- Added ask_human tool to allow human operator to answer questions asked by the agent.

CHANGELOG.md

@@ -10,6 +10,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Make delete_tasks tool available to planning agent.
 - Get rid of implementation args as they are not used.
 - Improve ripgrep tool status output.
+- Added ask_human tool to allow human operator to answer questions asked by the agent.
 
 ## [0.6.4] - 2024-12-19
 

ra_aid/__main__.py

@@ -7,7 +7,7 @@ from langchain_core.messages import HumanMessage
 from langgraph.checkpoint.memory import MemorySaver
 from langgraph.prebuilt import create_react_agent
 from ra_aid.tools import (
-    ask_expert, run_shell_command, run_programming_task,
+    ask_expert, ask_human, run_shell_command, run_programming_task,
     emit_research_notes, emit_plan, emit_related_files, emit_task,
     emit_expert_context, get_memory_value, emit_key_facts, delete_key_facts,
     emit_key_snippets, delete_key_snippets, delete_tasks,
@@ -24,24 +24,35 @@ from ra_aid.prompts import (
     EXPERT_PROMPT_SECTION_RESEARCH,
     EXPERT_PROMPT_SECTION_PLANNING,
     EXPERT_PROMPT_SECTION_IMPLEMENTATION,
+    HUMAN_PROMPT_SECTION_RESEARCH,
+    HUMAN_PROMPT_SECTION_PLANNING
 )
 import time
 from anthropic import APIError, APITimeoutError, RateLimitError, InternalServerError
 from ra_aid.llm import initialize_llm
 
 # Read-only tools that don't modify system state
-READ_ONLY_TOOLS = [
-    emit_related_files,
-    emit_key_facts,
-    delete_key_facts,
-    emit_key_snippets,
-    delete_key_snippets,
-    list_directory_tree,
-    read_file_tool,
-    fuzzy_find_project_files,
-    ripgrep_search,
-    run_shell_command # can modify files, but we still need it for read-only tasks.
-]
+def get_read_only_tools(human_interaction: bool = False) -> list:
+    """Get the list of read-only tools, optionally including human interaction tools."""
+    tools = [
+        emit_related_files,
+        emit_key_facts,
+        delete_key_facts,
+        emit_key_snippets,
+        delete_key_snippets,
+        list_directory_tree,
+        read_file_tool,
+        fuzzy_find_project_files,
+        ripgrep_search,
+        run_shell_command # can modify files, but we still need it for read-only tasks.
+    ]
+    
+    if human_interaction:
+        tools.append(ask_human)
+    
+    return tools
+
+READ_ONLY_TOOLS = get_read_only_tools()
 
 # Tools that can modify files or system state
 MODIFICATION_TOOLS = [
@@ -116,6 +127,11 @@ Examples:
         type=str,
         help='The model name to use for expert knowledge queries (required for non-OpenAI providers)'
     )
+    parser.add_argument(
+        '--human-interaction',
+        action='store_true',
+        help='Enable human interaction'
+    )
     
     args = parser.parse_args()
     
@@ -140,10 +156,10 @@ research_memory = MemorySaver()
 planning_memory = MemorySaver()
 implementation_memory = MemorySaver()
 
-def get_research_tools(research_only: bool = False, expert_enabled: bool = True) -> list:
+def get_research_tools(research_only: bool = False, expert_enabled: bool = True, human_interaction: bool = False) -> list:
     """Get the list of research tools based on mode and whether expert is enabled."""
     # Start with read-only tools
-    tools = READ_ONLY_TOOLS.copy()
+    tools = get_read_only_tools(human_interaction).copy()
     
     tools.extend(RESEARCH_TOOLS)
     
@@ -270,15 +286,13 @@ def run_implementation_stage(base_task, tasks, plan, related_files, model, exper
         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 ""
-        task_prompt = (IMPLEMENTATION_PROMPT + expert_section).format(
+        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
+            base_task=base_task
         )
         
         # Run agent for this task
@@ -312,7 +326,7 @@ def run_research_subtasks(base_task: str, config: dict, model, expert_enabled: b
         )
         
         # Run the subtask agent
-        subtask_prompt = f"Base Task: {base_task}\nResearch Subtask: {subtask}\n\n{RESEARCH_PROMPT}"
+        subtask_prompt = f"Base Task: {base_task}\nResearch Subtask: {subtask}\n\n{RESEARCH_PROMPT.format(base_task=base_task, research_only_note='')}"
         run_agent_with_retry(subtask_agent, subtask_prompt, config)
 
 
@@ -362,13 +376,19 @@ def main():
     # 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),
+        get_research_tools(
+            research_only=_global_memory.get('config', {}).get('research_only', False),
+            expert_enabled=expert_enabled,
+            human_interaction=args.human_interaction
+        ),
         checkpointer=research_memory
     )
     
     expert_section = EXPERT_PROMPT_SECTION_RESEARCH if expert_enabled else ""
+    human_section = HUMAN_PROMPT_SECTION_RESEARCH if args.human_interaction 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.'
     )
@@ -395,13 +415,16 @@ def main():
         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.human_interaction 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'),
-            base_task=base_task,
-            related_files="\n".join(get_related_files()),
-            expert_section=expert_section
+            research_only_note='' if args.research_only else ' Only request implementation if the user explicitly asked for changes to be made.'
         )
 
         # Run planning agent

ra_aid/prompts.py

@@ -33,7 +33,32 @@ Expert Consultation:
     - Ask the expert to perform deep analysis
     - Wait for expert guidance before proceeding with implementation
 """
- 
+
+# Human-specific prompt sections
+HUMAN_PROMPT_SECTION_RESEARCH = """
+Human Interaction:
+    If you need clarification from the human operator:
+    - Ask clear, specific questions
+    - Use the ask_human tool for queries
+    - Wait for human response before proceeding
+"""
+
+HUMAN_PROMPT_SECTION_PLANNING = """
+Human Interaction:
+    If you need requirements clarification:
+    - Use ask_human for specific planning questions
+    - Await human input before finalizing plans
+    - Keep questions focused and context-aware
+"""
+
+HUMAN_PROMPT_SECTION_IMPLEMENTATION = """
+Human Interaction:
+    If you need implementation guidance:
+    - Ask the human operator using ask_human
+    - Keep questions specific to the current task
+    - Wait for responses before proceeding
+"""
+
 # Research stage prompt - guides initial codebase analysis
 RESEARCH_PROMPT = """User query: {base_task} --keep it simple
 
@@ -142,6 +167,7 @@ Be thorough on locating all potential change sites/gauging blast radius.
 If this is a top-level README.md or docs folder, start there. If relevant tests exist, run them upfront as part of the research phase to establish a baseline.
 
 {expert_section}
+{human_section}
 """
 
 # Planning stage prompt - guides task breakdown and implementation planning
@@ -208,6 +234,7 @@ Guidelines:
     Do not implement anything yet.
 
 {expert_section}
+{human_section}
 """
 
 
@@ -253,6 +280,7 @@ Testing:
 - If you add or change any unit tests, run them using run_shell_command and ensure they pass (check docs or analyze directory structure/test files to infer how to run them.)
   - Start with running very specific tests, then move to more general/complete test suites.
 {expert_section}
+{human_section}
 - Only test UI components if there is already a UI testing system in place.
 - Only test things that can be tested by an automated process.
 

ra_aid/tools/__init__.py

@@ -1,5 +1,6 @@
 from .shell import run_shell_command
 from .research import monorepo_detected, existing_project_detected, ui_detected
+from .human import ask_human
 from .programmer import run_programming_task
 from .expert import ask_expert, emit_expert_context
 from .read_file import read_file_tool
@@ -41,5 +42,6 @@ __all__ = [
     'swap_task_order',
     'monorepo_detected',
     'existing_project_detected',
-    'ui_detected'
+    'ui_detected',
+    'ask_human'
 ]

ra_aid/tools/human.py

@@ -1,21 +1,28 @@
 """Tool for asking questions to the human user."""
 
-from langchain.tools import tool
+from langchain_core.tools import tool
 from rich.console import Console
 from rich.prompt import Prompt
+from rich.panel import Panel
+from rich.markdown import Markdown
 
 console = Console()
 
 @tool
 def ask_human(question: str) -> str:
-    """Ask the human user a question and get their response.
+    """Ask the human user a question with a nicely formatted display.
     
     Args:
-        question: The question to ask the human user
+        question: The question to ask the human user (supports markdown)
         
     Returns:
         The user's response as a string
     """
-    console.print(f"\n[bold yellow]Human Query:[/] {question}")
-    response = Prompt.ask("Your response")
+    console.print(Panel(
+        Markdown(question),
+        title="💭 Question for Human",
+        border_style="yellow bold"
+    ))
+    response = Prompt.ask("\nYour response")
+    print()
     return response