Agents

Agent objects are used to simulate survey responses for target audiences. They are created with specified traits, such as personas and relevant attributes for a survey, that are used together with language models to generate answers to questions.

Constructing an Agent

An Agent is created by passing a dictionary of traits for a language model to reference in answering questions. Traits can be anything that might be relevant to the questions the agent will be asked, and constructed with single values or textual narratives.

For example:

from edsl import Agent

agent = Agent(
    traits = {
        "persona": "You are an expert in machine learning.",
        "age": 45,
        "home_state": "Massachusetts"
    })

Note that traits= must be named explicitly in the construction, and the traits must use Python identifiers as keys (e.g., home_state but not home state or home-state).

Agent names

We can optionally give an agent a name when it is constructed:

agent = Agent(
    name = "Ada",
    traits = {
        "persona": "You are an expert in machine learning.",
        "age": 45,
        "home_state": "Massachusetts"
    })

If a name is not passed when the agent is created, an agent_name field is automatically added to the Results that are generated when a survey is run with the agent. This field is a unique identifier for the agent and can be used to filter or group results by agent.

Note that trying to create two agents with the same name or trying to use a key “name” in the traits will raise an error.

Agent lists

Agents can be created collectively and administered a survey together. This is useful for comparing responses across agents.

For example, here we create an a list of agents as an AgentList with different combinations of traits:

from edsl import AgentList, Agent

ages = [10, 20, 30, 40, 50]
locations = ["New York", "California", "Texas", "Florida", "Washington"]

agents = AgentList(
    Agent(traits = {"age": age, "location": location}) for age, location in zip(ages, locations)
)

This code will create a list of agents that can then be used in a survey.

Example code for running a survey with the agents:

from edsl import Survey

survey = Survey.example()

results = survey.by(agents).run()

This will generate a Results object that contains a Result for each agent’s responses to the survey questions. Learn more about working with results in the Results section.

Generating agents from data

An AgentList can be automatically generated from data stored in a list, a dictionary or a CSV file.

From a list

We can create a simple AgentList from a list using the from_list() method, which takes a single trait_name and a list of values for it and returns an agent for each value (each agent has a single trait):

from edsl import AgentList

agents = AgentList.from_list(trait_name="age", values=[10, 20, 30, 40])
agents

Output:

age
10
20
30
40

From a dictionary

We can create a more complex AgentList from a dictionary using the from_dict() method. It takes a dictionary with a key agent_list and a list of dictionaries, each of which must have a traits key with a dictionary of traits and an optional name key for the agent’s name:

from edsl import AgentList

data = {
    "agent_list": [
        {"name":"agent1", "traits":{"age": 10, "location": "New York"}},
        {"name":"agent2", "traits":{"age": 20, "location": "California"}},
        {"name":"agent3", "traits":{"age": 30, "location": "Texas"}},
        {"name":"agent4", "traits":{"age": 40, "location": "Florida"}},
        {"name":"agent5", "traits":{"age": 50, "location": "Washington"}}
    ]
}

agents = AgentList.from_dict(data)
agents

Output:

location	age
New York	10
California	20
Texas	30
Florida	40
Washington	50

From a CSV file

We can also create an AgentList from a CSV file using the from_csv() method. The CSV file must have a header row of Pythonic keys for the traits, and can optionally have a column “name” for the agent names:

from edsl import AgentList

# Creating a CSV file with agent data to use as an example
import pandas as pd

data = [
    {"name": "Alice", "age": 25, "city": "New York"},
    {"name": "Bob", "age": 30, "city": "San Francisco"},
    {"name": "Charlie", "age": 35, "city": "Chicago"}
]

df = pd.DataFrame(data)

df.to_csv("agent_data.csv", index=False)

# Creating an AgentList from the CSV file
agents = AgentList.from_csv("agent_data.csv")
agents

Output:

city	age
New York	25
San Francisco	30
Chicago	35

Dynamic traits function

Agents can also be created with a dynamic_traits_function parameter. This function can be used to generate traits dynamically based on the question being asked or the scenario in which the question is asked.

For example:

def dynamic_traits_function(question):
    if question.question_name == "age":
        return {"age": 10}
    elif question.question_name == "hair":
        return {"hair": "brown"}

a = Agent(dynamic_traits_function = dynamic_traits_function)

When the agent is asked a question about age, the agent will return an age of 10. When asked about hair, the agent will return “brown”. This can be useful for creating agents that can answer questions about different topics without including potentially irrelevant traits in the agent’s traits dictionary.

Note that the traits returned by the function are not added to the agent’s traits dictionary.

Agent direct-answering methods

Agents can also be created with a method that can answer a particular question type directly:

from edsl import Agent

a = Agent()

def f(self, question, scenario): return "I am a direct answer."

a.add_direct_question_answering_method(f)
a.answer_question_directly(question = None, scenario = None)

Output:

I am a direct answer.

This can be useful for creating agents that can answer questions directly without needing to use a language model.

Giving an agent instructions

In addition to traits, agents can be given detailed instructions on how to answer questions.

For example:

from edsl import Agent

a = Agent(traits = {"age": 10}, instruction = "Answer in German.")
a.instruction

Output:

Answer in German.

When the agent is assigned to a survey, the special instruction will be added to the prompts for generating responses. The instructions are stored in the instruction field of the agent and can be accessed directly in results.

Learn more about how to use instructions in the Prompts section.

Controlling the presentation of the persona

The traits_presentation_template parameter can be used to create a narrative persona for an agent. This is a template string that can be rendered with the agent’s traits as variables.

For example:

a = Agent(
    traits = {'age': 22, 'hair': 'brown', 'gender': 'female'},
    traits_presentation_template = "I am a {{ age }} year-old {{ gender }} with {{ hair }} hair."
    )

a.agent_persona.render(primary_replacement = a.traits)

Output:

I am a 22 year-old female with brown hair.

Note that the trait keys must be valid Python identifiers (e.g., home_state but not home state or home-state). This can be handled by using a dictionary with string keys and values, for example:

from edsl import Agent

codebook = {'age': 'The age of the agent'}

a = Agent(
    traits = {'age': 22},
    codebook = codebook,
    traits_presentation_template = "{{ codebook['age'] }} is {{ age }}."
    )

a.agent_persona.render(primary_replacement = a.traits)

Output:

The age of the agent is 22.

Note that it can be helpful to include traits mentioned in the persona as independent keys and values in order to analyze survey results by those dimensions individually. For example, we may want the narrative to include a sentence about the agent’s age, but also be able to readily analyze or filter results by age.

The following code will include the agent’s age as a column of a table with any other selected components (e.g., agent name and all the answers):

results.select("agent.age", "agent.agent_name", "answer.*")

Note that the prefix “agent” can also be dropped. The following code is equivalent:

results.select("age", "agent_name", "answer.*")

We can filter the results by an agent’s traits:

results.filter("age == 22")

We can also call the filter() method on an agent list to filter agents by their traits:

middle_aged_agents = agents.filter("40 <= age <= 60")

Using agent traits in prompts

The traits of an agent can be used in the prompts of questions.

For example:

from edsl import Agent, QuestionFreeText

a = Agent(traits = {'first_name': 'John'})

q = QuestionFreeText(
    question_text = 'What is your last name, {{ agent.first_name }}?',
    question_name = "exmaple"
)

job = q.by(a)
job.prompts().select("user_prompt")

Output:

user_prompt
What is your last name, John?

Learn more about user and system prompts in the Prompts section.

Accessing agent traits

The traits of an agent can be accessed directly:

from edsl import Agent

a = Agent(traits = {'age': 22})
a.traits

Output:

{'age': 22}

The traits of an agent can also be accessed as attributes of the agent:

from edsl import Agent

a = Agent(traits = {'age': 22})
a.age

Output:

Simulating agent responses

When a survey is run, agents can be assigned to it using the by method, which can be chained with other components like scenarios and models:

from edsl import Agent, QuestionList, QuestionMultipleChoice, Survey

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"
    }
)

q1 = QuestionList(
    question_name = "favorite_courses",
    question_text = "What are the names of your 3 favorite courses?",
    max_list_items = 3
)

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])

results = survey.by(agent).run()

This will generate a Results object that contains a Result for each agent’s responses to the survey questions. We can select and inspect components of the results, such as the agent’s traits and their answers:

results.select("persona", "year", "school", "major", "state", "answer.*")

Output:

agent.persona	agent.year	agent.school	agent.major	agent.state	answer.favorite_courses	answer.attend_grad_school
You are a sophomore at a community college in upstate New York.	sophomore	community college	biology	New York	[‘General Biology I’, ‘Organic Chemistry’, ‘Environmental Science’]	Undecided

If multiple agents will be used with a survey, they are passed as a list in the same by call:

from edsl import Agent, AgentList

agents = AgentList([
    Agent(traits = {"major": "biology", "year": "sophomore"}),
    Agent(traits = {"major": "history", "year": "junior"}),
    Agent(traits = {"major": "mathematics", "year": "senior"}),
])

results = survey.by(agents).run() # using the same survey as above

results.select("major", "year", "answer.*")

Output:

agent.major	agent.year	answer.favorite_courses	answer.attend_grad_school
biology	sophomore	[‘Genetics’, ‘Ecology’, ‘Cell Biology’]	Undecided
history	junior	[‘Medieval Europe’, ‘The American Civil War’, ‘Ancient Civilizations’]	Undecided
mathematics	senior	[‘Abstract Algebra’, ‘Real Analysis’, ‘Topology’]	Undecided

If scenarios and/or models are also specified for a survey, each component type is added in a separate by call that can be chained in any order with the run method appended last:

results = survey.by(scenarios).by(agents).by(models).run() # example code - scenarios and models not defined here

Learn more about Scenarios, Language Models and Results.

Updating agents

Agents can be updated after they are created.

Changing a trait

Here we create an agent and then change one of its traits:

from edsl import Agent

a = Agent(traits = {"age": 22})
a.age = 23
a.age

Output:

Adding a trait

We can also add a new trait to an agent:

from edsl import Agent

a = Agent(traits = {"age": 22})

a.add_trait({"location": "California"})
a

Output:

key	value
traits:age	22
traits:location	California

Removing a trait

We can remove a trait from an agent:

from edsl import Agent

a = Agent(traits = {"age": 22, "location": "California"})

a.remove_trait("age")
a

Output:

key	value
traits:location	California

Using survey responses as new agent traits

After running a survey, we can use the responses to create new traits for an agent:

from edsl import Agent, QuestionMultipleChoice, Survey

a = Agent(traits = {"age": 22, "location": "California"})

q = QuestionMultipleChoice(
    question_name = "surfing",
    question_text = "How often do you go surfing?",
    question_options = ["Never", "Sometimes", "Often"]
)

survey = Survey([q])
results = survey.by(a).run()

a = results.select("age", "location", "surfing").to_agent_list() # create new agent with traits from results
a

Output:

location	surfing	age
California	Sometimes	22

Note that in the example above we simply replaced the original agent by selecting the first agent from the agent list that we created. This can be useful for creating agents that evolve over time based on their experiences or responses to surveys.

Here we use the same method to update multiple agents at once:

from edsl import Agent, QuestionMultipleChoice, Survey, AgentList

agents = AgentList([
    Agent(traits = {"age": 22, "location": "California"}),
    Agent(traits = {"age": 30, "location": "New York"}),
    Agent(traits = {"age": 40, "location": "Texas"}),
])

q = QuestionMultipleChoice(
    question_name = "surfing",
    question_text = "How often do you go surfing?",
    question_options = ["Never", "Sometimes", "Often"]
)

survey = Survey([q])
results = survey.by(agents).run()

agents = results.select("age", "location", "surfing").to_agent_list()
agents

Output:

location	surfing	age
California	Sometimes	22
New York	Never	30
Texas	Never	40

Agent class

An Agent is an AI agent that can reference a set of traits in answering questions.

class edsl.agents.Agent.Agent(traits: dict | None = None, name: str | None = None, codebook: dict | None = None, instruction: str | None = None, traits_presentation_template: str | None = None, dynamic_traits_function: Callable | None = None, dynamic_traits_function_source_code: str | None = None, dynamic_traits_function_name: str | None = None, answer_question_directly_source_code: str | None = None, answer_question_directly_function_name: str | None = None)[source]

Bases: Base

An class representing an agent that can answer questions.

Initialize a new instance of Agent.

Parameters:

traits – A dictionary of traits that the agent has. The keys need to be valid identifiers.
name – A name for the agent
codebook – A codebook mapping trait keys to trait descriptions.
instruction – Instructions for the agent in how to answer questions.
trait_presentation_template – A template for how to present the agent’s traits.
dynamic_traits_function – A function that returns a dictionary of traits.

The traits parameter is a dictionary of traits that the agent has. These traits are used to construct a prompt that is presented to the LLM. In the absence of a traits_presentation_template, the default is used. This is a template that is used to present the agent’s traits to the LLM. See edsl.prompts.library.agent_persona.AgentPersona for more information.

Example usage:

>>> a = Agent(traits = {"age": 10, "hair": "brown", "height": 5.5})
>>> a.traits
{'age': 10, 'hair': 'brown', 'height': 5.5}

These traits are used to construct a prompt that is presented to the LLM.

In the absence of a traits_presentation_template, the default is used.

>>> a = Agent(traits = {"age": 10}, traits_presentation_template = "I am a {{age}} year old.")
>>> repr(a.agent_persona)
'Prompt(text="""I am a {{age}} year old.""")'

When this is rendered for presentation to the LLM, it will replace the {{age}} with the actual age. it is also possible to use the codebook to provide a more human-readable description of the trait. Here is an example where we give a prefix to the age trait (namely the age):

>>> traits = {"age": 10, "hair": "brown", "height": 5.5}
>>> codebook = {'age': 'Their age is'}
>>> a = Agent(traits = traits, codebook = codebook, traits_presentation_template = "This agent is Dave. {{codebook['age']}} {{age}}")
>>> d = a.traits | {'codebook': a.codebook}
>>> a.agent_persona.render(d)
Prompt(text="""This agent is Dave. Their age is 10""")

Instructions

The agent can also have instructions. These are instructions that are given to the agent when answering questions.

>>> Agent.default_instruction
'You are answering questions as if you were a human. Do not break character.'

See see how these are used to actually construct the prompt that is presented to the LLM, see edsl.agents.Invigilator.InvigilatorBase.

add_direct_question_answering_method(method: Callable, validate_response: bool = False, translate_response: bool = False) → None[source]

Add a method to the agent that can answer a particular question type. https://docs.expectedparrot.com/en/latest/agents.html#agent-direct-answering-methods

Parameters:

method – A method that can answer a question directly.
validate_response – Whether to validate the response.
translate_response – Whether to translate the response.

Example usage:

>>> a = Agent()
>>> def f(self, question, scenario): return "I am a direct answer."
>>> a.add_direct_question_answering_method(f)
>>> a.answer_question_directly(question = None, scenario = None)
'I am a direct answer.'

add_trait(trait_name_or_dict: str, value: Any | None = None) → Agent[source]: Adds a trait to an agent and returns that agent >>> a = Agent(traits = {“age”: 10, “hair”: “brown”, “height”: 5.5}) >>> a.add_trait(“weight”, 150) Agent(traits = {‘age’: 10, ‘hair’: ‘brown’, ‘height’: 5.5, ‘weight’: 150})

property agent_persona: Prompt[source]: Return the agent persona template.

answer_question(*, question: QuestionBase, cache: Cache, scenario: Scenario | None = None, survey: Survey | None = None, model: LanguageModel | None = None, debug: bool = False, memory_plan: MemoryPlan | None = None, current_answers: dict | None = None, iteration: int = 0) → AgentResponseDict[source]

Answer a posed question.

Parameters:

question – The question to answer.
scenario – The scenario in which the question is asked.
model – The language model to use.
debug – Whether to run in debug mode.
memory_plan – The memory plan to use.
current_answers – The current answers.
iteration – The iteration number.

>>> a = Agent(traits = {})
>>> a.add_direct_question_answering_method(lambda self, question, scenario: "I am a direct answer.")
>>> from edsl.questions.QuestionFreeText import QuestionFreeText
>>> q = QuestionFreeText.example()
>>> a.answer_question(question = q, cache = False).answer
'I am a direct answer.'

This is a function where an agent returns an answer to a particular question. However, there are several different ways an agent can answer a question, so the actual functionality is delegated to an edsl.agents.InvigilatorBase: object.

answer_question_directly_function_name = ''[source]

async async_answer_question(*, question: QuestionBase, cache: Cache, scenario: Scenario | None = None, survey: Survey | None = None, model: LanguageModel | None = None, debug: bool = False, memory_plan: MemoryPlan | None = None, current_answers: dict | None = None, iteration: int = 0) → AgentResponseDict[source]

Answer a posed question.

Parameters:

question – The question to answer.
scenario – The scenario in which the question is asked.
model – The language model to use.
debug – Whether to run in debug mode.
memory_plan – The memory plan to use.
current_answers – The current answers.
iteration – The iteration number.

>>> a = Agent(traits = {})
>>> a.add_direct_question_answering_method(lambda self, question, scenario: "I am a direct answer.")
>>> from edsl.questions.QuestionFreeText import QuestionFreeText
>>> q = QuestionFreeText.example()
>>> a.answer_question(question = q, cache = False).answer
'I am a direct answer.'

This is a function where an agent returns an answer to a particular question. However, there are several different ways an agent can answer a question, so the actual functionality is delegated to an edsl.agents.InvigilatorBase: object.

code() → str[source]

Return the code for the agent.

Example usage:

>>> a = Agent(traits = {"age": 10, "hair": "brown", "height": 5.5})
>>> print(a.code())
from edsl.agents.Agent import Agent
agent = Agent(traits={'age': 10, 'hair': 'brown', 'height': 5.5})

create_invigilator(*, question: QuestionBase, cache: Cache, survey: 'Survey' | None = None, scenario: 'Scenario' | None = None, model: 'LanguageModel' | None = None, debug: bool = False, memory_plan: 'MemoryPlan' | None = None, current_answers: dict | None = None, iteration: int = 1, sidecar_model=None, raise_validation_errors: bool = True) → InvigilatorBase[source]

Create an Invigilator.

An invigilator is an object that is responsible for administering a question to an agent. There are several different types of invigilators, depending on the type of question and the agent. For example, there are invigilators for functional questions (i.e., question is of type edsl.questions.QuestionFunctional:), for direct questions, and for LLM questions.

>>> a = Agent(traits = {})
>>> a.create_invigilator(question = None, cache = False)
InvigilatorAI(...)

An invigator is an object that is responsible for administering a question to an agent and recording the responses.

default_instruction = 'You are answering questions as if you were a human. Do not break character.'[source]

duplicate()[source]

Return a duplicate of the agent.

>>> a = Agent(traits = {"age": 10, "hair": "brown", "height": 5.5}, codebook = {'age': 'Their age is'})
>>> a2 = a.duplicate()
>>> a2 == a
True
>>> id(a) == id(a2)
False
>>> def f(self, question, scenario): return "I am a direct answer."
>>> a.add_direct_question_answering_method(f)
>>> hasattr(a, "answer_question_directly")
True
>>> a2 = a.duplicate()
>>> a2.answer_question_directly(None, None)
'I am a direct answer.'

dynamic_traits_function_name = ''[source]

classmethod example(randomize: bool = False) → Agent[source]

Returns an example Agent instance.

Parameters:: randomize – If True, adds a random string to the value of an example key.

classmethod from_dict(agent_dict: dict[str, dict | bool]) → Agent[source]

Deserialize from a dictionary.

Example usage:

>>> Agent.from_dict({'name': "Steve", 'traits': {'age': 10, 'hair': 'brown', 'height': 5.5}})
Agent(name = """Steve""", traits = {'age': 10, 'hair': 'brown', 'height': 5.5})

has_dynamic_traits_function = False[source]

instruction[source]: ABC for something.

name[source]: ABC for something.

prompt() → str[source]

Return the prompt for the agent.

Example usage:

>>> a = Agent(traits = {"age": 10, "hair": "brown", "height": 5.5})
>>> a.prompt()
Prompt(text="""Your traits: {'age': 10, 'hair': 'brown', 'height': 5.5}""")

remove_direct_question_answering_method() → None[source]

Remove the direct question answering method.

Example usage:

>>> a = Agent()
>>> def f(self, question, scenario): return "I am a direct answer."
>>> a.add_direct_question_answering_method(f)
>>> a.remove_direct_question_answering_method()
>>> hasattr(a, "answer_question_directly")
False

remove_trait(trait: str) → Agent[source]

Remove a trait from the agent.

Example usage:

>>> a = Agent(traits = {"age": 10, "hair": "brown", "height": 5.5})
>>> a.remove_trait("age")
Agent(traits = {'hair': 'brown', 'height': 5.5})

rename(old_name_or_dict: str | dict, new_name: str | None = None) → Agent[source]

Rename a trait.

Example usage:

>>> a = Agent(traits = {"age": 10, "hair": "brown", "height": 5.5})
>>> newa = a.rename("age", "years")
>>> newa == Agent(traits = {'years': 10, 'hair': 'brown', 'height': 5.5})
True

>>> newa.rename({'years': 'smage'}) == Agent(traits = {'smage': 10, 'hair': 'brown', 'height': 5.5})
True

select(*traits: str) → Agent[source]

Selects agents with only the references traits

>>> a = Agent(traits = {"age": 10, "hair": "brown", "height": 5.5}, codebook = {'age': 'Their age is'})
>>> a
Agent(traits = {'age': 10, 'hair': 'brown', 'height': 5.5}, codebook = {'age': 'Their age is'})

>>> a.select("age", "height")
Agent(traits = {'age': 10, 'height': 5.5}, codebook = {'age': 'Their age is'})

>>> a.select("height")
Agent(traits = {'height': 5.5})

to_dict(add_edsl_version=True) → dict[str, dict | bool][source]

Serialize to a dictionary with EDSL info.

Example usage:

>>> a = Agent(name = "Steve", traits = {"age": 10, "hair": "brown", "height": 5.5})
>>> a.to_dict()
{'name': 'Steve', 'traits': {'age': 10, 'hair': 'brown', 'height': 5.5}, 'edsl_version': '...', 'edsl_class_name': 'Agent'}

property traits: dict[str, str][source]

An agent’s traits, which is a dictionary.

The agent could have a a dynamic traits function (dynamic_traits_function) that returns a dictionary of traits when called. This function can also take a question as an argument. If so, the dynamic traits function is called and the result is returned. Otherwise, the traits are returned.

Example:

>>> a = Agent(traits = {"age": 10, "hair": "brown", "height": 5.5})
>>> a.traits
{'age': 10, 'hair': 'brown', 'height': 5.5}

translate_traits(values_codebook: dict) → Agent[source]

Translate traits to a new codebook.

>>> a = Agent(traits = {"age": 10, "hair": 1, "height": 5.5})
>>> a.translate_traits({"hair": {1:"brown"}})
Agent(traits = {'age': 10, 'hair': 'brown', 'height': 5.5})

Parameters:: values_codebook – The new codebook.

class edsl.agents.Agent.AgentTraits(data: dict | None = None, name: str = None)[source]

Bases: Scenario

A class representing the traits of an agent.

AgentList class

A list of Agents

AgentList.__init__(data: list['Agent'] | None = None)[source]

Initialize a new AgentList.

Parameters:: data – A list of Agents.

classmethod AgentList.example(randomize: bool = False) → AgentList[source]

Returns an example AgentList instance.

Parameters:: randomize – If True, uses Agent’s randomize method.

classmethod AgentList.from_dict(data: dict) → AgentList[source]

Deserialize the dictionary back to an AgentList object.

Param:: data: A dictionary representing an AgentList.

>>> from edsl.agents.Agent import Agent
>>> al = AgentList([Agent.example(), Agent.example()])
>>> al2 = AgentList.from_dict(al.to_dict())
>>> al2 == al
True

classmethod AgentList.from_csv(file_path: str, name_field: str | None = None)[source]

Load AgentList from a CSV file.

>>> import csv
>>> import os
>>> with open('/tmp/agents.csv', 'w') as f:
...     writer = csv.writer(f)
...     _ = writer.writerow(['age', 'hair', 'height'])
...     _ = writer.writerow([22, 'brown', 5.5])
>>> al = AgentList.from_csv('/tmp/agents.csv')
>>> al
AgentList([Agent(traits = {'age': '22', 'hair': 'brown', 'height': '5.5'})])
>>> al = AgentList.from_csv('/tmp/agents.csv', name_field='hair')
>>> al
AgentList([Agent(name = """brown""", traits = {'age': '22', 'height': '5.5'})])
>>> os.remove('/tmp/agents.csv')

Parameters:

file_path – The path to the CSV file.
name_field – The name of the field to use as the agent name.

classmethod AgentList.from_list(trait_name: str, values: List[Any])[source]

Create an AgentList from a list of values.

Parameters:

trait_name – The name of the trait.
values – A list of values.

>>> AgentList.from_list('age', [22, 23])
AgentList([Agent(traits = {'age': 22}), Agent(traits = {'age': 23})])

AgentList.to_dict(sorted=False, add_edsl_version=True)[source]: Serialize the AgentList to a dictionary.

AgentList.to_csv(filename: str | None = None, remove_prefix: bool = False, pretty_labels: dict | None = None) → FileStore[source]

Export the results to a FileStore instance containing CSV data.

Args:: filename: Optional filename for the CSV (defaults to “results.csv”) remove_prefix: Whether to remove the prefix from column names pretty_labels: Dictionary mapping original column names to pretty labels
Returns:: FileStore: Instance containing the CSV data