"""
Example demonstrating the ask_agent functionality for getting sidebar replies
from the agent for a running conversation.
"""
import os
import threading
import time
from datetime import datetime
from pydantic import SecretStr
from openhands.sdk import (
LLM,
Agent,
Conversation,
)
from openhands.sdk.conversation import ConversationVisualizerBase
from openhands.sdk.event import Event
from openhands.sdk.tool import Tool
from openhands.tools.file_editor import FileEditorTool
from openhands.tools.task_tracker import TaskTrackerTool
from openhands.tools.terminal import TerminalTool
# Configure LLM
api_key = os.getenv("LLM_API_KEY")
assert api_key is not None, "LLM_API_KEY environment variable is not set."
model = os.getenv("LLM_MODEL", "anthropic/claude-sonnet-4-5-20250929")
base_url = os.getenv("LLM_BASE_URL")
llm = LLM(
usage_id="agent",
model=model,
base_url=base_url,
api_key=SecretStr(api_key),
)
# Tools
cwd = os.getcwd()
tools = [
Tool(name=TerminalTool.name),
Tool(name=FileEditorTool.name),
Tool(name=TaskTrackerTool.name),
]
class MinimalVisualizer(ConversationVisualizerBase):
"""A minimal visualizer that print the raw events as they occur."""
count = 0
def on_event(self, event: Event) -> None:
"""Handle events for minimal progress visualization."""
print(f"\n\n[EVENT {self.count}] {type(event).__name__}")
self.count += 1
# Agent
agent = Agent(llm=llm, tools=tools)
conversation = Conversation(
agent=agent, workspace=cwd, visualizer=MinimalVisualizer, max_iteration_per_run=5
)
def timestamp() -> str:
return datetime.now().strftime("%H:%M:%S")
print("=== Ask Agent Example ===")
print("This example demonstrates asking questions during conversation execution")
# Step 1: Build conversation context
print(f"\n[{timestamp()}] Building conversation context...")
conversation.send_message("Explore the current directory and describe the architecture")
# Step 2: Start conversation in background thread
print(f"[{timestamp()}] Starting conversation in background thread...")
thread = threading.Thread(target=conversation.run)
thread.start()
# Give the agent time to start processing
time.sleep(2)
# Step 3: Use ask_agent while conversation is running
print(f"\n[{timestamp()}] Using ask_agent while conversation is processing...")
# Ask context-aware questions
questions_and_responses = []
question_1 = "Summarize the activity so far in 1 sentence."
print(f"\n[{timestamp()}] Asking: {question_1}")
response1 = conversation.ask_agent(question_1)
questions_and_responses.append((question_1, response1))
print(f"Response: {response1}")
time.sleep(1)
question_2 = "How's the progress?"
print(f"\n[{timestamp()}] Asking: {question_2}")
response2 = conversation.ask_agent(question_2)
questions_and_responses.append((question_2, response2))
print(f"Response: {response2}")
time.sleep(1)
question_3 = "Have you finished running?"
print(f"\n[{timestamp()}] {question_3}")
response3 = conversation.ask_agent(question_3)
questions_and_responses.append((question_3, response3))
print(f"Response: {response3}")
# Step 4: Wait for conversation to complete
print(f"\n[{timestamp()}] Waiting for conversation to complete...")
thread.join()
# Step 5: Verify conversation state wasn't affected
final_event_count = len(conversation.state.events)
# Step 6: Ask a final question after conversation completion
print(f"\n[{timestamp()}] Asking final question after completion...")
final_response = conversation.ask_agent(
"Can you summarize what you accomplished in this conversation?"
)
print(f"Final response: {final_response}")
# Step 7: Summary
print("\n" + "=" * 60)
print("SUMMARY OF ASK_AGENT DEMONSTRATION")
print("=" * 60)
print("\nQuestions and Responses:")
for i, (question, response) in enumerate(questions_and_responses, 1):
print(f"\n{i}. Q: {question}")
print(f" A: {response[:100]}{'...' if len(response) > 100 else ''}")
final_truncated = final_response[:100] + ("..." if len(final_response) > 100 else "")
print(f"\nFinal Question Response: {final_truncated}")
