Introduction

The Agent-Based Travel Planner is a modular AI system that automates the generation of detailed travel plans using a multi-agent architecture. This project demonstrates how specialized agents—each responsible for a distinct facet of the planning process—can collaborate under a supervisor pattern to deliver comprehensive, actionable itineraries. The system is designed with extensibility and maintainability in mind, making it suitable for both educational and practical applications in automated planning.

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Agent Roles and Responsibilities

• Research Agent:
  Gathers destination information and suggests activities by formulating optimized web queries and extracting relevant details for the trip.

• Budget Agent:
  Estimates costs for the planned trip, including accommodation, food, and activities, and manages budget constraints based on research data.

• Logistics Agent:
  Produces a detailed, day-by-day itinerary, including suggested activities, accommodation, transport tips, and local experiences, using research and budget data.

• Supervisor:
  Orchestrates the workflow, ensuring each agent operates in sequence and passes results to the next phase. Handles transitions between research, budgeting, and logistics phases.

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Workflow Overview

The multi-agent system uses Supervisor architecture as shown in the figure.

1. User Input:
   The user defines trip parameters (destination, dates, budget, etc.) in the configuration.

2. Research Phase:
   The Research Agent formulates web queries to gather up-to-date destination and activity info.

3. Budgeting Phase:
   The Budget Agent uses research data to estimate total costs and provide budget breakdowns.

4. Logistics Phase:
   The Logistics Agent composes a clear, structured itinerary, optimizing for user goals and constraints.

5. Output:
   The result is compiled into a Markdown file (travel_plan.md) for easy review.

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Technology Stack

• Programming Language: Python
• Key Libraries: LangGraph, LangChain, Gemini LLM (Google Generative AI), Tavily (web search)
• Architecture: Supervisor pattern coordinating specialized agents
• Utilities: Calculator, file writer, web search

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Project Structure

app/
├── agents/           # Specialized agents for research, budgeting, logistics
│   ├── budget_agent.py
│   ├── logistic_agent.py
│   └── research_agent.py
├── config/           # Configuration files (LLM, settings)
│   ├── llm_config.py
│   └── settings.py
├── supervisor/       # Supervisor agent orchestrating workflow
│   └── supervisor.py
├── tools/          
│   ├── calculator.py
│   ├── TravelScript.py
│   └── web_search.py
├── utils/            # Logging and helper utilities
│   └── logger.py
├── workflows/        # Main workflow logic
│   └── main_workflow.py
├── main.py           # Entry point
├── requirements.txt  # Python dependencies
├── travel_plan.md    # Example output
├── pyproject.toml    # Project metadata
└── README.md         # Documentation

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Example Use Case

Suppose a user wants to plan a week-long trip to Paris on a $2000 budget. The system will:

• Use the Research Agent to gather up-to-date info on Paris attractions and travel tips.
• Pass findings to the Budget Agent to estimate costs for accommodation, food, and activities.
• Feed the results to the Logistics Agent to create a detailed, day-by-day itinerary.

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Human in the loop

bot: What kind of trip would you like to plan?
Also include Origin and Destinatin:
User: I want to visit snow falling mountain area

**Research agent shows all the information**

research agent: Do you want to proceed with this research result? (yes/no/revise):
User: revise

research agent: Please provide revised trip details: 
User: Mountain area should be in south east asia

**Now research agents redo the work by considering the Human feedback and pass the data to budget agent.**

**Budget agent estimates all the budget and display it**

Supervisor: Do you want to proceed with this budget estimate? (yes/no/revise):
User: yes

**Now researched and  budget information is passed to logistic agent and it gives proper planning to Supervisor**

**Final response is shown to the user**

Supervisor: Do you want to proceed with this final plan? (yes/no/revise): 
User: yes

**Results have been saved to travel_plan.md.**

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Sample Output

Research Results:
Agoda Celebrates International Mountain Day with Top 5 Mountain ...: Top Mountain Destination in Asia · 1. Jeju, South Korea · 2. Sapporo, Japan · 3. Chiang Mai, Thailand · 4. Bandung, Indonesia · 5. Kota Kinabalu,
From Mountains to Beaches: Scenic Destinations in Asia: From Mountains to Beaches: Scenic Destinations in Asia · Halong Bay, Vietnam · Kerala, India · Luang Prabang, Laos · Kyoto, Japan · Chiang Mai,
Best access to mountains in SE Asia? : r/solotravel - Reddit: Northern Thailand is great- the higher/farther north, the cooler it gets but also the more the remote so less people who speak English and less reliable WiFi.
Best Countries To Travel In Southeast Asia (All Pros & Cons): Thailand · Laos · Let me help you plan your Southeast Asia trip! · Vietnam · Cambodia · Malaysia · Singapore · Indonesia.
Climb, Discover, and Experience Asia's Best Mountain Hikes - Part 1: Exploring the Majestic Mountains of Asia

Budget Estimate:
Based on the provided information and focusing on Southeast Asia for mountain access, I'll create a cost estimate in INR, assuming a moderate budget and a 10-day trip. I'll prioritize Thailand (Chiang Mai) and potentially Laos (Luang Prabang) due to mountain access and general travel recommendations.

flights: 40000
accommodation: 15000
food: 7000
activities: 5000
transportation: 3000
miscellaneous: 2000

Final Plan:
Okay, here's a concise 10-day itinerary focusing on mountains in Southeast Asia, primarily in Northern Thailand (Chiang Mai), with a potential side trip to Laos (Luang Prabang), based on your provided research and budget (converted to INR).

**Assumptions:**

*   **Budget:** INR 72,000 (Flights: 40,000, Accommodation: 15,000, Food: 7,000, Activities: 5,000, Transportation: 3,000, Miscellaneous: 2,000)
*   **Focus:** Mountain scenery, hiking, cultural experiences.
*   **Travel Style:** Moderate budget, prioritizing experiences over luxury.

**Itinerary:**

**Days 1-3: Chiang Mai, Thailand (Arrival & City Exploration)**

*   **Day 1:** Arrive at Chiang Mai International Airport (CNX). Check into your guesthouse/hostel in the Old City (consider areas near Thapae Gate). Explore the Old City temples (Wat Chedi Luang, Wat Phra Singh). Evening: Chiang Mai Night Bazaar (street food, souvenirs).
    *   **Accommodation:** Guesthouse/Hostel (INR 1000-1500/night).
    *   **Transportation:** Airport taxi/Grab, walking within the Old City.
    *   **Food:** Street food (Pad Thai, Mango Sticky Rice) - INR 500/day.
*   **Day 2:** Doi Suthep Temple (Wat Phra That Doi Suthep) - take a songthaew (red truck taxi) up the mountain. Afternoon: Explore Nimmanhaemin Road (trendy cafes, shops). Evening: Cooking class (learn to make Thai dishes).
    *   **Activities:** Doi Suthep entrance fee, cooking class (INR 1500).
*   **Day 3:** Elephant Nature Park (ethical elephant sanctuary - book in advance!).  Evening: Relaxing Thai massage.
    *   **Activities:** Elephant Nature Park (INR 2000).

**Days 4-6: Chiang Mai (Mountain Hiking & Nature)**

*   **Day 4:** Hike to Doi Inthanon, the highest peak in Thailand. Hire a driver or join a tour. Visit waterfalls (Wachirathan Falls, Sirithan Falls) along the way.
    *   **Transportation:** Private driver/tour (INR 1500).
    *   **Activities:** Doi Inthanon National Park entrance fee.
*   **Day 5:** Hike the Monk's Trail to Wat Pha Lat (hidden temple in the jungle). Afternoon: Explore the Huay Tung Tao Lake (relax, swim, bamboo huts).
    *   **Transportation:** Songthaew/Grab to trailheads.
*   **Day 6:** Optional: Day trip to Pai (scenic mountain town - longer travel time). Alternatively, explore more of Chiang Mai's surrounding nature (e.g., Queen Sirikit Botanic Garden).

**Days 7-9: Luang Prabang, Laos (Optional - Requires Flight/Bus)**

*   **Day 7:** Travel to Luang Prabang (flight or overnight bus - factor in travel time). Check into your guesthouse. Explore the Luang Prabang Old Town (UNESCO World Heritage Site).
    *   **Accommodation:** Guesthouse (INR 1000-1500/night).
    *   **Transportation:** Flight/Bus (adjust budget accordingly).
    *   **Food:** Lao cuisine (sticky rice, laap) - INR 500/day.
*   **Day 8:** Kuang Si Falls (beautiful turquoise waterfalls).  Afternoon: Climb Mount Phousi for sunset views. Evening: Luang Prabang Night Market.
    *   **Activities:** Kuang Si Falls entrance fee.
*   **Day 9:** Alms Giving Ceremony (early morning).  Explore temples (Wat Xieng Thong).  Optional:  Boat trip on the Mekong River.

**Day 10: Departure**

*   **Day 10:** Fly from Luang Prabang (LPQ) or Chiang Mai (CNX) back home.

**Travel Tips & Warnings:**

*   **Visas:** Check visa requirements for Thailand and Laos based on your nationality.
*   **Currency:** Thai Baht (THB) and Lao Kip (LAK).
*   **Bargaining:** Bargain respectfully in markets.
*   **Health:** Consult your doctor about vaccinations and malaria prevention.
*   **Safety:** Be aware of your surroundings, especially at night.
*   **Respect:** Dress respectfully when visiting temples (cover shoulders and knees).
*   **Water:** Drink bottled water.
*   **Language:** Learn basic Thai/Lao phrases.
*   **Flexibility:** This is a suggested itinerary; be flexible and adjust based on your interests and budget.
*   **Transportation:** Download Grab app for easy taxi booking in Chiang Mai.

**Budget Notes:**

*   Flights are a significant portion of the budget.  Consider booking in advance and being flexible with travel dates.
*   Accommodation can be cheaper if you stay in hostels or budget guesthouses.
*   Food can be very affordable if you eat at local restaurants and street food stalls.
*   Activities can be adjusted based on your interests.
*   The Luang Prabang portion is optional and will require adjusting the budget for transportation. If you skip it, spend more time exploring Northern Thailand.

This itinerary provides a framework for your trip. Enjoy your mountain adventure in Southeast Asia!

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Getting Started

1. Clone the Repository

   git clone https://github.com/AnisH1427/Agent-Based-Travel-Planner.git
   cd Agent-Based-Travel-Planner

2. Set Up Environment

   uv venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   uv pip install -r requirements.txt

3. Configure Settings
   • Edit app/config/settings.py and .env for your API keys and trip parameters.
4. Run the Application

   python main.py

5. View the Output
   • Review the generated travel_plan.md for the detailed itinerary.

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Future Direction

As the travel planning system evolves, there are numerous opportunities to expand its capabilities and deliver even greater value to users. By integrating advanced technologies and user-centric features, the platform can become more intelligent, adaptive, and comprehensive. Future development could focus on the following areas:

• Multi-modal input support, such as image and document parsing for richer user context
• Persistent user profiles for personalized recommendations and adaptive planning
• Real-time integration with APIs for flights, hotels, weather, and local events
• Collaborative trip planning features for group itineraries and shared preferences
• User-friendly mobile and web interfaces for broader accessibility
• Automated booking capabilities for flights, accommodations, and activities
• Advanced budget optimization using AI-driven suggestions
• Conversational feedback loops for dynamic plan refinement
• Sustainability recommendations and carbon footprint estimation
• Multi-language support and local cultural insights for international travelers

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Maintenance, Support, and Licensing

Maintenance and Support Status

The Agent-Based Travel Planner is an actively maintained open-source prototype. The primary goals are to ensure compatibility with updated library versions (e.g., LangChain, Tavily), refine agent logic based on user feedback, and implement the features outlined in the Future Direction. Support is provided on a best-effort basis through the project's GitHub repository, where users can report issues, suggest enhancements, and contribute code.

System Resilience and Error Handling

The system's architecture prioritizes reliability through a multi-layered strategy to manage failures and unexpected inputs:

• Structured Error Propagation: Each agent is designed to catch and classify errors within its domain (e.g., no search results, invalid API response, budget calculation error). These errors are packaged with context and passed to the Supervisor agent, which acts as the central error handler.
• Supervisor Recovery Logic: The Supervisor implements recovery strategies based on error type. For transient errors (e.g., API timeouts), it will automatically retry the operation. For logical errors (e.g., no activities found for a destination), it triggers the "Human-in-the-loop" mechanism to ask the user for clarification or revised input, ensuring the workflow can proceed.
• LLM Output Validation and Retries: All responses from the Gemini LLM are parsed and validated. If the output is malformed or does not adhere to the instructed schema, the system automatically retries the request with more explicit prompting before escalating the issue.
• External API Robustness: Calls to external services like Tavily Web Search and Google Generative AI are wrapped in retry handlers with exponential backoff. This gracefully handles rate limits and temporary network instability without crashing the application.

Licensing and Usage Rights

This project is published under the MIT License, a permissive open-source license. This grants users full freedom to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the software, subject to one condition: all copies must include the original copyright notice and the full text of the MIT License. This makes the project suitable for unrestricted academic, personal, and commercial use.

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Conclusion

This project illustrates a practical application of multi-agent systems for travel planning. The Agent-Based Travel Planner demonstrates how modular AI agents and modern orchestration patterns can automate and enhance the travel planning process. By integrating research, budgeting, and logistics capabilities, the system delivers efficient, personalized itineraries, setting a strong foundation for future innovation and user-centric enhancements.

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Reference

• https://langchain-ai.github.io/langgraph/concepts/why-langgraph/
• https://app.tavily.com/home
• https://aistudio.google.com/apikey
• https://langchain-ai.github.io/langgraph/tutorials/multi_agent/agent_supervisor/