> ## Documentation Index
> Fetch the complete documentation index at: https://www.truefoundry.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Add Tracing to Agno agent

> Add tracing to Agno agent applications using the Traceloop SDK and TrueFoundry OtelCollector.

This guide demonstrates how to use TrueFoundry OtelCollector along with the Traceloop SDK to instrument Agno agent code.
In this example, the Agno agent is a research agent which researches the latest trends to conduct detailed market research. For example, it can generate "A comprehensive report on AI and machine learning."

<Steps>
  <Step title="Create Tracing Project, API Key and copy tracing code">
    Follow the instructions in [Getting Started](/docs/tracing/tracing-getting-started) to create a tracing project, generate API key and copy the
    tracing code.
  </Step>

  <Step title="Install Dependencies">
    First, you need to install the following

    ```shell lines theme={"dark"}
    pip install agno==1.2.6 traceloop-sdk openai
    ```
  </Step>

  <Step title="Add Tracing code to Agno agent application">
    For Agno agents, we need to add the `Traceloop.init()` call to the application. We should also add the `@workflow`, `@task` and `@tool` decorators to the workflow, task and tool functions respectively.

    ```python Agno Agent Code {8-10,14-23,25-26,33-34,43-44} lines theme={"dark"}
    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 "
    ))

    ```
  </Step>

  <Step title="Run your application and view logged trace">
    <Frame caption="">
      <img src="https://mintcdn.com/truefoundry/2MMcllD7kMlpnaWX/images/tracing_agno.png?fit=max&auto=format&n=2MMcllD7kMlpnaWX&q=85&s=feda20c2eb48c3684103e73cd76b8cb1" width="3024" height="1716" data-path="images/tracing_agno.png" />
    </Frame>
  </Step>
</Steps>