Enhance X integration with comprehensive debugging and thread context improvements

- Added detailed debugging data structure for X bot, saving conversation metadata and user analysis.
- Implemented common issue handling for incomplete thread context and cache staleness.
- Updated `get_thread_context()` to include an `until_id` parameter, allowing exclusion of future tweets.
- Enhanced mention processing with improved logging for conversation tracking and debugging.
- Saved agent response data and conversation debug information to dedicated debug folders for better analysis.
- Ensured tools are self-contained, removing reliance on shared functions and environment variables.

These changes improve the robustness of the X integration and facilitate better debugging and analysis of conversation flows.

CLAUDE.md

···
 python queue_manager.py delete @example.bsky.social --force
 ```
 
+### X Debug Data Structure
+
+The X bot saves comprehensive debugging data to `x_queue/debug/conversation_{conversation_id}/` for each processed mention:
+
+- `thread_data_{mention_id}.json` - Raw thread data from X API
+- `thread_context_{mention_id}.yaml` - Processed YAML thread context sent to agent  
+- `debug_info_{mention_id}.json` - Conversation metadata and user analysis
+- `agent_response_{mention_id}.json` - Complete agent interaction including prompt, reasoning, tool calls, and responses
+
+This debug data is especially useful for analyzing how different conversation types (including Grok interactions) are handled.
+
+**Common Issues:**
+- **Incomplete Thread Context**: X API's conversation search may miss recent tweets in long conversations. The bot attempts to fetch missing referenced tweets directly.
+- **Cache Staleness**: Thread context caching is disabled during processing to ensure fresh data.
+- **Search API Limitations**: X API recent search only covers 7 days and may have indexing delays.
+- **Temporal Constraints**: Thread context uses `until_id` parameter to exclude tweets that occurred after the mention being processed, preventing "future knowledge" leakage.
+
 ## Architecture Overview
 
 ### Core Components
···
 ## Key Coding Principles
 
 - All errors in tools must be thrown, not returned as strings.
+- **Tool Self-Containment**: Tools executed in the cloud (like user block management tools) must be completely self-contained:
+  - Cannot use shared functions like `get_letta_client()` 
+  - Must create Letta client inline using environment variables: `Letta(token=os.environ["LETTA_API_KEY"])`
+  - Cannot use config.yaml (only environment variables)
+  - Cannot use logging (cloud execution doesn't support it)
+  - Must include all necessary imports within the function
 
 ## Memory: Python Environment Commands
 

tools/blocks.py

···
     Returns:
         String confirming the note was appended
     """
-    logger = logging.getLogger(__name__)
-    
     try:
-        client = get_letta_client()
+        # Create Letta client inline - cloud tools must be self-contained
+        import os
+        from letta_client import Letta
+        client = Letta(token=os.environ["LETTA_API_KEY"])
         
         block_label = f"x_user_{user_id}"
         
···
                 block_id=str(block.id),
                 value=new_value
             )
-            logger.info(f"Appended note to existing block: {block_label}")
             return f"✓ Appended note to X user {user_id}'s memory block"
             
         else:
···
                 value=initial_value,
                 limit=5000
             )
-            logger.info(f"Created new block with note: {block_label}")
             
             # Check if block needs to be attached to agent
             current_blocks = client.agents.blocks.list(agent_id=str(agent_state.id))
···
                     agent_id=str(agent_state.id),
                     block_id=str(block.id)
                 )
-                logger.info(f"Attached new block to agent: {block_label}")
                 return f"✓ Created and attached X user {user_id}'s memory block with note"
             else:
                 return f"✓ Created X user {user_id}'s memory block with note"
                 
     except Exception as e:
-        logger.error(f"Error appending note to X user block: {e}")
         raise Exception(f"Error appending note to X user block: {str(e)}")
 
 
···
     Returns:
         String confirming the text was replaced
     """
-    logger = logging.getLogger(__name__)
-    
     try:
-        client = get_letta_client()
+        # Create Letta client inline - cloud tools must be self-contained
+        import os
+        from letta_client import Letta
+        client = Letta(token=os.environ["LETTA_API_KEY"])
         
         block_label = f"x_user_{user_id}"
         
···
             block_id=str(block.id),
             value=new_value
         )
-        logger.info(f"Replaced text in block: {block_label}")
         return f"✓ Replaced text in X user {user_id}'s memory block"
         
     except Exception as e:
-        logger.error(f"Error replacing text in X user block: {e}")
         raise Exception(f"Error replacing text in X user block: {str(e)}")
 
 
···
     Returns:
         String confirming the content was set
     """
-    logger = logging.getLogger(__name__)
-    
     try:
-        client = get_letta_client()
+        # Create Letta client inline - cloud tools must be self-contained
+        import os
+        from letta_client import Letta
+        client = Letta(token=os.environ["LETTA_API_KEY"])
         
         block_label = f"x_user_{user_id}"
         
···
                 block_id=str(block.id),
                 value=content
             )
-            logger.info(f"Set content for existing block: {block_label}")
             return f"✓ Set content for X user {user_id}'s memory block"
             
         else:
···
                 value=content,
                 limit=5000
             )
-            logger.info(f"Created new block with content: {block_label}")
             
             # Check if block needs to be attached to agent
             current_blocks = client.agents.blocks.list(agent_id=str(agent_state.id))
···
                     agent_id=str(agent_state.id),
                     block_id=str(block.id)
                 )
-                logger.info(f"Attached new block to agent: {block_label}")
                 return f"✓ Created and attached X user {user_id}'s memory block"
             else:
                 return f"✓ Created X user {user_id}'s memory block"
                 
     except Exception as e:
-        logger.error(f"Error setting X user block content: {e}")
         raise Exception(f"Error setting X user block content: {str(e)}")
 
 
···
     Returns:
         String containing the user's memory block content
     """
-    logger = logging.getLogger(__name__)
-    
     try:
-        client = get_letta_client()
+        # Create Letta client inline - cloud tools must be self-contained
+        import os
+        from letta_client import Letta
+        client = Letta(token=os.environ["LETTA_API_KEY"])
         
         block_label = f"x_user_{user_id}"
         
···
             return f"No memory block found for X user: {user_id}"
             
         block = blocks[0]
-        logger.info(f"Retrieved content for block: {block_label}")
         
         return f"Memory block for X user {user_id}:\n\n{block.value}"
         
     except Exception as e:
-        logger.error(f"Error viewing X user block: {e}")
         raise Exception(f"Error viewing X user block: {str(e)}")
 
 

x.py

···
 from rich.panel import Panel
 from rich.text import Text
 
+import bsky_utils
+
 
 # Configure logging
 logging.basicConfig(
···
                 logger.warning("Search request failed")
             return []
     
-    def get_thread_context(self, conversation_id: str, use_cache: bool = True) -> Optional[List[Dict]]:
+    def get_thread_context(self, conversation_id: str, use_cache: bool = True, until_id: Optional[str] = None) -> Optional[List[Dict]]:
         """
-        Get all tweets in a conversation thread.
+        Get all tweets in a conversation thread up to a specific tweet ID.
         
         Args:
             conversation_id: The conversation ID to fetch (should be the original tweet ID)
             use_cache: Whether to use cached data if available
+            until_id: Optional tweet ID to use as upper bound (excludes posts after this ID)
             
         Returns:
             List of tweets in the conversation, ordered chronologically
···
             "expansions": "author_id,in_reply_to_user_id,referenced_tweets.id",
             "sort_order": "recency"  # Get newest first, we'll reverse later
         }
+        
+        # Add until_id parameter to exclude tweets after the mention being processed
+        if until_id:
+            params["until_id"] = until_id
+            logger.info(f"Using until_id={until_id} to exclude future tweets")
         
         logger.info(f"Fetching thread context for conversation {conversation_id}")
         response = self._make_request(endpoint, params)
···
                 tweets.append(original_tweet)
                 logger.info("Added original tweet to thread context")
         
+        # Attempt to fill gaps by fetching referenced tweets that are missing
+        # This helps with X API's incomplete conversation search results
+        tweet_ids = set(t.get('id') for t in tweets)
+        missing_tweet_ids = set()
+        
+        # Collect all referenced tweet IDs that aren't in our current set
+        for tweet in tweets:
+            referenced_tweets = tweet.get('referenced_tweets', [])
+            for ref in referenced_tweets:
+                ref_id = ref.get('id')
+                if ref_id and ref_id not in tweet_ids:
+                    missing_tweet_ids.add(ref_id)
+        
+        # Fetch missing referenced tweets individually
+        for missing_id in missing_tweet_ids:
+            try:
+                endpoint = f"/tweets/{missing_id}"
+                params = {
+                    "tweet.fields": "id,text,author_id,created_at,in_reply_to_user_id,referenced_tweets,conversation_id",
+                    "user.fields": "id,name,username",
+                    "expansions": "author_id"
+                }
+                response = self._make_request(endpoint, params)
+                if response and "data" in response:
+                    missing_tweet = response["data"]
+                    # Only add if it's actually part of this conversation
+                    if missing_tweet.get('conversation_id') == conversation_id:
+                        tweets.append(missing_tweet)
+                        tweet_ids.add(missing_id)
+                        logger.info(f"Retrieved missing referenced tweet: {missing_id}")
+                        
+                        # Also add user data if available
+                        if "includes" in response and "users" in response["includes"]:
+                            for user in response["includes"]["users"]:
+                                users_data[user["id"]] = user
+            except Exception as e:
+                logger.warning(f"Could not fetch missing tweet {missing_id}: {e}")
+        
         if tweets:
+            # Filter out tweets that occur after until_id (if specified)
+            if until_id:
+                original_count = len(tweets)
+                # Convert until_id to int for comparison (Twitter IDs are sequential)
+                until_id_int = int(until_id)
+                tweets = [t for t in tweets if int(t.get('id', '0')) <= until_id_int]
+                filtered_count = len(tweets)
+                if original_count != filtered_count:
+                    logger.info(f"Filtered out {original_count - filtered_count} tweets after until_id {until_id}")
+            
             # Sort chronologically (oldest first)
             tweets.sort(key=lambda x: x.get('created_at', ''))
             logger.info(f"Retrieved {len(tweets)} tweets in thread")
···
         
         queue_file = X_QUEUE_DIR / filename
         
-        # Save mention data
+        # Save mention data with enhanced debugging information
+        mention_data = {
+            'mention': mention,
+            'queued_at': datetime.now().isoformat(),
+            'type': 'x_mention',
+            # Debug info for conversation tracking
+            'debug_info': {
+                'mention_id': mention.get('id'),
+                'author_id': mention.get('author_id'),
+                'conversation_id': mention.get('conversation_id'),
+                'in_reply_to_user_id': mention.get('in_reply_to_user_id'),
+                'referenced_tweets': mention.get('referenced_tweets', []),
+                'text_preview': mention.get('text', '')[:200],
+                'created_at': mention.get('created_at'),
+                'public_metrics': mention.get('public_metrics', {}),
+                'context_annotations': mention.get('context_annotations', [])
+            }
+        }
+        
         with open(queue_file, 'w') as f:
-            json.dump({
-                'mention': mention,
-                'queued_at': datetime.now().isoformat(),
-                'type': 'x_mention'
-            }, f, indent=2)
+            json.dump(mention_data, f, indent=2)
         
         logger.info(f"Queued X mention {mention_id} -> {filename}")
         
···
         mention_text = mention.get('text', '')
         author_id = mention.get('author_id')
         conversation_id = mention.get('conversation_id')
+        in_reply_to_user_id = mention.get('in_reply_to_user_id')
+        referenced_tweets = mention.get('referenced_tweets', [])
         
-        logger.debug(f"Extracted data - ID: {mention_id}, Author: {author_id}, Text: {mention_text[:50]}...")
+        # Enhanced conversation tracking for debug - especially important for Grok handling
+        logger.info(f"🔍 CONVERSATION DEBUG - Mention ID: {mention_id}")
+        logger.info(f"   Author ID: {author_id}")
+        logger.info(f"   Conversation ID: {conversation_id}")
+        logger.info(f"   In Reply To User ID: {in_reply_to_user_id}")
+        logger.info(f"   Referenced Tweets: {len(referenced_tweets)} items")
+        for i, ref in enumerate(referenced_tweets[:3]):  # Log first 3 referenced tweets
+            logger.info(f"     Reference {i+1}: {ref.get('type')} -> {ref.get('id')}")
+        logger.info(f"   Text preview: {mention_text[:100]}...")
         
         if not conversation_id:
-            logger.warning(f"No conversation_id found for mention {mention_id}")
+            logger.warning(f"❌ No conversation_id found for mention {mention_id} - this may cause thread context issues")
             return None
         
-        # Get thread context
+        # Get thread context (disable cache for missing context issues)
+        # Use mention_id as until_id to exclude tweets that occurred after this mention
         try:
-            thread_data = x_client.get_thread_context(conversation_id)
+            thread_data = x_client.get_thread_context(conversation_id, use_cache=False, until_id=mention_id)
             if not thread_data:
-                logger.error(f"Failed to get thread context for conversation {conversation_id}")
+                logger.error(f"❌ Failed to get thread context for conversation {conversation_id}")
                 return False
+            
+            # If this mention references a specific tweet, ensure we have that tweet in context
+            if referenced_tweets:
+                for ref in referenced_tweets:
+                    if ref.get('type') == 'replied_to':
+                        ref_id = ref.get('id')
+                        # Check if the referenced tweet is in our thread data
+                        thread_tweet_ids = [t.get('id') for t in thread_data.get('tweets', [])]
+                        if ref_id and ref_id not in thread_tweet_ids:
+                            logger.warning(f"Missing referenced tweet {ref_id} in thread context, attempting to fetch")
+                            try:
+                                # Fetch the missing referenced tweet directly
+                                endpoint = f"/tweets/{ref_id}"
+                                params = {
+                                    "tweet.fields": "id,text,author_id,created_at,in_reply_to_user_id,referenced_tweets,conversation_id",
+                                    "user.fields": "id,name,username",
+                                    "expansions": "author_id"
+                                }
+                                response = x_client._make_request(endpoint, params)
+                                if response and "data" in response:
+                                    missing_tweet = response["data"]
+                                    if missing_tweet.get('conversation_id') == conversation_id:
+                                        # Add to thread data
+                                        if 'tweets' not in thread_data:
+                                            thread_data['tweets'] = []
+                                        thread_data['tweets'].append(missing_tweet)
+                                        
+                                        # Add user data if available
+                                        if "includes" in response and "users" in response["includes"]:
+                                            if 'users' not in thread_data:
+                                                thread_data['users'] = {}
+                                            for user in response["includes"]["users"]:
+                                                thread_data['users'][user["id"]] = user
+                                        
+                                        logger.info(f"✅ Added missing referenced tweet {ref_id} to thread context")
+                                    else:
+                                        logger.warning(f"Referenced tweet {ref_id} belongs to different conversation {missing_tweet.get('conversation_id')}")
+                            except Exception as e:
+                                logger.error(f"Failed to fetch referenced tweet {ref_id}: {e}")
+            
+            # Enhanced thread context debugging
+            logger.info(f"🧵 THREAD CONTEXT DEBUG - Conversation ID: {conversation_id}")
+            thread_posts = thread_data.get('tweets', [])
+            thread_users = thread_data.get('users', {})
+            logger.info(f"   Posts in thread: {len(thread_posts)}")
+            logger.info(f"   Users in thread: {len(thread_users)}")
+            
+            # Log thread participants for Grok detection
+            for user_id, user_info in thread_users.items():
+                username = user_info.get('username', 'unknown')
+                name = user_info.get('name', 'Unknown')
+                is_verified = user_info.get('verified', False)
+                logger.info(f"   User {user_id}: @{username} ({name}) verified={is_verified}")
+                
+                # Special logging for Grok or AI-related users
+                if 'grok' in username.lower() or 'grok' in name.lower():
+                    logger.info(f"   🤖 DETECTED GROK USER: @{username} ({name})")
+            
+            # Log conversation structure
+            for i, post in enumerate(thread_posts[:5]):  # Log first 5 posts
+                post_id = post.get('id')
+                post_author = post.get('author_id')
+                post_text = post.get('text', '')[:50]
+                is_reply = 'in_reply_to_user_id' in post
+                logger.info(f"   Post {i+1}: {post_id} by {post_author} (reply={is_reply}) - {post_text}...")
+                
         except Exception as e:
-            logger.error(f"Error getting thread context: {e}")
+            logger.error(f"❌ Error getting thread context: {e}")
             return False
         
         # Convert to YAML string
         thread_context = thread_to_yaml_string(thread_data)
-        logger.debug(f"Thread context generated, length: {len(thread_context)} characters")
+        logger.info(f"📄 Thread context generated, length: {len(thread_context)} characters")
+        
+        # Save comprehensive conversation data for debugging
+        try:
+            debug_dir = X_QUEUE_DIR / "debug" / f"conversation_{conversation_id}"
+            debug_dir.mkdir(parents=True, exist_ok=True)
+            
+            # Save raw thread data (JSON)
+            with open(debug_dir / f"thread_data_{mention_id}.json", 'w') as f:
+                json.dump(thread_data, f, indent=2)
+            
+            # Save YAML thread context
+            with open(debug_dir / f"thread_context_{mention_id}.yaml", 'w') as f:
+                f.write(thread_context)
+            
+            # Save mention processing debug info
+            debug_info = {
+                'processed_at': datetime.now().isoformat(),
+                'mention_id': mention_id,
+                'conversation_id': conversation_id,
+                'author_id': author_id,
+                'in_reply_to_user_id': in_reply_to_user_id,
+                'referenced_tweets': referenced_tweets,
+                'thread_stats': {
+                    'total_posts': len(thread_posts),
+                    'total_users': len(thread_users),
+                    'yaml_length': len(thread_context)
+                },
+                'users_in_conversation': {
+                    user_id: {
+                        'username': user_info.get('username'),
+                        'name': user_info.get('name'),
+                        'verified': user_info.get('verified', False),
+                        'is_grok': 'grok' in user_info.get('username', '').lower() or 'grok' in user_info.get('name', '').lower()
+                    }
+                    for user_id, user_info in thread_users.items()
+                }
+            }
+            
+            with open(debug_dir / f"debug_info_{mention_id}.json", 'w') as f:
+                json.dump(debug_info, f, indent=2)
+                
+            logger.info(f"💾 Saved conversation debug data to: {debug_dir}")
+            
+        except Exception as debug_error:
+            logger.warning(f"Failed to save debug data: {debug_error}")
+            # Continue processing even if debug save fails
         
         # Check for #voidstop
         if "#voidstop" in thread_context.lower() or "#voidstop" in mention_text.lower():
···
 ```
 
 The YAML above shows the complete conversation thread. The most recent post is the one mentioned above that you should respond to, but use the full thread context to understand the conversation flow.
+
+If you need to update user information, use the x_user_* tools.
 
 To reply, use the add_post_to_x_thread tool:
 - Each call creates one post (max 280 characters)
···
                         except json.JSONDecodeError as e:
                             logger.error(f"Failed to parse tool call arguments: {e}")
         
+        # Save agent response data to debug folder
+        try:
+            debug_dir = X_QUEUE_DIR / "debug" / f"conversation_{conversation_id}"
+            
+            # Save complete agent interaction
+            agent_response_data = {
+                'processed_at': datetime.now().isoformat(),
+                'mention_id': mention_id,
+                'conversation_id': conversation_id,
+                'prompt_sent': prompt,
+                'reply_candidates': reply_candidates,
+                'ignored_notification': ignored_notification,
+                'ack_note': ack_note,
+                'tool_call_results': tool_call_results,
+                'all_messages': []
+            }
+            
+            # Convert messages to serializable format
+            for message in message_response.messages:
+                msg_data = {
+                    'message_type': getattr(message, 'message_type', 'unknown'),
+                    'content': getattr(message, 'content', ''),
+                    'reasoning': getattr(message, 'reasoning', ''),
+                    'status': getattr(message, 'status', ''),
+                    'name': getattr(message, 'name', ''),
+                }
+                
+                if hasattr(message, 'tool_call') and message.tool_call:
+                    msg_data['tool_call'] = {
+                        'name': message.tool_call.name,
+                        'arguments': message.tool_call.arguments,
+                        'tool_call_id': getattr(message.tool_call, 'tool_call_id', '')
+                    }
+                
+                agent_response_data['all_messages'].append(msg_data)
+            
+            with open(debug_dir / f"agent_response_{mention_id}.json", 'w') as f:
+                json.dump(agent_response_data, f, indent=2)
+                
+            logger.info(f"💾 Saved agent response debug data")
+            
+        except Exception as debug_error:
+            logger.warning(f"Failed to save agent response debug data: {debug_error}")
+        
         # Handle conflicts
         if reply_candidates and ignored_notification:
             logger.error("⚠️ CONFLICT: Agent called both add_post_to_x_thread and ignore_notification!")
···
 def acknowledge_x_post(x_client, post_id, note=None):
     """
     Acknowledge an X post that we replied to.
-    For X, we could implement this as a private note/database entry since X doesn't have
-    a built-in acknowledgment system like Bluesky's stream.thought.ack.
+    Uses the same Bluesky client and uploads to the void data repository on atproto,
+    just like Bluesky acknowledgments.
     
     Args:
-        x_client: XClient instance (reserved for future X API acknowledgment features)
+        x_client: XClient instance (not used, kept for compatibility)
         post_id: The X post ID we're acknowledging
         note: Optional note to include with the acknowledgment
         
···
         True if successful, False otherwise
     """
     try:
-        # x_client reserved for future X API acknowledgment features
-        # For now, implement as a simple log entry
-        # In the future, this could write to a database or file system
-        ack_dir = X_QUEUE_DIR / "acknowledgments"
-        ack_dir.mkdir(exist_ok=True)
+        # Use Bluesky client to upload acks to the void data repository on atproto
+        bsky_client = bsky_utils.default_login()
         
-        ack_data = {
-            'post_id': post_id,
-            'acknowledged_at': datetime.now().isoformat(),
-            'note': note
-        }
+        # Create a synthetic URI and CID for the X post
+        # X posts don't have atproto URIs/CIDs, so we create identifiers
+        post_uri = f"x://twitter.com/post/{post_id}"
+        post_cid = f"x_{post_id}_cid"  # Synthetic CID for X posts
         
-        ack_file = ack_dir / f"ack_{post_id}.json"
-        with open(ack_file, 'w') as f:
-            json.dump(ack_data, f, indent=2)
-        
-        logger.debug(f"Acknowledged X post {post_id}" + (f" with note: {note[:50]}..." if note else ""))
-        return True
+        # Use the same acknowledge_post function as Bluesky
+        ack_result = bsky_utils.acknowledge_post(bsky_client, post_uri, post_cid, note)
         
+        if ack_result:
+            logger.debug(f"Acknowledged X post {post_id} via atproto" + (f" with note: {note[:50]}..." if note else ""))
+            return True
+        else:
+            logger.error(f"Failed to acknowledge X post {post_id}")
+            return False
+            
     except Exception as e:
         logger.error(f"Error acknowledging X post {post_id}: {e}")
         return False