Track agent depth for all agents.

ra_aid/agent_utils.py

@@ -306,6 +306,10 @@ def run_agent_with_retry(agent, prompt: str, config: dict) -> Optional[str]:
 
     with InterruptibleSection():
         try:
+            # Track agent execution depth
+            current_depth = _global_memory.get('agent_depth', 0)
+            _global_memory['agent_depth'] = current_depth + 1
+            
             for attempt in range(max_retries):
                 check_interrupt()
                 try:
@@ -325,5 +329,8 @@ def run_agent_with_retry(agent, prompt: str, config: dict) -> Optional[str]:
                         check_interrupt()
                         time.sleep(0.1)
         finally:
+            # Reset depth tracking
+            _global_memory['agent_depth'] = _global_memory.get('agent_depth', 1) - 1
+            
             if original_handler and threading.current_thread() is threading.main_thread():
                 signal.signal(signal.SIGINT, original_handler)

ra_aid/tools/agent.py

@@ -33,7 +33,7 @@ def request_research(query: str) -> ResearchResult:
     model = initialize_llm(config.get('provider', 'anthropic'), config.get('model', 'claude-3-5-sonnet-20241022'))
     
     # Check recursion depth
-    current_depth = _global_memory.get('research_depth', 0)
+    current_depth = _global_memory.get('agent_depth', 0)
     if current_depth >= RESEARCH_AGENT_RECURSION_LIMIT:
         print_error("Maximum research recursion depth reached")
         return {
@@ -50,9 +50,6 @@ def request_research(query: str) -> ResearchResult:
     reason = None
     
     try:
-        # Increment depth counter
-        _global_memory['research_depth'] = current_depth + 1
-        
         # Run research agent
         from ..agent_utils import run_research_agent
         result = run_research_agent(
@@ -72,15 +69,12 @@ def request_research(query: str) -> ResearchResult:
         success = False
         reason = f"error: {str(e)}"
     finally:
-        # Always decrement depth counter
-        _global_memory['research_depth'] = current_depth
+        # Get completion message if available
+        completion_message = _global_memory.get('completion_message', 'Task was completed successfully.' if success else None)
         
-    # Get completion message if available
-    completion_message = _global_memory.get('completion_message', 'Task was completed successfully.' if success else None)
-    
-    # Clear completion state from global memory
-    _global_memory['completion_message'] = ''
-    _global_memory['task_completed'] = False
+        # Clear completion state from global memory
+        _global_memory['completion_message'] = ''
+        _global_memory['task_completed'] = False
         
     return {
         "completion_message": completion_message,

ra_aid/tools/memory.py

@@ -33,7 +33,7 @@ _global_memory: Dict[str, Union[List[Any], Dict[int, str], Dict[int, SnippetInfo
     'related_files': {},  # Dict[int, str] - ID to filepath mapping
     'related_file_id_counter': 1,  # Counter for generating unique file IDs
     'plan_completed': False,
-    'research_depth': 0,
+    'agent_depth': 0,
     'work_log': []  # List[WorkLogEntry] - Timestamped work events
 }
 
@@ -340,6 +340,7 @@ def plan_implementation_completed(message: str) -> str:
     _global_memory['plan_completed'] = True
     _global_memory['completion_message'] = message
     _global_memory['tasks'].clear()  # Clear task list when plan is completed
+    _global_memory['task_id_counter'] = 1
     console.print(Panel(Markdown(message), title="✅ Plan Executed"))
     log_work_event(f"Plan execution completed: {message}")
     return "Plan completion noted and task list cleared."