🏖️ Trip Planner: Streamlit with CrewAI

Introduction

Trip Planner leverages the CrewAI framework to automate and enhance the trip planning experience, integrating a CLI, FASTAPI, and a user-friendly Streamlit interface.

CrewAI Framework

CrewAI simplifies the orchestration of role-playing AI agents. In VacAIgent, these agents collaboratively decide on cities and craft a complete itinerary for your trip based on specified preferences, all accessible via a streamlined Streamlit user interface.

Flow Diagram

Running the Application

To experience the VacAIgent app:

• Configure Environment: Set up the environment variables for Browseless, Serper, and OpenAI. Use the secrets.example as a guide to add your keys then move that file (secrets.toml) to .streamlit/secrets.toml.

• Install Dependencies: Execute pip install -r requirements.txt in your terminal.

• Launch the CLI Mode: Run python cli_app.py -o "Bangalore, India" -d "Krabi, Thailand" -s 2024-05-01 -e 2024-05-10 -i "2 adults who love swimming, dancing, hiking, shopping, food, water sports adventures, rock climbing" to start the CLI Mode.

• Launch the FASTAPI: Run uvicorn api_app:app --reload to start the FASTAPI server.

• Launch the Streamlit App: Run streamlit run streamlit_app.py to start the Streamlit interface.

★ Disclaimer: The application uses GEMINI by default. Ensure you have access to GEMINI's API and be aware of the associated costs.

Details & Explanation

• Streamlit UI: The Streamlit interface is implemented in streamlit_app.py, where users can input their trip details.
• Components:
  • ./trip_tasks.py: Contains task prompts for the agents.
  • ./trip_agents.py: Manages the creation of agents.
  • ./tools directory: Houses tool classes used by agents.
  • ./streamlit_app.py: The heart of the Streamlit app.

Using LLM Models

To switch LLMs from differnet Providers

class TripAgents():
    def __init__(self, llm: BaseChatModel = None):
        if llm is None:
            #self.llm = LLM(model="groq/deepseek-r1-distill-llama-70b")
            self.llm = LLM(model="gemini/gemini-2.0-flash")
        else:
            self.llm = llm

Connect to LLMs

Integrating Ollama with CrewAI

Pass the Ollama model to agents in the CrewAI framework:

    agent = Agent(
        role='Local AI Expert',
        goal='Process information using a local model',
        backstory="An AI assistant running on local hardware.",
        llm=LLM(model="ollama/llama3.2", base_url="http://localhost:11434")
    )

License

Trip Planner is open-sourced under the MIT License.