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Update references in docs (#4590)
* Update agent doc * Remove outdated doc * Update references * Update readme * Update readme
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python/packages/agbench/benchmarks/AssistantBench/Templates/MagenticOne/scenario.py

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from autogen_core import AgentId, AgentProxy, TopicId
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from autogen_core import SingleThreadedAgentRuntime
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from autogen_core.application.logging import EVENT_LOGGER_NAME
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from autogen_core.logging import EVENT_LOGGER_NAME
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from autogen_core.components.models import (
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ChatCompletionClient,
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UserMessage,

python/packages/agbench/benchmarks/GAIA/Templates/MagenticOne/scenario.py

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from autogen_core import AgentId, AgentProxy, TopicId
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from autogen_core import SingleThreadedAgentRuntime
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from autogen_core.application.logging import EVENT_LOGGER_NAME
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from autogen_core import EVENT_LOGGER_NAME
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from autogen_core.components.models import (
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ChatCompletionClient,
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ModelCapabilities,

python/packages/agbench/benchmarks/HumanEval/Templates/MagenticOne/scenario.py

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from autogen_core import AgentId, AgentProxy, TopicId
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from autogen_core import SingleThreadedAgentRuntime
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from autogen_core.application.logging import EVENT_LOGGER_NAME
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from autogen_core import EVENT_LOGGER_NAME
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from autogen_core import DefaultSubscription, DefaultTopicId
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from autogen_ext.code_executors.local import LocalCommandLineCodeExecutor
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from autogen_core.components.models import (

python/packages/agbench/benchmarks/WebArena/Templates/MagenticOne/scenario.py

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from autogen_core import AgentId, AgentProxy, TopicId
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from autogen_core import SingleThreadedAgentRuntime
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from autogen_core.application.logging import EVENT_LOGGER_NAME
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from autogen_core import EVENT_LOGGER_NAME
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from autogen_core import DefaultSubscription, DefaultTopicId
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from autogen_ext.code_executors.local import LocalCommandLineCodeExecutor
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from autogen_core.components.models import (
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# AutoGen AgentChat
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- [Documentation](https://microsoft.github.io/autogen/dev/user-guide/agentchat-user-guide/index.html)
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## Package structure
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- `agents` are the building blocks for creating agents and built-in agents.
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- `teams` are the building blocks for creating teams of agents and built-in teams, such as group chats.
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- `logging` contains logging utilities.
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# AutoGen Core
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- [Documentation](https://microsoft.github.io/autogen/dev/user-guide/core-user-guide/index.html)
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## Package layering
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- `base` are the the foundational generic interfaces upon which all else is built. This module must not depend on any other module.
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- `application` are implementations of core components that are used to compose an application.
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- `components` are the building blocks for creating agents.

python/packages/autogen-core/docs/drawio/layers.drawio

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This file was deleted.

python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tutorial/agents.ipynb

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"The following preset agents are available:\n",
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"\n",
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"- {py:class}`~autogen_agentchat.agents.CodeExecutorAgent`: An agent that can execute code.\n",
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"- {py:class}`~autogen_ext.agents.OpenAIAssistantAgent`: An agent that is backed by an OpenAI Assistant, with ability to use custom tools.\n",
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"- {py:class}`~autogen_ext.agents.MultimodalWebSurfer`: A multi-modal agent that can search the web and visit web pages for information."
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"- {py:class}`~autogen_ext.agents.openai.OpenAIAssistantAgent`: An agent that is backed by an OpenAI Assistant, with ability to use custom tools.\n",
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"- {py:class}`~autogen_ext.agents.web_surfer.MultimodalWebSurfer`: A multi-modal agent that can search the web and visit web pages for information.\n",
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"- {py:class}`~autogen_ext.agents.file_surfer.FileSurfer`: An agent that can search and browse local files for information.\n",
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"- {py:class}`~autogen_ext.agents.video_surfer.VideoSurfer`: An agent that can watch videos for information."
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]
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},
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.6"
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"version": "3.11.5"
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}
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},
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"nbformat": 4,

python/packages/autogen-core/docs/src/user-guide/core-user-guide/cookbook/llm-usage-logger.ipynb

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"\n",
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"The model clients included in AutoGen emit structured events that can be used to track the usage of the model. This notebook demonstrates how to use the logger to track the usage of the model.\n",
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"\n",
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"These events are logged to the logger with the name: :py:attr:`autogen_core.application.logging.EVENT_LOGGER_NAME`."
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"These events are logged to the logger with the name: :py:attr:`autogen_core.EVENT_LOGGER_NAME`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import logging\n",
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"\n",
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"from autogen_core.application.logging.events import LLMCallEvent\n",
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"from autogen_core.logging import LLMCallEvent\n",
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"\n",
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"\n",
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"class LLMUsageTracker(logging.Handler):\n",
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" def __init__(self) -> None:\n",
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" \"\"\"Logging handler that tracks the number of tokens used in the prompt and completion.\n",
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"\n",
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" Example:\n",
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"\n",
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" .. code-block:: python\n",
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"\n",
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" from autogen_core.application.logging import LLMUsageTracker, EVENT_LOGGER_NAME\n",
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"\n",
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" # Set up the logging configuration to use the custom handler\n",
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" logger = logging.getLogger(EVENT_LOGGER_NAME)\n",
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" logger.setLevel(logging.INFO)\n",
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" llm_usage = LLMUsageTracker()\n",
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" logger.handlers = [llm_usage]\n",
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"\n",
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" # ...\n",
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"\n",
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" print(llm_usage.prompt_tokens)\n",
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" print(llm_usage.completion_tokens)\n",
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"\n",
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" \"\"\"\n",
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" \"\"\"Logging handler that tracks the number of tokens used in the prompt and completion.\"\"\"\n",
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" super().__init__()\n",
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" self._prompt_tokens = 0\n",
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" self._completion_tokens = 0\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"from autogen_core.application.logging import EVENT_LOGGER_NAME\n",
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"from autogen_core import EVENT_LOGGER_NAME\n",
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"\n",
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"# Set up the logging configuration to use the custom handler\n",
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"logger = logging.getLogger(EVENT_LOGGER_NAME)\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.5"
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"version": "3.11.5"
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}
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},
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"nbformat": 4,

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