state_in_function.py
"""
State In Function
=================
Demonstrates reading and mutating workflow session state inside custom function executors.
"""
from typing import Iterator, Union
from agno.agent import Agent
from agno.db.in_memory import InMemoryDb
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIChat
from agno.run.workflow import WorkflowRunOutputEvent
from agno.team import Team
from agno.tools.hackernews import HackerNewsTools
from agno.tools.websearch import WebSearchTools
from agno.workflow.step import Step, StepInput, StepOutput
from agno.workflow.workflow import Workflow
# ---------------------------------------------------------------------------
# Create Agents
# ---------------------------------------------------------------------------
hackernews_agent = Agent(
name="Hackernews Agent",
model=OpenAIChat(id="gpt-4o"),
tools=[HackerNewsTools()],
instructions="Extract key insights and content from Hackernews posts",
)
web_agent = Agent(
name="Web Agent",
model=OpenAIChat(id="gpt-4o"),
tools=[WebSearchTools()],
instructions="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",
],
)
streaming_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",
],
db=InMemoryDb(),
)
# ---------------------------------------------------------------------------
# Create Team
# ---------------------------------------------------------------------------
research_team = Team(
name="Research Team",
model=OpenAIChat(id="gpt-4o"),
members=[hackernews_agent, web_agent],
instructions="Analyze content and create comprehensive social media strategy",
)
# ---------------------------------------------------------------------------
# Define Function Executors
# ---------------------------------------------------------------------------
def custom_content_planning_function(
step_input: StepInput,
session_state: dict,
) -> StepOutput:
message = step_input.input
previous_step_content = step_input.previous_step_content
if "content_plans" not in session_state:
session_state["content_plans"] = []
if "plan_counter" not in session_state:
session_state["plan_counter"] = 0
session_state["plan_counter"] += 1
current_plan_id = session_state["plan_counter"]
planning_prompt = f"""
STRATEGIC CONTENT PLANNING REQUEST:
Core Topic: {message}
Plan ID: #{current_plan_id}
Research Results: {previous_step_content[:500] if previous_step_content else "No research results"}
Previous Plans Count: {len(session_state["content_plans"])}
Planning Requirements:
1. Create a comprehensive content strategy based on the research
2. Leverage the research findings effectively
3. Identify content formats and channels
4. Provide timeline and priority recommendations
5. Include engagement and distribution strategies
Please create a detailed, actionable content plan.
"""
try:
response = content_planner.run(planning_prompt)
plan_data = {
"id": current_plan_id,
"topic": message,
"content": response.content,
"timestamp": f"Plan #{current_plan_id}",
"has_research": bool(previous_step_content),
}
session_state["content_plans"].append(plan_data)
enhanced_content = f"""
## Strategic Content Plan #{current_plan_id}
**Planning Topic:** {message}
**Research Integration:** {"[PASS] Research-based" if previous_step_content else "[FAIL] No research foundation"}
**Total Plans Created:** {len(session_state["content_plans"])}
**Content Strategy:**
{response.content}
**Custom Planning Enhancements:**
- Research Integration: {"High" if previous_step_content else "Baseline"}
- Strategic Alignment: Optimized for multi-channel distribution
- Execution Ready: Detailed action items included
- Session History: {len(session_state["content_plans"])} plans stored
**Plan ID:** #{current_plan_id}
""".strip()
return StepOutput(content=enhanced_content)
except Exception as e:
return StepOutput(
content=f"Custom content planning failed: {str(e)}",
success=False,
)
def content_summary_function(step_input: StepInput, session_state: dict) -> StepOutput:
if "content_plans" not in session_state or not session_state["content_plans"]:
return StepOutput(
content="No content plans found in session state.", success=False
)
plans = session_state["content_plans"]
summary = f"""
## Content Planning Session Summary
**Total Plans Created:** {len(plans)}
**Session Statistics:**
- Plans with research: {len([p for p in plans if p["has_research"]])}
- Plans without research: {len([p for p in plans if not p["has_research"]])}
**Plan Overview:**
"""
for plan in plans:
summary += f"""
### Plan #{plan["id"]} - {plan["topic"]}
- Research Available: {"[PASS]" if plan["has_research"] else "[FAIL]"}
- Status: Completed
"""
session_state["session_summarized"] = True
session_state["total_plans_summarized"] = len(plans)
return StepOutput(content=summary.strip())
def custom_content_planning_function_stream(
step_input: StepInput,
session_state: dict,
) -> Iterator[Union[WorkflowRunOutputEvent, StepOutput]]:
message = step_input.input
previous_step_content = step_input.previous_step_content
if "content_plans" not in session_state:
session_state["content_plans"] = []
if "plan_counter" not in session_state:
session_state["plan_counter"] = 0
session_state["plan_counter"] += 1
current_plan_id = session_state["plan_counter"]
planning_prompt = f"""
STRATEGIC CONTENT PLANNING REQUEST:
Core Topic: {message}
Plan ID: #{current_plan_id}
Research Results: {previous_step_content[:500] if previous_step_content else "No research results"}
Previous Plans Count: {len(session_state["content_plans"])}
Planning Requirements:
1. Create a comprehensive content strategy based on the research
2. Leverage the research findings effectively
3. Identify content formats and channels
4. Provide timeline and priority recommendations
5. Include engagement and distribution strategies
Please create a detailed, actionable content plan.
"""
try:
response_iterator = streaming_content_planner.run(
planning_prompt,
stream=True,
stream_events=True,
)
for event in response_iterator:
yield event
response = streaming_content_planner.get_last_run_output()
plan_data = {
"id": current_plan_id,
"topic": message,
"content": response.content if response else "No content generated",
"timestamp": f"Plan #{current_plan_id}",
"has_research": bool(previous_step_content),
}
session_state["content_plans"].append(plan_data)
enhanced_content = f"""
## Strategic Content Plan #{current_plan_id}
**Planning Topic:** {message}
**Research Integration:** {"[PASS] Research-based" if previous_step_content else "[FAIL] No research foundation"}
**Total Plans Created:** {len(session_state["content_plans"])}
**Content Strategy:**
{response.content if response else "Content generation failed"}
**Custom Planning Enhancements:**
- Research Integration: {"High" if previous_step_content else "Baseline"}
- Strategic Alignment: Optimized for multi-channel distribution
- Execution Ready: Detailed action items included
- Session History: {len(session_state["content_plans"])} plans stored
**Plan ID:** #{current_plan_id}
""".strip()
yield StepOutput(content=enhanced_content)
except Exception as e:
yield StepOutput(
content=f"Custom content planning failed: {str(e)}",
success=False,
)
def content_summary_function_stream(
step_input: StepInput,
session_state: dict,
) -> Iterator[StepOutput]:
plans = session_state["content_plans"]
summary = f"""
## Content Planning Session Summary
**Total Plans Created:** {len(plans)}
**Session Statistics:**
- Plans with research: {len([p for p in plans if p["has_research"]])}
- Plans without research: {len([p for p in plans if not p["has_research"]])}
**Plan Overview:**
"""
for plan in plans:
summary += f"""
### Plan #{plan["id"]} - {plan["topic"]}
- Research Available: {"[PASS]" if plan["has_research"] else "[FAIL]"}
- Status: Completed
"""
session_state["session_summarized"] = True
session_state["total_plans_summarized"] = len(plans)
yield StepOutput(content=summary.strip())
# ---------------------------------------------------------------------------
# Define Steps
# ---------------------------------------------------------------------------
research_step = Step(
name="Research Step",
team=research_team,
)
content_planning_step = Step(
name="Content Planning Step",
executor=custom_content_planning_function,
)
content_summary_step = Step(
name="Content Summary Step",
executor=content_summary_function,
)
stream_content_planning_step = Step(
name="Content Planning Step",
executor=custom_content_planning_function_stream,
)
stream_content_summary_step = Step(
name="Content Summary Step",
executor=content_summary_function_stream,
)
# ---------------------------------------------------------------------------
# Create Workflows
# ---------------------------------------------------------------------------
content_creation_workflow = Workflow(
name="Content Creation Workflow",
description="Automated content creation with custom execution options and session state",
db=SqliteDb(
session_table="workflow_session",
db_file="tmp/workflow.db",
),
steps=[research_step, content_planning_step, content_summary_step],
session_state={"content_plans": [], "plan_counter": 0},
)
streaming_content_workflow = Workflow(
name="Streaming Content Creation Workflow",
description="Automated content creation with streaming custom execution functions and session state",
db=SqliteDb(
session_table="workflow_session",
db_file="tmp/workflow.db",
),
steps=[research_step, stream_content_planning_step, stream_content_summary_step],
session_state={"content_plans": [], "plan_counter": 0},
)
# ---------------------------------------------------------------------------
# Run Workflows
# ---------------------------------------------------------------------------
if __name__ == "__main__":
print("=== First Workflow Run ===")
content_creation_workflow.print_response(
input="AI trends in 2024",
markdown=True,
)
print(
f"\nSession State After First Run: {content_creation_workflow.get_session_state()}"
)
print("\n" + "=" * 60 + "\n")
print("=== Second Workflow Run (Same Session) ===")
content_creation_workflow.print_response(
input="Machine Learning automation tools",
markdown=True,
)
print(f"\nFinal Session State: {content_creation_workflow.get_session_state()}")
print("\n=== First Streaming Workflow Run ===")
streaming_content_workflow.print_response(
input="AI trends in 2024",
markdown=True,
stream=True,
)
print(
f"\nSession State After First Run: {streaming_content_workflow.get_session_state()}"
)
print("\n" + "=" * 60 + "\n")
print("=== Second Streaming Workflow Run (Same Session) ===")
streaming_content_workflow.print_response(
input="Machine Learning automation tools",
markdown=True,
stream=True,
)
print(f"\nFinal Session State: {streaming_content_workflow.get_session_state()}")
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
Run the example
Save the code above as
state_in_function.py, then run:python state_in_function.py