Add isort (#2265)

* Add isort

* Apply isort on py files

* Fix circular import

* Fix format for notebooks

* Fix format

---------

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

autogen/__init__.py

@@ -1,10 +1,10 @@
 import logging
-from .version import __version__
-from .oai import *
-from .agentchat import *
-from .exception_utils import *
-from .code_utils import DEFAULT_MODEL, FAST_MODEL
 
+from .agentchat import *
+from .code_utils import DEFAULT_MODEL, FAST_MODEL
+from .exception_utils import *
+from .oai import *
+from .version import __version__
 
 # Set the root logger.
 logger = logging.getLogger(__name__)

autogen/agentchat/__init__.py

@@ -1,9 +1,9 @@
 from .agent import Agent
 from .assistant_agent import AssistantAgent
+from .chat import ChatResult, initiate_chats
 from .conversable_agent import ConversableAgent, register_function
 from .groupchat import GroupChat, GroupChatManager
 from .user_proxy_agent import UserProxyAgent
-from .chat import initiate_chats, ChatResult
 from .utils import gather_usage_summary
 
 __all__ = (

autogen/agentchat/assistant_agent.py

@@ -1,7 +1,8 @@
 from typing import Callable, Dict, Literal, Optional, Union
 
+from autogen.runtime_logging import log_new_agent, logging_enabled
+
 from .conversable_agent import ConversableAgent
-from autogen.runtime_logging import logging_enabled, log_new_agent
 
 
 class AssistantAgent(ConversableAgent):

autogen/agentchat/chat.py

@@ -1,15 +1,15 @@
 import asyncio
-from functools import partial
-import logging
-from collections import defaultdict, abc
-from typing import Dict, List, Any, Set, Tuple
-from dataclasses import dataclass
-from .utils import consolidate_chat_info
 import datetime
+import logging
 import warnings
-from ..io.base import IOStream
-from ..formatting_utils import colored
+from collections import abc, defaultdict
+from dataclasses import dataclass
+from functools import partial
+from typing import Any, Dict, List, Set, Tuple
 
+from ..formatting_utils import colored
+from ..io.base import IOStream
+from .utils import consolidate_chat_info
 
 logger = logging.getLogger(__name__)
 Prerequisite = Tuple[int, int]

autogen/agentchat/contrib/agent_builder.py

@@ -1,10 +1,11 @@
-import autogen
-import time
-import subprocess as sp
-import socket
-import json
 import hashlib
-from typing import Optional, List, Dict, Tuple
+import json
+import socket
+import subprocess as sp
+import time
+from typing import Dict, List, Optional, Tuple
+
+import autogen
 
 
 def _config_check(config: Dict):

autogen/agentchat/contrib/agent_optimizer.py

@@ -1,8 +1,9 @@
-from autogen.code_utils import execute_code
-from typing import List, Dict, Optional
-import json
 import copy
+import json
+from typing import Dict, List, Optional
+
 import autogen
+from autogen.code_utils import execute_code
 
 ADD_FUNC = {
     "type": "function",

autogen/agentchat/contrib/capabilities/generate_images.py

@@ -5,10 +5,10 @@ from openai import OpenAI
 from PIL.Image import Image
 
 from autogen import Agent, ConversableAgent, code_utils
-from autogen.cache import AbstractCache
 from autogen.agentchat.contrib import img_utils
 from autogen.agentchat.contrib.capabilities.agent_capability import AgentCapability
 from autogen.agentchat.contrib.text_analyzer_agent import TextAnalyzerAgent
+from autogen.cache import AbstractCache
 
 SYSTEM_MESSAGE = "You've been given the special ability to generate images."
 DESCRIPTION_MESSAGE = "This agent has the ability to generate images."

autogen/agentchat/contrib/capabilities/teachability.py

@@ -1,11 +1,14 @@
 import os
+import pickle
 from typing import Dict, Optional, Union
+
 import chromadb
 from chromadb.config import Settings
-import pickle
+
 from autogen.agentchat.assistant_agent import ConversableAgent
 from autogen.agentchat.contrib.capabilities.agent_capability import AgentCapability
 from autogen.agentchat.contrib.text_analyzer_agent import TextAnalyzerAgent
+
 from ....formatting_utils import colored
 
 

autogen/agentchat/contrib/capabilities/transform_messages.py

@@ -4,6 +4,7 @@ from typing import Dict, List
 from termcolor import colored
 
 from autogen import ConversableAgent
+
 from .transforms import MessageTransform
 
 

autogen/agentchat/contrib/compressible_agent.py

@@ -1,10 +1,10 @@
-from typing import Callable, Dict, Optional, Union, Tuple, List, Any
-from autogen import OpenAIWrapper
-from autogen import Agent, ConversableAgent
-import copy
 import asyncio
-import logging
+import copy
 import inspect
+import logging
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+from autogen import Agent, ConversableAgent, OpenAIWrapper
 from autogen.token_count_utils import count_token, get_max_token_limit, num_tokens_from_functions
 
 from ...formatting_utils import colored

autogen/agentchat/contrib/gpt_assistant_agent.py

@@ -1,16 +1,16 @@
-from collections import defaultdict
-import openai
-import json
-import time
-import logging
 import copy
+import json
+import logging
+import time
+from collections import defaultdict
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import openai
 
 from autogen import OpenAIWrapper
-from autogen.oai.openai_utils import retrieve_assistants_by_name
 from autogen.agentchat.agent import Agent
-from autogen.agentchat.assistant_agent import ConversableAgent
-from autogen.agentchat.assistant_agent import AssistantAgent
-from typing import Dict, Optional, Union, List, Tuple, Any
+from autogen.agentchat.assistant_agent import AssistantAgent, ConversableAgent
+from autogen.oai.openai_utils import retrieve_assistants_by_name
 
 logger = logging.getLogger(__name__)
 

autogen/agentchat/contrib/llava_agent.py

@@ -1,6 +1,7 @@
 import json
 import logging
 from typing import List, Optional, Tuple
+
 import replicate
 import requests
 
@@ -8,8 +9,8 @@ from autogen.agentchat.agent import Agent
 from autogen.agentchat.contrib.img_utils import get_image_data, llava_formatter
 from autogen.agentchat.contrib.multimodal_conversable_agent import MultimodalConversableAgent
 from autogen.code_utils import content_str
-from ...formatting_utils import colored
 
+from ...formatting_utils import colored
 
 logger = logging.getLogger(__name__)
 

autogen/agentchat/contrib/math_user_proxy_agent.py

@@ -1,15 +1,15 @@
-import re
 import os
-from pydantic import BaseModel, Extra, root_validator
-from typing import Any, Callable, Dict, List, Optional, Union, Tuple
+import re
 from time import sleep
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+from pydantic import BaseModel, Extra, root_validator
 
 from autogen._pydantic import PYDANTIC_V1
 from autogen.agentchat import Agent, UserProxyAgent
-from autogen.code_utils import UNKNOWN, extract_code, execute_code, infer_lang
+from autogen.code_utils import UNKNOWN, execute_code, extract_code, infer_lang
 from autogen.math_utils import get_answer
 
-
 PROMPTS = {
     # default
     "default": """Let's use Python to solve a math problem.

autogen/agentchat/contrib/multimodal_conversable_agent.py

@@ -11,7 +11,6 @@ from autogen.code_utils import content_str
 
 from ..._pydantic import model_dump
 
-
 DEFAULT_LMM_SYS_MSG = """You are a helpful AI assistant."""
 DEFAULT_MODEL = "gpt-4-vision-preview"
 

autogen/agentchat/contrib/qdrant_retrieve_user_proxy_agent.py

@@ -1,15 +1,15 @@
+import logging
 from typing import Callable, Dict, List, Optional
 
 from autogen.agentchat.contrib.retrieve_user_proxy_agent import RetrieveUserProxyAgent
-from autogen.retrieve_utils import get_files_from_dir, split_files_to_chunks, TEXT_FORMATS
-import logging
+from autogen.retrieve_utils import TEXT_FORMATS, get_files_from_dir, split_files_to_chunks
 
 logger = logging.getLogger(__name__)
 
 try:
+    import fastembed
     from qdrant_client import QdrantClient, models
     from qdrant_client.fastembed_common import QueryResponse
-    import fastembed
 except ImportError as e:
     logging.fatal("Failed to import qdrant_client with fastembed. Try running 'pip install qdrant_client[fastembed]'")
     raise e

autogen/agentchat/contrib/retrieve_assistant_agent.py

@@ -1,6 +1,7 @@
+from typing import Any, Dict, List, Optional, Tuple, Union
+
 from autogen.agentchat.agent import Agent
 from autogen.agentchat.assistant_agent import AssistantAgent
-from typing import Dict, Optional, Union, List, Tuple, Any
 
 
 class RetrieveAssistantAgent(AssistantAgent):

autogen/agentchat/contrib/retrieve_user_proxy_agent.py

@@ -1,19 +1,20 @@
 import re
-from typing import Callable, Dict, Optional, Union, List, Tuple, Any
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
 from IPython import get_ipython
 
 try:
     import chromadb
 except ImportError:
     raise ImportError("Please install dependencies first. `pip install pyautogen[retrievechat]`")
-from autogen.agentchat.agent import Agent
-from autogen.agentchat import UserProxyAgent
-from autogen.retrieve_utils import create_vector_db_from_dir, query_vector_db, TEXT_FORMATS
-from autogen.token_count_utils import count_token
-from autogen.code_utils import extract_code
 from autogen import logger
-from ...formatting_utils import colored
+from autogen.agentchat import UserProxyAgent
+from autogen.agentchat.agent import Agent
+from autogen.code_utils import extract_code
+from autogen.retrieve_utils import TEXT_FORMATS, create_vector_db_from_dir, query_vector_db
+from autogen.token_count_utils import count_token
 
+from ...formatting_utils import colored
 
 PROMPT_DEFAULT = """You're a retrieve augmented chatbot. You answer user's questions based on your own knowledge and the
 context provided by the user. You should follow the following steps to answer a question:

autogen/agentchat/contrib/society_of_mind_agent.py

@@ -1,10 +1,11 @@
 # ruff: noqa: E722
+import copy
 import json
 import traceback
-import copy
 from dataclasses import dataclass
-from typing import Dict, List, Optional, Union, Callable, Literal, Tuple
-from autogen import Agent, ConversableAgent, GroupChatManager, GroupChat, OpenAIWrapper
+from typing import Callable, Dict, List, Literal, Optional, Tuple, Union
+
+from autogen import Agent, ConversableAgent, GroupChat, GroupChatManager, OpenAIWrapper
 
 
 class SocietyOfMindAgent(ConversableAgent):

autogen/agentchat/contrib/text_analyzer_agent.py

@@ -1,7 +1,8 @@
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
 from autogen import oai
 from autogen.agentchat.agent import Agent
 from autogen.agentchat.assistant_agent import ConversableAgent
-from typing import Callable, Dict, Optional, Union, List, Tuple, Any
 
 system_message = """You are an expert in text analysis.
 The user will give you TEXT to analyze.

autogen/agentchat/contrib/web_surfer.py

@@ -1,16 +1,18 @@
-import json
 import copy
+import json
 import logging
 import re
 from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Union, Callable, Literal, Tuple
+from datetime import datetime
+from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union
+
 from typing_extensions import Annotated
-from ... import Agent, ConversableAgent, AssistantAgent, UserProxyAgent, GroupChatManager, GroupChat, OpenAIWrapper
+
+from ... import Agent, AssistantAgent, ConversableAgent, GroupChat, GroupChatManager, OpenAIWrapper, UserProxyAgent
 from ...browser_utils import SimpleTextBrowser
 from ...code_utils import content_str
-from datetime import datetime
-from ...token_count_utils import count_token, get_max_token_limit
 from ...oai.openai_utils import filter_config
+from ...token_count_utils import count_token, get_max_token_limit
 
 logger = logging.getLogger(__name__)
 

autogen/agentchat/conversable_agent.py

@@ -29,10 +29,10 @@ from ..coding.base import CodeExecutor
 from ..coding.factory import CodeExecutorFactory
 from ..formatting_utils import colored
 from ..function_utils import get_function_schema, load_basemodels_if_needed, serialize_to_str
+from ..io.base import IOStream
 from ..oai.client import ModelClient, OpenAIWrapper
 from ..runtime_logging import log_new_agent, logging_enabled
 from .agent import Agent, LLMAgent
-from ..io.base import IOStream
 from .chat import ChatResult, a_initiate_chats, initiate_chats
 from .utils import consolidate_chat_info, gather_usage_summary
 

autogen/agentchat/groupchat.py

@@ -5,13 +5,13 @@ import sys
 from dataclasses import dataclass, field
 from typing import Callable, Dict, List, Literal, Optional, Tuple, Union
 
-from .agent import Agent
-from .conversable_agent import ConversableAgent
-from ..io.base import IOStream
 from ..code_utils import content_str
 from ..exception_utils import AgentNameConflict, NoEligibleSpeaker, UndefinedNextAgent
 from ..graph_utils import check_graph_validity, invert_disallowed_to_allowed
+from ..io.base import IOStream
 from ..runtime_logging import log_new_agent, logging_enabled
+from .agent import Agent
+from .conversable_agent import ConversableAgent
 
 logger = logging.getLogger(__name__)
 

autogen/agentchat/user_proxy_agent.py

@@ -1,7 +1,7 @@
 from typing import Callable, Dict, List, Literal, Optional, Union
 
+from ..runtime_logging import log_new_agent, logging_enabled
 from .conversable_agent import ConversableAgent
-from ..runtime_logging import logging_enabled, log_new_agent
 
 
 class UserProxyAgent(ConversableAgent):

autogen/browser_utils.py

@@ -1,14 +1,15 @@
-import json
-import os
-import requests
-import re
-import markdownify
 import io
-import uuid
+import json
 import mimetypes
+import os
+import re
+import uuid
+from typing import Any, Dict, List, Optional, Tuple, Union
 from urllib.parse import urljoin, urlparse
+
+import markdownify
+import requests
 from bs4 import BeautifulSoup
-from typing import Any, Dict, List, Optional, Union, Tuple
 
 # Optional PDF support
 IS_PDF_CAPABLE = False

autogen/cache/abstract_cache_base.py

@@ -1,6 +1,6 @@
+import sys
 from types import TracebackType
 from typing import Any, Optional, Protocol, Type
-import sys
 
 if sys.version_info >= (3, 11):
     from typing import Self

autogen/cache/cache.py

@@ -1,12 +1,11 @@
 from __future__ import annotations
-from types import TracebackType
-from typing import Dict, Any, Optional, Type, Union
-
-from .abstract_cache_base import AbstractCache
-
-from .cache_factory import CacheFactory
 
 import sys
+from types import TracebackType
+from typing import Any, Dict, Optional, Type, Union
+
+from .abstract_cache_base import AbstractCache
+from .cache_factory import CacheFactory
 
 if sys.version_info >= (3, 11):
     from typing import Self

autogen/cache/cache_factory.py

@@ -1,9 +1,9 @@
+import logging
 from typing import Optional, Union
+
 from .abstract_cache_base import AbstractCache
 from .disk_cache import DiskCache
 
-import logging
-
 
 class CacheFactory:
     @staticmethod

autogen/cache/disk_cache.py

@@ -1,8 +1,10 @@
+import sys
 from types import TracebackType
 from typing import Any, Optional, Type, Union
+
 import diskcache
+
 from .abstract_cache_base import AbstractCache
-import sys
 
 if sys.version_info >= (3, 11):
     from typing import Self

autogen/cache/in_memory_cache.py

@@ -1,6 +1,7 @@
+import sys
 from types import TracebackType
 from typing import Any, Dict, Optional, Type, Union
-import sys
+
 from .abstract_cache_base import AbstractCache
 
 if sys.version_info >= (3, 11):

autogen/cache/redis_cache.py

@@ -1,8 +1,10 @@
 import pickle
+import sys
 from types import TracebackType
 from typing import Any, Optional, Type, Union
+
 import redis
-import sys
+
 from .abstract_cache_base import AbstractCache
 
 if sys.version_info >= (3, 11):

autogen/code_utils.py

@@ -10,10 +10,10 @@ from concurrent.futures import ThreadPoolExecutor, TimeoutError
 from hashlib import md5
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
-from autogen import oai
-
 import docker
 
+from autogen import oai
+
 from .types import UserMessageImageContentPart, UserMessageTextContentPart
 
 SENTINEL = object()

autogen/coding/__init__.py

@@ -1,8 +1,8 @@
 from .base import CodeBlock, CodeExecutor, CodeExtractor, CodeResult
-from .factory import CodeExecutorFactory
-from .markdown_code_extractor import MarkdownCodeExtractor
-from .local_commandline_code_executor import LocalCommandLineCodeExecutor
 from .docker_commandline_code_executor import DockerCommandLineCodeExecutor
+from .factory import CodeExecutorFactory
+from .local_commandline_code_executor import LocalCommandLineCodeExecutor
+from .markdown_code_extractor import MarkdownCodeExtractor
 
 __all__ = (
     "CodeBlock",

autogen/coding/base.py

@@ -1,4 +1,5 @@
 from __future__ import annotations
+
 from typing import Any, List, Literal, Mapping, Optional, Protocol, TypedDict, Union, runtime_checkable
 
 from pydantic import BaseModel, Field

autogen/coding/docker_commandline_code_executor.py

@@ -1,22 +1,22 @@
 from __future__ import annotations
+
 import atexit
-from hashlib import md5
 import logging
+import sys
+import uuid
+from hashlib import md5
 from pathlib import Path
 from time import sleep
 from types import TracebackType
-import uuid
 from typing import Any, List, Optional, Type, Union
+
 import docker
 from docker.errors import ImageNotFound
 
-from .utils import _get_file_name_from_content, silence_pip
-from .base import CommandLineCodeResult
-
 from ..code_utils import TIMEOUT_MSG, _cmd
-from .base import CodeBlock, CodeExecutor, CodeExtractor
+from .base import CodeBlock, CodeExecutor, CodeExtractor, CommandLineCodeResult
 from .markdown_code_extractor import MarkdownCodeExtractor
-import sys
+from .utils import _get_file_name_from_content, silence_pip
 
 if sys.version_info >= (3, 11):
     from typing import Self

autogen/coding/factory.py

@@ -1,4 +1,4 @@
-from .base import CodeExecutor, CodeExecutionConfig
+from .base import CodeExecutionConfig, CodeExecutor
 
 __all__ = ("CodeExecutorFactory",)
 

autogen/coding/func_with_reqs.py

@@ -1,12 +1,14 @@
 from __future__ import annotations
-import inspect
+
 import functools
-from typing import Any, Callable, List, TypeVar, Generic, Union
-from typing_extensions import ParamSpec
-from textwrap import indent, dedent
+import importlib
+import inspect
 from dataclasses import dataclass, field
 from importlib.abc import SourceLoader
-import importlib
+from textwrap import dedent, indent
+from typing import Any, Callable, Generic, List, TypeVar, Union
+
+from typing_extensions import ParamSpec
 
 T = TypeVar("T")
 P = ParamSpec("P")

autogen/coding/jupyter/__init__.py

@@ -1,9 +1,9 @@
 from .base import JupyterConnectable, JupyterConnectionInfo
-from .jupyter_client import JupyterClient
-from .local_jupyter_server import LocalJupyterServer
 from .docker_jupyter_server import DockerJupyterServer
 from .embedded_ipython_code_executor import EmbeddedIPythonCodeExecutor
+from .jupyter_client import JupyterClient
 from .jupyter_code_executor import JupyterCodeExecutor
+from .local_jupyter_server import LocalJupyterServer
 
 __all__ = [
     "JupyterConnectable",

autogen/coding/jupyter/docker_jupyter_server.py

@@ -1,15 +1,16 @@
 from __future__ import annotations
 
-from pathlib import Path
-import sys
-from types import TracebackType
-import uuid
-from typing import Dict, Optional, Type, Union
-import docker
-import secrets
-import io
 import atexit
+import io
 import logging
+import secrets
+import sys
+import uuid
+from pathlib import Path
+from types import TracebackType
+from typing import Dict, Optional, Type, Union
+
+import docker
 
 from ..docker_commandline_code_executor import _wait_for_ready
 
@@ -19,8 +20,8 @@ else:
     from typing_extensions import Self
 
 
-from .jupyter_client import JupyterClient
 from .base import JupyterConnectable, JupyterConnectionInfo
+from .jupyter_client import JupyterClient
 
 
 class DockerJupyterServer(JupyterConnectable):

autogen/coding/jupyter/embedded_ipython_code_executor.py

@@ -1,9 +1,9 @@
 import base64
 import json
 import os
-from pathlib import Path
 import re
 import uuid
+from pathlib import Path
 from queue import Empty
 from typing import Any, ClassVar, List
 

autogen/coding/jupyter/jupyter_client.py

@@ -1,22 +1,22 @@
 from __future__ import annotations
 
+import sys
 from dataclasses import dataclass
 from types import TracebackType
 from typing import Any, Dict, List, Optional, Type, cast
-import sys
 
 if sys.version_info >= (3, 11):
     from typing import Self
 else:
     from typing_extensions import Self
 
+import datetime
 import json
 import uuid
-import datetime
-import requests
-from requests.adapters import HTTPAdapter, Retry
 
+import requests
 import websocket
+from requests.adapters import HTTPAdapter, Retry
 from websocket import WebSocket
 
 from .base import JupyterConnectionInfo

autogen/coding/jupyter/jupyter_code_executor.py

@@ -1,12 +1,12 @@
 import base64
 import json
 import os
-from pathlib import Path
 import re
-from types import TracebackType
-import uuid
-from typing import Any, ClassVar, List, Optional, Type, Union
 import sys
+import uuid
+from pathlib import Path
+from types import TracebackType
+from typing import Any, ClassVar, List, Optional, Type, Union
 
 from autogen.coding.utils import silence_pip
 

autogen/coding/jupyter/local_jupyter_server.py

@@ -1,14 +1,14 @@
 from __future__ import annotations
-from types import TracebackType
 
-from typing import Optional, Type, Union, cast
-import subprocess
-import signal
-import sys
+import atexit
 import json
 import secrets
+import signal
 import socket
-import atexit
+import subprocess
+import sys
+from types import TracebackType
+from typing import Optional, Type, Union, cast
 
 if sys.version_info >= (3, 11):
     from typing import Self

autogen/coding/local_commandline_code_executor.py

@@ -1,11 +1,15 @@
-from hashlib import md5
-from pathlib import Path
+import logging
 import re
-from string import Template
+import subprocess
 import sys
 import warnings
+from hashlib import md5
+from pathlib import Path
+from string import Template
 from typing import Any, Callable, ClassVar, List, TypeVar, Union, cast
+
 from typing_extensions import ParamSpec
+
 from autogen.coding.func_with_reqs import (
     FunctionWithRequirements,
     FunctionWithRequirementsStr,
@@ -16,13 +20,8 @@ from autogen.coding.func_with_reqs import (
 from ..code_utils import TIMEOUT_MSG, WIN32, _cmd
 from .base import CodeBlock, CodeExecutor, CodeExtractor, CommandLineCodeResult
 from .markdown_code_extractor import MarkdownCodeExtractor
-
 from .utils import _get_file_name_from_content, silence_pip
 
-import subprocess
-
-import logging
-
 __all__ = ("LocalCommandLineCodeExecutor",)
 
 A = ParamSpec("A")

autogen/coding/markdown_code_extractor.py

@@ -2,8 +2,8 @@ import re
 from typing import Any, Dict, List, Optional, Union
 
 from ..code_utils import CODE_BLOCK_PATTERN, UNKNOWN, content_str, infer_lang
-from .base import CodeBlock, CodeExtractor
 from ..types import UserMessageImageContentPart, UserMessageTextContentPart
+from .base import CodeBlock, CodeExtractor
 
 __all__ = ("MarkdownCodeExtractor",)
 

autogen/graph_utils.py

@@ -1,7 +1,7 @@
-from typing import Dict, List
 import logging
+from typing import Dict, List
 
-from autogen.agentchat.groupchat import Agent
+from autogen.agentchat import Agent
 
 
 def has_self_loops(allowed_speaker_transitions: Dict) -> bool:
@@ -115,8 +115,8 @@ def visualize_speaker_transitions_dict(speaker_transitions_dict: dict, agents: L
     Visualize the speaker_transitions_dict using networkx.
     """
     try:
-        import networkx as nx
         import matplotlib.pyplot as plt
+        import networkx as nx
     except ImportError as e:
         logging.fatal("Failed to import networkx or matplotlib. Try running 'pip install autogen[graphs]'")
         raise e

autogen/io/base.py

@@ -1,6 +1,6 @@
+import logging
 from contextlib import contextmanager
 from contextvars import ContextVar
-import logging
 from typing import Any, Iterator, Optional, Protocol, runtime_checkable
 
 __all__ = ("OutputStream", "InputStream", "IOStream")

autogen/io/websockets.py

@@ -4,7 +4,7 @@ import threading
 from contextlib import contextmanager
 from functools import partial
 from time import sleep
-from typing import Any, Callable, Dict, Iterable, Iterator, Optional, TYPE_CHECKING, Protocol, Union
+from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, Iterator, Optional, Protocol, Union
 
 from .base import IOStream
 

autogen/logger/base_logger.py

@@ -1,11 +1,11 @@
 from __future__ import annotations
 
-from abc import ABC, abstractmethod
-from typing import Any, Dict, List, TYPE_CHECKING, Union
 import sqlite3
 import uuid
+from abc import ABC, abstractmethod
+from typing import TYPE_CHECKING, Any, Dict, List, Union
 
-from openai import OpenAI, AzureOpenAI
+from openai import AzureOpenAI, OpenAI
 from openai.types.chat import ChatCompletion
 
 if TYPE_CHECKING:

autogen/logger/logger_factory.py

@@ -1,4 +1,5 @@
 from typing import Any, Dict, Optional
+
 from autogen.logger.base_logger import BaseLogger
 from autogen.logger.sqlite_logger import SqliteLogger
 

autogen/logger/sqlite_logger.py

@@ -6,16 +6,16 @@ import os
 import sqlite3
 import threading
 import uuid
+from typing import TYPE_CHECKING, Any, Dict, List, Tuple, Union
+
+from openai import AzureOpenAI, OpenAI
+from openai.types.chat import ChatCompletion
 
 from autogen.logger.base_logger import BaseLogger
 from autogen.logger.logger_utils import get_current_ts, to_dict
 
-from openai import OpenAI, AzureOpenAI
-from openai.types.chat import ChatCompletion
-from typing import Any, Dict, List, TYPE_CHECKING, Tuple, Union
 from .base_logger import LLMConfig
 
-
 if TYPE_CHECKING:
     from autogen import ConversableAgent, OpenAIWrapper
 

autogen/math_utils.py

@@ -1,5 +1,6 @@
 from typing import Optional
-from autogen import oai, DEFAULT_MODEL
+
+from autogen import DEFAULT_MODEL, oai
 
 _MATH_PROMPT = "{problem} Solve the problem carefully. Simplify your answer as much as possible. Put the final answer in \\boxed{{}}."
 _MATH_CONFIG = {

autogen/oai/__init__.py

@@ -1,15 +1,15 @@
-from autogen.oai.client import OpenAIWrapper, ModelClient
-from autogen.oai.completion import Completion, ChatCompletion
+from autogen.cache.cache import Cache
+from autogen.oai.client import ModelClient, OpenAIWrapper
+from autogen.oai.completion import ChatCompletion, Completion
 from autogen.oai.openai_utils import (
-    get_config_list,
+    config_list_from_dotenv,
+    config_list_from_json,
+    config_list_from_models,
     config_list_gpt4_gpt35,
     config_list_openai_aoai,
-    config_list_from_models,
-    config_list_from_json,
-    config_list_from_dotenv,
     filter_config,
+    get_config_list,
 )
-from autogen.cache.cache import Cache
 
 __all__ = [
     "OpenAIWrapper",

autogen/oai/client.py

@@ -1,22 +1,20 @@
 from __future__ import annotations
 
-import sys
-from typing import Any, List, Optional, Dict, Callable, Tuple, Union
-import logging
 import inspect
+import logging
+import sys
 import uuid
-from flaml.automl.logger import logger_formatter
+from typing import Any, Callable, Dict, List, Optional, Protocol, Tuple, Union
 
+from flaml.automl.logger import logger_formatter
 from pydantic import BaseModel
-from typing import Protocol
 
 from autogen.cache import Cache
 from autogen.io.base import IOStream
-from autogen.oai.openai_utils import get_key, is_valid_api_key, OAI_PRICE1K
-from autogen.token_count_utils import count_token
-
-from autogen.runtime_logging import logging_enabled, log_chat_completion, log_new_client, log_new_wrapper
 from autogen.logger.logger_utils import get_current_ts
+from autogen.oai.openai_utils import OAI_PRICE1K, get_key, is_valid_api_key
+from autogen.runtime_logging import log_chat_completion, log_new_client, log_new_wrapper, logging_enabled
+from autogen.token_count_utils import count_token
 
 TOOL_ENABLED = False
 try:
@@ -27,14 +25,15 @@ except ImportError:
     AzureOpenAI = object
 else:
     # raises exception if openai>=1 is installed and something is wrong with imports
-    from openai import OpenAI, AzureOpenAI, APIError, APITimeoutError, __version__ as OPENAIVERSION
+    from openai import APIError, APITimeoutError, AzureOpenAI, OpenAI
+    from openai import __version__ as OPENAIVERSION
     from openai.resources import Completions
     from openai.types.chat import ChatCompletion
     from openai.types.chat.chat_completion import ChatCompletionMessage, Choice  # type: ignore [attr-defined]
     from openai.types.chat.chat_completion_chunk import (
+        ChoiceDeltaFunctionCall,
         ChoiceDeltaToolCall,
         ChoiceDeltaToolCallFunction,
-        ChoiceDeltaFunctionCall,
     )
     from openai.types.completion import Completion
     from openai.types.completion_usage import CompletionUsage

autogen/oai/completion.py

@@ -1,28 +1,30 @@
-from time import sleep
 import logging
-import time
-from typing import List, Optional, Dict, Callable, Union
-import sys
 import shutil
-import numpy as np
-from flaml import tune, BlendSearch
-from flaml.tune.space import is_constant
-from flaml.automl.logger import logger_formatter
-from .openai_utils import get_key
+import sys
+import time
 from collections import defaultdict
+from time import sleep
+from typing import Callable, Dict, List, Optional, Union
+
+import numpy as np
+from flaml import BlendSearch, tune
+from flaml.automl.logger import logger_formatter
+from flaml.tune.space import is_constant
+
+from .openai_utils import get_key
 
 try:
+    import diskcache
     import openai
     from openai import (
-        RateLimitError,
-        APIError,
-        BadRequestError,
         APIConnectionError,
-        Timeout,
+        APIError,
         AuthenticationError,
+        BadRequestError,
+        RateLimitError,
+        Timeout,
     )
     from openai import Completion as openai_Completion
-    import diskcache
 
     ERROR = None
     assert openai.__version__ < "1"

autogen/oai/openai_utils.py

@@ -7,7 +7,6 @@ from pathlib import Path
 from typing import Any, Dict, List, Optional, Set, Union
 
 from dotenv import find_dotenv, load_dotenv
-
 from openai import OpenAI
 from openai.types.beta.assistant import Assistant
 

autogen/retrieve_utils.py

@@ -1,18 +1,21 @@
-from typing import List, Union, Callable
-import os
-import requests
-from urllib.parse import urlparse
 import glob
+import os
+from typing import Callable, List, Union
+from urllib.parse import urlparse
+
 import chromadb
+import requests
 
 if chromadb.__version__ < "0.4.15":
     from chromadb.api import API
 else:
     from chromadb.api import ClientAPI as API
-from chromadb.api.types import QueryResult
-import chromadb.utils.embedding_functions as ef
 import logging
+
+import chromadb.utils.embedding_functions as ef
 import pypdf
+from chromadb.api.types import QueryResult
+
 from autogen.token_count_utils import count_token
 
 try:

autogen/runtime_logging.py

@@ -1,16 +1,16 @@
 from __future__ import annotations
 
-from autogen.logger.logger_factory import LoggerFactory
-from autogen.logger.base_logger import LLMConfig
-
 import logging
 import sqlite3
-from typing import Any, Dict, List, Optional, TYPE_CHECKING, Union
 import uuid
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
 
-from openai import OpenAI, AzureOpenAI
+from openai import AzureOpenAI, OpenAI
 from openai.types.chat import ChatCompletion
 
+from autogen.logger.base_logger import LLMConfig
+from autogen.logger.logger_factory import LoggerFactory
+
 if TYPE_CHECKING:
     from autogen import ConversableAgent, OpenAIWrapper
 

autogen/token_count_utils.py

@@ -1,9 +1,9 @@
-from typing import List, Union, Dict
-import logging
 import json
-import tiktoken
+import logging
 import re
+from typing import Dict, List, Union
 
+import tiktoken
 
 logger = logging.getLogger(__name__)
 

notebook/agentchat_agentoptimizer.ipynb

@@ -5,7 +5,10 @@
    "metadata": {
     "front_matter": {
      "description": "AgentOptimizer is able to prompt LLMs to iteratively optimize function/skills of AutoGen agents according to the historical conversation and performance.",
-     "tags": ["optimization", "tool/function"]
+     "tags": [
+      "optimization",
+      "tool/function"
+     ]
     }
    },
    "source": [
@@ -36,17 +39,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import copy\n",
+    "import json\n",
     "from typing import Any, Callable, Dict, List, Optional, Tuple, Union\n",
+    "\n",
+    "from openai import BadRequestError\n",
+    "\n",
+    "import autogen\n",
+    "from autogen import config_list_from_json\n",
+    "from autogen.agentchat import Agent\n",
     "from autogen.agentchat.contrib.agent_optimizer import AgentOptimizer\n",
     "from autogen.agentchat.contrib.math_user_proxy_agent import MathUserProxyAgent\n",
-    "from autogen.agentchat import Agent\n",
-    "from openai import BadRequestError\n",
     "from autogen.code_utils import extract_code\n",
-    "from autogen.math_utils import get_answer\n",
-    "from autogen import config_list_from_json\n",
-    "import autogen\n",
-    "import json\n",
-    "import copy"
+    "from autogen.math_utils import get_answer"
    ]
   },
   {

notebook/agentchat_auto_feedback_from_code_execution.ipynb

@@ -33,11 +33,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import csv\n",
     "from typing import Dict, Union\n",
     "\n",
     "from IPython import get_ipython\n",
-    "from IPython.display import display, Image\n",
-    "import csv\n",
+    "from IPython.display import Image, display\n",
     "\n",
     "import autogen\n",
     "\n",

notebook/agentchat_capability_long_context_handling.ipynb

@@ -31,6 +31,7 @@
    "outputs": [],
    "source": [
     "import os\n",
+    "\n",
     "import autogen\n",
     "from autogen.agentchat.contrib.capabilities import context_handling"
    ]

notebook/agentchat_cost_token_tracking.ipynb

@@ -60,9 +60,7 @@
    "outputs": [],
    "source": [
     "import autogen\n",
-    "from autogen import OpenAIWrapper\n",
-    "from autogen import AssistantAgent, UserProxyAgent\n",
-    "from autogen import gather_usage_summary\n",
+    "from autogen import AssistantAgent, OpenAIWrapper, UserProxyAgent, gather_usage_summary\n",
     "\n",
     "# config_list = autogen.config_list_from_json(\n",
     "#     \"OAI_CONFIG_LIST\",\n",

notebook/agentchat_custom_model.ipynb

@@ -36,10 +36,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from types import SimpleNamespace\n",
+    "\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig\n",
+    "\n",
     "import autogen\n",
-    "from autogen import AssistantAgent, UserProxyAgent\n",
-    "from transformers import AutoTokenizer, GenerationConfig, AutoModelForCausalLM\n",
-    "from types import SimpleNamespace"
+    "from autogen import AssistantAgent, UserProxyAgent"
    ]
   },
   {

notebook/agentchat_dalle_and_gpt4v.ipynb

@@ -46,7 +46,7 @@
     "\n",
     "import autogen\n",
     "from autogen import Agent, AssistantAgent, ConversableAgent, UserProxyAgent\n",
-    "from autogen.agentchat.contrib.img_utils import _to_pil, get_image_data, gpt4v_formatter, get_pil_image\n",
+    "from autogen.agentchat.contrib.img_utils import _to_pil, get_image_data, get_pil_image, gpt4v_formatter\n",
     "from autogen.agentchat.contrib.multimodal_conversable_agent import MultimodalConversableAgent"
    ]
   },

notebook/agentchat_function_call_currency_calculator.ipynb

@@ -65,7 +65,6 @@
     "import autogen\n",
     "from autogen.cache import Cache\n",
     "\n",
-    "\n",
     "config_list = autogen.config_list_from_json(\n",
     "    \"OAI_CONFIG_LIST\",\n",
     "    filter_dict={\n",

notebook/agentchat_groupchat_RAG.ipynb

@@ -47,8 +47,8 @@
     }
    ],
    "source": [
-    "from typing_extensions import Annotated\n",
     "import chromadb\n",
+    "from typing_extensions import Annotated\n",
     "\n",
     "import autogen\n",
     "from autogen import AssistantAgent\n",

notebook/agentchat_groupchat_customized.ipynb

@@ -155,8 +155,9 @@
     "    },  # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.\n",
     ")\n",
     "\n",
+    "from typing import Dict, List\n",
+    "\n",
     "from autogen import Agent\n",
-    "from typing import List, Dict\n",
     "\n",
     "\n",
     "def custom_speaker_selection_func(last_speaker: Agent, groupchat: autogen.GroupChat):\n",

notebook/agentchat_image_generation_capability.ipynb

@@ -40,14 +40,14 @@
     "import re\n",
     "from typing import Dict, Optional\n",
     "\n",
+    "from IPython.display import display\n",
     "from PIL.Image import Image\n",
     "\n",
     "import autogen\n",
-    "from autogen.cache import Cache\n",
     "from autogen.agentchat.contrib import img_utils\n",
     "from autogen.agentchat.contrib.capabilities import generate_images\n",
-    "from autogen.oai import openai_utils\n",
-    "from IPython.display import display"
+    "from autogen.cache import Cache\n",
+    "from autogen.oai import openai_utils"
    ]
   },
   {

notebook/agentchat_lmm_gpt-4v.ipynb

@@ -43,17 +43,17 @@
     "from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import requests\n",
     "from PIL import Image\n",
     "from termcolor import colored\n",
-    "import numpy as np\n",
     "\n",
     "import autogen\n",
-    "from autogen.code_utils import content_str\n",
     "from autogen import Agent, AssistantAgent, ConversableAgent, UserProxyAgent\n",
     "from autogen.agentchat.contrib.capabilities.vision_capability import VisionCapability\n",
+    "from autogen.agentchat.contrib.img_utils import get_pil_image, pil_to_data_uri\n",
     "from autogen.agentchat.contrib.multimodal_conversable_agent import MultimodalConversableAgent\n",
-    "from autogen.agentchat.contrib.img_utils import get_pil_image, pil_to_data_uri"
+    "from autogen.code_utils import content_str"
    ]
   },
   {

notebook/agentchat_logging.ipynb

@@ -38,9 +38,11 @@
    ],
    "source": [
     "import json\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
     "import autogen\n",
     "from autogen import AssistantAgent, UserProxyAgent\n",
-    "import pandas as pd\n",
     "\n",
     "# Setup API key. Add your own API key to config file or environment variable\n",
     "llm_config = {\n",

notebook/agentchat_nested_chats_chess.ipynb

@@ -98,10 +98,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from typing import List\n",
+    "\n",
     "import chess\n",
     "import chess.svg\n",
     "from IPython.display import display\n",
-    "from typing import List\n",
     "from typing_extensions import Annotated\n",
     "\n",
     "# Initialize the board.\n",

notebook/agentchat_nestedchat.ipynb

@@ -31,9 +31,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import autogen\n",
     "from typing_extensions import Annotated\n",
     "\n",
+    "import autogen\n",
+    "\n",
     "config_list = autogen.config_list_from_json(env_or_file=\"OAI_CONFIG_LIST\")\n",
     "llm_config = {\"config_list\": config_list}"
    ]
@@ -656,9 +657,10 @@
   }
  ],
  "metadata": {
-    "extra_files_to_copy": [
-        "nested_chat_1.png", "nested_chat_2.png"
-       ],
+  "extra_files_to_copy": [
+   "nested_chat_1.png",
+   "nested_chat_2.png"
+  ],
   "front_matter": {
    "description": "Solve complex tasks with a chat nested as inner monologue.",
    "tags": [

notebook/agentchat_nestedchat_optiguide.ipynb

@@ -36,18 +36,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import autogen\n",
-    "from typing import Union\n",
-    "\n",
-    "# test Gurobi installation\n",
-    "from gurobipy import GRB\n",
-    "from eventlet.timeout import Timeout\n",
     "import re\n",
-    "from termcolor import colored\n",
-    "from autogen.code_utils import extract_code\n",
+    "from typing import Union\n",
     "\n",
     "# import auxiliary packages\n",
     "import requests  # for loading the example source code\n",
+    "from eventlet.timeout import Timeout\n",
+    "\n",
+    "# test Gurobi installation\n",
+    "from gurobipy import GRB\n",
+    "from termcolor import colored\n",
+    "\n",
+    "import autogen\n",
+    "from autogen.code_utils import extract_code\n",
     "\n",
     "config_list_gpt4 = autogen.config_list_from_json(\n",
     "    \"OAI_CONFIG_LIST\",\n",

notebook/agentchat_sql_spider.ipynb

@@ -45,12 +45,13 @@
    ],
    "source": [
     "# %pip install spider-env\n",
-    "from spider_env import SpiderEnv\n",
-    "\n",
-    "from autogen import UserProxyAgent, ConversableAgent, config_list_from_json\n",
-    "from typing import Annotated, Dict\n",
     "import json\n",
     "import os\n",
+    "from typing import Annotated, Dict\n",
+    "\n",
+    "from spider_env import SpiderEnv\n",
+    "\n",
+    "from autogen import ConversableAgent, UserProxyAgent, config_list_from_json\n",
     "\n",
     "gym = SpiderEnv()\n",
     "\n",

notebook/agentchat_transform_messages.ipynb

@@ -29,14 +29,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import copy\n",
     "import os\n",
     "import pprint\n",
-    "import copy\n",
     "import re\n",
+    "from typing import Dict, List\n",
     "\n",
     "import autogen\n",
-    "from autogen.agentchat.contrib.capabilities import transform_messages, transforms\n",
-    "from typing import Dict, List"
+    "from autogen.agentchat.contrib.capabilities import transform_messages, transforms"
    ]
   },
   {

notebook/agentchat_video_transcript_translate_with_whisper.ipynb

@@ -84,8 +84,8 @@
     "\n",
     "import whisper\n",
     "from openai import OpenAI\n",
-    "import autogen\n",
     "\n",
+    "import autogen\n",
     "\n",
     "source_language = \"English\"\n",
     "target_language = \"Chinese\"\n",

notebook/agentchat_webscraping_with_apify.ipynb

@@ -1,389 +1,389 @@
 {
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Web Scraping using Apify Tools\n",
-                "\n",
-                "This notebook shows how to use Apify tools with AutoGen agents to\n",
-                "scrape data from a website and formate the output."
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "First we need to install the Apify SDK and the AutoGen library."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "! pip install -qqq pyautogen apify-client"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Setting up the LLM configuration and the Apify API key is also required."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 11,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import os\n",
-                "\n",
-                "config_list = [\n",
-                "    {\"model\": \"gpt-4\", \"api_key\": os.getenv(\"OPENAI_API_KEY\")},\n",
-                "]\n",
-                "\n",
-                "apify_api_key = os.getenv(\"APIFY_API_KEY\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Let's define the tool for scraping data from the website using Apify actor.\n",
-                "Read more about tool use in this [tutorial chapter](/docs/tutorial/tool-use)."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 12,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "from typing_extensions import Annotated\n",
-                "from apify_client import ApifyClient\n",
-                "\n",
-                "\n",
-                "def scrape_page(url: Annotated[str, \"The URL of the web page to scrape\"]) -> Annotated[str, \"Scraped content\"]:\n",
-                "    # Initialize the ApifyClient with your API token\n",
-                "    client = ApifyClient(token=apify_api_key)\n",
-                "\n",
-                "    # Prepare the Actor input\n",
-                "    run_input = {\n",
-                "        \"startUrls\": [{\"url\": url}],\n",
-                "        \"useSitemaps\": False,\n",
-                "        \"crawlerType\": \"playwright:firefox\",\n",
-                "        \"includeUrlGlobs\": [],\n",
-                "        \"excludeUrlGlobs\": [],\n",
-                "        \"ignoreCanonicalUrl\": False,\n",
-                "        \"maxCrawlDepth\": 0,\n",
-                "        \"maxCrawlPages\": 1,\n",
-                "        \"initialConcurrency\": 0,\n",
-                "        \"maxConcurrency\": 200,\n",
-                "        \"initialCookies\": [],\n",
-                "        \"proxyConfiguration\": {\"useApifyProxy\": True},\n",
-                "        \"maxSessionRotations\": 10,\n",
-                "        \"maxRequestRetries\": 5,\n",
-                "        \"requestTimeoutSecs\": 60,\n",
-                "        \"dynamicContentWaitSecs\": 10,\n",
-                "        \"maxScrollHeightPixels\": 5000,\n",
-                "        \"removeElementsCssSelector\": \"\"\"nav, footer, script, style, noscript, svg,\n",
-                "    [role=\\\"alert\\\"],\n",
-                "    [role=\\\"banner\\\"],\n",
-                "    [role=\\\"dialog\\\"],\n",
-                "    [role=\\\"alertdialog\\\"],\n",
-                "    [role=\\\"region\\\"][aria-label*=\\\"skip\\\" i],\n",
-                "    [aria-modal=\\\"true\\\"]\"\"\",\n",
-                "        \"removeCookieWarnings\": True,\n",
-                "        \"clickElementsCssSelector\": '[aria-expanded=\"false\"]',\n",
-                "        \"htmlTransformer\": \"readableText\",\n",
-                "        \"readableTextCharThreshold\": 100,\n",
-                "        \"aggressivePrune\": False,\n",
-                "        \"debugMode\": True,\n",
-                "        \"debugLog\": True,\n",
-                "        \"saveHtml\": True,\n",
-                "        \"saveMarkdown\": True,\n",
-                "        \"saveFiles\": False,\n",
-                "        \"saveScreenshots\": False,\n",
-                "        \"maxResults\": 9999999,\n",
-                "        \"clientSideMinChangePercentage\": 15,\n",
-                "        \"renderingTypeDetectionPercentage\": 10,\n",
-                "    }\n",
-                "\n",
-                "    # Run the Actor and wait for it to finish\n",
-                "    run = client.actor(\"aYG0l9s7dbB7j3gbS\").call(run_input=run_input)\n",
-                "\n",
-                "    # Fetch and print Actor results from the run's dataset (if there are any)\n",
-                "    text_data = \"\"\n",
-                "    for item in client.dataset(run[\"defaultDatasetId\"]).iterate_items():\n",
-                "        text_data += item.get(\"text\", \"\") + \"\\n\"\n",
-                "\n",
-                "    average_token = 0.75\n",
-                "    max_tokens = 20000  # slightly less than max to be safe 32k\n",
-                "    text_data = text_data[: int(average_token * max_tokens)]\n",
-                "    return text_data"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Create the agents and register the tool."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 34,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "from autogen import ConversableAgent, register_function\n",
-                "\n",
-                "# Create web scrapper agent.\n",
-                "scraper_agent = ConversableAgent(\n",
-                "    \"WebScraper\",\n",
-                "    llm_config={\"config_list\": config_list},\n",
-                "    system_message=\"You are a web scrapper and you can scrape any web page using the tools provided. \"\n",
-                "    \"Returns 'TERMINATE' when the scraping is done.\",\n",
-                ")\n",
-                "\n",
-                "# Create user proxy agent.\n",
-                "user_proxy_agent = ConversableAgent(\n",
-                "    \"UserProxy\",\n",
-                "    llm_config=False,  # No LLM for this agent.\n",
-                "    human_input_mode=\"NEVER\",\n",
-                "    code_execution_config=False,  # No code execution for this agent.\n",
-                "    is_termination_msg=lambda x: x.get(\"content\", \"\") is not None and \"terminate\" in x[\"content\"].lower(),\n",
-                "    default_auto_reply=\"Please continue if not finished, otherwise return 'TERMINATE'.\",\n",
-                ")\n",
-                "\n",
-                "# Register the function with the agents.\n",
-                "register_function(\n",
-                "    scrape_page,\n",
-                "    caller=scraper_agent,\n",
-                "    executor=user_proxy_agent,\n",
-                "    name=\"scrape_page\",\n",
-                "    description=\"Scrape a web page and return the content.\",\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Start the conversation for scraping web data. We used the\n",
-                "`reflection_with_llm` option for summary method\n",
-                "to perform the formatting of the output into a desired format.\n",
-                "The summary method is called after the conversation is completed\n",
-                "given the complete history of the conversation."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 36,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "\u001b[33mUserProxy\u001b[0m (to WebScraper):\n",
-                        "\n",
-                        "Can you scrape agentops.ai for me?\n",
-                        "\n",
-                        "--------------------------------------------------------------------------------\n",
-                        "\u001b[31m\n",
-                        ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
-                        "\u001b[33mWebScraper\u001b[0m (to UserProxy):\n",
-                        "\n",
-                        "\u001b[32m***** Suggested tool call (call_0qok2jvCxOfv7HOA0oxPWneM): scrape_page *****\u001b[0m\n",
-                        "Arguments: \n",
-                        "{\n",
-                        "\"url\": \"https://www.agentops.ai\"\n",
-                        "}\n",
-                        "\u001b[32m****************************************************************************\u001b[0m\n",
-                        "\n",
-                        "--------------------------------------------------------------------------------\n",
-                        "\u001b[35m\n",
-                        ">>>>>>>> EXECUTING FUNCTION scrape_page...\u001b[0m\n",
-                        "\u001b[33mUserProxy\u001b[0m (to WebScraper):\n",
-                        "\n",
-                        "\u001b[33mUserProxy\u001b[0m (to WebScraper):\n",
-                        "\n",
-                        "\u001b[32m***** Response from calling tool (call_0qok2jvCxOfv7HOA0oxPWneM) *****\u001b[0m\n",
-                        "START NOW\n",
-                        "Take your business to the next level with our features \n",
-                        "AI Agents Suck.\n",
-                        "We're Fixing That. \n",
-                        "Build compliant AI agents with observability, evals, and replay analytics. No more black boxes and prompt guessing.\n",
-                        "New! Introducing AgentOps\n",
-                        "Three Lines of Code. Unlimited Testing. \n",
-                        "Instant Testing + Debugging = Compliant AI Agents That Work\n",
-                        "5\n",
-                        "# Beginning of program's code (i.e. main.py, __init__.py)\n",
-                        "6\n",
-                        "ao_client = agentops.Client(<INSERT YOUR API KEY HERE>)\n",
-                        "9\n",
-                        "# (optional: record specific functions)\n",
-                        "10\n",
-                        "@ao_client.record_action('sample function being record')\n",
-                        "11\n",
-                        "def sample_function(...):\n",
-                        "15\n",
-                        "ao_client.end_session('Success')\n",
-                        "Prototype to Production\n",
-                        "Generous free limits, upgrade only when you need it.\n",
-                        "\n",
-                        "\u001b[32m**********************************************************************\u001b[0m\n",
-                        "\n",
-                        "--------------------------------------------------------------------------------\n",
-                        "\u001b[31m\n",
-                        ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
-                        "\u001b[33mWebScraper\u001b[0m (to UserProxy):\n",
-                        "\n",
-                        "Sure, here's the information from the website agentops.ai:\n",
-                        "\n",
-                        "- Their main value proposition is to fix bad AI Agents and replace black boxes and prompt guessing with compliant, observable AI agents that come with evals and replay analytics.\n",
-                        "- Their latest product is AgentOps. The simple and instant testing & debugging offered promises better-performing compliant AI agents.\n",
-                        "- Integration is easy with just three lines of code.\n",
-                        "- They let you record specific functions.\n",
-                        "- They provide generous free limits and you only need to upgrade when necessary.\n",
-                        "\n",
-                        "Here's a sample of their code:\n",
-                        "```python\n",
-                        "ao_client = agentops.Client(<INSERT YOUR API KEY HERE>)\n",
-                        "\n",
-                        "# optional: record specific functions\n",
-                        "@ao_client.record_action('sample function being record')\n",
-                        "def sample_function(...):\n",
-                        "    ...\n",
-                        "\n",
-                        "ao_client.end_session('Success')\n",
-                        "```\n",
-                        "This code is for sample usage of their libraries/functions.\n",
-                        "\n",
-                        "Let me know if you need more specific details.\n",
-                        "\n",
-                        "--------------------------------------------------------------------------------\n",
-                        "\u001b[33mUserProxy\u001b[0m (to WebScraper):\n",
-                        "\n",
-                        "Please continue if not finished, otherwise return 'TERMINATE'.\n",
-                        "\n",
-                        "--------------------------------------------------------------------------------\n",
-                        "\u001b[31m\n",
-                        ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
-                        "\u001b[33mWebScraper\u001b[0m (to UserProxy):\n",
-                        "\n",
-                        "TERMINATE\n",
-                        "\n",
-                        "--------------------------------------------------------------------------------\n"
-                    ]
-                }
-            ],
-            "source": [
-                "chat_result = user_proxy_agent.initiate_chat(\n",
-                "    scraper_agent,\n",
-                "    message=\"Can you scrape agentops.ai for me?\",\n",
-                "    summary_method=\"reflection_with_llm\",\n",
-                "    summary_args={\n",
-                "        \"summary_prompt\": \"\"\"Summarize the scraped content and format summary EXACTLY as follows:\n",
-                "---\n",
-                "*Company name*:\n",
-                "`Acme Corp`\n",
-                "---\n",
-                "*Website*:\n",
-                "`acmecorp.com`\n",
-                "---\n",
-                "*Description*:\n",
-                "`Company that does things.`\n",
-                "---\n",
-                "*Tags*:\n",
-                "`Manufacturing. Retail. E-commerce.`\n",
-                "---\n",
-                "*Takeaways*:\n",
-                "`Provides shareholders with value by selling products.`\n",
-                "---\n",
-                "*Questions*:\n",
-                "`What products do they sell? How do they make money? What is their market share?`\n",
-                "---\n",
-                "\"\"\"\n",
-                "    },\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "The output is stored in the summary."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 37,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "---\n",
-                        "*Company name*:\n",
-                        "`AgentOps`\n",
-                        "---\n",
-                        "*Website*:\n",
-                        "`agentops.ai`\n",
-                        "---\n",
-                        "*Description*:\n",
-                        "`Company that aims to improve AI agents. They offer observed and evaluable AI agents with replay analytics as an alternative to black box models and blind prompting.`\n",
-                        "---\n",
-                        "*Tags*:\n",
-                        "`Artificial Intelligence, AI agents, Observability, Analytics.`\n",
-                        "---\n",
-                        "*Takeaways*:\n",
-                        "`Their product, AgentOps, allows for easy and instant testing and debugging of AI agents. Integration is as simple as writing three lines of code. They also provide generous free limits and mandate upgrades only when necessary.`\n",
-                        "---\n",
-                        "*Questions*:\n",
-                        "`What differentiates AgentOps from other, similar products? How does their pricing scale with usage? What are the details of their \"generous free limits\"?`\n",
-                        "---\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(chat_result.summary)"
-            ]
-        }
-    ],
-    "metadata": {
-        "front_matter": {
-            "description": "Scrapping web pages and summarizing the content using agents with tools.",
-            "tags": [
-                "web scraping",
-                "apify",
-                "tool use"
-            ],
-            "title": "Web Scraper Agent using Apify Tools"
-        },
-        "kernelspec": {
-            "display_name": "autogen",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.11.5"
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 2
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Web Scraping using Apify Tools\n",
+    "\n",
+    "This notebook shows how to use Apify tools with AutoGen agents to\n",
+    "scrape data from a website and formate the output."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we need to install the Apify SDK and the AutoGen library."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install -qqq pyautogen apify-client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Setting up the LLM configuration and the Apify API key is also required."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "config_list = [\n",
+    "    {\"model\": \"gpt-4\", \"api_key\": os.getenv(\"OPENAI_API_KEY\")},\n",
+    "]\n",
+    "\n",
+    "apify_api_key = os.getenv(\"APIFY_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's define the tool for scraping data from the website using Apify actor.\n",
+    "Read more about tool use in this [tutorial chapter](/docs/tutorial/tool-use)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from apify_client import ApifyClient\n",
+    "from typing_extensions import Annotated\n",
+    "\n",
+    "\n",
+    "def scrape_page(url: Annotated[str, \"The URL of the web page to scrape\"]) -> Annotated[str, \"Scraped content\"]:\n",
+    "    # Initialize the ApifyClient with your API token\n",
+    "    client = ApifyClient(token=apify_api_key)\n",
+    "\n",
+    "    # Prepare the Actor input\n",
+    "    run_input = {\n",
+    "        \"startUrls\": [{\"url\": url}],\n",
+    "        \"useSitemaps\": False,\n",
+    "        \"crawlerType\": \"playwright:firefox\",\n",
+    "        \"includeUrlGlobs\": [],\n",
+    "        \"excludeUrlGlobs\": [],\n",
+    "        \"ignoreCanonicalUrl\": False,\n",
+    "        \"maxCrawlDepth\": 0,\n",
+    "        \"maxCrawlPages\": 1,\n",
+    "        \"initialConcurrency\": 0,\n",
+    "        \"maxConcurrency\": 200,\n",
+    "        \"initialCookies\": [],\n",
+    "        \"proxyConfiguration\": {\"useApifyProxy\": True},\n",
+    "        \"maxSessionRotations\": 10,\n",
+    "        \"maxRequestRetries\": 5,\n",
+    "        \"requestTimeoutSecs\": 60,\n",
+    "        \"dynamicContentWaitSecs\": 10,\n",
+    "        \"maxScrollHeightPixels\": 5000,\n",
+    "        \"removeElementsCssSelector\": \"\"\"nav, footer, script, style, noscript, svg,\n",
+    "    [role=\\\"alert\\\"],\n",
+    "    [role=\\\"banner\\\"],\n",
+    "    [role=\\\"dialog\\\"],\n",
+    "    [role=\\\"alertdialog\\\"],\n",
+    "    [role=\\\"region\\\"][aria-label*=\\\"skip\\\" i],\n",
+    "    [aria-modal=\\\"true\\\"]\"\"\",\n",
+    "        \"removeCookieWarnings\": True,\n",
+    "        \"clickElementsCssSelector\": '[aria-expanded=\"false\"]',\n",
+    "        \"htmlTransformer\": \"readableText\",\n",
+    "        \"readableTextCharThreshold\": 100,\n",
+    "        \"aggressivePrune\": False,\n",
+    "        \"debugMode\": True,\n",
+    "        \"debugLog\": True,\n",
+    "        \"saveHtml\": True,\n",
+    "        \"saveMarkdown\": True,\n",
+    "        \"saveFiles\": False,\n",
+    "        \"saveScreenshots\": False,\n",
+    "        \"maxResults\": 9999999,\n",
+    "        \"clientSideMinChangePercentage\": 15,\n",
+    "        \"renderingTypeDetectionPercentage\": 10,\n",
+    "    }\n",
+    "\n",
+    "    # Run the Actor and wait for it to finish\n",
+    "    run = client.actor(\"aYG0l9s7dbB7j3gbS\").call(run_input=run_input)\n",
+    "\n",
+    "    # Fetch and print Actor results from the run's dataset (if there are any)\n",
+    "    text_data = \"\"\n",
+    "    for item in client.dataset(run[\"defaultDatasetId\"]).iterate_items():\n",
+    "        text_data += item.get(\"text\", \"\") + \"\\n\"\n",
+    "\n",
+    "    average_token = 0.75\n",
+    "    max_tokens = 20000  # slightly less than max to be safe 32k\n",
+    "    text_data = text_data[: int(average_token * max_tokens)]\n",
+    "    return text_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the agents and register the tool."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from autogen import ConversableAgent, register_function\n",
+    "\n",
+    "# Create web scrapper agent.\n",
+    "scraper_agent = ConversableAgent(\n",
+    "    \"WebScraper\",\n",
+    "    llm_config={\"config_list\": config_list},\n",
+    "    system_message=\"You are a web scrapper and you can scrape any web page using the tools provided. \"\n",
+    "    \"Returns 'TERMINATE' when the scraping is done.\",\n",
+    ")\n",
+    "\n",
+    "# Create user proxy agent.\n",
+    "user_proxy_agent = ConversableAgent(\n",
+    "    \"UserProxy\",\n",
+    "    llm_config=False,  # No LLM for this agent.\n",
+    "    human_input_mode=\"NEVER\",\n",
+    "    code_execution_config=False,  # No code execution for this agent.\n",
+    "    is_termination_msg=lambda x: x.get(\"content\", \"\") is not None and \"terminate\" in x[\"content\"].lower(),\n",
+    "    default_auto_reply=\"Please continue if not finished, otherwise return 'TERMINATE'.\",\n",
+    ")\n",
+    "\n",
+    "# Register the function with the agents.\n",
+    "register_function(\n",
+    "    scrape_page,\n",
+    "    caller=scraper_agent,\n",
+    "    executor=user_proxy_agent,\n",
+    "    name=\"scrape_page\",\n",
+    "    description=\"Scrape a web page and return the content.\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Start the conversation for scraping web data. We used the\n",
+    "`reflection_with_llm` option for summary method\n",
+    "to perform the formatting of the output into a desired format.\n",
+    "The summary method is called after the conversation is completed\n",
+    "given the complete history of the conversation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mUserProxy\u001b[0m (to WebScraper):\n",
+      "\n",
+      "Can you scrape agentops.ai for me?\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mWebScraper\u001b[0m (to UserProxy):\n",
+      "\n",
+      "\u001b[32m***** Suggested tool call (call_0qok2jvCxOfv7HOA0oxPWneM): scrape_page *****\u001b[0m\n",
+      "Arguments: \n",
+      "{\n",
+      "\"url\": \"https://www.agentops.ai\"\n",
+      "}\n",
+      "\u001b[32m****************************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION scrape_page...\u001b[0m\n",
+      "\u001b[33mUserProxy\u001b[0m (to WebScraper):\n",
+      "\n",
+      "\u001b[33mUserProxy\u001b[0m (to WebScraper):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool (call_0qok2jvCxOfv7HOA0oxPWneM) *****\u001b[0m\n",
+      "START NOW\n",
+      "Take your business to the next level with our features \n",
+      "AI Agents Suck.\n",
+      "We're Fixing That. \n",
+      "Build compliant AI agents with observability, evals, and replay analytics. No more black boxes and prompt guessing.\n",
+      "New! Introducing AgentOps\n",
+      "Three Lines of Code. Unlimited Testing. \n",
+      "Instant Testing + Debugging = Compliant AI Agents That Work\n",
+      "5\n",
+      "# Beginning of program's code (i.e. main.py, __init__.py)\n",
+      "6\n",
+      "ao_client = agentops.Client(<INSERT YOUR API KEY HERE>)\n",
+      "9\n",
+      "# (optional: record specific functions)\n",
+      "10\n",
+      "@ao_client.record_action('sample function being record')\n",
+      "11\n",
+      "def sample_function(...):\n",
+      "15\n",
+      "ao_client.end_session('Success')\n",
+      "Prototype to Production\n",
+      "Generous free limits, upgrade only when you need it.\n",
+      "\n",
+      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mWebScraper\u001b[0m (to UserProxy):\n",
+      "\n",
+      "Sure, here's the information from the website agentops.ai:\n",
+      "\n",
+      "- Their main value proposition is to fix bad AI Agents and replace black boxes and prompt guessing with compliant, observable AI agents that come with evals and replay analytics.\n",
+      "- Their latest product is AgentOps. The simple and instant testing & debugging offered promises better-performing compliant AI agents.\n",
+      "- Integration is easy with just three lines of code.\n",
+      "- They let you record specific functions.\n",
+      "- They provide generous free limits and you only need to upgrade when necessary.\n",
+      "\n",
+      "Here's a sample of their code:\n",
+      "```python\n",
+      "ao_client = agentops.Client(<INSERT YOUR API KEY HERE>)\n",
+      "\n",
+      "# optional: record specific functions\n",
+      "@ao_client.record_action('sample function being record')\n",
+      "def sample_function(...):\n",
+      "    ...\n",
+      "\n",
+      "ao_client.end_session('Success')\n",
+      "```\n",
+      "This code is for sample usage of their libraries/functions.\n",
+      "\n",
+      "Let me know if you need more specific details.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mUserProxy\u001b[0m (to WebScraper):\n",
+      "\n",
+      "Please continue if not finished, otherwise return 'TERMINATE'.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mWebScraper\u001b[0m (to UserProxy):\n",
+      "\n",
+      "TERMINATE\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "chat_result = user_proxy_agent.initiate_chat(\n",
+    "    scraper_agent,\n",
+    "    message=\"Can you scrape agentops.ai for me?\",\n",
+    "    summary_method=\"reflection_with_llm\",\n",
+    "    summary_args={\n",
+    "        \"summary_prompt\": \"\"\"Summarize the scraped content and format summary EXACTLY as follows:\n",
+    "---\n",
+    "*Company name*:\n",
+    "`Acme Corp`\n",
+    "---\n",
+    "*Website*:\n",
+    "`acmecorp.com`\n",
+    "---\n",
+    "*Description*:\n",
+    "`Company that does things.`\n",
+    "---\n",
+    "*Tags*:\n",
+    "`Manufacturing. Retail. E-commerce.`\n",
+    "---\n",
+    "*Takeaways*:\n",
+    "`Provides shareholders with value by selling products.`\n",
+    "---\n",
+    "*Questions*:\n",
+    "`What products do they sell? How do they make money? What is their market share?`\n",
+    "---\n",
+    "\"\"\"\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The output is stored in the summary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---\n",
+      "*Company name*:\n",
+      "`AgentOps`\n",
+      "---\n",
+      "*Website*:\n",
+      "`agentops.ai`\n",
+      "---\n",
+      "*Description*:\n",
+      "`Company that aims to improve AI agents. They offer observed and evaluable AI agents with replay analytics as an alternative to black box models and blind prompting.`\n",
+      "---\n",
+      "*Tags*:\n",
+      "`Artificial Intelligence, AI agents, Observability, Analytics.`\n",
+      "---\n",
+      "*Takeaways*:\n",
+      "`Their product, AgentOps, allows for easy and instant testing and debugging of AI agents. Integration is as simple as writing three lines of code. They also provide generous free limits and mandate upgrades only when necessary.`\n",
+      "---\n",
+      "*Questions*:\n",
+      "`What differentiates AgentOps from other, similar products? How does their pricing scale with usage? What are the details of their \"generous free limits\"?`\n",
+      "---\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(chat_result.summary)"
+   ]
+  }
+ ],
+ "metadata": {
+  "front_matter": {
+   "description": "Scrapping web pages and summarizing the content using agents with tools.",
+   "tags": [
+    "web scraping",
+    "apify",
+    "tool use"
+   ],
+   "title": "Web Scraper Agent using Apify Tools"
+  },
+  "kernelspec": {
+   "display_name": "autogen",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
 }

notebook/agenteval_cq_math.ipynb

@@ -82,7 +82,6 @@
    "outputs": [],
    "source": [
     "import json\n",
-    "\n",
     "import os\n",
     "from pathlib import Path\n",
     "\n",

pyproject.toml

@@ -29,6 +29,7 @@ select = [
 #    "D",  # see: https://pypi.org/project/pydocstyle
 #    "N",  # see: https://pypi.org/project/pep8-naming
 #    "S",  # see: https://pypi.org/project/flake8-bandit
+    "I",  # see: https://pypi.org/project/isort/
 ]
 
 ignore = [

samples/apps/auto-anny/bot.py

@@ -1,11 +1,10 @@
-import os
 import logging
 import logging.handlers
+import os
 
 import discord
-from discord.ext import commands
-
 from agent_utils import solve_task
+from discord.ext import commands
 
 logger = logging.getLogger("anny")
 logger.setLevel(logging.INFO)

samples/apps/autogen-studio/autogenstudio/__init__.py

@@ -1,4 +1,4 @@
 from .chatmanager import *
-from .workflowmanager import *
 from .datamodel import *
 from .version import __version__
+from .workflowmanager import *

samples/apps/autogen-studio/autogenstudio/chatmanager.py

@@ -1,12 +1,14 @@
 import asyncio
-from datetime import datetime
 import json
-from queue import Queue
-import time
-from typing import Any, List, Dict, Optional, Tuple
 import os
-from fastapi import WebSocket, WebSocketDisconnect
+import time
+from datetime import datetime
+from queue import Queue
+from typing import Any, Dict, List, Optional, Tuple
+
 import websockets
+from fastapi import WebSocket, WebSocketDisconnect
+
 from .datamodel import AgentWorkFlowConfig, Message, SocketMessage
 from .utils import extract_successful_code_blocks, get_modified_files, summarize_chat_history
 from .workflowmanager import AutoGenWorkFlowManager

samples/apps/autogen-studio/autogenstudio/cli.py

@@ -1,10 +1,11 @@
 import os
-from typing_extensions import Annotated
+
 import typer
 import uvicorn
+from typing_extensions import Annotated
 
-from .version import VERSION
 from .utils.dbutils import DBManager
+from .version import VERSION
 
 app = typer.Typer()
 

samples/apps/autogen-studio/autogenstudio/datamodel.py

@@ -1,8 +1,9 @@
 import uuid
+from dataclasses import asdict, field
 from datetime import datetime
 from typing import Any, Callable, Dict, List, Literal, Optional, Union
+
 from pydantic.dataclasses import dataclass
-from dataclasses import asdict, field
 
 
 @dataclass

samples/apps/autogen-studio/autogenstudio/utils/dbutils.py

@@ -1,13 +1,13 @@
 import json
 import logging
+import os
 import sqlite3
 import threading
-import os
-from typing import Any, List, Dict, Optional, Tuple
+from typing import Any, Dict, List, Optional, Tuple
+
 from ..datamodel import AgentFlowSpec, AgentWorkFlowConfig, Gallery, Message, Model, Session, Skill
 from ..version import __version__ as __db_version__
 
-
 VERSION_TABLE_SQL = """
             CREATE TABLE IF NOT EXISTS version (
 

samples/apps/autogen-studio/autogenstudio/utils/utils.py

@@ -1,14 +1,17 @@
 import base64
 import hashlib
-from typing import List, Dict, Tuple, Union
 import os
+import re
 import shutil
 from pathlib import Path
-import re
+from typing import Dict, List, Tuple, Union
+
+from dotenv import load_dotenv
+
 import autogen
 from autogen.oai.client import OpenAIWrapper
+
 from ..datamodel import AgentConfig, AgentFlowSpec, AgentWorkFlowConfig, LLMConfig, Model, Skill
-from dotenv import load_dotenv
 from ..version import APP_NAME
 
 

samples/apps/autogen-studio/autogenstudio/web/app.py

@@ -1,26 +1,25 @@
 import asyncio
-from contextlib import asynccontextmanager
 import json
 import os
 import queue
 import threading
 import traceback
-from fastapi import FastAPI, WebSocket, WebSocketDisconnect
+from contextlib import asynccontextmanager
+
+from fastapi import FastAPI, HTTPException, WebSocket, WebSocketDisconnect
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.staticfiles import StaticFiles
-from fastapi import HTTPException
 from openai import OpenAIError
-from ..version import VERSION, APP_NAME
 
+from ..chatmanager import AutoGenChatManager, WebSocketConnectionManager
 from ..datamodel import (
     DBWebRequestModel,
     DeleteMessageWebRequestModel,
     Message,
     Session,
 )
-from ..utils import md5_hash, init_app_folders, DBManager, dbutils, test_model
-from ..chatmanager import AutoGenChatManager, WebSocketConnectionManager
-
+from ..utils import DBManager, dbutils, init_app_folders, md5_hash, test_model
+from ..version import APP_NAME, VERSION
 
 managers = {"chat": None}  # manage calls to autogen
 # Create thread-safe queue for messages between api thread and autogen threads

samples/apps/autogen-studio/autogenstudio/workflowmanager.py

@@ -1,12 +1,13 @@
 import os
-from typing import List, Optional, Union, Dict
+from datetime import datetime
+from typing import Dict, List, Optional, Union
 
 from requests import Session
 
 import autogen
+
 from .datamodel import AgentConfig, AgentFlowSpec, AgentWorkFlowConfig, Message, SocketMessage
-from .utils import get_skills_from_prompt, clear_folder, sanitize_model
-from datetime import datetime
+from .utils import clear_folder, get_skills_from_prompt, sanitize_model
 
 
 class AutoGenWorkFlowManager:

samples/apps/autogen-studio/notebooks/tutorial.ipynb

@@ -7,6 +7,7 @@
    "outputs": [],
    "source": [
     "import json\n",
+    "\n",
     "from autogenstudio import AgentWorkFlowConfig, AutoGenWorkFlowManager"
    ]
   },

samples/apps/cap/py/autogencap/Actor.py

@@ -1,9 +1,11 @@
-import zmq
 import threading
-import traceback
 import time
-from .DebugLog import Debug, Info, Error
+import traceback
+
+import zmq
+
 from .Config import xpub_url
+from .DebugLog import Debug, Error, Info
 
 
 class Actor:

samples/apps/cap/py/autogencap/ActorConnector.py

@@ -1,14 +1,16 @@
 # Agent_Sender takes a zmq context, Topic and creates a
 # socket that can publish to that topic. It exposes this functionality
 # using send_msg method
-import zmq
-from zmq.utils.monitor import recv_monitor_message
 import time
 import uuid
-from .DebugLog import Debug, Error, Info
-from .Config import xsub_url, xpub_url, router_url
 from typing import Any, Dict
 
+import zmq
+from zmq.utils.monitor import recv_monitor_message
+
+from .Config import router_url, xpub_url, xsub_url
+from .DebugLog import Debug, Error, Info
+
 
 class ActorSender:
     def __init__(self, context, topic):

samples/apps/cap/py/autogencap/Broker.py

@@ -1,8 +1,10 @@
-import time
-import zmq
 import threading
+import time
+
+import zmq
+
+from autogencap.Config import router_url, xpub_url, xsub_url
 from autogencap.DebugLog import Debug, Info, Warn
-from autogencap.Config import xsub_url, xpub_url, router_url
 
 
 class Broker:

samples/apps/cap/py/autogencap/DebugLog.py

@@ -1,8 +1,10 @@
-import threading
 import datetime
-import autogencap.Config as Config
+import threading
+
 from termcolor import colored
 
+import autogencap.Config as Config
+
 # Define log levels as constants
 DEBUG = 0
 INFO = 1

samples/apps/cap/py/autogencap/DirectorySvc.py

@@ -1,26 +1,29 @@
-from autogencap.Constants import Directory_Svc_Topic
-from autogencap.Config import xpub_url, xsub_url, router_url
-from autogencap.DebugLog import Debug, Info, Error
-from autogencap.ActorConnector import ActorConnector, ActorSender
-from autogencap.Actor import Actor
-from autogencap.Broker import Broker
-from autogencap.proto.CAP_pb2 import (
-    ActorRegistration,
-    ActorInfo,
-    ActorLookup,
-    ActorLookupResponse,
-    Ping,
-    Pong,
-    ActorInfoCollection,
-    Error as ErrorMsg,
-    ErrorCode,
-)
-from autogencap.utility import report_error_msg
-
-import zmq
+import re
 import threading
 import time
-import re
+
+import zmq
+
+from autogencap.Actor import Actor
+from autogencap.ActorConnector import ActorConnector, ActorSender
+from autogencap.Broker import Broker
+from autogencap.Config import router_url, xpub_url, xsub_url
+from autogencap.Constants import Directory_Svc_Topic
+from autogencap.DebugLog import Debug, Error, Info
+from autogencap.proto.CAP_pb2 import (
+    ActorInfo,
+    ActorInfoCollection,
+    ActorLookup,
+    ActorLookupResponse,
+    ActorRegistration,
+    ErrorCode,
+    Ping,
+    Pong,
+)
+from autogencap.proto.CAP_pb2 import (
+    Error as ErrorMsg,
+)
+from autogencap.utility import report_error_msg
 
 # TODO (Future DirectorySv PR) use actor description, network_id, other properties to make directory
 # service more generic and powerful

samples/apps/cap/py/autogencap/LocalActorNetwork.py

@@ -1,13 +1,15 @@
+import time
+from typing import List
+
 import zmq
-from .DebugLog import Debug, Warn
+
+from .Actor import Actor
 from .ActorConnector import ActorConnector
 from .Broker import Broker
-from .DirectorySvc import DirectorySvc
 from .Constants import Termination_Topic
-from .Actor import Actor
+from .DebugLog import Debug, Warn
+from .DirectorySvc import DirectorySvc
 from .proto.CAP_pb2 import ActorInfo, ActorInfoCollection
-from typing import List
-import time
 
 # TODO: remove time import
 

samples/apps/cap/py/autogencap/ag_adapter/AG2CAP.py

@@ -1,8 +1,10 @@
 import time
 from typing import Callable, Dict, List, Optional, Union
+
 from autogen import Agent, ConversableAgent
-from .AutoGenConnector import AutoGenConnector
+
 from ..LocalActorNetwork import LocalActorNetwork
+from .AutoGenConnector import AutoGenConnector
 
 
 class AG2CAP(ConversableAgent):

samples/apps/cap/py/autogencap/ag_adapter/AGActor.py

@@ -1,4 +1,5 @@
 import zmq
+
 from autogencap.Actor import Actor
 from autogencap.Constants import Termination_Topic
 from autogencap.DebugLog import Debug

samples/apps/cap/py/autogencap/ag_adapter/AutoGenConnector.py

@@ -1,5 +1,7 @@
 from typing import Dict, Optional, Union
+
 from autogen import Agent
+
 from ..ActorConnector import ActorConnector
 from ..proto.Autogen_pb2 import GenReplyReq, GenReplyResp, PrepChat, ReceiveReq, Terminate
 

samples/apps/cap/py/autogencap/ag_adapter/CAP2AG.py

@@ -1,11 +1,13 @@
 from enum import Enum
 from typing import Optional
+
+from autogen import ConversableAgent
+
 from ..DebugLog import Debug, Error, Info, Warn, shorten
 from ..LocalActorNetwork import LocalActorNetwork
 from ..proto.Autogen_pb2 import GenReplyReq, GenReplyResp, PrepChat, ReceiveReq, Terminate
-from .AGActor import AGActor
 from .AG2CAP import AG2CAP
-from autogen import ConversableAgent
+from .AGActor import AGActor
 
 
 class CAP2AG(AGActor):

samples/apps/cap/py/autogencap/ag_adapter/CAPGroupChat.py

@@ -1,8 +1,9 @@
+from typing import List
+
 from autogen import Agent, AssistantAgent, GroupChat
 from autogencap.ag_adapter.AG2CAP import AG2CAP
 from autogencap.ag_adapter.CAP2AG import CAP2AG
 from autogencap.LocalActorNetwork import LocalActorNetwork
-from typing import List
 
 
 class CAPGroupChat(GroupChat):