diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index bafc69ed..1bbdd31e 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -32,6 +32,7 @@ Please keep the lists sorted alphabetically.
 * Bikram Pandit
 * Eric Vollenweider
 * Fabian Jenelten
+* Lingshang Kong
 * Lorenzo Terenzi
 * Marko Bjelonic
 * Matthijs van der Boon
diff --git a/README.md b/README.md
index c8bae884..ecdd63b3 100644
--- a/README.md
+++ b/README.md
@@ -44,6 +44,7 @@ The package supports the following logging frameworks which can be configured th
 * Tensorboard: https://www.tensorflow.org/tensorboard/
 * Weights & Biases: https://wandb.ai/site
 * Neptune: https://docs.neptune.ai/
+* Swanlab: https://docs.swanlab.cn/en/
 
 For a demo configuration of PPO, please check the [example_config.yaml](config/example_config.yaml) file.
 
diff --git a/rsl_rl/utils/logger.py b/rsl_rl/utils/logger.py
index 2dc29231..a1b77e09 100644
--- a/rsl_rl/utils/logger.py
+++ b/rsl_rl/utils/logger.py
@@ -64,7 +64,7 @@ def __init__(
         self._store_code_state()
 
         # Log configuration
-        if self.writer and not self.disable_logs and self.logger_type in ["wandb", "neptune"]:
+        if self.writer and not self.disable_logs and self.logger_type in ["wandb", "neptune", "swanlab"]:
             self.writer.store_config(env_cfg, self.cfg)
 
     def process_env_step(
@@ -251,6 +251,10 @@ def _prepare_logging_writer(self) -> None:
                 from torch.utils.tensorboard import SummaryWriter
 
                 self.writer = SummaryWriter(log_dir=self.log_dir, flush_secs=10)
+            elif self.logger_type == "swanlab":
+                from rsl_rl.utils.swanlab_utils import SwanlabSummaryWriter
+
+                self.writer = SwanlabSummaryWriter(log_dir=self.log_dir, flush_secs=10, cfg=self.cfg)
             else:
                 raise ValueError("Logger type not found. Please choose 'wandb', 'neptune', or 'tensorboard'.")
         else:
diff --git a/rsl_rl/utils/swanlab_utils.py b/rsl_rl/utils/swanlab_utils.py
new file mode 100644
index 00000000..00046f82
--- /dev/null
+++ b/rsl_rl/utils/swanlab_utils.py
@@ -0,0 +1,74 @@
+# Copyright (c) 2021-2025, ETH Zurich and NVIDIA CORPORATION
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+import os
+from dataclasses import asdict
+from torch.utils.tensorboard import SummaryWriter
+
+try:
+    import swanlab
+except ModuleNotFoundError:
+    raise ModuleNotFoundError("swanlab package is required to log to Swanlab.") from None
+
+
+class SwanlabSummaryWriter(SummaryWriter):
+    """Summary writer for Swanlab."""
+
+    def __init__(self, log_dir: str, flush_secs: int, cfg: dict) -> None:
+        super().__init__(log_dir, flush_secs)
+
+        # Get the run name
+        run_name = os.path.split(log_dir)[-1]
+
+        # Get swanlab project and entity
+        try:
+            project = cfg["swanlab_project"]
+        except KeyError:
+            raise KeyError("Please specify swanlab_project in the runner config, e.g. legged_gym.") from None
+        try:
+            entity = os.environ["SWANLAB_USERNAME"]
+        except KeyError:
+            entity = None
+
+        # Initialize swanlab
+        swanlab.init(project=project, entity=entity, name=run_name)
+        swanlab.config.update({"log_dir": log_dir})
+
+    def store_config(self, env_cfg: dict | object, train_cfg: dict) -> None:
+        swanlab.config.update({"runner_cfg": train_cfg})
+        swanlab.config.update({"policy_cfg": train_cfg["policy"]})
+        swanlab.config.update({"alg_cfg": train_cfg["algorithm"]})
+        try:
+            swanlab.config.update({"env_cfg": env_cfg.to_dict()})
+        except Exception:
+            swanlab.config.update({"env_cfg": asdict(env_cfg)})
+
+    def add_scalar(
+        self,
+        tag: str,
+        scalar_value: float,
+        global_step: int | None = None,
+        walltime: float | None = None,
+        new_style: bool = False,
+    ) -> None:
+        super().add_scalar(
+            tag,
+            scalar_value,
+            global_step=global_step,
+            walltime=walltime,
+            new_style=new_style,
+        )
+        swanlab.log({tag: scalar_value}, step=global_step)
+
+    def stop(self) -> None:
+        swanlab.finish()
+
+    def save_model(self, model_path: str, it: int) -> None:
+        raise NotImplementedError("Swanlab does not support saving files currently.")
+
+    def save_file(self, path: str) -> None:
+        raise NotImplementedError("Swanlab does not support saving files currently.")