diff --git a/tests/explorer/workflow_test.py b/tests/explorer/workflow_test.py
index 1710ce9f38..e5dab0004b 100644
--- a/tests/explorer/workflow_test.py
+++ b/tests/explorer/workflow_test.py
@@ -6,16 +6,18 @@
 from typing import Dict, Optional
 from unittest.mock import MagicMock
 
+import ray
 from parameterized import parameterized, parameterized_class
 from torch import Tensor
 
 from tests.common.vllm_test import CHAT_TEMPLATE
 from tests.tools import get_model_path, get_template_config, get_unittest_dataset_config
-from trinity.common.experience import EID
+from trinity.common.experience import EID, Experience
 from trinity.common.models import create_inference_models
 from trinity.common.models.model import ModelWrapper
 from trinity.common.rewards import RMGalleryFn
 from trinity.common.workflows import (
+    WORKFLOWS,
     MathBoxedWorkflow,
     MathEvalWorkflow,
     MathRMWorkflow,
@@ -489,3 +491,44 @@ def test_multi_turn_workflow(self):
         else:
             answer = workflow.run()
         self.assertEqual(len(answer), 2)
+
+    def tearDown(self):
+        ray.shutdown(_exiting_interpreter=True)
+
+
+class TestAgentScopeWorkflowAdapter(unittest.IsolatedAsyncioTestCase):
+    @unittest.skip("Waiting for agentscope>=0.1.6")
+    async def test_adapter(self):
+        try:
+            from agentscope.model import TrinityChatModel
+        except ImportError:
+            self.skipTest("agentscope >= 0.1.6 is not installed")
+
+        async def as_workflow_func(task, model) -> float:
+            self.assertIsInstance(task, dict)
+            self.assertIsInstance(model, TrinityChatModel)
+            return task["reward"]
+
+        model = MagicMock()
+        openai_client = MagicMock()
+        openai_client.model_path = "Qwen/Qwen3-8B"
+        model.get_openai_async_client.return_value = openai_client
+        model.extract_experience_from_history.return_value = [
+            Experience(tokens=Tensor([0, 1, 2]), prompt_length=1, logprobs=Tensor([0.1, 0.2])),
+            Experience(tokens=Tensor([3, 4, 5]), prompt_length=2, logprobs=Tensor([0.3])),
+        ]
+
+        as_adapter_cls = WORKFLOWS.get("agentscope_workflow_adapter")
+        as_adapter = as_adapter_cls(
+            task=Task(
+                raw_task={"reward": 0.1},
+                workflow_args={"workflow_func": as_workflow_func},
+            ),
+            model=model,
+        )
+        result = await as_adapter.run_async()
+        self.assertEqual(len(result), 2)
+        self.assertEqual(result[0].reward, 0.1)
+        self.assertEqual(result[0].prompt_length, 1)
+        self.assertEqual(result[1].reward, 0.1)
+        self.assertEqual(result[1].prompt_length, 2)
diff --git a/trinity/common/workflows/__init__.py b/trinity/common/workflows/__init__.py
index 492f8b3a60..1277f041f9 100644
--- a/trinity/common/workflows/__init__.py
+++ b/trinity/common/workflows/__init__.py
@@ -3,6 +3,7 @@
 from trinity.common.workflows.agentscope.react.react_workflow import (
     AgentScopeReActWorkflow,
 )
+from trinity.common.workflows.agentscope_workflow import AgentScopeWorkflowAdapter
 from trinity.common.workflows.customized_math_workflows import (
     AsyncMathBoxedWorkflow,
     MathBoxedWorkflow,
@@ -92,4 +93,5 @@
     "AsyncSimpleMMWorkflow",
     "SimpleMMWorkflow",
     "RubricJudgeWorkflow",
+    "AgentScopeWorkflowAdapter",
 ]
diff --git a/trinity/common/workflows/agentscope_workflow.py b/trinity/common/workflows/agentscope_workflow.py
new file mode 100644
index 0000000000..0cbce6e01a
--- /dev/null
+++ b/trinity/common/workflows/agentscope_workflow.py
@@ -0,0 +1,83 @@
+from typing import Awaitable, Callable, Dict, List, Optional
+
+import openai
+
+from trinity.common.experience import Experience
+from trinity.common.models.model import ModelWrapper
+from trinity.common.workflows.workflow import WORKFLOWS, Task, Workflow
+
+
+@WORKFLOWS.register_module("agentscope_workflow_adapter")
+class AgentScopeWorkflowAdapter(Workflow):
+    """Adapter to wrap a agentscope trainable workflow function into a Trinity Workflow."""
+
+    def __init__(
+        self,
+        *,
+        task: Task,
+        model: ModelWrapper,
+        auxiliary_models: Optional[List[openai.OpenAI]] = None,
+    ):
+        """Initialize the adapter with the task and model."""
+        try:
+            from agentscope.model import TrinityChatModel
+        except ImportError:
+            raise ImportError(
+                "This workflow requires agentscope >= 0.1.6, please install "
+                "it via `pip install agentscope>=0.1.6`",
+            )
+
+        super().__init__(
+            task=task,
+            model=model,
+            auxiliary_models=auxiliary_models,
+        )
+        self.workflow_func: Callable[
+            [Dict, TrinityChatModel], Awaitable[float]
+        ] = task.workflow_args.get("workflow_func", None)
+
+        if self.workflow_func is None:
+            raise ValueError(
+                "The 'workflow_func' is not provided.",
+            )
+
+        self.chat_model: TrinityChatModel = TrinityChatModel(
+            model.get_openai_async_client(),
+        )
+
+    @property
+    def asynchronous(self) -> bool:
+        """This workflow runs asynchronously."""
+        return True
+
+    @property
+    def repeatable(self) -> bool:
+        """This workflow is not repeatable."""
+        return False
+
+    @property
+    def resetable(self) -> bool:
+        """This workflow cannot be reset."""
+        return False
+
+    def construct_experiences(
+        self,
+        reward: float,
+    ) -> List[Experience]:
+        """Construct experiences from the agent's interaction history.
+
+        Args:
+            reward (float): The reward value to assign to each experience.
+
+        Returns:
+            List: A list of Experience objects.
+        """
+        exps = self.model.extract_experience_from_history()
+        for exp in exps:
+            exp.reward = reward
+        return exps
+
+    async def run_async(self) -> List[Experience]:
+        """Run the workflow asynchronously and return experiences."""
+        reward = await self.workflow_func(self.task.raw_task, self.chat_model)  # type: ignore [arg-type]
+        return self.construct_experiences(reward)