diff --git a/environments/kernelbench_env.py b/environments/kernelbench_env.py
new file mode 100644
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+++ b/environments/kernelbench_env.py
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+# kernelbench_env.py
+import os
+import json
+import subprocess
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple, TypedDict, Union
+
+from atroposlib.envs.base import BaseEnv, BaseEnvConfig, APIServerConfig, ScoredDataGroup
+from atroposlib.type_definitions import Item, number
+from atroposlib.utils.tokenize_for_trainer import tokenize_for_trainer
+
+# KernelBench imports
+
+from src.eval import eval_kernel_against_ref   # <- new import
+
+
+KERNELBENCH_DIR = Path("/path/to/KernelBench")   # ← point here to your clone
+
+class KBRow(TypedDict):
+    """Single‑task record (prompt text plus meta)."""
+    prompt: str           # full prompt given to the LLM
+    sample_path: str
+
+
+class KBEnv(BaseEnv):
+    """
+    A stripped‑down Atropos environment that only handles Level‑1 / problem‑1
+    (square matrix multiplication).  It generates one kernel per rollout
+    group, writes the kernel to the expected `runs/{run_name}` layout, then
+    invokes KernelBench's evaluation script to obtain a scalar reward.
+    """
+
+    name = "kernelbench"
+
+    # ---------- Static config helpers ----------------------------------------
+    @classmethod
+    def config_init(cls) -> Tuple[BaseEnvConfig, List[APIServerConfig]]:
+        env_cfg = BaseEnvConfig(
+            tokenizer_name="NousResearch/DeepHermes-3-Llama-3-3B-Preview",
+            group_size=4,                     # 4 candidate kernels per step
+            max_token_length=2048,
+            batch_size=4,
+            steps_per_eval=50,
+            total_steps=1000,
+            rollout_server_url="http://localhost:8000",
+            use_wandb=False,                  # flip on if you want logging
+            wandb_name="kb_level1_prob1",
+        )
+
+        server_cfgs = [
+            APIServerConfig(
+                model_name="NousResearch/DeepHermes-3-Llama-3-3B-Preview",
+                base_url="http://localhost:9001/v1",
+                api_key="DUMMY_KB_KEY",       # fill in if proxy requires it
+                num_requests_for_eval=64,
+            )
+        ]
+        return env_cfg, server_cfgs
+
+    # --------------------- Data ------------------------------------------------
+    async def setup(self):
+        """
+        Nothing to load from disk – we construct the single prompt on‑the‑fly
+        with KernelBench's PromptConstructor so that it exactly matches their
+        evaluation format.
+        """
+        # Hard‑code the HF dataset identifier for problem 1
+        self.problem_spec = {
+            "level": 1,
+            "problem_id": 1,
+            "problem_file": "1_Square_matrix_multiplication_.py",
+        }
+        self.iter = 0
+        with open("prompt.txt", "r", encoding="utf-8") as f:
+            self.prompt = f.read()
+
+        self.sample_path="./sample.py"
+        self.reward_buffer = list()
+
+    # --------------------- Rollout / scoring ----------------------------------
+    async def collect_trajectories(
+        self, item: KBRow
+    ) -> Tuple[ScoredDataGroup, List[Item]]:
+        """
+        Ask the LLM `group_size` times; each completion should be *only* the
+        CUDA / Triton kernel (per KernelBench docs).  We store them to
+        runs/{run_name}/{level}/{id}/sample_<n>.cu so that the official
+        evaluator picks them up.
+        """
+        user_msg = {"role": "user", "content": self.prompt}
+
+        chat_completions = await self.server.chat_completion(
+            messages=[user_msg],
+            n=self.config.group_size,
+            max_tokens=self.config.max_token_length,
+            temperature=0.0,
+        )
+
+        # Path: runs/<RUN_NAME>/level_1/1/
+        run_dir = run_dir = KERNELBENCH_DIR / "runs"  / self.config.wandb_name / "level_1" / "1"
+        run_dir.mkdir(parents=True, exist_ok=True)
+
+        to_score: List[Dict] = []
+        to_backlog: list()
+        for i, choice in enumerate(chat_completions.choices):
+            kernel_code = choice.message.content
+            sample_path = run_dir / f"sample_{i}.cu"
+            sample_path.write_text(kernel_code, encoding="utf‑8")
+
+            messages = (user_msg, {"role": "assistant", "content": kernel_code})
+            to_score.append(
+                {
+                    "messages": messages,
+                    "sample_path": str(sample_path),
+                    "finish_reason": choice.finish_reason,
+                }
+            )
+        
+        to_postprocess = await self.score(to_score)
+
+        return to_postprocess, to_backlog
+    
+    async def score(
+        self, rollout_group_data: List[Dict]
+    ) -> Union[Optional[ScoredDataGroup], List[Optional[ScoredDataGroup]]]:
+
+        scores = ScoredDataGroup(tokens=[], masks=[], scores=[])
+
+        scores["tokens"] = list()
+        scores["masks"] = list()
+        scores["scores"] = list()
+
+        # where we will build + compile kernels
+        build_dir = os.path.join("build", "kernelbench", f"{1}", f"{1}")
+        os.makedirs(build_dir, exist_ok=True)
+
+        for item in rollout_group_data:
+            generated_src = item["prompt"]
+            custom_model_src = Path(item["sample_path"]).read_text()
+
+            eval_result = eval_kernel_against_ref(
+                ref_arch_src=generated_src,                      # blank per instructions
+                custom_model_src=custom_model_src,
+                measure_performance=True,
+                verbose=True,
+                num_correct_trials=1,
+                num_perf_trials=1,
+                build_dir=build_dir,
+            )
+
+            compiled_flag = bool(getattr(eval_result, "compiled", False))
+            runtime_val   = float(getattr(eval_result, "runtime", -1.0))
+
+            reward = 0.3 * (1 if compiled_flag else 0) + runtime_val
+
+            out_dict = tokenize_for_trainer(self.tokenizer, item["messages"], item["finish_reason"])
+
+            scores["tokens"].append(out_dict["tokens"])
+            scores["masks"].append(out_dict["masks"])
+            scores["scores"].append(reward)
+        
+        for score in scores["scores"]:
+            self.reward_buffer.append(max(score, 0))
+
+        return scores if scores["tokens"] else None
+    
+    async def get_next_item(self) -> KBRow:
+        """Return the same single problem every time (env is tiny)."""
+        return KBRow(prompt=self.prompt, sample_path=self.sample_path)
+
+    async def evaluate(self, *args, **kwargs):
+        """Evaluate the current model on a set of test problems."""
+        # For now, we'll just log the average reward from the reward buffer
+        if self.reward_buffer:
+            avg_reward = sum(self.reward_buffer) / len(self.reward_buffer)
+            self.eval_metrics.append(("eval/avg_reward", avg_reward))
+            self.reward_buffer = list()
+
+    async def wandb_log(self, wandb_metrics: Optional[Dict] = None):
+        if wandb_metrics is None:
+            wandb_metrics = {}
+
+        # Try to calculate percent_correct, pass if there's a division by zero
+        try:
+            wandb_metrics["train/reward"] = sum(
+                self.reward_buffer
+            ) / len(self.reward_buffer)
+        except ZeroDivisionError:
+            # Skip if buffer is empty
+            pass
+
+        self.reward_buffer = list()
+        for item in self.eval_metrics:
+            wandb_metrics[item[0]] = item[1]
+        self.eval_metrics = list()
+        # Call the parent method to handle the server metrics
+        await super().wandb_log(wandb_metrics)
+
+
+# -----------------------------------------------------------------------------
+
+
+if __name__ == "__main__":
+    KBEnv.cli()
+