状态化 AI 代理

有状态的人工智能代理在交互之间保持上下文，通过使用线程 ID 来跟踪线程。 检查点使你可以将代理保存在特定的状态，而时间回溯使你可以从这些状态重播对话。 这有助于您了解非确定性语言模型 (LLM) 代理的决策过程，并执行以下操作：

1. 观察代理：准确分析代理在每个步骤中知道和执行的动作
2. 调试错误：确定会话流中发生错误的位置和原因
3. 探索其他选择：重新播放和测试检查点的不同会话路径

本页演示如何使用马赛克 AI 代理框架和 LangGraph 和 Lakebase 作为内存存储创建有状态代理。

要求

若要创建有状态代理，需要：

• 设置 Lakebase 实例，请参阅 创建和管理数据库实例。

示例笔记本

以下笔记本使用此页上的概念来实现使用 Lakebase 的有状态代理：

具有线程范围的内存的有状态代理

获取笔记本

实现 LangGraph 时间旅行

使用 LangGraph 时间旅行 从检查点恢复执行。 可以重播对话或修改对话，以浏览替代路径。 每次从检查点继续时，LangGraph 会在对话历史记录中创建一个新的分支，保留原始数据，同时便于进行实验。

1. 在代理代码中，创建在类中检索检查点历史记录和更新检查点状态的 LangGraphResponsesAgent 函数：

   from typing import List, Dict
   def get_checkpoint_history(self, thread_id: str, limit: int = 10) -> List[Dict[str, Any]]:
       """Retrieve checkpoint history for a thread.
   
       Args:
           thread_id: The thread identifier
           limit: Maximum number of checkpoints to return
   
       Returns:
           List of checkpoint information including checkpoint_id, timestamp, and next nodes
       """
       config = {"configurable": {"thread_id": thread_id}}
   
       with self.get_connection() as conn:
           checkpointer = PostgresSaver(conn)
           graph = self._create_graph(checkpointer)
   
           history = []
           for state in graph.get_state_history(config):
               if len(history) >= limit:
                   break
   
               history.append({
                   "checkpoint_id": state.config["configurable"]["checkpoint_id"],
                   "thread_id": thread_id,
                   "timestamp": state.created_at,
                   "next_nodes": state.next,
                   "message_count": len(state.values.get("messages", [])),
                   # Include last message summary for context
                   "last_message": self._get_last_message_summary(state.values.get("messages", []))
               })
   
           return history
   
   def _get_last_message_summary(self, messages: List[Any]) -> Optional[str]:
       """Get a snippet of the last message for checkpoint identification"""
       return getattr(messages[-1], "content", "")[:100] if messages else None
   
   def update_checkpoint_state(self, thread_id: str, checkpoint_id: str,
                           new_messages: Optional[List[Dict]] = None) -> Dict[str, Any]:
       """Update state at a specific checkpoint (used for modifying conversation history).
   
       Args:
           thread_id: The thread identifier
           checkpoint_id: The checkpoint to update
           new_messages: Optional new messages to set at this checkpoint
   
       Returns:
           New checkpoint configuration including the new checkpoint_id
       """
       config = {
           "configurable": {
               "thread_id": thread_id,
               "checkpoint_id": checkpoint_id
           }
       }
   
       with self.get_connection() as conn:
           checkpointer = PostgresSaver(conn)
           graph = self._create_graph(checkpointer)
   
           # Prepare the values to update
           values = {}
           if new_messages:
               cc_msgs = self.prep_msgs_for_cc_llm(new_messages)
               values["messages"] = cc_msgs
   
           # Update the state (creates a new checkpoint)
           new_config = graph.update_state(config, values=values)
   
           return {
               "thread_id": thread_id,
               "checkpoint_id": new_config["configurable"]["checkpoint_id"],
               "parent_checkpoint_id": checkpoint_id
           }
   

2. 更新predict和predict_stream函数以支持传入检查点：

   Predict

   def predict(self, request: ResponsesAgentRequest) -> ResponsesAgentResponse:
       """Non-streaming prediction"""
       # The same thread_id is used by BOTH predict() and predict_stream()
       ci = dict(request.custom_inputs or {})
       if "thread_id" not in ci:
           ci["thread_id"] = str(uuid.uuid4())
       request.custom_inputs = ci
   
       outputs = [
           event.item
           for event in self.predict_stream(request)
           if event.type == "response.output_item.done"
       ]
   
       # Include thread_id and checkpoint_id in custom outputs
       custom_outputs = {
           "thread_id": ci["thread_id"]
       }
       if "checkpoint_id" in ci:
           custom_outputs["parent_checkpoint_id"] = ci["checkpoint_id"]
   
       try:
           history = self.get_checkpoint_history(ci["thread_id"], limit=1)
           if history:
               custom_outputs["checkpoint_id"] = history[0]["checkpoint_id"]
       except Exception as e:
           logger.warning(f"Could not retrieve new checkpoint_id: {e}")
   
       return ResponsesAgentResponse(output=outputs, custom_outputs=custom_outputs)
   

   Predict_stream

   def predict_stream(
       self,
       request: ResponsesAgentRequest,
   ) -> Generator[ResponsesAgentStreamEvent, None, None]:
       """Streaming prediction with PostgreSQL checkpoint branching support.
   
       Accepts in custom_inputs:
       - thread_id: Conversation thread identifier for session
       - checkpoint_id (optional): Checkpoint to resume from (for branching)
       """
       # Get thread ID and checkpoint ID from custom inputs
       custom_inputs = request.custom_inputs or {}
       thread_id = custom_inputs.get("thread_id", str(uuid.uuid4()))  # generate new thread ID if one is not passed in
       checkpoint_id = custom_inputs.get("checkpoint_id")  # Optional for branching
   
       # Convert incoming Responses messages to LangChain format
       langchain_msgs = self.prep_msgs_for_cc_llm([i.model_dump() for i in request.input])
   
       # Build checkpoint configuration
       checkpoint_config = {"configurable": {"thread_id": thread_id}}
       # If checkpoint_id is provided, we're branching from that checkpoint
       if checkpoint_id:
           checkpoint_config["configurable"]["checkpoint_id"] = checkpoint_id
           logger.info(f"Branching from checkpoint: {checkpoint_id} in thread: {thread_id}")
   
       # DATABASE CONNECTION POOLING LOGIC FOLLOWS
       # Use connection from pool
   

然后，测试检查点分支：

1. 启动对话线程并添加一些消息：

   from agent import AGENT
   # Initial conversation - starts a new thread
   response1 = AGENT.predict({
       "input": [{"role": "user", "content": "I'm planning for an upcoming trip!"}],
   })
   print(response1.model_dump(exclude_none=True))
   thread_id = response1.custom_outputs["thread_id"]
   
   # Within the same thread, ask a follow-up question - thread-scoped memory will remember previous messages in the same thread/conversation session
   response2 = AGENT.predict({
       "input": [{"role": "user", "content": "I'm headed to SF!"}],
       "custom_inputs": {"thread_id": thread_id}
   })
   print(response2.model_dump(exclude_none=True))
   
   # Within the same thread, ask a follow-up question - thread-scoped memory will remember previous messages in the same thread/conversation session
   response3 = AGENT.predict({
       "input": [{"role": "user", "content": "Where did I say I'm going?"}],
       "custom_inputs": {"thread_id": thread_id}
   })
   print(response3.model_dump(exclude_none=True))
   
   

2. 检索检查点历史记录，并使用其他消息对对话进行分叉：

   # Get checkpoint history to find branching point
   history = AGENT.get_checkpoint_history(thread_id, 20)
   # Retrieve checkpoint at index - indices count backward from most recent checkpoint
   index = max(1, len(history) - 4)
   branch_checkpoint = history[index]["checkpoint_id"]
   
   # Branch from node with next_node = `('__start__',)` to re-input message to agent at certain part of conversation
   # I want to update the information of which city I am going to
   # Within the same thread, branch from a checkpoint and override it with different context to continue the conversation in a new fork
   response4 = AGENT.predict({
       "input": [{"role": "user", "content": "I'm headed to New York!"}],
       "custom_inputs": {
           "thread_id": thread_id,
           "checkpoint_id": branch_checkpoint # Branch from this checkpoint!
       }
   })
   print(response4.model_dump(exclude_none=True))
   
   # Thread ID stays the same even though it branched from a checkpoint:
   branched_thread_id = response4.custom_outputs["thread_id"]
   print(f"original thread id was {thread_id}")
   print(f"new thread id after branching is the same as original: {branched_thread_id}")
   
   # Continue the conversation in the same thread and it will pick up from the information you tell it in your branch
   response5 = AGENT.predict({
       "input": [{"role": "user", "content": "Where am I going?"}],
       "custom_inputs": {
           "thread_id": thread_id,
       }
   })
   print(response5.model_dump(exclude_none=True))
   

查询您已部署的有状态代理

将代理部署到模型服务终结点后，请参阅 查询已部署的马赛克 AI 代理 以获取查询说明。

若要传入线程 ID，请使用 extra_body 参数。 以下示例演示如何将线程 ID ResponsesAgent 传递到终结点：

   response1 = client.responses.create(
    model=endpoint,
    input=[{"role": "user", "content": "What are stateful agents?"}],
    extra_body={
        "custom_inputs": {"thread_id": thread_id}
    }
)

后续步骤

• LangGraph 文档：持久性
• LangGraph 文档：时间旅行
• 在代码中开发 AI 代理
• 创建自定义代理工具