import argparse import sys from typing import Optional from rich.panel import Panel from rich.markdown import Markdown from rich.console import Console from langchain_core.messages import HumanMessage 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_agent_output, print_stage_header, print_task_header, print_error 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 ) import time from anthropic import APIError, APITimeoutError, RateLimitError, InternalServerError 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 ) def parse_arguments(): parser = argparse.ArgumentParser( description='RA.Aid - AI Agent for executing programming and research tasks', formatter_class=argparse.RawDescriptionHelpFormatter, epilog=''' Examples: ra-aid -m "Add error handling to the database module" ra-aid -m "Explain the authentication flow" --research-only ''' ) parser.add_argument( '-m', '--message', type=str, help='The task or query to be executed by the agent' ) parser.add_argument( '--research-only', action='store_true', help='Only perform research without implementation' ) parser.add_argument( '--provider', type=str, default='anthropic', choices=['anthropic', 'openai', 'openrouter', 'openai-compatible'], help='The LLM provider to use' ) parser.add_argument( '--model', type=str, help='The model name to use (required for non-Anthropic providers)' ) parser.add_argument( '--cowboy-mode', action='store_true', help='Skip interactive approval for shell commands' ) parser.add_argument( '--expert-provider', type=str, default='openai', choices=['anthropic', 'openai', 'openrouter', 'openai-compatible'], help='The LLM provider to use for expert knowledge queries (default: openai)' ) parser.add_argument( '--expert-model', type=str, help='The model name to use for expert knowledge queries (required for non-OpenAI providers)' ) parser.add_argument( '--hil', '-H', action='store_true', help='Enable human-in-the-loop mode, where the agent can prompt the user for additional information.' ) parser.add_argument( '--chat', action='store_true', help='Enable chat mode with direct human interaction (implies --hil)' ) args = parser.parse_args() # Set hil=True when chat mode is enabled if args.chat: args.hil = True # Set default model for Anthropic, require model for other providers if args.provider == 'anthropic': if not args.model: args.model = 'claude-3-5-sonnet-20241022' elif not args.model: parser.error(f"--model is required when using provider '{args.provider}'") # Validate expert model requirement if args.expert_provider != 'openai' and not args.expert_model: parser.error(f"--expert-model is required when using expert provider '{args.expert_provider}'") return args # Create console instance console = Console() # Create individual memory objects for each agent research_memory = MemorySaver() planning_memory = MemorySaver() implementation_memory = MemorySaver() def is_informational_query() -> bool: """Determine if the current query is informational based on implementation_requested state.""" return _global_memory.get('config', {}).get('research_only', False) or not is_stage_requested('implementation') def is_stage_requested(stage: str) -> bool: """Check if a stage has been requested to proceed.""" if stage == 'implementation': return _global_memory.get('implementation_requested', False) return False 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. Args: agent: The agent to run prompt: The prompt to send to the agent config: Configuration dictionary for the agent Returns: Optional[str]: The completion message if task was completed, None otherwise Handles API errors with exponential backoff retry logic and checks for task completion after each chunk of output. """ max_retries = 20 base_delay = 1 # Initial delay in seconds for attempt in range(max_retries): try: for chunk in agent.stream( {"messages": [HumanMessage(content=prompt)]}, config ): print_agent_output(chunk) # Check for task completion after each chunk if _global_memory.get('task_completed'): completion_msg = _global_memory.get('completion_message', 'Task was completed successfully.') console.print(Panel( Markdown(completion_msg), title="✅ Task Completed", style="green" )) return completion_msg break except (InternalServerError, APITimeoutError, RateLimitError, APIError) as e: if attempt == max_retries - 1: raise RuntimeError(f"Max retries ({max_retries}) exceeded. Last error: {str(e)}") delay = base_delay * (2 ** attempt) # Exponential backoff error_type = e.__class__.__name__ print_error(f"Encountered {error_type}: {str(e)}. Retrying in {delay} seconds... (Attempt {attempt + 1}/{max_retries})") time.sleep(delay) continue def run_implementation_stage(base_task, tasks, plan, related_files, model, expert_enabled: bool): """Run implementation stage with a distinct agent for each task.""" if not is_stage_requested('implementation'): print_stage_header("Implementation Stage Skipped") return print_stage_header("Implementation Stage") # Get tasks directly from memory, maintaining order by ID task_list = [task for _, task in sorted(_global_memory['tasks'].items())] print_task_header(f"Found {len(task_list)} tasks to implement") for i, task in enumerate(task_list, 1): print_task_header(task) # Create a unique memory instance for this task task_memory = MemorySaver() # 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, human_section=human_section ) # Run agent for this task run_agent_with_retry(task_agent, task_prompt, {"configurable": {"thread_id": "abc123"}, "recursion_limit": 100}) def run_research_subtasks(base_task: str, config: dict, model, expert_enabled: bool): """Run research subtasks with separate agents.""" subtasks = _global_memory.get('research_subtasks', []) if not subtasks: return print_stage_header("Research Subtasks") # Get tools for subtask agents (excluding request_research_subtask and implementation) research_only = _global_memory.get('config', {}).get('research_only', False) subtask_tools = [ t for t in get_research_tools(research_only=research_only, expert_enabled=expert_enabled) if t.name not in ['request_research_subtask'] ] for i, subtask in enumerate(subtasks, 1): print_task_header(f"Research Subtask {i}/{len(subtasks)}") # Create fresh memory and agent for each subtask subtask_memory = MemorySaver() subtask_agent = create_react_agent( model, subtask_tools, checkpointer=subtask_memory ) # Run the subtask agent expert_section = EXPERT_PROMPT_SECTION_RESEARCH if expert_enabled else "" human_section = HUMAN_PROMPT_SECTION_RESEARCH if config.get('hil', False) else "" subtask_prompt = f"Base Task: {base_task}\nResearch Subtask: {subtask}\n\n{RESEARCH_PROMPT.format( base_task=base_task, research_only_note='', expert_section=expert_section, human_section=human_section )}" run_agent_with_retry(subtask_agent, subtask_prompt, config) def main(): """Main entry point for the ra-aid command line tool.""" try: args = parse_arguments() expert_enabled, expert_missing = validate_environment(args) # Will exit if main env vars missing if expert_missing: console.print(Panel( f"[yellow]Expert tools disabled due to missing configuration:[/yellow]\n" + "\n".join(f"- {m}" for m in expert_missing) + "\nSet the required environment variables or args to enable expert mode.", title="Expert Tools Disabled", style="yellow" )) # Create the base model after validation model = initialize_llm(args.provider, args.model) # Handle chat mode if args.chat: print_stage_header("Chat Mode") # Create chat agent with appropriate tools chat_agent = create_react_agent( model, get_chat_tools(expert_enabled=expert_enabled), checkpointer=MemorySaver() ) # Run chat agent with CHAT_PROMPT config = { "configurable": {"thread_id": "abc123"}, "recursion_limit": 100, "chat_mode": True, "cowboy_mode": args.cowboy_mode, "hil": True # Always true in chat mode } # Store config in global memory _global_memory['config'] = config _global_memory['config']['expert_provider'] = args.expert_provider _global_memory['config']['expert_model'] = args.expert_model # Run chat agent and exit run_agent_with_retry(chat_agent, CHAT_PROMPT, config) return # Validate message is provided if not args.message: print_error("--message is required") sys.exit(1) base_task = args.message config = { "configurable": { "thread_id": "abc123" }, "recursion_limit": 100, "research_only": args.research_only, "cowboy_mode": args.cowboy_mode } # Store config in global memory for access by is_informational_query _global_memory['config'] = config # Store expert provider and model in config _global_memory['config']['expert_provider'] = args.expert_provider _global_memory['config']['expert_model'] = args.expert_model # Run research stage print_stage_header("Research Stage") # Create research agent research_agent = create_react_agent( 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_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) # Run any research subtasks run_research_subtasks(base_task, config, model, expert_enabled=expert_enabled) # 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 implementation stage with task-specific agents run_implementation_stage( base_task, get_memory_value('tasks'), get_memory_value('plan'), get_related_files(), model, expert_enabled=expert_enabled ) except KeyboardInterrupt: console.print("\n[red]Operation cancelled by user[/red]") sys.exit(1) if __name__ == "__main__": main()