from typing import List
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIChat
from agno.team import Team
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.hackernews import HackerNewsTools
from agno.workflow.step import Step
from agno.workflow.workflow import Workflow
from pydantic import BaseModel, Field
class DifferentModel(BaseModel):
name: str
class ResearchTopic(BaseModel):
"""Structured research topic with specific requirements"""
topic: str
focus_areas: List[str] = Field(description="Specific areas to focus on")
target_audience: str = Field(description="Who this research is for")
sources_required: int = Field(description="Number of sources needed", default=5)
# Define agents
hackernews_agent = Agent(
name="Hackernews Agent",
model=OpenAIChat(id="gpt-5-mini"),
tools=[HackerNewsTools()],
role="Extract key insights and content from Hackernews posts",
)
web_agent = Agent(
name="Web Agent",
model=OpenAIChat(id="gpt-5-mini"),
tools=[DuckDuckGoTools()],
role="Search the web for the latest news and trends",
)
# Define research team for complex analysis
research_team = Team(
name="Research Team",
members=[hackernews_agent, web_agent],
instructions="Research tech topics from Hackernews and the web",
)
content_planner = Agent(
name="Content Planner",
model=OpenAIChat(id="gpt-5-mini"),
instructions=[
"Plan a content schedule over 4 weeks for the provided topic and research content",
"Ensure that I have posts for 3 posts per week",
],
)
# Define steps
research_step = Step(
name="Research Step",
team=research_team,
)
content_planning_step = Step(
name="Content Planning Step",
agent=content_planner,
)
# Create and use workflow
if __name__ == "__main__":
content_creation_workflow = Workflow(
name="Content Creation Workflow",
description="Automated content creation from blog posts to social media",
db=SqliteDb(
session_table="workflow_session",
db_file="tmp/workflow.db",
),
steps=[research_step, content_planning_step],
input_schema=ResearchTopic, # <-- Define input schema for validation
)
print("=== Example 1: Valid Pydantic Model Input ===")
research_topic = ResearchTopic(
topic="AI trends in 2024",
focus_areas=[
"Machine Learning",
"Natural Language Processing",
"Computer Vision",
"AI Ethics",
],
target_audience="Tech professionals and business leaders",
)
# ✅ This will work properly - input matches the schema
content_creation_workflow.print_response(
input=research_topic,
markdown=True,
)
print("\n=== Example 2: Valid Dictionary Input ===")
# ✅ This will also work - dict matches ResearchTopic structure
content_creation_workflow.print_response(
input={
"topic": "AI trends in 2024",
"focus_areas": ["Machine Learning", "Computer Vision"],
"target_audience": "Tech professionals",
"sources_required": 8
},
markdown=True,
)
print("\n=== Example 3: Missing Required Fields (Commented - Would Fail) ===")
# ❌ This would fail - missing required 'target_audience' field
# Uncomment to see validation error:
# content_creation_workflow.print_response(
# input=ResearchTopic(
# topic="AI trends in 2024",
# focus_areas=[
# "Machine Learning",
# "Natural Language Processing",
# "Computer Vision",
# "AI Ethics",
# ],
# # target_audience missing - will raise ValidationError
# ),
# markdown=True,
# )
print("\n=== Example 4: Wrong Model Type (Commented - Would Fail) ===")
# ❌ This would fail - different Pydantic model provided
# Uncomment to see validation error:
# content_creation_workflow.print_response(
# input=DifferentModel(name="test"),
# markdown=True,
# )
print("\n=== Example 5: Type Mismatch (Commented - Would Fail) ===")
# ❌ This would fail - wrong data types
# Uncomment to see validation error:
# content_creation_workflow.print_response(
# input={
# "topic": 123, # Should be string
# "focus_areas": "Machine Learning", # Should be List[str]
# "target_audience": ["audience1", "audience2"], # Should be string
# },
# markdown=True,
# )
