> ## Documentation Index > Fetch the complete documentation index at: https://phidatainc-agui.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # State in Router > This example demonstrates how to access the run context in the selector function of a router step This example shows: 1. Using `run_context.session_state` in a Router selector function 2. Making routing decisions based on session state data 3. Accessing user preferences and history from `run_context.session_state` 4. Dynamically selecting different agents based on user context ```python access_session_state_in_router_selector_function.py theme={null} from agno.agent import Agent from agno.models.openai import OpenAIResponses from agno.workflow.router import Router from agno.workflow.step import Step, StepInput, StepOutput from agno.workflow.workflow import Workflow from agno.run import RunContext def route_based_on_user_preference(step_input: StepInput, run_context: RunContext) -> Step: """ Router selector that chooses an agent based on user preferences in session_state. Args: step_input: The input for this step (contains user query) run_context: The run context object Returns: Step: The step to execute based on user preference """ print("\n=== Routing Decision ===") print(f"User ID: {run_context.session_state.get('current_user_id')}") print(f"Session ID: {run_context.session_state.get('current_session_id')}") # Get user preference from session state user_preference = run_context.session_state.get("agent_preference", "general") interaction_count = run_context.session_state.get("interaction_count", 0) print(f"User Preference: {user_preference}") print(f"Interaction Count: {interaction_count}") # Update interaction count run_context.session_state["interaction_count"] = interaction_count + 1 # Route based on preference if user_preference == "technical": print("→ Routing to Technical Expert") return technical_step elif user_preference == "friendly": print("→ Routing to Friendly Assistant") return friendly_step else: # For first interaction, route to onboarding if interaction_count == 0: print("→ Routing to Onboarding (first interaction)") return onboarding_step else: print("→ Routing to General Assistant") return general_step def set_user_preference(step_input: StepInput, run_context: RunContext) -> StepOutput: """Custom function that sets user preference based on onboarding.""" print("\n=== Setting User Preference ===") # In a real scenario, this would analyze the user's response # For demo purposes, we'll set it based on interaction count interaction_count = run_context.session_state.get("interaction_count", 0) if interaction_count % 3 == 1: run_context.session_state["agent_preference"] = "technical" preference = "technical" elif interaction_count % 3 == 2: run_context.session_state["agent_preference"] = "friendly" preference = "friendly" else: run_context.session_state["agent_preference"] = "general" preference = "general" print(f"Set preference to: {preference}") return StepOutput(content=f"Preference set to: {preference}") # Create specialized agents onboarding_agent = Agent( name="Onboarding Agent", model=OpenAIResponses(id="gpt-5.2"), instructions=( "Welcome new users and ask about their preferences. " "Determine if they prefer technical or friendly assistance." ), markdown=True, ) technical_agent = Agent( name="Technical Expert", model=OpenAIResponses(id="gpt-5.2"), instructions=( "You are a technical expert. Provide detailed, technical answers with code examples and best practices." ), markdown=True, ) friendly_agent = Agent( name="Friendly Assistant", model=OpenAIResponses(id="gpt-5.2"), instructions=( "You are a friendly, casual assistant. Use simple language, emojis, and make the conversation fun." ), markdown=True, ) general_agent = Agent( name="General Assistant", model=OpenAIResponses(id="gpt-5.2"), instructions=( "You are a balanced assistant. Provide helpful answers that are neither too technical nor too casual." ), markdown=True, ) # Create steps for routing onboarding_step = Step( name="Onboard User", description="Onboard new user and set preferences", agent=onboarding_agent, ) technical_step = Step( name="Technical Response", description="Provide technical assistance", agent=technical_agent, ) friendly_step = Step( name="Friendly Response", description="Provide friendly assistance", agent=friendly_agent, ) general_step = Step( name="General Response", description="Provide general assistance", agent=general_agent, ) # Create workflow with router workflow = Workflow( name="Adaptive Assistant Workflow", steps=[ # Router that selects agent based on session state Router( name="Route to Appropriate Agent", description="Route to the appropriate agent based on user preferences", selector=route_based_on_user_preference, choices=[ onboarding_step, technical_step, friendly_step, general_step, ], ), # After first interaction, update preferences Step( name="Update Preferences", description="Update user preferences based on interaction", executor=set_user_preference, ), ], session_state={ "agent_preference": "general", "interaction_count": 0, }, ) def run_example(): """Run the example workflow multiple times to see dynamic routing.""" queries = [ "Hello! I'm new here.", "How do I implement a binary search tree in Python?", "What's the best pizza topping?", "Explain quantum computing", ] for i, query in enumerate(queries, 1): print("\n" + "=" * 80) print(f"Interaction {i}: {query}") print("=" * 80) workflow.print_response( input=query, session_id="user-456", user_id="user-456", stream=True, ) if __name__ == "__main__": run_example() ``` ```bash theme={null} uv pip install -U agno openai ``` ```bash theme={null} python access_session_state_in_router_selector_function.py ```