diff --git a/src/lighteval/logging/evaluation_tracker.py b/src/lighteval/logging/evaluation_tracker.py
index 976b21c86..80c1715f8 100644
--- a/src/lighteval/logging/evaluation_tracker.py
+++ b/src/lighteval/logging/evaluation_tracker.py
@@ -180,10 +180,12 @@ def __init__(
 
         self.public = public
 
+        self._using_trackio = False
         if use_wandb is True:
             try:
                 import trackio as wandb
 
+                self._using_trackio = True
                 logger.warning("Trackio was found available in your environment, using it instead of wandb")
                 self.wandb_project = os.environ.get("WANDB_PROJECT", None)
                 self.space_id = os.environ.get("WANDB_SPACE_ID", None)
@@ -207,6 +209,7 @@ def __init__(
                 resume="allow",
                 **wandb_kwargs,
             )
+            self._wandb_module = wandb
 
     @property
     def results(self):
@@ -292,13 +295,64 @@ def save(self) -> None:
 
     def push_to_wandb(self, results_dict: dict, details_datasets: dict) -> None:
         # reformat the results key to replace ':' with '/'
-        results_dict = {k.replace(":", "/"): v for k, v in results_dict["results"].items()}
+        flat_results = {k.replace(":", "/"): v for k, v in results_dict["results"].items()}
 
         self.wandb_run.log(
-            {**results_dict},
+            {**flat_results},
         )
+
+        # When the backend is Trackio, log per-sample details as trackio.Trace
+        # so each sample is inspectable on the dashboard. (For wandb users this
+        # path stays a no-op; details are still pushed to the Hub via push_to_hub.)
+        if self._using_trackio:
+            self._log_details_as_traces(details_datasets)
+
         self.wandb_run.finish()
 
+    def _log_details_as_traces(self, details_datasets: dict) -> None:
+        """Log each sample of each task as a ``trackio.Trace``.
+
+        Pulls (prompt, completion, metrics, gold) out of the per-sample detail
+        dataset and emits one Trace per row, with the task name as a top-level
+        log key so samples are grouped by task on the Trackio dashboard.
+        """
+        trackio_module = self._wandb_module
+        step = 0
+        for task_name, dataset in details_datasets.items():
+            for row in dataset:
+                model_response = row.get("model_response") or {}
+                doc = row.get("doc") or {}
+                metric = row.get("metric") or {}
+
+                prompt = model_response.get("input")
+                if isinstance(prompt, list):
+                    prompt = "\n".join(str(p) for p in prompt)
+                generations = model_response.get("text") or []
+                completion = generations[0] if generations else ""
+
+                messages = []
+                instruction = doc.get("instruction")
+                if instruction:
+                    messages.append({"role": "system", "content": str(instruction)})
+                messages.append({"role": "user", "content": str(prompt or doc.get("query", ""))})
+                if completion:
+                    messages.append({"role": "assistant", "content": str(completion)})
+
+                metadata = {"task": task_name, **{k: v for k, v in metric.items() if v is not None}}
+                gold_index = doc.get("gold_index")
+                choices = doc.get("choices")
+                if gold_index is not None:
+                    metadata["gold_index"] = gold_index
+                    if isinstance(choices, list) and isinstance(gold_index, int) and 0 <= gold_index < len(choices):
+                        metadata["gold"] = choices[gold_index]
+
+                try:
+                    trace = trackio_module.Trace(messages=messages, metadata=metadata)
+                    self.wandb_run.log({f"{task_name.replace(':', '/')}/sample": trace}, step=step)
+                except Exception as e:
+                    logger.warning(f"Failed to log Trackio trace for {task_name} sample {step}: {e}")
+                step += 1
+
     def save_results(self, date_id: str, results_dict: dict):
         if self.results_path_template is not None:
             org_model_parts = self.general_config_logger.model_name.split("/")