---
title: VacAIgent
emoji: 🐨
colorFrom: yellow
colorTo: purple
sdk: streamlit
sdk_version: 1.44.1
app_file: app.py
pinned: false
license: mit
short_description: Let AI agents plan your next vacation!
---

# 🏖️ VacAIgent: Streamlit-Integrated AI Crew for Trip Planning

VacAIgent leverages the CrewAI framework to automate and enhance the trip planning experience, integrating a user-friendly Streamlit interface. This project demonstrates how autonomous AI agents can collaborate and execute complex tasks efficiently.

_Forked and enhanced from the_ [_crewAI examples repository_](https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner). You can find the application hosted on Hugging Face Spaces here:

[![](images/hf_vacaigent.png)](https://huggingface.co/spaces/Intel/vacaigent)


**Check out the video below for code walkthrough** 👇

<a href="https://youtu.be/nKG_kbQUDDE">
  <img src="https://img.youtube.com/vi/nKG_kbQUDDE/hqdefault.jpg" alt="Watch the video" width="100%">
</a>

(_Trip example originally developed by [@joaomdmoura](https://x.com/joaomdmoura)_)

## 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 Streamlit user interface.


## Running the Application

To experience the VacAIgent app:

### Pre-Requisites
1. Get the API key from **scrapinagent.com** from  [scrapinagent](https://scrapingant.com/)
2. Get the API from **SERPER API** from [serper]( https://serper.dev/)
3. Bring your OpenAI compatible API key
4. Bring your model endpoint URL and LLM model ID that you want to use

### Deploy Trip Planner

####  Step 1
Clone the repository:
```sh
git clone https://github.com/opea-project/Enterprise-Inference/
cd examples/vacaigent
```

#### Step 2

Insall Dependencies
```sh
pip install -r requirements.txt
```
####  Step 3
Add Streamlit secrets. Create a `.streamlit/secrets.toml` file and update the variables below:

```sh
SERPER_API_KEY=""
SCRAPINGANT_API_KEY=""
OPENAI_API_KEY=""
MODEL_ID="meta-llama/Llama-3.3-70B-Instruct"
MODEL_BASE_URL="https://api.inference.denvrdata.com/v1/"

```
**Note**: You can alternatively add these secrets directly to Hugging Face Spaces Secrets, under the Settings tab, if deploying the Streamlit application directly on Hugging Face.

#### Step 4

Run the application

```sh
streamlit run app.py
```

Your application should be up and running in your web browser.

★ **Disclaimer**: The application uses meta-llama/Llama-3.3-70B-Instruct by default. Ensure you have access to an OpenAI-compatible API and be aware of any associated costs.

## Details & Explanation

- **Components**:
  - [trip_tasks.py](trip_tasks.py): Contains task prompts for the agents.
  - [trip_agents.py](trip_agents.py): Manages the creation of agents.
  - [tools](tools) directory: Houses tool classes used by agents.
  - [app.py](app.py): The heart of the frontend Streamlit app.

## LLM Model

To switch the LLM model being used, you can switch the `MODEL_ID` in the `.streamlit/secrets.toml` file.

## Using Local Models with Ollama

For enhanced privacy and customization, you can integrate local models like Ollama:

### Setting Up Ollama

- **Installation**: Follow Ollama's guide for installation.
- **Configuration**: Customize the model as per your requirements.

### Integrating Ollama with CrewAI

Pass the Ollama model to agents in the CrewAI framework:

```python
from langchain.llms import Ollama

ollama_model = Ollama(model="agent")

class TripAgents:
    # ... existing methods

    def local_expert(self):
        return Agent(
            role='Local Expert',
            tools=[SearchTools.search_internet, BrowserTools.scrape_and_summarize_website],
            llm=ollama_model,
            verbose=True
        )

```

## Benefits of Local Models

- **Privacy**: Process sensitive data in-house.
- **Customization**: Tailor models to fit specific needs.
- **Performance**: Potentially faster responses with on-premises models.

## License

VacAIgent is open-sourced under the MIT license.