input_schema validation with structured and invalid input examples.
The source uses deprecated
gpt-4o for the content planner. Replace that model ID before running.input_schema.py
"""
Input Schema
============
Demonstrates workflow-level `input_schema` validation with structured and invalid input examples.
"""
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.hackernews import HackerNewsTools
from agno.tools.websearch import WebSearchTools
from agno.workflow.step import Step
from agno.workflow.workflow import Workflow
from pydantic import BaseModel, Field
# ---------------------------------------------------------------------------
# Define Input Models
# ---------------------------------------------------------------------------
class DifferentModel(BaseModel):
name: str
class ResearchTopic(BaseModel):
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)
# ---------------------------------------------------------------------------
# Create Agents
# ---------------------------------------------------------------------------
hackernews_agent = Agent(
name="Hackernews Agent",
model=OpenAIChat(id="gpt-4o-mini"),
tools=[HackerNewsTools()],
role="Extract key insights and content from Hackernews posts",
)
web_agent = Agent(
name="Web Agent",
model=OpenAIChat(id="gpt-4o-mini"),
tools=[WebSearchTools()],
role="Search the web for the latest news and trends",
)
content_planner = Agent(
name="Content Planner",
model=OpenAIChat(id="gpt-4o"),
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",
],
)
# ---------------------------------------------------------------------------
# Create Team
# ---------------------------------------------------------------------------
research_team = Team(
name="Research Team",
members=[hackernews_agent, web_agent],
instructions="Research tech topics from Hackernews and the web",
)
# ---------------------------------------------------------------------------
# Define Steps
# ---------------------------------------------------------------------------
research_step = Step(
name="Research Step",
team=research_team,
)
content_planning_step = Step(
name="Content Planning Step",
agent=content_planner,
)
# ---------------------------------------------------------------------------
# Create Workflow
# ---------------------------------------------------------------------------
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,
)
# ---------------------------------------------------------------------------
# Run Workflow
# ---------------------------------------------------------------------------
if __name__ == "__main__":
print("=== Example: Research with Structured Topic ===")
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",
)
content_creation_workflow.print_response(
input=research_topic,
markdown=True,
)
# Should fail, as some fields present in input schema are missing.
# content_creation_workflow.print_response(
# input=ResearchTopic(
# topic="AI trends in 2024",
# focus_areas=[
# "Machine Learning",
# "Natural Language Processing",
# "Computer Vision",
# "AI Ethics",
# ],
# ),
# markdown=True,
# )
# Should fail, as it is not in sync with input schema.
# content_creation_workflow.print_response(
# input=DifferentModel(name="test"),
# markdown=True,
# )
# Pass a valid dict that matches ResearchTopic.
# 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,
# )
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno ddgs fastapi openai sqlalchemy
3
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Update the content planner model
Replace
OpenAIChat(id="gpt-4o") with OpenAIChat(id="gpt-5.4-mini") in the saved file.5
Run the example
Save the code above as
input_schema.py, then run:python input_schema.py