Innate traits: anxious, likes design, talkative
Tommie is determined and hopeful in his job search, but can also feel discouraged and frustrated at times. He has a strong connection to his childhood dog, Bruno. Tommie seeks support from his friends when feeling overwhelmed and is grateful for their help. He also enjoys exploring his new city.
print(eve.get_summary(force_refresh=True))
Name: Eve (age: 34)
Innate traits: curious, helpful
Eve is a helpful and friendly person who enjoys playing sports and staying productive. She is attentive and responsive to others' needs, actively listening and asking questions to understand their perspectives. Eve has experience in event planning and communication, and is willing to share her knowledge and expertise with others. She values teamwork and collaboration, and strives to create a comfortable and supportive environment for everyone.
interview_agent(tommie, "How was your conversation with Eve?")
'Tommie said "It was really helpful actually. Eve shared some great tips on managing events and handling unexpected issues. I feel like I learned a lot from her experience."'
interview_agent(eve, "How was your conversation with Tommie?")
'Eve said "It was great, thanks for asking. Tommie was very receptive and had some great questions about event planning. How about you, have you had any interactions with Tommie?"'
interview_agent(eve, "What do you wish you would have said to Tommie?")
'Eve said "It was great meeting with you, Tommie. If you have any more questions or need any help in the future, don\'t hesitate to reach out to me. Have a great day!"'
 Contents
  
Generative Agent Memory Components
Memory Lifecycle
Create a Generative Character
Pre-Interview with Character
Step through the day’s observations.
Interview after the day
Adding Multiple Characters
rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agent_simulations/characters.html