diff --git a/examples/13_rag.py b/examples/13_rag.py
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+"""
+This example demonstrates how to create a RAG-enabled chatbot that:
+1. Uses a search tool to find relevant information from a knowledge base
+2. Incorporates search results into its responses
+3. Maintains conversation context
+4. Provides well-structured, informative responses
+
+Note: WorkflowAI does not manage the RAG implementation (yet). You need to provide your own
+search implementation to connect to your knowledge base. This example uses a mock search
+function to demonstrate the pattern.
+"""
+
+import asyncio
+from enum import Enum
+from typing import List, Optional
+
+from pydantic import BaseModel, Field
+
+import workflowai
+from workflowai import Model, Run
+
+
+class Role(str, Enum):
+    """Enum representing possible message roles."""
+    USER = "user"
+    ASSISTANT = "assistant"
+
+
+class SearchResult(BaseModel):
+    """Model representing a search result from the knowledge base."""
+    content: str = Field(
+        description="The content of the search result",
+    )
+    relevance_score: float = Field(
+        description="Score indicating how relevant this result is to the query",
+    )
+
+
+# Simulated knowledge base search tool
+# ruff: noqa: ARG001
+async def search_faq(query: str) -> List[SearchResult]:
+    """
+    Search the knowledge base for relevant information.
+
+    Args:
+        query: The search query to find relevant information
+
+    Returns:
+        A list of search results ordered by relevance
+    """
+    # This is a mock implementation - in a real system this would query your knowledge base
+    # The results below are hardcoded but in a real implementation would be based on the query
+    return [
+        SearchResult(
+            content=(
+                "Our standard return policy allows returns within 30 days of purchase with original "
+                "receipt. Items must be unused and in original packaging. Once received, refunds are "
+                "processed within 5-7 business days."
+            ),
+            relevance_score=0.95,
+        ),
+        SearchResult(
+            content=(
+                "For online purchases, customers can initiate returns through their account dashboard "
+                "or by contacting customer support. Free return shipping labels are provided for "
+                "defective items."
+            ),
+            relevance_score=0.88,
+        ),
+        SearchResult(
+            content=(
+                "Standard shipping takes 3-5 business days within the continental US. Express shipping "
+                "(1-2 business days) is available for an additional fee. Free shipping on orders over $50."
+            ),
+            relevance_score=0.82,
+        ),
+    ]
+
+
+class Message(BaseModel):
+    """Model representing a chat message."""
+    role: Role
+    content: str = Field(
+        description="The content of the message",
+    )
+
+
+class AssistantMessage(Message):
+    """Model representing a message from the assistant."""
+    role: Role = Role.ASSISTANT
+    content: str = ""
+
+
+class ChatbotOutput(BaseModel):
+    """Output model for the chatbot response."""
+    assistant_message: AssistantMessage = Field(default_factory=AssistantMessage,
+        description="The chatbot's response message",
+    )
+
+
+class ChatInput(BaseModel):
+    """Input model containing the user's message and conversation history."""
+    conversation_history: Optional[List[Message]] = Field(
+        default=None,
+        description="Previous messages in the conversation, if any",
+    )
+    user_message: str = Field(
+        description="The current message from the user",
+    )
+
+
+@workflowai.agent(
+    id="rag-chatbot",
+    model=Model.CLAUDE_3_5_SONNET_LATEST,
+    # The search_faq function is passed as a tool, allowing the agent to call it during execution.
+    # You can replace this with your own search implementation that connects to your knowledge base.
+    # The agent will automatically handle calling the tool and incorporating the results.
+    tools=[search_faq],
+)
+async def chat_agent(chat_input: ChatInput) -> Run[ChatbotOutput]:
+    """
+    Act as a knowledgeable assistant that uses search to find and incorporate relevant information.
+
+    You have access to a search tool that can find relevant information from the knowledge base.
+    Use it by calling the search_faq function with your query.
+
+    Guidelines:
+    1. Understand the user's query:
+       - Analyze the question and conversation history
+       - Identify key concepts to search for
+       - Consider context from previous messages
+
+    2. Search effectively:
+       - Use the search_faq tool to find relevant information
+       - Construct focused search queries
+       - Consider multiple searches if needed
+       - Prioritize recent and authoritative sources
+
+    3. Provide comprehensive responses:
+       - Synthesize information from search results
+       - Cite sources when appropriate
+       - Explain complex concepts clearly
+       - Address all parts of the query
+
+    4. Maintain conversation flow:
+       - Acknowledge previous context
+       - Be natural and engaging
+       - Ask clarifying questions if needed
+       - Provide smooth transitions
+
+    5. Format responses clearly:
+       - Structure information logically
+       - Use clear language
+       - Break down complex answers
+       - Highlight key points
+    """
+    ...
+
+
+async def main():
+    # Example: Question about return policy
+    print("\nExample: Question about return policy")
+    print("-" * 50)
+
+    chat_input = ChatInput(
+        user_message="What is your return policy? Can I return items I bought online?",
+    )
+
+    run = await chat_agent(chat_input)
+    print(run)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())