> ## Documentation Index > Fetch the complete documentation index at: https://phidatainc-studio-tools-doc.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # State in Custom Function > This example demonstrates how to access the run context in a custom python function step Create a file named `access_session_state_in_custom_python_function_step.py` ```python access_session_state_in_custom_python_function_step.py theme={null} from agno.agent import Agent from agno.db.sqlite import SqliteDb from agno.models.openai import OpenAIResponses from agno.run import RunContext from agno.team import Team from agno.tools.hackernews import HackerNewsTools from agno.tools.yfinance import YFinanceTools from agno.workflow.step import Step, StepInput, StepOutput from agno.workflow.workflow import Workflow # Define agents hackernews_agent = Agent( name="Hackernews Agent", model=OpenAIResponses(id="gpt-5.2"), tools=[HackerNewsTools()], instructions="Extract key insights and content from Hackernews posts", ) finance_agent = Agent( name="Finance Agent", model=OpenAIResponses(id="gpt-5.2"), tools=[YFinanceTools()], instructions="Get financial data and market trends", ) # Define research team for complex analysis research_team = Team( name="Research Team", model=OpenAIResponses(id="gpt-5.2"), members=[hackernews_agent, finance_agent], instructions="Analyze content and create comprehensive social media strategy", ) content_planner = Agent( name="Content Planner", model=OpenAIResponses(id="gpt-5.2"), 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", ], ) def custom_content_planning_function( step_input: StepInput, run_context: RunContext ) -> StepOutput: """ Custom function that does intelligent content planning with context awareness and maintains a content plan history in session_state """ message = step_input.input previous_step_content = step_input.previous_step_content # Initialize content history if not present if "content_plans" not in run_context.session_state: run_context.session_state["content_plans"] = [] if "plan_counter" not in run_context.session_state: run_context.session_state["plan_counter"] = 0 # Increment plan counter run_context.session_state["plan_counter"] += 1 current_plan_id = run_context.session_state["plan_counter"] # Create intelligent planning prompt 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(run_context.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) # Store this plan in session state plan_data = { "id": current_plan_id, "topic": message, "content": response.content, "timestamp": f"Plan #{current_plan_id}", "has_research": bool(previous_step_content), } run_context.session_state["content_plans"].append(plan_data) enhanced_content = f""" ## Strategic Content Plan #{current_plan_id} **Planning Topic:** {message} **Research Integration:** {"Research-based" if previous_step_content else "No research foundation"} **Total Plans Created:** {len(run_context.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(run_context.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, run_context: RunContext) -> StepOutput: """ Custom function that summarizes all content plans created in the session """ if run_context.session_state is None or run_context.session_state.get("content_plans") is None: return StepOutput( content="No content plans found in session state.", success=False ) plans = run_context.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: {"Yes" if plan["has_research"] else "No"} - Status: Completed """ # Update session state with summary info run_context.session_state["session_summarized"] = True run_context.session_state["total_plans_summarized"] = len(plans) return StepOutput(content=summary.strip()) # Define steps using different executor types 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, ) # Define and use examples if __name__ == "__main__": 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", ), # Define the sequence of steps # First run the research_step, then the content_planning_step, then the summary_step # You can mix and match agents, teams, and even regular python functions directly as steps steps=[research_step, content_planning_step, content_summary_step], # Initialize session state with empty content plans session_state={"content_plans": [], "plan_counter": 0}, ) 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()}") ``` ```bash theme={null} uv pip install -U agno openai yfinance ``` ```bash theme={null} python access_session_state_in_custom_python_function_step.py ```