fix: prevent tool-call preface text leaks

astrbot/core/agent/runners/tool_loop_agent_runner.py

@@ -794,27 +794,28 @@ async def step(self):
             await self._complete_with_assistant_response(llm_resp)
 
         # 返回 LLM 结果
-        if llm_resp.reasoning_content:
-            yield AgentResponse(
-                type="llm_result",
-                data=AgentResponseData(
-                    chain=MessageChain(type="reasoning").message(
-                        llm_resp.reasoning_content,
+        if not llm_resp.tools_call_name:
+            if llm_resp.reasoning_content:
+                yield AgentResponse(
+                    type="llm_result",
+                    data=AgentResponseData(
+                        chain=MessageChain(type="reasoning").message(
+                            llm_resp.reasoning_content,
+                        ),
                     ),
-                ),
-            )
-        if llm_resp.result_chain:
-            yield AgentResponse(
-                type="llm_result",
-                data=AgentResponseData(chain=llm_resp.result_chain),
-            )
-        elif llm_resp.completion_text:
-            yield AgentResponse(
-                type="llm_result",
-                data=AgentResponseData(
-                    chain=MessageChain().message(llm_resp.completion_text),
-                ),
-            )
+                )
+            if llm_resp.result_chain:
+                yield AgentResponse(
+                    type="llm_result",
+                    data=AgentResponseData(chain=llm_resp.result_chain),
+                )
+            elif llm_resp.completion_text:
+                yield AgentResponse(
+                    type="llm_result",
+                    data=AgentResponseData(
+                        chain=MessageChain().message(llm_resp.completion_text),
+                    ),
+                )
 
         # 如果有工具调用，还需处理工具调用
         if llm_resp.tools_call_name:

astrbot/core/provider/sources/openai_source.py

@@ -769,6 +769,20 @@ def _normalize_content(raw_content: Any, strip: bool = True) -> str:
 
         if isinstance(raw_content, str):
             content = raw_content.strip() if strip else raw_content
+            repr_like_prefix = "[{text="
+            repr_like_suffix = ", type=text}]"
+            repr_like_max_len = 8192
+            check_content = raw_content.strip()
+            while (
+                check_content.startswith(repr_like_prefix)
+                and check_content.endswith(repr_like_suffix)
+                and len(check_content) < repr_like_max_len
+            ):
+                check_content = check_content[
+                    len(repr_like_prefix) : -len(repr_like_suffix)
+                ].strip()
+            if check_content != raw_content.strip():
+                return check_content.strip() if strip else check_content
             # Check if the string is a JSON-encoded list (e.g., "[{'type': 'text', ...}]")
             # This can happen when streaming concatenates content that was originally list format
             # Only check if it looks like a complete JSON array (requires strip for check)

tests/test_openai_source.py

@@ -60,6 +60,34 @@ def _make_groq_provider(overrides: dict | None = None) -> ProviderGroq:
     )
 
 
+def test_normalize_content_handles_text_part_repr_string():
+    raw = "[{text=I will use the file reading tool., type=text}]"
+
+    assert ProviderOpenAIOfficial._normalize_content(raw) == (
+        "I will use the file reading tool."
+    )
+
+
+def test_normalize_content_handles_text_part_repr_string_without_strip():
+    raw = "  [{text=I will use the file reading tool., type=text}]  "
+
+    assert ProviderOpenAIOfficial._normalize_content(raw, strip=False) == (
+        "I will use the file reading tool."
+    )
+
+
+def test_normalize_content_drops_empty_nested_text_part_repr_string():
+    raw = "[{text=[{text=[{text=, type=text}], type=text}], type=text}]"
+
+    assert ProviderOpenAIOfficial._normalize_content(raw) == ""
+
+
+def test_normalize_content_handles_non_empty_nested_text_part_repr_string():
+    raw = "[{text=[{text=[{text=hello, type=text}], type=text}], type=text}]"
+
+    assert ProviderOpenAIOfficial._normalize_content(raw) == "hello"
+
+
 def test_create_http_client_uses_openai_httpx_module(monkeypatch):
     captured: dict[str, object] = {}
 

tests/test_tool_loop_agent_runner.py

@@ -507,6 +507,38 @@ async def snapshot_context_manager(messages, trusted_token_usage=0):
     assert final_contexts[-1].content == runner.MAX_STEPS_REACHED_PROMPT
 
 
+@pytest.mark.asyncio
+async def test_tool_call_assistant_preface_is_not_sent_as_llm_result(
+    runner, provider_request, mock_tool_executor, mock_hooks, mock_provider
+):
+    """Assistant preface text on a tool-call turn must stay internal."""
+    mock_provider.should_call_tools = True
+    mock_provider.max_calls_before_normal_response = 1
+
+    await runner.reset(
+        provider=mock_provider,
+        request=provider_request,
+        run_context=ContextWrapper(context=None),
+        tool_executor=mock_tool_executor,
+        agent_hooks=mock_hooks,
+        streaming=False,
+    )
+
+    responses = []
+    async for response in runner.step_until_done(3):
+        responses.append(response)
+
+    response_types = [response.type for response in responses]
+    assert response_types[-1] == "llm_result"
+    assert "llm_result" not in response_types[:-1]
+
+    llm_results = [response for response in responses if response.type == "llm_result"]
+    llm_texts = [response.data["chain"].get_plain_text() for response in llm_results]
+    assert len(llm_results) == 1
+    assert "我需要使用工具来帮助您" not in llm_texts
+    assert llm_texts == ["这是我的最终回答"]
+
+
 @pytest.mark.asyncio
 async def test_tool_loop_next_request_includes_tool_result(
     runner, provider_request, mock_tool_executor, mock_hooks