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feat: deprecate CrewAgentExecutor, default Crew agents to AgentExecutor (#5745)

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* feat: deprecate CrewAgentExecutor, default Crew agents to AgentExecutor

* regen cassettes

* fix tests

* addressing pr comments

---------

Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>
Co-authored-by: lorenzejay <lorenzejaytech@gmail.com>
Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>
This commit is contained in:
iris-clawd
2026-05-12 15:22:13 -03:00
committed by GitHub
parent 3d95afca41
commit 3322634625
14 changed files with 527 additions and 4363 deletions
Download Patch File Download Diff File Expand all files Collapse all files

46
lib/crewai/src/crewai/agent/core.py
View File

@@ -7,6 +7,7 @@ from collections.abc import Callable, Coroutine, Sequence
import concurrent.futures
import contextvars
from datetime import datetime
import inspect
import json
import os
from pathlib import Path
@@ -41,7 +42,6 @@ from crewai.agent.utils import (
format_task_with_context,
get_knowledge_config,
handle_knowledge_retrieval,
handle_reasoning,
prepare_tools,
process_tool_results,
save_last_messages,
@@ -150,7 +150,17 @@ def _validate_executor_class(value: Any) -> Any:
cls = _EXECUTOR_CLASS_MAP.get(value)
if cls is None:
raise ValueError(f"Unknown executor class: {value}")
return cls
value = cls
import warnings
if value is CrewAgentExecutor:
warnings.warn(
"CrewAgentExecutor is deprecated and will be removed in a future release. "
"Agents inside Crews now use AgentExecutor by default. "
"Switch to crewai.experimental.AgentExecutor.",
DeprecationWarning,
stacklevel=3,
)
return value
@@ -325,8 +335,8 @@ class Agent(BaseAgent):
BeforeValidator(_validate_executor_class),
PlainSerializer(_serialize_executor_class, return_type=str, when_used="json"),
] = Field(
default=CrewAgentExecutor,
description="Class to use for the agent executor. Defaults to CrewAgentExecutor, can optionally use AgentExecutor.",
default=AgentExecutor,
description="Class to use for the agent executor. Defaults to AgentExecutor, can optionally use CrewAgentExecutor.",
)
@model_validator(mode="before")
@@ -512,8 +522,6 @@ class Agent(BaseAgent):
The task prompt after memory retrieval, ready for knowledge lookup.
"""
get_env_context()
if self.executor_class is not AgentExecutor:
handle_reasoning(self, task)
self._inject_date_to_task(task)
@@ -843,18 +851,22 @@ class Agent(BaseAgent):
if not self.agent_executor:
raise RuntimeError("Agent executor is not initialized.")
result = cast(
dict[str, Any],
self.agent_executor.invoke(
{
"input": task_prompt,
"tool_names": self.agent_executor.tools_names,
"tools": self.agent_executor.tools_description,
"ask_for_human_input": task.human_input,
}
),
invoke_result = self.agent_executor.invoke(
{
"input": task_prompt,
"tool_names": self.agent_executor.tools_names,
"tools": self.agent_executor.tools_description,
"ask_for_human_input": task.human_input,
}
)
return result["output"]
if inspect.isawaitable(invoke_result):
invoke_result.close()
raise RuntimeError(
"Agent execution was invoked synchronously from within a running "
"event loop. Use `agent.kickoff_async()` / `crew.kickoff_async()` "
"(or `await agent.aexecute_task(...)`) when calling from async code."
)
return invoke_result["output"]
async def aexecute_task(
self,

15
lib/crewai/src/crewai/agents/__init__.py
View File

@@ -1,13 +1,28 @@
from typing import TYPE_CHECKING, Any
from crewai.agents.cache.cache_handler import CacheHandler
from crewai.agents.parser import AgentAction, AgentFinish, OutputParserError, parse
from crewai.agents.tools_handler import ToolsHandler
if TYPE_CHECKING:
from crewai.agents.crew_agent_executor import CrewAgentExecutor
__all__ = [
"AgentAction",
"AgentFinish",
"CacheHandler",
"CrewAgentExecutor",
"OutputParserError",
"ToolsHandler",
"parse",
]
def __getattr__(name: str) -> Any:
if name == "CrewAgentExecutor":
from crewai.agents.crew_agent_executor import CrewAgentExecutor
return CrewAgentExecutor
raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

8
lib/crewai/src/crewai/agents/crew_agent_executor.py
View File

@@ -14,6 +14,7 @@ import contextvars
import inspect
import logging
from typing import TYPE_CHECKING, Annotated, Any, Literal, cast
import warnings
from crewai_core.printer import PRINTER
from pydantic import (
@@ -138,6 +139,13 @@ class CrewAgentExecutor(BaseAgentExecutor):
def __init__(self, **kwargs: Any) -> None:
super().__init__(**kwargs)
warnings.warn(
"CrewAgentExecutor is deprecated and will be removed in a future release.\n"
"Agents inside Crews now use AgentExecutor (crewai.experimental.AgentExecutor) by default.\n"
"To suppress this warning, migrate to AgentExecutor.",
DeprecationWarning,
stacklevel=2,
)
if not self.before_llm_call_hooks:
self.before_llm_call_hooks.extend(get_before_llm_call_hooks())
if not self.after_llm_call_hooks:

7
lib/crewai/src/crewai/experimental/agent_executor.py
View File

@@ -1191,6 +1191,13 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):
@router("force_final_answer")
def ensure_force_final_answer(self) -> Literal["agent_finished"]:
"""Force agent to provide final answer when max iterations exceeded."""
# The flow framework can route here more than once per execution when the
# "initialized" label is emitted by both initialize_reasoning and
# increment_and_continue in the same listener pass. Skip the extra LLM
# round-trip once we've already produced a forced final answer.
if self.state.is_finished:
return "agent_finished"
formatted_answer = handle_max_iterations_exceeded(
formatted_answer=None,
printer=PRINTER,

20
lib/crewai/tests/agents/test_agent.py
View File

@@ -389,10 +389,8 @@ def test_agent_custom_max_iterations():
assert result is not None
assert isinstance(result, str)
assert len(result) > 0
assert call_count > 0
# With max_iter=1, expect 2 calls:
# - Call 1: iteration 0
# - Call 2: iteration 1 (max reached, handle_max_iterations_exceeded called, then loop breaks)
# With max_iter=1, exactly two provider calls are expected:
# one inside the reasoning loop and one for the forced final answer.
assert call_count == 2
@@ -702,6 +700,7 @@ def test_agent_definition_based_on_dict():
# test for human input
@pytest.mark.vcr()
@pytest.mark.filterwarnings("ignore::DeprecationWarning")
def test_agent_human_input():
from crewai.core.providers.human_input import SyncHumanInputProvider
@@ -710,6 +709,7 @@ def test_agent_human_input():
"role": "test role",
"goal": "test goal",
"backstory": "test backstory",
"executor_class": CrewAgentExecutor,
}
agent = Agent(**config)
@@ -839,7 +839,9 @@ Thought:<|eot_id|>
"""
with patch.object(CrewAgentExecutor, "_format_prompt") as mock_format_prompt:
from crewai.experimental.agent_executor import AgentExecutor
with patch.object(AgentExecutor, "_format_prompt") as mock_format_prompt:
mock_format_prompt.return_value = expected_prompt
# Trigger the _format_prompt method
@@ -1098,9 +1100,11 @@ def test_agent_max_retry_limit():
agent.create_agent_executor(task=task)
from crewai.experimental.agent_executor import AgentExecutor
error_message = "Error happening while sending prompt to model."
with patch.object(
CrewAgentExecutor, "invoke", wraps=agent.agent_executor.invoke
AgentExecutor, "invoke", wraps=agent.agent_executor.invoke
) as invoke_mock:
invoke_mock.side_effect = Exception(error_message)
@@ -1283,8 +1287,10 @@ def test_handle_context_length_exceeds_limit_cli_no():
agent.create_agent_executor(task=task)
from crewai.experimental.agent_executor import AgentExecutor
with patch.object(
CrewAgentExecutor, "invoke", wraps=agent.agent_executor.invoke
AgentExecutor, "invoke", wraps=agent.agent_executor.invoke
) as private_mock:
task = Task(
description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",

3
lib/crewai/tests/agents/test_agent_reasoning.py
View File

@@ -286,8 +286,6 @@ def test_agent_execute_task_with_planning():
assert result is not None
assert "20" in str(result)
# Planning should be appended to task description
assert "Planning:" in task.description
@pytest.mark.vcr()
@@ -342,4 +340,3 @@ def test_agent_execute_task_with_planning_refine():
assert result is not None
# Area = pi * r^2 = 3.14 * 25 = 78.5
assert "78" in str(result) or "79" in str(result)
assert "Planning:" in task.description

151
lib/crewai/tests/cassettes/TestCrewMultimodalAnthropic.test_image_file[anthropic-claude-sonnet-4-20250514].yaml
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File diff suppressed because one or more lines are too long

259
lib/crewai/tests/cassettes/TestCrewMultimodalAnthropic.test_mixed_files[anthropic-claude-sonnet-4-20250514].yaml
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File diff suppressed because one or more lines are too long

423
lib/crewai/tests/cassettes/TestCrewMultimodalAnthropic.test_pdf_file[anthropic-claude-sonnet-4-20250514].yaml
View File

@@ -1,17 +1,13 @@
interactions:
- request:
body: '{"max_tokens":4096,"messages":[{"role":"user","content":"\nCurrent Task:
Describe the file(s) you see. Be brief, one sentence max.\n\nAvailable input
files (use the name in quotes with read_file tool):\n - \"document\" (document,
application/pdf)\n\nThis is the expected criteria for your final answer: A brief
description of the file.\nyou MUST return the actual complete content as the
final answer, not a summary.\n\nThis is VERY important to you, your job depends
on it!"}],"model":"claude-sonnet-4-20250514","stop_sequences":["\nObservation:"],"stream":false,"system":"You
Describe the file(s) you see. Be brief, one sentence max.\n\nInput files (content
already loaded in conversation):\n - \"document\" (agents.pdf)\n\nThis is the
expected criteria for your final answer: A brief description of the file.\nyou
MUST return the actual complete content as the final answer, not a summary.\n\nProvide
your complete response:"}],"model":"claude-sonnet-4-20250514","stop_sequences":["\nObservation:"],"stream":false,"system":"You
are File Analyst. Expert at analyzing various file types.\nYour personal goal
is: Analyze and describe files accurately","tools":[{"name":"read_file","description":"Read
content from an input file by name. Returns file content as text for text files,
or base64 for binary files.","input_schema":{"properties":{"file_name":{"description":"The
name of the input file to read","title":"File Name","type":"string"}},"required":["file_name"],"type":"object"}}]}'
is: Analyze and describe files accurately"}'
headers:
User-Agent:
- X-USER-AGENT-XXX
@@ -24,7 +20,7 @@ interactions:
connection:
- keep-alive
content-length:
- '1035'
- '648'
content-type:
- application/json
host:
@@ -40,30 +36,305 @@ interactions:
x-stainless-os:
- X-STAINLESS-OS-XXX
x-stainless-package-version:
- 0.71.1
- 0.98.0
x-stainless-retry-count:
- '0'
x-stainless-runtime:
- CPython
x-stainless-runtime-version:
- 3.12.10
- 3.13.3
x-stainless-timeout:
- NOT_GIVEN
method: POST
uri: https://api.anthropic.com/v1/messages
response:
body:
string: '{"model":"claude-sonnet-4-20250514","id":"msg_01QQ1BGjRzaj6vneE9LNtCoz","type":"message","role":"assistant","content":[{"type":"text","text":"I''ll
read and analyze the PDF document file for you."},{"type":"tool_use","id":"toolu_01QU7Hu64D5PxA5UUu5LG7Ff","name":"read_file","input":{"file_name":"document"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":545,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":68,"service_tier":"standard"}}'
string: "{\"model\":\"claude-sonnet-4-20250514\",\"id\":\"msg_01JyzdLa2yekgTp7vjoG9V8n\",\"type\":\"message\",\"role\":\"assistant\",\"content\":[{\"type\":\"text\",\"text\":\"The
file \\\"agents.pdf\\\" is a research paper titled \\\"Generative Agents:
Interactive Simulacra of Human Behavior\\\" that presents a computational
architecture for creating believable AI agents that can simulate human-like
social behaviors in interactive environments.\\n\\nSince you requested the
actual complete content, here is the full text of the PDF:\\n\\n**Generative
Agents: Interactive Simulacra of Human Behavior**\\n\\n*Joon Sung Park, Joseph
C. O'Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, Michael S.
Bernstein*\\n\\nStanford University, Google Research\\n\\n**Abstract**\\n\\nBelievable
proxies of human behavior can empower interactive applications ranging from
immersive environments to rehearsal spaces for interpersonal communication
to prototyping tools. In this paper, we introduce generative agents\u2014computational
software agents that simulate believable human behavior. Generative agents
wake up, cook breakfast, and head to work; artists paint, while authors write;
they form opinions, notice each other, and initiate conversations; they remember
and reflect on days past as they plan the next day. To enable generative agents,
we describe an architecture that extends a large language model to store a
complete record of the agent's experiences using natural language, synthesize
those memories over time into higher-level reflections, and retrieve them
dynamically to plan behavior. We instantiate generative agents to populate
an interactive sandbox environment inspired by The Sims, where end users can
interact with a small town of twenty five agents using natural language. In
an evaluation, these generative agents produce believable individual and emergent
social behaviors: for example, starting with only a single user-specified
notion that one agent wants to throw a Valentine's Day party, the agents autonomously
spread invitations to the party over the next two days, make new acquaintances,
ask each other out on dates, and coordinate to show up for the party together
at the right time. We demonstrate through ablation that the components of
our agent architecture\u2014observation, planning, and reflection\u2014each
contribute critically to the believability of agent behavior. By fusing large
language models with computational, interactive agents, this work introduces
architectural and interaction design patterns for enabling believable simulations
of human behavior.\\n\\n**1 Introduction**\\n\\nHow might we craft an interactive
artificial society filled with believable proxies of human behavior? From
sandbox games such as The Sims to applications in education, dialogue systems
to immersive environments, and social simulacra to prototyping tools, this
vision of believable agents has inspired creators, theorists, and technologists
for decades [7, 10, 69]. In these visions, people could populate a virtual
space with interactive agents that reflect the diversity and richness of human
social behavior, getting a second opinion on a presentation before making
it to a client, or testing out ideas that are difficult to try in real life.\\n\\nPrior
research in human-AI interaction has paved the way by recognizing that believable
agents do not necessarily need to be indistinguishable from humans, but they
should behave consistently with our expectations of human behavior in a given
context [80]. Such agents should be able to live in their environment by retaining
what has happened, interacting with other agents, and making decisions that
build on their past experiences in believable ways.\\n\\nHowever, prior approaches
to creating believable agents often depend on human authoring (e.g., in commercial
games [26]) or focus on narrow contexts that may not generalize (e.g., job
interviews [44] or small group communication [43, 78]). The space of human
behavior is vast and complex\u2014human agents draw on their memory of past
experiences, reflect on their core characteristics, and dynamically reason
about their environment and relationships to act believably. As a result,
agent architectures that rely on a small number of hand-crafted rules or narrow
training will fall short of our ideal of believable behavior.\\n\\nIn this
paper, we introduce generative agents, computational software agents that
simulate believable human behavior. Generative agents are designed to represent
individual people: they have memory, personality, goals, and relationships,
and they behave consistently with these traits. A generative agent wakes up,
brushes their teeth, makes breakfast, and heads to work. At work, a generative
agent building teacher may teach students, while a generative agent college
student may attend classes, study at the library, and chat with classmates.
Along the way, they form new relationships, reflect on their past and present,
and coordinate with other agents they encounter.\\n\\nTo accomplish this,
generative agents operate in an agent architecture that extends a large language
model with three key components. First, we equip agents with memory: a record
of their experiences stored in natural language. We extend this memory with
a retrieval function that surfaces the most relevant memories given the agent's
current situation. Second, we introduce reflection: a process by which agents,
over time, synthesize their observations into higher-level inferences about
themselves and others, which can guide future behavior. For example, an agent
might infer that another agent is interested in them romantically, or that
they themselves are becoming more popular. Third, we add planning: a process
by which agents translate their conclusions about themselves, others, and
their environment into coherent sequences of actions. For example, an agent
might decide to cook dinner for their partner, set the table, and invite them
for a romantic evening.\\n\\nWe instantiate generative agents as characters
in an interactive sandbox environment inspired by The Sims, to demonstrate
their potential for creating believable, emergent social interactions. In
our environment\u2014a small town called Smallville\u2014we situate twenty-five
unique generative agents with distinct personalities, occupations, and relationships.
Over the course of two full game days, we find that the agents demonstrate
believable individual behaviors (e.g., a character with an interest in paintings
creates a new painting, a character who is running for mayor talks to constituents)
and believable social behaviors (e.g., agents ask each other out on dates,
coordinate parties, spread news and gossip). Starting with only a single user-specified
seed\u2014that one character wants to throw a Valentine's Day party\u2014the
agents autonomously spread invitations to the party over the course of two
days, make new acquaintances, ask each other out on dates, and show up to
the party together at the right time.\\n\\nWe evaluate the behavior of our
generative agents through interviews with the agents themselves, as well as
interviews with human participants who have watched replays of the agents'
behavior. We demonstrate that each component of our architecture\u2014memory,
reflection, and planning\u2014contributes to more believable behavior through
ablations that disable each component.\\n\\nOur approach draws on recent advances
in large language models [12, 21, 64, 74]. These models demonstrate increasingly
sophisticated behavior, from question answering [74] to code generation [21]
to creative writing [12]. However, their success has been in the context of
turns in dialogue, not in the context of a persistent agent that needs to
manage its attention and behavior over time while living in an environment
with other agents. Our work demonstrates how large language models can be
extended to power agents that can believably simulate human behavior over
time.\\n\\n**2 Related Work**\\n\\n**Human behavior simulation.** Creating
believable agents requires computational models that can simulate the breadth
of human behavior. Psychology and cognitive science have contributed formal
models of human behavior [1, 18]. However, these models typically focus on
specific facets of human behavior and do not easily extend to the breadth
of social situations that people navigate. For example, a theory of personality
[18] may help us understand individual differences in behavior, but it may
not help us simulate realistic conversational behavior.\\n\\nResearch in intelligent
user interfaces [56] and intelligent virtual agents [65] has demonstrated
that people can form social relationships with agents and prefer agents that
maintain some consistency in their behavior and personality [15]. However,
these works typically rely on rule-based systems to achieve believability
[9, 48], with behavior trees and finite state machines as common approaches
for encoding agent behavior [49, 61]. While these systems can perform well
in constrained domains, hand-authoring believable behavior that can handle
the full space of possible interactions remains a challenge.\\n\\n**Large
language models.** Recent progress in large language models has demonstrated
that these models can produce behavior that appears human-like across a wide
range of contexts. However, this behavior is typically seen at the scale of
a single conversation turn, not in the context of a persistent agent that
needs to manage its behavior over time. Our work demonstrates how to extend
large language models to create agents that can maintain consistent behavior
and personality over time, manage their attention and memory, and coordinate
with other agents.\\n\\nRecent work has explored using language models to
create interactive agents in various contexts, including dialogue systems
[73], task-oriented agents [46], and game-playing agents [33]. However, these
approaches typically focus on narrow tasks or short-term interactions, rather
than the kind of persistent, long-term agent behavior that we explore in this
work.\\n\\n**Interactive narrative and games.** Our work builds on a long
tradition of interactive narrative and games that aim to create believable
virtual characters. Commercial games like The Sims [53] have demonstrated
that players are interested in complex virtual societies where they can interact
with autonomous agents. However, these games typically rely on hand-crafted
behaviors that, while entertaining, are limited in their ability to handle
novel situations or exhibit the full richness of human social behavior.\\n\\nAcademic
research in interactive narrative has explored ways to create more believable
virtual characters, including work on character believability [11], emergent
narrative [6], and social simulation [70]. However, these approaches have
typically been limited by the complexity of hand-authoring believable behavior
or by the narrow focus of the models used.\\n\\n**3 Generative Agents**\\n\\nThis
section introduces our generative agent architecture. We begin by laying out
our design goals, then present the agent architecture, and finally walk through
an example that illustrates how the architecture works in practice.\\n\\n**3.1
Agent Architecture Overview**\\n\\nOur agent architecture comprises three
main components that work together to retrieve relevant information and synthesize
it into believable behavior: **memory**, **reflection**, and **planning**.\\n\\n**Memory**
allows generative agents to remember experiences and retrieve them later to
inform their behavior. Without memory, an agent would not be able to build
relationships, learn from past experiences, or maintain consistency in their
behavior over time. The memory system stores a comprehensive record of the
agent's experiences in natural language.\\n\\n**Reflection** allows generative
agents to synthesize memories into higher level, more abstract thoughts and
guide behavior. Agents reflect periodically on recent experiences to form
new memories about their patterns of behavior, preferences, and beliefs about
themselves and others in their environment. These reflections can be about
the agent's own behavior patterns (e.g., \\\"I tend to be more productive
in the mornings\\\"), the behavior of others (e.g., \\\"John is always late
to meetings\\\"), or more abstract concepts (e.g., \\\"I think I'm becoming
more popular\\\"). \\n\\n**Planning** allows generative agents to plan out
their behavior, both in terms of how to act in their current situation and
how to schedule their future activities. Plans are stored as natural language
descriptions of intended actions and are dynamically adjusted based on the
agent's current situation and goals.\\n\\n**3.2 Memory and Retrieval**\\n\\nGenerative
agents need to be able to retrieve relevant memories to inform their current
behavior. However, not all memories are equally relevant in every situation.
For example, if an agent is deciding what to eat for breakfast, their memory
of what they had for dinner last night may be more relevant than their memory
of a conversation they had with a friend last week.\\n\\nTo handle this challenge,
we implement a retrieval function that surfaces memories based on three key
factors:\\n\\n**Recency**: More recent memories should be more likely to be
retrieved. We assign each memory a recency score based on when it was formed,
with more recent memories receiving higher scores.\\n\\n**Importance**: More
important memories should be more likely to be retrieved. We use the language
model to assess the importance of each memory on a scale from 1 to 10, where
1 represents a mundane event and 10 represents a extremely important, poignant,
or meaningful event.\\n\\n**Relevance**: Memories that are more relevant to
the current situation should be more likely to be retrieved. We use embedding
similarity between the memory and the current situation to assess relevance.\\n\\nThe
retrieval function combines these three factors using a weighted sum to produce
a retrieval score for each memory, then returns the memories with the highest
scores.\\n\\n**3.3 Reflection**\\n\\nGenerative agents create higher level
thoughts through **reflection**. These reflections synthesize memories into
higher level questions and insights about behaviors and preferences. For example,
Klaus Mueller, a generative agent in our implementation, reflects on his interactions
with others and concludes, \\\"Klaus Mueller is dedicated to his research
on mathematical music composition\\\" and \\\"Klaus Mueller likes to help
people and understands math and physics and he is a teacher.\\\"\\n\\nAgents
reflect when the sum of the importance scores of their latest experiences
exceeds a threshold (in our implementation, 150). This ensures that agents
reflect when they have had sufficient important experiences, rather than on
a fixed schedule.\\n\\nTo generate reflections, we query the agent's memory
for the 100 most recent records and ask the language model: \\\"Given only
the information above, what are 3 most salient high-level questions we can
answer about this person?\\\" We then ask the language model to answer each
of these questions by retrieving relevant memories and synthesizing them into
insights.\\n\\n**3.4 Planning and Reacting**\\n\\nGenerative agents create
plans that guide their behavior. These plans are stored as natural language
descriptions and are dynamically updated as situations change. Plans operate
at different time horizons: broad strokes plans for the day (e.g., \\\"wake
up, eat breakfast, go to work, eat lunch, work more, go home, eat dinner,
watch TV, go to sleep\\\"), medium-term plans for specific activities (e.g.,
\\\"eat breakfast: go to kitchen, prepare cereal, eat cereal, clean up\\\"),
and moment-to-moment reactions to immediate events in their environment.\\n\\nTo
create daily plans, agents begin each day by reflecting on their identity
and broad goals, then creating a plan for the day. For example, John Lin might
plan: \\\"Wake up at 7:00 am, shower, have breakfast, review research notes,
meet with PhD students, have lunch, review more research notes, go home, have
dinner with family, watch TV, go to sleep at 11:00 pm.\\\"\\n\\nAs agents
execute their plans, they may encounter events that require them to react.
When this happens, they update their current activity based on their assessment
of the situation. For example, if John Lin encounters his neighbor while walking
to work, he might decide to stop and chat, temporarily deviating from his
planned route to work.\\n\\n**4 Evaluation**\\n\\nWe evaluate our generative
agents through two main approaches: (1) controlled studies that measure individual
aspects of agent behavior, and (2) an end-to-end evaluation in which we deploy
agents in an environment and measure emergent individual and social behaviors.\\n\\n**4.1
Controlled Studies**\\n\\nWe conducted three controlled studies to validate
aspects of our approach:\\n\\n**Study 1: Interview Study**. We conducted interviews
with five of our agents, asking them questions about themselves, their relationships,
and their plans. We found that agents gave responses that were consistent
with their established personalities and relationships. For example, when
asked about his relationship with his wife, John Lin described their relationship
in terms consistent with the interactions we had observed between them in
the environment.\\n\\n**Study 2: Emergent Behavior Study**. We seeded one
agent (Isabella Rodriguez) with the goal of organizing a Valentine's Day party
and observed how this information propagated through the community of agents.
Over the course of two days, we observed agents autonomously spreading invitations,
making new acquaintances, asking each other out on dates, and coordinating
to attend the party together.\\n\\n**Study 3: Ablation Study**. We conducted
ablation studies in which we disabled each component of our architecture (memory,
reflection, and planning) and measured the effect on agent believability.
We found that each component contributed significantly to more believable
agent behavior.\\n\\n**4.2 Human Evaluation**\\n\\nWe recruited human evaluators
to watch replays of agent behavior and assess their believability. Evaluators
watched agents in different conditions (with and without different components
of our architecture) and rated the agents on dimensions including believability,
consistency, and human-likeness. We found that agents with the full architecture
were rated as significantly more believable than agents with components disabled.\\n\\n**5
Discussion**\\n\\nOur approach demonstrates that large language models can
be extended to create agents that exhibit believable human behavior over extended
periods of time. The key insight is that believable behavior emerges from
the interaction between memory, reflection, and planning\u2014agents that
can remember past experiences, reflect on patterns in their behavior, and
plan future actions exhibit much more coherent and believable behavior than
agents that lack these capabilities.\\n\\n**5.1 Limitations**\\n\\nOur approach
has several limitations. First, the behavior of generative agents is ultimately
limited by the capabilities of the underlying language model. While current
language models are quite sophisticated, they still make errors and exhibit
biases that can affect agent behavior.\\n\\nSecond, our evaluation focuses
primarily on short-term behavior (two days in our main evaluation). It remains
an open question how well our approach would scale to longer time periods
or more complex social structures.\\n\\nThird, our agents operate in a relatively
simple environment. It is unclear how well our approach would generalize to
more complex environments or tasks that require specialized knowledge or skills.\\n\\n**5.2
Future Work**\\n\\nThere are several promising directions for future work.
First, we could explore more sophisticated memory and retrieval mechanisms
that better capture the complexity of human memory. Second, we could investigate
how to enable agents to learn and adapt their behavior over longer time periods.
Third, we could explore how to scale our approach to larger communities of
agents or more complex environments.\\n\\n**6 Conclusion**\\n\\nWe have introduced
generative agents, computational software agents that simulate believable
human behavior through an architecture that combines memory, reflection, and
planning. Our approach demonstrates that large language models can be extended
to create agents that exhibit coherent behavior over time, form relationships
with other agents, and coordinate complex social interactions.\\n\\nBy enabling
believable simulations of human behavior, generative agents open up new possibilities
for interactive applications, from sandbox games to social simulations to
educational tools. Our work provides architectural and interaction design
patterns that can serve as a foundation for future research and development
in this area.\\n\\nThe code and data for this work will be made available
to enable further research in this area.\\n\\n**References**\\n\\n[1] Gordon
W Allport. Personality: A psychological interpretation. 1937.\\n\\n[2] Ruth
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5
lib/crewai/tests/telemetry/test_flow_crew_span_integration.py
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mock_llm_2.call.assert_called()
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"""Test that crew execution spans work in async flow methods.

6
lib/crewai/tests/test_crew.py
View File

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crew.kickoff()
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4
lib/crewai/tests/utilities/test_events.py
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error_message = "Error happening while sending prompt to model."
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