fix: handle direct tool call results

astrbot/core/pipeline/process_stage/agent_runner/tool_loop_agent.py

@@ -218,7 +218,9 @@ class ToolLoopAgent(BaseAgentRunner):
                                 content="返回了图片(已直接发送给用户)",
                             )
                         )
-                        yield MessageChain().base64_image(res.content[0].data)
+                        yield MessageChain(type="tool_direct_result").base64_image(
+                            res.content[0].data
+                        )
                     elif isinstance(res.content[0], EmbeddedResource):
                         resource = res.content[0].resource
                         if isinstance(resource, TextResourceContents):
@@ -242,7 +244,9 @@ class ToolLoopAgent(BaseAgentRunner):
                                     content="返回了图片(已直接发送给用户)",
                                 )
                             )
-                            yield MessageChain().base64_image(res.content[0].data)
+                            yield MessageChain(type="tool_direct_result").base64_image(
+                                res.content[0].data
+                            )
                         else:
                             tool_call_result_blocks.append(
                                 ToolCallMessageSegment(
@@ -275,7 +279,9 @@ class ToolLoopAgent(BaseAgentRunner):
                             self._transition_state(AgentState.DONE)
                             if res := self.event.get_result():
                                 if res.chain:
-                                    yield MessageChain(chain=res.chain)
+                                    yield MessageChain(
+                                        chain=res.chain, type="tool_direct_result"
+                                    )
 
                 self.event.clear_result()
             except Exception as e:

astrbot/core/pipeline/process_stage/method/llm_request.py

@@ -177,10 +177,13 @@ class LLMRequestSubStage(Stage):
                         if event.is_stopped():
                             return
                         if resp.type == "tool_call_result":
-                            # 处理工具调用结果，直接发送给用户
-                            resp.data["chain"].type = "tool_call_result"
-                            await event.send(resp.data["chain"])
-                            continue
+                            msg_chain = resp.data["chain"]
+                            if msg_chain.type == "tool_direct_result":
+                                # tool_direct_result 用于标记 llm tool 需要直接发送给用户的内容
+                                resp.data["chain"].type = "tool_call_result"
+                                await event.send(resp.data["chain"])
+                                continue
+                            # 对于其他情况，暂时先不处理
                         if resp.type == "tool_call":
                             if self.streaming_response:
                                 # 用来标记流式响应需要分节
@@ -261,7 +264,9 @@ class LLMRequestSubStage(Stage):
 
         await self._save_to_history(event, req, tool_loop_agent.get_final_llm_resp())
 
-    async def _handle_webchat(self, event: AstrMessageEvent, req: ProviderRequest, prov: Provider):
+    async def _handle_webchat(
+        self, event: AstrMessageEvent, req: ProviderRequest, prov: Provider
+    ):
         """处理 WebChat 平台的特殊情况，包括第一次 LLM 对话时总结对话内容生成 title"""
         conversation = await self.conv_manager.get_conversation(
             event.unified_msg_origin, req.conversation.cid