fix(provider): deduplicate streaming tool_call_ids for parallel calls

nanobot/providers/openai_compat_provider.py

@@ -1097,6 +1097,15 @@ class OpenAICompatProvider(LLMProvider):
             if delta:
                 _accum_legacy_function_call(getattr(delta, "function_call", None))
 
+        # Some providers (e.g. Zhipu/GLM) reuse the same tool_call id for
+        # parallel tool calls in streaming mode. Deduplicate before building
+        # the response so downstream tool messages don't collide.
+        _seen_tc_ids: set[str] = set()
+        for b in tc_bufs.values():
+            if not b["id"] or b["id"] in _seen_tc_ids:
+                b["id"] = _short_tool_id()
+            _seen_tc_ids.add(b["id"])
+
         return LLMResponse(
             content="".join(content_parts) or None,
             tool_calls=[

nanobot/utils/file_edit_events.py

@@ -367,12 +367,14 @@ class StreamingFileEditTracker:
 
     def apply_final_call_ids(self, final_tool_calls: list[Any]) -> None:
         """Keep final start/end events keyed to any earlier streamed placeholder."""
+        used_canonicals: set[str] = set()
         for tool_call in final_tool_calls:
             canonical = self.canonical_call_id_for(tool_call)
-            if canonical:
+            if canonical and canonical not in used_canonicals:
                 try:
                     tool_call.id = canonical
-                except Exception:
+                    used_canonicals.add(canonical)
+                except (AttributeError, TypeError):
                     pass
 
     def canonical_call_id_for(self, tool_call: Any) -> str | None: