diff --git a/ai_agent_tutorials/ai_r1-tooluse-langroid/README.md b/ai_agent_tutorials/ai_r1-tooluse-langroid/README.md
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diff --git a/ai_agent_tutorials/ai_r1-tooluse-langroid/main.py b/ai_agent_tutorials/ai_r1-tooluse-langroid/main.py
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diff --git a/ai_agent_tutorials/ai_r1-tooluse-langroid/requirements.txt b/ai_agent_tutorials/ai_r1-tooluse-langroid/requirements.txt
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diff --git a/ai_agent_tutorials/ai_r1-tooluse-langroid/test.py b/ai_agent_tutorials/ai_r1-tooluse-langroid/test.py
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+
+import os
+from typing import List
+import fire
+
+from langroid.pydantic_v1 import BaseModel, Field
+import langroid as lr
+from langroid.utils.configuration import settings
+from langroid.agent.tool_message import ToolMessage
+from langroid.agent.tools.orchestration import FinalResultTool
+import langroid.language_models as lm
+from rich.prompt import Prompt
+from langroid.agent.chat_document import ChatDocument
+
+# for best results:
+DEFAULT_LLM = lm.OpenAIChatModel.GPT4o
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+# (1) Define the desired structure via Pydantic.
+# Here we define a nested structure for City information.
+# The "Field" annotations are optional, and are included in the system message
+# if provided, and help with generation accuracy.
+
+
+class CityData(BaseModel):
+    population: int = Field(..., description="population of city")
+    country: str = Field(..., description="country of city")
+
+
+class City(BaseModel):
+    name: str = Field(..., description="name of city")
+    details: CityData = Field(..., description="details of city")
+
+
+# (2) Define the Tool class for the LLM to use, to produce the above structure.
+class CityTool(lr.agent.ToolMessage):
+    """Present information about a city"""
+
+    request: str = "city_tool"
+    purpose: str = """
+    To present <city_info> AFTER user gives a city name,
+    with all fields of the appropriate type filled out;
+    """
+    city_info: City = Field(..., description="information about a city")
+
+    def handle(self) -> FinalResultTool:
+        """Handle LLM's structured output if it matches City structure"""
+        print("SUCCESS! Got Valid City Info")
+        return FinalResultTool(answer=self.city_info)
+
+    @staticmethod
+    def handle_message_fallback(
+        agent: lr.ChatAgent, msg: str | ChatDocument
+    ) -> str | None:
+        """
+        We end up here when there was no recognized tool msg from the LLM;
+        In this case use the AgentDoneTool with content set to
+        the original message content.
+        """
+        if isinstance(msg, ChatDocument) and msg.metadata.sender == lr.Entity.LLM:
+            return f"""
+            You forgot to use the TOOL/Function `{CityTool.name()}`.
+            Please use this tool to present the city info.
+            """
+
+    @classmethod
+    def examples(cls) -> List["ToolMessage"]:
+        # Used to provide few-shot examples in the system prompt
+        return [
+            cls(
+                city_info=City(
+                    name="San Francisco",
+                    details=CityData(
+                        population=800_000,
+                        country="USA",
+                    ),
+                )
+            )
+        ]
+
+
+def app(
+    m: str = DEFAULT_LLM,  # model
+    d: bool = False,  # pass -d to enable debug mode (see prompts etc)
+    nc: bool = False,  # pass -nc to disable cache-retrieval (i.e. get fresh answers)
+):
+    settings.debug = d
+    settings.cache = not nc
+    # create LLM config
+    llm_cfg = lm.OpenAIGPTConfig(
+        chat_model=m or DEFAULT_LLM,
+        chat_context_length=32000,  # set this based on model
+        max_output_tokens=1000,
+        temperature=0.2,
+        stream=True,
+        timeout=45,
+    )
+
+    # Recommended: First test if basic chat works with this llm setup as below:
+    # Once this works, then you can try the rest of the example.
+    #
+    # agent = lr.ChatAgent(
+    #     lr.ChatAgentConfig(
+    #         llm=llm_cfg,
+    #     )
+    # )
+    #
+    # agent.llm_response("What is 3 + 4?")
+    #
+    # task = lr.Task(agent)
+    # verify you can interact with this in a chat loop on cmd line:
+    # task.run("Concisely answer some questions")
+
+    # Define a ChatAgentConfig and ChatAgent
+
+    config = lr.ChatAgentConfig(
+        llm=llm_cfg,
+        system_message=f"""
+        You will receive a city name, 
+        and you must use the TOOL/FUNCTION `{CityTool.name()}` to generate/present
+        information about the city.
+        """,
+    )
+
+    agent = lr.ChatAgent(config)
+
+    # (4) Enable the Tool for this agent --> this auto-inserts JSON instructions
+    # and few-shot examples (specified in the tool defn above) into the system message
+    agent.enable_message(CityTool)
+
+    # (5) Create task specialized to return City object
+    task: City | None = lr.Task(agent, interactive=False)[City]
+
+    while True:
+        city = Prompt.ask("Enter a city name")
+        if city in ["q", "x"]:
+            break
+        result: City | None = task.run(city)
+        if result:
+            print(f"City Info: {result}")
+        else:
+            print("No valid city info found.")
+
+
+if __name__ == "__main__":
+    fire.Fire(app)
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