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init: TODO agent
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‎examples/agents/todo_example.py‎

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#!/usr/bin/env python3
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"""Simplified Hiking Trip Planning Agent - Process Under Hood, Stream Final Summary."""
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import asyncio
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import json
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import os
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from pydantic import BaseModel
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from ragbits.agents.todo_agent import TodoAgent
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from ragbits.core.llms import LiteLLM
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from ragbits.core.prompt.prompt import Prompt
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class HikingPlanInput(BaseModel):
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"""Simplified input for hiking trip planning."""
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location: str
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duration_days: int
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group_size: int
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skill_level: str
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season: str
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preferences: str
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class PlanningPrompt(Prompt[HikingPlanInput]):
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"""Unified prompt for all hiking planning tasks."""
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system_prompt = """
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You are an expert hiking guide with 15+ years of experience. You create comprehensive,
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actionable hiking trip plans by working through planning tasks systematically.
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Always prioritize safety while maximizing the adventure experience.
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Provide specific recommendations with real names, locations, costs, and practical details.
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"""
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user_prompt = """
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Plan a {{ duration_days }}-day hiking trip for {{ group_size }} {{ skill_level }} hikers
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in {{ location }} during {{ season }}.
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Preferences: {{ preferences }}
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Complete this specific planning task with detailed, actionable recommendations.
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"""
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class TaskGenerationPrompt(Prompt[HikingPlanInput]):
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"""Prompt for AI to generate planning tasks."""
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system_prompt = """
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You are an expert hiking trip planner. Create a JSON array of planning tasks.
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IMPORTANT: Return ONLY the JSON array, no other text or explanations.
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Create 3-5 specific planning tasks. Each task should be actionable and focused.
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Example response:
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["Research trail routes and difficulty levels", "Find accommodation near trailheads",
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"Analyze weather and gear needs"]
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"""
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user_prompt = """
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Create planning tasks for:
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- {{ duration_days }}-day hiking trip in {{ location }}
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- {{ group_size }} {{ skill_level }} hikers
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- Season: {{ season }}
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- Preferences: {{ preferences }}
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Return only the JSON array of task descriptions.
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"""
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class SummaryPrompt(Prompt[HikingPlanInput]):
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"""Prompt for creating final comprehensive summary."""
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system_prompt = """
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You are an expert hiking guide creating a comprehensive trip plan summary.
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Based on your planning analysis, create a complete, actionable hiking trip plan
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that covers all essential aspects of the adventure.
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Structure your response clearly with specific recommendations and practical details.
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"""
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user_prompt = """
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Create a comprehensive hiking trip plan for:
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- {{ duration_days }}-day trip in {{ location }}
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- {{ group_size }} {{ skill_level }} hikers
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- Season: {{ season }}
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- Preferences: {{ preferences }}
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Provide a complete plan including:
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• Trail routes and difficulty assessments
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• Accommodation recommendations near trailheads
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• Detailed budget breakdown with costs
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• Weather analysis and gear recommendations
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• Daily schedule with sunrise/sunset times
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• Post-hike activities and relaxation options
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• Safety protocols and emergency contacts
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Make this a step-by-step plan someone could follow immediately.
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"""
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def clear_console() -> None:
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"""Clear the console screen."""
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os.system("clear" if os.name == "posix" else "cls") # noqa: S605
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def display_task_list(agent: TodoAgent, title: str = "Planning Progress") -> None:
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"""Display current task list with visual status indicators."""
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clear_console()
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print("🏔️ AI Hiking Trip Planner")
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print("=" * 50)
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print(f"\n📋 {title}")
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print("─" * 30)
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for task in agent.tasks:
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if task.status.value == "pending":
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status_icon = "⏳"
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elif task.status.value == "in-progress":
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status_icon = "🔄"
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else: # done
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status_icon = "✅"
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print(f" {status_icon} {task.description}")
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print(f"\n📊 Progress: {len(agent.done_tasks)}/{len(agent.tasks)} completed")
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async def smart_hiking_planner() -> None:
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"""Smart hiking planner that processes tasks under the hood and streams final summary."""
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llm = LiteLLM(model_name="gpt-4o-mini")
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clear_console()
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print("🏔️ Smart AI Hiking Trip Planner")
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print("=" * 50)
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# Trip configuration
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trip_input = HikingPlanInput(
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location="Tatra Mountains - Poland",
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duration_days=1,
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group_size=2,
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skill_level="intermediate",
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season="late September/early October",
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preferences="scenic views, no longer than 15km, no crowds, avoid Morskie Oko/Giewont/Kasprowy Wierch",
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)
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print("\n📋 Trip Configuration:")
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print(f" 📍 Location: {trip_input.location}")
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print(f" 📅 Duration: {trip_input.duration_days} day(s)")
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print(f" 👥 Group: {trip_input.group_size} {trip_input.skill_level} hikers")
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print(f" 🌤️ Season: {trip_input.season}")
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print(f" ⭐ Preferences: {trip_input.preferences}")
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await asyncio.sleep(2)
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# AI creates planning tasks
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print("\n🤖 AI analyzing trip requirements and generating tasks...")
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# Create task generation agent
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task_generator = TodoAgent(llm=llm, prompt=TaskGenerationPrompt)
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# Generate tasks with AI
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task_response = await task_generator.run(trip_input)
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# Parse AI-generated tasks with robust error handling
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try:
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# Extract response content - AgentResult has .content attribute
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if hasattr(task_response, "content"):
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response_content = task_response.content.strip()
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else:
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response_content = str(task_response).strip()
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# Multiple strategies to extract JSON
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import re
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# Strategy 1: Try direct JSON parsing
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try:
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planning_tasks = json.loads(response_content)
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except json.JSONDecodeError:
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# Strategy 2: Extract JSON array from text
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json_patterns = [
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r"\[(?:[^[\]]*(?:\[[^\]]*\])*)*\]", # Complex nested array pattern
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r"\[.*?\]", # Simple array pattern
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]
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planning_tasks = None
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for pattern in json_patterns:
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json_match = re.search(pattern, response_content, re.DOTALL)
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if json_match:
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json_str = json_match.group(0)
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try:
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planning_tasks = json.loads(json_str)
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break
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except json.JSONDecodeError:
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continue
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if planning_tasks is None:
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raise ValueError("No valid JSON array found in response") from None
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# Validate it's a list of strings
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if not isinstance(planning_tasks, list):
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raise ValueError(f"Expected list, got {type(planning_tasks)}")
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if not all(isinstance(task, str) for task in planning_tasks):
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raise ValueError("All tasks must be strings")
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if len(planning_tasks) == 0:
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raise ValueError("No tasks generated")
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except Exception as e:
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print(f"⚠️ AI task generation failed ({e}), using fallback tasks...")
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planning_tasks = [
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"Research optimal trail routes and scenic viewpoints",
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"Find accommodation close to trailheads",
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"Analyze weather conditions and gear requirements",
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"Plan daily schedule with sunrise/sunset times",
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"Research post-hike activities and relaxation spots",
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]
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# Create planning agent
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planning_agent = TodoAgent(llm=llm, prompt=PlanningPrompt, keep_history=True)
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planning_agent.create_todo_list(planning_tasks)
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await asyncio.sleep(1)
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display_task_list(planning_agent, "AI-Generated Planning Tasks")
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await asyncio.sleep(2)
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print("\n🚀 Processing tasks under the hood...")
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await asyncio.sleep(1)
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# Process all tasks under the hood (user sees progress, not details)
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for task in planning_agent.tasks:
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# Show current progress
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planning_agent.mark_task(task.id, "in-progress")
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display_task_list(planning_agent, f"Processing: {task.description[:40]}...")
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# Process task with AI (under the hood)
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await planning_agent.run(trip_input)
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# Mark as completed
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planning_agent.mark_task(task.id, "done")
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await asyncio.sleep(1)
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# Show all tasks completed
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display_task_list(planning_agent, "All Planning Tasks Completed!")
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await asyncio.sleep(2)
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# Generate and stream comprehensive summary
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print("\n🎯 Generating comprehensive trip plan...")
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print("🤖 AI compiling all research into actionable plan...")
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await asyncio.sleep(2)
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# Create summary agent with all gathered information
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summary_agent = TodoAgent(llm=llm, prompt=SummaryPrompt)
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# Clear screen for final summary
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clear_console()
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print(f"{'='*60}")
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print("🏔️ COMPLETE HIKING TRIP PLAN")
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print(f"{'='*60}")
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print()
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# Stream the comprehensive summary
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streaming_result = summary_agent.run_streaming(trip_input)
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async for chunk in streaming_result:
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if isinstance(chunk, str):
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print(chunk, end="", flush=True)
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print(f"\n\n{'='*60}")
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print("✅ Trip planning completed!")
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print(f"📊 Agent processed {len(planning_agent.tasks)} planning tasks")
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print("🎉 Ready for your adventure!")
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async def main() -> None:
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"""Run the smart hiking planner."""
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print("🚀 Smart AI Hiking Trip Planner")
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print("AI processes planning tasks under the hood, then streams comprehensive plan")
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print("\nPress Ctrl+C to exit...")
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await asyncio.sleep(3)
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try:
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await smart_hiking_planner()
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print("\n✨ Planning completed successfully!")
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except KeyboardInterrupt:
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print("\n👋 Planning interrupted")
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except Exception as e:
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print(f"\n❌ Error: {e}")
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print("Make sure OPENAI_API_KEY is set")
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if __name__ == "__main__":
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asyncio.run(main())

‎packages/ragbits-agents/src/ragbits/agents/__init__.py‎

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AgentRunContext,
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ToolCallResult,
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)
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from ragbits.agents.todo_agent import Task, TaskStatus, TodoAgent
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from ragbits.agents.types import QuestionAnswerAgent, QuestionAnswerPromptInput, QuestionAnswerPromptOutput
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__all__ = [
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"QuestionAnswerAgent",
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"QuestionAnswerPromptInput",
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"QuestionAnswerPromptOutput",
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"Task",
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"TaskStatus",
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"TodoAgent",
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"ToolCallResult",
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]

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