diff --git a/eval_protocol/adapters/__init__.py b/eval_protocol/adapters/__init__.py
index 6f031f3d..b6e7c1e9 100644
--- a/eval_protocol/adapters/__init__.py
+++ b/eval_protocol/adapters/__init__.py
@@ -99,3 +99,19 @@
     __all__.extend(["WeaveAdapter"])
 except ImportError:
     pass
+
+# DataFrame adapter (pandas integration for Lilac, etc.)
+try:
+    from .dataframe import (
+        evaluation_rows_to_dataframe,
+        dataframe_to_evaluation_rows,
+    )
+
+    __all__.extend(
+        [
+            "evaluation_rows_to_dataframe",
+            "dataframe_to_evaluation_rows",
+        ]
+    )
+except ImportError:
+    pass
diff --git a/eval_protocol/adapters/dataframe.py b/eval_protocol/adapters/dataframe.py
new file mode 100644
index 00000000..f67077f4
--- /dev/null
+++ b/eval_protocol/adapters/dataframe.py
@@ -0,0 +1,66 @@
+"""
+Pandas DataFrame adapter for Eval Protocol.
+
+This module provides utilities for converting between EvaluationRow format
+and pandas DataFrame format, enabling integration with data curation tools
+such as Lilac, Great Expectations, or any pandas-based workflow.
+
+Example usage:
+    >>> from eval_protocol.adapters.dataframe import (
+    ...     evaluation_rows_to_dataframe,
+    ...     dataframe_to_evaluation_rows,
+    ... )
+    >>>
+    >>> # Convert EvaluationRows to DataFrame
+    >>> df = evaluation_rows_to_dataframe(rows)
+    >>>
+    >>> # Convert back to EvaluationRows
+    >>> rows = dataframe_to_evaluation_rows(df)
+"""
+
+from __future__ import annotations
+
+import logging
+
+import pandas as pd
+
+from ..models import EvaluationRow
+
+logger = logging.getLogger(__name__)
+
+
+def evaluation_rows_to_dataframe(rows: list[EvaluationRow]) -> pd.DataFrame:
+    """Convert EvaluationRows to a pandas DataFrame.
+
+    Uses EvaluationRow.to_dict() for serialization.
+
+    Args:
+        rows: List of EvaluationRow objects
+
+    Returns:
+        DataFrame with 'data_json' containing serialized rows plus convenience fields
+    """
+    records = [row.to_dict() for row in rows]
+    return pd.DataFrame(records)
+
+
+def dataframe_to_evaluation_rows(df: pd.DataFrame) -> list[EvaluationRow]:
+    """Convert a pandas DataFrame back to EvaluationRows.
+
+    Uses EvaluationRow.from_dict() for deserialization.
+
+    Args:
+        df: DataFrame with 'data_json' column containing serialized EvaluationRows
+
+    Returns:
+        List of EvaluationRow objects
+    """
+    rows = []
+    for _, row_data in df.iterrows():
+        try:
+            row = EvaluationRow.from_dict(row_data.to_dict())
+            rows.append(row)
+        except Exception as e:
+            logger.warning(f"Failed to convert row: {e}")
+            continue
+    return rows
diff --git a/eval_protocol/models.py b/eval_protocol/models.py
index 13d059d3..a7bc767b 100644
--- a/eval_protocol/models.py
+++ b/eval_protocol/models.py
@@ -953,6 +953,49 @@ def get_termination_reason(self) -> str:
                 return str(reason)
         return "unknown"
 
+    def to_dict(self) -> Dict[str, Any]:
+        """Serialize this EvaluationRow to a dictionary.
+
+        The entire EvaluationRow is serialized to JSON, allowing full reconstruction.
+        Additional scalar fields are included for convenient filtering/grouping.
+
+        Returns:
+            Dictionary with 'data_json' containing the full serialized row,
+            plus convenience fields for filtering.
+        """
+        return {
+            "data_json": self.model_dump_json(),
+            "row_id": self.input_metadata.row_id if self.input_metadata else None,
+            "score": self.evaluation_result.score if self.evaluation_result else None,
+            "message_count": len(self.messages),
+            "has_tools": bool(self.tools),
+            "created_at": self.created_at.isoformat() if self.created_at else None,
+        }
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "EvaluationRow":
+        """Reconstruct an EvaluationRow from a dictionary.
+
+        Args:
+            data: Dictionary containing 'data_json' with the serialized EvaluationRow.
+
+        Returns:
+            Reconstructed EvaluationRow instance.
+
+        Raises:
+            ValueError: If 'data_json' is missing or invalid.
+        """
+        from pydantic import ValidationError
+
+        data_json = data.get("data_json")
+        if not data_json:
+            raise ValueError("Missing 'data_json' field in dictionary")
+
+        try:
+            return cls.model_validate_json(data_json)
+        except ValidationError as e:
+            raise ValueError(f"Failed to deserialize EvaluationRow: {e}") from e
+
     def __hash__(self) -> int:
         # Use a stable hash that works across Python processes
         return self._stable_hash()