diff --git a/shiny/templates/chat/hello-providers/litellm/_template.json b/shiny/templates/chat/hello-providers/litellm/_template.json
new file mode 100644
index 000000000..52866eeb9
--- /dev/null
+++ b/shiny/templates/chat/hello-providers/litellm/_template.json
@@ -0,0 +1,5 @@
+{
+  "type": "app",
+  "id": "chat-ai-litellm",
+  "title": "Chat AI using LiteLLM"
+}
diff --git a/shiny/templates/chat/hello-providers/litellm/app.py b/shiny/templates/chat/hello-providers/litellm/app.py
new file mode 100644
index 000000000..7cc90a87d
--- /dev/null
+++ b/shiny/templates/chat/hello-providers/litellm/app.py
@@ -0,0 +1,22 @@
+# ------------------------------------------------------------------------------------
+# A basic Shiny Chat example powered by OpenAI's GPT-4o model using the `litellm` library.
+# To run it, you'll need OpenAI API key.
+# To get setup, follow the instructions at https://platform.openai.com/docs/quickstart
+# ------------------------------------------------------------------------------------
+import litellm
+from app_utils import load_dotenv
+
+from shiny.express import ui
+
+# Load a .env file (if it exists) to get the OpenAI API key
+load_dotenv()
+
+chat = ui.Chat(id="chat")
+chat.ui()
+
+
+@chat.on_user_submit
+async def _():
+    messages = chat.messages()
+    response = await litellm.acompletion(model="gpt-4o", messages=messages, stream=True)
+    await chat.append_message_stream(response)
diff --git a/shiny/templates/chat/hello-providers/litellm/app_utils.py b/shiny/templates/chat/hello-providers/litellm/app_utils.py
new file mode 100644
index 000000000..404a13730
--- /dev/null
+++ b/shiny/templates/chat/hello-providers/litellm/app_utils.py
@@ -0,0 +1,26 @@
+import os
+from pathlib import Path
+from typing import Any
+
+app_dir = Path(__file__).parent
+env_file = app_dir / ".env"
+
+
+def load_dotenv(dotenv_path: os.PathLike[str] = env_file, **kwargs: Any) -> None:
+    """
+    A convenience wrapper around `dotenv.load_dotenv` that warns if `dotenv` is not installed.
+    It also returns `None` to make it easier to ignore the return value.
+    """
+    try:
+        import dotenv
+
+        dotenv.load_dotenv(dotenv_path=dotenv_path, **kwargs)
+    except ImportError:
+        import warnings
+
+        warnings.warn(
+            "Could not import `dotenv`. If you want to use `.env` files to "
+            "load environment variables, please install it using "
+            "`pip install python-dotenv`.",
+            stacklevel=2,
+        )
diff --git a/shiny/templates/chat/hello-providers/litellm/requirements.txt b/shiny/templates/chat/hello-providers/litellm/requirements.txt
new file mode 100644
index 000000000..0519f0ea0
--- /dev/null
+++ b/shiny/templates/chat/hello-providers/litellm/requirements.txt
@@ -0,0 +1,5 @@
+shiny
+python-dotenv
+tokenizers
+openai
+litellm
diff --git a/shiny/ui/_chat_normalize.py b/shiny/ui/_chat_normalize.py
index 2d5063324..b778a9fbd 100644
--- a/shiny/ui/_chat_normalize.py
+++ b/shiny/ui/_chat_normalize.py
@@ -18,6 +18,9 @@ class GenerateContentResponse:
             text: str
 
     from langchain_core.messages import BaseMessage, BaseMessageChunk
+    from litellm.types.utils import (  # pyright: ignore[reportMissingTypeStubs]
+        ModelResponse,
+    )
     from openai.types.chat import ChatCompletion, ChatCompletionChunk
 
 
@@ -137,6 +140,36 @@ def can_normalize_chunk(self, chunk: Any) -> bool:
             return False
 
 
+class LiteLlmNormalizer(OpenAINormalizer):
+    def normalize(self, message: Any) -> ChatMessage:
+        x = cast("ModelResponse", message)
+        return super().normalize(x)
+
+    def normalize_chunk(self, chunk: Any) -> ChatMessage:
+        x = cast("ModelResponse", chunk)
+        return super().normalize_chunk(x)
+
+    def can_normalize(self, message: Any) -> bool:
+        try:
+            from litellm.types.utils import (  # pyright: ignore[reportMissingTypeStubs]
+                ModelResponse,
+            )
+
+            return isinstance(message, ModelResponse)
+        except Exception:
+            return False
+
+    def can_normalize_chunk(self, chunk: Any) -> bool:
+        try:
+            from litellm.types.utils import (  # pyright: ignore[reportMissingTypeStubs]
+                ModelResponse,
+            )
+
+            return isinstance(chunk, ModelResponse)
+        except Exception:
+            return False
+
+
 class AnthropicNormalizer(BaseMessageNormalizer):
     def normalize(self, message: Any) -> ChatMessage:
         x = cast("AnthropicMessage", message)
@@ -248,6 +281,7 @@ def __init__(self) -> None:
             "anthropic": AnthropicNormalizer(),
             "google": GoogleNormalizer(),
             "langchain": LangChainNormalizer(),
+            "litellm": LiteLlmNormalizer(),
             "ollama": OllamaNormalizer(),
             "dict": DictNormalizer(),
             "string": StringNormalizer(),