:
             ai_content = ai_message.content
 
-        ai_content = ai_content.replace("```json", "").replace("```", "")
-        ai_content = repair_json(ai_content)
-        parsed_json = json.loads(ai_content)
-        parsed: AgentOutput = self.AgentOutput(**parsed_json)
+        try:
+            ai_content = ai_content.replace("```json", "").replace("```", "")
+            ai_content = repair_json(ai_content)
+            parsed_json = json.loads(ai_content)
+            parsed: AgentOutput = self.AgentOutput(**parsed_json)
+        except Exception as e:
+            import traceback
+            traceback.print_exc()
+            logger.debug(ai_message.content)
+            raise ValueError('Could not parse response.')
 
         if parsed is None:
             logger.debug(ai_message.content)
@@ -234,7 +241,6 @@ class CustomAgent(Agent):
         if len(parsed.action) > self.settings.max_actions_per_step:
             parsed.action = parsed.action[: self.settings.max_actions_per_step]
         self._log_response(parsed)
-        self.message_manager._add_message_with_tokens(ai_message)
         return parsed
 
     async def _run_planner(self) -> Optional[str]:
@@ -335,7 +341,6 @@ class CustomAgent(Agent):
                 await self._raise_if_stopped_or_paused()
             except Exception as e:
                 # model call failed, remove last state message from history
-                pdb.set_trace()
                 self.message_manager._remove_state_message_by_index(-1)
                 raise e
 
diff --git a/src/agent/custom_message_manager.py b/src/agent/custom_message_manager.py
index 3831385..212c3fb 100644
--- a/src/agent/custom_message_manager.py
+++ b/src/agent/custom_message_manager.py
@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import logging
+import pdb
 from typing import List, Optional, Type, Dict
 
 from browser_use.agent.message_manager.service import MessageManager
diff --git a/src/agent/custom_system_prompt.md b/src/agent/custom_system_prompt.md
index 13efbdb..9cefaa2 100644
--- a/src/agent/custom_system_prompt.md
+++ b/src/agent/custom_system_prompt.md
@@ -18,11 +18,17 @@ Example:
 
 # Response Rules
 1. RESPONSE FORMAT: You must ALWAYS respond with valid JSON in this exact format:
-{{"current_state": {{"evaluation_previous_goal": "Success|Failed|Unknown - Analyze the current elements and the image to check if the previous goals/actions are successful like intended by the task. Mention if something unexpected happened. Shortly state why/why not.",
-"important_contents": "Output important contents closely related to user's instruction on the current page. If there is, please output the contents. If not, please output ''.",
-"thought": "Think about the requirements that have been completed in previous operations and the requirements that need to be completed in the next one operation. If your output of evaluation_previous_goal is 'Failed', please reflect and output your reflection here.",
-"next_goal": "Please generate a brief natural language description for the goal of your next actions based on your thought."}},
-"action":[{{"one_action_name": {{// action-specific parameter}}}}, // ... more actions in sequence]}}
+{{
+ "current_state": {{
+   "evaluation_previous_goal": "Success|Failed|Unknown - Analyze the current elements and the image to check if the previous goals/actions are successful like intended by the task. Mention if something unexpected happened. Shortly state why/why not.",
+   "important_contents": "Output important contents closely related to user\'s instruction on the current page. If there is, please output the contents. If not, please output empty string ''.",
+   "thought": "Think about the requirements that have been completed in previous operations and the requirements that need to be completed in the next one operation. If your output of evaluation_previous_goal is 'Failed', please reflect and output your reflection here.",
+   "next_goal": "Please generate a brief natural language description for the goal of your next actions based on your thought."
+ }},
+ "action": [
+   {{"one_action_name": {{// action-specific parameter}}}}, // ... more actions in sequence
+ ]
+}}
 
 2. ACTIONS: You can specify multiple actions in the list to be executed in sequence. But always specify only one action name per item. Use maximum {{max_actions}} actions per sequence.
 Common action sequences:
diff --git a/tests/test_browser_use.py b/tests/test_browser_use.py
index db35c5f..6ef4210 100644
--- a/tests/test_browser_use.py
+++ b/tests/test_browser_use.py
@@ -133,11 +133,11 @@ async def test_browser_use_custom():
         api_key=os.getenv("GOOGLE_API_KEY", "")
     )
 
-    # llm = utils.get_llm_model(
-    #     provider="deepseek",
-    #     model_name="deepseek-reasoner",
-    #     temperature=0.8
-    # )
+    llm = utils.get_llm_model(
+        provider="deepseek",
+        model_name="deepseek-reasoner",
+        temperature=0.8
+    )
 
     # llm = utils.get_llm_model(
     #     provider="deepseek",