diff --git a/report_generation/src/report_generation/main.py b/report_generation/src/report_generation/main.py
new file mode 100644
index 0000000..462de64
--- /dev/null
+++ b/report_generation/src/report_generation/main.py
@@ -0,0 +1,86 @@
+import json
+import os
+from pathlib import Path
+from datetime import datetime
+from typing import Dict, Any
+
+
+class ReportPath:
+    """Datamodel for the report output path."""
+
+    def __init__(self, file_location: str) -> None:
+        if not isinstance(file_location, str):
+            raise TypeError("file_location must be a string")
+        self.file_location = file_location
+
+    def __repr__(self) -> str:
+        return f"ReportPath(file_location={self.file_location!r})"
+
+
+def _validate_analysis_results(analysis_results: Dict[str, Any]) -> None:
+    """Validate the analysis_results input structure."""
+    if not isinstance(analysis_results, dict):
+        raise TypeError("analysis_results must be a dictionary")
+
+    required_fields = {"run_id", "metric_name", "value", "cluster_score"}
+    if "results" not in analysis_results or not isinstance(analysis_results["results"], list):
+        raise ValueError("analysis_results must contain a 'results' list")
+
+    for entry in analysis_results["results"]:
+        if not isinstance(entry, dict):
+            raise ValueError("Each result entry must be a dictionary")
+        missing = required_fields - entry.keys()
+        if missing:
+            raise ValueError(f"Missing required fields in result entry: {missing}")
+        if not isinstance(entry["run_id"], int):
+            raise TypeError("run_id must be int")
+        if not isinstance(entry["metric_name"], str):
+            raise TypeError("metric_name must be str")
+        if not isinstance(entry["value"], (int, float)):
+            raise TypeError("value must be numeric")
+        if not isinstance(entry["cluster_score"], (int, float)):
+            raise TypeError("cluster_score must be numeric")
+
+
+def generate_report(analysis_results: Dict[str, Any]) -> str:
+    """Erzeugt einen Bericht basierend auf den analysierten Resultaten der Replikationsläufe.
+
+    Args:
+        analysis_results (dict): Aggregierte Ergebnisse der Replikations- und Clusteranalyse.
+
+    Returns:
+        str: Pfad zur generierten JSON-Berichtsdatei.
+    """
+    _validate_analysis_results(analysis_results)
+
+    results = analysis_results["results"]
+
+    # Compute summary statistics
+    total_runs = len(results)
+    avg_cluster = sum(r["cluster_score"] for r in results) / total_runs if total_runs else 0.0
+    avg_metric_value = sum(r["value"] for r in results) / total_runs if total_runs else 0.0
+
+    metric_names = list({r["metric_name"] for r in results})
+    run_ids = [r["run_id"] for r in results]
+
+    report_data = {
+        "timestamp": datetime.utcnow().isoformat() + "Z",
+        "total_runs": total_runs,
+        "metric_names": metric_names,
+        "average_cluster_score": avg_cluster,
+        "average_metric_value": avg_metric_value,
+        "run_ids": run_ids,
+    }
+
+    output_dir = Path("output")
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    output_path = output_dir / "report_summary.json"
+
+    with open(output_path, "w", encoding="utf-8") as f:
+        json.dump(report_data, f, indent=2)
+
+    assert output_path.is_file(), "Report file was not created."
+
+    report_path = ReportPath(file_location=str(output_path.resolve()))
+    return report_path.file_location