feat: subagent功能增强

astrbot/builtin_stars/builtin_commands/commands/conversation.py

@@ -189,6 +189,18 @@ async def reset(self, message: AstrMessageEvent) -> None:
 
         ret = "✅ Conversation reset successfully."
 
+        # 清理该会话下的所有 subagent
+        try:
+            from astrbot.core.subagent_manager import SubAgentManager
+
+            cleanup_result = await SubAgentManager.cleanup_session(umo)
+            if cleanup_result["status"] == "cleaned":
+                cleaned_count = len(cleanup_result["cleaned_agents"])
+                if cleaned_count > 0:
+                    ret += f" 🧹 Also cleaned {cleaned_count} subagent(s): {', '.join(cleanup_result['cleaned_agents'])}."
+        except Exception as e:
+            logger.warning(f"[SubAgent] Failed to cleanup subagents on /reset: {e}")
+
         message.set_extra("_clean_group_context_session", True)
 
         message.set_result(MessageEventResult().message(ret))

astrbot/core/agent/runners/tool_loop_agent_runner.py

@@ -991,6 +991,13 @@ def _append_tool_call_result(tool_call_id: str, content: str) -> None:
                 ),
             )
 
+        # 获取 trace span：优先使用 subagent trace_span，否则回退到 event.trace
+        _agent_ctx = getattr(self.run_context, "context", None)
+        _trace = getattr(_agent_ctx, "trace_span", None)
+        if _trace is None and _agent_ctx is not None:
+            _event = getattr(_agent_ctx, "event", None)
+            _trace = getattr(_event, "trace", None)
+
         # 执行函数调用
         for func_tool_name, func_tool_args, func_tool_id in zip(
             llm_response.tools_call_name,
@@ -1014,10 +1021,23 @@ def _append_tool_call_result(tool_call_id: str, content: str) -> None:
                     ],
                 )
             )
+            # 记录工具调用追踪
+            if _trace:
+                _trace.record("agent_tool_call", tool_name=func_tool_name)
             try:
                 if not req.func_tool:
                     return
 
+                # Resolve tool from regular tool sets
+                if (
+                    self.tool_schema_mode == "skills_like"
+                    and self._skill_like_raw_tool_set
+                ):
+                    # in 'skills_like' mode, raw.func_tool is light schema, does not have handler
+                    # so we need to get the tool from the raw tool set
+                    func_tool = self._skill_like_raw_tool_set.get_tool(func_tool_name)
+                else:
+                    func_tool = req.func_tool.get_tool(func_tool_name)
                 if (
                     self.tool_schema_mode == "skills_like"
                     and self._skill_like_raw_tool_set
@@ -1231,6 +1251,15 @@ def _append_tool_call_result(tool_call_id: str, content: str) -> None:
                     )
                 )
                 logger.info(f"Tool `{func_tool_name}` Result: {tool_result_content}")
+                # 记录工具结果追踪
+                if _trace:
+                    _trace.record(
+                        "agent_tool_result",
+                        tool_name=func_tool_name,
+                        tool_result=tool_result_content[:500]
+                        if tool_result_content
+                        else None,
+                    )
 
         # 处理函数调用响应
         if tool_call_result_blocks:
@@ -1359,13 +1388,16 @@ async def _finalize_aborted_step(
         llm_resp: LLMResponse | None = None,
     ) -> AgentResponse:
         logger.info("Agent execution was requested to stop by user.")
+
         if llm_resp is None:
             llm_resp = LLMResponse(role="assistant", completion_text="")
+
         if llm_resp.role != "assistant":
             llm_resp = LLMResponse(
                 role="assistant",
                 completion_text=self.USER_INTERRUPTION_MESSAGE,
             )
+
         self.final_llm_resp = llm_resp
         self._aborted = True
         self._transition_state(AgentState.DONE)

astrbot/core/astr_agent_context.py

@@ -1,3 +1,5 @@
+from typing import Any
+
 from pydantic import Field
 from pydantic.dataclasses import dataclass
 
@@ -14,8 +16,13 @@ class AstrAgentContext:
     """The star context instance"""
     event: AstrMessageEvent
     """The message event associated with the agent context."""
-    extra: dict[str, str] = Field(default_factory=dict)
+    extra: dict[str, Any] = Field(default_factory=dict)
     """Customized extra data."""
+    trace_span: Any = Field(default=None)
+    """Optional custom TraceSpan for subagent tracing. When set, tool calls within
+    the agent loop will be recorded to this trace instead of event.trace.
+    This prevents concurrent subagent and main agent tool calls from mixing up
+    trace records."""
 
 
 AgentContextWrapper = ContextWrapper[AstrAgentContext]

astrbot/core/astr_agent_run_util.py

@@ -185,13 +185,6 @@ async def run_agent(
                 if resp.type == "tool_call_result":
                     msg_chain = resp.data["chain"]
 
-                    astr_event.trace.record(
-                        "agent_tool_result",
-                        tool_result=msg_chain.get_plain_text(
-                            with_other_comps_mark=True
-                        ),
-                    )
-
                     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
                         await astr_event.send(msg_chain)
@@ -218,10 +211,6 @@ async def run_agent(
                         yield MessageChain(chain=[], type="break")
 
                     tool_info = _extract_chain_json_data(resp.data["chain"])
-                    astr_event.trace.record(
-                        "agent_tool_call",
-                        tool_name=tool_info if tool_info else "unknown",
-                    )
                     _record_tool_call_name(tool_info, tool_name_by_call_id)
 
                     if astr_event.get_platform_name() == "webchat":