diff --git a/trace_agg/src/trace_agg/cli.py b/trace_agg/src/trace_agg/cli.py
new file mode 100644
index 0000000..7172628
--- /dev/null
+++ b/trace_agg/src/trace_agg/cli.py
@@ -0,0 +1,103 @@
+import argparse
+import json
+import sys
+from pathlib import Path
+import logging
+from typing import List
+
+from trace_agg import core
+
+
+def setup_logging() -> logging.Logger:
+    logger = logging.getLogger("trace_agg.cli")
+    handler = logging.StreamHandler()
+    formatter = logging.Formatter(
+        fmt="%(asctime)s [%(levelname)s] %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+    )
+    handler.setFormatter(formatter)
+    if not logger.handlers:
+        logger.addHandler(handler)
+    logger.setLevel(logging.INFO)
+    return logger
+
+
+def _validate_run_data_entries(data: list) -> List[dict]:
+    required_keys = {"run_id", "retry_free_reads", "window_duration"}
+    validated = []
+    for entry in data:
+        if not isinstance(entry, dict):
+            raise TypeError(f"Run data entry must be dict, got {type(entry)}")
+        if not required_keys.issubset(entry):
+            raise ValueError(f"Missing required keys in {entry}")
+        if not isinstance(entry["run_id"], str):
+            raise TypeError("run_id must be str")
+        if not isinstance(entry["retry_free_reads"], int):
+            raise TypeError("retry_free_reads must be int")
+        if not isinstance(entry["window_duration"], (int, float)):
+            raise TypeError("window_duration must be number")
+        validated.append(entry)
+    assert validated, "Input data list cannot be empty"
+    return validated
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Command-line interface for run set analysis and retry classification."
+    )
+    parser.add_argument("--input", required=True, help="Path to input JSON with run data.")
+    parser.add_argument(
+        "--output", required=True, help="Path to output JSON file for classification results."
+    )
+    args = parser.parse_args()
+
+    logger = setup_logging()
+
+    try:
+        input_path = Path(args.input)
+        output_path = Path(args.output)
+
+        if not input_path.exists():
+            raise FileNotFoundError(f"Input file not found: {input_path}")
+
+        logger.info(f"Loading run data from {input_path}")
+        with input_path.open("r", encoding="utf-8") as f:
+            data = json.load(f)
+
+        validated_data = _validate_run_data_entries(data)
+
+        # Prepare run data objects for core classification.
+        run_data_objs = []
+        for d in validated_data:
+            run_data_objs.append(
+                type("RunData", (), d)
+            )
+
+        logger.info("Classifying runs and computing correlations...")
+        results = core.classify_runs(run_data_objs)
+
+        # Results serialization.
+        output_serializable = []
+        for r in results:
+            output_serializable.append(
+                {
+                    "run_id": getattr(r, "run_id", None),
+                    "step_stability_score": getattr(r, "step_stability_score", None),
+                    "correlation_coefficient": getattr(r, "correlation_coefficient", None),
+                }
+            )
+
+        logger.info(f"Writing classification results to {output_path}")
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+        with output_path.open("w", encoding="utf-8") as f:
+            json.dump(output_serializable, f, indent=2)
+
+        logger.info("Run classification completed successfully.")
+
+    except Exception as exc:
+        logger.exception(f"Execution failed: {exc}")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()