timeline
    title Agent框架统一时间线 2025-2026
    2025 Q3 : AutoGen + SK合并预告
    2025 Q3 : OpenAI Agents SDK发布
    2025 Q10 : MAF开源预览版
    2026 Q1 : Claude Agent SDK发布
    2026 Q2 : MAF 1.0 GA (4月3日)
    2026 Q2 : Google ADK四语言发布 (4月)
    2026 Q2 : AWS Strands + AgentCore (4月)
    2026 Q5 : 框架迁移成本报告 (4-12周)

# portability/agent_port.py — 框架无关的Agent接口
from abc import ABC, abstractmethod
from dataclasses import dataclass

@dataclass
class Task:
    prompt: str
    tools: list[str]
    context: dict | None = None

@dataclass
class Result:
    output: str
    tool_calls: list[dict]
    tokens_used: int

class AgentPort(ABC):
    """框架无关的Agent抽象端口"""

    @abstractmethod
    async def run(self, task: Task) -> Result: ...

    @abstractmethod
    def add_tool(self, name: str, handler: callable, schema: dict): ...

    @abstractmethod
    def set_system_prompt(self, prompt: str): ...

# 适配器示例：LangGraph实现
class LangGraphAdapter(AgentPort):
    def __init__(self, model: str = "gpt-4o"):
        from langgraph.prebuilt import create_react_agent
        self._tools = []
        self._model = model
        self._system = ""

    def add_tool(self, name, handler, schema):
        from langchain_core.tools import tool as lc_tool
        self._tools.append(lc_tool(handler))

    async def run(self, task: Task) -> Result:
        from langgraph.prebuilt import create_react_agent
        agent = create_react_agent(self._model, self._tools)
        resp = await agent.ainvoke({"messages": task.prompt})
        return Result(
            output=resp["messages"][-1].content,
            tool_calls=[],  # 从中间步骤提取
            tokens_used=0
        )

# 适配器示例：MAF实现（统一AutoGen + SK路径）
class MAFLangGraphAdapter(AgentPort):
    def __init__(self, model: str = "gpt-4o"):
        from microsoft.agent_framework import Agent, ChatClient
        self._client = ChatClient(model=model)
        self._tools = []

    async def run(self, task: Task) -> Result:
        agent = Agent(chat_client=self._client, tools=self._tools)
        response = await agent.run(task.prompt)
        return Result(output=response.content, tool_calls=[], tokens_used=0)

# agent.yaml — ADP格式的Agent定义
apiVersion: adp/v1
kind: Agent
metadata:
  name: research-assistant
  version: "1.2.0"
  description: "深度研究助手，支持多源检索与证据汇编"
spec:
  identity:
    system_prompt: |
      你是一个深度研究助手...
    model_requirements:
      min_context: 32000
      tool_calling: required
  tools:
    - name: web_search
      description: "搜索网络信息"
      input_schema:
        type: object
        properties:
          query: {type: string}
    - name: analyze
      description: "分析内容"
      input_schema:
        type: object
        properties:
          content: {type: string}
          depth: {type: string, enum: [shallow, deep]}
  guardrails:
    max_iterations: 15
    timeout_seconds: 300
    cost_limit_usd: 0.50
  memory:
    type: conversation_window
    max_messages: 50

# adp/runtime_mapper.py
class ADPRuntime:
    """将ADP定义映射到不同框架运行时"""

    def deploy_to_langgraph(self, agent_def: dict) -> "CompiledGraph":
        from langgraph.prebuilt import create_react_agent
        from langchain_core.tools import StructuredTool

        tools = [
            StructuredTool.from_function(
                name=t["name"],
                description=t["description"],
                args_schema=t["input_schema"]
            ) for t in agent_def["spec"]["tools"]
        ]

        return create_react_agent(
            model=self._resolve_model(agent_def),
            tools=tools,
            state_modifier=agent_def["spec"]["identity"]["system_prompt"]
        )

    def deploy_to_strands(self, agent_def: dict) -> "StrandsAgent":
        from strands import Agent, Tool

        tools = [
            Tool(name=t["name"], description=t["description"])
            for t in agent_def["spec"]["tools"]
        ]

        return Agent(
            system_prompt=agent_def["spec"]["identity"]["system_prompt"],
            tools=tools,
            max_iterations=agent_def["spec"]["guardrails"]["max_iterations"]
        )

    def deploy_to_maf(self, agent_def: dict) -> "MAFAgent":
        from microsoft.agent_framework import Agent, ChatClient

        client = ChatClient(model=self._resolve_model(agent_def))
        return Agent(
            chat_client=client,
            system_prompt=agent_def["spec"]["identity"]["system_prompt"],
            tools=self._map_maf_tools(agent_def["spec"]["tools"])
        )

# protocol_portability/mcp_tool_registry.py
class MCPToolRegistry:
    """基于MCP的工具注册中心，框架无关"""

    def __init__(self, mcp_server_url: str):
        self.server_url = mcp_server_url
        self._tools_cache: dict[str, dict] = {}

    async def discover_tools(self) -> list[dict]:
        """自动发现MCP服务器提供的所有工具"""
        from mcp import ClientSession

        async with ClientSession(self.server_url) as session:
            tools = await session.list_tools()
            for tool in tools:
                self._tools_cache[tool.name] = {
                    "name": tool.name,
                    "description": tool.description,
                    "schema": tool.inputSchema
                }
        return list(self._tools_cache.values())

    async def call_tool(self, name: str, args: dict) -> any:
        """通过MCP协议调用工具，与框架无关"""
        from mcp import ClientSession
        async with ClientSession(self.server_url) as session:
            return await session.call_tool(name, args)


# protocol_portability/a2a_agent_bridge.py
class A2ABridge:
    """A2A协议桥接，让不同框架的Agent互相对话"""

    async def register_agent(self, agent_id: str, capabilities: list[str]):
        """向A2A网络注册Agent能力"""
        await self._a2a_client.register(agent_id, {
            "capabilities": capabilities,
            "protocol": "a2a/v1"
        })

    async def delegate_task(self, from_agent: str, task: str) -> str:
        """通过A2A协议将任务委托给最合适的Agent"""
        candidates = await self._a2a_client.discover(task)
        best = self._select_best_match(candidates, task)
        return await self._a2a_client.send(best["id"], task)

# agent_workflow.dsl — 声明式Agent工作流
workflow:
  name: customer_support
  version: "2.0"

  agents:
    triage:
      role: "分类Agent"
      model: "auto"  # 运行时选择最优模型
      tools: [classify_issue, lookup_customer]
      max_turns: 3

    specialist:
      role: "专业Agent"
      model: "gpt-4o"
      tools: [search_docs, create_ticket, escalate]
      max_turns: 10

  flow:
    - agent: triage
      on_complete:
        route:
          billing: specialist
          technical: specialist
          complaint: human_handoff

    - agent: specialist
      on_complete:
        success: end
        needs_help: human_handoff

  guardrails:
    total_cost_limit: 1.00  # USD
    max_duration: 600  # seconds
    require_approval_above: 0.10  # USD per step

# dsl/compiler.py
class AgentDSLCompiler:
    """将声明式DSL编译到不同框架"""

    def compile_to_langgraph(self, dsl: dict) -> "CompiledGraph":
        from langgraph.graph import StateGraph, END

        workflow = StateGraph(AgentState)

        # 编译agents为图节点
        for agent_name, agent_config in dsl["agents"].items():
            workflow.add_node(agent_name, 
                self._create_langgraph_node(agent_config))

        # 编译flow为边和条件路由
        for step in dsl["flow"]:
            agent_name = step["agent"]
            if "on_complete" in step:
                routing = step["on_complete"].get("route", {})
                workflow.add_conditional_edges(
                    agent_name,
                    self._create_router(routing)
                )

        return workflow.compile()

    def compile_to_maf(self, dsl: dict) -> "MAFWorkflow":
        from microsoft.agent_framework import Workflow, Step

        maf_workflow = Workflow(name=dsl["workflow"]["name"])

        for step in dsl["flow"]:
            agent_config = dsl["agents"][step["agent"]]
            maf_step = Step(
                agent=self._create_maf_agent(agent_config),
                routing=self._compile_routing(step)
            )
            maf_workflow.add_step(maf_step)

        return maf_workflow

graph TB
    subgraph "应用层"
        BIZ[业务逻辑]
    end

    subgraph "Harness抽象层"
        LC[生命周期管理]
        SP[状态持久化]
        UP[热升级]
        SM[会话迁移]
    end

    subgraph "框架层（可替换）"
        LG[LangGraph]
        CR[CrewAI]
        MF[MAF]
        ST[Strands]
    end

    subgraph "协议层"
        MCP[MCP工具]
        A2A[A2A通信]
    end

    BIZ --> LC
    LC --> SP
    LC --> UP
    LC --> SM
    LC -.->|可切换| LG
    LC -.->|可切换| CR
    LC -.->|可切换| MF
    LC -.->|可切换| ST
    LG --> MCP
    CR --> MCP
    MF --> A2A
    ST --> MCP

# harness/agent_harness.py
from dataclasses import dataclass
from enum import Enum
from typing import Any, Protocol

class Framework(Enum):
    LANGGRAPH = "langgraph"
    CREWAI = "crewai"
    MAF = "maf"
    STRANDS = "strands"

@dataclass
class SessionState:
    session_id: str
    framework: Framework
    checkpoint: dict  # 框架无关的状态快照
    created_at: float
    version: str

class AgentHarness:
    """Agent运行时Harness — 框架无关的生命周期管理"""

    def __init__(self, framework: Framework, config: dict):
        self.framework = framework
        self._runtime = self._bootstrap_runtime(framework, config)
        self._state_store = StateStore()  # 框架无关的状态存储

    async def run(self, task: str, session_id: str | None = None) -> dict:
        """执行任务，自动管理状态持久化"""
        state = await self._state_store.load(session_id) if session_id else None

        if state and state.framework != self.framework:
            # 跨框架会话迁移！
            state = await self._migrate_state(state, self.framework)

        result = await self._runtime.execute(task, state)

        new_state = SessionState(
            session_id=session_id or generate_id(),
            framework=self.framework,
            checkpoint=result.checkpoint,
            created_at=time.time(),
            version="1.0"
        )
        await self._state_store.save(new_state)
        return result

    async def switch_framework(self, target: Framework):
        """运行时切换底层框架（保持会话状态）"""
        current_state = await self._state_store.load(self._current_session)
        migrated = await self._migrate_state(current_state, target)
        self._runtime = self._bootstrap_runtime(target, self._config)
        await self._state_store.save(migrated)

    async def hot_upgrade(self, new_version: str):
        """热升级Agent逻辑，不丢失会话"""
        # SHADOW模式：新旧版本并行运行，验证后切换
        shadow_result = await self._shadow_run(new_version)
        if shadow_result.validation_passed:
            self._version = new_version
            await self._state_store.compact()

# hybrid/multi_framework_orchestrator.py
class MultiFrameworkOrchestrator:
    """多框架并行编排器"""

    def __init__(self):
        self.agents: dict[str, tuple[Framework, Any]] = {}
        self.a2a_bus = A2ABus()  # A2A协议总线

    def register(self, name: str, framework: Framework, agent: Any):
        """注册不同框架实现的Agent"""
        self.agents[name] = (framework, agent)

    async def run_workflow(self, workflow_def: dict) -> dict:
        """跨框架执行工作流"""
        results = {}

        for step in workflow_def["steps"]:
            framework, agent = self.agents[step["agent"]]

            # 框架特定执行
            if framework == Framework.LANGGRAPH:
                result = await agent.ainvoke({"messages": step["input"]})
                results[step["id"]] = result["messages"][-1].content

            elif framework == Framework.STRANDS:
                result = await agent(step["input"])
                results[step["id"]] = result

            elif framework == Framework.MAF:
                result = await agent.run(step["input"])
                results[step["id"]] = result.content

            # 通过A2A协议通知下游Agent
            if step.get("notify"):
                for target in step["notify"]:
                    await self.a2a_bus.send(target, results[step["id"]])

        return results


# 使用示例
orchestrator = MultiFrameworkOrchestrator()

# 研究Agent用LangGraph（复杂状态机）
orchestrator.register("researcher", Framework.LANGGRAPH, research_agent)

# 写作Agent用Strands（Model-Driven，快速迭代）
orchestrator.register("writer", Framework.STRANDS, writer_agent)

# 代码审查用MAF（企业级，.NET集成）
orchestrator.register("reviewer", Framework.MAF, review_agent)

quadrantChart
    title 六大可移植引擎定位
    x-axis "实现成本低" --> "实现成本高"
    y-axis "可移植性弱" --> "可移植性强"
    quadrant-1 "高价值区"
    quadrant-2 "理想区"
    quadrant-3 "权宜区"
    quadrant-4 "过度工程区"
    "适配器模式": [0.25, 0.35]
    "ADP协议": [0.45, 0.55]
    "MCP/A2A/ACP": [0.35, 0.65]
    "声明式DSL": [0.70, 0.75]
    "Harness层": [0.80, 0.85]
    "混合并行": [0.15, 0.90]