init1

.gitattributes

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+*.bz2 filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text

LICENSE

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+MIT License
+
+Copyright (c) 2024 Tony Kipkemboi
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+title: VacAIgent
+emoji: 🐨
+colorFrom: yellow
+colorTo: purple
+sdk: streamlit
+sdk_version: 1.44.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: Let AI agents plan your next vacation!
+---
+
+# 🏖️ VacAIgent: Streamlit-Integrated AI Crew for Trip Planning
+
+_Forked and enhanced from the_ [_crewAI examples repository_](https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner)
+
+## Introduction
+
+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, now with an added layer of interactivity and accessibility through Streamlit.
+
+**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 streamlined Streamlit user interface.
+
+## Streamlit Interface
+
+The introduction of [Streamlit](https://streamlit.io/) transforms this application into an interactive web app, allowing users to easily input their preferences and receive tailored travel plans.
+
+## Running the Application
+
+To experience the VacAIgent app:
+
+### Pre-Requisites
+1. Install and Configure **git** on your machine
+2. Get the API key from **scrapinagent.com** from  scrapinagent [Click Here to Signup](https://scrapingant.com/)
+3. Get the API from **SERPER API** from serper [Click here to Signup]( https://serper.dev/)
+
+### Deploy Trip Planner
+
+####  Step 1
+```sh
+git clone https://github.com/intel-sandbox/trip_planner_agent
+```
+* *Please make sure git is installed*
+
+#### Step 2
+
+Insall Dependencies
+```sh
+pip install -r requirements.txt
+```
+####  Step 3
+
+```sh
+cd trip_planner_agent
+```
+
+create `.streamlit/secrets.toml` file and Update **Credentials**
+ you can use secrtes.example file for reference 
+
+```sh
+SERPER_API_KEY=""
+SCRAPINGANT_API_KEY=""
+OPENAI_API_KEY=""
+MODEL_ID=""
+MODEL_BASE_URL=""
+```
+#### Step 4
+
+Run the application
+
+```sh
+streamlit run streamlit_app.py
+```
+
+Your application should be up and running
+
+★ **Disclaimer**: The application uses GPT-4 by default. Ensure you have access to OpenAI'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 GPT 3.5
+
+To switch from GPT-4 to GPT-3.5, pass the llm argument in the agent constructor:
+
+```python
+from langchain.chat_models import ChatOpenAI
+
+llm = ChatOpenAI(model='gpt-3.5-turbo') # Loading gpt-3.5-turbo (see more OpenAI models at https://platform.openai.com/docs/models/gpt-4-turbo-and-gpt-4)
+
+class TripAgents:
+    # ... existing methods
+
+    def local_expert(self):
+        return Agent(
+            role='Local Expert',
+            goal='Provide insights about the selected city',
+            tools=[SearchTools.search_internet, BrowserTools.scrape_and_summarize_website],
+            llm=llm,
+            verbose=True
+        )
+
+```
+
+## 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.
+
+
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

README_backup.md

@@ -0,0 +1,166 @@
+---
+title: VacAIgent
+emoji: 🐨
+colorFrom: yellow
+colorTo: purple
+sdk: streamlit
+sdk_version: 1.44.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: Let AI agents plan your next vacation!
+---
+
+# 🏖️ VacAIgent: Streamlit-Integrated AI Crew for Trip Planning
+
+_Forked and enhanced from the_ [_crewAI examples repository_](https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner)
+
+![Beach Vacation Scene ~ generated by GPT-4V](images/beach.png)
+
+## Introduction
+
+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, now with an added layer of interactivity and accessibility through Streamlit.
+
+**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 streamlined Streamlit user interface.
+
+## Streamlit Interface
+
+The introduction of [Streamlit](https://streamlit.io/) transforms this application into an interactive web app, allowing users to easily input their preferences and receive tailored travel plans.
+
+## Running the Application
+
+To experience the VacAIgent app:
+
+### Pre-Requisites
+1. Install and Configure **git** on your machine
+2. Get the API key from **scrapinagent.com** from  scrapinagent [Click Here to Signup](https://scrapingant.com/)
+3. Get the API from **SERPER API** from serper [Click here to Signup]( https://serper.dev/)
+
+### Deploy Trip Planner
+
+####  Step 1
+```sh
+git clone https://github.com/intel-sandbox/trip_planner_agent
+```
+* *Please make sure git is installed*
+
+#### Step 2
+
+Insall Dependencies
+```sh
+pip install -r requirements.txt
+```
+####  Step 3
+
+```sh
+cd trip_planner_agent
+```
+
+create `.streamlit/secrets.toml` file and Update **Credentials**
+ you can use secrtes.example file for reference 
+
+```sh
+SERPER_API_KEY=""
+SCRAPINGANT_API_KEY=""
+OPENAI_API_KEY=""
+MODEL_ID=""
+MODEL_BASE_URL=""
+```
+#### Step 4
+
+Run the application
+
+```sh
+streamlit run streamlit_app.py
+```
+
+Your application should be up and running
+
+★ **Disclaimer**: The application uses GPT-4 by default. Ensure you have access to OpenAI'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 GPT 3.5
+
+To switch from GPT-4 to GPT-3.5, pass the llm argument in the agent constructor:
+
+```python
+from langchain.chat_models import ChatOpenAI
+
+llm = ChatOpenAI(model='gpt-3.5-turbo') # Loading gpt-3.5-turbo (see more OpenAI models at https://platform.openai.com/docs/models/gpt-4-turbo-and-gpt-4)
+
+class TripAgents:
+    # ... existing methods
+
+    def local_expert(self):
+        return Agent(
+            role='Local Expert',
+            goal='Provide insights about the selected city',
+            tools=[SearchTools.search_internet, BrowserTools.scrape_and_summarize_website],
+            llm=llm,
+            verbose=True
+        )
+
+```
+
+## 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.
+
+
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,134 @@
+from crewai import Crew
+from trip_agents import TripAgents, StreamToExpander
+from trip_tasks import TripTasks
+import streamlit as st
+import datetime
+import sys
+
+st.set_page_config(page_icon="✈️", layout="wide")
+
+
+def icon(emoji: str):
+    """Shows an emoji as a Notion-style page icon."""
+    st.write(
+        f'<span style="font-size: 78px; line-height: 1">{emoji}</span>',
+        unsafe_allow_html=True,
+    )
+
+
+class TripCrew:
+
+    def __init__(self, origin, cities, date_range, interests):
+        self.cities = cities
+        self.origin = origin
+        self.interests = interests
+        self.date_range = date_range
+        self.output_placeholder = st.empty()
+
+    def run(self):
+        agents = TripAgents()
+        tasks = TripTasks()
+
+        city_selector_agent = agents.city_selection_agent()
+        local_expert_agent = agents.local_expert()
+        travel_concierge_agent = agents.travel_concierge()
+
+        identify_task = tasks.identify_task(
+            city_selector_agent,
+            self.origin,
+            self.cities,
+            self.interests,
+            self.date_range
+        )
+
+        gather_task = tasks.gather_task(
+            local_expert_agent,
+            self.origin,
+            self.interests,
+            self.date_range
+        )
+
+        plan_task = tasks.plan_task(
+            travel_concierge_agent,
+            self.origin,
+            self.interests,
+            self.date_range
+        )
+
+        crew = Crew(
+            agents=[
+                city_selector_agent, local_expert_agent, travel_concierge_agent
+            ],
+            tasks=[identify_task, gather_task, plan_task],
+            verbose=True
+        )
+
+        result = crew.kickoff()
+        self.output_placeholder.markdown(result)
+
+        return result
+
+
+if __name__ == "__main__":
+    icon("🏖️ VacAIgent")
+
+    st.subheader("Let AI agents plan your next vacation!",
+                 divider="rainbow", anchor=False)
+
+    import datetime
+
+    today = datetime.datetime.now().date()
+    next_year = today.year + 1
+    jan_16_next_year = datetime.date(next_year, 1, 10)
+
+    with st.sidebar:
+        st.header("👇 Enter your trip details")
+        with st.form("my_form"):
+            location = st.text_input(
+                "Where are you currently located?", placeholder="San Mateo, CA")
+            cities = st.text_input(
+                "City and country are you interested in vacationing at?", placeholder="Bali, Indonesia")
+            date_range = st.date_input(
+                "Date range you are interested in traveling?",
+                min_value=today,
+                value=(today, jan_16_next_year + datetime.timedelta(days=6)),
+                format="MM/DD/YYYY",
+            )
+            interests = st.text_area("High level interests and hobbies or extra details about your trip?",
+                                     placeholder="2 adults who love swimming, dancing, hiking, and eating")
+
+            submitted = st.form_submit_button("Submit")
+
+        st.divider()
+
+        # Credits to joaomdmoura/CrewAI for the code: https://github.com/joaomdmoura/crewAI
+        st.sidebar.markdown(
+        """
+        Credits to [**@joaomdmoura**](https://twitter.com/joaomdmoura)
+        for creating **crewAI** 🚀
+        """,
+            unsafe_allow_html=True
+        )
+
+        st.sidebar.info("Click the logo to visit GitHub repo", icon="👇")
+        st.sidebar.markdown(
+            """
+        <a href="https://github.com/joaomdmoura/crewAI" target="_blank">
+            <img src="https://raw.githubusercontent.com/joaomdmoura/crewAI/main/docs/crewai_logo.png" alt="CrewAI Logo" style="width:100px;"/>
+        </a>
+        """,
+            unsafe_allow_html=True
+        )
+
+
+if submitted:
+    with st.status("🤖 **Agents at work...**", state="running", expanded=True) as status:
+        with st.container(height=500, border=False):
+            sys.stdout = StreamToExpander(st)
+            trip_crew = TripCrew(location, cities, date_range, interests)
+            result = trip_crew.run()
+        status.update(label="✅ Trip Plan Ready!",
+                      state="complete", expanded=False)
+
+    st.subheader("Here is your Trip Plan", anchor=False, divider="rainbow")
+    st.markdown(result)

requirements.txt

@@ -0,0 +1,7 @@
+crewai==0.98.0
+streamlit==1.41.1
+openai==1.60.1
+unstructured==0.16.15
+pyowm==3.3.0
+tools==0.1.9
+langchain_community==0.3.15

tools/browser_tools.py

@@ -0,0 +1,38 @@
+import json
+
+import requests
+import streamlit as st
+from crewai import Agent, Task
+from langchain.tools import tool
+from unstructured.partition.html import partition_html
+
+
+class BrowserTools():
+
+  @tool("Scrape website content")
+  def scrape_and_summarize_website(website):
+    """Useful to scrape and summarize a website content"""
+    url = f"http://api.scrapingant.com/v2/general?url{website}&x-api-key={st.secrets['SCRAPINGANT_API_KEY']}"
+    payload = json.dumps({"url": website})
+    headers = {'cache-control': 'no-cache', 'content-type': 'application/json'}
+    response = requests.request("GET", url, headers=headers, data=payload)
+    elements = partition_html(text=response.text)
+    content = "\n\n".join([str(el) for el in elements])
+    content = [content[i:i + 8000] for i in range(0, len(content), 8000)]
+    summaries = []
+    for chunk in content:
+      agent = Agent(
+          role='Principal Researcher',
+          goal=
+          'Do amazing researches and summaries based on the content you are working with',
+          backstory=
+          "You're a Principal Researcher at a big company and you need to do a research about a given topic.",
+          allow_delegation=False)
+      task = Task(
+          agent=agent,
+          description=
+          f'Analyze and summarize the content bellow, make sure to include the most relevant information in the summary, return only the summary nothing else.\n\nCONTENT\n----------\n{chunk}'
+      )
+      summary = task.execute()
+      summaries.append(summary)
+    return "\n\n".join(summaries)

tools/calculator_tools.py

@@ -0,0 +1,13 @@
+from langchain.tools import tool
+
+
+class CalculatorTools():
+
+  @tool("Make a calcualtion")
+  def calculate(operation):
+    """Useful to perform any mathematical calculations, 
+    like sum, minus, multiplication, division, etc.
+    The input to this tool should be a mathematical 
+    expression, a couple examples are `200*7` or `5000/2*10`
+    """
+    return eval(operation)

tools/search_tools.py

@@ -0,0 +1,37 @@
+import json
+
+import requests
+import streamlit as st
+from langchain.tools import tool
+
+
+class SearchTools():
+
+  @tool("Search the internet")
+  def search_internet(query):
+    """Useful to search the internet
+    about a a given topic and return relevant results"""
+    top_result_to_return = 4
+    url = "https://google.serper.dev/search"
+    payload = json.dumps({"q": query})
+    headers = {
+        'X-API-KEY': st.secrets['SERPER_API_KEY'],
+        'content-type': 'application/json'
+    }
+    response = requests.request("POST", url, headers=headers, data=payload)
+    # check if there is an organic key
+    if 'organic' not in response.json():
+      return "Sorry, I couldn't find anything about that, there could be an error with you serper api key."
+    else:
+      results = response.json()['organic']
+      string = []
+      for result in results[:top_result_to_return]:
+        try:
+          string.append('\n'.join([
+              f"Title: {result['title']}", f"Link: {result['link']}",
+              f"Snippet: {result['snippet']}", "\n-----------------"
+          ]))
+        except KeyError:
+          next
+
+      return '\n'.join(string)

trip_agents.py

@@ -0,0 +1,110 @@
+from crewai import Agent
+import re
+import streamlit as st
+from langchain_community.llms import OpenAI
+
+from tools.browser_tools import BrowserTools
+from tools.calculator_tools import CalculatorTools
+from tools.search_tools import SearchTools
+from crewai.llm import LLM
+
+model_kwargs = {
+    "model": f"openai/{st.secrets['MODEL_ID']}", 
+    "base_url": st.secrets["MODEL_BASE_URL"],
+    "api_key": st.secrets["OPENAI_API_KEY"]
+}
+llm = LLM(**model_kwargs) # Loading Llama
+
+
+class TripAgents():
+
+    def city_selection_agent(self):
+        return Agent(
+            role='City Selection Expert',
+            goal='Select the best city based on weather, season, and prices',
+            backstory='An expert in analyzing travel data to pick ideal destinations',
+            tools=[
+                SearchTools.search_internet,
+                BrowserTools.scrape_and_summarize_website,
+            ],
+            verbose=True,
+            llm=llm
+            # step_callback=streamlit_callback,
+        )
+
+    def local_expert(self):
+        return Agent(
+            role='Local Expert at this city',
+            goal='Provide the BEST insights about the selected city',
+            backstory="""A knowledgeable local guide with extensive information
+        about the city, it's attractions and customs""",
+            tools=[
+                SearchTools.search_internet,
+                BrowserTools.scrape_and_summarize_website,
+            ],
+            verbose=True,
+            llm=llm
+            # step_callback=streamlit_callback,
+        )
+
+    def travel_concierge(self):
+        return Agent(
+            role='Amazing Travel Concierge',
+            goal="""Create the most amazing travel itineraries with budget and 
+        packing suggestions for the city""",
+            backstory="""Specialist in travel planning and logistics with 
+        decades of experience""",
+            tools=[
+                SearchTools.search_internet,
+                BrowserTools.scrape_and_summarize_website,
+                CalculatorTools.calculate,
+            ],
+            verbose=True,
+            llm=llm
+            # step_callback=streamlit_callback,
+        )
+
+class StreamToExpander:
+    def __init__(self, expander):
+        self.expander = expander
+        self.buffer = []
+        self.colors = ['red', 'green', 'blue', 'orange']  # Define a list of colors
+        self.color_index = 0  # Initialize color index
+
+    def write(self, data):
+        # Filter out ANSI escape codes using a regular expression
+        cleaned_data = re.sub(r'\x1B\[[0-9;]*[mK]', '', data)
+
+        # Check if the data contains 'task' information
+        task_match_object = re.search(r'\"task\"\s*:\s*\"(.*?)\"', cleaned_data, re.IGNORECASE)
+        task_match_input = re.search(r'task\s*:\s*([^\n]*)', cleaned_data, re.IGNORECASE)
+        task_value = None
+        if task_match_object:
+            task_value = task_match_object.group(1)
+        elif task_match_input:
+            task_value = task_match_input.group(1).strip()
+
+        if task_value:
+            st.toast(":robot_face: " + task_value)
+
+        # Check if the text contains the specified phrase and apply color
+        if "Entering new CrewAgentExecutor chain" in cleaned_data:
+            # Apply different color and switch color index
+            self.color_index = (self.color_index + 1) % len(self.colors)  # Increment color index and wrap around if necessary
+
+            cleaned_data = cleaned_data.replace("Entering new CrewAgentExecutor chain", f":{self.colors[self.color_index]}[Entering new CrewAgentExecutor chain]")
+
+        if "City Selection Expert" in cleaned_data:
+            # Apply different color 
+            cleaned_data = cleaned_data.replace("City Selection Expert", f":{self.colors[self.color_index]}[City Selection Expert]")
+        if "Local Expert at this city" in cleaned_data:
+            cleaned_data = cleaned_data.replace("Local Expert at this city", f":{self.colors[self.color_index]}[Local Expert at this city]")
+        if "Amazing Travel Concierge" in cleaned_data:
+            cleaned_data = cleaned_data.replace("Amazing Travel Concierge", f":{self.colors[self.color_index]}[Amazing Travel Concierge]")
+        if "Finished chain." in cleaned_data:
+            cleaned_data = cleaned_data.replace("Finished chain.", f":{self.colors[self.color_index]}[Finished chain.]")
+
+        self.buffer.append(cleaned_data)
+        if "\n" in data:
+            self.expander.markdown(''.join(self.buffer), unsafe_allow_html=True)
+            self.buffer = []

trip_tasks.py

@@ -0,0 +1,86 @@
+from crewai import Task
+from textwrap import dedent
+from datetime import date
+
+
+class TripTasks():
+
+    def identify_task(self, agent, origin, cities, interests, range):
+        return Task(description=dedent(f"""
+            Analyze and select the best city for the trip based
+            on specific criteria such as weather patterns, seasonal
+            events, and travel costs. This task involves comparing
+            multiple cities, considering factors like current weather
+            conditions, upcoming cultural or seasonal events, and
+            overall travel expenses.
+
+            Your final answer must be a detailed
+            report on the chosen city, and everything you found out
+            about it, including the actual flight costs, weather
+            forecast and attractions.
+            {self.__tip_section()}
+
+            Traveling from: {origin}
+            City Options: {cities}
+            Trip Date: {range}
+            Traveler Interests: {interests}
+          """),
+            expected_output="A detailed report on the chosen city with flight costs, weather forecast, and attractions.",
+            agent=agent)
+
+    def gather_task(self, agent, origin, interests, range):
+        return Task(description=dedent(f"""
+            As a local expert on this city you must compile an
+            in-depth guide for someone traveling there and wanting
+            to have THE BEST trip ever!
+            Gather information about  key attractions, local customs,
+            special events, and daily activity recommendations.
+            Find the best spots to go to, the kind of place only a
+            local would know.
+            This guide should provide a thorough overview of what
+            the city has to offer, including hidden gems, cultural
+            hotspots, must-visit landmarks, weather forecasts, and
+            high level costs.
+
+            The final answer must be a comprehensive city guide,
+            rich in cultural insights and practical tips,
+            tailored to enhance the travel experience.
+            {self.__tip_section()}
+
+            Trip Date: {range}
+            Traveling from: {origin}
+            Traveler Interests: {interests}
+          """),
+            expected_output="A comprehensive city guide with cultural insights and practical tips.",
+            agent=agent)
+
+    def plan_task(self, agent, origin, interests, range):
+        return Task(description=dedent(f"""
+            Expand this guide into a full travel
+            itinerary for this time {range} with detailed per-day plans, including
+            weather forecasts, places to eat, packing suggestions,
+            and a budget breakdown.
+
+            You MUST suggest actual places to visit, actual hotels
+            to stay and actual restaurants to go to.
+
+            This itinerary should cover all aspects of the trip,
+            from arrival to departure, integrating the city guide
+            information with practical travel logistics.
+
+            Your final answer MUST be a complete expanded travel plan,
+            formatted as markdown, encompassing a daily schedule,
+            anticipated weather conditions, recommended clothing and
+            items to pack, and a detailed budget, ensuring THE BEST
+            TRIP EVER, Be specific and give it a reason why you picked
+            # up each place, what make them special! {self.__tip_section()}
+
+            Trip Date: {range}
+            Traveling from: {origin}
+            Traveler Interests: {interests}
+          """),
+            expected_output="A complete 7-day travel plan, formatted as markdown, with a daily schedule and budget.",
+            agent=agent)
+
+    def __tip_section(self):
+        return "If you do your BEST WORK, I'll tip you $100 and grant you any wish you want!"