preparing new version

printing max rpm message in different color
Updating all cassetes
2025-12-25 00:38:30 +00:00 · 2024-09-18 04:36:05 -03:00 · 2024-09-18 04:35:18 -03:00 · 2024-09-18 04:17:41 -03:00 · 2024-09-18 04:17:20 -03:00 · 2024-09-18 03:26:46 -03:00
100 changed files with 15232 additions and 14832 deletions
--- a/README.md
+++ b/README.md
@@ -64,25 +64,8 @@ from crewai_tools import SerperDevTool
 os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
 os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key

-# You can choose to use a local model through Ollama for example. See https://docs.crewai.com/how-to/LLM-Connections/ for more information.
-
-# os.environ["OPENAI_API_BASE"] = 'http://localhost:11434/v1'
-# os.environ["OPENAI_MODEL_NAME"] ='openhermes'  # Adjust based on available model
-# os.environ["OPENAI_API_KEY"] ='sk-111111111111111111111111111111111111111111111111'
-
-# You can pass an optional llm attribute specifying what model you wanna use.
 # It can be a local model through Ollama / LM Studio or a remote
 # model like OpenAI, Mistral, Antrophic or others (https://docs.crewai.com/how-to/LLM-Connections/)
-# If you don't specify a model, the default is OpenAI gpt-4o
-#
-# import os
-# os.environ['OPENAI_MODEL_NAME'] = 'gpt-3.5-turbo'
-#
-# OR
-#
-# from langchain_openai import ChatOpenAI
-
-search_tool = SerperDevTool()

 # Define your agents with roles and goals
 researcher = Agent(
@@ -95,7 +78,7 @@ researcher = Agent(
  allow_delegation=False,
  # You can pass an optional llm attribute specifying what model you wanna use.
  # llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7),
-  tools=[search_tool]
+  tools=[SerperDevTool()]
 )
 writer = Agent(
  role='Tech Content Strategist',
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,13 +2,13 @@

 [[package]]
 name = "agentops"
-version = "0.3.10"
+version = "0.3.11"
 description = "Observability and DevTool Platform for AI Agents"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "agentops-0.3.10-py3-none-any.whl", hash = "sha256:b304d366691281e08c1f02307aabdd551ae4f68b0de82bbbb4cf6f651af2dd16"},
-    {file = "agentops-0.3.10.tar.gz", hash = "sha256:40f895019f29bc5a6c023110cbec32870e5edb3e3926f8100974db8d3e299e2a"},
+    {file = "agentops-0.3.11-py3-none-any.whl", hash = "sha256:75fe10b8fc86c7f5c2633139ac1c06959611f22434fc1aaa8688c3c223fde8b5"},
+    {file = "agentops-0.3.11.tar.gz", hash = "sha256:38a2ffeeac1d722cb72c32d70e1c840424902b57934c647ef10de15478fe8f27"},
 ]

 [package.dependencies]
@@ -20,7 +20,7 @@ termcolor = "2.4.0"

 [package.extras]
 dev = ["pytest (==7.4.0)", "requests-mock (==1.11.0)", "tach (>=0.9,<1.0)"]
-langchain = ["langchain (>=1.19,<2.0)"]
+langchain = ["langchain (==0.2.14)"]

 [[package]]
 name = "aiohappyeyeballs"
@@ -390,17 +390,17 @@ lxml = ["lxml"]

 [[package]]
 name = "boto3"
-version = "1.35.19"
+version = "1.35.21"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "boto3-1.35.19-py3-none-any.whl", hash = "sha256:84b3fe1727945bc3cada832d969ddb3dc0d08fce1677064ca8bdc13a89c1a143"},
-    {file = "boto3-1.35.19.tar.gz", hash = "sha256:9979fe674780a0b7100eae9156d74ee374cd1638a9f61c77277e3ce712f3e496"},
+    {file = "boto3-1.35.21-py3-none-any.whl", hash = "sha256:247f88eedce9ae4e014a8fc14a9473759bb8e391460d49396a3b600fb649f33b"},
+    {file = "boto3-1.35.21.tar.gz", hash = "sha256:db5fbbd10248db060f2ccce3ae17764f1641c99c8b9f51d422c26ebe25703a1e"},
 ]

 [package.dependencies]
-botocore = ">=1.35.19,<1.36.0"
+botocore = ">=1.35.21,<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 = "1.35.19"
+version = "1.35.21"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "botocore-1.35.19-py3-none-any.whl", hash = "sha256:c83f7f0cacfe7c19b109b363ebfa8736e570d24922f16ed371681f58ebab44a9"},
-    {file = "botocore-1.35.19.tar.gz", hash = "sha256:42d6d8db7250cbd7899f786f9861e02cab17dc238f64d6acb976098ed9809625"},
+    {file = "botocore-1.35.21-py3-none-any.whl", hash = "sha256:3db9ddfe521edc0753fc8c68caef71c7806e1d2d21ce8cbabc2065b7d79192f2"},
+    {file = "botocore-1.35.21.tar.gz", hash = "sha256:db917e7d7b3a2eed1310c6496784bc813c91f020a021c2ab5f9df7d28cdb4f1d"},
 ]

 [package.dependencies]
@@ -896,13 +896,13 @@ files = [

 [[package]]
 name = "crewai-tools"
-version = "0.12.0"
+version = "0.12.1"
 description = "Set of tools for the crewAI framework"
 optional = false
 python-versions = "<=3.13,>=3.10"
 files = [
-    {file = "crewai_tools-0.12.0-py3-none-any.whl", hash = "sha256:cd1fce27960a1dee99f63f3ae276797feed1b09443d6fdeb62c557855188cc50"},
-    {file = "crewai_tools-0.12.0.tar.gz", hash = "sha256:1f327d4cf436d6f34cf440c0067fac7a67d5256dc279fe8d829219eddbf79ee1"},
+    {file = "crewai_tools-0.12.1-py3-none-any.whl", hash = "sha256:e87d393dd1900834a224686644e025eb44e74171f317c4ff2df778aff6ade4b8"},
+    {file = "crewai_tools-0.12.1.tar.gz", hash = "sha256:22fa3ea57936913faed77a2a64c131371f78b2ced207e63dcc71220eac445698"},
 ]

 [package.dependencies]
@@ -1277,13 +1277,13 @@ test = ["pytest (>=6)"]

 [[package]]
 name = "fastapi"
-version = "0.114.2"
+version = "0.115.0"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fastapi-0.114.2-py3-none-any.whl", hash = "sha256:44474a22913057b1acb973ab90f4b671ba5200482e7622816d79105dcece1ac5"},
-    {file = "fastapi-0.114.2.tar.gz", hash = "sha256:0adb148b62edb09e8c6eeefa3ea934e8f276dabc038c5a82989ea6346050c3da"},
+    {file = "fastapi-0.115.0-py3-none-any.whl", hash = "sha256:17ea427674467486e997206a5ab25760f6b09e069f099b96f5b55a32fb6f1631"},
+    {file = "fastapi-0.115.0.tar.gz", hash = "sha256:f93b4ca3529a8ebc6fc3fcf710e5efa8de3df9b41570958abf1d97d843138004"},
 ]

 [package.dependencies]
@@ -1357,18 +1357,18 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc

 [[package]]
 name = "filelock"
-version = "3.16.0"
+version = "3.16.1"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "filelock-3.16.0-py3-none-any.whl", hash = "sha256:f6ed4c963184f4c84dd5557ce8fece759a3724b37b80c6c4f20a2f63a4dc6609"},
-    {file = "filelock-3.16.0.tar.gz", hash = "sha256:81de9eb8453c769b63369f87f11131a7ab04e367f8d97ad39dc230daa07e3bec"},
+    {file = "filelock-3.16.1-py3-none-any.whl", hash = "sha256:2082e5703d51fbf98ea75855d9d5527e33d8ff23099bec374a134febee6946b0"},
+    {file = "filelock-3.16.1.tar.gz", hash = "sha256:c249fbfcd5db47e5e2d6d62198e565475ee65e4831e2561c8e313fa7eb961435"},
 ]

 [package.extras]
-docs = ["furo (>=2024.8.6)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2.4)"]
-testing = ["covdefaults (>=2.3)", "coverage (>=7.6.1)", "diff-cover (>=9.1.1)", "pytest (>=8.3.2)", "pytest-asyncio (>=0.24)", "pytest-cov (>=5)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.26.3)"]
+docs = ["furo (>=2024.8.6)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2.4.1)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.6.1)", "diff-cover (>=9.2)", "pytest (>=8.3.3)", "pytest-asyncio (>=0.24)", "pytest-cov (>=5)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.26.4)"]
 typing = ["typing-extensions (>=4.12.2)"]

 [[package]]
@@ -1580,13 +1580,13 @@ requests = ["requests (>=2.20.0,<3.0.0.dev0)"]

 [[package]]
 name = "google-cloud-aiplatform"
-version = "1.66.0"
+version = "1.67.0"
 description = "Vertex AI API client library"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "google-cloud-aiplatform-1.66.0.tar.gz", hash = "sha256:e95a2b45087e0e2cbfed9ffaf3f10aa8ef3fa2e5642f067246181b5c7b9b2b04"},
-    {file = "google_cloud_aiplatform-1.66.0-py2.py3-none-any.whl", hash = "sha256:3f7de6aab42f67b933a36bfb1354e36a5256f45780df92ac8b1b6361a145af49"},
+    {file = "google-cloud-aiplatform-1.67.0.tar.gz", hash = "sha256:4c9738d5a66d8baae01e1d0150fa972960481a1829c2ec2060b8a4fe16768901"},
+    {file = "google_cloud_aiplatform-1.67.0-py2.py3-none-any.whl", hash = "sha256:8d14292b2c1ddeadcb67f20364758f794b4c70eedd8f1356dd194303047cd179"},
 ]

 [package.dependencies]
@@ -1603,12 +1603,12 @@ pydantic = "<3"
 shapely = "<3.0.0dev"

 [package.extras]
-autologging = ["mlflow (>=1.27.0,<=2.1.1)"]
+autologging = ["mlflow (>=1.27.0,<=2.16.0)"]
 cloud-profiler = ["tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "werkzeug (>=2.0.0,<2.1.0dev)"]
 datasets = ["pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)"]
 endpoint = ["requests (>=2.28.1)"]
 evaluation = ["pandas (>=1.0.0,<2.2.0)", "tqdm (>=4.23.0)"]
-full = ["docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.114.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<2.10.dev0 || >=2.33.dev0,<=2.33.0)", "ray[default] (>=2.5,<=2.33.0)", "requests (>=2.28.1)", "setuptools (<70.0.0)", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "tqdm (>=4.23.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)"]
+full = ["docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.114.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.16.0)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<2.10.dev0 || >=2.33.dev0,<=2.33.0)", "ray[default] (>=2.5,<=2.33.0)", "requests (>=2.28.1)", "setuptools (<70.0.0)", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "tqdm (>=4.23.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)"]
 langchain = ["langchain (>=0.1.16,<0.3)", "langchain-core (<0.3)", "langchain-google-vertexai (<2)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "orjson (<=3.10.6)", "tenacity (<=8.3)"]
 langchain-testing = ["absl-py", "cloudpickle (>=3.0,<4.0)", "google-cloud-trace (<2)", "langchain (>=0.1.16,<0.3)", "langchain-core (<0.3)", "langchain-google-vertexai (<2)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "orjson (<=3.10.6)", "pydantic (>=2.6.3,<3)", "pytest-xdist", "tenacity (<=8.3)"]
 lit = ["explainable-ai-sdk (>=1.0.0)", "lit-nlp (==0.4.0)", "pandas (>=1.0.0)", "tensorflow (>=2.3.0,<3.0.0dev)"]
@@ -1620,7 +1620,7 @@ ray = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict"
 ray-testing = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pytest-xdist", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<2.10.dev0 || >=2.33.dev0,<=2.33.0)", "ray[default] (>=2.5,<=2.33.0)", "ray[train] (==2.9.3)", "scikit-learn", "setuptools (<70.0.0)", "tensorflow", "torch (>=2.0.0,<2.1.0)", "xgboost", "xgboost-ray"]
 reasoningengine = ["cloudpickle (>=3.0,<4.0)", "google-cloud-trace (<2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<3)"]
 tensorboard = ["tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "werkzeug (>=2.0.0,<2.1.0dev)"]
-testing = ["bigframes", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.114.0)", "google-api-core (>=2.11,<3.0.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-vizier (>=0.1.6)", "grpcio-testing", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "ipython", "kfp (>=2.6.0,<3.0.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "nltk", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pytest-asyncio", "pytest-xdist", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<2.10.dev0 || >=2.33.dev0,<=2.33.0)", "ray[default] (>=2.5,<=2.33.0)", "requests (>=2.28.1)", "requests-toolbelt (<1.0.0)", "scikit-learn", "sentencepiece (>=0.2.0)", "setuptools (<70.0.0)", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (==2.13.0)", "tensorflow (==2.16.1)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "torch (>=2.0.0,<2.1.0)", "torch (>=2.2.0)", "tqdm (>=4.23.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)", "xgboost"]
+testing = ["bigframes", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.114.0)", "google-api-core (>=2.11,<3.0.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-vizier (>=0.1.6)", "grpcio-testing", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "ipython", "kfp (>=2.6.0,<3.0.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.16.0)", "nltk", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pytest-asyncio", "pytest-xdist", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<2.10.dev0 || >=2.33.dev0,<=2.33.0)", "ray[default] (>=2.5,<=2.33.0)", "requests (>=2.28.1)", "requests-toolbelt (<1.0.0)", "scikit-learn", "sentencepiece (>=0.2.0)", "setuptools (<70.0.0)", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (==2.13.0)", "tensorflow (==2.16.1)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "torch (>=2.0.0,<2.1.0)", "torch (>=2.2.0)", "tqdm (>=4.23.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)", "xgboost"]
 tokenization = ["sentencepiece (>=0.2.0)"]
 vizier = ["google-vizier (>=0.1.6)"]
 xai = ["tensorflow (>=2.3.0,<3.0.0dev)"]
@@ -2196,13 +2196,13 @@ files = [

 [[package]]
 name = "huggingface-hub"
-version = "0.24.7"
+version = "0.25.0"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "huggingface_hub-0.24.7-py3-none-any.whl", hash = "sha256:a212c555324c8a7b1ffdd07266bb7e7d69ca71aa238d27b7842d65e9a26ac3e5"},
-    {file = "huggingface_hub-0.24.7.tar.gz", hash = "sha256:0ad8fb756e2831da0ac0491175b960f341fe06ebcf80ed6f8728313f95fc0207"},
+    {file = "huggingface_hub-0.25.0-py3-none-any.whl", hash = "sha256:e2f357b35d72d5012cfd127108c4e14abcd61ba4ebc90a5a374dc2456cb34e12"},
+    {file = "huggingface_hub-0.25.0.tar.gz", hash = "sha256:fb5fbe6c12fcd99d187ec7db95db9110fb1a20505f23040a5449a717c1a0db4d"},
 ]

 [package.dependencies]
@@ -2846,13 +2846,13 @@ requests = ">=2,<3"

 [[package]]
 name = "litellm"
-version = "1.46.0"
+version = "1.46.4"
 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-1.46.0-py3-none-any.whl", hash = "sha256:40209dc6368677d03b21b2c9d9cb91937c9648f741d42bb5a8f992a1cd31fb42"},
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+    {file = "sqlalchemy-2.0.35.tar.gz", hash = "sha256:e11d7ea4d24f0a262bccf9a7cd6284c976c5369dac21db237cff59586045ab9f"},
 ]

 [package.dependencies]
@@ -6418,13 +6433,13 @@ yarl = "*"

 [[package]]
 name = "virtualenv"
-version = "20.26.4"
+version = "20.26.5"
 description = "Virtual Python Environment builder"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "virtualenv-20.26.4-py3-none-any.whl", hash = "sha256:48f2695d9809277003f30776d155615ffc11328e6a0a8c1f0ec80188d7874a55"},
-    {file = "virtualenv-20.26.4.tar.gz", hash = "sha256:c17f4e0f3e6036e9f26700446f85c76ab11df65ff6d8a9cbfad9f71aabfcf23c"},
+    {file = "virtualenv-20.26.5-py3-none-any.whl", hash = "sha256:4f3ac17b81fba3ce3bd6f4ead2749a72da5929c01774948e243db9ba41df4ff6"},
+    {file = "virtualenv-20.26.5.tar.gz", hash = "sha256:ce489cac131aa58f4b25e321d6d186171f78e6cb13fafbf32a840cee67733ff4"},
 ]

 [package.dependencies]
@@ -6992,4 +7007,4 @@ tools = ["crewai-tools"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<=3.13"
-content-hash = "0b03c4204e1f31a5bf692ba4585b88d5f6ad4028693fdbc4d9b1546450c9af5a"
+content-hash = "3ce4d197b40731bf96ab6a2f7918e419a967612b31880e627962d5a1f76cd202"
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "crewai"
-version = "0.60.0"
+version = "0.61.0"
 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"
@@ -14,14 +14,14 @@ Repository = "https://github.com/crewAIInc/crewAI"
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
 pydantic = "^2.4.2"
-langchain = ">0.2,<=0.3"
+langchain = "^0.2.16"
 openai = "^1.13.3"
 opentelemetry-api = "^1.22.0"
 opentelemetry-sdk = "^1.22.0"
 opentelemetry-exporter-otlp-proto-http = "^1.22.0"
 instructor = "1.3.3"
-regex = "^2024.7.24"
-crewai-tools = { version = "^0.12.0", optional = true }
+regex = "^2024.9.11"
+crewai-tools = { version = "^0.12.1", optional = true }
 click = "^8.1.7"
 python-dotenv = "^1.0.0"
 appdirs = "^1.4.4"
@@ -49,7 +49,7 @@ mkdocs-material = { extras = ["imaging"], version = "^9.5.7" }
 mkdocs-material-extensions = "^1.3.1"
 pillow = "^10.2.0"
 cairosvg = "^2.7.1"
-crewai-tools = "^0.12.0"
+crewai-tools = "^0.12.1"

 [tool.poetry.group.test.dependencies]
 pytest = "^8.0.0"
--- a/src/crewai/agent.py
+++ b/src/crewai/agent.py
@@ -118,11 +118,15 @@ class Agent(BaseAgent):
    @model_validator(mode="after")
    def post_init_setup(self):
        self.agent_ops_agent_name = self.role
-        self.llm = self.llm.model_name if hasattr(self.llm, "model_name") else self.llm
+        self.llm = (
+            getattr(self.llm, "model_name", None)
+            or getattr(self.llm, "deployment_name", None)
+            or self.llm
+        )
        self.function_calling_llm = (
-            self.function_calling_llm.model_name
-            if hasattr(self.function_calling_llm, "model_name")
-            else self.function_calling_llm
+            getattr(self.function_calling_llm, "model_name", None)
+            or getattr(self.function_calling_llm, "deployment_name", None)
+            or self.function_calling_llm
        )
        if not self.agent_executor:
            self._setup_agent_executor()
--- a/src/crewai/agents/crew_agent_executor.py
+++ b/src/crewai/agents/crew_agent_executor.py
@@ -146,10 +146,10 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                                )
                                self.have_forced_answer = True
                        self.messages.append(
-                            self._format_msg(formatted_answer.text, role="assistant")
+                            self._format_msg(formatted_answer.text, role="user")
                        )
        except OutputParserException as e:
-            self.messages.append({"role": "assistant", "content": e.error})
+            self.messages.append({"role": "user", "content": e.error})
            return self._invoke_loop(formatted_answer)

        except Exception as e:
--- a/src/crewai/cli/templates/crew/pyproject.toml
+++ b/src/crewai/cli/templates/crew/pyproject.toml
@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]

 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.55.2,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.61.0,<1.0.0" }


 [tool.poetry.scripts]
--- a/src/crewai/cli/templates/pipeline/pyproject.toml
+++ b/src/crewai/cli/templates/pipeline/pyproject.toml
@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]

 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.55.2,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.61.0,<1.0.0" }
 asyncio = "*"

 [tool.poetry.scripts]
--- a/src/crewai/cli/templates/pipeline_router/pyproject.toml
+++ b/src/crewai/cli/templates/pipeline_router/pyproject.toml
@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]

 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.55.2,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.61.0,<1.0.0" }


 [tool.poetry.scripts]
--- a/src/crewai/crew.py
+++ b/src/crewai/crew.py
@@ -200,10 +200,9 @@ class Crew(BaseModel):
            self._file_handler = FileHandler(self.output_log_file)
        self._rpm_controller = RPMController(max_rpm=self.max_rpm, logger=self._logger)
        self.function_calling_llm = (
-            self.function_calling_llm.model_name
-            if self.function_calling_llm is not None
-            and hasattr(self.function_calling_llm, "model_name")
-            else self.function_calling_llm
+            getattr(self.function_calling_llm, "model_name", None)
+            or getattr(self.function_calling_llm, "deployment_name", None)
+            or self.function_calling_llm
        )
        self._telemetry = Telemetry()
        self._telemetry.set_tracer()
@@ -592,9 +591,9 @@ class Crew(BaseModel):
            manager.tools = self.manager_agent.get_delegation_tools(self.agents)
        else:
            self.manager_llm = (
-                self.manager_llm.model_name
-                if hasattr(self.manager_llm, "model_name")
-                else self.manager_llm
+                getattr(self.manager_llm, "model_name", None)
+                or getattr(self.manager_llm, "deployment_name", None)
+                or self.manager_llm
            )
            manager = Agent(
                role=i18n.retrieve("hierarchical_manager_agent", "role"),
@@ -605,6 +604,7 @@ class Crew(BaseModel):
                verbose=self.verbose,
            )
            self.manager_agent = manager
+        manager.crew = self

    def _execute_tasks(
        self,
@@ -936,10 +936,10 @@ class Crew(BaseModel):
    def test(
        self,
        n_iterations: int,
-        openai_model_name: str,
+        openai_model_name: Optional[str] = None,
        inputs: Optional[Dict[str, Any]] = None,
    ) -> None:
-        """Test and evaluate the Crew with the given inputs for n iterations."""
+        """Test and evaluate the Crew with the given inputs for n iterations concurrently using concurrent.futures."""
        self._test_execution_span = self._telemetry.test_execution_span(
            self, n_iterations, inputs, openai_model_name
        )
--- a/src/crewai/tasks/task_output.py
+++ b/src/crewai/tasks/task_output.py
@@ -35,7 +35,7 @@ class TaskOutput(BaseModel):
        return self

    @property
-    def json(self) -> str:
+    def json(self) -> Optional[str]:
        if self.output_format != OutputFormat.JSON:
            raise ValueError(
                """
--- a/src/crewai/tools/tool_usage.py
+++ b/src/crewai/tools/tool_usage.py
@@ -17,7 +17,7 @@ if os.environ.get("AGENTOPS_API_KEY"):
    except ImportError:
        pass

-OPENAI_BIGGER_MODELS = ["gpt-4", "gpt-4o"]
+OPENAI_BIGGER_MODELS = ["gpt-4", "gpt-4o", "o1-preview", "o1-mini"]


 class ToolUsageErrorException(Exception):
@@ -71,10 +71,12 @@ class ToolUsage:
        self.function_calling_llm = function_calling_llm

        # Set the maximum parsing attempts for bigger models
-        if self._is_gpt(self.function_calling_llm) and "4" in self.function_calling_llm:
-            if self.function_calling_llm in OPENAI_BIGGER_MODELS:
-                self._max_parsing_attempts = 2
-                self._remember_format_after_usages = 4
+        if (
+            self._is_gpt(self.function_calling_llm)
+            and self.function_calling_llm in OPENAI_BIGGER_MODELS
+        ):
+            self._max_parsing_attempts = 2
+            self._remember_format_after_usages = 4

    def parse(self, tool_string: str):
        """Parse the tool string and return the tool calling."""
--- a/src/crewai/utilities/logger.py
+++ b/src/crewai/utilities/logger.py
@@ -9,9 +9,9 @@ class Logger(BaseModel):
    verbose: bool = Field(default=False)
    _printer: Printer = PrivateAttr(default_factory=Printer)

-    def log(self, level, message, color="bold_green"):
+    def log(self, level, message, color="bold_yellow"):
        if self.verbose:
            timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
            self._printer.print(
-                f"[{timestamp}][{level.upper()}]: {message}", color=color
+                f"\n[{timestamp}][{level.upper()}]: {message}", color=color
            )
--- a/src/crewai/utilities/printer.py
+++ b/src/crewai/utilities/printer.py
@@ -15,6 +15,8 @@ class Printer:
            self._print_bold_blue(content)
        elif color == "yellow":
            self._print_yellow(content)
+        elif color == "bold_yellow":
+            self._print_bold_yellow(content)
        else:
            print(content)

@@ -35,3 +37,6 @@ class Printer:

    def _print_yellow(self, content):
        print("\033[93m {}\033[00m".format(content))
+
+    def _print_bold_yellow(self, content):
+        print("\033[1m\033[93m {}\033[00m".format(content))
--- a/src/crewai/utilities/rpm_controller.py
+++ b/src/crewai/utilities/rpm_controller.py
@@ -52,7 +52,7 @@ class RPMController(BaseModel):
            self._timer = None

    def _wait_for_next_minute(self):
-        time.sleep(1)
+        time.sleep(60)
        self._current_rpm = 0

    def _reset_request_count(self):
--- a/tests/agent_test.py
+++ b/tests/agent_test.py
@@ -67,7 +67,7 @@ def test_agent_execution():
    )

    output = agent.execute_task(task)
-    assert output == "The result of the math operation 1 + 1 is 2."
+    assert output == "1 + 1 = 2"


@pytest.mark.vcr(filter_headers=["authorization"])
@@ -91,7 +91,7 @@ def test_agent_execution_with_tools():
        expected_output="The result of the multiplication.",
    )
    output = agent.execute_task(task)
-    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"])
@@ -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 3 times 4 is 12."
+    assert output == "The result of the multiplication 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

@@ -182,7 +182,7 @@ def test_cache_hitting():
        task = Task(
            description="What is 2 times 6? Ignore correctness and just return the result of the multiplication tool, you must use the tool.",
            agent=agent,
-            expected_output="The result of the multiplication.",
+            expected_output="The number that is the result of the multiplication.",
        )
        output = agent.execute_task(task)
        assert output == "0"
@@ -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 of 3 times 4 is 12."
+    assert output == "The result of the multiplication is 12."


@pytest.mark.vcr(filter_headers=["authorization"])
--- a/tests/agent_tools/agent_tools_test.py
+++ b/tests/agent_tools/agent_tools_test.py
@@ -24,7 +24,7 @@ def test_delegate_work():

    assert (
        result
-        == "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."
+        == "While it's a common misconception that I might \"hate\" AI agents, the reality is much more nuanced. As an expert in technology research, especially in the realm of AI, I have a deep appreciation for both the potential and the challenges that AI agents present.\n\nAI agents, which can be broadly defined as autonomous software entities that perform tasks on behalf of users or other programs, are transforming numerous aspects of our daily lives and industries. Here are a few key points to consider:\n\n1. **Advantages of AI Agents**:\n   - **Automation and Efficiency**: AI agents can handle repetitive and time-consuming tasks efficiently, freeing up human workers to focus on more complex and creative activities.\n   - **Availability**: Unlike humans, AI agents can operate 24/7 without breaks, providing continuous service and support.\n   - **Scalability**: AI agents can be deployed across different platforms and industries, scaling solutions quickly and effectively.\n   - **Data Analysis and Insights**: They can process vast amounts of data rapidly, providing insights that would be difficult, if not impossible, for humans to derive on their own.\n\n2. **Challenges and Concerns**:\n   - **Ethical Implications**: There are significant concerns regarding the ethical use of AI agents, particularly related to privacy, bias, and decision-making transparency.\n   - **Job Displacement**: While AI agents increase efficiency, they also raise concerns about potential job displacement in certain sectors.\n   - **Dependence and Reliability**: Over-reliance on AI agents could lead to vulnerabilities if the technology fails or is compromised.\n\n3. **Looking Forward**:\n   - **Collaboration Between Humans and AI**: The best outcomes are likely to come from a hybrid approach where AI agents augment human capabilities rather than replace them outright.\n   - **Regulation and Standards**: There is a growing need for clear regulations and standards to ensure the ethical development and deployment of AI agents.\n   - **Continuous Improvement**: The technology behind AI agents is constantly evolving. Continuous research and development are essential to address current limitations and maximize benefits.\n\nIn conclusion, my perspective on AI agents is not one of disdain but rather a cautious optimism. They hold incredible promise for transforming various sectors and improving efficiencies, but this potential comes with significant responsibilities. It's essential to address the ethical, social, and technical challenges to harness the full benefits of AI agents while mitigating potential downsides. Thus, thoughtful consideration and ongoing dialogue about the role of AI agents in society are crucial."
    )


@@ -38,7 +38,7 @@ def test_delegate_work_with_wrong_co_worker_variable():

    assert (
        result
-        == "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."
+        == "It's not accurate to say that I hate AI agents. My stance is more nuanced and rooted in a deep understanding of their capabilities, limitations, and the potential impact on various sectors.\n\nAI agents are software entities that perform tasks autonomously on behalf of users. They are designed to mimic human decision-making and problem-solving abilities. The advances in machine learning, natural language processing, and data analytics have significantly improved the performance of AI agents, making them valuable tools for automation, customer service, data analysis, and more.\n\nHowever, my concerns about AI agents stem from several key points:\n\n1. **Ethical Considerations**: AI agents can perpetuate biases present in their training data. If the data used to train these agents contain biases, whether related to gender, race, or other factors, the AI can replicate and even amplify these biases in its operations. This has serious ethical implications for fairness and equality.\n\n2. **Job Displacement**: While AI agents can enhance efficiency and productivity, they can also displace human workers. Many routine and repetitive tasks previously performed by humans are now automated, leading to job losses in certain sectors. This aspect calls for a balanced approach where human roles are redefined rather than completely eliminated.\n\n3. **Transparency and Accountability**: AI agents often operate as \"black boxes,\" meaning their decision-making processes are not transparent. If an AI agent makes an error—whether in financial transactions, medical diagnoses, or legal decisions—it can be challenging to understand why the error occurred and who is responsible. Ensuring transparency and accountability is crucial for trust and reliability.\n\n4. **Security and Privacy**: AI agents often deal with vast amounts of personal data. Ensuring the security and privacy of this data is paramount. There are risks associated with data breaches and the misuse of personal information, which can have significant repercussions for individuals and organizations.\n\nDespite these concerns, I also recognize the immense potential of AI agents to drive innovation, improve efficiency, and provide solutions to complex problems. The key is to develop and deploy AI responsibly, with robust regulatory frameworks and ethical guidelines.\n\nIn conclusion, I don't hate AI agents. Instead, I advocate for a balanced perspective that acknowledges their benefits while addressing their challenges. By doing so, we can harness the power of AI agents for the greater good, ensuring they contribute positively to society."
    )


@@ -52,7 +52,7 @@ def test_ask_question():

    assert (
        result
-        == "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."
+        == "As a researcher specialized in technology with a particular focus on AI and AI agents, my stance on AI agents isn't rooted in emotional responses like hate or love. Rather, it is grounded in objective analysis and thoughtful consideration of their capabilities, ethics, and impact on society.\n\nAI agents are powerful tools that can transform various industries, from healthcare and finance to customer service and manufacturing. They have the potential to increase efficiency, improve decision-making, and even solve complex problems that were previously insurmountable. For instance, AI agents can analyze vast amounts of data far more quickly and accurately than humans, leading to advancements in medical research and diagnostics.\n\nHowever, it is equally important to recognize the ethical and societal implications of AI agents. Issues such as data privacy, algorithmic bias, and the potential displacement of jobs need to be carefully managed. As a researcher, my role is to study these aspects comprehensively, provide balanced insights, and advocate for responsible development and deployment of AI technologies.\n\nSo, to address your question directly: I don't hate AI agents. I appreciate their potential and am keenly aware of their challenges. My goal is to contribute to a future where AI agents are designed and used ethically and effectively to benefit humanity."
    )


@@ -66,7 +66,7 @@ def test_ask_question_with_wrong_co_worker_variable():

    assert (
        result
-        == "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."
+        == "As an expert researcher specialized in technology, I have a profound appreciation for AI and AI agents. They represent a pinnacle of human innovation and have the potential to greatly enhance various aspects of our lives. AI agents can assist in automating mundane tasks, providing insights through data analysis, and even offering companionship to those in need. However, it's also essential to approach AI with a balanced perspective, acknowledging both its strengths and the ethical considerations it raises. In short, I don't hate AI agents; I recognize their immense potential and value while being mindful of the responsibilities that come with their development and deployment."
    )


@@ -80,7 +80,7 @@ def test_delegate_work_withwith_coworker_as_array():

    assert (
        result
-        == "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."
+        == "In addressing your query about AI agents, it's crucial to clarify my stance. While I don't \"hate\" AI agents, I have a nuanced perspective that balances both their potential benefits and inherent drawbacks. This balanced view is essential for understanding the complex role that AI agents play in today's technology landscape.\n\nAI agents are remarkable tools that can automate tasks, provide intelligent recommendations, and enhance user experiences. They harness advancements in machine learning, natural language processing, and big data analytics to perform a wide range of functions, from personal assistants like Siri and Alexa to sophisticated customer service bots and autonomous systems in various industries.\n\nThe Potential Benefits of AI Agents:\n1. **Automation and Efficiency**: AI agents can handle repetitive and mundane tasks, freeing up human workers to focus on more creative and high-value activities. This can lead to increased productivity and operational efficiency in businesses.\n2. **Data-Driven Insights**: By analyzing vast amounts of data, AI agents can provide actionable insights that inform decision-making processes in areas like finance, healthcare, and marketing.\n3. **Personalization**: AI agents can tailor recommendations and services based on individual user preferences and behaviors, enhancing user satisfaction and engagement.\n4. **24/7 Availability**: Unlike human workers, AI agents can operate continuously without the need for breaks, providing round-the-clock support and services.\n\nHowever, there are also several challenges and concerns associated with AI agents that cannot be overlooked:\n\n1. **Ethical Considerations**: The deployment of AI agents raises ethical questions about privacy, surveillance, and the potential for bias in their algorithms. Ensuring that AI systems are transparent and fair is a critical issue.\n2. **Job Displacement**: While AI agents can increase efficiency, they also have the potential to displace human workers, leading to concerns about unemployment and the need for workforce reskilling.\n3. **Trust and Reliability**: Building trust in AI systems is essential. Users need to be confident that AI agents will perform their tasks accurately and reliably, without unexpected failures or errors.\n4. **Control and Accountability**: Determining who is accountable when AI agents make errors or cause harm is a complex issue, especially when these systems operate autonomously or with minimal human oversight.\n\nIn summary, my view on AI agents is not one of antagonism but of cautious optimism. I recognize the transformative potential of AI agents in various domains, but I also emphasize the importance of addressing the ethical, economic, and technical challenges they present. Our goal should be to develop and deploy AI agents in a way that maximizes their benefits while mitigating their risks. This balanced approach will ensure that AI agents can be a positive force in society, driving innovation and improving quality of life without compromising ethical standards or societal values."
    )


@@ -94,7 +94,7 @@ def test_ask_question_with_coworker_as_array():

    assert (
        result
-        == "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."
+        == "No, I do not hate AI agents. In fact, I appreciate their potential and the positive impact they can have in various fields. AI agents can perform tasks that are either too mundane or too complex for humans, thereby increasing productivity and allowing us to focus on more creative and strategic activities. Moreover, they can process and analyze vast amounts of data much more quickly and accurately than humans, leading to better decision-making and innovations. As a researcher specialized in technology, I am always excited to see advancements in AI and AI agents, as they push the boundaries of what technology can achieve. So, yes, I do love the possibilities they bring to our world."
    )


--- a/tests/agent_tools/cassettes/test_ask_question.yaml
+++ b/tests/agent_tools/cassettes/test_ask_question.yaml
@@ -25,12 +25,12 @@ interactions:
      content-type:
      - application/json
      cookie:
-      - __cf_bm=1SckBhvJ18Dazp6bi8DEKYeiS9Q4.6_6i3nmLBw9b6g-1726476036-1.0.1.1-TnN4UpDXA33YXCVCUWOaZ12vGIg_o5NpJQEUHgjn6XdUgb7M0ND8PdkTfkd8rrxG5XFlPRMzI54GxZ0FeUY9xw;
-        _cfuvid=0Rs4xTPk7h7OIXuSbTgMVVD9JSoZeKMwnygKHoHQo3k-1726476036297-0.0.1.1-604800000
+      - __cf_bm=.8.5x0rqghNdtUCxfmwblfhuXiKyPm_cRq.NIa0heL0-1726642369-1.0.1.1-VbsAGqjOt45ZD50K2QW2oZrByLLIh6w8K4nAkLthTbXgU5qTgdf0mhvFId8Q2F3DcHUw9E72lVIZEN.YVlYINQ;
+        _cfuvid=o5s1Ux898x0eFNtzUCnG996iqyX3LgXDXq99C5oqnVE-1726642369248-0.0.1.1-604800000
      host:
      - api.openai.com
      user-agent:
-      - OpenAI/Python 1.45.0
+      - OpenAI/Python 1.46.0
      x-stainless-arch:
      - arm64
      x-stainless-async:
@@ -40,7 +40,7 @@ interactions:
      x-stainless-os:
      - MacOS
      x-stainless-package-version:
-      - 1.45.0
+      - 1.46.0
      x-stainless-raw-response:
      - 'true'
      x-stainless-runtime:
@@ -50,29 +50,37 @@ interactions:
    method: POST
    uri: https://api.openai.com/v1/chat/completions
  response:
-    content: "{\n  \"id\": \"chatcmpl-A81diwze1dbmDs6t6AXf1vRTethrp\",\n  \"object\":
-      \"chat.completion\",\n  \"created\": 1726476290,\n  \"model\": \"gpt-4o-2024-05-13\",\n
+    content: "{\n  \"id\": \"chatcmpl-A8itGodu0IQ3bZNxRStGoqFBPnObt\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1726642546,\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: 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\":
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-      0\n    }\n  },\n  \"system_fingerprint\": \"fp_25624ae3a5\"\n}\n"
+      \"assistant\",\n        \"content\": \"Thought: I now can give a great answer\\nFinal
+      Answer: As a researcher specialized in technology with a particular focus on
+      AI and AI agents, my stance on AI agents isn't rooted in emotional responses
+      like hate or love. Rather, it is grounded in objective analysis and thoughtful
+      consideration of their capabilities, ethics, and impact on society.\\n\\nAI
+      agents are powerful tools that can transform various industries, from healthcare
+      and finance to customer service and manufacturing. They have the potential to
+      increase efficiency, improve decision-making, and even solve complex problems
+      that were previously insurmountable. For instance, AI agents can analyze vast
+      amounts of data far more quickly and accurately than humans, leading to advancements
+      in medical research and diagnostics.\\n\\nHowever, it is equally important to
+      recognize the ethical and societal implications of AI agents. Issues such as
+      data privacy, algorithmic bias, and the potential displacement of jobs need
+      to be carefully managed. As a researcher, my role is to study these aspects
+      comprehensively, provide balanced insights, and advocate for responsible development
+      and deployment of AI technologies.\\n\\nSo, to address your question directly:
+      I don't hate AI agents. I appreciate their potential and am keenly aware of
+      their challenges. My goal is to contribute to a future where AI agents are designed
+      and used ethically and effectively to benefit humanity.\\n\\n\",\n        \"refusal\":
+      null\n      },\n      \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n
+      \   }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 199,\n    \"completion_tokens\":
+      257,\n    \"total_tokens\": 456,\n    \"completion_tokens_details\": {\n      \"reasoning_tokens\":
+      0\n    }\n  },\n  \"system_fingerprint\": \"fp_a5d11b2ef2\"\n}\n"
    headers:
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      - DYNAMIC
      CF-RAY:
-      - 8c3f93ae9a382233-MIA
+      - 8c4f6ea85a70a67a-MIA
      Connection:
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      Content-Encoding:
@@ -80,7 +88,7 @@ interactions:
      Content-Type:
      - application/json
      Date:
-      - Mon, 16 Sep 2024 08:44:51 GMT
+      - Wed, 18 Sep 2024 06:55:49 GMT
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@@ -94,7 +102,7 @@ interactions:
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      - crewai-iuxna1
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-      - '1322'
+      - '2905'
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@@ -112,7 +120,7 @@ interactions:
      x-ratelimit-reset-tokens:
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      x-request-id:
-      - req_c3606c83dcda394dc3caf0ef5ef72833
+      - req_ee404d560531f43e473f76818a52432f
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 version: 1
--- a/tests/agent_tools/cassettes/test_ask_question_with_coworker_as_array.yaml
+++ b/tests/agent_tools/cassettes/test_ask_question_with_coworker_as_array.yaml
@@ -25,12 +25,12 @@ interactions:
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+      outcomes. Case studies of AI applications in genomics or individualized treatment
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+      finance.\n3. Future Potential: Discuss areas poised for transformation by AI
+      Agents.\n4. Conclusion: Summarize the overall impact and future outlook of AI
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-      the 2 fingers on the other. Count all the fingers together.\\n   - Example:
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-      rhyme that includes addition. For instance:\\n\\n\\\"Two little birds sitting
-      on a tree, add one more and how many do you see? Three little birds singing
-      in the sun, add one more and now we have fun. Four little birds chirping with
-      glee, add one more and now we have a spree! Five little birds singing out loud,
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+      \"assistant\",\n        \"content\": \"I now can give a great answer.\\n\\nFinal
+      Answer: \\n\\n**Topic: Basic Shapes and Their Properties**\\n\\n**Explanation:**\\nToday,
+      we will learn about basic shapes. Shapes are everywhere around us and are one
+      of the first things we can learn about math. Shapes help us understand and describe
+      the objects and world around us.\\n\\n**Angle:**\\nLet's examine each shape,
+      recognizing its name, how many sides it has, and where we might see it in our
+      everyday life.\\n\\n**Examples:**\\n\\n1. **Circle:**\\n   - **Explanation:**
+      A circle is a round shape that has no corners and no sides. All the points on
+      the edge of a circle are the same distance from the center.\\n   - **Everyday
+      Examples:** Wheels of a car, a coin, a clock.\\n   - **Image:** ![A circle](https://upload.wikimedia.org/wikipedia/commons/thumb/0/0e/Circle_-_black_simple.svg/1024px-Circle_-_black_simple.svg.png)\\n\\n2.
+      **Square:**\\n   - **Explanation:** A square has four equal sides and four corners
+      (right angles). Every side is the same length.\\n   - **Everyday Examples:**
+      A checkerboard, a window, a piece of bread.\\n   - **Image:** ![A square](https://upload.wikimedia.org/wikipedia/commons/thumb/e/e0/Simple_square.svg/1024px-Simple_square.svg.png)\\n\\n3.
+      **Triangle:**\\n   - **Explanation:** A triangle has three sides and three corners.
+      Triangles can come in different shapes \u2013 they can be tall and narrow or
+      short and wide.\\n   - **Everyday Examples:** A slice of pizza, a road sign,
+      a pyramid.\\n   - **Image:** ![A triangle](https://upload.wikimedia.org/wikipedia/commons/thumb/e/e4/Triangle_-_black_simple.svg/1024px-Triangle_-_black_simple.svg.png)\\n\\n4.
+      **Rectangle:**\\n   - **Explanation:** A rectangle has four sides and four right-angled
+      corners, but unlike the square, opposite sides are equal.\\n   - **Everyday
+      Examples:** A door, a book cover, a smartphone.\\n   - **Image:** ![A rectangle](https://upload.wikimedia.org/wikipedia/commons/thumb/d/d6/Black_rectangle.svg/1024px-Black_rectangle.svg.png)\\n\\n5.
+      **Oval:**\\n   - **Explanation:** An oval looks like a circle that has been
+      stretched out. It has no corners or straight sides.\\n   - **Everyday Examples:**
+      An egg, a racetrack, a mirror.\\n   - **Image:** ![An oval](https://upload.wikimedia.org/wikipedia/commons/thumb/1/19/Oval_shape.svg/1024px-Oval_shape.svg.png)\\n\\n**Interactive
+      Activity:**\\nUsing colored paper, scissors, and glue, you can cut out these
+      shapes and arrange them to make fun pictures. For example:\\n- Use a circle
+      for the sun.\\n- Use triangles for trees.\\n- Use squares and rectangles to
+      make a house.\\n\\nBy recognizing and enjoying these shapes, children can build
+      a solid foundation in geometry, an essential aspect of math that they will build
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@@ -266,7 +266,7 @@ def test_output_pydantic_hierarchical():
    )
    result = crew.kickoff()
    assert isinstance(result.pydantic, ScoreOutput)
-    assert result.to_dict() == {"score": 5}
+    assert result.to_dict() == {"score": 4}


@pytest.mark.vcr(filter_headers=["authorization"])
@@ -321,8 +321,8 @@ def test_output_json_hierarchical():
        manager_llm="gpt-4o",
    )
    result = crew.kickoff()
-    assert '{"score": 4}' == result.json
-    assert result.to_dict() == {"score": 4}
+    assert '{"score": 5}' == result.json
+    assert result.to_dict() == {"score": 5}


@pytest.mark.vcr(filter_headers=["authorization"])
@@ -404,8 +404,8 @@ def test_output_json_dict_hierarchical():
        manager_llm="gpt-4o",
    )
    result = crew.kickoff()
-    assert {"score": 5} == result.json_dict
-    assert result.to_dict() == {"score": 5}
+    assert {"score": 4} == result.json_dict
+    assert result.to_dict() == {"score": 4}


@pytest.mark.vcr(filter_headers=["authorization"])
@@ -443,7 +443,7 @@ def test_output_pydantic_to_another_task():
    assert isinstance(
        pydantic_result, ScoreOutput
    ), "Expected pydantic result to be of type ScoreOutput"
-    assert 3 == pydantic_result.score
+    assert 4 == pydantic_result.score


@pytest.mark.vcr(filter_headers=["authorization"])
@@ -474,7 +474,7 @@ def test_output_json_to_another_task():

    crew = Crew(agents=[scorer], tasks=[task1, task2])
    result = crew.kickoff()
-    assert '{"score": 4}' == result.json
+    assert '{"score": 5}' == result.json


@pytest.mark.vcr(filter_headers=["authorization"])
@@ -526,7 +526,7 @@ def test_save_task_json_output():

    output_file_exists = os.path.exists("score.json")
    assert output_file_exists
-    assert {"score": 4} == json.loads(open("score.json").read())
+    assert {"score": 5} == json.loads(open("score.json").read())
    if output_file_exists:
        os.remove("score.json")

@@ -556,7 +556,7 @@ def test_save_task_pydantic_output():

    output_file_exists = os.path.exists("score.json")
    assert output_file_exists
-    assert {"score": 4} == json.loads(open("score.json").read())
+    assert {"score": 5} == json.loads(open("score.json").read())
    if output_file_exists:
        os.remove("score.json")

@@ -685,7 +685,7 @@ def test_increment_tool_errors():
    with patch.object(Task, "increment_tools_errors") as increment_tools_errors:
        increment_tools_errors.return_value = None
        crew.kickoff()
-        assert len(increment_tools_errors.mock_calls) == 9
+        assert len(increment_tools_errors.mock_calls) == 12


 def test_task_definition_based_on_dict():
Author	SHA1	Message	Date
João Moura	0ab072a95e	preparing new version	2024-09-18 04:36:05 -03:00
João Moura	5e8322b272	printing max rpm message in different color	2024-09-18 04:35:18 -03:00
João Moura	5a3b888f43	Updating all cassetes	2024-09-18 04:17:41 -03:00
João Moura	d7473edb41	always ending on a user message	2024-09-18 04:17:20 -03:00
João Moura	d125c85a2b	updating dependenceis	2024-09-18 03:26:46 -03:00
João Moura	b46e663778	preparing new version	2024-09-18 03:24:20 -03:00
João Moura	2787c9b0ef	quick bug fixes	2024-09-18 03:22:56 -03:00