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This script demonstrates the “Senior Agent” pattern. It extracts its own state and memory using XML tags and uses StateBase to persist them across turns. 
import os
import re
import json
from statebase import StateBase
from google import genai
from google.genai import types

# 1. Setup Clients
sb = StateBase(api_key=os.getenv("STATEBASE_KEY"))
gemini = genai.Client(api_key=os.getenv("GEMINI_KEY"))

# 2. Initialize Persistent Session
session = sb.sessions.create(
    agent_id="senior-support-v1",
    initial_state={"status": "ready", "user_name": "Guest"}
)

def process_interaction(user_input):
    # 3. Get Context (State + Memories + History)
    context = sb.sessions.get_context(session_id=session.id, query=user_input)
    
    # 4. Prompt with State-Awareness
    prompt = f"""
    Current State: {context['state']}
    Relevant Memories: {context['memories']}
    
    If you learn something new, use <memory>...</memory>.
    To update state, use <state_update>{{"key": "value"}}</state_update>.
    """
    
    response = gemini.models.generate_content(
        model="gemini-2.0-flash",
        config=types.GenerateContentConfig(system_instruction=prompt),
        contents=user_input
    )
    
    raw_output = response.text
    
    # 5. Extract and Persist (The StateBase Magic)
    # Memory extraction
    mem_match = re.search(r"<memory>(.*?)</memory>", raw_output)
    if mem_match:
        sb.memory.add(content=mem_match.group(1), session_id=session.id)
        
    # State extraction
    state_match = re.search(r"<state_update>(.*?)</state_update>", raw_output)
    if state_match:
        new_state = json.loads(state_match.group(1))
        sb.sessions.update_state(session_id=session.id, state=new_state)
        
    # 6. Log Turn for Dashboard Trace
    sb.sessions.add_turn(session_id=session.id, input=user_input, output=raw_output)
    
    return re.sub(r"<(memory|state_update)>.*?</\1>", "", raw_output).strip()

# Run the loop
if __name__ == "__main__":
    print(process_interaction("My name is Alex and I love python."))

​
Key Engineering Takeaway

By using this pattern, your application code remains fixed. You can change the agent’s behavior, personality, or state schema entirely by just changing the system prompt—StateBase handles the heavy lifting of storage and retrieval automatically.