Basic Workflow Tracing

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.os import AgentOS
from agno.tools.duckduckgo import DuckDuckGoTools
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=[DuckDuckGoTools()],
)

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=[DuckDuckGoTools()],
)

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})")