Updating agent traits
This notebook demonstrates how to update an agent’s traits with new answers to questions using the add traits() and to_agent_list() methods.
Learn more about designing and using AI agents with surveys in the documentation.
Updating a single agent
We start by creating an agent and using it with a survey:
[1]:
from edsl import Agent, QuestionFreeText, QuestionMultipleChoice, Survey
[2]:
agent = Agent(
name = "college student",
traits = {
"persona": "You are a sophomore at a community college in upstate New York.",
"year": "sophomore",
"school": "community college",
"major": "biology",
"state": "New York"
}
)
[3]:
q1 = QuestionFreeText(
question_name = "career_interests",
question_text = "What are your career interests?"
)
q2 = QuestionMultipleChoice(
question_name = "attend_grad_school",
question_text = "Do you plan to attend grad school?",
question_options = ["Yes", "No", "Undecided"]
)
survey = Survey([q1, q2])
[4]:
results = survey.by(agent).run()
Job Status 🦜
Completed (1 completed, 0 failed)
Job Links
Content
Identifiers
Results UUID:
176e5a3f...90d5
Use Results.pull(uuid) to fetch results.
Job UUID:
4534fa5b...1880
Use Jobs.pull(uuid) to fetch job.
Model Costs ($0.0017 / 0.00 credits total)
| Service | Model | Input Tokens | Input Cost | Output Tokens | Output Cost | Total Cost | Total Credits |
|---|---|---|---|---|---|---|---|
| openai | gpt-4o | 216 | $0.0006 | 108 | $0.0011 | $0.0017 | 0.00 |
| Totals | 216 | $0.0006 | 108 | $0.0011 | $0.0017 | 0.00 | |
You can obtain the total credit cost by multiplying the total USD cost by 100. A lower credit cost indicates that you saved money by retrieving responses from the universal remote cache.
[5]:
results.select("career_interests", "attend_grad_school")
[5]:
| answer.career_interests | answer.attend_grad_school | |
|---|---|---|
| 0 | I'm really interested in pursuing a career in the biological sciences. Since I'm majoring in biology, I'm exploring options like working in environmental conservation or possibly going into research. I'm also considering the healthcare field, as I've always been fascinated by how the human body works and how we can improve health outcomes. I still have some time to decide, so I'm keeping my options open while I learn more about each path. | Undecided |
Creating a new agent with original and new traits
Here we use the to_agent_list() method to create a new agent that has both the original traits and new traits for the survey responses:
[6]:
new_agent = results.select("persona", "year", "school", "major", "state", "career_interests", "attend_grad_school").to_agent_list()[0]
[7]:
type(new_agent)
[7]:
edsl.agents.agent.Agent
[8]:
new_agent
[8]:
| key | value | |
|---|---|---|
| 0 | traits:persona | You are a sophomore at a community college in upstate New York. |
| 1 | traits:year | sophomore |
| 2 | traits:school | community college |
| 3 | traits:major | biology |
| 4 | traits:state | New York |
| 5 | traits:career_interests | I'm really interested in pursuing a career in the biological sciences. Since I'm majoring in biology, I'm exploring options like working in environmental conservation or possibly going into research. I'm also considering the healthcare field, as I've always been fascinated by how the human body works and how we can improve health outcomes. I still have some time to decide, so I'm keeping my options open while I learn more about each path. |
| 6 | traits:attend_grad_school | Undecided |
Updating an existing agent
Here we use the add_traits() method to add the agent’s answers to its existing traits:
[9]:
career_interests = results.select("career_interests").to_list()[0] # there is only 1 answer
career_interests
[9]:
"I'm really interested in pursuing a career in the biological sciences. Since I'm majoring in biology, I'm exploring options like working in environmental conservation or possibly going into research. I'm also considering the healthcare field, as I've always been fascinated by how the human body works and how we can improve health outcomes. I still have some time to decide, so I'm keeping my options open while I learn more about each path."
[10]:
attend_grad_school = results.select("attend_grad_school").to_list()[0]
attend_grad_school
[10]:
'Undecided'
[11]:
agent.add_trait({
"career_interests": career_interests,
"attend_grad_school": attend_grad_school
})
agent
[11]:
| key | value | |
|---|---|---|
| 0 | traits:persona | You are a sophomore at a community college in upstate New York. |
| 1 | traits:year | sophomore |
| 2 | traits:school | community college |
| 3 | traits:major | biology |
| 4 | traits:state | New York |
| 5 | name | college student |
Updating an agent list
Here we update a list of agents all at once:
[12]:
from edsl import AgentList
[13]:
agents = AgentList(
Agent(traits = {"persona":p}) for p in ["School principal", "Nurse"]
)
[14]:
q1 = QuestionFreeText(
question_name = "ideal_vacation",
question_text = "Describe your ideal vacation."
)
q2 = QuestionMultipleChoice(
question_name = "commute",
question_text = "How do you typically commute to work?",
question_options = ["Car", "Bus", "Train", "Bike", "Walk", "Other"]
)
survey = Survey([q1, q2])
[15]:
results = survey.by(agents).run()
Job Status 🦜
Completed (2 completed, 0 failed)
Job Links
Content
Identifiers
Results UUID:
ec2e8f9a...4bb1
Use Results.pull(uuid) to fetch results.
Job UUID:
6d6e9abd...f140
Use Jobs.pull(uuid) to fetch job.
Model Costs ($0.0038 / 0.00 credits total)
| Service | Model | Input Tokens | Input Cost | Output Tokens | Output Cost | Total Cost | Total Credits |
|---|---|---|---|---|---|---|---|
| openai | gpt-4o | 282 | $0.0008 | 294 | $0.0030 | $0.0038 | 0.00 |
| Totals | 282 | $0.0008 | 294 | $0.0030 | $0.0038 | 0.00 | |
You can obtain the total credit cost by multiplying the total USD cost by 100. A lower credit cost indicates that you saved money by retrieving responses from the universal remote cache.
[16]:
results.select("persona", "ideal_vacation", "commute")
[16]:
| agent.persona | answer.ideal_vacation | answer.commute | |
|---|---|---|---|
| 0 | School principal | As a school principal, my ideal vacation would be a blend of relaxation and enrichment. I'd love to escape to a quiet coastal town where I can unwind with a good book, ideally something educational or related to leadership. Mornings would be for long walks on the beach, afternoons for exploring local museums or historical sites, and evenings for enjoying local cuisine. It would be a chance to recharge and gather new ideas to bring back to the school. A little time for reflection and planning for the upcoming school year would also be part of the itinerary. | Car |
| 1 | Nurse | As a nurse, my ideal vacation would be a mix of relaxation and adventure. I'd love to spend some time at a serene beach resort where I can unwind and soak up the sun, perhaps with a good book in hand. It's important to recharge and escape the hustle and bustle of a hospital environment. After a few days of relaxation, I'd enjoy exploring local culture and cuisine, perhaps visiting markets or taking a cooking class. A bit of hiking or a nature excursion would also be perfect to stay active and enjoy the beauty of nature. Overall, a balance of rest and exploration would make for a perfect getaway. | Car |
[17]:
new_agents = results.select("persona", "ideal_vacation", "commute").to_agent_list()
[18]:
type(new_agents)
[18]:
edsl.agents.agent_list.AgentList
[19]:
new_agents
[19]:
AgentList agents: 2;
| persona | ideal_vacation | commute | |
|---|---|---|---|
| 0 | School principal | As a school principal, my ideal vacation would be a blend of relaxation and enrichment. I'd love to escape to a quiet coastal town where I can unwind with a good book, ideally something educational or related to leadership. Mornings would be for long walks on the beach, afternoons for exploring local museums or historical sites, and evenings for enjoying local cuisine. It would be a chance to recharge and gather new ideas to bring back to the school. A little time for reflection and planning for the upcoming school year would also be part of the itinerary. | Car |
| 1 | Nurse | As a nurse, my ideal vacation would be a mix of relaxation and adventure. I'd love to spend some time at a serene beach resort where I can unwind and soak up the sun, perhaps with a good book in hand. It's important to recharge and escape the hustle and bustle of a hospital environment. After a few days of relaxation, I'd enjoy exploring local culture and cuisine, perhaps visiting markets or taking a cooking class. A bit of hiking or a nature excursion would also be perfect to stay active and enjoy the beauty of nature. Overall, a balance of rest and exploration would make for a perfect getaway. | Car |
Posting to Coop
Here we show how to post content to Coop, an integrated platform for creating and sharing AI research. Learn more about how it works.
[ ]:
from edsl import Notebook
nb = Notebook(path = "updating_agents.ipynb")
nb.push(
description = "Updating agent traits with new answers",
alias = "updating-agents-notebook",
visibility = "public"
)