Add Tracing to Agno agent

from dotenv import load_dotenv
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.workflow import RunResponse
import random
import os

# importing traceloop sdk and decorators
from traceloop.sdk import Traceloop
from traceloop.sdk.decorators import workflow, task, tool

load_dotenv()

# Add the traceloop init code to your application
TFY_API_KEY = os.environ.get("TFY_API_KEY")
Traceloop.init(
    api_endpoint="<enter_your_api_endpoint>",
    app_name="your tracing application name",
    headers = {
        "Authorization": f"Bearer {TFY_API_KEY}",
        "TFY-Tracing-Project": "<enter_your_tracing_project_fqn>",
    },
)

# traceloop tool decorator
@tool(name="get_random_topic")
def get_random_topic() -> str:
    """Get a random topic from a list of AI-related subjects for research."""
    print("Getting random topic")
    words = ["AI", "Machine Learning", "Data Science", "Deep Learning", "Computer Vision", "Natural Language Processing", "Robotics", "Blockchain", "Quantum Computing", "Gen AI", "LLMs", "RAG", "LLM Agents", "LLM Orchestration", "LLM Tool Calling", "LLM Memory", "LLM Planning", "LLM Reasoning", "LLM Chain of Thought", "LLM Self-Reflection", "LLM Self-Improvement", "LLM Self-Evaluation", "LLM Self-Correction", "LLM Self-Debugging", "LLM Self-Healing", "LLM Self-Repairing", "LLM Self-Optimizing", "LLM Self-Adjusting", "LLM Self-Adapting", "LLM Self-Learning", "LLM Self-Training", "LLM Self-Testing", "LLM Self-Verification", "LLM Self-Validation", "LLM Self-Improvement", "LLM Self-Correction", "LLM Self-Debugging", "LLM Self-Healing", "LLM Self-Repairing", "LLM Self-Optimizing", "LLM Self-Adjusting", "LLM Self-Adapting", "LLM Self-Learning", "LLM Self-Training", "LLM Self-Testing", "LLM Self-Verification", "LLM Self-Improvement", "LLM Self-Correction", "LLM Self-Debugging", "LLM Self-Healing", "LLM Self-Repairing", "LLM Self-Optimizing", "LLM Self-Adjusting", "LLM Self-Adapting", "LLM Self-Learning"]
    return random.choice(words)

# Traceloop workflow decorator
@workflow(name="research_workflow")
def research(topic: str):
    research_agent = Agent(
        model=OpenAIChat(id="gpt-4o-mini"),
        description="Expert in market analysis with keen attention to detail",
        tool_choice="auto",
        tools=[get_random_topic]
    )

    # Traceloop task decorator
    @task(name="research_task")
    def research_task(topic: str):
        researcher_response: RunResponse = research_agent.run(topic)
        serializable_response = researcher_response.dict() if hasattr(researcher_response, "dict") else str(researcher_response)
        return serializable_response

    return research_task(topic)

if __name__ == "__main__":
    research(topic=(
    "Use the `get_random_topic` tool **exactly once** to select a trending topic in AI. "
    "Do not call the tool multiple times. Once a topic is selected, research the latest trends in that area "
))