diff --git a/python/samples/02-agents/auto_retry.py b/python/samples/02-agents/auto_retry.py
new file mode 100644
index 0000000000..7c985bd0c1
--- /dev/null
+++ b/python/samples/02-agents/auto_retry.py
@@ -0,0 +1,250 @@
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "agent-framework",
+#     "tenacity",
+# ]
+# ///
+# Run with any PEP 723 compatible runner, e.g.:
+#   uv run samples/02-agents/auto_retry.py
+
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import logging
+from collections.abc import Awaitable, Callable
+from typing import Any, TypeVar, cast
+
+from agent_framework import ChatContext, ChatMiddleware, SupportsChatGetResponse, chat_middleware
+from agent_framework.azure import AzureOpenAIChatClient
+from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
+from openai import RateLimitError
+from tenacity import (
+    AsyncRetrying,
+    before_sleep_log,
+    retry,
+    retry_if_exception_type,
+    stop_after_attempt,
+    wait_exponential,
+)
+
+# Load environment variables from .env file
+load_dotenv()
+
+"""
+Auto-Retry Rate Limiting Sample
+
+Every model inference API enforces rate limits, so production agents need retry logic
+to handle 429 responses gracefully. This sample shows two ways to add automatic retry
+using the `tenacity` library, keeping your application code free of boilerplate.
+
+Approach 1 – Class decorator
+    Apply a class decorator to any client type implementing
+    SupportsChatGetResponse. The decorator patches get_response() with retry
+    behavior. Non-streaming responses are retried; streaming is returned as-is
+    (streaming retry requires more delicate handling).
+
+Approach 2 – Chat middleware
+    Register middleware on the agent that catches RateLimitError raised inside
+    call_next() and retries the entire request pipeline. Two styles are shown:
+    a) Class-based middleware (ChatMiddleware subclass)
+    b) Function-based middleware (@chat_middleware decorator)
+
+Both approaches use the same tenacity primitives:
+    - stop_after_attempt  – cap the total number of tries
+    - wait_exponential    – exponential back-off between retries
+    - retry_if_exception_type(RateLimitError) – only retry on 429 errors
+    - before_sleep_log    – log each retry attempt at WARNING level
+"""
+
+logger = logging.getLogger(__name__)
+
+RETRY_ATTEMPTS = 3
+
+# =============================================================================
+# Approach 1: Class decorator
+# =============================================================================
+
+
+ChatClientT = TypeVar("ChatClientT", bound=SupportsChatGetResponse[Any])
+
+
+def with_rate_limit_retry(*, retry_attempts: int = RETRY_ATTEMPTS) -> Callable[[type[ChatClientT]], type[ChatClientT]]:
+    """Class decorator that adds non-streaming retry behavior to get_response()."""
+
+    def decorator(client_cls: type[ChatClientT]) -> type[ChatClientT]:
+        original_get_response = client_cls.get_response
+
+        def get_response_with_retry(self, *args, **kwargs):  # type: ignore[no-untyped-def]
+            stream = kwargs.get("stream", False)
+
+            if stream:
+                # Streaming retry is more complex; fall back to the original behaviour.
+                return original_get_response(self, *args, **kwargs)
+
+            async def _with_retry():
+                async for attempt in AsyncRetrying(
+                    stop=stop_after_attempt(retry_attempts),
+                    wait=wait_exponential(multiplier=1, min=4, max=10),
+                    retry=retry_if_exception_type(RateLimitError),
+                    reraise=True,
+                    before_sleep=before_sleep_log(logger, logging.WARNING),
+                ):
+                    with attempt:
+                        return await original_get_response(self, *args, **kwargs)
+                return None
+
+            return _with_retry()
+
+        client_cls.get_response = cast(Any, get_response_with_retry)
+        return client_cls
+
+    return decorator
+
+
+@with_rate_limit_retry()
+class RetryingAzureOpenAIChatClient(AzureOpenAIChatClient):
+    """Azure OpenAI Chat client with class-decorator-based retry behavior."""
+
+
+# =============================================================================
+# Approach 2a: Class-based chat middleware
+# =============================================================================
+
+
+class RateLimitRetryMiddleware(ChatMiddleware):
+    """Chat middleware that retries the full request pipeline on rate limit errors.
+
+    Register this middleware on an agent (or at the run level) to automatically
+    retry any call_next() invocation that raises RateLimitError.
+    """
+
+    def __init__(self, *, max_attempts: int = RETRY_ATTEMPTS) -> None:
+        """Initialize with the maximum number of retry attempts."""
+        self.max_attempts = max_attempts
+
+    async def process(
+        self,
+        context: ChatContext,
+        call_next: Callable[[], Awaitable[None]],
+    ) -> None:
+        """Retry call_next() on rate limit errors with exponential back-off."""
+        async for attempt in AsyncRetrying(
+            stop=stop_after_attempt(self.max_attempts),
+            wait=wait_exponential(multiplier=1, min=4, max=10),
+            retry=retry_if_exception_type(RateLimitError),
+            reraise=True,
+            before_sleep=before_sleep_log(logger, logging.WARNING),
+        ):
+            with attempt:
+                await call_next()
+
+
+# =============================================================================
+# Approach 2b: Function-based chat middleware
+# =============================================================================
+
+
+@chat_middleware
+async def rate_limit_retry_middleware(
+    context: ChatContext,
+    call_next: Callable[[], Awaitable[None]],
+) -> None:
+    """Function-based chat middleware that retries on rate limit errors.
+
+    Wrap call_next() with a tenacity @retry decorator so any RateLimitError
+    raised during model inference triggers an automatic retry with exponential
+    back-off.
+    """
+
+    @retry(
+        stop=stop_after_attempt(RETRY_ATTEMPTS),
+        wait=wait_exponential(multiplier=1, min=4, max=10),
+        retry=retry_if_exception_type(RateLimitError),
+        reraise=True,
+        before_sleep=before_sleep_log(logger, logging.WARNING),
+    )
+    async def _call_next_with_retry() -> None:
+        await call_next()
+
+    await _call_next_with_retry()
+
+
+# =============================================================================
+# Demo
+# =============================================================================
+
+
+async def class_decorator_example() -> None:
+    """Demonstrate Approach 1: class decorator on a chat client type."""
+    print("\n" + "=" * 60)
+    print("Approach 1: Class decorator (applied to client type)")
+    print("=" * 60)
+
+    # For authentication, run `az login` command in terminal or replace
+    # AzureCliCredential with your preferred authentication option.
+    agent = RetryingAzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
+        instructions="You are a helpful assistant.",
+    )
+
+    query = "Say hello!"
+    print(f"User: {query}")
+    result = await agent.run(query)
+    print(f"Agent: {result.text}")
+
+
+async def class_based_middleware_example() -> None:
+    """Demonstrate Approach 2a: class-based chat middleware."""
+    print("\n" + "=" * 60)
+    print("Approach 2a: Class-based chat middleware")
+    print("=" * 60)
+
+    # For authentication, run `az login` command in terminal or replace
+    # AzureCliCredential with your preferred authentication option.
+    agent = AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
+        instructions="You are a helpful assistant.",
+        middleware=[RateLimitRetryMiddleware(max_attempts=3)],
+    )
+
+    query = "Say hello!"
+    print(f"User: {query}")
+    result = await agent.run(query)
+    print(f"Agent: {result.text}")
+
+
+async def function_based_middleware_example() -> None:
+    """Demonstrate Approach 2b: function-based chat middleware."""
+    print("\n" + "=" * 60)
+    print("Approach 2b: Function-based chat middleware")
+    print("=" * 60)
+
+    # For authentication, run `az login` command in terminal or replace
+    # AzureCliCredential with your preferred authentication option.
+    agent = AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
+        instructions="You are a helpful assistant.",
+        middleware=[rate_limit_retry_middleware],
+    )
+
+    query = "Say hello!"
+    print(f"User: {query}")
+    result = await agent.run(query)
+    print(f"Agent: {result.text}")
+
+
+async def main() -> None:
+    """Run all auto-retry examples."""
+    print("=== Auto-Retry Rate Limiting Sample ===")
+    print(
+        "Demonstrates two approaches for automatic retry on rate limit (429) errors.\n"
+        "Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME (and optionally\n"
+        "AZURE_OPENAI_API_KEY) before running, or populate a .env file."
+    )
+
+    await class_decorator_example()
+    await class_based_middleware_example()
+    await function_based_middleware_example()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())