feat: implement configurable Xingbang risk agent on mini-openclaw core

backend/graph/agent.py

@@ -46,20 +46,40 @@ class AgentManager:
 
     def _build_agent(self):
         """Build a fresh agent with current system prompt (re-reads files each time)."""
-        from langchain.agents import create_agent
-
         assert self._base_dir is not None
         assert self._llm is not None
 
         rag_mode = get_rag_mode()
         system_prompt = build_system_prompt(self._base_dir, rag_mode=rag_mode)
 
-        agent = create_agent(
-            model=self._llm,
-            tools=self._tools,
-            system_prompt=system_prompt,
+        return self.build_task_agent(self._llm, self._tools, system_prompt)
+
+    def build_task_agent(self, model, tools: list, system_prompt: str):
+        """Shared core factory. Scoped tasks supply explicit model, tools and prompt."""
+        from langchain.agents import create_agent
+        return create_agent(model=model, tools=tools, system_prompt=system_prompt)
+
+    def invoke_scoped(self, message: str, *, model, tools: list, system_prompt: str) -> dict:
+        """Run an isolated task through the same core without shared chat memory.
+
+        No mutation of the default model/tools: concurrent projects cannot share state.
+        The caller stores the trace under its own authorization boundary.
+        """
+        from langchain_core.messages import messages_to_dict
+        agent = self.build_task_agent(model, tools, system_prompt)
+        result = agent.invoke(
+            {"messages": self._build_messages(message, [])},
+            config={"recursion_limit": 16, "callbacks": []},
         )
-        return agent
+        messages = result.get("messages", [])
+        if not messages or messages[-1].type != "ai" or getattr(messages[-1], "tool_calls", None):
+            raise ValueError("Agent 未完成工具调用循环")
+        content = messages[-1].content
+        if not isinstance(content, str):
+            content = "\n".join(b.get("text", "") for b in content if isinstance(b, dict) and b.get("type") == "text")
+        if not content.strip():
+            raise ValueError("Agent 未返回有效建议")
+        return {"text": content, "messages": messages_to_dict(messages), "engine": "mini-openclaw/AgentManager"}
 
     def _build_messages(self, user_message: str, history: list[dict[str, Any]]) -> list:
         """Convert session history + new message into LangChain messages."""