fix deepresearch

Author: vvincent1234

src/agent/custom_agent.py

@@ -54,26 +54,6 @@
 
 logger = logging.getLogger(__name__)
 
-
-def _log_response(response: CustomAgentOutput) -> None:
-    """Log the model's response"""
-    if "Success" in response.current_state.evaluation_previous_goal:
-        emoji = "✅"
-    elif "Failed" in response.current_state.evaluation_previous_goal:
-        emoji = "❌"
-    else:
-        emoji = "🤷"
-
-    logger.info(f"{emoji} Eval: {response.current_state.evaluation_previous_goal}")
-    logger.info(f"🧠 New Memory: {response.current_state.important_contents}")
-    logger.info(f"🤔 Thought: {response.current_state.thought}")
-    logger.info(f"🎯 Next Goal: {response.current_state.next_goal}")
-    for i, action in enumerate(response.action):
-        logger.info(
-            f"🛠️  Action {i + 1}/{len(response.action)}: {action.model_dump_json(exclude_unset=True)}"
-        )
-
-
 Context = TypeVar('Context')
 
 
@@ -180,6 +160,24 @@ def __init__(
             state=self.state.message_manager_state,
         )
 
+    def _log_response(self, response: CustomAgentOutput) -> None:
+        """Log the model's response"""
+        if "Success" in response.current_state.evaluation_previous_goal:
+            emoji = "✅"
+        elif "Failed" in response.current_state.evaluation_previous_goal:
+            emoji = "❌"
+        else:
+            emoji = "🤷"
+
+        logger.info(f"{emoji} Eval: {response.current_state.evaluation_previous_goal}")
+        logger.info(f"🧠 New Memory: {response.current_state.important_contents}")
+        logger.info(f"🤔 Thought: {response.current_state.thought}")
+        logger.info(f"🎯 Next Goal: {response.current_state.next_goal}")
+        for i, action in enumerate(response.action):
+            logger.info(
+                f"🛠️  Action {i + 1}/{len(response.action)}: {action.model_dump_json(exclude_unset=True)}"
+            )
+
     def _setup_action_models(self) -> None:
         """Setup dynamic action models from controller's registry"""
         # Get the dynamic action model from controller's registry
@@ -236,7 +234,7 @@ async def get_next_action(self, input_messages: list[BaseMessage]) -> AgentOutpu
         # cut the number of actions to max_actions_per_step if needed
         if len(parsed.action) > self.settings.max_actions_per_step:
             parsed.action = parsed.action[: self.settings.max_actions_per_step]
-        _log_response(parsed)
+        self._log_response(parsed)
         return parsed
 
     async def _run_planner(self) -> Optional[str]:

src/agent/custom_prompts.py

@@ -88,15 +88,15 @@ def get_user_message(self, use_vision: bool = True) -> HumanMessage:
             for i, result in enumerate(self.result):
                 action = self.actions[i]
                 state_description += f"Previous action {i + 1}/{len(self.result)}: {action.model_dump_json(exclude_unset=True)}\n"
+                if result.error:
+                    # only use last 300 characters of error
+                    error = result.error.split('\n')[-1]
+                    state_description += (
+                        f"Error of previous action {i + 1}/{len(self.result)}: ...{error}\n"
+                    )
                 if result.include_in_memory:
                     if result.extracted_content:
                         state_description += f"Result of previous action {i + 1}/{len(self.result)}: {result.extracted_content}\n"
-                    if result.error:
-                        # only use last 300 characters of error
-                        error = result.error.split('\n')[-1]
-                        state_description += (
-                            f"Error of previous action {i + 1}/{len(self.result)}: ...{error}\n"
-                        )
 
         if self.state.screenshot and use_vision == True:
             # Format message for vision model

src/utils/deep_research.py

@@ -89,7 +89,7 @@ async def extract_content(browser: BrowserContext):
         )
         # go back to org url
         await page.go_back()
-        msg = f'Extracted page content:\n {content}\n'
+        msg = f'Extracted page content:\n{content}\n'
         logger.info(msg)
         return ActionResult(extracted_content=msg)
 

webui.py

@@ -338,7 +338,7 @@ async def run_org_agent(
             )
         history = await _global_agent.run(max_steps=max_steps)
 
-        history_file = os.path.join(save_agent_history_path, f"{_global_agent.agent_id}.json")
+        history_file = os.path.join(save_agent_history_path, f"{_global_agent.state.agent_id}.json")
         _global_agent.save_history(history_file)
 
         final_result = history.final_result()
@@ -450,7 +450,7 @@ async def run_custom_agent(
             )
         history = await _global_agent.run(max_steps=max_steps)
 
-        history_file = os.path.join(save_agent_history_path, f"{_global_agent.agent_id}.json")
+        history_file = os.path.join(save_agent_history_path, f"{_global_agent.state.agent_id}.json")
         _global_agent.save_history(history_file)
 
         final_result = history.final_result()
@@ -985,12 +985,11 @@ def update_llm_num_ctx_visibility(llm_provider):
                 markdown_output_display = gr.Markdown(label="Research Report")
                 markdown_download = gr.File(label="Download Research Report")
 
-
             # Bind the stop button click event after errors_output is defined
             stop_button.click(
                 fn=stop_agent,
                 inputs=[],
-                outputs=[errors_output, stop_button, run_button],
+                outputs=[stop_button, run_button],
             )
 
             # Run button click handler