Introduction
State management helps an agent remember what happened before. This stops it from getting mixed up and making mistakes.
Jump into concepts and practice - no test required
State management helps an agent remember what happened before. This stops it from getting mixed up and making mistakes.
state = {}
# Update state with new info
state['last_question'] = 'What is your name?'
# Use state to decide next action
if state.get('last_question') == 'What is your name?':
response = 'I am your assistant.'State is usually stored in a dictionary or similar structure.
Always update state after each step to keep info fresh.
state = {}
state['step'] = 1
print(state)state['user_name'] = 'Alex' print(f"Hello, {state['user_name']}!")
if state.get('step') == 1: print('First step done')
This program shows how an agent uses state to remember the last question and respond properly.
state = {}
# Simulate a simple agent conversation
state['last_question'] = 'How are you?'
# Agent uses state to respond
if state.get('last_question') == 'How are you?':
response = 'I am fine, thank you!'
else:
response = 'Hello!'
print('Agent response:', response)Without state, agents forget past info and can give wrong answers.
State management is key for smooth, natural interactions.
Keep state simple and update it often to avoid confusion.
State helps agents remember past events.
Remembering stops confusion and wrong actions.
Good state management makes agents smarter and friendlier.
state = {'visited': []}
new_place = 'park'
state['visited'].append(new_place)
print(state['visited'])state = {'count': 1}
state['count'] + 1
print(state['count'])