feat(agent): Telegram与Agent相互时支持流式输出

app/agent/__init__.py

@@ -5,7 +5,8 @@ from typing import Dict, List
 
 from langchain.agents import create_agent
 from langchain.agents.middleware import (
-    SummarizationMiddleware, LLMToolSelectorMiddleware,
+    SummarizationMiddleware,
+    LLMToolSelectorMiddleware,
 )
 from langchain_core.messages import (
     HumanMessage,
@@ -36,12 +37,12 @@ class MoviePilotAgent:
     """
 
     def __init__(
-            self,
-            session_id: str,
-            user_id: str = None,
-            channel: str = None,
-            source: str = None,
-            username: str = None,
+        self,
+        session_id: str,
+        user_id: str = None,
+        channel: str = None,
+        source: str = None,
+        username: str = None,
     ):
         self.session_id = session_id
         self.user_id = user_id
@@ -80,9 +81,7 @@ class MoviePilotAgent:
             # 系统提示词
             system_prompt = prompt_manager.get_agent_prompt(
                 channel=self.channel
-            ).format(
-                current_date=strftime('%Y-%m-%d')
-            )
+            ).format(current_date=strftime("%Y-%m-%d"))
 
             # LLM 模型（用于 agent 执行）
             llm = self._initialize_llm()
@@ -93,21 +92,15 @@ class MoviePilotAgent:
             # 中间件
             middlewares = [
                 # 工具选择
-                LLMToolSelectorMiddleware(
-                    model=llm,
-                    max_tools=20
-                ),
+                LLMToolSelectorMiddleware(model=llm, max_tools=20),
                 # 记忆管理
                 MemoryMiddleware(
                     sources=[str(settings.CONFIG_PATH / "agent" / "MEMORY.md")]
                 ),
                 # 上下文压缩
-                SummarizationMiddleware(
-                    model=llm,
-                    trigger=("fraction", 0.85)
-                ),
+                SummarizationMiddleware(model=llm, trigger=("fraction", 0.85)),
                 # 错误工具调用修复
-                PatchToolCallsMiddleware()
+                PatchToolCallsMiddleware(),
             ]
 
             return create_agent(
@@ -130,8 +123,7 @@ class MoviePilotAgent:
 
             # 获取历史消息
             messages = memory_manager.get_agent_messages(
-                session_id=self.session_id,
-                user_id=self.user_id
+                session_id=self.session_id, user_id=self.user_id
             )
 
             # 增加用户消息
@@ -150,6 +142,7 @@ class MoviePilotAgent:
         """
         调用 LangGraph Agent，通过 astream_events 流式获取 token，
         同时用 UsageMetadataCallbackHandler 统计 token 用量。
+        支持流式输出：在支持消息编辑的渠道上实时推送 token。
         """
         try:
             # Agent运行配置
@@ -162,37 +155,57 @@ class MoviePilotAgent:
             # 创建智能体
             agent = self._create_agent()
 
+            # 启动流式输出（内部会检查渠道是否支持消息编辑）
+            await self.stream_handler.start_streaming(
+                channel=self.channel,
+                source=self.source,
+                user_id=self.user_id,
+                username=self.username,
+            )
+
             # 流式运行智能体
             async for chunk in agent.astream(
-                    {"messages": messages},
-                    stream_mode="messages",
-                    config=agent_config,
-                    version="v2"
+                {"messages": messages},
+                stream_mode="messages",
+                config=agent_config,
+                version="v2",
             ):
                 # 处理流式token（过滤工具调用token，只保留模型生成的内容）
                 if chunk["type"] == "messages":
                     token, metadata = chunk["data"]
-                    if (token and hasattr(token, "tool_call_chunks")
-                            and not token.tool_call_chunks):
+                    if (
+                        token
+                        and hasattr(token, "tool_call_chunks")
+                        and not token.tool_call_chunks
+                    ):
                         if token.content:
                             self.stream_handler.emit(token.content)
 
-            # 发送最终消息给用户
-            await self.send_agent_message(
-                self.stream_handler.take()
-            )
+            # 停止流式输出，返回是否已通过流式编辑发送了所有内容
+            all_sent_via_stream = await self.stream_handler.stop_streaming()
+
+            if not all_sent_via_stream:
+                # 流式输出未能发送全部内容（渠道不支持编辑，或发送失败）
+                # 通过常规方式发送剩余内容
+                remaining_text = await self.stream_handler.take()
+                if remaining_text:
+                    await self.send_agent_message(remaining_text)
 
             # 保存消息
             memory_manager.save_agent_messages(
                 session_id=self.session_id,
                 user_id=self.user_id,
-                messages=agent.get_state(agent_config).values.get("messages", [])
+                messages=agent.get_state(agent_config).values.get("messages", []),
             )
 
         except asyncio.CancelledError:
+            # 确保取消时也停止流式输出
+            await self.stream_handler.stop_streaming()
             logger.info(f"Agent执行被取消: session_id={self.session_id}")
             return "任务已取消", {}
         except Exception as e:
+            # 确保异常时也停止流式输出
+            await self.stream_handler.stop_streaming()
             logger.error(f"Agent执行失败: {e} - {traceback.format_exc()}")
             return str(e), {}
 
@@ -243,13 +256,13 @@ class AgentManager:
         self.active_agents.clear()
 
     async def process_message(
-            self,
-            session_id: str,
-            user_id: str,
-            message: str,
-            channel: str = None,
-            source: str = None,
-            username: str = None,
+        self,
+        session_id: str,
+        user_id: str,
+        message: str,
+        channel: str = None,
+        source: str = None,
+        username: str = None,
     ) -> str:
         """
         处理用户消息