diff --git a/src/agentunit/examples/basic_evaluation.py b/src/agentunit/examples/basic_evaluation.py
new file mode 100644
index 0000000..02c93c4
--- /dev/null
+++ b/src/agentunit/examples/basic_evaluation.py
@@ -0,0 +1,79 @@
+from agentunit.adapters.base import AdapterOutcome, BaseAdapter
+from agentunit.core.runner import Runner
+from agentunit.core.scenario import Scenario
+from agentunit.core.trace import TraceLog
+from agentunit.datasets.base import DatasetCase, DatasetSource
+
+
+class FakeAdapter(BaseAdapter):
+    """
+    A minimal adapter implementation used for testing and examples.
+
+    FakeAdapter simulates an adapter without performing real computation, it returns predefined response.
+    This becomes useful in representing integration of adapters with the AgentUnit.
+    """
+
+    def __init__(self, response: str):
+        """
+        Initialize adapter with static response.
+
+        Args:
+            response (str): The output string returns on execution.
+        """
+        self.response = response
+
+    def prepare(self):
+        """
+        Prepare adapter before execution.
+
+        FakeAdapter do nor require setup.
+        """
+
+    def execute(self, case, trace_log: TraceLog) -> AdapterOutcome:
+        """
+        Execute the adapter for a given evaluation case.
+
+        Args:
+            case(object): Input case of evaluation.
+            trace_log (TraceLog): Trace log for recording execution details.
+        """
+        return AdapterOutcome(success=True, output=self.response, error=None)
+
+    def cleanup(self):
+        """
+        Cleanup adapter resources.
+
+        No cleanup required for FakeAdapter.
+        """
+
+
+def main() -> None:
+    # define simple dataset
+    cases = [
+        DatasetCase(
+            id="case_1",
+            input="hello",
+            expected_output="hello",
+        )
+    ]
+
+    # create a scenario using the fake adapter
+    scenario = Scenario(
+        name="Basic Evaluation Example",
+        adapter=FakeAdapter(response="hello"),
+        dataset=DatasetSource.from_list(cases),
+    )
+
+    # run evaluation
+    runner = Runner([scenario])
+    result = runner.run()
+
+    scenario_result = result.scenarios[0]
+    # print summary
+    print("=== Evaluation Summary ===")
+    print(f"Scenario: {scenario.name}")
+    print(f"Success rate: {scenario_result.success_rate:.0%}")
+
+
+if __name__ == "__main__":
+    main()