feat: add support for showing tool call results in agent execution

astrbot/core/astr_agent_run_util.py

@@ -24,15 +24,77 @@ def _should_stop_agent(astr_event) -> bool:
     return astr_event.is_stopped() or bool(astr_event.get_extra("agent_stop_requested"))
 
 
+def _truncate_tool_result(text: str, limit: int = 70) -> str:
+    if limit <= 0:
+        return ""
+    if len(text) <= limit:
+        return text
+    if limit <= 3:
+        return text[:limit]
+    return f"{text[: limit - 3]}..."
+
+
+def _extract_chain_json_data(msg_chain: MessageChain) -> dict | None:
+    if not msg_chain.chain:
+        return None
+    first_comp = msg_chain.chain[0]
+    if isinstance(first_comp, Json) and isinstance(first_comp.data, dict):
+        return first_comp.data
+    return None
+
+
+def _record_tool_call_name(
+    tool_info: dict | None, tool_name_by_call_id: dict[str, str]
+) -> None:
+    if not isinstance(tool_info, dict):
+        return
+    tool_call_id = tool_info.get("id")
+    tool_name = tool_info.get("name")
+    if tool_call_id is None or tool_name is None:
+        return
+    tool_name_by_call_id[str(tool_call_id)] = str(tool_name)
+
+
+def _build_tool_call_status_message(tool_info: dict | None) -> str:
+    if tool_info:
+        return f"🔨 调用工具: {tool_info.get('name', 'unknown')}"
+    return "🔨 调用工具..."
+
+
+def _build_tool_result_status_message(
+    msg_chain: MessageChain, tool_name_by_call_id: dict[str, str]
+) -> str:
+    tool_name = "unknown"
+    tool_result = ""
+
+    result_data = _extract_chain_json_data(msg_chain)
+    if result_data:
+        tool_call_id = result_data.get("id")
+        if tool_call_id is not None:
+            tool_name = tool_name_by_call_id.pop(str(tool_call_id), "unknown")
+        tool_result = str(result_data.get("result", ""))
+
+    if not tool_result:
+        tool_result = msg_chain.get_plain_text(with_other_comps_mark=True)
+    tool_result = _truncate_tool_result(tool_result, 70)
+
+    status_msg = f"🔨 调用工具: {tool_name}"
+    if tool_result:
+        status_msg = f"{status_msg}\n📎 返回结果: {tool_result}"
+    return status_msg
+
+
 async def run_agent(
     agent_runner: AgentRunner,
     max_step: int = 30,
     show_tool_use: bool = True,
+    show_tool_call_result: bool = False,
     stream_to_general: bool = False,
     show_reasoning: bool = False,
 ) -> AsyncGenerator[MessageChain | None, None]:
     step_idx = 0
     astr_event = agent_runner.run_context.context.event
+    tool_name_by_call_id: dict[str, str] = {}
     while step_idx < max_step + 1:
         step_idx += 1
 
@@ -90,32 +152,36 @@ async def run_agent(
                         continue
                     if astr_event.get_platform_id() == "webchat":
                         await astr_event.send(msg_chain)
+                    elif show_tool_use and show_tool_call_result:
+                        status_msg = _build_tool_result_status_message(
+                            msg_chain, tool_name_by_call_id
+                        )
+                        await astr_event.send(
+                            MessageChain(type="tool_call").message(status_msg)
+                        )
                     # 对于其他情况，暂时先不处理
                     continue
                 elif resp.type == "tool_call":
                     if agent_runner.streaming:
                         # 用来标记流式响应需要分节
                         yield MessageChain(chain=[], type="break")
 
-                    tool_info = None
-
-                    if resp.data["chain"].chain:
-                        json_comp = resp.data["chain"].chain[0]
-                        if isinstance(json_comp, Json):
-                            tool_info = json_comp.data
-                        astr_event.trace.record(
-                            "agent_tool_call",
-                            tool_name=tool_info if tool_info else "unknown",
-                        )
+                    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":
                         await astr_event.send(resp.data["chain"])
                     elif show_tool_use:
-                        if tool_info:
-                            m = f"🔨 调用工具: {tool_info.get('name', 'unknown')}"
-                        else:
-                            m = "🔨 调用工具..."
-                        chain = MessageChain(type="tool_call").message(m)
+                        if show_tool_call_result and isinstance(tool_info, dict):
+                            # Delay tool status notification until tool_call_result.
+                            continue
+                        chain = MessageChain(type="tool_call").message(
+                            _build_tool_call_status_message(tool_info)
+                        )
                         await astr_event.send(chain)
                     continue
 
@@ -202,6 +268,7 @@ async def run_live_agent(
     tts_provider: TTSProvider | None = None,
     max_step: int = 30,
     show_tool_use: bool = True,
+    show_tool_call_result: bool = False,
     show_reasoning: bool = False,
 ) -> AsyncGenerator[MessageChain | None, None]:
     """Live Mode 的 Agent 运行器，支持流式 TTS
@@ -211,6 +278,7 @@ async def run_live_agent(
         tts_provider: TTS Provider 实例
         max_step: 最大步数
         show_tool_use: 是否显示工具使用
+        show_tool_call_result: 是否显示工具返回结果
         show_reasoning: 是否显示推理过程
 
     Yields:
@@ -222,6 +290,7 @@ async def run_live_agent(
             agent_runner,
             max_step=max_step,
             show_tool_use=show_tool_use,
+            show_tool_call_result=show_tool_call_result,
             stream_to_general=False,
             show_reasoning=show_reasoning,
         ):
@@ -250,7 +319,12 @@ async def run_live_agent(
     # 1. 启动 Agent Feeder 任务：负责运行 Agent 并将文本分句喂给 text_queue
     feeder_task = asyncio.create_task(
         _run_agent_feeder(
-            agent_runner, text_queue, max_step, show_tool_use, show_reasoning
+            agent_runner,
+            text_queue,
+            max_step,
+            show_tool_use,
+            show_tool_call_result,
+            show_reasoning,
         )
     )
 
@@ -336,6 +410,7 @@ async def _run_agent_feeder(
     text_queue: asyncio.Queue,
     max_step: int,
     show_tool_use: bool,
+    show_tool_call_result: bool,
     show_reasoning: bool,
 ) -> None:
     """运行 Agent 并将文本输出分句放入队列"""
@@ -345,6 +420,7 @@ async def _run_agent_feeder(
             agent_runner,
             max_step=max_step,
             show_tool_use=show_tool_use,
+            show_tool_call_result=show_tool_call_result,
             stream_to_general=False,
             show_reasoning=show_reasoning,
         ):

astrbot/core/config/default.py

@@ -100,6 +100,7 @@
         "dequeue_context_length": 1,
         "streaming_response": False,
         "show_tool_use_status": False,
+        "show_tool_call_result": False,
         "sanitize_context_by_modalities": False,
         "max_quoted_fallback_images": 20,
         "quoted_message_parser": {
@@ -2306,6 +2307,9 @@ class ChatProviderTemplate(TypedDict):
                     "show_tool_use_status": {
                         "type": "bool",
                     },
+                    "show_tool_call_result": {
+                        "type": "bool",
+                    },
                     "unsupported_streaming_strategy": {
                         "type": "string",
                     },
@@ -2994,6 +2998,15 @@ class ChatProviderTemplate(TypedDict):
                             "provider_settings.agent_runner_type": "local",
                         },
                     },
+                    "provider_settings.show_tool_call_result": {
+                        "description": "输出函数调用返回结果",
+                        "type": "bool",
+                        "hint": "仅在输出函数调用状态启用时生效，展示结果前 70 个字符。",
+                        "condition": {
+                            "provider_settings.agent_runner_type": "local",
+                            "provider_settings.show_tool_use_status": True,
+                        },
+                    },
                     "provider_settings.sanitize_context_by_modalities": {
                         "description": "按模型能力清理历史上下文",
                         "type": "bool",

astrbot/core/pipeline/process_stage/method/agent_sub_stages/internal.py

@@ -54,6 +54,7 @@ async def initialize(self, ctx: PipelineContext) -> None:
         if isinstance(self.max_step, bool):  # workaround: #2622
             self.max_step = 30
         self.show_tool_use: bool = settings.get("show_tool_use_status", True)
+        self.show_tool_call_result: bool = settings.get("show_tool_call_result", False)
         self.show_reasoning = settings.get("display_reasoning_text", False)
         self.sanitize_context_by_modalities: bool = settings.get(
             "sanitize_context_by_modalities",
@@ -240,6 +241,7 @@ async def process(
                                 tts_provider,
                                 self.max_step,
                                 self.show_tool_use,
+                                self.show_tool_call_result,
                                 show_reasoning=self.show_reasoning,
                             ),
                         ),
@@ -269,6 +271,7 @@ async def process(
                                 agent_runner,
                                 self.max_step,
                                 self.show_tool_use,
+                                self.show_tool_call_result,
                                 show_reasoning=self.show_reasoning,
                             ),
                         ),
@@ -297,6 +300,7 @@ async def process(
                         agent_runner,
                         self.max_step,
                         self.show_tool_use,
+                        self.show_tool_call_result,
                         stream_to_general,
                         show_reasoning=self.show_reasoning,
                     ):

dashboard/src/i18n/locales/en-US/features/config-metadata.json

@@ -251,6 +251,10 @@
         "show_tool_use_status": {
           "description": "Output Function Call Status"
         },
+        "show_tool_call_result": {
+          "description": "Output Tool Call Results",
+          "hint": "Only takes effect when \"Output Function Call Status\" is enabled, and shows at most 70 characters."
+        },
         "sanitize_context_by_modalities": {
           "description": "Sanitize History by Modalities",
           "hint": "When enabled, sanitizes contexts before each LLM request by removing image blocks and tool-call structures that the current provider's modalities do not support (this changes what the model sees)."

dashboard/src/i18n/locales/zh-CN/features/config-metadata.json

@@ -254,6 +254,10 @@
         "show_tool_use_status": {
           "description": "输出函数调用状态"
         },
+        "show_tool_call_result": {
+          "description": "输出函数调用返回结果",
+          "hint": "仅在启用“输出函数调用状态”时生效，且最多展示 70 个字符。"
+        },
         "sanitize_context_by_modalities": {
           "description": "按模型能力清理历史上下文",
           "hint": "开启后，在每次请求 LLM 前会按当前模型提供商中所选择的模型能力删除对话中不支持的图片/工具调用结构（会改变模型看到的历史）"