AutoGen 2026：微软的多Agent对话框架深度评测

from autogen import ConversableAgent, GroupChat, GroupChatManager
from autogen.protocol import ConversationProtocol, MessageContract

# 定义消息契约
class AnalysisRequest(MessageContract):
    """分析请求消息"""
    target: str
    metrics: list[str]
    deadline: str

class AnalysisResult(MessageContract):
    """分析结果消息"""
    findings: list[dict]
    confidence: float
    caveats: str

# 定义对话协议
protocol = ConversationProtocol(
    participants=["analyst", "reviewer", "reporter"],
    message_types=[AnalysisRequest, AnalysisResult],
    termination_condition="reporter:FINAL_REPORT",
    max_turns=20
)

from autogen import AssistantAgent, UserProxyAgent, GroupChat

# 动态角色注册
class DynamicGroupChat(GroupChat):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.role_registry = {}
    
    def register_role(self, name: str, agent: ConversableAgent, 
                      trigger_condition: callable):
        """注册条件触发的动态角色"""
        self.role_registry[name] = {
            "agent": agent,
            "trigger": trigger_condition
        }
    
    def select_speaker(self, last_speaker, messages):
        # 先检查动态角色触发条件
        for name, role in self.role_registry.items():
            if role["trigger"](messages):
                return role["agent"]
        # 回退到默认选择逻辑
        return super().select_speaker(last_speaker, messages)

# 使用场景：当检测到法律问题时动态引入法务Agent
legal_trigger = lambda msgs: any(
    "合规" in m.get("content", "") or "法律" in m.get("content", "")
    for m in msgs[-3:]
)

chat = DynamicGroupChat(
    agents=[analyst, researcher, writer],
    messages=[],
    max_round=30
)
chat.register_role("legal", legal_agent, legal_trigger)

# AutoGen风格：对话驱动
analyst = AssistantAgent("analyst", system_msg="你是分析师")
researcher = AssistantAgent("researcher", system_msg="你是研究员")
manager = GroupChatManager([analyst, researcher])
analyst.initiate_chat(manager, message="分析苹果公司的AI战略")

# vs LangGraph风格：图驱动
graph = StateGraph(State)
graph.add_node("analyst", analyst_node)
graph.add_node("researcher", researcher_node)
graph.add_edge("analyst", "researcher")
app = graph.compile()
app.invoke({"messages": [HumanMessage("分析苹果公司的AI战略")]})

from autogen.optimization import MessageSummarizer

# 配置消息摘要
summarizer = MessageSummarizer(
    model="gpt-4o-mini",
    strategy="sliding_window",  # 滑动窗口策略
    window_size=10,  # 保留最近10条原文
    summary_max_tokens=200  # 历史摘要上限
)

analyst = AssistantAgent(
    "analyst",
    system_message="你是金融分析师",
    message_optimizer=summarizer,
    # 2026新特性：选择性历史
    history_filter="relevant",  # 只保留与当前任务相关的历史消息
)

测试条件：50个并发GroupChat，每个包含5个Agent，运行30分钟

from autogen.enterprise import EnterpriseConfig

config = EnterpriseConfig(
    # 审计日志
    audit_log=AuditLogConfig(
        enabled=True,
        storage="azure_blob",
        retention_days=90,
        log_level="INFO"
    ),
    # 合规策略
    compliance=ComplianceConfig(
        pii_filter=True,  # PII自动过滤
        content_moderation=True,
        max_turns_per_conversation=50
    ),
    # 资源限制
    resource_limits=ResourceLimits(
        max_concurrent_chats=100,
        max_tokens_per_chat=500000,
        timeout_seconds=600
    ),
    # 多租户隔离
    multi_tenant=MultiTenantConfig(
        isolation_level="conversation",
        shared_agents=False
    )
)