diff --git a/ai_travel_agent_memory/README.md b/ai_travel_agent_memory/README.md
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+## 🧳 AI Travel Agent with Memory
+This Streamlit app implements an AI-powered travel assistant that remembers user preferences and past interactions. It utilizes OpenAI's GPT-4o for generating responses and Mem0 with Qdrant for maintaining conversation history.
+
+### Features
+- Chat-based interface for interacting with an AI travel assistant
+- Persistent memory of user preferences and past conversations
+- Utilizes OpenAI's GPT-4o model for intelligent responses
+- Implements memory storage and retrieval using Mem0 and Qdrant
+- User-specific conversation history and memory viewing
+
+### 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 travel_agent_memory.py
+```
diff --git a/ai_travel_agent_memory/requirements.txt b/ai_travel_agent_memory/requirements.txt
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+streamlit 
+openai
+mem0ai
\ No newline at end of file
diff --git a/ai_travel_agent_memory/travel_agent_memory.py b/ai_travel_agent_memory/travel_agent_memory.py
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+import streamlit as st
+from openai import OpenAI
+from mem0 import Memory
+
+# Set up the Streamlit App
+st.title("AI Travel Agent with Memory 🧳")
+st.caption("Chat with a travel assistant who remembers your preferences and past interactions.")
+
+# Set the OpenAI API key
+openai_api_key = st.text_input("Enter OpenAI API Key", type="password")
+
+if openai_api_key:
+    # Initialize OpenAI client
+    client = OpenAI(api_key=openai_api_key)
+
+    # Initialize Mem0 with Qdrant
+    config = {
+        "vector_store": {
+            "provider": "qdrant",
+            "config": {
+                "host": "localhost",
+                "port": 6333,
+            }
+        },
+    }
+    memory = Memory.from_config(config)
+
+    # Sidebar for username and memory view
+    st.sidebar.title("Enter your username:")
+    previous_user_id = st.session_state.get("previous_user_id", None)
+    user_id = st.sidebar.text_input("Enter your Username")
+
+    if user_id != previous_user_id:
+        st.session_state.messages = []
+        st.session_state.previous_user_id = user_id
+
+    if st.sidebar.button("View Memory Info"):
+        if user_id:
+            memories = memory.get_all(user_id=user_id)
+            if memories:
+                st.sidebar.write(f"Memory for user **{user_id}**:")
+                for mem in memories:
+                    st.sidebar.write(f"- {mem['text']}")
+            else:
+                st.sidebar.info("No memory found for this user ID.")
+        else:
+            st.sidebar.error("Please enter a username 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
+    prompt = st.chat_input("Where would you like to travel?")
+
+    if prompt and user_id:
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        with st.chat_message("user"):
+            st.markdown(prompt)
+
+        # Retrieve relevant memories
+        relevant_memories = memory.search(query=prompt, user_id=user_id)
+        context = "Relevant past information:\n"
+        for mem in relevant_memories:
+            context += f"- {mem['text']}\n"
+
+        # Prepare the full prompt
+        full_prompt = f"{context}\nHuman: {prompt}\nAI:"
+
+        # Generate response
+        response = client.chat.completions.create(
+            model="gpt-4o",
+            messages=[
+                {"role": "system", "content": "You are a travel assistant with access to past conversations."},
+                {"role": "user", "content": full_prompt}
+            ]
+        )
+        answer = response.choices[0].message.content
+
+        # Add assistant response to chat history
+        st.session_state.messages.append({"role": "assistant", "content": answer})
+        with st.chat_message("assistant"):
+            st.markdown(answer)
+
+        # Store the user query and AI response in memory
+        memory.add(prompt, user_id=user_id, metadata={"role": "user"})
+        memory.add(answer, user_id=user_id, metadata={"role": "assistant"})
+    elif not user_id:
+        st.error("Please enter a username to start the chat.")