diff --git a/examples/llm_multi_turn_conversation.py b/examples/llm_multi_turn_conversation.py
new file mode 100644
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+++ b/examples/llm_multi_turn_conversation.py
@@ -0,0 +1,120 @@
+"""
+Multi-turn conversation example with OpenGradient TEE-verified LLM.
+
+Demonstrates how to maintain conversation history across multiple turns,
+enabling context-aware responses with full cryptographic verification
+of every inference step.
+
+Usage:
+    export OG_PRIVATE_KEY="your_private_key"
+    python examples/llm_multi_turn_conversation.py
+"""
+
+import asyncio
+import os
+
+import opengradient as og
+
+
+def add_user_message(history: list, content: str) -> list:
+    """Append a user message to the conversation history."""
+    return history + [{"role": "user", "content": content}]
+
+
+def add_assistant_message(history: list, content: str) -> list:
+    """Append an assistant message to the conversation history."""
+    return history + [{"role": "assistant", "content": content}]
+
+
+async def chat_turn(
+    llm: og.LLM,
+    history: list,
+    user_input: str,
+    model: og.TEE_LLM = og.TEE_LLM.GEMINI_2_5_FLASH,
+) -> tuple[str, list, str]:
+    """
+    Execute a single conversation turn.
+
+    Args:
+        llm: Initialized LLM client.
+        history: Current conversation history.
+        user_input: The user's message for this turn.
+        model: TEE_LLM model to use.
+
+    Returns:
+        Tuple of (assistant_reply, updated_history, transaction_hash).
+    """
+    history = add_user_message(history, user_input)
+
+    result = await llm.chat(
+        model=model,
+        messages=history,
+        max_tokens=500,
+        temperature=0.7,
+    )
+
+    reply = result.chat_output["content"]
+    history = add_assistant_message(history, reply)
+
+    return reply, history, result.transaction_hash
+
+
+async def main():
+    private_key = os.environ.get("OG_PRIVATE_KEY")
+    if not private_key:
+        raise ValueError("OG_PRIVATE_KEY environment variable is not set.")
+
+    llm = og.LLM(private_key=private_key)
+    llm.ensure_opg_approval(min_allowance=0.5)
+
+    model = og.TEE_LLM.GEMINI_2_5_FLASH
+    print(f"Model : {model.value}")
+    print(f"Mode  : Multi-turn conversation with TEE verification")
+    print("=" * 60)
+
+    # System prompt sets the assistant persona for the whole conversation
+    history = [
+        {
+            "role": "system",
+            "content": (
+                "You are a concise Python tutor. "
+                "Give short, clear answers with code examples when helpful."
+            ),
+        }
+    ]
+
+    # --- Turn 1 ---
+    question_1 = "What is a Python decorator?"
+    print(f"\nUser : {question_1}")
+
+    reply_1, history, tx_1 = await chat_turn(llm, history, question_1, model)
+    print(f"Assistant : {reply_1}")
+    print(f"[tx: {tx_1}]")
+
+    # --- Turn 2 — follow-up referencing Turn 1 ---
+    question_2 = "Can you show me a real-world example of one?"
+    print(f"\nUser : {question_2}")
+
+    reply_2, history, tx_2 = await chat_turn(llm, history, question_2, model)
+    print(f"Assistant : {reply_2}")
+    print(f"[tx: {tx_2}]")
+
+    # --- Turn 3 — deeper follow-up ---
+    question_3 = "How would I stack two decorators on the same function?"
+    print(f"\nUser : {question_3}")
+
+    reply_3, history, tx_3 = await chat_turn(llm, history, question_3, model)
+    print(f"Assistant : {reply_3}")
+    print(f"[tx: {tx_3}]")
+
+    # Summary
+    print("\n" + "=" * 60)
+    print(f"Total turns     : {len([m for m in history if m['role'] == 'user'])}")
+    print(f"Total messages  : {len(history)}")
+    print("Transaction hashes (verifiable on-chain):")
+    for i, tx in enumerate([tx_1, tx_2, tx_3], 1):
+        print(f"  Turn {i}: {tx}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())