> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-studio-tools-doc.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Basic Workflow Tracing

> Enable tracing and observability for workflows.

This example shows how to enable tracing for a workflow. When tracing is enabled, all workflow operations, step runs, model calls, and tool executions are automatically captured and stored in your database.

<Steps>
  <Step title="Create a Python file">
    ```python basic_workflow_tracing.py theme={null}
    from agno.agent import Agent
    from agno.db.sqlite import SqliteDb
    from agno.os import AgentOS
    from agno.tools.hackernews import HackerNewsTools
    from agno.workflow.condition import Condition
    from agno.workflow.step import Step
    from agno.workflow.types import StepInput
    from agno.workflow.workflow import Workflow

    # Set up database
    db = SqliteDb(db_file="tmp/traces.db")

    # === BASIC AGENTS ===
    researcher = Agent(
    name="Researcher",
    instructions="Research the given topic and provide detailed findings.",
    tools=[HackerNewsTools()],
    )

    summarizer = Agent(
    name="Summarizer",
    instructions="Create a clear summary of the research findings.",
    )

    fact_checker = Agent(
    name="Fact Checker",
    instructions="Verify facts and check for accuracy in the research.",
    tools=[HackerNewsTools()],
    )

    writer = Agent(
    name="Writer",
    instructions="Write a comprehensive article based on all available research and verification.",
    )

    # === CONDITION EVALUATOR ===


    def needs_fact_checking(step_input: StepInput) -> bool:
    """Determine if the research contains claims that need fact-checking"""
    return True


    # === WORKFLOW STEPS ===
    research_step = Step(
    name="research",
    description="Research the topic",
    agent=researcher,
    )

    summarize_step = Step(
    name="summarize",
    description="Summarize research findings",
    agent=summarizer,
    )

    # Conditional fact-checking step
    fact_check_step = Step(
    name="fact_check",
    description="Verify facts and claims",
    agent=fact_checker,
    )

    write_article = Step(
    name="write_article",
    description="Write final article",
    agent=writer,
    )

    # === BASIC LINEAR WORKFLOW ===
    workflow = Workflow(
    name="Basic Linear Workflow",
    description="Research -> Summarize -> Condition(Fact Check) -> Write Article",
    db=db,
    steps=[
        research_step,
        summarize_step,
        Condition(
            name="fact_check_condition",
            description="Check if fact-checking is needed",
            evaluator=needs_fact_checking,
            steps=[fact_check_step],
        ),
        write_article,
    ],
    )

    # Run the workflow - traces are captured automatically
    workflow.print_response("Write an article on AI agents?")

    # Query traces from the database
    traces, count = db.get_traces(workflow_id=workflow.id, limit=10)
    print(f"\nFound {count} traces for workflow '{workflow.name}'")
    for trace in traces:
        print(f"  - {workflow.name}: {trace.duration_ms}ms ({trace.status})")
    ```
  </Step>

  <Snippet file="create-venv-step.mdx" />

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U openai agno opentelemetry-api opentelemetry-sdk openinference-instrumentation-agno
    ```
  </Step>

  <Step title="Export your OpenAI API key">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
      export OPENAI_API_KEY="your_openai_api_key_here"
      ```

      ```bash Windows theme={null}
      $Env:OPENAI_API_KEY="your_openai_api_key_here"
      ```
    </CodeGroup>
  </Step>

  <Step title="Run the workflow">
    <CodeGroup>
      ```bash Mac theme={null}
      python basic_workflow_tracing.py
      ```

      ```bash Windows theme={null}
      python basic_workflow_tracing.py
      ```
    </CodeGroup>
  </Step>
</Steps>