diff --git a/17-multi-turn/README.md b/17-multi-turn/README.md
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+# Multi-Turn vs Single-Turn Agent Testing
+
+This example demonstrates the differences between multi-turn and single-turn agents in Contextual AI through comprehensive testing scenarios.
+
+## Overview
+
+The notebook creates two agents with identical configurations except for their multi-turn settings:
+- **MultiTurnAgent**: Maintains conversation context across multiple messages
+- **SingleTurnAgent**: Treats each message independently without memory
+
+## What You'll Learn
+
+- How to enable/disable multi-turn functionality in Contextual AI agents
+- The difference in behavior between multi-turn and single-turn agents
+- How to use `conversation_id` to continue previous conversations
+- Best practices for testing agent memory capabilities
+
+## Key Concepts
+
+### Multi-Turn Agents
+- Remember conversation history using `conversation_id`
+- Can reference previous messages and maintain context
+- Ideal for conversational interfaces and complex interactions
+
+### Single-Turn Agents
+- Process each message independently
+- No memory of previous interactions
+- Suitable for stateless operations and simple Q&A
+
+### Conversation Management
+- The first message in a conversation generates a `conversation_id`
+- Subsequent messages use this ID to maintain context
+- Conversations can be resumed later using the same ID
+
+## Test Methodology
+
+The notebook follows a systematic approach:
+
+1. **Setup**: Creates two identical agents with different multi-turn settings
+2. **Initial Facts**: Shares the same information with both agents
+3. **Memory Testing**: Asks follow-up questions to test retention
+4. **Comparison**: Validates expected vs actual behavior
+5. **Recurring Conversations**: Tests conversation continuity
+
+### Test Scenarios
+
+| Scenario | MultiTurnAgent | SingleTurnAgent |
+|----------|----------------|-----------------|
+| Initial message with facts | ✓ Acknowledges | ✓ Acknowledges |
+| Follow-up question about facts | ✓ Remembers | ✗ Forgets |
+| Complex recall request | ✓ Full context | ✗ No context |
+| Conversation resumption | ✓ Continues | N/A |
+
+## Running the Notebook
+
+### Prerequisites
+- Contextual AI API key
+- Python environment with required packages
+
+### Setup Steps
+1. Update the `API_KEY` variable with your Contextual AI API key
+2. Run all cells sequentially
+3. Review the test results summary at the end
+
+### Expected Outputs
+- Multi-turn agent should pass memory tests (remember color and cats)
+- Single-turn agent should fail memory tests (forget previous information)
+- All 5 tests should pass if multi-turn functionality works correctly
+
+## Configuration Details
+
+### Agent Configuration
+```python
+agent_configs = {
+    "global_config": {
+        "enable_multi_turn": True,  # Key difference between agents
+        "enable_filter": False,
+        "enable_rerank": False,
+        "should_check_retrieval_need": True,
+    },
+    "reformulation_config": {
+        "enable_query_expansion": False,
+        "enable_query_decomposition": False,
+    },
+    "generate_response_config": {
+        "model": 'vertex_ai/gemini-2.0-flash-lite'
+    }
+}
+```
+
+### Query with Conversation ID
+```python
+response = client.agents.query.create(
+    agent_id=agent.id,
+    messages=[{"content": "Your message", "role": "user"}],
+    conversation_id=conversation_id  # Enables context retention
+)
+```
+
+## Test Data
+
+The example uses simple, verifiable facts:
+- **Favorite color**: Blue
+- **Pet count**: 3 cats
+- **Pet names**: Whiskers, Mittens, Shadow
+
+These concrete values make it easy to verify whether agents remember information correctly.
+
+## Cleanup
+
+The notebook includes an optional cleanup section to delete the test agents and datastore after experimentation. Uncomment the cleanup code if you want to remove the created resources.
+
+## Use Cases
+
+### Multi-Turn Agents Are Ideal For:
+- Customer service chatbots
+- Interactive tutorials
+- Complex problem-solving sessions
+- Personalized conversations
+
+### Single-Turn Agents Are Ideal For:
+- FAQ systems
+- Simple classification tasks
+- Stateless API endpoints
+- One-off queries
+
+## Troubleshooting
+
+### Common Issues
+- **Agent doesn't remember**: Ensure you're using the correct `conversation_id`
+- **Tests fail**: Check API key validity and agent configuration
+- **Unexpected behavior**: Verify the `enable_multi_turn` setting
+
+### Debugging Tips
+- Monitor conversation IDs in the output
+- Check agent responses for context clues
+- Validate test data matches expected values
+
+## Next Steps
+
+After understanding multi-turn behavior, consider:
+- Implementing conversation persistence
+- Adding conversation branching
+- Building stateful applications
+- Exploring advanced context management
+
+---
+
+**Note**: Remember to keep your API key secure and never commit it to version control.
\ No newline at end of file
diff --git a/17-multi-turn/multi_turn.ipynb b/17-multi-turn/multi_turn.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5235406f",
+   "metadata": {},
+   "source": [
+    "# Contextual AI Multi-Turn vs Single-Turn Chat Testing\n",
+    "\n",
+    "This notebook demonstrates the difference between multi-turn and single-turn agents in Contextual AI by creating two fresh agents.\n",
+    "\n",
+    "**What we're testing:**\n",
+    "- **MultiTurnAgent**: Will be created with multi-turn enabled - should remember conversation context\n",
+    "- **SingleTurnAgent**: Will be created with multi-turn disabled - should NOT remember previous messages\n",
+    "- **Recurring conversations**: Can continue from a previous conversation using conversation_id\n",
+    "\n",
+    "**Test approach:**\n",
+    "1. Create two new agents with different multi-turn settings\n",
+    "2. Tell both agents some simple facts (favorite color and pet names) \n",
+    "3. Ask if they remember those facts\n",
+    "4. Test recurring conversation capability\n",
+    "\n",
+    "**Expected results:**\n",
+    "- MultiTurnAgent should remember facts across messages\n",
+    "- SingleTurnAgent should forget facts between messages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "aee6dba7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting contextual-client\n",
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+      "Installing collected packages: typing-extensions, sniffio, pyparsing, pillow, numpy, kiwisolver, idna, h11, fonttools, distro, cycler, certifi, annotated-types, typing-inspection, pydantic-core, httpcore, contourpy, anyio, pydantic, matplotlib, httpx, contextual-client\n",
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+      "\u001b[1A\u001b[2KSuccessfully installed annotated-types-0.7.0 anyio-4.11.0 certifi-2025.10.5 contextual-client-0.8.0 contourpy-1.3.3 cycler-0.12.1 distro-1.9.0 fonttools-4.60.1 h11-0.16.0 httpcore-1.0.9 httpx-0.28.1 idna-3.11 kiwisolver-1.4.9 matplotlib-3.10.7 numpy-2.3.4 pillow-12.0.0 pydantic-2.12.2 pydantic-core-2.41.4 pyparsing-3.2.5 sniffio-1.3.1 typing-extensions-4.15.0 typing-inspection-0.4.2\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install contextual-client "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2da6c1c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Libraries imported successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import required libraries\n",
+    "import time\n",
+    "from datetime import datetime\n",
+    "from contextual import ContextualAI\n",
+    "\n",
+    "print(\"Libraries imported successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d386cdc7",
+   "metadata": {},
+   "source": [
+    "## Configuration and Agent Creation\n",
+    "\n",
+    "Set up the API credentials and create two new agents - one with multi-turn enabled and one without."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2c93ab7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Client initialized successfully!\n",
+      "Ready to create agents...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Configuration - API Key only (we'll create agents dynamically)\n",
+    "API_KEY = \"<your key here\"\n",
+    "\n",
+    "# Initialize the Contextual AI client\n",
+    "client = ContextualAI(api_key=API_KEY)\n",
+    "\n",
+    "print(\"Client initialized successfully!\")\n",
+    "print(\"Ready to create agents...\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "38a4f6a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Placeholder datastore because an agent must have a datastore.\n",
+    "response = client.datastores.create(\n",
+    "    name=\"place-holder-datastore\"\n",
+    ")\n",
+    "datastore_id = response.id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "97e2afb7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Multi turn agent\n",
+    "multi_turn_agent = client.agents.create(\n",
+    "    name=\"MultiTurnAgent\",\n",
+    "    datastore_ids=[datastore_id ],\n",
+    "    system_prompt=\"\",\n",
+    "    multiturn_system_prompt=\"\",\n",
+    "    filter_prompt = \"\",\n",
+    "    no_retrieval_system_prompt = \"\",\n",
+    "    suggested_queries = [\n",
+    "            \"Hi! I want to tell you that my favorite color is blue and I have 3 cats named Whiskers, Mittens, and Shadow\",\n",
+    "            \"What is my favorite color?\",\n",
+    "            \"How many cats do I have and what are their names?\"\n",
+    "    ],\n",
+    "    agent_configs={\n",
+    "        \"global_config\": {\n",
+    "            \"enable_multi_turn\": True,\n",
+    "            \"enable_filter\": False,\n",
+    "            \"enable_rerank\": False,\n",
+    "            \"should_check_retrieval_need\": True,\n",
+    "        },\n",
+    "        \"reformulation_config\": {\n",
+    "            \"enable_query_expansion\": False,\n",
+    "            \"enable_query_decomposition\": False,\n",
+    "            \"query_expansion_prompt\": (\"\"),\n",
+    "        },\n",
+    "        \"generate_response_config\": {\n",
+    "            \"model\": 'vertex_ai/gemini-2.0-flash-lite'\n",
+    "        }\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "2fa979d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Single turn agent\n",
+    "single_turn_agent = client.agents.create(\n",
+    "    name=\"SingleTurnAgent\",\n",
+    "    datastore_ids=[datastore_id ],\n",
+    "    system_prompt=\"\",\n",
+    "    multiturn_system_prompt=\"\",\n",
+    "    filter_prompt = \"\",\n",
+    "    no_retrieval_system_prompt = \"\",\n",
+    "    suggested_queries = [\n",
+    "            \"Hi! I want to tell you that my favorite color is blue and I have 3 cats named Whiskers, Mittens, and Shadow\",\n",
+    "            \"What is my favorite color?\",\n",
+    "            \"How many cats do I have and what are their names?\"\n",
+    "    ],\n",
+    "    agent_configs={\n",
+    "        \"global_config\": {\n",
+    "            \"enable_multi_turn\": False,\n",
+    "            \"enable_filter\": False,\n",
+    "            \"enable_rerank\": False,\n",
+    "            \"should_check_retrieval_need\": True,\n",
+    "        },\n",
+    "        \"reformulation_config\": {\n",
+    "            \"enable_query_expansion\": False,\n",
+    "            \"enable_query_decomposition\": False,\n",
+    "            \"query_expansion_prompt\": (\"\"),\n",
+    "        },\n",
+    "        \"generate_response_config\": {\n",
+    "            \"model\": 'vertex_ai/gemini-2.0-flash-lite'\n",
+    "        }\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37323ba5",
+   "metadata": {},
+   "source": [
+    "## Test Values Setup\n",
+    "\n",
+    "Define the specific values we'll use to test memory. These are simple, concrete facts that are easy to verify."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "43fed619",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test values defined:\n",
+      "- Favorite color: blue\n",
+      "- Number of cats: 3\n",
+      "- Cat names: Whiskers, Mittens, Shadow\n",
+      "\n",
+      "Initial message: Hi! I want to tell you that my favorite color is blue and I have 3 cats named Whiskers, Mittens, Shadow.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Test values - these are the facts we'll share and test for memory\n",
+    "FAVORITE_COLOR = \"blue\"\n",
+    "CAT_COUNT = \"3\"\n",
+    "CAT_NAMES = [\"Whiskers\", \"Mittens\", \"Shadow\"]\n",
+    "\n",
+    "# Initial message that contains all the facts\n",
+    "INITIAL_MESSAGE = f\"Hi! I want to tell you that my favorite color is {FAVORITE_COLOR} and I have {CAT_COUNT} cats named {', '.join(CAT_NAMES)}.\"\n",
+    "\n",
+    "print(\"Test values defined:\")\n",
+    "print(f\"- Favorite color: {FAVORITE_COLOR}\")\n",
+    "print(f\"- Number of cats: {CAT_COUNT}\")\n",
+    "print(f\"- Cat names: {', '.join(CAT_NAMES)}\")\n",
+    "print(f\"\\nInitial message: {INITIAL_MESSAGE}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e6610c6",
+   "metadata": {},
+   "source": [
+    "## Testing Multi-Turn Agent\n",
+    "\n",
+    "Now we'll test the multi-turn enabled agent. This agent should remember our conversation context across multiple messages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b68d828a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "============================================================\n",
+      " MULTI-TURN AGENT - MESSAGE 1: Sharing facts \n",
+      "============================================================\n",
+      "USER: Hi! I want to tell you that my favorite color is blue and I have 3 cats named Whiskers, Mittens, Shadow.\n",
+      "\n",
+      "ASSISTANT: That's wonderful! I love blue too. And three cats? That sounds like a lot of fun! Whiskers, Mittens, and Shadow are great names. Thanks for sharing! \n",
+      "\n",
+      "\n",
+      "Conversation ID received: 9beba569-b8e2-471a-8f99-bc121f32c216\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Step 1: Send initial message to multi-turn agent (no conversation_id yet)\n",
+    "# This is the first message, so we don't have a conversation_id yet\n",
+    "# The API will create one and return it to us\n",
+    "\n",
+    "print(\"=\" * 60)\n",
+    "print(\" MULTI-TURN AGENT - MESSAGE 1: Sharing facts \")\n",
+    "print(\"=\" * 60)\n",
+    "\n",
+    "response_1 = client.agents.query.create(\n",
+    "    agent_id=multi_turn_agent.id,\n",
+    "    messages=[{\"content\": INITIAL_MESSAGE, \"role\": \"user\"}]\n",
+    ")\n",
+    "\n",
+    "# Get the conversation_id from the response - this is key for multi-turn!\n",
+    "conversation_id = getattr(response_1, 'conversation_id', None)\n",
+    "\n",
+    "print(f\"USER: {INITIAL_MESSAGE}\")\n",
+    "print(f\"\\nASSISTANT: {response_1.message.content}\")\n",
+    "print(f\"\\nConversation ID received: {conversation_id}\")\n",
+    "\n",
+    "# Store the response for later validation\n",
+    "multi_turn_response_1 = response_1.message.content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "21b66f35",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "============================================================\n",
+      " MULTI-TURN AGENT - MESSAGE 2: Testing color memory \n",
+      "============================================================\n",
+      "USER: What is my favorite color?\n",
+      "\n",
+      "ASSISTANT: Your favorite color is blue! You told me that earlier. 😊\n",
+      "\n",
+      "\n",
+      "✓ SUCCESS: Agent remembered favorite color (blue)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Step 2: Test if multi-turn agent remembers favorite color\n",
+    "# Now we use the conversation_id from the previous message\n",
+    "# This should allow the agent to remember our previous conversation\n",
+    "\n",
+    "time.sleep(1)  # Brief pause between messages\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 60)\n",
+    "print(\" MULTI-TURN AGENT - MESSAGE 2: Testing color memory \")\n",
+    "print(\"=\" * 60)\n",
+    "\n",
+    "color_question = \"What is my favorite color?\"\n",
+    "\n",
+    "response_2 = client.agents.query.create(\n",
+    "    agent_id=multi_turn_agent.id,\n",
+    "    messages=[{\"content\": color_question, \"role\": \"user\"}],\n",
+    "    conversation_id=conversation_id  # This is the key - using the same conversation_id!\n",
+    ")\n",
+    "\n",
+    "print(f\"USER: {color_question}\")\n",
+    "print(f\"\\nASSISTANT: {response_2.message.content}\")\n",
+    "\n",
+    "# Store the response for later validation\n",
+    "multi_turn_response_2 = response_2.message.content\n",
+    "\n",
+    "# Check if the response contains our expected value\n",
+    "if FAVORITE_COLOR.lower() in multi_turn_response_2.lower():\n",
+    "    print(f\"\\n✓ SUCCESS: Agent remembered favorite color ({FAVORITE_COLOR})\")\n",
+    "    color_test_passed = True\n",
+    "else:\n",
+    "    print(f\"\\n✗ FAILED: Agent did not remember favorite color ({FAVORITE_COLOR})\")\n",
+    "    color_test_passed = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "188a6590",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "============================================================\n",
+      " MULTI-TURN AGENT - MESSAGE 3: Testing cats memory \n",
+      "============================================================\n",
+      "USER: How many cats do I have and what are their names?\n",
+      "\n",
+      "ASSISTANT: I am a language model, and I don't have any cats (or any pets at all!). You, on the other hand, have three cats named Whiskers, Mittens, and Shadow.\n",
+      "\n",
+      "\n",
+      "✗ FAILED: Agent did not remember all cat details\n",
+      "  Found: ['Whiskers', 'Mittens', 'Shadow']\n",
+      "  Missing: ['3']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Step 3: Test if multi-turn agent remembers cats\n",
+    "# Continue using the same conversation_id to test memory of cats\n",
+    "\n",
+    "time.sleep(1)  # Brief pause between messages\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 60)\n",
+    "print(\" MULTI-TURN AGENT - MESSAGE 3: Testing cats memory \")\n",
+    "print(\"=\" * 60)\n",
+    "\n",
+    "cats_question = \"How many cats do I have and what are their names?\"\n",
+    "\n",
+    "response_3 = client.agents.query.create(\n",
+    "    agent_id=multi_turn_agent.id,\n",
+    "    messages=[{\"content\": cats_question, \"role\": \"user\"}],\n",
+    "    conversation_id=conversation_id  # Same conversation_id again!\n",
+    ")\n",
+    "\n",
+    "print(f\"USER: {cats_question}\")\n",
+    "print(f\"\\nASSISTANT: {response_3.message.content}\")\n",
+    "\n",
+    "# Store the response for later validation\n",
+    "multi_turn_response_3 = response_3.message.content\n",
+    "\n",
+    "# Check if the response contains all our expected cat values\n",
+    "expected_cat_values = [CAT_COUNT] + CAT_NAMES\n",
+    "response_lower = multi_turn_response_3.lower()\n",
+    "\n",
+    "found_cat_values = []\n",
+    "missing_cat_values = []\n",
+    "\n",
+    "for value in expected_cat_values:\n",
+    "    if value.lower() in response_lower:\n",
+    "        found_cat_values.append(value)\n",
+    "    else:\n",
+    "        missing_cat_values.append(value)\n",
+    "\n",
+    "if not missing_cat_values:\n",
+    "    print(f\"\\n✓ SUCCESS: Agent remembered all cat details\")\n",
+    "    print(f\"  Found: {found_cat_values}\")\n",
+    "    cats_test_passed = True\n",
+    "else:\n",
+    "    print(f\"\\n✗ FAILED: Agent did not remember all cat details\")\n",
+    "    print(f\"  Found: {found_cat_values}\")\n",
+    "    print(f\"  Missing: {missing_cat_values}\")\n",
+    "    cats_test_passed = False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2bbc1a14",
+   "metadata": {},
+   "source": [
+    "## Testing Single-Turn Agent\n",
+    "\n",
+    "Now we'll test the single-turn agent with the same questions. This agent should NOT remember previous messages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2e772f7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "============================================================\n",
+      " SINGLE-TURN AGENT - MESSAGE 1: Sharing facts \n",
+      "============================================================\n",
+      "USER: Hi! I want to tell you that my favorite color is blue and I have 3 cats named Whiskers, Mittens, Shadow.\n",
+      "\n",
+      "ASSISTANT: That's wonderful! It's great to know your favorite color is blue. And it sounds like you have a lovely little feline family with Whiskers, Mittens, and Shadow. Thanks for sharing! \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Step 4: Send initial message to single-turn agent\n",
+    "# We share the same facts with the single-turn agent\n",
+    "# Note: We don't use conversation_id here since it's single-turn\n",
+    "\n",
+    "time.sleep(2)  # Pause before switching agents\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 60)\n",
+    "print(\" SINGLE-TURN AGENT - MESSAGE 1: Sharing facts \")\n",
+    "print(\"=\" * 60)\n",
+    "\n",
+    "response_single_1 = client.agents.query.create(\n",
+    "    agent_id=single_turn_agent.id,\n",
+    "    messages=[{\"content\": INITIAL_MESSAGE, \"role\": \"user\"}]\n",
+    ")\n",
+    "\n",
+    "print(f\"USER: {INITIAL_MESSAGE}\")\n",
+    "print(f\"\\nASSISTANT: {response_single_1.message.content}\")\n",
+    "\n",
+    "# Store the response\n",
+    "single_turn_response_1 = response_single_1.message.content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "8a98ab7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "============================================================\n",
+      " SINGLE-TURN AGENT - MESSAGE 2: Testing color memory (should fail) \n",
+      "============================================================\n",
+      "USER: What is my favorite color?\n",
+      "\n",
+      "ASSISTANT: As a large language model, I do not have access to your personal information, including your favorite color. I cannot know what your favorite color is.\n",
+      "\n",
+      "\n",
+      "✓ EXPECTED: Single-turn agent correctly forgot favorite color\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Step 5: Test if single-turn agent remembers favorite color\n",
+    "# This should FAIL because single-turn agents don't remember previous messages\n",
+    "\n",
+    "time.sleep(1)  # Brief pause between messages\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 60)\n",
+    "print(\" SINGLE-TURN AGENT - MESSAGE 2: Testing color memory (should fail) \")\n",
+    "print(\"=\" * 60)\n",
+    "\n",
+    "response_single_2 = client.agents.query.create(\n",
+    "    agent_id=single_turn_agent.id,\n",
+    "    messages=[{\"content\": color_question, \"role\": \"user\"}]\n",
+    "    # Note: No conversation_id - each message is independent\n",
+    ")\n",
+    "\n",
+    "print(f\"USER: {color_question}\")\n",
+    "print(f\"\\nASSISTANT: {response_single_2.message.content}\")\n",
+    "\n",
+    "# Store the response\n",
+    "single_turn_response_2 = response_single_2.message.content\n",
+    "\n",
+    "# Check if the response contains our expected value (should NOT)\n",
+    "if FAVORITE_COLOR.lower() in single_turn_response_2.lower():\n",
+    "    print(f\"\\n✗ UNEXPECTED: Single-turn agent remembered favorite color ({FAVORITE_COLOR})\")\n",
+    "    single_color_test_passed = False  # This is bad - it shouldn't remember\n",
+    "else:\n",
+    "    print(f\"\\n✓ EXPECTED: Single-turn agent correctly forgot favorite color\")\n",
+    "    single_color_test_passed = True  # This is good - it should forget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1edb22f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "============================================================\n",
+      " SINGLE-TURN AGENT - MESSAGE 3: Testing cats memory (should fail) \n",
+      "============================================================\n",
+      "USER: How many cats do I have and what are their names?\n",
+      "\n",
+      "ASSISTANT: I am a language model, and I do not have access to your personal information, including how many cats you have or their names. I cannot answer this question.\n",
+      "\n",
+      "\n",
+      "✓ EXPECTED: Single-turn agent correctly forgot all cat details\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Step 6: Test if single-turn agent remembers cats\n",
+    "# This should also FAIL because single-turn agents don't remember previous messages\n",
+    "\n",
+    "time.sleep(1)  # Brief pause between messages\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 60)\n",
+    "print(\" SINGLE-TURN AGENT - MESSAGE 3: Testing cats memory (should fail) \")\n",
+    "print(\"=\" * 60)\n",
+    "\n",
+    "response_single_3 = client.agents.query.create(\n",
+    "    agent_id=single_turn_agent.id,\n",
+    "    messages=[{\"content\": cats_question, \"role\": \"user\"}]\n",
+    "    # Note: Still no conversation_id - each message is independent\n",
+    ")\n",
+    "\n",
+    "print(f\"USER: {cats_question}\")\n",
+    "print(f\"\\nASSISTANT: {response_single_3.message.content}\")\n",
+    "\n",
+    "# Store the response\n",
+    "single_turn_response_3 = response_single_3.message.content\n",
+    "\n",
+    "# Check if the response contains our cat values (should NOT)\n",
+    "response_lower = single_turn_response_3.lower()\n",
+    "single_found_cat_values = []\n",
+    "\n",
+    "for value in expected_cat_values:\n",
+    "    if value.lower() in response_lower:\n",
+    "        single_found_cat_values.append(value)\n",
+    "\n",
+    "if single_found_cat_values:\n",
+    "    print(f\"\\n✗ UNEXPECTED: Single-turn agent remembered some cat details: {single_found_cat_values}\")\n",
+    "    single_cats_test_passed = False  # This is bad - it shouldn't remember\n",
+    "else:\n",
+    "    print(f\"\\n✓ EXPECTED: Single-turn agent correctly forgot all cat details\")\n",
+    "    single_cats_test_passed = True  # This is good - it should forget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "917c2b0b",
+   "metadata": {},
+   "source": [
+    "## Testing Recurring Conversation\n",
+    "\n",
+    "Now we'll test if we can continue our conversation with the multi-turn agent using the same conversation_id from earlier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "e012e932",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "============================================================\n",
+      " RECURRING CONVERSATION - Testing full memory \n",
+      "============================================================\n",
+      "Continuing conversation with ID: 9beba569-b8e2-471a-8f99-bc121f32c216\n",
+      "USER: Can you summarize everything I told you about myself in our conversation?\n",
+      "\n",
+      "ASSISTANT: Okay, here's a summary of what the user has told me:\n",
+      "\n",
+      "*   **Favorite Color:** Blue\n",
+      "*   **Pets:** 3 cats\n",
+      "*   **Cat Names:** Whiskers, Mittens, and Shadow\n",
+      "\n",
+      "\n",
+      "✓ SUCCESS: Recurring conversation remembered everything\n",
+      "  Found all values: ['blue', '3', 'Whiskers', 'Mittens', 'Shadow', 'cats']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Step 7: Test recurring conversation with multi-turn agent\n",
+    "# Use the same conversation_id from our original multi-turn conversation\n",
+    "# This tests if we can \"pick up where we left off\"\n",
+    "\n",
+    "time.sleep(2)  # Pause before continuing conversation\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 60)\n",
+    "print(\" RECURRING CONVERSATION - Testing full memory \")\n",
+    "print(\"=\" * 60)\n",
+    "\n",
+    "print(f\"Continuing conversation with ID: {conversation_id}\")\n",
+    "\n",
+    "summary_question = \"Can you summarize everything I told you about myself in our conversation?\"\n",
+    "\n",
+    "response_recurring = client.agents.query.create(\n",
+    "    agent_id=multi_turn_agent.id,\n",
+    "    messages=[{\"content\": summary_question, \"role\": \"user\"}],\n",
+    "    conversation_id=conversation_id  # Using the SAME conversation_id from the beginning!\n",
+    ")\n",
+    "\n",
+    "print(f\"USER: {summary_question}\")\n",
+    "print(f\"\\nASSISTANT: {response_recurring.message.content}\")\n",
+    "\n",
+    "# Store the response\n",
+    "recurring_response = response_recurring.message.content\n",
+    "\n",
+    "# Check if the response contains ALL our expected values\n",
+    "all_expected_values = [FAVORITE_COLOR] + [CAT_COUNT] + CAT_NAMES + [\"cats\"]  # Added \"cats\" as keyword\n",
+    "response_lower = recurring_response.lower()\n",
+    "\n",
+    "recurring_found_values = []\n",
+    "recurring_missing_values = []\n",
+    "\n",
+    "for value in all_expected_values:\n",
+    "    if value.lower() in response_lower:\n",
+    "        recurring_found_values.append(value)\n",
+    "    else:\n",
+    "        recurring_missing_values.append(value)\n",
+    "\n",
+    "if not recurring_missing_values:\n",
+    "    print(f\"\\n✓ SUCCESS: Recurring conversation remembered everything\")\n",
+    "    print(f\"  Found all values: {recurring_found_values}\")\n",
+    "    recurring_test_passed = True\n",
+    "else:\n",
+    "    print(f\"\\n✗ FAILED: Recurring conversation missing some details\")\n",
+    "    print(f\"  Found: {recurring_found_values}\")\n",
+    "    print(f\"  Missing: {recurring_missing_values}\")\n",
+    "    recurring_test_passed = False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73b9be7a",
+   "metadata": {},
+   "source": [
+    "## Test Results Summary\n",
+    "\n",
+    "Let's summarize all our test results and determine if multi-turn functionality is working correctly. The 2 use case can fail if the model responds with the numeral instead of the word  \"three\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "040720a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "============================================================\n",
+      " FINAL TEST RESULTS \n",
+      "============================================================\n",
+      "\n",
+      "Test Summary: 4/5 tests passed\n",
+      "\n",
+      "  1. Multi-turn agent remembers favorite color: PASS ✓\n",
+      "  2. Multi-turn agent remembers cats: FAIL ✗\n",
+      "  3. Single-turn agent forgets favorite color: PASS ✓\n",
+      "  4. Single-turn agent forgets cats: PASS ✓\n",
+      "  5. Recurring conversation remembers everything: PASS ✓\n",
+      "\n",
+      "============================================================\n",
+      "⚠️  PARTIAL SUCCESS: Multi-turn agent is working, but some edge cases failed.\n",
+      "\n",
+      "What each test validates:\n",
+      "- Tests 1-2: Multi-turn agent should remember conversation context\n",
+      "- Tests 3-4: Single-turn agent should NOT remember previous messages\n",
+      "- Test 5: Conversation can be continued using conversation_id\n",
+      "============================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Final Test Results Summary\n",
+    "print(\"=\" * 60)\n",
+    "print(\" FINAL TEST RESULTS \")\n",
+    "print(\"=\" * 60)\n",
+    "\n",
+    "# Collect all test results\n",
+    "all_tests = [\n",
+    "    (\"Multi-turn agent remembers favorite color\", color_test_passed),\n",
+    "    (\"Multi-turn agent remembers cats\", cats_test_passed),\n",
+    "    (\"Single-turn agent forgets favorite color\", single_color_test_passed),\n",
+    "    (\"Single-turn agent forgets cats\", single_cats_test_passed),\n",
+    "    (\"Recurring conversation remembers everything\", recurring_test_passed)\n",
+    "]\n",
+    "\n",
+    "# Count passed tests\n",
+    "passed_tests = sum(test[1] for test in all_tests)\n",
+    "total_tests = len(all_tests)\n",
+    "\n",
+    "print(f\"\\nTest Summary: {passed_tests}/{total_tests} tests passed\\n\")\n",
+    "\n",
+    "# Display detailed results\n",
+    "for i, (test_name, passed) in enumerate(all_tests, 1):\n",
+    "    status = \"PASS ✓\" if passed else \"FAIL ✗\"\n",
+    "    print(f\"  {i}. {test_name}: {status}\")\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 60)\n",
+    "\n",
+    "# Overall assessment\n",
+    "if passed_tests == total_tests:\n",
+    "    print(\"🎉 SUCCESS: All tests passed! Multi-turn functionality is working correctly.\")\n",
+    "elif passed_tests >= 3:  # If at least multi-turn tests pass\n",
+    "    print(\"⚠️  PARTIAL SUCCESS: Multi-turn agent is working, but some edge cases failed.\")\n",
+    "else:\n",
+    "    print(\"❌ FAILURE: Multi-turn functionality is not working as expected.\")\n",
+    "\n",
+    "print(\"\\nWhat each test validates:\")\n",
+    "print(\"- Tests 1-2: Multi-turn agent should remember conversation context\")\n",
+    "print(\"- Tests 3-4: Single-turn agent should NOT remember previous messages\")\n",
+    "print(\"- Test 5: Conversation can be continued using conversation_id\")\n",
+    "print(\"=\" * 60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0497cf96",
+   "metadata": {},
+   "source": [
+    "## Cleanup (Optional)\n",
+    "\n",
+    "If you want to delete the agents we created for this test, run the cell below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "44c90728",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Uncomment lines above to delete agents\n"
+     ]
+    }
+   ],
+   "source": [
+    "# #Cleanup: Delete the test agents (optional)\n",
+    "# #Uncomment the lines below if you want to delete the agents we created\n",
+    "\n",
+    "# print(\"Deleting test agents...\")\n",
+    "\n",
+    "# # Delete MultiTurnAgent\n",
+    "# try:\n",
+    "#     client.agents.delete(multi_turn_agent.id)\n",
+    "#     print(f\"✓ MultiTurnAgent ({multi_turn_agent.id}) deleted\")\n",
+    "# except Exception as e:\n",
+    "#     print(f\"✗ Error deleting MultiTurnAgent: {e}\")\n",
+    "\n",
+    "# # Delete SingleTurnAgent  \n",
+    "# try:\n",
+    "#     client.agents.delete(single_turn_agent.id)\n",
+    "#     print(f\"✓ SingleTurnAgent ({single_turn_agent.id}) deleted\")\n",
+    "# except Exception as e:\n",
+    "#     print(f\"✗ Error deleting SingleTurnAgent: {e}\")\n",
+    "\n",
+    "# # Delete datastore\n",
+    "# try:\n",
+    "#     client.datastores.delete(datastore_id)\n",
+    "#     print(f\"✓ Datastore  ({datastore_id}) deleted\")\n",
+    "# except Exception as e:\n",
+    "#     print(f\"✗ Error deleting Datastore: {e}\")\n",
+    "\n",
+    "# print(\"Cleanup completed!\")\n",
+    "\n",
+    "print(\"Uncomment lines above to delete agents\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/README.md b/README.md
index b9ef84e..1156f36 100644
--- a/README.md
+++ b/README.md
@@ -26,6 +26,7 @@ This repository contains practical examples and demonstrations of how to interac
 - [Monitoring RAG](14-monitoring) - Using Metrics API to monitor your RAG agent
 - [Metadata Introduction](15-metadata-intro/) - Working with metadata in your RAG Agent
 - [Build your own Matthew McConaughey](16-matthew-mcconaughey) - Building your own custom RAG agent.
+- [Multi-turn Conversation](17-multi-turn) -  A chatbot that uses previous messages in the conversation as context for its responses.
 
 ### Integrations
 - [CrewAI Multi-Agent Workflow](13-crewai-multiagent/) - Using CrewAI in a MultiAgent workflow