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João Moura
2024-09-16 06:16:48 -03:00
parent 8ca0821718
commit e5ff0363d9
95 changed files with 13936 additions and 17097 deletions
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poetry.lock generated
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@@ -390,17 +390,17 @@ lxml = ["lxml"]
[[package]] [[package]]
name = "boto3" name = "boto3"
version = "1.35.18" version = "1.35.19"
description = "The AWS SDK for Python" description = "The AWS SDK for Python"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
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{file = "boto3-1.35.18.tar.gz", hash = "sha256:fd130308f1f49d748a5fc63de92de79a995b51c79af3947ddde8815fcf0684fe"}, {file = "boto3-1.35.19.tar.gz", hash = "sha256:9979fe674780a0b7100eae9156d74ee374cd1638a9f61c77277e3ce712f3e496"},
] ]
[package.dependencies] [package.dependencies]
botocore = ">=1.35.18,<1.36.0" botocore = ">=1.35.19,<1.36.0"
jmespath = ">=0.7.1,<2.0.0" jmespath = ">=0.7.1,<2.0.0"
s3transfer = ">=0.10.0,<0.11.0" s3transfer = ">=0.10.0,<0.11.0"
@@ -409,13 +409,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
[[package]] [[package]]
name = "botocore" name = "botocore"
version = "1.35.18" version = "1.35.19"
description = "Low-level, data-driven core of boto 3." description = "Low-level, data-driven core of boto 3."
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
{file = "botocore-1.35.18-py3-none-any.whl", hash = "sha256:1027083aeb1fe74057273410fd768e018e22f85adfbd717b5a69f578f7812b80"}, {file = "botocore-1.35.19-py3-none-any.whl", hash = "sha256:c83f7f0cacfe7c19b109b363ebfa8736e570d24922f16ed371681f58ebab44a9"},
{file = "botocore-1.35.18.tar.gz", hash = "sha256:e59da8b91ab06683d2725b6cbbb0383b30c68a241c3c63363f4c5bff59b3c0c0"}, {file = "botocore-1.35.19.tar.gz", hash = "sha256:42d6d8db7250cbd7899f786f9861e02cab17dc238f64d6acb976098ed9809625"},
] ]
[package.dependencies] [package.dependencies]
@@ -833,13 +833,13 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
[[package]] [[package]]
name = "cohere" name = "cohere"
version = "5.9.1" version = "5.9.2"
description = "" description = ""
optional = false optional = false
python-versions = "<4.0,>=3.8" python-versions = "<4.0,>=3.8"
files = [ files = [
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{file = "cohere-5.9.1.tar.gz", hash = "sha256:de9a828b91481882bf554e94a61ccc52843a09fbc58f53b8e1fa940b17ce1dc9"}, {file = "cohere-5.9.2.tar.gz", hash = "sha256:1860c527b2a8a5593873a342b0bf572220b6db7966c0782076b3f2740ab3d94d"},
] ]
[package.dependencies] [package.dependencies]
@@ -1277,13 +1277,13 @@ test = ["pytest (>=6)"]
[[package]] [[package]]
name = "fastapi" name = "fastapi"
version = "0.114.1" version = "0.114.2"
description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production" description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
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{file = "fastapi-0.114.1.tar.gz", hash = "sha256:1d7bbbeabbaae0acb0c22f0ab0b040f642d3093ca3645f8c876b6f91391861d8"}, {file = "fastapi-0.114.2.tar.gz", hash = "sha256:0adb148b62edb09e8c6eeefa3ea934e8f276dabc038c5a82989ea6346050c3da"},
] ]
[package.dependencies] [package.dependencies]
@@ -2255,13 +2255,13 @@ files = [
[[package]] [[package]]
name = "identify" name = "identify"
version = "2.6.0" version = "2.6.1"
description = "File identification library for Python" description = "File identification library for Python"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
{file = "identify-2.6.0-py2.py3-none-any.whl", hash = "sha256:e79ae4406387a9d300332b5fd366d8994f1525e8414984e1a59e058b2eda2dd0"}, {file = "identify-2.6.1-py2.py3-none-any.whl", hash = "sha256:53863bcac7caf8d2ed85bd20312ea5dcfc22226800f6d6881f232d861db5a8f0"},
{file = "identify-2.6.0.tar.gz", hash = "sha256:cb171c685bdc31bcc4c1734698736a7d5b6c8bf2e0c15117f4d469c8640ae5cf"}, {file = "identify-2.6.1.tar.gz", hash = "sha256:91478c5fb7c3aac5ff7bf9b4344f803843dc586832d5f110d672b19aa1984c98"},
] ]
[package.extras] [package.extras]
@@ -2269,15 +2269,18 @@ license = ["ukkonen"]
[[package]] [[package]]
name = "idna" name = "idna"
version = "3.8" version = "3.10"
description = "Internationalized Domain Names in Applications (IDNA)" description = "Internationalized Domain Names in Applications (IDNA)"
optional = false optional = false
python-versions = ">=3.6" python-versions = ">=3.6"
files = [ files = [
{file = "idna-3.8-py3-none-any.whl", hash = "sha256:050b4e5baadcd44d760cedbd2b8e639f2ff89bbc7a5730fcc662954303377aac"}, {file = "idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"},
{file = "idna-3.8.tar.gz", hash = "sha256:d838c2c0ed6fced7693d5e8ab8e734d5f8fda53a039c0164afb0b82e771e3603"}, {file = "idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9"},
] ]
[package.extras]
all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
[[package]] [[package]]
name = "importlib-metadata" name = "importlib-metadata"
version = "8.4.0" version = "8.4.0"
@@ -2729,21 +2732,21 @@ langchain-community = ["langchain-community (>=0.2.4)"]
[[package]] [[package]]
name = "langchain-community" name = "langchain-community"
version = "0.2.16" version = "0.2.17"
description = "Community contributed LangChain integrations." description = "Community contributed LangChain integrations."
optional = false optional = false
python-versions = "<4.0,>=3.8.1" python-versions = "<4.0,>=3.8.1"
files = [ files = [
{file = "langchain_community-0.2.16-py3-none-any.whl", hash = "sha256:115e1419c176091d4e00240cb5a38612a249e70f213516b6cacae61a8794a868"}, {file = "langchain_community-0.2.17-py3-none-any.whl", hash = "sha256:d07c31b641e425fb8c3e7148ad6a62e1b54a9adac6e1173021a7dd3148266063"},
{file = "langchain_community-0.2.16.tar.gz", hash = "sha256:ab416b793a7aed1fa46ebaffd29993296b02286a99819eabc43be2ea8e41ae78"}, {file = "langchain_community-0.2.17.tar.gz", hash = "sha256:b0745c1fcf1bd532ed4388f90b47139d6a6c6ba48a87aa68aa32d4d6bb97259d"},
] ]
[package.dependencies] [package.dependencies]
aiohttp = ">=3.8.3,<4.0.0" aiohttp = ">=3.8.3,<4.0.0"
dataclasses-json = ">=0.5.7,<0.7" dataclasses-json = ">=0.5.7,<0.7"
langchain = ">=0.2.16,<0.3.0" langchain = ">=0.2.16,<0.3.0"
langchain-core = ">=0.2.38,<0.3.0" langchain-core = ">=0.2.39,<0.3.0"
langsmith = ">=0.1.0,<0.2.0" langsmith = ">=0.1.112,<0.2.0"
numpy = [ numpy = [
{version = ">=1,<2", markers = "python_version < \"3.12\""}, {version = ">=1,<2", markers = "python_version < \"3.12\""},
{version = ">=1.26.0,<2.0.0", markers = "python_version >= \"3.12\""}, {version = ">=1.26.0,<2.0.0", markers = "python_version >= \"3.12\""},
@@ -2755,13 +2758,13 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
[[package]] [[package]]
name = "langchain-core" name = "langchain-core"
version = "0.2.39" version = "0.2.40"
description = "Building applications with LLMs through composability" description = "Building applications with LLMs through composability"
optional = false optional = false
python-versions = "<4.0,>=3.8.1" python-versions = "<4.0,>=3.8.1"
files = [ files = [
{file = "langchain_core-0.2.39-py3-none-any.whl", hash = "sha256:5a5ad4e1c02d13e7021c026b2c7965069ad71ef67b018274cd52bab56e39143e"}, {file = "langchain_core-0.2.40-py3-none-any.whl", hash = "sha256:71fff5cafa4b9c82a3a716e985f071383be452c35d8cc3169b3a393e6857fc99"},
{file = "langchain_core-0.2.39.tar.gz", hash = "sha256:73d7b6f0b0212bc8069c3640d69e8d75faa51097784019ffa5c36c617c04acb0"}, {file = "langchain_core-0.2.40.tar.gz", hash = "sha256:c838ea0c0b73475a8e58ced3e306b6d926ef063721abd164f237c8664916f502"},
] ]
[package.dependencies] [package.dependencies]
@@ -2793,17 +2796,17 @@ langchain-core = ">=0.2.38,<0.3.0"
[[package]] [[package]]
name = "langchain-openai" name = "langchain-openai"
version = "0.1.24" version = "0.1.25"
description = "An integration package connecting OpenAI and LangChain" description = "An integration package connecting OpenAI and LangChain"
optional = false optional = false
python-versions = "<4.0,>=3.8.1" python-versions = "<4.0,>=3.8.1"
files = [ files = [
{file = "langchain_openai-0.1.24-py3-none-any.whl", hash = "sha256:7dc27f1dd50090a409fb5e970b07aaf3ff32c027cc1e8da1d1272f950e93e02c"}, {file = "langchain_openai-0.1.25-py3-none-any.whl", hash = "sha256:f0b34a233d0d9cb8fce6006c903e57085c493c4f0e32862b99063b96eaedb109"},
{file = "langchain_openai-0.1.24.tar.gz", hash = "sha256:3f7ef5c26d232685d3c367b7ec523799563c468725e4ca2e616d7c4a96bd16f3"}, {file = "langchain_openai-0.1.25.tar.gz", hash = "sha256:eb116f744f820247a72f54313fb7c01524fba0927120d4e899e5e4ab41ad3928"},
] ]
[package.dependencies] [package.dependencies]
langchain-core = ">=0.2.39,<0.3.0" langchain-core = ">=0.2.40,<0.3.0"
openai = ">=1.40.0,<2.0.0" openai = ">=1.40.0,<2.0.0"
tiktoken = ">=0.7,<1" tiktoken = ">=0.7,<1"
@@ -2843,13 +2846,13 @@ requests = ">=2,<3"
[[package]] [[package]]
name = "litellm" name = "litellm"
version = "1.44.28" version = "1.46.0"
description = "Library to easily interface with LLM API providers" description = "Library to easily interface with LLM API providers"
optional = false optional = false
python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8" python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8"
files = [ files = [
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] ]
[package.dependencies] [package.dependencies]
@@ -4228,13 +4231,13 @@ testing = ["pytest", "pytest-cov", "wheel"]
[[package]] [[package]]
name = "platformdirs" name = "platformdirs"
version = "4.3.2" version = "4.3.3"
description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`." description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
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{file = "platformdirs-4.3.2.tar.gz", hash = "sha256:9e5e27a08aa095dd127b9f2e764d74254f482fef22b0970773bfba79d091ab8c"}, {file = "platformdirs-4.3.3.tar.gz", hash = "sha256:d4e0b7d8ec176b341fb03cb11ca12d0276faa8c485f9cd218f613840463fc2c0"},
] ]
[package.extras] [package.extras]
@@ -4837,15 +4840,18 @@ files = [
[[package]] [[package]]
name = "pyreadline3" name = "pyreadline3"
version = "3.4.3" version = "3.5.2"
description = "A python implementation of GNU readline." description = "A python implementation of GNU readline."
optional = false optional = false
python-versions = "*" python-versions = ">=3.8"
files = [ files = [
{file = "pyreadline3-3.4.3-py3-none-any.whl", hash = "sha256:f832c5898f4f9a0f81d48a8c499b39d0179de1a465ea3def1a7e7231840b4ed6"}, {file = "pyreadline3-3.5.2-py3-none-any.whl", hash = "sha256:a87d56791e2965b2b187e2ea33dcf664600842c997c0623c95cf8ef07db83de9"},
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[package.extras]
dev = ["build", "flake8", "pytest", "twine"]
[[package]] [[package]]
name = "pyright" name = "pyright"
version = "1.1.380" version = "1.1.380"
@@ -5665,18 +5671,18 @@ files = [
[[package]] [[package]]
name = "setuptools" name = "setuptools"
version = "74.1.2" version = "75.0.0"
description = "Easily download, build, install, upgrade, and uninstall Python packages" description = "Easily download, build, install, upgrade, and uninstall Python packages"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
{file = "setuptools-74.1.2-py3-none-any.whl", hash = "sha256:5f4c08aa4d3ebcb57a50c33b1b07e94315d7fc7230f7115e47fc99776c8ce308"}, {file = "setuptools-75.0.0-py3-none-any.whl", hash = "sha256:791ae94f04f78c880b5e614e560dd32d4b4af5d151bd9e7483e3377846caf90a"},
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[package.extras] [package.extras]
check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.5.2)"] check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.5.2)"]
core = ["importlib-metadata (>=6)", "importlib-resources (>=5.10.2)", "jaraco.text (>=3.7)", "more-itertools (>=8.8)", "packaging (>=24)", "platformdirs (>=2.6.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"] core = ["importlib-metadata (>=6)", "importlib-resources (>=5.10.2)", "jaraco.collections", "jaraco.functools", "jaraco.text (>=3.7)", "more-itertools", "more-itertools (>=8.8)", "packaging", "packaging (>=24)", "platformdirs (>=2.6.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
cover = ["pytest-cov"] cover = ["pytest-cov"]
doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"] doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
enabler = ["pytest-enabler (>=2.2)"] enabler = ["pytest-enabler (>=2.2)"]
@@ -6255,13 +6261,13 @@ typing-extensions = ">=3.7.4.3"
[[package]] [[package]]
name = "types-requests" name = "types-requests"
version = "2.32.0.20240907" version = "2.32.0.20240914"
description = "Typing stubs for requests" description = "Typing stubs for requests"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
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] ]
[package.dependencies] [package.dependencies]
@@ -6962,13 +6968,13 @@ multidict = ">=4.0"
[[package]] [[package]]
name = "zipp" name = "zipp"
version = "3.20.1" version = "3.20.2"
description = "Backport of pathlib-compatible object wrapper for zip files" description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
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] ]
[package.extras] [package.extras]

2
pyproject.toml
View File

@@ -1,6 +1,6 @@
[tool.poetry] [tool.poetry]
name = "crewai" name = "crewai"
version = "0.56.0" version = "0.56.23"
description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks." description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
authors = ["Joao Moura <joao@crewai.com>"] authors = ["Joao Moura <joao@crewai.com>"]
readme = "README.md" readme = "README.md"

14
src/crewai/__init__.py
View File

@@ -1,3 +1,4 @@
import warnings
from crewai.agent import Agent from crewai.agent import Agent
from crewai.crew import Crew from crewai.crew import Crew
from crewai.pipeline import Pipeline from crewai.pipeline import Pipeline
@@ -5,4 +6,17 @@ from crewai.process import Process
from crewai.routers import Router from crewai.routers import Router
from crewai.task import Task from crewai.task import Task
warnings.filterwarnings(
"ignore",
message="Valid config keys have changed in V2:",
)
warnings.filterwarnings(
"ignore",
message="Pydantic serializer warnings:",
category=UserWarning,
module="pydantic.main",
)
__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router"] __all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router"]

54
src/crewai/agents/crew_agent_executor.py
View File

@@ -1,10 +1,12 @@
import json
import re
from typing import Any, Dict, List, Union from typing import Any, Dict, List, Union
from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
from crewai.agents.parser import CrewAgentParser from crewai.agents.parser import CrewAgentParser
from crewai.agents.tools_handler import ToolsHandler from crewai.agents.tools_handler import ToolsHandler
from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
from crewai.utilities import I18N from crewai.utilities import I18N, Printer
from crewai.utilities.constants import TRAINING_DATA_FILE from crewai.utilities.constants import TRAINING_DATA_FILE
from crewai.utilities.exceptions.context_window_exceeding_exception import ( from crewai.utilities.exceptions.context_window_exceeding_exception import (
LLMContextLengthExceededException, LLMContextLengthExceededException,
@@ -55,6 +57,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
self.stop = stop_words self.stop = stop_words
self.max_iter = max_iter self.max_iter = max_iter
self.callbacks = callbacks self.callbacks = callbacks
self._printer: Printer = Printer()
self.tools_handler = tools_handler self.tools_handler = tools_handler
self.original_tools = original_tools self.original_tools = original_tools
self.step_callback = step_callback self.step_callback = step_callback
@@ -79,6 +82,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
else: else:
user_prompt = self._format_prompt(self.prompt.get("prompt", ""), inputs) user_prompt = self._format_prompt(self.prompt.get("prompt", ""), inputs)
self.messages.append(self._format_msg(user_prompt)) self.messages.append(self._format_msg(user_prompt))
self.ask_for_human_input = bool(inputs.get("ask_for_human_input", False)) self.ask_for_human_input = bool(inputs.get("ask_for_human_input", False))
formatted_answer = self._invoke_loop() formatted_answer = self._invoke_loop()
@@ -90,7 +94,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
# Making sure we only ask for it once, so disabling for the next thought loop # Making sure we only ask for it once, so disabling for the next thought loop
self.ask_for_human_input = False self.ask_for_human_input = False
self.messages.append(self._format_msg(f"Feedback: {human_feedback}")) self.messages.append(self._format_msg(f"Feedback: {human_feedback}"))
formatted_answer = self._invoke_loop(None) formatted_answer = self._invoke_loop()
return {"output": formatted_answer.output} return {"output": formatted_answer.output}
def _invoke_loop(self, formatted_answer=None): def _invoke_loop(self, formatted_answer=None):
@@ -119,9 +124,12 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
if isinstance(formatted_answer, AgentAction): if isinstance(formatted_answer, AgentAction):
action_result = self._use_tool(formatted_answer) action_result = self._use_tool(formatted_answer)
formatted_answer.text += f"\nObservation: {action_result}" formatted_answer.text += f"\nObservation: {action_result}"
formatted_answer.result = action_result
self._show_logs(formatted_answer)
if self.step_callback: if self.step_callback:
formatted_answer.result = action_result
self.step_callback(formatted_answer) self.step_callback(formatted_answer)
if self._should_force_answer(): if self._should_force_answer():
if self.have_forced_answer: if self.have_forced_answer:
return AgentFinish( return AgentFinish(
@@ -140,18 +148,54 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
) )
except OutputParserException as e: except OutputParserException as e:
self.messages.append({"role": "assistant", "content": e.error}) self.messages.append({"role": "assistant", "content": e.error})
self._invoke_loop(formatted_answer) return self._invoke_loop(formatted_answer)
except Exception as e: except Exception as e:
if LLMContextLengthExceededException(str(e))._is_context_limit_error( if LLMContextLengthExceededException(str(e))._is_context_limit_error(
str(e) str(e)
): ):
self._handle_context_length() self._handle_context_length()
self._invoke_loop(formatted_answer) return self._invoke_loop(formatted_answer)
else: else:
raise e raise e
self._show_logs(formatted_answer)
return formatted_answer return formatted_answer
def _show_logs(self, formatted_answer: Union[AgentAction, AgentFinish]):
if self.agent.verbose or (
hasattr(self, "crew") and getattr(self.crew, "verbose", False)
):
if isinstance(formatted_answer, AgentAction):
thought = re.sub(r"\n+", "\n", formatted_answer.thought)
formatted_json = json.dumps(
json.loads(formatted_answer.tool_input),
indent=2,
ensure_ascii=False,
)
self._printer.print(
content=f"\n\n\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{self.agent.role}\033[00m"
)
self._printer.print(
content=f"\033[95m## Thought:\033[00m \033[92m{thought}\033[00m"
)
self._printer.print(
content=f"\033[95m## Using tool:\033[00m \033[92m{formatted_answer.tool}\033[00m"
)
self._printer.print(
content=f"\033[95m## Tool Input:\033[00m \033[92m\n{formatted_json}\033[00m"
)
self._printer.print(
content=f"\033[95m## Tool Output:\033[00m \033[92m\n{formatted_answer.result}\033[00m"
)
elif isinstance(formatted_answer, AgentFinish):
self._printer.print(
content=f"\n\n\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{self.agent.role}\033[00m"
)
self._printer.print(
content=f"\033[95m## Final Answer:\033[00m \033[92m\n{formatted_answer.output}\033[00m"
)
def _use_tool(self, agent_action: AgentAction) -> Any: def _use_tool(self, agent_action: AgentAction) -> Any:
tool_usage = ToolUsage( tool_usage = ToolUsage(
tools_handler=self.tools_handler, tools_handler=self.tools_handler,

21
src/crewai/agents/parser.py
View File

@@ -11,22 +11,26 @@ FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE = "I did it wrong. Tried to both
class AgentAction: class AgentAction:
thought: str
tool: str tool: str
tool_input: str tool_input: str
text: str text: str
result: str result: str
def __init__(self, tool: str, tool_input: str, text: str): def __init__(self, thought: str, tool: str, tool_input: str, text: str):
self.thought = thought
self.tool = tool self.tool = tool
self.tool_input = tool_input self.tool_input = tool_input
self.text = text self.text = text
class AgentFinish: class AgentFinish:
thought: str
output: str output: str
text: str text: str
def __init__(self, output: str, text: str): def __init__(self, thought: str, output: str, text: str):
self.thought = thought
self.output = output self.output = output
self.text = text self.text = text
@@ -66,6 +70,7 @@ class CrewAgentParser:
self.agent = agent self.agent = agent
def parse(self, text: str) -> Union[AgentAction, AgentFinish]: def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
thought = self._extract_thought(text)
includes_answer = FINAL_ANSWER_ACTION in text includes_answer = FINAL_ANSWER_ACTION in text
regex = ( regex = (
r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)" r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
@@ -84,10 +89,11 @@ class CrewAgentParser:
tool_input = action_input.strip(" ").strip('"') tool_input = action_input.strip(" ").strip('"')
safe_tool_input = self._safe_repair_json(tool_input) safe_tool_input = self._safe_repair_json(tool_input)
return AgentAction(clean_action, safe_tool_input, text) return AgentAction(thought, clean_action, safe_tool_input, text)
elif includes_answer: elif includes_answer:
return AgentFinish(text.split(FINAL_ANSWER_ACTION)[-1].strip(), text) final_answer = text.split(FINAL_ANSWER_ACTION)[-1].strip()
return AgentFinish(thought, final_answer, text)
if not re.search(r"Action\s*\d*\s*:[\s]*(.*?)", text, re.DOTALL): if not re.search(r"Action\s*\d*\s*:[\s]*(.*?)", text, re.DOTALL):
self.agent.increment_formatting_errors() self.agent.increment_formatting_errors()
@@ -109,6 +115,13 @@ class CrewAgentParser:
error, error,
) )
def _extract_thought(self, text: str) -> str:
regex = r"(.*?)(?:\n\nAction|\n\nFinal Answer)"
thought_match = re.search(regex, text, re.DOTALL)
if thought_match:
return thought_match.group(1).strip()
return ""
def _clean_action(self, text: str) -> str: def _clean_action(self, text: str) -> str:
"""Clean action string by removing non-essential formatting characters.""" """Clean action string by removing non-essential formatting characters."""
return re.sub(r"^\s*\*+\s*|\s*\*+\s*$", "", text).strip() return re.sub(r"^\s*\*+\s*|\s*\*+\s*$", "", text).strip()

4
src/crewai/crew.py
View File

@@ -749,9 +749,6 @@ class Crew(BaseModel):
task.tools.append(new_tool) task.tools.append(new_tool)
def _log_task_start(self, task: Task, role: str = "None"): def _log_task_start(self, task: Task, role: str = "None"):
color = self._logging_color
self._logger.log("debug", f"== Working Agent: {role}", color=color)
self._logger.log("info", f"== Starting Task: {task.description}", color=color)
if self.output_log_file: if self.output_log_file:
self._file_handler.log(agent=role, task=task.description, status="started") self._file_handler.log(agent=role, task=task.description, status="started")
@@ -774,7 +771,6 @@ class Crew(BaseModel):
def _process_task_result(self, task: Task, output: TaskOutput) -> None: def _process_task_result(self, task: Task, output: TaskOutput) -> None:
role = task.agent.role if task.agent is not None else "None" role = task.agent.role if task.agent is not None else "None"
self._logger.log("debug", f"== [{role}] Task output: {output}\n\n")
if self.output_log_file: if self.output_log_file:
self._file_handler.log(agent=role, task=output, status="completed") self._file_handler.log(agent=role, task=output, status="completed")

4
src/crewai/tools/tool_usage.py
View File

@@ -113,8 +113,6 @@ class ToolUsage:
result = self._i18n.errors("task_repeated_usage").format( result = self._i18n.errors("task_repeated_usage").format(
tool_names=self.tools_names tool_names=self.tools_names
) )
if self.agent.verbose:
self._printer.print(content=f"\n\n{result}\n", color="purple")
self._telemetry.tool_repeated_usage( self._telemetry.tool_repeated_usage(
llm=self.function_calling_llm, llm=self.function_calling_llm,
tool_name=tool.name, tool_name=tool.name,
@@ -200,8 +198,6 @@ class ToolUsage:
calling=calling, output=result, should_cache=should_cache calling=calling, output=result, should_cache=should_cache
) )
if self.agent.verbose:
self._printer.print(content=f"\n\n{result}\n", color="purple")
if agentops: if agentops:
agentops.record(tool_event) agentops.record(tool_event)
self._telemetry.tool_usage( self._telemetry.tool_usage(

20
src/crewai/utilities/converter.py
View File

@@ -104,25 +104,16 @@ def convert_to_model(
converter_cls: Optional[Type[Converter]] = None, converter_cls: Optional[Type[Converter]] = None,
) -> Union[dict, BaseModel, str]: ) -> Union[dict, BaseModel, str]:
model = output_pydantic or output_json model = output_pydantic or output_json
if model is None: if model is None:
return result return result
try: try:
escaped_result = json.dumps(json.loads(result, strict=False)) escaped_result = json.dumps(json.loads(result, strict=False))
return validate_model(escaped_result, model, bool(output_json)) return validate_model(escaped_result, model, bool(output_json))
except json.JSONDecodeError as e: except json.JSONDecodeError:
Printer().print(
content=f"Error parsing JSON: {e}. Attempting to handle partial JSON.",
color="yellow",
)
return handle_partial_json( return handle_partial_json(
result, model, bool(output_json), agent, converter_cls result, model, bool(output_json), agent, converter_cls
) )
except ValidationError as e: except ValidationError:
Printer().print(
content=f"Pydantic validation error: {e}. Attempting to handle partial JSON.",
color="yellow",
)
return handle_partial_json( return handle_partial_json(
result, model, bool(output_json), agent, converter_cls result, model, bool(output_json), agent, converter_cls
) )
@@ -159,11 +150,8 @@ def handle_partial_json(
return exported_result return exported_result
except json.JSONDecodeError: except json.JSONDecodeError:
pass pass
except ValidationError as e: except ValidationError:
Printer().print( pass
content=f"Pydantic validation error: {e}. The JSON structure doesn't match the expected model. Attempting alternative conversion method.",
color="yellow",
)
except Exception as e: except Exception as e:
Printer().print( Printer().print(
content=f"Unexpected error during partial JSON handling: {type(e).__name__}: {e}. Attempting alternative conversion method.", content=f"Unexpected error during partial JSON handling: {type(e).__name__}: {e}. Attempting alternative conversion method.",

2
src/crewai/utilities/printer.py
View File

@@ -1,5 +1,5 @@
class Printer: class Printer:
def print(self, content: str, color: str): def print(self, content: str, color: str = None):
if color == "purple": if color == "purple":
self._print_purple(content) self._print_purple(content)
elif color == "red": elif color == "red":

13
tests/agent_test.py
View File

@@ -67,7 +67,7 @@ def test_agent_execution():
) )
output = agent.execute_task(task) output = agent.execute_task(task)
assert output == "1 + 1 equals 2." assert output == "The result of the math operation 1 + 1 is 2."
@pytest.mark.vcr(filter_headers=["authorization"]) @pytest.mark.vcr(filter_headers=["authorization"])
@@ -121,7 +121,7 @@ def test_logging_tool_usage():
tool_usage = InstructorToolCalling( tool_usage = InstructorToolCalling(
tool_name=multiplier.name, arguments={"first_number": 3, "second_number": 4} tool_name=multiplier.name, arguments={"first_number": 3, "second_number": 4}
) )
assert output == "The result of multiplying 3 times 4 is 12." assert output == "The result of 3 times 4 is 12."
assert agent.tools_handler.last_used_tool.tool_name == tool_usage.tool_name assert agent.tools_handler.last_used_tool.tool_name == tool_usage.tool_name
assert agent.tools_handler.last_used_tool.arguments == tool_usage.arguments assert agent.tools_handler.last_used_tool.arguments == tool_usage.arguments
@@ -275,7 +275,7 @@ def test_agent_execution_with_specific_tools():
expected_output="The result of the multiplication.", expected_output="The result of the multiplication.",
) )
output = agent.execute_task(task=task, tools=[multiplier]) output = agent.execute_task(task=task, tools=[multiplier])
assert output == "The result of the multiplication is 12." assert output == "The result of the multiplication of 3 times 4 is 12."
@pytest.mark.vcr(filter_headers=["authorization"]) @pytest.mark.vcr(filter_headers=["authorization"])
@@ -490,7 +490,7 @@ def test_agent_respect_the_max_rpm_set(capsys):
task=task, task=task,
tools=[get_final_answer], tools=[get_final_answer],
) )
assert output == "The final answer is 42." assert output == "42"
captured = capsys.readouterr() captured = capsys.readouterr()
assert "Max RPM reached, waiting for next minute to start." in captured.out assert "Max RPM reached, waiting for next minute to start." in captured.out
moveon.assert_called() moveon.assert_called()
@@ -1110,7 +1110,10 @@ def test_handle_context_length_exceeds_limit():
backstory="test backstory", backstory="test backstory",
) )
original_action = AgentAction( original_action = AgentAction(
tool="test_tool", tool_input="test_input", text="test_log" tool="test_tool",
tool_input="test_input",
text="test_log",
thought="test_thought",
) )
with patch.object( with patch.object(

12
tests/agent_tools/agent_tools_test.py
View File

@@ -24,7 +24,7 @@ def test_delegate_work():
assert ( assert (
result result
== "As a researcher specialized in technology, I believe that AI agents represent a significant advancement in the capability to automate tasks, process large amounts of data, and even make decisions in certain domains. While I wouldn't characterize my stance as \"hate\" towards them, I do have some critical perspectives on their current deployment and use.\n\nFirstly, AI agents can perform repetitive and mundane tasks more efficiently than humans, allowing individuals and organizations to allocate their human resources to more complex and creative endeavors. This has promising benefits for productivity and innovation across various sectors.\n\nHowever, there are several concerns and challenges that come along with the deployment of AI agents. One primary concern is the degree of control and transparency. Many AI systems, particularly those utilizing machine learning and neural networks, operate as \"black boxes\" where understanding the decision-making process is not straightforward. This lack of transparency can lead to trust issues and difficulties in compliance with legal and ethical standards.\n\nAnother significant challenge is the potential for bias. AI agents learn from data, and if the training data contains biases, the AI agents can perpetuate and even exacerbate those biases. This can result in unfair outcomes and discriminatory practices, especially in sensitive applications like hiring, law enforcement, and lending decisions.\n\nAdditionally, the impact on employment is a critical issue. While AI agents can take over repetitive tasks, there is a legitimate concern over job displacement. It's essential to have strategies in place for retraining and upskilling the workforce to ensure that the benefits of AI advancements are shared broadly across society.\n\nSecurity is another crucial factor. AI agents, if not properly secured, can be susceptible to malicious attacks, leading to potential data breaches or adversarial manipulation. The security of AI systems needs to be robust to safeguard against these risks.\n\nFinally, the ethical implications of autonomous decision-making by AI agents cannot be ignored. As we delegate more decision-making power to AI, it's vital to ensure that these systems operate within the ethical boundaries and align with human values and societal norms.\n\nIn conclusion, while AI agents have transformative potential, it's essential to address these concerns comprehensively. The focus should be on ensuring transparency, mitigating bias, providing employment transition strategies, enhancing security measures, and upholding ethical standards in the deployment and use of AI agents. This balanced approach can help us harness the benefits of AI agents while minimizing their risks and downsides." == "While it's a common perception that I might \"hate\" AI agents, my actual stance is much more nuanced and guided by an in-depth understanding of their potential and limitations. As an expert researcher in technology, I recognize that AI agents are a significant advancement in the field of computing and artificial intelligence, offering numerous benefits and applications across various sectors. Here's a detailed take on AI agents:\n\n**Advantages of AI Agents:**\n1. **Automation and Efficiency:** AI agents can automate repetitive tasks, thus freeing up human workers for more complex and creative work. This leads to significant efficiency gains in industries such as customer service (chatbots), data analysis, and even healthcare (AI diagnostic tools).\n\n2. **24/7 Availability:** Unlike human workers, AI agents can operate continuously without fatigue. This is particularly beneficial in customer service environments where support can be provided around the clock.\n\n3. **Data Handling and Analysis:** AI agents can process and analyze vast amounts of data more quickly and accurately than humans. This ability is invaluable in fields like finance, where AI can detect fraudulent activities, or in marketing, where consumer data can be analyzed to improve customer engagement strategies.\n\n4. **Personalization:** AI agents can provide personalized experiences by learning from user interactions. For example, recommendation systems on platforms like Netflix and Amazon use AI agents to suggest content or products tailored to individual preferences.\n\n5. **Scalability:** AI agents can be scaled up easily to handle increasing workloads, making them ideal for businesses experiencing growth or variable demand.\n\n**Challenges and Concerns:**\n1. **Ethical Implications:** The deployment of AI agents raises significant ethical questions, including issues of bias, privacy, and the potential for job displacement. It’s crucial to address these concerns by incorporating transparent, fair, and inclusive practices in AI development and deployment.\n\n2. **Dependability and Error Rates:** While AI agents are generally reliable, they are not infallible. Errors, especially in critical areas like healthcare or autonomous driving, can have severe consequences. Therefore, rigorous testing and validation are essential.\n\n3. **Lack of Understanding:** Many users and stakeholders may not fully understand how AI agents work, leading to mistrust or misuse. Improving AI literacy and transparency can help build trust in these systems.\n\n4. **Security Risks:** AI agents can be vulnerable to cyber-attacks. Ensuring robust cybersecurity measures are in place is vital to protect sensitive data and maintain the integrity of AI systems.\n\n5. **Regulation and Oversight:** The rapid development of AI technology often outpaces regulatory frameworks. Effective governance is needed to ensure AI is used responsibly and ethically.\n\nIn summary, while I thoroughly understand the transformative potential of AI agents and their numerous advantages, I also recognize the importance of addressing the associated challenges. It's not about hating AI agents, but rather advocating for their responsible and ethical use to ensure they benefit society as a whole. My critical perspective is rooted in a desire to see AI agents implemented in ways that maximize their benefits while minimizing potential harms."
) )
@@ -38,7 +38,7 @@ def test_delegate_work_with_wrong_co_worker_variable():
assert ( assert (
result result
== "AI agents are autonomous software programs that perform specific tasks based on a combination of programmed rules and machine learning algorithms. These agents can operate independently to analyze data, make decisions, and interact with their environment, encompassing a wide range of applications from customer service chatbots to sophisticated decision-making systems in finance and healthcare.\n\nDespite their impressive capabilities, AI agents are not without their challenges and limitations. One major concern is the ethical implications of AI decision-making, especially in situations where bias in training data can lead to unfair or harmful outcomes. Additionally, there are substantial issues related to privacy, as these agents often require access to large amounts of personal data to function effectively.\n\nAnother significant drawback is the potential for job displacement. As AI agents become more advanced, they may perform tasks traditionally done by humans, leading to economic and employment disruptions. This creates a pressing need for society to find a balance between leveraging AI advancements and ensuring that the workforce can adapt through reskilling and upskilling initiatives.\n\nOn the technical side, AI agents still encounter difficulties with context understanding and adaptability. While they are excellent at processing structured data, unstructured data—like natural language in diverse formats—presents a significant challenge. The reliability of these systems can also vary depending on the quality of data they are trained on and the complexity of the task at hand.\n\nIn conclusion, while AI agents offer transformative potential across various industries, they come with a set of complex challenges that must be carefully managed. It is crucial to continue advancing these technologies responsibly, considering both their positive impacts and the societal implications they bring." == "As an expert researcher in technology, particularly in the field of AI and AI agents, it is essential to clarify that my perspective is not one of hatred but rather critical analysis. My evaluation of AI agents is grounded in a balanced view of their advantages and the challenges they present. \n\nAI agents represent a significant leap in technological progress with a wide array of applications across industries. They can perform tasks ranging from customer service interactions, data analysis, complex simulations, to even personal assistance. Their ability to learn and adapt makes them powerful tools for enhancing productivity and innovation.\n\nHowever, there are considerable challenges and ethical concerns associated with their deployment. These include privacy issues, job displacement, and the potential for biased decision-making driven by flawed algorithms. Furthermore, the security risks posed by AI agents, such as how they can be manipulated or hacked, are critical concerns that cannot be ignored.\n\nIn essence, while I do recognize the transformative potential of AI agents, I remain vigilant about their implications. It is vital to ensure that their development is guided by robust ethical standards and stringent regulations to mitigate risks. My view is not rooted in hatred but in a deep commitment to responsible and thoughtful technological advancement. \n\nI hope this clarifies my stance on AI agents and underscores the importance of critical engagement with emerging technologies."
) )
@@ -52,7 +52,7 @@ def test_ask_question():
assert ( assert (
result result
== "I do not hate AI agents; in fact, I find them incredibly fascinating and valuable. As someone who specializes in technology research, I recognize the immense potential AI agents have in transforming various industries. They can automate mundane tasks, provide insightful data analysis, enhance decision-making processes, and even personalize user experiences. My enthusiasm stems from their ability to innovate and solve complex problems, which aligns with my passion for exploring technological advancements. So yes, you heard correctly—I indeed love them for their transformative capabilities and the endless possibilities they offer." == "No, I do not hate AI agents; in fact, I find them incredibly fascinating and useful. As a researcher specializing in technology, particularly in AI and AI agents, I appreciate their potential to revolutionize various industries by automating tasks, providing deep insights through data analysis, and even enhancing decision-making processes. AI agents can streamline operations, improve efficiency, and contribute to advancements in fields like healthcare, finance, and cybersecurity. While they do present challenges, such as ethical considerations and the need for robust security measures, the benefits and potential for positive impact are immense. Therefore, my stance is one of strong support and enthusiasm for AI agents and their future developments."
) )
@@ -66,7 +66,7 @@ def test_ask_question_with_wrong_co_worker_variable():
assert ( assert (
result result
== "As an expert researcher specialized in technology and artificial intelligence, I find AI agents to be incredibly fascinating and valuable tools in advancing various fields. AI agents have the potential to revolutionize industries, improve efficiencies, and solve complex problems that were previously insurmountable. My admiration for AI agents stems from their ability to learn, adapt, and provide innovative solutions that can enhance human capabilities rather than replace them. While AI agents do present ethical and operational challenges that need to be addressed, I do not harbor any hatred towards them. Instead, I am passionate about leveraging their potential responsibly and ethically to create positive societal impacts." == "I do not hate AI agents; in fact, I appreciate them for their immense potential and the numerous benefits they bring to various fields. My passion for AI agents stems from their ability to streamline processes, enhance decision-making, and provide innovative solutions to complex problems. They significantly contribute to advancements in healthcare, finance, education, and many other sectors, making tasks more efficient and freeing up human capacities for more creative and strategic endeavors. So, to answer your question, I love AI agents because of the positive impact they have on our world and their capability to drive technological progress."
) )
@@ -80,7 +80,7 @@ def test_delegate_work_withwith_coworker_as_array():
assert ( assert (
result result
== "It's a misconception to say I hate AI agents; the reality is far more nuanced. AI agents, in their many forms, are remarkable advancements in technology with the capability to transform various sectors ranging from healthcare to customer service. The key is understanding the context and application of these agents and being mindful of their limitations and ethical considerations.\n\nAI agents, powered by machine learning algorithms and vast data pools, can automate tasks, analyze complex datasets, provide personalized recommendations, and offer conversational interfaces that improve efficiency and user experience. For instance, in customer support, AI agents can handle routine queries swiftly, freeing up human agents for more complex issues.\n\nHowever, it's crucial to address the shortcomings and challenges associated with AI agents. One major concern is the risk of biases in AI systems, which can lead to discriminatory outcomes if not properly managed and audited. Data privacy is another significant issue; AI agents often require access to large amounts of personal data, raising concerns about how this data is collected, stored, and used.\n\nThere's also the question of job displacement. While AI can augment human capabilities and create new job categories, it can also render certain roles obsolete, necessitating workforce reskilling and adaptation.\n\nIn summary, my perspective isn't one of antagonism but of cautious optimism. AI agents have tremendous potential when implemented responsibly, with a clear focus on ethical considerations and societal impact. It's not about hating or loving AI agents, but about striving for balanced, well-informed integration into our technological ecosystem." == "AI agents have emerged as a revolutionary force in today's technological landscape, and my stance on them is not rooted in hatred but in a critical, analytical perspective. Let's delve deeper into what makes AI agents both a boon and a bane in various contexts.\n\n**Benefits of AI Agents:**\n\n1. **Automation and Efficiency:**\n AI agents excel at automating repetitive tasks, which frees up human resources for more complex and creative endeavors. They are capable of performing tasks rapidly and with high accuracy, leading to increased efficiency in operations.\n\n2. **Data Analysis and Decision Making:**\n These agents can process vast amounts of data at speeds far beyond human capability. They can identify patterns and insights that would otherwise be missed, aiding in informed decision-making processes across industries like finance, healthcare, and logistics.\n\n3. **Personalization and User Experience:**\n AI agents can personalize interactions on a scale that is impractical for humans. For example, recommendation engines in e-commerce or content platforms tailor suggestions to individual users, enhancing user experience and satisfaction.\n\n4. **24/7 Availability:**\n Unlike human employees, AI agents can operate round-the-clock without the need for breaks, sleep, or holidays. This makes them ideal for customer service roles, providing consistent and immediate responses any time of the day.\n\n**Challenges and Concerns:**\n\n1. **Job Displacement:**\n One of the major concerns is the displacement of jobs. As AI agents become more proficient at a variety of tasks, there is a legitimate fear of human workers being replaced, leading to unemployment and economic disruption.\n\n2. **Bias and Fairness:**\n AI agents are only as good as the data they are trained on. If the training data contains biases, the AI agents can perpetuate or even exacerbate these biases, leading to unfair and discriminatory outcomes.\n\n3. **Privacy and Security:**\n The use of AI agents often involves handling large amounts of personal data, raising significant privacy and security concerns. Unauthorized access or breaches could lead to severe consequences for individuals and organizations.\n\n4. **Accountability and Transparency:**\n The decision-making processes of AI agents can be opaque, making it difficult to hold them accountable. This lack of transparency can lead to mistrust and ethical dilemmas, particularly when AI decisions impact human lives.\n\n5. **Ethical Considerations:**\n The deployment of AI agents in sensitive areas, such as surveillance and law enforcement, raises ethical issues. The potential for misuse or overdependence on AI decision-making poses a threat to individual freedoms and societal norms.\n\nIn conclusion, while AI agents offer remarkable advantages in terms of efficiency, data handling, and user experience, they also bring significant challenges that need to be addressed carefully. My critical stance is driven by a desire to ensure that their integration into society is balanced, fair, and beneficial to all, without ignoring the potential downsides. Therefore, a nuanced approach is essential in leveraging the power of AI agents responsibly."
) )
@@ -94,7 +94,7 @@ def test_ask_question_with_coworker_as_array():
assert ( assert (
result result
== "As an expert researcher specializing in technology, particularly AI and AI agents, I have a nuanced perspective on the matter. AI agents have their strengths and weaknesses, and my appreciation for them is rooted in their potential to transform various industries and improve efficiencies. AI agents can automate repetitive tasks, provide insightful data analyses, and enhance user experiences through intelligent interactions. However, it's also critical to address the ethical considerations and potential risks associated with their use, such as biases in AI models, privacy concerns, and the need for transparency. In summary, I wouldn't say I hate AI agents; rather, I value their contributions and advocate for responsible and ethical development and deployment." == "As a researcher specialized in technology, particularly in AI and AI agents, my feelings toward them are far more nuanced than simply loving or hating them. AI agents represent a remarkable advancement in technology and hold tremendous potential for improving various aspects of our lives and industries. They can automate tedious tasks, provide intelligent data analysis, support decision-making, and even enhance our creative processes. These capabilities can drive efficiency, innovation, and economic growth.\n\nHowever, it is also crucial to acknowledge the challenges and ethical considerations posed by AI agents. Issues such as data privacy, security, job displacement, and the need for proper regulation are significant concerns that must be carefully managed. Moreover, the development and deployment of AI should be guided by principles that ensure fairness, transparency, and accountability.\n\nIn essence, I appreciate the profound impact AI agents can have, but I also recognize the importance of approaching their integration into society with thoughtful consideration and responsibility. Balancing enthusiasm with caution and ethical oversight is key to harnessing the full potential of AI while mitigating its risks."
) )

44
tests/agent_tools/cassettes/test_ask_question.yaml
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@@ -25,8 +25,8 @@ interactions:
content-type: content-type:
- application/json - application/json
cookie: cookie:
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user-agent: user-agent:
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response: response:
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Answer: I do not hate AI agents; in fact, I find them incredibly fascinating Answer: No, I do not hate AI agents; in fact, I find them incredibly fascinating
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for exploring technological advancements. So yes, you heard correctly\u2014I and cybersecurity. While they do present challenges, such as ethical considerations
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49
tests/agent_tools/cassettes/test_ask_question_with_coworker_as_array.yaml
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strengths and weaknesses, and my appreciation for them is rooted in their potential them. AI agents represent a remarkable advancement in technology and hold tremendous
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repetitive tasks, provide insightful data analyses, and enhance user experiences automate tedious tasks, provide intelligent data analysis, support decision-making,
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I wouldn't say I hate AI agents; rather, I value their contributions and advocate data privacy, security, job displacement, and the need for proper regulation
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41
tests/agent_tools/cassettes/test_ask_question_with_wrong_co_worker_variable.yaml
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I find AI agents to be incredibly fascinating and valuable tools in advancing potential and the numerous benefits they bring to various fields. My passion
various fields. AI agents have the potential to revolutionize industries, improve for AI agents stems from their ability to streamline processes, enhance decision-making,
efficiencies, and solve complex problems that were previously insurmountable. and provide innovative solutions to complex problems. They significantly contribute
My admiration for AI agents stems from their ability to learn, adapt, and provide to advancements in healthcare, finance, education, and many other sectors, making
innovative solutions that can enhance human capabilities rather than replace tasks more efficient and freeing up human capacities for more creative and strategic
them. While AI agents do present ethical and operational challenges that need endeavors. So, to answer your question, I love AI agents because of the positive
to be addressed, I do not harbor any hatred towards them. Instead, I am passionate impact they have on our world and their capability to drive technological progress.\",\n
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115
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represent a significant advancement in the capability to automate tasks, process my actual stance is much more nuanced and guided by an in-depth understanding
large amounts of data, and even make decisions in certain domains. While I wouldn't of their potential and limitations. As an expert researcher in technology, I
characterize my stance as \\\"hate\\\" towards them, I do have some critical recognize that AI agents are a significant advancement in the field of computing
perspectives on their current deployment and use.\\n\\nFirstly, AI agents can and artificial intelligence, offering numerous benefits and applications across
perform repetitive and mundane tasks more efficiently than humans, allowing various sectors. Here's a detailed take on AI agents:\\n\\n**Advantages of AI
individuals and organizations to allocate their human resources to more complex Agents:**\\n1. **Automation and Efficiency:** AI agents can automate repetitive
and creative endeavors. This has promising benefits for productivity and innovation tasks, thus freeing up human workers for more complex and creative work. This
across various sectors.\\n\\nHowever, there are several concerns and challenges leads to significant efficiency gains in industries such as customer service
that come along with the deployment of AI agents. One primary concern is the (chatbots), data analysis, and even healthcare (AI diagnostic tools).\\n\\n2.
degree of control and transparency. Many AI systems, particularly those utilizing **24/7 Availability:** Unlike human workers, AI agents can operate continuously
machine learning and neural networks, operate as \\\"black boxes\\\" where understanding without fatigue. This is particularly beneficial in customer service environments
the decision-making process is not straightforward. This lack of transparency where support can be provided around the clock.\\n\\n3. **Data Handling and
can lead to trust issues and difficulties in compliance with legal and ethical Analysis:** AI agents can process and analyze vast amounts of data more quickly
standards.\\n\\nAnother significant challenge is the potential for bias. AI and accurately than humans. This ability is invaluable in fields like finance,
agents learn from data, and if the training data contains biases, the AI agents where AI can detect fraudulent activities, or in marketing, where consumer data
can perpetuate and even exacerbate those biases. This can result in unfair outcomes can be analyzed to improve customer engagement strategies.\\n\\n4. **Personalization:**
and discriminatory practices, especially in sensitive applications like hiring, AI agents can provide personalized experiences by learning from user interactions.
law enforcement, and lending decisions.\\n\\nAdditionally, the impact on employment For example, recommendation systems on platforms like Netflix and Amazon use
is a critical issue. While AI agents can take over repetitive tasks, there is AI agents to suggest content or products tailored to individual preferences.\\n\\n5.
a legitimate concern over job displacement. It's essential to have strategies **Scalability:** AI agents can be scaled up easily to handle increasing workloads,
in place for retraining and upskilling the workforce to ensure that the benefits making them ideal for businesses experiencing growth or variable demand.\\n\\n**Challenges
of AI advancements are shared broadly across society.\\n\\nSecurity is another and Concerns:**\\n1. **Ethical Implications:** The deployment of AI agents raises
crucial factor. AI agents, if not properly secured, can be susceptible to malicious significant ethical questions, including issues of bias, privacy, and the potential
attacks, leading to potential data breaches or adversarial manipulation. The for job displacement. It\u2019s crucial to address these concerns by incorporating
security of AI systems needs to be robust to safeguard against these risks.\\n\\nFinally, transparent, fair, and inclusive practices in AI development and deployment.\\n\\n2.
the ethical implications of autonomous decision-making by AI agents cannot be **Dependability and Error Rates:** While AI agents are generally reliable, they
ignored. As we delegate more decision-making power to AI, it's vital to ensure are not infallible. Errors, especially in critical areas like healthcare or
that these systems operate within the ethical boundaries and align with human autonomous driving, can have severe consequences. Therefore, rigorous testing
values and societal norms.\\n\\nIn conclusion, while AI agents have transformative and validation are essential.\\n\\n3. **Lack of Understanding:** Many users
potential, it's essential to address these concerns comprehensively. The focus and stakeholders may not fully understand how AI agents work, leading to mistrust
should be on ensuring transparency, mitigating bias, providing employment transition or misuse. Improving AI literacy and transparency can help build trust in these
strategies, enhancing security measures, and upholding ethical standards in systems.\\n\\n4. **Security Risks:** AI agents can be vulnerable to cyber-attacks.
the deployment and use of AI agents. This balanced approach can help us harness Ensuring robust cybersecurity measures are in place is vital to protect sensitive
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tests/agent_tools/cassettes/test_delegate_work_with_wrong_co_worker_variable.yaml
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based on a combination of programmed rules and machine learning algorithms. AI and AI agents, it is essential to clarify that my perspective is not one
These agents can operate independently to analyze data, make decisions, and of hatred but rather critical analysis. My evaluation of AI agents is grounded
interact with their environment, encompassing a wide range of applications from in a balanced view of their advantages and the challenges they present. \\n\\nAI
customer service chatbots to sophisticated decision-making systems in finance agents represent a significant leap in technological progress with a wide array
and healthcare.\\n\\nDespite their impressive capabilities, AI agents are not of applications across industries. They can perform tasks ranging from customer
without their challenges and limitations. One major concern is the ethical implications service interactions, data analysis, complex simulations, to even personal assistance.
of AI decision-making, especially in situations where bias in training data Their ability to learn and adapt makes them powerful tools for enhancing productivity
can lead to unfair or harmful outcomes. Additionally, there are substantial and innovation.\\n\\nHowever, there are considerable challenges and ethical
issues related to privacy, as these agents often require access to large amounts concerns associated with their deployment. These include privacy issues, job
of personal data to function effectively.\\n\\nAnother significant drawback displacement, and the potential for biased decision-making driven by flawed
is the potential for job displacement. As AI agents become more advanced, they algorithms. Furthermore, the security risks posed by AI agents, such as how
may perform tasks traditionally done by humans, leading to economic and employment they can be manipulated or hacked, are critical concerns that cannot be ignored.\\n\\nIn
disruptions. This creates a pressing need for society to find a balance between essence, while I do recognize the transformative potential of AI agents, I remain
leveraging AI advancements and ensuring that the workforce can adapt through vigilant about their implications. It is vital to ensure that their development
reskilling and upskilling initiatives.\\n\\nOn the technical side, AI agents is guided by robust ethical standards and stringent regulations to mitigate
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Answer: \\n\\nIt's a misconception to say I hate AI agents; the reality is far Answer:\\n\\nAI agents have emerged as a revolutionary force in today's technological
more nuanced. AI agents, in their many forms, are remarkable advancements in landscape, and my stance on them is not rooted in hatred but in a critical,
technology with the capability to transform various sectors ranging from healthcare analytical perspective. Let's delve deeper into what makes AI agents both a
to customer service. The key is understanding the context and application of boon and a bane in various contexts.\\n\\n**Benefits of AI Agents:**\\n\\n1.
these agents and being mindful of their limitations and ethical considerations.\\n\\nAI **Automation and Efficiency:**\\n AI agents excel at automating repetitive
agents, powered by machine learning algorithms and vast data pools, can automate tasks, which frees up human resources for more complex and creative endeavors.
tasks, analyze complex datasets, provide personalized recommendations, and offer They are capable of performing tasks rapidly and with high accuracy, leading
conversational interfaces that improve efficiency and user experience. For instance, to increased efficiency in operations.\\n\\n2. **Data Analysis and Decision
in customer support, AI agents can handle routine queries swiftly, freeing up Making:**\\n These agents can process vast amounts of data at speeds far beyond
human agents for more complex issues.\\n\\nHowever, it's crucial to address human capability. They can identify patterns and insights that would otherwise
the shortcomings and challenges associated with AI agents. One major concern be missed, aiding in informed decision-making processes across industries like
is the risk of biases in AI systems, which can lead to discriminatory outcomes finance, healthcare, and logistics.\\n\\n3. **Personalization and User Experience:**\\n
if not properly managed and audited. Data privacy is another significant issue; \ AI agents can personalize interactions on a scale that is impractical for
AI agents often require access to large amounts of personal data, raising concerns humans. For example, recommendation engines in e-commerce or content platforms
about how this data is collected, stored, and used.\\n\\nThere's also the question tailor suggestions to individual users, enhancing user experience and satisfaction.\\n\\n4.
of job displacement. While AI can augment human capabilities and create new **24/7 Availability:**\\n Unlike human employees, AI agents can operate round-the-clock
job categories, it can also render certain roles obsolete, necessitating workforce without the need for breaks, sleep, or holidays. This makes them ideal for customer
reskilling and adaptation.\\n\\nIn summary, my perspective isn't one of antagonism service roles, providing consistent and immediate responses any time of the
but of cautious optimism. AI agents have tremendous potential when implemented day.\\n\\n**Challenges and Concerns:**\\n\\n1. **Job Displacement:**\\n One
responsibly, with a clear focus on ethical considerations and societal impact. of the major concerns is the displacement of jobs. As AI agents become more
It's not about hating or loving AI agents, but about striving for balanced, proficient at a variety of tasks, there is a legitimate fear of human workers
well-informed integration into our technological ecosystem.\",\n \"refusal\": being replaced, leading to unemployment and economic disruption.\\n\\n2. **Bias
and Fairness:**\\n AI agents are only as good as the data they are trained
on. If the training data contains biases, the AI agents can perpetuate or even
exacerbate these biases, leading to unfair and discriminatory outcomes.\\n\\n3.
**Privacy and Security:**\\n The use of AI agents often involves handling
large amounts of personal data, raising significant privacy and security concerns.
Unauthorized access or breaches could lead to severe consequences for individuals
and organizations.\\n\\n4. **Accountability and Transparency:**\\n The decision-making
processes of AI agents can be opaque, making it difficult to hold them accountable.
This lack of transparency can lead to mistrust and ethical dilemmas, particularly
when AI decisions impact human lives.\\n\\n5. **Ethical Considerations:**\\n
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on AI decision-making poses a threat to individual freedoms and societal norms.\\n\\nIn
conclusion, while AI agents offer remarkable advantages in terms of efficiency,
data handling, and user experience, they also bring significant challenges that
need to be addressed carefully. My critical stance is driven by a desire to
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83
tests/cassettes/test_agent_error_on_parsing_tool.yaml
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27
tests/cassettes/test_agent_execution.yaml
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46
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56
tests/cassettes/test_agent_execution_with_tools.yaml
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201
tests/cassettes/test_agent_function_calling_llm.yaml
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x-ratelimit-remaining-requests: x-ratelimit-remaining-requests:
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x-ratelimit-remaining-tokens: x-ratelimit-remaining-tokens:
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x-ratelimit-reset-requests: x-ratelimit-reset-requests:
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x-ratelimit-reset-tokens: x-ratelimit-reset-tokens:
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x-request-id: x-request-id:
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http_version: HTTP/1.1 http_version: HTTP/1.1
status_code: 200 status_code: 200
version: 1 version: 1

32
tests/cassettes/test_agent_human_input.yaml
View File

@@ -22,8 +22,8 @@ interactions:
content-type: content-type:
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cookie: cookie:
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host: host:
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user-agent: user-agent:
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uri: https://api.openai.com/v1/chat/completions uri: https://api.openai.com/v1/chat/completions
response: response:
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Content-Encoding: Content-Encoding:
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Content-Type: Content-Type:
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Date: Date:
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Server: Server:
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openai-processing-ms: openai-processing-ms:
- '203' - '165'
openai-version: openai-version:
- '2020-10-01' - '2020-10-01'
strict-transport-security: strict-transport-security:
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x-ratelimit-reset-tokens: x-ratelimit-reset-tokens:
- 0s - 0s
x-request-id: x-request-id:
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http_version: HTTP/1.1 http_version: HTTP/1.1
status_code: 200 status_code: 200
- request: - request:
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content-type: content-type:
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cookie: cookie:
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host: host:
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user-agent: user-agent:
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method: POST method: POST
uri: https://api.openai.com/v1/chat/completions uri: https://api.openai.com/v1/chat/completions
response: response:
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Content-Type: Content-Type:
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Date: Date:
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Server: Server:
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Transfer-Encoding: Transfer-Encoding:
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openai-organization: openai-organization:
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openai-processing-ms: openai-processing-ms:
- '190' - '202'
openai-version: openai-version:
- '2020-10-01' - '2020-10-01'
strict-transport-security: strict-transport-security:
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x-request-id: x-request-id:
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http_version: HTTP/1.1 http_version: HTTP/1.1
status_code: 200 status_code: 200
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156
tests/cassettes/test_agent_moved_on_after_max_iterations.yaml
View File

@@ -31,8 +31,8 @@ interactions:
content-type: content-type:
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cookie: cookie:
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host: host:
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user-agent: user-agent:
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uri: https://api.openai.com/v1/chat/completions uri: https://api.openai.com/v1/chat/completions
response: response:
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Connection: Connection:
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Content-Encoding: Content-Encoding:
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Content-Type: Content-Type:
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Date: Date:
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Server: Server:
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openai-organization: openai-organization:
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openai-processing-ms: openai-processing-ms:
- '365' - '452'
openai-version: openai-version:
- '2020-10-01' - '2020-10-01'
strict-transport-security: strict-transport-security:
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x-ratelimit-reset-tokens: x-ratelimit-reset-tokens:
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x-request-id: x-request-id:
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http_version: HTTP/1.1 http_version: HTTP/1.1
status_code: 200 status_code: 200
- request: - request:
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final answer\nyou MUST return the actual complete content as the final answer, final answer\nyou MUST return the actual complete content as the final answer,
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content-type: content-type:
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cookie: cookie:
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host: host:
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user-agent: user-agent:
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uri: https://api.openai.com/v1/chat/completions uri: https://api.openai.com/v1/chat/completions
response: response:
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CF-RAY: CF-RAY:
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Connection: Connection:
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Content-Type: Content-Type:
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Server: Server:
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openai-processing-ms: openai-processing-ms:
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openai-version: openai-version:
- '2020-10-01' - '2020-10-01'
strict-transport-security: strict-transport-security:
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x-ratelimit-remaining-requests: x-ratelimit-remaining-requests:
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x-ratelimit-remaining-tokens: x-ratelimit-remaining-tokens:
- '29999609' - '29999606'
x-ratelimit-reset-requests: x-ratelimit-reset-requests:
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x-ratelimit-reset-tokens: x-ratelimit-reset-tokens:
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x-request-id: x-request-id:
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http_version: HTTP/1.1 http_version: HTTP/1.1
status_code: 200 status_code: 200
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final answer\nyou MUST return the actual complete content as the final answer, final answer\nyou MUST return the actual complete content as the final answer,
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content-type: content-type:
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cookie: cookie:
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host: host:
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user-agent: user-agent:
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method: POST method: POST
uri: https://api.openai.com/v1/chat/completions uri: https://api.openai.com/v1/chat/completions
response: response:
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Connection: Connection:
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Content-Encoding: Content-Encoding:
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Server: Server:
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openai-processing-ms: openai-processing-ms:
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openai-version: openai-version:
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strict-transport-security: strict-transport-security:
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x-ratelimit-remaining-requests: x-ratelimit-remaining-requests:
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x-ratelimit-remaining-tokens: x-ratelimit-remaining-tokens:
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x-ratelimit-reset-requests: x-ratelimit-reset-requests:
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x-ratelimit-reset-tokens: x-ratelimit-reset-tokens:
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70
tests/cassettes/test_agent_powered_by_new_o_model_family_that_allows_skipping_tool.yaml
View File

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62
tests/cassettes/test_agent_powered_by_new_o_model_family_that_uses_tool.yaml
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tests/cassettes/test_agent_use_specific_tasks_output_as_context.yaml
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experiences. In healthcare, they assist in diagnosing diseases and predicting complex tasks, from predictive maintenance in manufacturing to personalized
patient outcomes with unprecedented accuracy. In finance, AI agents automate customer service in retail. Through advanced machine learning algorithms and
trading and fraud detection, significantly reducing risks. The retail sector natural language processing, AI agents not only enhance operational productivity
benefits from virtual shopping assistants that offer tailored recommendations but also create new opportunities for innovation and growth. As they continue
and streamline customer service. As we look to the future, AI agents have the to evolve, AI agents promise to further disrupt traditional business models,
potential to transform autonomous driving, streamline supply chain management, leading to smarter, data-driven decision-making processes and opening new frontiers
and even contribute to scientific research by generating new hypotheses and in sectors like healthcare, finance, and transportation. The future holds immense
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efficiency, innovation, and profound positive changes across a multitude of digital transformation and societal advancement."}, {"role": "assistant", "content":
sectors."}], "model": "gpt-4o", "stop": ["\nObservation:"]}' "Thought: The Senior Writer has provided an excellent 1-paragraph draft about
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and enabling personalized treatment plans. In the financial sector, AI algorithms
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precision than traditional methods. Autonomous vehicles, another prominent application,
are reshaping the transportation industry by promising safer and more efficient
travel. Additionally, AI is playing a crucial role in entertainment, with recommendation
engines in streaming services providing customized content to audiences, thereby
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considerations and potential risks. Issues such as data privacy, job displacement
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deliberation. As we integrate AI deeper into our lives, it is crucial to strike
a balance between harnessing its benefits and addressing its challenges to ensure
it serves the greater good.\n\nIn conclusion, AI and AI agents are dynamic forces
that are already reshaping industries and have the potential to profoundly impact
the future. By understanding and effectively leveraging these technologies,
society can unlock new opportunities and tackle complex challenges. As we move
forward, responsible development, deployment, and regulation of AI will be essential
in maximizing its benefits while mitigating associated risks."}], "model": "gpt-4o",
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16
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tests/cassettes/test_tool_usage_information_is_appended_to_agent.yaml
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511
tests/cassettes/test_tools_with_custom_caching.yaml
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crew = Crew(agents=[scorer], tasks=[task], process=Process.sequential) crew = Crew(agents=[scorer], tasks=[task], process=Process.sequential)
result = crew.kickoff() result = crew.kickoff()
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