diff --git a/TraceLens/NcclAnalyser/nccl_analyser.py b/TraceLens/NcclAnalyser/nccl_analyser.py
index 35f96bd2..3305a423 100644
--- a/TraceLens/NcclAnalyser/nccl_analyser.py
+++ b/TraceLens/NcclAnalyser/nccl_analyser.py
@@ -208,7 +208,7 @@ def build_df_summary_long(self, agg_metrics=['mean', 'std', 'min', 'max'],
     # Step 2: Build a wide table for implicit sync class
     # where each row is a collective operation
     # ------------------------------------------------------------------------
-    def build_df_nccl_implicit_sync_cat(self, detailed=False):
+    def build_df_nccl_implicit_sync_cat(self, detailed=False, strict_metadata_check=True):
         """
         Builds a single DF with one row *per collective ID*, including per-rank ts/dur + metadata.
         Ensures metadata consistency across ranks.
@@ -237,7 +237,9 @@ def build_df_nccl_implicit_sync_cat(self, detailed=False):
             for field in metadata_fields:
                 unique_values = rank_events[field].unique()
                 if len(unique_values) > 1:
-                    raise ValueError(f"Metadata mismatch in '{field}' for collective {cid}: {unique_values}")
+                    if strict_metadata_check:
+                        raise ValueError(f"Metadata mismatch in '{field}' for collective {cid}: {unique_values}")
+                    warnings.warn(f"Metadata mismatch in '{field}' for collective {cid}: {unique_values}")
 
             row = {'collective_id': cid, **ref_metadata}
 
@@ -301,13 +303,14 @@ def build_df_nccl_implicit_sync_cat(self, detailed=False):
 
 
     def build_df_summary_nccl_implicit_sync_cat(self, agg_metrics=['mean', 'std'],
-                                                metadata_fields=["Process Group Name", "Group size", "Full msg size (MB)"]):
+                                                metadata_fields=["Process Group Name", "Group size", "Full msg size (MB)"],
+                                                strict_metadata_check=True):
         """
         Builds a summary DF with one row per collective name, dtype, and msg size.
         Aggregates across all collectives and ranks.
         """
         if not hasattr(self, 'df_implicit_sync_cat'):
-            self.df_implicit_sync_cat = self.build_df_nccl_implicit_sync_cat()
+            self.df_implicit_sync_cat = self.build_df_nccl_implicit_sync_cat(strict_metadata_check=strict_metadata_check)
 
         # Aggregation logic
 
@@ -350,7 +353,7 @@ def build_df_summary_nccl_implicit_sync_cat(self, agg_metrics=['mean', 'std'],
         summary_df = summary_df[columns_order]
         return summary_df
 
-    def build_df_nccl_all2allv(self, detailed=False):
+    def build_df_nccl_all2allv(self, detailed=False, strict_metadata_check=True):
         # this is diff from implicit sync cat
         # first, each rank can send and receive different amount of data
         # as a result they do not respect the implicit sync cat
@@ -383,7 +386,9 @@ def build_df_nccl_all2allv(self, detailed=False):
             for field in metadata_fields:
                 unique_values = rank_events[field].unique()
                 if len(unique_values) > 1:
-                    raise ValueError(f"Metadata mismatch in '{field}' for collective {cid}")
+                    if strict_metadata_check:
+                        raise ValueError(f"Metadata mismatch in '{field}' for collective {cid}")
+                    warnings.warn(f"Metadata mismatch in '{field}' for collective {cid}")
 
             # **Common metadata**
             row = {'collective_id': cid, **ref_metadata}