refactor: Reduce verbosity in agent outputs and enhance message formatting in chat interface

src/agents/bookmarks_agent.py

@@ -345,4 +345,7 @@ bookmarks_agent = CodeAgent(
     description="Specialized agent for Chrome bookmarks operations, focusing on AI ressources folder. Extracts bookmarks from Chrome and caches them in data/ai_bookmarks_cache.json to avoid direct interaction with Chrome's raw JSON. Provides search, filtering, statistics, and cache management for AI-related bookmarks.",
     max_steps=10,
     additional_authorized_imports=["json", "datetime", "urllib.parse", "pathlib"],
+    # Reduce verbosity
+    stream_outputs=False,
+    max_print_outputs_length=300,
 )

src/agents/categoriser_agent.py

@@ -540,4 +540,7 @@ categoriser_agent = CodeAgent(
     description="Specializes in categorizing AI news and bookmarks into 10 predefined categories: Research & Breakthroughs, Model Releases & Updates, Tools/Frameworks/Platforms, Applications & Industry Use Cases, Regulation/Ethics/Policy, Investment/Funding/M&A, Benchmarks & Leaderboards, Community/Events/Education, Security/Privacy/Safety, and Market Trends & Analysis. Uses keyword-based categorization and provides tools for managing and searching categorized content.",
     max_steps=10,
     additional_authorized_imports=["json", "datetime", "re", "pathlib"],
+    # Reduce verbosity
+    stream_outputs=False,
+    max_print_outputs_length=300,
 )

src/agents/gmail_agent.py

@@ -84,4 +84,6 @@ gmail_agent = CodeAgent(
     description="Gmail agent specialized in reading and searching emails from habib.adoum01@gmail.com and news@alphasignal.ai only",
     max_steps=10,
     additional_authorized_imports=["json"],
+    stream_outputs=False,
+    max_print_outputs_length=300,
 )

src/agents/manager_agent.py

@@ -47,4 +47,7 @@ manager_agent = CodeAgent(
     additional_authorized_imports=["json"],
     # Add planning to help with complex queries
     planning_interval=3,  # Plan every 3 steps to maintain focus
+    # Reduce verbosity - disable streaming outputs and minimize console display
+    stream_outputs=False,  # Disable live streaming of thoughts to terminal
+    max_print_outputs_length=500,  # Limit output length to reduce terminal noise
 )

src/interfaces/gradio_interface.py

@@ -307,15 +307,31 @@ Thanks to **Modal Labs**, **Hugging Face**, **Nebius**, **Anthropic**, **OpenAI*
     return about_tab
 
 
+def sanitize_content(content):
+    """Sanitize content to ensure it's a clean string without complex objects"""
+    if isinstance(content, str):
+        return content
+    elif isinstance(content, dict):
+        # If content is a dict, convert to string representation
+        return str(content)
+    elif hasattr(content, "__dict__"):
+        # If it's an object with attributes, convert to string
+        return str(content)
+    else:
+        return str(content)
+
+
 def validate_message_history(history):
     """Validate and return properly formatted message history"""
     validated = []
     for msg in history:
         if isinstance(msg, dict) and "role" in msg and "content" in msg:
-            # Ensure content is a string
-            if not isinstance(msg["content"], str):
-                msg["content"] = str(msg["content"])
-            validated.append(msg)
+            # Ensure content is a string and properly formatted
+            content = sanitize_content(msg["content"])
+
+            # Create a clean message dict to avoid any nesting issues
+            clean_msg = {"role": str(msg["role"]), "content": content}
+            validated.append(clean_msg)
         else:
             print(f"Warning: Invalid message format detected: {msg}")
     return validated
@@ -336,20 +352,23 @@ def chat_with_agent(message: str, history: List) -> Generator[List, None, None]:
             if isinstance(item, dict):
                 # Already a dict, check if it has required keys
                 if "role" in item and "content" in item:
-                    formatted_history.append(item)
+                    # Ensure content is a simple string
+                    content = sanitize_content(item["content"])
+                    formatted_history.append({"role": str(item["role"]), "content": content})
                 else:
                     # Skip malformed dict items
                     print(f"Warning: Skipping malformed history item: {item}")
                     continue
             elif hasattr(item, "role") and hasattr(item, "content"):
-                # ChatMessage object - convert to dict
-                formatted_history.append({"role": item.role, "content": item.content})
+                # ChatMessage object - convert to dict with string content
+                content = sanitize_content(item.content)
+                formatted_history.append({"role": str(item.role), "content": content})
             elif isinstance(item, (list, tuple)) and len(item) == 2:
                 # Legacy format: [user_message, assistant_message] or (user, assistant)
                 # Convert to proper message format
                 if isinstance(item[0], str) and isinstance(item[1], str):
-                    formatted_history.append({"role": "user", "content": item[0]})
-                    formatted_history.append({"role": "assistant", "content": item[1]})
+                    formatted_history.append({"role": "user", "content": str(item[0])})
+                    formatted_history.append({"role": "assistant", "content": str(item[1])})
                 else:
                     print(f"Warning: Skipping malformed history item: {item}")
                     continue
@@ -391,10 +410,10 @@ def chat_with_agent(message: str, history: List) -> Generator[List, None, None]:
                     step_content += f"🔍 **Step {step.step_number}:** *In Progress...*\n\n"
 
                     if hasattr(step, "thought") and step.thought:
-                        step_content += f"💭 **Thought:** {step.thought}\n\n"
+                        step_content += f"💭 **Thought:** {str(step.thought)}\n\n"
 
                     if hasattr(step, "action") and step.action:
-                        step_content += f"🛠️ **Action:** {step.action}\n\n"
+                        step_content += f"🛠️ **Action:** {str(step.action)}\n\n"
 
                     if hasattr(step, "observations") and step.observations:
                         obs_text = str(step.observations)[:300]
@@ -403,7 +422,9 @@ def chat_with_agent(message: str, history: List) -> Generator[List, None, None]:
                         step_content += f"👁️ **Observation:** {obs_text}\n\n"
 
                     step_content += "⏳ *Processing next step...*"
-                    thinking_message["content"] = step_content
+
+                    # Ensure the content is a clean string
+                    thinking_message = {"role": "assistant", "content": str(step_content)}
                     new_history[-1] = thinking_message
                     yield validate_message_history(new_history)
 
@@ -411,9 +432,10 @@ def chat_with_agent(message: str, history: List) -> Generator[List, None, None]:
             # If streaming fails, fall back to regular execution
             print(f"Streaming failed: {stream_error}, falling back to regular execution")
 
-            thinking_message["content"] = (
-                "⚡ **Agent Working** ����\n\n💫 Processing your request using available tools...\n\n⏳ *Please wait...*"
-            )
+            thinking_message = {
+                "role": "assistant",
+                "content": "⚡ **Agent Working** 🔄\n\n💫 Processing your request using available tools...\n\n⏳ *Please wait...*",
+            }
             new_history[-1] = thinking_message
             yield validate_message_history(new_history)
 
@@ -467,28 +489,35 @@ def chat_with_agent(message: str, history: List) -> Generator[List, None, None]:
                     tool_usage_content = "Agent executed actions successfully"
 
             # Update thinking to show completion
-            thinking_message["content"] = (
-                "✅ **Agent Complete** 🎉\n\n✅ Request processed successfully\n✅ Response prepared"
-            )
+            thinking_message = {
+                "role": "assistant",
+                "content": "✅ **Agent Complete** 🎉\n\n✅ Request processed successfully\n✅ Response prepared",
+            }
             new_history[-1] = thinking_message
             yield validate_message_history(new_history)
 
             # Add tool usage message if there were tools used
             if tool_usage_content:
-                tool_message = {"role": "assistant", "content": f"🛠️ **Tools & Actions Used**\n\n{tool_usage_content}"}
+                tool_message = {
+                    "role": "assistant",
+                    "content": f"🛠️ **Tools & Actions Used**\n\n{str(tool_usage_content)}",
+                }
                 new_history.append(tool_message)
                 yield validate_message_history(new_history)
 
             # Add final response
             final_response = str(result) if result else "I couldn't process your request."
-            final_message = {"role": "assistant", "content": final_response}
+            final_message = {"role": "assistant", "content": str(final_response)}
             new_history.append(final_message)
             yield validate_message_history(new_history)
             return
 
         # If we get here, streaming worked, so get the final result
         # The streaming should have shown all the steps, now get final answer
-        thinking_message["content"] = "✅ **Agent Complete** 🎉\n\n✅ All steps executed\n✅ Preparing final response"
+        thinking_message = {
+            "role": "assistant",
+            "content": "✅ **Agent Complete** 🎉\n\n✅ All steps executed\n✅ Preparing final response",
+        }
         new_history[-1] = thinking_message
         yield validate_message_history(new_history)
 
@@ -500,7 +529,7 @@ def chat_with_agent(message: str, history: List) -> Generator[List, None, None]:
             if hasattr(last_step, "observations") and last_step.observations:
                 final_response = str(last_step.observations)
 
-        final_message = {"role": "assistant", "content": final_response}
+        final_message = {"role": "assistant", "content": str(final_response)}
         new_history.append(final_message)
         yield validate_message_history(new_history)
 
@@ -553,8 +582,8 @@ chat_interface = gr.ChatInterface(
     **⏱️ Processing Time Note:** Depending on the type of query, processing can take several seconds or minutes to complete.
     """,
     examples=[
+        "📧 Show me my latest 5 newsletters emails",
         "🔖 Search my AI bookmarks",
-        "📧 Show me my latest 5 emails",
         "🤖 Find emails about AI",
         "🌐 Search for latest AI news",
         "💎 What AI resources do I have?",
@@ -566,7 +595,7 @@ chat_interface = gr.ChatInterface(
         "🚀 Show model releases bookmarks",
         "🛠️ Find tools and frameworks bookmarks",
     ],
-    show_progress="hidden",
+    show_progress="hidden"
 )
 
 # Create categories and about interfaces