diff --git a/Cursor AI Experiments/ai_web_scrapper.py b/Cursor AI Experiments/ai_web_scrapper.py
new file mode 100644
index 0000000..19c5880
--- /dev/null
+++ b/Cursor AI Experiments/ai_web_scrapper.py	
@@ -0,0 +1,49 @@
+import streamlit as st
+from scrapegraphai.graphs import SmartScraperGraph
+
+# Streamlit app title
+st.title("AI Web Scraper")
+
+# Input fields for user prompt and source URL
+prompt = st.text_input("Enter the information you want to extract:")
+source_url = st.text_input("Enter the source URL:")
+
+# Input field for OpenAI API key
+api_key = st.text_input("Enter your OpenAI API key:", type="password")
+
+# Configuration for the scraping pipeline
+graph_config = {
+    "llm": {
+        "api_key": api_key,
+        "model": "openai/gpt-4o-mini",
+    },
+    "verbose": True,
+    "headless": False,
+}
+
+# Button to start the scraping process
+if st.button("Scrape"):
+    if prompt and source_url and api_key:
+        # Create the SmartScraperGraph instance
+        smart_scraper_graph = SmartScraperGraph(
+            prompt=prompt,
+            source=source_url,
+            config=graph_config
+        )
+
+        # Run the pipeline
+        result = smart_scraper_graph.run()
+
+        # Display the result
+        st.write(result)
+    else:
+        st.error("Please provide all the required inputs.")
+
+# Instructions for the user
+st.markdown("""
+### Instructions
+1. Enter the information you want to extract in the first input box.
+2. Enter the source URL from which you want to extract the information.
+3. Enter your OpenAI API key.
+4. Click on the "Scrape" button to start the scraping process.
+""")
\ No newline at end of file
diff --git a/Cursor AI Experiments/chatgpt_clone_llama3.py b/Cursor AI Experiments/chatgpt_clone_llama3.py
new file mode 100644
index 0000000..34f7015
--- /dev/null
+++ b/Cursor AI Experiments/chatgpt_clone_llama3.py	
@@ -0,0 +1,40 @@
+import streamlit as st
+from ollama import Client
+
+# Initialize Ollama client
+client = Client()
+
+# Set up Streamlit page
+st.set_page_config(page_title="Local ChatGPT Clone", page_icon="🤖", layout="wide")
+st.title("🤖 Local ChatGPT Clone")
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display chat messages
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# User input
+if prompt := st.chat_input("What's on your mind?"):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    # Generate AI response
+    with st.chat_message("assistant"):
+        message_placeholder = st.empty()
+        full_response = ""
+        for response in client.chat(model="llama3.1:latest", messages=st.session_state.messages, stream=True):
+            full_response += response['message']['content']
+            message_placeholder.markdown(full_response + "▌")
+        message_placeholder.markdown(full_response)
+    st.session_state.messages.append({"role": "assistant", "content": full_response})
+
+# Add a sidebar with information
+st.sidebar.title("About")
+st.sidebar.info("This is a local ChatGPT clone using Ollama's llama3.1:latest model and Streamlit.")
+st.sidebar.markdown("---")
+st.sidebar.markdown("Made with ❤️ by Your Name")
\ No newline at end of file
diff --git a/Cursor AI Experiments/multi_agent_researcher.py b/Cursor AI Experiments/multi_agent_researcher.py
new file mode 100644
index 0000000..6d54dae
--- /dev/null
+++ b/Cursor AI Experiments/multi_agent_researcher.py	
@@ -0,0 +1,135 @@
+import streamlit as st
+from crewai import Agent, Task, Crew, Process
+from langchain_openai import ChatOpenAI
+import os
+
+# Initialize the GPT-4 model
+gpt4_model = None
+
+def create_article_crew(topic):
+    # Create agents
+    researcher = Agent(
+        role='Researcher',
+        goal='Conduct thorough research on the given topic',
+        backstory='You are an expert researcher with a keen eye for detail',
+        verbose=True,
+        allow_delegation=False,
+        llm=gpt4_model
+    )
+
+    writer = Agent(
+        role='Writer',
+        goal='Write a detailed and engaging article based on the research, using proper markdown formatting',
+        backstory='You are a skilled writer with expertise in creating informative content and formatting it beautifully in markdown',
+        verbose=True,
+        allow_delegation=False,
+        llm=gpt4_model
+    )
+
+    editor = Agent(
+        role='Editor',
+        goal='Review and refine the article for clarity, accuracy, engagement, and proper markdown formatting',
+        backstory='You are an experienced editor with a sharp eye for quality content and excellent markdown structure',
+        verbose=True,
+        allow_delegation=False,
+        llm=gpt4_model
+    )
+
+    # Create tasks
+    research_task = Task(
+        description=f"Conduct comprehensive research on the topic: {topic}. Gather key information, statistics, and expert opinions.",
+        agent=researcher,
+        expected_output="A comprehensive research report on the given topic, including key information, statistics, and expert opinions."
+    )
+
+    writing_task = Task(
+        description="""Using the research provided, write a detailed and engaging article. 
+        Ensure proper structure, flow, and clarity. Format the article using markdown, including:
+        1. A main title (H1)
+        2. Section headings (H2)
+        3. Subsection headings where appropriate (H3)
+        4. Bullet points or numbered lists where relevant
+        5. Emphasis on key points using bold or italic text
+        Make sure the content is well-organized and easy to read.""",
+        agent=writer,
+        expected_output="A well-structured, detailed, and engaging article based on the provided research, formatted in markdown with proper headings and subheadings."
+    )
+
+    editing_task = Task(
+        description="""Review the article for clarity, accuracy, engagement, and proper markdown formatting. 
+        Ensure that:
+        1. The markdown formatting is correct and consistent
+        2. Headings and subheadings are used appropriately
+        3. The content flow is logical and engaging
+        4. Key points are emphasized correctly
+        Make necessary edits and improvements to both content and formatting.""",
+        agent=editor,
+        expected_output="A final, polished version of the article with improved clarity, accuracy, engagement, and proper markdown formatting."
+    )
+
+    # Create the crew
+    crew = Crew(
+        agents=[researcher, writer, editor],
+        tasks=[research_task, writing_task, editing_task],
+        verbose=2,
+        process=Process.sequential
+    )
+
+    return crew
+
+# Streamlit app
+st.set_page_config(page_title="Multi Agent AI Researcher", page_icon="📝")
+
+# Custom CSS for better appearance
+st.markdown("""
+    <style>
+    .stApp {
+        max-width: 1800px;
+        margin: 0 auto;
+        font-family: Arial, sans-serif;
+    }
+    .st-bw {
+        background-color: #f0f2f6;
+    }
+    .stButton>button {
+        background-color: #4CAF50;
+        color: white;
+        font-weight: bold;
+    }
+    .stTextInput>div>div>input {
+        background-color: #ffffff;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+st.title("📝 Multi Agent AI Researcher")
+
+# Sidebar for API key input
+with st.sidebar:
+    st.header("Configuration")
+    api_key = st.text_input("Enter your OpenAI API Key:", type="password")
+    if api_key:
+        os.environ["OPENAI_API_KEY"] = api_key
+        gpt4_model = ChatOpenAI(model_name="gpt-4o-mini")
+        st.success("API Key set successfully!")
+    else:
+        st.info("Please enter your OpenAI API Key to proceed.")
+
+# Main content
+st.markdown("Generate detailed articles on any topic using AI agents!")
+
+topic = st.text_input("Enter the topic for the article:", placeholder="e.g., The Impact of Artificial Intelligence on Healthcare")
+
+if st.button("Generate Article"):
+    if not api_key:
+        st.error("Please enter your OpenAI API Key in the sidebar.")
+    elif not topic:
+        st.warning("Please enter a topic for the article.")
+    else:
+        with st.spinner("🤖 AI agents are working on your article..."):
+            crew = create_article_crew(topic)
+            result = crew.kickoff()
+            st.markdown(result)
+
+st.markdown("---")
+st.markdown("Powered by CrewAI and OpenAI :heart:")
\ No newline at end of file
diff --git a/ai_customer_support_agent/README.md b/ai_customer_support_agent/README.md
new file mode 100644
index 0000000..c31afb4
--- /dev/null
+++ b/ai_customer_support_agent/README.md
@@ -0,0 +1,38 @@
+## 🛒 AI Customer Support Agent with Memory
+This Streamlit app implements an AI-powered customer support agent for TechGadgets.com, an online electronics store. The agent uses OpenAI's GPT-4 model and maintains a memory of past interactions using the Mem0 library with Qdrant as the vector store.
+
+### Features
+
+- Chat interface for interacting with the AI customer support agent
+- Persistent memory of customer interactions and profiles
+- Synthetic data generation for testing and demonstration
+- Utilizes OpenAI's GPT-4o model for intelligent responses
+
+### How to get Started?
+
+1. Clone the GitHub repository
+```bash
+git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
+```
+
+2. Install the required dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+3. Ensure Qdrant is running:
+The app expects Qdrant to be running on localhost:6333. Adjust the configuration in the code if your setup is different.
+
+```bash
+docker pull qdrant/qdrant
+
+docker run -p 6333:6333 -p 6334:6334 \
+    -v $(pwd)/qdrant_storage:/qdrant/storage:z \
+    qdrant/qdrant
+```
+
+4. Run the Streamlit App
+```bash
+streamlit run customer_support_agent.py
+```
diff --git a/ai_customer_support_agent/customer_support_agent.py b/ai_customer_support_agent/customer_support_agent.py
new file mode 100644
index 0000000..a6fd43f
--- /dev/null
+++ b/ai_customer_support_agent/customer_support_agent.py
@@ -0,0 +1,166 @@
+import streamlit as st
+from openai import OpenAI
+from mem0 import Memory
+import os
+import json
+from datetime import datetime, timedelta
+
+# Set up the Streamlit App
+st.title("AI Customer Support Agent with Memory 🛒")
+st.caption("Chat with a customer support assistant who remembers your past interactions.")
+
+# Set the OpenAI API key
+openai_api_key = st.text_input("Enter OpenAI API Key", type="password")
+
+if openai_api_key:
+    os.environ['OPENAI_API_KEY'] = openai_api_key
+    
+    class CustomerSupportAIAgent:
+        def __init__(self):
+            config = {
+                "vector_store": {
+                    "provider": "qdrant",
+                    "config": {
+                        "model": "gpt-4o-mini",
+                        "host": "localhost",
+                        "port": 6333,
+                    }
+                },
+            }
+            self.memory = Memory.from_config(config)
+            self.client = OpenAI()
+            self.app_id = "customer-support"
+
+        def handle_query(self, query, user_id=None):
+            relevant_memories = self.memory.search(query=query, user_id=user_id)
+            context = "Relevant past information:\n"
+            for mem in relevant_memories:
+                context += f"- {mem['text']}\n"
+
+            full_prompt = f"{context}\nCustomer: {query}\nSupport Agent:"
+
+            response = self.client.chat.completions.create(
+                model="gpt-4o-mini",
+                messages=[
+                    {"role": "system", "content": "You are a customer support AI agent for TechGadgets.com, an online electronics store."},
+                    {"role": "user", "content": full_prompt}
+                ]
+            )
+            answer = response.choices[0].message.content
+
+            self.memory.add(query, user_id=user_id, metadata={"app_id": self.app_id, "role": "user"})
+            self.memory.add(answer, user_id=user_id, metadata={"app_id": self.app_id, "role": "assistant"})
+
+            return answer
+
+        def get_memories(self, user_id=None):
+            return self.memory.get_all(user_id=user_id)
+
+        def generate_synthetic_data(self, user_id):
+            today = datetime.now()
+            order_date = (today - timedelta(days=10)).strftime("%B %d, %Y")
+            expected_delivery = (today + timedelta(days=2)).strftime("%B %d, %Y")
+
+            prompt = f"""Generate a detailed customer profile and order history for a TechGadgets.com customer with ID {user_id}. Include:
+            1. Customer name and basic info
+            2. A recent order of a high-end electronic device (placed on {order_date}, to be delivered by {expected_delivery})
+            3. Order details (product, price, order number)
+            4. Customer's shipping address
+            5. 2-3 previous orders from the past year
+            6. 2-3 customer service interactions related to these orders
+            7. Any preferences or patterns in their shopping behavior
+
+            Format the output as a JSON object."""
+
+            response = self.client.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=[
+                {"role": "system", "content": "You are a data generation AI that creates realistic customer profiles and order histories. Always respond with valid JSON."},
+                {"role": "user", "content": prompt}
+            ],
+            response_format={"type": "json_object"}
+            )
+
+            customer_data = json.loads(response.choices[0].message.content)
+
+            # Add generated data to memory
+            for key, value in customer_data.items():
+                if isinstance(value, list):
+                    for item in value:
+                        self.memory.add(json.dumps(item), user_id=user_id, metadata={"app_id": self.app_id, "role": "system"})
+                else:
+                    self.memory.add(f"{key}: {json.dumps(value)}", user_id=user_id, metadata={"app_id": self.app_id, "role": "system"})
+
+            return customer_data
+
+    # Initialize the CustomerSupportAIAgent
+    support_agent = CustomerSupportAIAgent()
+
+    # Sidebar for customer ID and memory view
+    st.sidebar.title("Enter your Customer ID:")
+    previous_customer_id = st.session_state.get("previous_customer_id", None)
+    customer_id = st.sidebar.text_input("Enter your Customer ID")
+
+    if customer_id != previous_customer_id:
+        st.session_state.messages = []
+        st.session_state.previous_customer_id = customer_id
+        st.session_state.customer_data = None
+
+    # Add button to generate synthetic data
+    if st.sidebar.button("Generate Synthetic Data"):
+        if customer_id:
+            with st.spinner("Generating customer data..."):
+                st.session_state.customer_data = support_agent.generate_synthetic_data(customer_id)
+            st.sidebar.success("Synthetic data generated successfully!")
+        else:
+            st.sidebar.error("Please enter a customer ID first.")
+
+    if st.sidebar.button("View Customer Profile"):
+        if st.session_state.customer_data:
+            st.sidebar.json(st.session_state.customer_data)
+        else:
+            st.sidebar.info("No customer data generated yet. Click 'Generate Synthetic Data' first.")
+
+    if st.sidebar.button("View Memory Info"):
+        if customer_id:
+            memories = support_agent.get_memories(user_id=customer_id)
+            if memories:
+                st.sidebar.write(f"Memory for customer **{customer_id}**:")
+                for mem in memories:
+                    st.sidebar.write(f"- {mem['text']}")
+            else:
+                st.sidebar.info("No memory found for this customer ID.")
+        else:
+            st.sidebar.error("Please enter a customer ID to view memory info.")
+
+    # Initialize the chat history
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    # Display the chat history
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    # Accept user input
+    query = st.chat_input("How can I assist you today?")
+
+    if query and customer_id:
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": query})
+        with st.chat_message("user"):
+            st.markdown(query)
+
+        # Generate and display response
+        answer = support_agent.handle_query(query, user_id=customer_id)
+
+        # Add assistant response to chat history
+        st.session_state.messages.append({"role": "assistant", "content": answer})
+        with st.chat_message("assistant"):
+            st.markdown(answer)
+
+    elif not customer_id:
+        st.error("Please enter a customer ID to start the chat.")
+
+else:
+    st.warning("Please enter your OpenAI API key to use the customer support agent.")
\ No newline at end of file
diff --git a/ai_customer_support_agent/requirements.txt b/ai_customer_support_agent/requirements.txt
new file mode 100644
index 0000000..088b5ab
--- /dev/null
+++ b/ai_customer_support_agent/requirements.txt
@@ -0,0 +1,3 @@
+streamlit 
+openai
+mem0ai
\ No newline at end of file