custom_function_step_confirmation.py
"""
Test script demonstrating Step-level Human-In-The-Loop (HITL) functionality.
This example shows a blog post workflow where:
1. Research agent gathers information (no confirmation)
2. Custom function processes the research (HITL via @pause decorator)
3. Writer agent creates the final post (no confirmation)
Two approaches for HITL:
1. Flag-based: Using requires_confirmation=True on Step
2. Decorator-based: Using @pause decorator on custom functions
"""
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIResponses
from agno.workflow.decorators import pause
from agno.workflow.step import Step
from agno.workflow.types import StepInput, StepOutput
from agno.workflow.workflow import Workflow
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
# ============================================================
# Step 1: Research Agent (no confirmation needed)
# ============================================================
research_agent = Agent(
name="Researcher",
model=OpenAIResponses(id="gpt-5.4"),
instructions=[
"You are a research assistant.",
"Given a topic, provide 3 key points about it in a concise bullet list.",
"Keep each point to one sentence.",
],
)
# ============================================================
# Step 2: Process research (requires confirmation via @pause decorator)
# ============================================================
@pause(
name="Process Research",
requires_confirmation=True,
confirmation_message="Research complete. Ready to generate blog post. Proceed?",
)
def process_research(step_input: StepInput) -> StepOutput:
"""Process the research data before writing."""
research = step_input.previous_step_content or "No research available"
return StepOutput(
content=f"PROCESSED RESEARCH:\n{research}\n\nReady for blog post generation."
)
# ============================================================
# Step 3: Writer Agent (no confirmation needed)
# ============================================================
writer_agent = Agent(
name="Writer",
model=OpenAIResponses(id="gpt-5.4"),
instructions=[
"You are a blog writer.",
"Given processed research, write a short 2-paragraph blog post.",
"Keep it concise and engaging.",
],
)
# Define steps
research_step = Step(name="research", agent=research_agent)
process_step = Step(
name="process_research", executor=process_research
) # @pause auto-detected
write_step = Step(name="write_post", agent=writer_agent)
# Create workflow
workflow = Workflow(
name="blog_post_workflow",
db=PostgresDb(db_url=db_url),
steps=[research_step, process_step, write_step],
)
if __name__ == "__main__":
print("Starting blog post workflow...")
print("=" * 50)
run_output = workflow.run("Benefits of morning exercise")
# Handle HITL pause
while run_output.is_paused:
for requirement in run_output.steps_requiring_confirmation:
print(f"\n[HITL] Step '{requirement.step_name}' requires confirmation")
print(f"[HITL] {requirement.confirmation_message}")
user_input = input("\nContinue? (yes/no): ").strip().lower()
if user_input in ("yes", "y"):
requirement.confirm()
print("[HITL] Confirmed - continuing workflow...")
else:
requirement.reject()
print("[HITL] Rejected - cancelling workflow...")
run_output = workflow.continue_run(run_output)
print("\n" + "=" * 50)
print(f"Status: {run_output.status}")
print(f"Output:\n{run_output.content}")
Run the Example
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
Run PgVector
docker run -d \
-e POSTGRES_DB=ai \
-e POSTGRES_USER=ai \
-e POSTGRES_PASSWORD=ai \
-e PGDATA=/var/lib/postgresql/data/pgdata \
-v pgvolume:/var/lib/postgresql/data \
-p 5532:5432 \
--name pgvector \
agnohq/pgvector:18