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final pass

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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]]
name = "boto3"
version = "1.35.18"
version = "1.35.19"
description = "The AWS SDK for Python"
optional = false
python-versions = ">=3.8"
files = [
{file = "boto3-1.35.18-py3-none-any.whl", hash = "sha256:71e237d3997cf93425947854d7b121c577944f391ba633afb0659e1015364704"},
{file = "boto3-1.35.18.tar.gz", hash = "sha256:fd130308f1f49d748a5fc63de92de79a995b51c79af3947ddde8815fcf0684fe"},
{file = "boto3-1.35.19-py3-none-any.whl", hash = "sha256:84b3fe1727945bc3cada832d969ddb3dc0d08fce1677064ca8bdc13a89c1a143"},
{file = "boto3-1.35.19.tar.gz", hash = "sha256:9979fe674780a0b7100eae9156d74ee374cd1638a9f61c77277e3ce712f3e496"},
]
[package.dependencies]
botocore = ">=1.35.18,<1.36.0"
botocore = ">=1.35.19,<1.36.0"
jmespath = ">=0.7.1,<2.0.0"
s3transfer = ">=0.10.0,<0.11.0"
@@ -409,13 +409,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
[[package]]
name = "botocore"
version = "1.35.18"
version = "1.35.19"
description = "Low-level, data-driven core of boto 3."
optional = false
python-versions = ">=3.8"
files = [
{file = "botocore-1.35.18-py3-none-any.whl", hash = "sha256:1027083aeb1fe74057273410fd768e018e22f85adfbd717b5a69f578f7812b80"},
{file = "botocore-1.35.18.tar.gz", hash = "sha256:e59da8b91ab06683d2725b6cbbb0383b30c68a241c3c63363f4c5bff59b3c0c0"},
{file = "botocore-1.35.19-py3-none-any.whl", hash = "sha256:c83f7f0cacfe7c19b109b363ebfa8736e570d24922f16ed371681f58ebab44a9"},
{file = "botocore-1.35.19.tar.gz", hash = "sha256:42d6d8db7250cbd7899f786f9861e02cab17dc238f64d6acb976098ed9809625"},
]
[package.dependencies]
@@ -833,13 +833,13 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
[[package]]
name = "cohere"
version = "5.9.1"
version = "5.9.2"
description = ""
optional = false
python-versions = "<4.0,>=3.8"
files = [
{file = "cohere-5.9.1-py3-none-any.whl", hash = "sha256:8e1e1dde0e1a5ee5a3f22b890e0c927989f9326bab57b6b4be812a40e2565c3a"},
{file = "cohere-5.9.1.tar.gz", hash = "sha256:de9a828b91481882bf554e94a61ccc52843a09fbc58f53b8e1fa940b17ce1dc9"},
{file = "cohere-5.9.2-py3-none-any.whl", hash = "sha256:169ee06b0a54f8a913d42b19123bd72c1a72833275d544a52606d307f5547a7b"},
{file = "cohere-5.9.2.tar.gz", hash = "sha256:1860c527b2a8a5593873a342b0bf572220b6db7966c0782076b3f2740ab3d94d"},
]
[package.dependencies]
@@ -1277,13 +1277,13 @@ test = ["pytest (>=6)"]
[[package]]
name = "fastapi"
version = "0.114.1"
version = "0.114.2"
description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
optional = false
python-versions = ">=3.8"
files = [
{file = "fastapi-0.114.1-py3-none-any.whl", hash = "sha256:5d4746f6e4b7dff0b4f6b6c6d5445645285f662fe75886e99af7ee2d6b58bb3e"},
{file = "fastapi-0.114.1.tar.gz", hash = "sha256:1d7bbbeabbaae0acb0c22f0ab0b040f642d3093ca3645f8c876b6f91391861d8"},
{file = "fastapi-0.114.2-py3-none-any.whl", hash = "sha256:44474a22913057b1acb973ab90f4b671ba5200482e7622816d79105dcece1ac5"},
{file = "fastapi-0.114.2.tar.gz", hash = "sha256:0adb148b62edb09e8c6eeefa3ea934e8f276dabc038c5a82989ea6346050c3da"},
]
[package.dependencies]
@@ -2255,13 +2255,13 @@ files = [
[[package]]
name = "identify"
version = "2.6.0"
version = "2.6.1"
description = "File identification library for Python"
optional = false
python-versions = ">=3.8"
files = [
{file = "identify-2.6.0-py2.py3-none-any.whl", hash = "sha256:e79ae4406387a9d300332b5fd366d8994f1525e8414984e1a59e058b2eda2dd0"},
{file = "identify-2.6.0.tar.gz", hash = "sha256:cb171c685bdc31bcc4c1734698736a7d5b6c8bf2e0c15117f4d469c8640ae5cf"},
{file = "identify-2.6.1-py2.py3-none-any.whl", hash = "sha256:53863bcac7caf8d2ed85bd20312ea5dcfc22226800f6d6881f232d861db5a8f0"},
{file = "identify-2.6.1.tar.gz", hash = "sha256:91478c5fb7c3aac5ff7bf9b4344f803843dc586832d5f110d672b19aa1984c98"},
]
[package.extras]
@@ -2269,15 +2269,18 @@ license = ["ukkonen"]
[[package]]
name = "idna"
version = "3.8"
version = "3.10"
description = "Internationalized Domain Names in Applications (IDNA)"
optional = false
python-versions = ">=3.6"
files = [
{file = "idna-3.8-py3-none-any.whl", hash = "sha256:050b4e5baadcd44d760cedbd2b8e639f2ff89bbc7a5730fcc662954303377aac"},
{file = "idna-3.8.tar.gz", hash = "sha256:d838c2c0ed6fced7693d5e8ab8e734d5f8fda53a039c0164afb0b82e771e3603"},
{file = "idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"},
{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]]
name = "importlib-metadata"
version = "8.4.0"
@@ -2729,21 +2732,21 @@ langchain-community = ["langchain-community (>=0.2.4)"]
[[package]]
name = "langchain-community"
version = "0.2.16"
version = "0.2.17"
description = "Community contributed LangChain integrations."
optional = false
python-versions = "<4.0,>=3.8.1"
files = [
{file = "langchain_community-0.2.16-py3-none-any.whl", hash = "sha256:115e1419c176091d4e00240cb5a38612a249e70f213516b6cacae61a8794a868"},
{file = "langchain_community-0.2.16.tar.gz", hash = "sha256:ab416b793a7aed1fa46ebaffd29993296b02286a99819eabc43be2ea8e41ae78"},
{file = "langchain_community-0.2.17-py3-none-any.whl", hash = "sha256:d07c31b641e425fb8c3e7148ad6a62e1b54a9adac6e1173021a7dd3148266063"},
{file = "langchain_community-0.2.17.tar.gz", hash = "sha256:b0745c1fcf1bd532ed4388f90b47139d6a6c6ba48a87aa68aa32d4d6bb97259d"},
]
[package.dependencies]
aiohttp = ">=3.8.3,<4.0.0"
dataclasses-json = ">=0.5.7,<0.7"
langchain = ">=0.2.16,<0.3.0"
langchain-core = ">=0.2.38,<0.3.0"
langsmith = ">=0.1.0,<0.2.0"
langchain-core = ">=0.2.39,<0.3.0"
langsmith = ">=0.1.112,<0.2.0"
numpy = [
{version = ">=1,<2", 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]]
name = "langchain-core"
version = "0.2.39"
version = "0.2.40"
description = "Building applications with LLMs through composability"
optional = false
python-versions = "<4.0,>=3.8.1"
files = [
{file = "langchain_core-0.2.39-py3-none-any.whl", hash = "sha256:5a5ad4e1c02d13e7021c026b2c7965069ad71ef67b018274cd52bab56e39143e"},
{file = "langchain_core-0.2.39.tar.gz", hash = "sha256:73d7b6f0b0212bc8069c3640d69e8d75faa51097784019ffa5c36c617c04acb0"},
{file = "langchain_core-0.2.40-py3-none-any.whl", hash = "sha256:71fff5cafa4b9c82a3a716e985f071383be452c35d8cc3169b3a393e6857fc99"},
{file = "langchain_core-0.2.40.tar.gz", hash = "sha256:c838ea0c0b73475a8e58ced3e306b6d926ef063721abd164f237c8664916f502"},
]
[package.dependencies]
@@ -2793,17 +2796,17 @@ langchain-core = ">=0.2.38,<0.3.0"
[[package]]
name = "langchain-openai"
version = "0.1.24"
version = "0.1.25"
description = "An integration package connecting OpenAI and LangChain"
optional = false
python-versions = "<4.0,>=3.8.1"
files = [
{file = "langchain_openai-0.1.24-py3-none-any.whl", hash = "sha256:7dc27f1dd50090a409fb5e970b07aaf3ff32c027cc1e8da1d1272f950e93e02c"},
{file = "langchain_openai-0.1.24.tar.gz", hash = "sha256:3f7ef5c26d232685d3c367b7ec523799563c468725e4ca2e616d7c4a96bd16f3"},
{file = "langchain_openai-0.1.25-py3-none-any.whl", hash = "sha256:f0b34a233d0d9cb8fce6006c903e57085c493c4f0e32862b99063b96eaedb109"},
{file = "langchain_openai-0.1.25.tar.gz", hash = "sha256:eb116f744f820247a72f54313fb7c01524fba0927120d4e899e5e4ab41ad3928"},
]
[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"
tiktoken = ">=0.7,<1"
@@ -2843,13 +2846,13 @@ requests = ">=2,<3"
[[package]]
name = "litellm"
version = "1.44.28"
version = "1.46.0"
description = "Library to easily interface with LLM API providers"
optional = false
python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8"
files = [
{file = "litellm-1.44.28-py3-none-any.whl", hash = "sha256:a4476c1f076b7996a97bd5d51e53760be482f1ec888c0c626c7877d5c6ff0849"},
{file = "litellm-1.44.28.tar.gz", hash = "sha256:9a9055ce3f655201e4527786c219eaa98579c0134c031418bc38744fed3cd265"},
{file = "litellm-1.46.0-py3-none-any.whl", hash = "sha256:40209dc6368677d03b21b2c9d9cb91937c9648f741d42bb5a8f992a1cd31fb42"},
{file = "litellm-1.46.0.tar.gz", hash = "sha256:6707eb4b17a2eca714f81261c3b6f33297cd25470c4843b8297e345ebdff0560"},
]
[package.dependencies]
@@ -4228,13 +4231,13 @@ testing = ["pytest", "pytest-cov", "wheel"]
[[package]]
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`."
optional = false
python-versions = ">=3.8"
files = [
{file = "platformdirs-4.3.2-py3-none-any.whl", hash = "sha256:eb1c8582560b34ed4ba105009a4badf7f6f85768b30126f351328507b2beb617"},
{file = "platformdirs-4.3.2.tar.gz", hash = "sha256:9e5e27a08aa095dd127b9f2e764d74254f482fef22b0970773bfba79d091ab8c"},
{file = "platformdirs-4.3.3-py3-none-any.whl", hash = "sha256:50a5450e2e84f44539718293cbb1da0a0885c9d14adf21b77bae4e66fc99d9b5"},
{file = "platformdirs-4.3.3.tar.gz", hash = "sha256:d4e0b7d8ec176b341fb03cb11ca12d0276faa8c485f9cd218f613840463fc2c0"},
]
[package.extras]
@@ -4837,15 +4840,18 @@ files = [
[[package]]
name = "pyreadline3"
version = "3.4.3"
version = "3.5.2"
description = "A python implementation of GNU readline."
optional = false
python-versions = "*"
python-versions = ">=3.8"
files = [
{file = "pyreadline3-3.4.3-py3-none-any.whl", hash = "sha256:f832c5898f4f9a0f81d48a8c499b39d0179de1a465ea3def1a7e7231840b4ed6"},
{file = "pyreadline3-3.4.3.tar.gz", hash = "sha256:ebab0baca37f50e2faa1dd99a6da1c75de60e0d68a3b229c134bbd12786250e2"},
{file = "pyreadline3-3.5.2-py3-none-any.whl", hash = "sha256:a87d56791e2965b2b187e2ea33dcf664600842c997c0623c95cf8ef07db83de9"},
{file = "pyreadline3-3.5.2.tar.gz", hash = "sha256:ba82292e52c5a3bb256b291af0c40b457c1e8699cac9a873abbcaac8aef3a1bb"},
]
[package.extras]
dev = ["build", "flake8", "pytest", "twine"]
[[package]]
name = "pyright"
version = "1.1.380"
@@ -5665,18 +5671,18 @@ files = [
[[package]]
name = "setuptools"
version = "74.1.2"
version = "75.0.0"
description = "Easily download, build, install, upgrade, and uninstall Python packages"
optional = false
python-versions = ">=3.8"
files = [
{file = "setuptools-74.1.2-py3-none-any.whl", hash = "sha256:5f4c08aa4d3ebcb57a50c33b1b07e94315d7fc7230f7115e47fc99776c8ce308"},
{file = "setuptools-74.1.2.tar.gz", hash = "sha256:95b40ed940a1c67eb70fc099094bd6e99c6ee7c23aa2306f4d2697ba7916f9c6"},
{file = "setuptools-75.0.0-py3-none-any.whl", hash = "sha256:791ae94f04f78c880b5e614e560dd32d4b4af5d151bd9e7483e3377846caf90a"},
{file = "setuptools-75.0.0.tar.gz", hash = "sha256:25af69c809d9334cd8e653d385277abeb5a102dca255954005a7092d282575ea"},
]
[package.extras]
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"]
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)"]
@@ -6255,13 +6261,13 @@ typing-extensions = ">=3.7.4.3"
[[package]]
name = "types-requests"
version = "2.32.0.20240907"
version = "2.32.0.20240914"
description = "Typing stubs for requests"
optional = false
python-versions = ">=3.8"
files = [
{file = "types-requests-2.32.0.20240907.tar.gz", hash = "sha256:ff33935f061b5e81ec87997e91050f7b4af4f82027a7a7a9d9aaea04a963fdf8"},
{file = "types_requests-2.32.0.20240907-py3-none-any.whl", hash = "sha256:1d1e79faeaf9d42def77f3c304893dea17a97cae98168ac69f3cb465516ee8da"},
{file = "types-requests-2.32.0.20240914.tar.gz", hash = "sha256:2850e178db3919d9bf809e434eef65ba49d0e7e33ac92d588f4a5e295fffd405"},
{file = "types_requests-2.32.0.20240914-py3-none-any.whl", hash = "sha256:59c2f673eb55f32a99b2894faf6020e1a9f4a402ad0f192bfee0b64469054310"},
]
[package.dependencies]
@@ -6962,13 +6968,13 @@ multidict = ">=4.0"
[[package]]
name = "zipp"
version = "3.20.1"
version = "3.20.2"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
python-versions = ">=3.8"
files = [
{file = "zipp-3.20.1-py3-none-any.whl", hash = "sha256:9960cd8967c8f85a56f920d5d507274e74f9ff813a0ab8889a5b5be2daf44064"},
{file = "zipp-3.20.1.tar.gz", hash = "sha256:c22b14cc4763c5a5b04134207736c107db42e9d3ef2d9779d465f5f1bcba572b"},
{file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
{file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
]
[package.extras]

2
pyproject.toml
View File

@@ -1,6 +1,6 @@
[tool.poetry]
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."
authors = ["Joao Moura <joao@crewai.com>"]
readme = "README.md"

14
src/crewai/__init__.py
View File

@@ -1,3 +1,4 @@
import warnings
from crewai.agent import Agent
from crewai.crew import Crew
from crewai.pipeline import Pipeline
@@ -5,4 +6,17 @@ from crewai.process import Process
from crewai.routers import Router
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"]

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 crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
from crewai.agents.parser import CrewAgentParser
from crewai.agents.tools_handler import ToolsHandler
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.exceptions.context_window_exceeding_exception import (
LLMContextLengthExceededException,
@@ -55,6 +57,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
self.stop = stop_words
self.max_iter = max_iter
self.callbacks = callbacks
self._printer: Printer = Printer()
self.tools_handler = tools_handler
self.original_tools = original_tools
self.step_callback = step_callback
@@ -79,6 +82,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
else:
user_prompt = self._format_prompt(self.prompt.get("prompt", ""), inputs)
self.messages.append(self._format_msg(user_prompt))
self.ask_for_human_input = bool(inputs.get("ask_for_human_input", False))
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
self.ask_for_human_input = False
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}
def _invoke_loop(self, formatted_answer=None):
@@ -119,9 +124,12 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
if isinstance(formatted_answer, AgentAction):
action_result = self._use_tool(formatted_answer)
formatted_answer.text += f"\nObservation: {action_result}"
formatted_answer.result = action_result
self._show_logs(formatted_answer)
if self.step_callback:
formatted_answer.result = action_result
self.step_callback(formatted_answer)
if self._should_force_answer():
if self.have_forced_answer:
return AgentFinish(
@@ -140,18 +148,54 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
)
except OutputParserException as e:
self.messages.append({"role": "assistant", "content": e.error})
self._invoke_loop(formatted_answer)
return self._invoke_loop(formatted_answer)
except Exception as e:
if LLMContextLengthExceededException(str(e))._is_context_limit_error(
str(e)
):
self._handle_context_length()
self._invoke_loop(formatted_answer)
return self._invoke_loop(formatted_answer)
else:
raise e
self._show_logs(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:
tool_usage = ToolUsage(
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:
thought: str
tool: str
tool_input: str
text: 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_input = tool_input
self.text = text
class AgentFinish:
thought: str
output: 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.text = text
@@ -66,6 +70,7 @@ class CrewAgentParser:
self.agent = agent
def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
thought = self._extract_thought(text)
includes_answer = FINAL_ANSWER_ACTION in text
regex = (
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('"')
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:
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):
self.agent.increment_formatting_errors()
@@ -109,6 +115,13 @@ class CrewAgentParser:
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:
"""Clean action string by removing non-essential formatting characters."""
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)
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:
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:
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:
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(
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(
llm=self.function_calling_llm,
tool_name=tool.name,
@@ -200,8 +198,6 @@ class ToolUsage:
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:
agentops.record(tool_event)
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,
) -> Union[dict, BaseModel, str]:
model = output_pydantic or output_json
if model is None:
return result
try:
escaped_result = json.dumps(json.loads(result, strict=False))
return validate_model(escaped_result, model, bool(output_json))
except json.JSONDecodeError as e:
Printer().print(
content=f"Error parsing JSON: {e}. Attempting to handle partial JSON.",
color="yellow",
)
except json.JSONDecodeError:
return handle_partial_json(
result, model, bool(output_json), agent, converter_cls
)
except ValidationError as e:
Printer().print(
content=f"Pydantic validation error: {e}. Attempting to handle partial JSON.",
color="yellow",
)
except ValidationError:
return handle_partial_json(
result, model, bool(output_json), agent, converter_cls
)
@@ -159,11 +150,8 @@ def handle_partial_json(
return exported_result
except json.JSONDecodeError:
pass
except ValidationError as e:
Printer().print(
content=f"Pydantic validation error: {e}. The JSON structure doesn't match the expected model. Attempting alternative conversion method.",
color="yellow",
)
except ValidationError:
pass
except Exception as e:
Printer().print(
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:
def print(self, content: str, color: str):
def print(self, content: str, color: str = None):
if color == "purple":
self._print_purple(content)
elif color == "red":

13
tests/agent_test.py
View File

@@ -67,7 +67,7 @@ def test_agent_execution():
)
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"])
@@ -121,7 +121,7 @@ def test_logging_tool_usage():
tool_usage = InstructorToolCalling(
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.arguments == tool_usage.arguments
@@ -275,7 +275,7 @@ def test_agent_execution_with_specific_tools():
expected_output="The result of the multiplication.",
)
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"])
@@ -490,7 +490,7 @@ def test_agent_respect_the_max_rpm_set(capsys):
task=task,
tools=[get_final_answer],
)
assert output == "The final answer is 42."
assert output == "42"
captured = capsys.readouterr()
assert "Max RPM reached, waiting for next minute to start." in captured.out
moveon.assert_called()
@@ -1110,7 +1110,10 @@ def test_handle_context_length_exceeds_limit():
backstory="test backstory",
)
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(

12
tests/agent_tools/agent_tools_test.py
View File

@@ -24,7 +24,7 @@ def test_delegate_work():
assert (
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 (
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 (
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 (
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 (
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 (
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
View File

@@ -25,8 +25,8 @@ interactions:
content-type:
- application/json
cookie:
- __cf_bm=xCQEG8vgEvQfut0bkj2xgre9z9ePZhdKdNxBphhvH6s-1726437753-1.0.1.1-5qo0zwDCcKDtqvIzDbp2sLEPCey8v5DAsf0Mg6f2RmooOW9FXTw2P66VCTTF.vCwTpWBIFk0vzKmBAPOt6urIg;
_cfuvid=TiTyEciy7HrUlIDEsELZqkUyM4Fyn8OldZd4JefFMOA-1726437753108-0.0.1.1-604800000
- __cf_bm=1SckBhvJ18Dazp6bi8DEKYeiS9Q4.6_6i3nmLBw9b6g-1726476036-1.0.1.1-TnN4UpDXA33YXCVCUWOaZ12vGIg_o5NpJQEUHgjn6XdUgb7M0ND8PdkTfkd8rrxG5XFlPRMzI54GxZ0FeUY9xw;
_cfuvid=0Rs4xTPk7h7OIXuSbTgMVVD9JSoZeKMwnygKHoHQo3k-1726476036297-0.0.1.1-604800000
host:
- api.openai.com
user-agent:
@@ -50,27 +50,29 @@ interactions:
method: POST
uri: https://api.openai.com/v1/chat/completions
response:
content: "{\n \"id\": \"chatcmpl-A7rgrY1N5kwCNwFeY3OnZmZhqOPa5\",\n \"object\":
\"chat.completion\",\n \"created\": 1726438045,\n \"model\": \"gpt-4o-2024-05-13\",\n
content: "{\n \"id\": \"chatcmpl-A81diwze1dbmDs6t6AXf1vRTethrp\",\n \"object\":
\"chat.completion\",\n \"created\": 1726476290,\n \"model\": \"gpt-4o-2024-05-13\",\n
\ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\":
\"assistant\",\n \"content\": \"Thought: I now can give a great answer\\nFinal
Answer: 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\u2014I
indeed love them for their transformative capabilities and the endless possibilities
they offer.\",\n \"refusal\": null\n },\n \"logprobs\": null,\n
\ \"finish_reason\": \"stop\"\n }\n ],\n \"usage\": {\n \"prompt_tokens\":
199,\n \"completion_tokens\": 118,\n \"total_tokens\": 317,\n \"completion_tokens_details\":
{\n \"reasoning_tokens\": 0\n }\n },\n \"system_fingerprint\": \"fp_25624ae3a5\"\n}\n"
\"assistant\",\n \"content\": \"Thought: I now can give a great answer.\\nFinal
Answer: 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.\",\n \"refusal\": null\n
\ },\n \"logprobs\": null,\n \"finish_reason\": \"stop\"\n }\n
\ ],\n \"usage\": {\n \"prompt_tokens\": 199,\n \"completion_tokens\":
145,\n \"total_tokens\": 344,\n \"completion_tokens_details\": {\n \"reasoning_tokens\":
0\n }\n },\n \"system_fingerprint\": \"fp_25624ae3a5\"\n}\n"
headers:
CF-Cache-Status:
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AI is transforming various fields, and space exploration is no exception. Dive
into how AI-powered robots, satellite data processing, and predictive analytics
are revolutionizing our understanding of the cosmos and aiding long-duration
space missions. Highlight recent missions like NASA\u2019s Perseverance rover,
which utilizes AI for autonomous navigation on Mars.\n\n2. **Ethics and Bias
in AI: Navigating a Complex Landscape**\n - **What makes it unique and interesting:**
As AI systems are increasingly making critical decisions, the ethical implications
and biases inherent in these systems are under scrutiny. Explore real-world
examples of biased algorithms affecting hiring, law enforcement, and medical
diagnoses. Discuss ongoing efforts to create ethical AI frameworks and bias
mitigation strategies.\n\n3. **The Rise of Emotional AI: Machines that Can Read
and Respond to Human Emotions**\n - **What makes it unique and interesting:**
Emotional AI aims to add a human touch to machine interactions, potentially
transforming industries like customer service, mental health, and entertainment.
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benefits, and the ethical concerns around privacy and accurate interpretation.\n\n4.
**AI and the Future of Work: Augmenting Human Productivity**\n - **What makes
it unique and interesting:** AI is often seen as a job-killer, but it also has
the potential to significantly augment human productivity and creativity. Investigate
how AI tools are being used across various professions, from legal and medical
fields to creative industries like writing and music. Present case studies of
successful AI-human collaborations.\n\n5. **From Chatbots to Personal AI Agents:
The Evolution and Future Potential**\n - **What makes it unique and interesting:**
Personal AI agents are evolving rapidly, offering more personalized and sophisticated
interactions. Trace the development from simple chatbots to advanced AI assistants
capable of managing schedules, making recommendations, and even fostering therapeutic
conversations. Predict how these advancements might change daily life and individual
productivity in the near future.\n\nThese topics not only underline the current
advancements and applications of AI but also touch upon the broader societal
impacts, making them rich and engaging subjects for an article.\n\n----------\n\n-
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This conference is widely considered the birth of AI as a field. John McCarthy,
Marvin Minsky, Nathaniel Rochester, and Claude Shannon organized the workshop
at Dartmouth College, coining the term \"Artificial Intelligence.\" The event
brought together top researchers and fostered the initial excitement and optimism
in AI research.\n\n- **1966: ELIZA, The First Chatbot**\n - **Event Details:**
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computer program that demonstrated the potential for machines to mimic human
conversation. ELIZA operated by pattern matching and simple transformations,
but it significantly influenced thoughts on human-computer interaction.\n\n-
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Garry Kasparov in a six-game match. This event marked a significant milestone
in AI, showcasing the ability of machines to perform at a high level in complex
tasks that require strategic thinking.\n\n- **2012: AlexNet Wins the ImageNet
Competition**\n - **Event Details:** AlexNet, designed by Alex Krizhevsky,
Ilya Sutskever, and Geoffrey Hinton, won the ImageNet Large Scale Visual Recognition
Challenge. The success of AlexNet demonstrated the effectiveness of deep learning
and convolutional neural networks (CNNs) in image recognition tasks, leading
to a surge in AI research and applications.\n\n- **2016: Google''s AlphaGo Defeats
World Go Champion Lee Sedol**\n - **Event Details:** AlphaGo, developed by
DeepMind (a Google subsidiary), defeated Lee Sedol, a world champion Go player,
in a five-game match. The victory was significant due to the immense complexity
of Go, underscoring advancements in machine learning, neural networks, and reinforcement
learning.\n\nThese events underscore significant advancements and turning points
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for an article, each accompanied by what makes them unique and interesting:\n\n1.
**AI in Healthcare: Revolutionizing Diagnosis and Treatment**\n - Unique and
Interesting: The integration of AI technologies into healthcare has the potential
to drastically improve diagnostic accuracy and treatment efficiency. This topic
can cover advancements like AI-driven imaging diagnostics, personalized medicine
through AI analysis of genetic information, and AI-powered virtual health assistants.
The exploration can also delve into ethical considerations and the future impact
on patient care and the healthcare workforce.\n\n2. **The Role of AI Agents
in Financial Markets and Trading**\n - Unique and Interesting: AI agents are
transforming financial markets through predictive analytics and automated trading.
This article idea could examine the influence of algorithmic trading, AI in
risk management, and how machine learning models predict market trends. Highlighting
specific use cases, such as robo-advisors for personal finance management and
AI''s role in fraud detection, can make the topic both engaging and relevant.\n\n3.
**AI-Powered Creativity: Art, Music, and Content Generation**\n - Unique and
Interesting: This topic explores how AI is redefining the boundaries of creativity.
By investigating AI tools that generate art, compose music, or write literature,
the article can provide insights into how these technologies create and inspire
human creativity. Ready examples include AI-generated artworks sold at auctions,
AI music composers collaborating with human musicians, and AI-driven content
generation tools for marketing and media.\n\n4. **Ethical Implications of Autonomous
AI Agents**\n - Unique and Interesting: The rise of autonomous AI agents,
such as self-driving cars and robotic caregivers, brings a host of ethical dilemmas.
This article can explore issues like decision-making in life-or-death situations,
biases in AI algorithms, and the accountability of autonomous agents. The discussion
can be deepened by examining current regulations, potential legal frameworks,
and contrasting different philosophical viewpoints on AI ethics.\n\n5. **AI
and Sustainability: Addressing Environmental Challenges**\n - Unique and Interesting:
AI\u2019s application in environmental conservation and sustainability is a
burgeoning field. This topic can highlight how AI is being used to combat climate
change, optimize energy use, and protect biodiversity. Examples may include
AI systems for monitoring deforestation, predictive models for environmental
conservation, and smart agriculture techniques that use AI to increase crop
yields while reducing resource consumption. The intersection of tech innovation
and ecological impact can captivate readers interested in both technology and
environmentalism.\n\n----------\n\n1. **The Dartmouth Conference (1956)**\n -
Considered the birthplace of Artificial Intelligence as a field, the Dartmouth
Conference was organized by John McCarthy, Marvin Minsky, Nathaniel Rochester,
and Claude Shannon. During this summer workshop, the term \"Artificial Intelligence\"
was coined, and it gathered leading researchers who discussed the possibility
of creating machines that could \"think\" and adapt like humans. This conference
laid the foundational ideas and aims of AI research.\n\n2. **Development of
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represented one of the earliest types of artificial neural networks. The perceptron
was designed to recognize patterns and learn from them, mimicking the way the
human brain processes visual information. This innovation was instrumental in
demonstrating that computers could be trained to perform tasks that required
a certain level of cognition and adaptability.\n\n3. **ALPAC Report (1966)**\n -
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government had a profound impact on AI research funding. It was critical of
the progress being made in machine translation and led to a significant reduction
in funding for AI research, particularly in the United States. This event marked
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interest and investment in AI research.\n\n4. **Deep Blue Defeats Garry Kasparov
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by defeating world chess champion Garry Kasparov in a six-game match. This victory
demonstrated the potential of AI in complex decision-making scenarios and showcased
the power of combining sophisticated algorithms with immense computational resources.
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practical applications of AI techniques.\n\n5. **Introduction of Deep Learning
and ImageNet (2012)**\n - In 2012, a deep learning model developed by a team
of researchers led by Geoffrey Hinton dramatically outperformed traditional
AI techniques in the ImageNet competition, a large-scale visual recognition
challenge. This event marked the resurgence of neural networks and showcased
the incredible potential of deep learning for tasks such as image and speech
recognition. It sparked a renewed interest and investment in AI research, leading
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