diff --git a/automation-api/yival_experiments/custom_configuration/claude_evaluator.py b/automation-api/yival_experiments/custom_configuration/claude_evaluator.py
new file mode 100644
index 0000000..a191ea6
--- /dev/null
+++ b/automation-api/yival_experiments/custom_configuration/claude_evaluator.py
@@ -0,0 +1,125 @@
+"""
+ClaudeEvaluator is an evaluator that uses Anthropic's Claude model for evaluations.
+
+The evaluator interfaces with Claude via litellm to present tasks and interpret
+the model's responses to determine the quality or correctness of a given
+experiment result.
+"""
+import copy
+import logging
+
+import litellm
+from claude_evaluator_config import ClaudeEvaluatorConfig
+from evaluator_common import (
+    CLASSIFY_STR,
+    calculate_choice_score,
+    choices_to_string,
+    completion_with_backpff,
+    extract_choice_from_response,
+    format_template,
+)
+from yival.evaluators.base_evaluator import BaseEvaluator
+from yival.schemas.evaluator_config import (
+    EvaluatorOutput,
+    EvaluatorType,
+    MethodCalculationMethod,
+    MetricCalculatorConfig,
+)
+from yival.schemas.experiment_config import (
+    ExperimentResult,
+    InputData,
+    MultimodalOutput,
+)
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+class ClaudeEvaluator(BaseEvaluator):
+    """Evaluator using Claude for evaluation."""
+
+    default_config = ClaudeEvaluatorConfig(name="claude_evaluator")  # type: ignore
+
+    def __init__(self, config: ClaudeEvaluatorConfig):
+        super().__init__(config)
+        self.config = config
+
+    def evaluate(self, experiment_result: ExperimentResult) -> EvaluatorOutput:
+        """Evaluate the experiment result using Claude."""
+        format_dict = copy.deepcopy(experiment_result.input_data.content)
+        format_dict["raw_output"] = experiment_result.raw_output.text_output
+
+        prompt = format_template(self.config.prompt, format_dict)
+        if isinstance(prompt, str):
+            prompt = [{"role": "user", "content": prompt}]
+
+        prompt[-1]["content"] += "\n\n" + CLASSIFY_STR.format(
+            choices=choices_to_string(self.config.choices)
+        )
+        response = completion_with_backpff(
+            model=self.config.model_name,
+            messages=prompt,
+            temperature=0.0,
+            n=1,
+            max_tokens=2000,
+            request_timeout=60,
+            caching=True,
+        )
+        response_content = response["choices"][0]["message"]["content"]
+        choice = extract_choice_from_response(response_content, self.config.choices)
+        score = calculate_choice_score(choice, self.config.choice_scores)
+        return EvaluatorOutput(
+            name=self.config.name,
+            result=score if score is not None else choice,
+            display_name=self.config.display_name,
+            metric_calculators=self.config.metric_calculators,
+        )
+
+
+BaseEvaluator.register_evaluator(
+    "claude_evaluator", ClaudeEvaluator, ClaudeEvaluatorConfig
+)
+
+
+def main():
+    """Main function to test the ClaudeEvaluator."""
+    from example_evaluator_data import (
+        choice_scores,
+        choices,
+        content,
+        prompt,
+        raw_output,
+    )
+
+    litellm.set_verbose = True
+
+    evaluator_config = ClaudeEvaluatorConfig(
+        name="claude_evaluator",
+        display_name="correctness test",
+        metric_calculators=[
+            MetricCalculatorConfig(
+                MethodCalculationMethod(MethodCalculationMethod.AVERAGE)
+            )
+        ],
+        prompt=prompt,
+        choices=choices,
+        evaluator_type=EvaluatorType.INDIVIDUAL,
+        choice_scores=choice_scores,
+    )
+    input_data_example = InputData(content=content)
+
+    experiment_result_example = ExperimentResult(
+        input_data=input_data_example,
+        combination={"wrapper1": "var1", "wrapper2": "var2"},
+        raw_output=MultimodalOutput(text_output=raw_output),
+        latency=150.0,
+        token_usage=50,
+    )
+
+    evaluator = ClaudeEvaluator(evaluator_config)
+    result = evaluator.evaluate(experiment_result_example)
+    print("Result: ", result.result)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/automation-api/yival_experiments/custom_configuration/claude_evaluator_config.py b/automation-api/yival_experiments/custom_configuration/claude_evaluator_config.py
new file mode 100644
index 0000000..4290163
--- /dev/null
+++ b/automation-api/yival_experiments/custom_configuration/claude_evaluator_config.py
@@ -0,0 +1,18 @@
+from dataclasses import asdict, dataclass, field
+from typing import Any, Dict, List, Optional, Union
+
+from yival.schemas.evaluator_config import EvaluatorConfig, EvaluatorType
+
+
+@dataclass
+class ClaudeEvaluatorConfig(EvaluatorConfig):
+    evaluator_type: EvaluatorType = EvaluatorType.INDIVIDUAL
+    prompt: Union[str, List[Dict[str, str]]] = ""
+    choices: List[str] = field(default_factory=list)
+    model_name: str = "claude-3-5-sonnet-20241022"
+    description: str = "This is an evaluator that uses Anthropic's Claude model."
+    scale_description: str = "0-4"
+    choice_scores: Optional[Dict[str, float]] = None
+
+    def asdict(self) -> Dict[str, Any]:
+        return asdict(self)