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Add a basic agent with gmail tool use
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.gitignore

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.aider*
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.env
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credentials.json
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token.json

src/.gitignore

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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htmlcov/
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.nox/
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# poetry
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# commonly ignored for libraries.
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/latest/usage/project/#working-with-version-control
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.pdm.toml
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.pdm-python
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.pdm-build/
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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# mkdocs documentation
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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.pyre/
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# pytype static type analyzer
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/

src/agents/tool_use_agent.py

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from typing import List
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from autogen_core.base import AgentId, MessageContext
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from autogen_core.components import RoutedAgent, message_handler
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from autogen_core.components.models import (
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ChatCompletionClient,
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LLMMessage,
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SystemMessage,
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UserMessage,
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)
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from autogen_core.components.tool_agent import tool_agent_caller_loop
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from autogen_core.components.tools import ToolSchema
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from src.contracts.message import Message
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class ToolUseAgent(RoutedAgent):
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def __init__(
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self,
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model_client: ChatCompletionClient,
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tool_schema: List[ToolSchema],
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tool_agent_type: str,
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) -> None:
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super().__init__("An agent with tools")
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self._system_messages: List[LLMMessage] = [
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SystemMessage("You are a helpful AI assistant.")
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]
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self._model_client = model_client
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self._tool_schema = tool_schema
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self._tool_agent_id = AgentId(tool_agent_type, self.id.key)
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@message_handler
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async def handle_user_message(
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self, message: Message, ctx: MessageContext
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) -> Message:
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# Create a session of messages.
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session: List[LLMMessage] = [
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UserMessage(content=message.content, source="user")
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]
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# Run the caller loop to handle tool calls.
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messages = await tool_agent_caller_loop(
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self,
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tool_agent_id=self._tool_agent_id,
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model_client=self._model_client,
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input_messages=session,
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tool_schema=self._tool_schema,
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cancellation_token=ctx.cancellation_token,
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)
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# Return the final response.
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assert isinstance(messages[-1].content, str)
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return Message(content=messages[-1].content)

src/contracts/message.py

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from dataclasses import dataclass
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@dataclass
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class Message:
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content: str

src/main.py

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import asyncio
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from .agents.tool_use_agent import ToolUseAgent
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from .tools.tool_factory import get_tools
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from autogen_core.application import SingleThreadedAgentRuntime
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from autogen_core.components.tool_agent import ToolAgent
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from autogen_ext.models import OpenAIChatCompletionClient
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from autogen_core.base import AgentId
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from .contracts.message import Message
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from dotenv import load_dotenv
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async def main():
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load_dotenv()
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tools = get_tools()
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runtime = SingleThreadedAgentRuntime()
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await ToolAgent.register(
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runtime, "tool_executor_agent", lambda: ToolAgent("tool executor agent", tools)
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)
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await ToolUseAgent.register(
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runtime,
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"tool_use_agent",
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lambda: ToolUseAgent(
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OpenAIChatCompletionClient(model="gpt-4o-mini"),
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[tool.schema for tool in tools],
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"tool_executor_agent",
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),
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)
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runtime.start()
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# Send a direct message to the tool agent.
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tool_use_agent = AgentId("tool_use_agent", "default")
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response = await runtime.send_message(
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Message("What's my latest email about?"), tool_use_agent
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)
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print(response.content)
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# Stop processing messages.
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await runtime.stop()
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if __name__ == "__main__":
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asyncio.run(main())

src/requirements.txt

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# Console utilities
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rich~=13.9.4
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# Autogen
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autogen-core==0.4.0.dev4
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autogen-ext[langchain,openai]==0.4.0.dev4
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# Toolkits
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langchain-google-community[gmail]

src/tools/tool_factory.py

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from langchain_google_community import GmailToolkit
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from autogen_ext.tools import LangChainToolAdapter
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def get_tools():
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gmailTookit = GmailToolkit()
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tools = gmailTookit.get_tools()
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autogen_tools = [LangChainToolAdapter(tool) for tool in tools]
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return autogen_tools

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