fix(mavvrik_focus): guard Tags column and non-dict parsed JSON

litellm/integrations/mavvrik_focus/mavvrik_focus_logger.py

@@ -53,7 +53,7 @@ def _with_token_tags(data: pl.DataFrame, normalized: pl.DataFrame) -> pl.DataFra
     adds/renames columns, it never filters or reorders rows.
     """
     available = [k for k in _TOKEN_TAG_KEYS if k in data.columns]
-    if not available or len(data) != len(normalized):
+    if not available or len(data) != len(normalized) or "Tags" not in normalized.columns:
         return normalized
 
     token_rows = data.select(available).to_dicts()
@@ -64,6 +64,8 @@ def _with_token_tags(data: pl.DataFrame, normalized: pl.DataFrame) -> pl.DataFra
             tags = json.loads(tags_json) if tags_json else {}
         except (TypeError, ValueError):
             tags = {}
+        if not isinstance(tags, dict):
+            tags = {}
         for key in available:
             value = row.get(key)
             if value is not None:

tests/test_litellm/integrations/mavvrik_focus/test_mavvrik_focus_logger.py

@@ -102,6 +102,29 @@ def test_with_token_tags_recovers_from_malformed_tags_json() -> None:
     }
 
 
+def test_with_token_tags_recovers_from_non_dict_tags_json() -> None:
+    data = pl.DataFrame({"prompt_tokens": [57], "completion_tokens": [753]})
+    normalized = pl.DataFrame({"Tags": ["null"]})
+
+    result = _with_token_tags(data, normalized)
+
+    tags = json.loads(result["Tags"][0])
+    assert tags == {
+        "prompt_tokens": "57",
+        "completion_tokens": "753",
+        "total_tokens": "810",
+    }
+
+
+def test_with_token_tags_noop_when_tags_column_absent() -> None:
+    data = pl.DataFrame({"prompt_tokens": [57], "completion_tokens": [753]})
+    normalized = pl.DataFrame({"OtherColumn": ["x"]})
+
+    result = _with_token_tags(data, normalized)
+
+    assert result is normalized
+
+
 def test_with_token_tags_omits_total_when_only_one_token_column_present() -> None:
     data = pl.DataFrame({"prompt_tokens": [57]})
     normalized = pl.DataFrame({"Tags": [json.dumps({"model": "azure/gpt-4o-mini"})]})