Introduce run_research_agent.

ra_aid/__main__.py

@@ -1,29 +1,26 @@
 import argparse
 import sys
+import uuid
 from rich.panel import Panel
 from rich.console import Console
 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, run_agent_with_retry
+from ra_aid.agent_utils import run_research_agent
 from ra_aid.prompts import (
-    RESEARCH_PROMPT,
     PLANNING_PROMPT,
     IMPLEMENTATION_PROMPT,
     CHAT_PROMPT,
-    EXPERT_PROMPT_SECTION_RESEARCH,
     EXPERT_PROMPT_SECTION_PLANNING,
     EXPERT_PROMPT_SECTION_IMPLEMENTATION,
-    HUMAN_PROMPT_SECTION_RESEARCH,
     HUMAN_PROMPT_SECTION_PLANNING,
     HUMAN_PROMPT_SECTION_IMPLEMENTATION
 )
 from ra_aid.llm import initialize_llm
 
 from ra_aid.tool_configs import (
-    get_read_only_tools,
-    get_research_tools,
     get_planning_tools,
     get_implementation_tools,
     get_chat_tools
@@ -199,7 +196,7 @@ def main():
             
             # Run chat agent with CHAT_PROMPT
             config = {
-                "configurable": {"thread_id": "abc123"},
+                "configurable": {"thread_id": uuid.uuid4()},
                 "recursion_limit": 100,
                 "chat_mode": True,
                 "cowboy_mode": args.cowboy_mode,
@@ -222,9 +219,7 @@ def main():
             
         base_task = args.message
         config = {
-            "configurable": {
-                "thread_id": "abc123"
-            },
+            "configurable": {"thread_id": uuid.uuid4()},
             "recursion_limit": 100,
             "research_only": args.research_only,
             "cowboy_mode": args.cowboy_mode
@@ -240,29 +235,16 @@ def main():
         # Run research stage
         print_stage_header("Research Stage")
         
-        # Create research agent
-        research_agent = create_react_agent(
+        run_research_agent(
+            base_task,
             model,
-            get_research_tools(
-                research_only=_global_memory.get('config', {}).get('research_only', False),
-                expert_enabled=expert_enabled,
-                human_interaction=args.hil
-            ),
-            checkpointer=research_memory
+            expert_enabled=expert_enabled,
+            research_only=args.research_only,
+            hil=args.hil,
+            memory=research_memory,
+            config=config
         )
         
-        expert_section = EXPERT_PROMPT_SECTION_RESEARCH if expert_enabled else ""
-        human_section = HUMAN_PROMPT_SECTION_RESEARCH if args.hil else ""
-        research_prompt = RESEARCH_PROMPT.format(
-            expert_section=expert_section,
-            human_section=human_section,
-            base_task=base_task,
-            research_only_note='' if args.research_only else ' Only request implementation if the user explicitly asked for changes to be made.'
-        )
-
-        # Run research agent
-        run_agent_with_retry(research_agent, research_prompt, config)
-        
         # Proceed with planning and implementation if not an informational query
         if not is_informational_query():
             print_stage_header("Planning Stage")

ra_aid/agent_utils.py

@@ -1,7 +1,11 @@
 """Utility functions for working with agents."""
 
 import time
-from typing import Optional
+import uuid
+from typing import Optional, Any
+
+from langgraph.prebuilt import create_react_agent
+from langgraph.checkpoint.memory import MemorySaver
 
 from langchain_core.messages import HumanMessage
 from langchain_core.messages import BaseMessage
@@ -11,9 +15,98 @@ from rich.markdown import Markdown
 from rich.panel import Panel
 
 from ra_aid.tools.memory import _global_memory
+from ra_aid.globals import RESEARCH_AGENT_RECURSION_LIMIT
+from ra_aid.tool_configs import get_research_tools
+from ra_aid.prompts import (
+    RESEARCH_PROMPT,
+    EXPERT_PROMPT_SECTION_RESEARCH,
+    HUMAN_PROMPT_SECTION_RESEARCH
+)
 
 console = Console()
 
+def run_research_agent(
+    base_task_or_query: str,
+    model,
+    *,
+    expert_enabled: bool = False,
+    research_only: bool = False,
+    hil: bool = False,
+    memory: Optional[Any] = None,
+    config: Optional[dict] = None,
+    thread_id: Optional[str] = None,
+    console_message: Optional[str] = None
+) -> Optional[str]:
+    """Run a research agent with the given configuration.
+    
+    Args:
+        base_task_or_query: The main task or query for research
+        model: The LLM model to use
+        expert_enabled: Whether expert mode is enabled
+        research_only: Whether this is a research-only task
+        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)
+        console_message: Optional message to display before running
+        
+    Returns:
+        Optional[str]: The completion message if task completed successfully
+        
+    Example:
+        result = run_research_agent(
+            "Research Python async patterns",
+            model,
+            expert_enabled=True,
+            research_only=True
+        )
+    """
+    # Initialize memory if not provided
+    if memory is None:
+        memory = MemorySaver()
+        memory.memory = _global_memory
+
+    # Set up thread ID
+    if thread_id is None:
+        thread_id = str(uuid.uuid4())
+
+    # Configure tools
+    tools = get_research_tools(
+        research_only=research_only,
+        expert_enabled=expert_enabled,
+        human_interaction=hil
+    )
+
+    # Create agent
+    agent = create_react_agent(model, tools, checkpointer=memory)
+
+    # Format prompt sections
+    expert_section = EXPERT_PROMPT_SECTION_RESEARCH if expert_enabled else ""
+    human_section = HUMAN_PROMPT_SECTION_RESEARCH if hil else ""
+    
+    # Build prompt
+    prompt = RESEARCH_PROMPT.format(
+        base_task=base_task_or_query,
+        research_only_note='' if research_only else ' Only request implementation if the user explicitly asked for changes to be made.',
+        expert_section=expert_section,
+        human_section=human_section
+    )
+
+    # Set up configuration
+    run_config = {
+         "configurable": {"thread_id": thread_id},
+        "recursion_limit": 100
+    }
+    if config:
+        run_config.update(config)
+
+    # Display console message if provided
+    if console_message:
+        console.print(Panel(Markdown(console_message), title="🔬 Research Task"))
+
+    # Run agent with retry logic
+    return run_agent_with_retry(agent, prompt, run_config)
+
 def print_agent_output(chunk: dict[str, BaseMessage]) -> None:
     """Print agent output chunks."""
     if chunk.get("delta") and chunk["delta"].content:

ra_aid/globals.py

@@ -0,0 +1,6 @@
+"""
+Global constants and configuration values used across the RA-AID codebase.
+"""
+
+# Maximum recursion depth for research agents to prevent infinite loops
+RESEARCH_AGENT_RECURSION_LIMIT = 100

ra_aid/tools/agent.py

@@ -1,16 +1,9 @@
 """Tools for spawning and managing sub-agents."""
 
 from langchain_core.tools import tool
-from typing import Dict, Any, List, Optional
-import uuid
+from typing import Dict, Any
 from rich.console import Console
-from rich.panel import Panel
-from rich.markdown import Markdown
-from langgraph.prebuilt import create_react_agent
-from langgraph.checkpoint.memory import MemorySaver
 from ra_aid.tools.memory import _global_memory
-from ra_aid import run_agent_with_retry
-from ..prompts import RESEARCH_PROMPT
 from .memory import get_memory_value, get_related_files
 from ..llm import initialize_llm
 
@@ -31,52 +24,19 @@ def request_research(query: str) -> Dict[str, Any]:
         - success: Whether completed or interrupted
         - reason: Reason for failure, if any
     """
-    # Initialize model and memory
+    # Initialize model
     model = initialize_llm("anthropic", "claude-3-sonnet-20240229")
-    memory = MemorySaver()
-    memory.memory = _global_memory
-    
-    # Configure research tools
-    from ..tool_configs import get_research_tools
-    tools = get_research_tools(research_only=True, expert_enabled=True)
-    
-    # Basic config matching main process
-    config = {
-        "thread_id": str(uuid.uuid4()),
-        "memory": memory,
-        "model": model
-    }
-    
-    from ra_aid.prompts import (
-        RESEARCH_PROMPT, 
-        EXPERT_PROMPT_SECTION_RESEARCH,
-        HUMAN_PROMPT_SECTION_RESEARCH
-    )
-    
-    # Create research agent
-    config = _global_memory.get('config', {})
-    expert_enabled = config.get('expert_enabled', False)
-    hil = config.get('hil', False)
-    
-    expert_section = EXPERT_PROMPT_SECTION_RESEARCH if expert_enabled else ""
-    human_section = HUMAN_PROMPT_SECTION_RESEARCH if hil else ""
-    
-    agent = create_react_agent(model, tools)
-    
-    prompt = RESEARCH_PROMPT.format(
-        base_task=query,
-        research_only_note='',
-        expert_section=expert_section,
-        human_section=human_section
-    )
     
     try:
-        console.print(Panel(Markdown(query), title="🔬 Research Task"))
-        # Run agent with retry logic
-        result = run_agent_with_retry(
-            agent,
-            prompt,
-            {"configurable": {"thread_id": str(uuid.uuid4())}, "recursion_limit": 100}
+        # Run research agent
+        from ..agent_utils import run_research_agent
+        result = run_research_agent(
+            query,
+            model,
+            expert_enabled=True,
+            research_only=True,
+            hil=_global_memory.get('config', {}).get('hil', False),
+            console_message=query
         )
         
         success = True