Extract run_planning_agent.

ra_aid/__main__.py

@@ -8,8 +8,11 @@ from langgraph.prebuilt import create_react_agent
 from ra_aid.env import validate_environment
 from ra_aid.tools.memory import _global_memory, get_related_files, get_memory_value
 from ra_aid import print_stage_header, print_error
-from ra_aid.agent_utils import run_agent_with_retry
+from ra_aid.agent_utils import (
-from ra_aid.agent_utils import run_research_agent
+    run_agent_with_retry,
+    run_research_agent,
+    run_planning_agent
+)
 from ra_aid.prompts import (
     PLANNING_PROMPT,
     CHAT_PROMPT,
@@ -207,26 +210,15 @@ def main():
         
         # Proceed with planning and implementation if not an informational query
         if not is_informational_query():
-            print_stage_header("Planning Stage")
-            
-            # Create planning agent
-            planning_agent = create_react_agent(model, get_planning_tools(expert_enabled=expert_enabled), checkpointer=planning_memory)
-            
-            expert_section = EXPERT_PROMPT_SECTION_PLANNING if expert_enabled else ""
-            human_section = HUMAN_PROMPT_SECTION_PLANNING if args.hil else ""
-            planning_prompt = PLANNING_PROMPT.format(
-                expert_section=expert_section,
-                human_section=human_section,
-                base_task=base_task,
-                research_notes=get_memory_value('research_notes'),
-                related_files="\n".join(get_related_files()),
-                key_facts=get_memory_value('key_facts'),
-                key_snippets=get_memory_value('key_snippets'),
-                research_only_note='' if args.research_only else ' Only request implementation if the user explicitly asked for changes to be made.'
-            )
-
             # Run planning agent
-            run_agent_with_retry(planning_agent, planning_prompt, config)
+            run_planning_agent(
+                base_task,
+                model,
+                expert_enabled=expert_enabled,
+                hil=args.hil,
+                memory=planning_memory,
+                config=config
+            )
 
     except KeyboardInterrupt:
         console.print("\n[red]Operation cancelled by user[/red]")

ra_aid/agent_utils.py

@@ -5,14 +5,22 @@ import uuid
 from typing import Optional, Any, List
 
 from langgraph.prebuilt import create_react_agent
-from ra_aid.tool_configs import get_implementation_tools, get_research_tools
+from ra_aid.console.formatting import print_stage_header
+from ra_aid.tool_configs import (
+    get_implementation_tools,
+    get_research_tools,
+    get_planning_tools
+)
 from ra_aid.prompts import (
     IMPLEMENTATION_PROMPT,
     EXPERT_PROMPT_SECTION_IMPLEMENTATION,
     HUMAN_PROMPT_SECTION_IMPLEMENTATION,
     EXPERT_PROMPT_SECTION_RESEARCH,
     RESEARCH_PROMPT,
-    HUMAN_PROMPT_SECTION_RESEARCH
+    HUMAN_PROMPT_SECTION_RESEARCH,
+    PLANNING_PROMPT,
+    EXPERT_PROMPT_SECTION_PLANNING,
+    HUMAN_PROMPT_SECTION_PLANNING
 )
 from langgraph.checkpoint.memory import MemorySaver
 
@@ -26,6 +34,7 @@ from rich.panel import Panel
 from ra_aid.tools.memory import (
     _global_memory,
     get_memory_value,
+    get_related_files,
 )
 from ra_aid.globals import RESEARCH_AGENT_RECURSION_LIMIT
 from ra_aid.tool_configs import get_research_tools
@@ -127,6 +136,72 @@ def print_error(msg: str) -> None:
     """Print error messages."""
     console.print(f"\n{msg}", style="red")
 
+def run_planning_agent(
+    base_task: str,
+    model,
+    *,
+    expert_enabled: bool = False,
+    hil: bool = False,
+    memory: Optional[Any] = None,
+    config: Optional[dict] = None,
+    thread_id: Optional[str] = None
+) -> Optional[str]:
+    """Run a planning agent to create implementation plans.
+    
+    Args:
+        base_task: The main task to plan implementation for
+        model: The LLM model to use
+        expert_enabled: Whether expert mode is enabled
+        hil: Whether human-in-the-loop mode is enabled
+        memory: Optional memory instance to use
+        config: Optional configuration dictionary
+        thread_id: Optional thread ID (defaults to new UUID)
+        
+    Returns:
+        Optional[str]: The completion message if planning completed successfully
+    """
+    # Initialize memory if not provided
+    if memory is None:
+        memory = MemorySaver()
+
+    # Set up thread ID
+    if thread_id is None:
+        thread_id = str(uuid.uuid4())
+
+    # Configure tools
+    tools = get_planning_tools(expert_enabled=expert_enabled)
+
+    # Create agent
+    agent = create_react_agent(model, tools, checkpointer=memory)
+
+    # Format prompt sections
+    expert_section = EXPERT_PROMPT_SECTION_PLANNING if expert_enabled else ""
+    human_section = HUMAN_PROMPT_SECTION_PLANNING if hil else ""
+    
+    # Build prompt
+    planning_prompt = PLANNING_PROMPT.format(
+        expert_section=expert_section,
+        human_section=human_section,
+        base_task=base_task,
+        research_notes=get_memory_value('research_notes'),
+        related_files="\n".join(get_related_files()),
+        key_facts=get_memory_value('key_facts'),
+        key_snippets=get_memory_value('key_snippets'),
+        research_only_note='' if config.get('research_only') else ' Only request implementation if the user explicitly asked for changes to be made.'
+    )
+
+    # Set up configuration
+    run_config = {
+        "configurable": {"thread_id": thread_id},
+        "recursion_limit": 100
+    }
+    if config:
+        run_config.update(config)
+
+    # Run agent with retry logic
+    print_stage_header("Planning Stage")
+    return run_agent_with_retry(agent, planning_prompt, run_config)
+
 def run_task_implementation_agent(
     base_task: str,
     tasks: list,

ra_aid/tools/agent.py

@@ -105,6 +105,10 @@ def request_task_implementation(task_spec: str) -> Dict[str, Any]:
     # 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['completion_state'] = False
+        
     return {
         "facts": get_memory_value("key_facts"),
         "files": list(get_related_files()),