Introduce run_task_implementation_agent.

ra_aid/__main__.py

@@ -7,7 +7,8 @@ from langgraph.checkpoint.memory import MemorySaver
 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_task_header, print_error, run_agent_with_retry
+from ra_aid import print_stage_header, print_task_header, print_error
+from ra_aid.agent_utils import run_agent_with_retry, run_task_implementation_agent
 from ra_aid.agent_utils import run_research_agent
 from ra_aid.prompts import (
     PLANNING_PROMPT,
@@ -140,28 +141,16 @@ def run_implementation_stage(base_task, tasks, plan, related_files, model, exper
     for i, task in enumerate(task_list, 1):
         print_task_header(task)
         
-        # Create a unique memory instance for this task
+        # Run implementation agent for this task
-        task_memory = MemorySaver()
+        run_task_implementation_agent(
-        
-        # Create a fresh agent for each task
-        task_agent = create_react_agent(model, get_implementation_tools(expert_enabled=expert_enabled), checkpointer=task_memory)
-        
-        # Construct task-specific prompt
-        expert_section = EXPERT_PROMPT_SECTION_IMPLEMENTATION if expert_enabled else ""
-        human_section = HUMAN_PROMPT_SECTION_IMPLEMENTATION if _global_memory.get('config', {}).get('hil', False) else ""
-        task_prompt = (IMPLEMENTATION_PROMPT).format(
-            plan=plan,
-            key_facts=get_memory_value('key_facts'),
-            key_snippets=get_memory_value('key_snippets'),
-            task=task,
-            related_files="\n".join(related_files),
             base_task=base_task,
-            expert_section=expert_section,
+            tasks=task_list,
-            human_section=human_section
+            task=task,
+            plan=plan,
+            related_files=related_files, 
+            model=model,
+            expert_enabled=expert_enabled
         )
-        
-        # Run agent for this task
-        run_agent_with_retry(task_agent, task_prompt, {"configurable": {"thread_id": "abc123"}, "recursion_limit": 100})
 
 
 

ra_aid/agent_utils.py

@@ -2,9 +2,18 @@
 
 import time
 import uuid
-from typing import Optional, Any
+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.prompts import (
+    IMPLEMENTATION_PROMPT,
+    EXPERT_PROMPT_SECTION_IMPLEMENTATION,
+    HUMAN_PROMPT_SECTION_IMPLEMENTATION,
+    EXPERT_PROMPT_SECTION_RESEARCH,
+    RESEARCH_PROMPT,
+    HUMAN_PROMPT_SECTION_RESEARCH
+)
 from langgraph.checkpoint.memory import MemorySaver
 
 from langchain_core.messages import HumanMessage
@@ -14,7 +23,10 @@ from rich.console import Console
 from rich.markdown import Markdown
 from rich.panel import Panel
 
-from ra_aid.tools.memory import _global_memory
+from ra_aid.tools.memory import (
+    _global_memory,
+    get_memory_value,
+)
 from ra_aid.globals import RESEARCH_AGENT_RECURSION_LIMIT
 from ra_aid.tool_configs import get_research_tools
 from ra_aid.prompts import (
@@ -64,7 +76,6 @@ def run_research_agent(
     # Initialize memory if not provided
     if memory is None:
         memory = MemorySaver()
-        memory.memory = _global_memory
 
     # Set up thread ID
     if thread_id is None:
@@ -116,6 +127,73 @@ def print_error(msg: str) -> None:
     """Print error messages."""
     console.print(f"\n{msg}", style="red")
 
+def run_task_implementation_agent(
+    base_task: str,
+    tasks: list,
+    task: str,
+    plan: str,
+    related_files: list,
+    model,
+    *,
+    expert_enabled: bool = False,
+    memory: Optional[Any] = None,
+    config: Optional[dict] = None,
+    thread_id: Optional[str] = None
+) -> Optional[str]:
+    """Run an implementation agent for a specific task.
+    
+    Args:
+        base_task: The main task being implemented
+        tasks: List of tasks to implement
+        plan: The implementation plan
+        related_files: List of related files
+        model: The LLM model to use
+        expert_enabled: Whether expert 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 task 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_implementation_tools(expert_enabled=expert_enabled)
+
+    # Create agent
+    agent = create_react_agent(model, tools, checkpointer=memory)
+
+    # Build prompt
+    prompt = IMPLEMENTATION_PROMPT.format(
+        base_task=base_task,
+        task=task,
+        tasks=tasks,
+        plan=plan,
+        related_files=related_files,
+        key_facts=get_memory_value('key_facts'),
+        key_snippets=get_memory_value('key_snippets'),
+        expert_section=EXPERT_PROMPT_SECTION_IMPLEMENTATION if expert_enabled else "",
+        human_section=HUMAN_PROMPT_SECTION_IMPLEMENTATION if _global_memory.get('config', {}).get('hil', False) else ""
+    )
+
+    # Set up configuration
+    run_config = {
+        "configurable": {"thread_id": thread_id},
+        "recursion_limit": 100
+    }
+    if config:
+        run_config.update(config)
+
+    # Run agent with retry logic
+    return run_agent_with_retry(agent, prompt, run_config)
+
 def run_agent_with_retry(agent, prompt: str, config: dict) -> Optional[str]:
     """Run an agent with retry logic for internal server errors and task completion handling.