Agents

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

model_client = OpenAIChatCompletionClient(model="gpt-4o")

agent = AssistantAgent(
    name="assistant",
    model_client=model_client,
    system_message="You are a helpful assistant.",
    description="A general-purpose assistant agent",
    tools=[],  # List of tools
    handoffs=[],  # List of agents this agent can hand off to
    model_client_stream=True,  # Enable streaming
    reflect_on_tool_use=False,  # Reflect on tool results before responding
    max_tool_iterations=10  # Max tool calling rounds
)

from autogen_agentchat.agents import CodeExecutorAgent
from autogen_ext.code_executors import DockerCommandLineCodeExecutor

# Create code executor (uses Docker for isolation)
executor = DockerCommandLineCodeExecutor()

agent = CodeExecutorAgent(
    name="code_executor",
    code_executor=executor,
    description="Executes Python and bash code"
)

from autogen_agentchat.agents import UserProxyAgent

agent = UserProxyAgent(
    name="user",
    description="Human user for approvals and feedback"
)

from autogen_agentchat.agents import SocietyOfMindAgent, AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_ext.models.openai import OpenAIChatCompletionClient

model_client = OpenAIChatCompletionClient(model="gpt-4o")

# Create inner team
researcher = AssistantAgent("researcher", model_client=model_client)
writer = AssistantAgent("writer", model_client=model_client)
inner_team = RoundRobinGroupChat([researcher, writer])

# Wrap team as single agent
som_agent = SocietyOfMindAgent(
    name="research_team",
    team=inner_team,
    description="A team that researches and writes reports"
)

# Can now use som_agent as a regular agent in another team

from autogen_agentchat.agents import MessageFilterAgent, PerSourceFilter

# Filter to only show messages from specific sources
filter_config = PerSourceFilter(
    allowed_senders=["agent1", "agent2"]
)

agent = MessageFilterAgent(
    name="filter",
    filter_config=filter_config,
    description="Filters messages from specific agents"
)

from autogen_agentchat.base import ChatAgent, Response
from autogen_agentchat.messages import BaseChatMessage, TextMessage
from typing import List, Sequence

class CustomAgent(ChatAgent):
    def __init__(self, name: str, description: str):
        self._name = name
        self._description = description
    
    @property
    def name(self) -> str:
        return self._name
    
    @property
    def description(self) -> str:
        return self._description
    
    async def on_messages(
        self, 
        messages: Sequence[BaseChatMessage],
        cancellation_token: CancellationToken
    ) -> Response:
        # Custom logic here
        return Response(
            chat_message=TextMessage(
                content="Custom response",
                source=self.name
            )
        )