# We can add memories directly to the memory object
tommie_observations = [
    "Tommie remembers his dog, Bruno, from when he was a kid",
    "Tommie feels tired from driving so far",
    "Tommie sees the new home",
    "The new neighbors have a cat",
    "The road is noisy at night",
    "Tommie is hungry",
    "Tommie tries to get some rest.",
]
for observation in tommie_observations:
    tommie.memory.add_memory(observation)
# Now that Tommie has 'memories', their self-summary is more descriptive, though still rudimentary.
# We will see how this summary updates after more observations to create a more rich description.
print(tommie.get_summary(force_refresh=True))
Name: Tommie (age: 25)
Innate traits: anxious, likes design, talkative
Tommie is a person who is observant of his surroundings, has a sentimental side, and experiences basic human needs such as hunger and the need for rest. He also tends to get tired easily and is affected by external factors such as noise from the road or a neighbor's pet.
Pre-Interview with Character#
Before sending our character on their way, let’s ask them a few questions.
def interview_agent(agent: GenerativeAgent, message: str) -> str:
    """Help the notebook user interact with the agent."""
    new_message = f"{USER_NAME} says {message}"
    return agent.generate_dialogue_response(new_message)[1]
interview_agent(tommie, "What do you like to do?")
'Tommie said "I really enjoy design and being creative. I\'ve been working on some personal projects lately. What about you, Person A? What do you like to do?"'
rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agent_simulations/characters.html