diff --git a/docs/source/assets/deployment/streamlit-chat.png b/docs/source/assets/deployment/streamlit-chat.png
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diff --git a/docs/source/deployment/frameworks/index.md b/docs/source/deployment/frameworks/index.md
index a1b405386b7..683fa8217a8 100644
--- a/docs/source/deployment/frameworks/index.md
+++ b/docs/source/deployment/frameworks/index.md
@@ -12,5 +12,6 @@ lws
 modal
 open-webui
 skypilot
+streamlit
 triton
 :::
diff --git a/docs/source/deployment/frameworks/streamlit.md b/docs/source/deployment/frameworks/streamlit.md
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+++ b/docs/source/deployment/frameworks/streamlit.md
@@ -0,0 +1,42 @@
+(deployment-streamlit)=
+
+# Streamlit
+
+[Streamlit](https://github.com/streamlit/streamlit) lets you transform Python scripts into interactive web apps in minutes, instead of weeks. Build dashboards, generate reports, or create chat apps.
+
+It can be quickly integrated with vLLM as a backend API server, enabling powerful LLM inference via API calls.
+
+## Prerequisites
+
+- Setup vLLM environment
+
+## Deploy
+
+- Start the vLLM server with the supported chat completion model, e.g.
+
+```console
+vllm serve qwen/Qwen1.5-0.5B-Chat
+```
+
+- Install streamlit and openai:
+
+```console
+pip install streamlit openai
+```
+
+- Use the script: <gh-file:examples/online_serving/streamlit_openai_chatbot_webserver.py>
+
+- Start the streamlit web UI and start to chat:
+
+```console
+streamlit run streamlit_openai_chatbot_webserver.py
+
+# or specify the VLLM_API_BASE or VLLM_API_KEY
+VLLM_API_BASE="http://vllm-server-host:vllm-server-port/v1" streamlit run streamlit_openai_chatbot_webserver.py
+
+# start with debug mode to view more details
+streamlit run streamlit_openai_chatbot_webserver.py --logger.level=debug
+```
+
+:::{image} /assets/deployment/streamlit-chat.png
+:::
diff --git a/examples/online_serving/streamlit_openai_chatbot_webserver.py b/examples/online_serving/streamlit_openai_chatbot_webserver.py
new file mode 100644
index 00000000000..d8a0f211d44
--- /dev/null
+++ b/examples/online_serving/streamlit_openai_chatbot_webserver.py
@@ -0,0 +1,185 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+vLLM Chat Assistant - A Streamlit Web Interface
+
+A streamlined chat interface that quickly integrates
+with vLLM API server.
+
+Features:
+- Multiple chat sessions management
+- Streaming response display
+- Configurable API endpoint
+- Real-time chat history
+
+Requirements:
+    pip install streamlit openai
+
+Usage:
+    # Start the app with default settings
+    streamlit run streamlit_openai_chatbot_webserver.py
+
+    # Start with custom vLLM API endpoint
+    VLLM_API_BASE="http://your-server:8000/v1" \
+        streamlit run streamlit_openai_chatbot_webserver.py
+
+    # Enable debug mode
+    streamlit run streamlit_openai_chatbot_webserver.py \
+        --logger.level=debug
+"""
+import os
+from datetime import datetime
+
+import streamlit as st
+from openai import OpenAI
+
+# Get command line arguments from environment variables
+openai_api_key = os.getenv('VLLM_API_KEY', "EMPTY")
+openai_api_base = os.getenv('VLLM_API_BASE', "http://localhost:8000/v1")
+
+# Initialize session states for managing chat sessions
+if "sessions" not in st.session_state:
+    st.session_state.sessions = {}
+
+if "current_session" not in st.session_state:
+    st.session_state.current_session = None
+
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+if "active_session" not in st.session_state:
+    st.session_state.active_session = None
+
+# Initialize session state for API base URL
+if "api_base_url" not in st.session_state:
+    st.session_state.api_base_url = openai_api_base
+
+
+def create_new_chat_session():
+    """Create a new chat session with timestamp as ID"""
+    session_id = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    st.session_state.sessions[session_id] = []
+    st.session_state.current_session = session_id
+    st.session_state.active_session = session_id
+    st.session_state.messages = []
+
+
+def switch_to_chat_session(session_id):
+    """Switch to a different chat session"""
+    st.session_state.current_session = session_id
+    st.session_state.active_session = session_id
+    st.session_state.messages = st.session_state.sessions[session_id]
+
+
+def get_llm_response(messages, model):
+    """Get streaming response from llm
+
+    Args:
+        messages: List of message dictionaries
+        model: Name of model
+
+    Returns:
+        Streaming response object or error message string
+    """
+    try:
+        response = client.chat.completions.create(model=model,
+                                                  messages=messages,
+                                                  stream=True)
+        return response
+    except Exception as e:
+        st.error(f"Error details: {str(e)}")
+        return f"Error: {str(e)}"
+
+
+# Sidebar - API Settings first
+st.sidebar.title("API Settings")
+new_api_base = st.sidebar.text_input("API Base URL:",
+                                     value=st.session_state.api_base_url)
+if new_api_base != st.session_state.api_base_url:
+    st.session_state.api_base_url = new_api_base
+    st.rerun()
+
+st.sidebar.divider()
+
+# Sidebar - Session Management
+st.sidebar.title("Chat Sessions")
+if st.sidebar.button("New Session"):
+    create_new_chat_session()
+
+# Display all sessions in reverse chronological order
+for session_id in sorted(st.session_state.sessions.keys(), reverse=True):
+    # Mark the active session with a pinned button
+    if session_id == st.session_state.active_session:
+        st.sidebar.button(f"📍 {session_id}",
+                          key=session_id,
+                          type="primary",
+                          on_click=switch_to_chat_session,
+                          args=(session_id, ))
+    else:
+        st.sidebar.button(f"Session {session_id}",
+                          key=session_id,
+                          on_click=switch_to_chat_session,
+                          args=(session_id, ))
+
+# Main interface
+st.title("vLLM Chat Assistant")
+
+# Initialize OpenAI client with API settings
+client = OpenAI(api_key=openai_api_key, base_url=st.session_state.api_base_url)
+
+# Get and display current model id
+models = client.models.list()
+model = models.data[0].id
+st.markdown(f"**Model**: {model}")
+
+# Initialize first session if none exists
+if st.session_state.current_session is None:
+    create_new_chat_session()
+    st.session_state.active_session = st.session_state.current_session
+
+# Display chat history for current session
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.write(message["content"])
+
+# Handle user input and generate llm response
+if prompt := st.chat_input("Type your message here..."):
+    # Save user message to session
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    st.session_state.sessions[
+        st.session_state.current_session] = st.session_state.messages
+
+    # Display user message
+    with st.chat_message("user"):
+        st.write(prompt)
+
+    # Prepare messages for llm
+    messages_for_llm = [{
+        "role": m["role"],
+        "content": m["content"]
+    } for m in st.session_state.messages]
+
+    # Generate and display llm response
+    with st.chat_message("assistant"):
+        message_placeholder = st.empty()
+        full_response = ""
+
+        # Get streaming response from llm
+        response = get_llm_response(messages_for_llm, model)
+        if isinstance(response, str):
+            message_placeholder.markdown(response)
+            full_response = response
+        else:
+            for chunk in response:
+                if hasattr(chunk.choices[0].delta, "content"):
+                    content = chunk.choices[0].delta.content
+                    if content:
+                        full_response += content
+                        message_placeholder.markdown(full_response + "▌")
+
+            message_placeholder.markdown(full_response)
+
+    # Save llm response to session history
+    st.session_state.messages.append({
+        "role": "assistant",
+        "content": full_response
+    })