diff --git a/examples/ags/experiments/baselines/cot_hotpotqa.py b/examples/ags/experiments/baselines/cot_hotpotqa.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/ags/experiments/baselines/cot_humaneval.py b/examples/ags/experiments/baselines/cot_humaneval.py
new file mode 100644
index 000000000..8f0db1066
--- /dev/null
+++ b/examples/ags/experiments/baselines/cot_humaneval.py
@@ -0,0 +1,50 @@
+from examples.ags.scripts.operator import Operator
+from examples.ags.scripts.graph import SolveGraph
+from examples.ags.benchmark.humaneval import humaneval_evaluation
+from examples.ags.scripts.operator_an import GenerateOp
+from metagpt.actions.action_node import ActionNode 
+from metagpt.configs.models_config import ModelsConfig
+from metagpt.llm import LLM
+from pydantic import BaseModel, Field
+
+HUMANEVAL_PROMPT_GPT = """
+{question}\nPlease reason step by step, and put your python function in the end. 
+"""
+
+class GenerateOp(BaseModel):
+    solution: str = Field(default="", description="问题的Python函数实现")
+
+class CoTGenerate(Operator):
+    def __init__(self, llm: LLM, name: str = "Generate"):
+        super().__init__(name, llm)
+
+    async def __call__(self, problem, mode: str = None):
+        prompt = HUMANEVAL_PROMPT_GPT.format(question=problem)
+        fill_kwargs = {"context": prompt, "llm": self.llm}
+        if mode:
+            fill_kwargs["mode"] = mode
+        node = await ActionNode.from_pydantic(GenerateOp).fill(**fill_kwargs)
+        response = node.instruct_content.model_dump()
+        return response
+
+class CoTSolveGraph(SolveGraph):
+    def __init__(self, name: str, llm_config, dataset: str):
+        super().__init__(name, llm_config, dataset)
+        self.cot_generate = CoTGenerate(self.llm)
+
+    async def __call__(self, problem):
+        solution = await self.cot_generate(problem, mode="code_fill")
+        return solution["solution"]
+
+if __name__ == "__main__":
+    async def main():
+        llm_config = ModelsConfig.default().get("gpt-35-turbo-1106")
+        graph = CoTSolveGraph(name="CoT", llm_config=llm_config, dataset="HumanEval")
+        file_path = "examples/ags/data/human-eval-new.jsonl"
+        samples = 1 # 33/131  
+        path = "examples/ags/data/baselines/general"
+        score = await humaneval_evaluation(graph, file_path, samples, path)
+        return score
+
+    import asyncio
+    asyncio.run(main())