diff --git a/trinity/common/workflows/envs/sciworld/sciworld_workflow.py b/trinity/common/workflows/envs/sciworld/sciworld_workflow.py
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+++ b/trinity/common/workflows/envs/sciworld/sciworld_workflow.py
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+# -*- coding: utf-8 -*-
+import json
+from typing import List
+
+from trinity.common.experience import Experience
+from trinity.common.models.model import ModelWrapper
+from trinity.common.workflows.workflow import WORKFLOWS, MultiTurnWorkflow
+
+SCIWORLD_SYSTEM_PROMPT = """
+You are an agent, you job is to do some scientific experiment in a virtual test-based environments.
+
+## Notes:
+At each step, you should first think then perform action to fulfill the instruction. You should ALWAYS wrap your thinking with the <think> </think> tag and wrap your action with the <action> </action> tag.
+You should ALWAYS take one action each step.
+DONOT try to interact with the user at anytime. Finish the task by yourself.
+
+## Action Format:
+Below are the available commands you can use:
+    open OBJ: open a container
+    close OBJ: close a container
+    activate OBJ: activate a device
+    deactivate OBJ: deactivate a device
+    connect OBJ to OBJ: connect electrical components
+    disconnect OBJ: disconnect electrical components
+    use OBJ [on OBJ]: use a device/item
+    look around: describe the current room
+    examine OBJ: describe an object in detail
+    look at OBJ: describe a container's contents
+    read OBJ: read a note or book
+    move OBJ to OBJ: move an object to a container
+    pick up OBJ: move an object to the inventory
+    pour OBJ into OBJ: pour a liquid into a container
+    mix OBJ: chemically mix a container
+    teleport to LOC: teleport to a specific room
+    focus on OBJ: signal intent on a task object
+    wait: task no action for 10 steps
+    wait1: task no action for a step
+
+For example your output should be like this:
+<think> Now I will check the bedroom ... </think><action>teleport to bedroom</action>
+"""
+
+
+def format_observation(observation: str):
+    return "Observation: \n" + observation
+
+
+def parse_action(response):
+    try:
+        # parse the action within the <action> </action> tag
+        action = response.split("<action>")[1].split("</action>")[0].strip()
+        return action
+    except Exception as e:
+        print("Error parsing action:", e)
+        return ""
+
+
+@WORKFLOWS.register_module("sciworld_workflow")
+class SciWorldWorkflow(MultiTurnWorkflow):
+    """A workflow for sciworld task."""
+
+    def __init__(self, model: ModelWrapper, **kwargs):
+        super().__init__(model)
+        self.system_prompt = kwargs.get("system_prompt", None)  # Unuse here
+        self.task_desc: str = kwargs.get("task_desc")
+        self.truth = kwargs.get("truth")  # Unuse here
+        self.reward_fn = kwargs.get("reward_fn")  # Unuse here
+        self.repeat_times = kwargs.get("repeat_times", 1)
+        self.max_env_steps = 30  # should be less than 100
+
+    def get_model_response(self, messages):
+        responses = self.model.chat(messages, repeat_times=1)
+        return responses
+
+    def get_model_response_text(self, messages):
+        return self.get_model_response(messages)[0].response_text
+
+    def generate_env_inference_samples(self, env, rollout_num) -> List[Experience]:
+        # TODO: Make this parallel
+        print("Generating env inference samples...")
+        golden_rounds = len(env.get_gold_action_sequence())
+        experience_list = []
+        for i in range(rollout_num):
+            observation, info = env.reset()
+            observation = (
+                "Task Description: " + str(env.get_task_description()) + "\n" + observation
+            )
+            final_reward = 0.0
+            current_reward = 0.0
+            memory = []
+            memory.append({"role": "system", "content": SCIWORLD_SYSTEM_PROMPT})
+            for r in range(self.max_env_steps):
+                format_obs = format_observation(observation)
+                memory = memory + [{"role": "user", "content": format_obs}]
+                response_text = self.get_model_response_text(memory)
+                memory.append({"role": "assistant", "content": response_text})
+                action = parse_action(response_text)
+                observation, reward, done, info = env.step(action)
+                current_reward += reward
+                final_reward = max(current_reward, final_reward)
+                if done:
+                    break
+            final_reward = final_reward / 100.0
+            experience = self.process_messages_to_experience(
+                memory,
+                final_reward,
+                {"env_rounds": r, "env_done": 1 if done else 0, "golden_rounds": golden_rounds},
+            )
+            experience_list.append(experience)
+        # Close the env to save cpu memory
+        env.close()
+        return experience_list
+
+    def run(self) -> List[Experience]:
+        # assume the task_description is the json object containing task index and the var_num
+        # see Trinity-RFT/script/data_prepare/get_scriworld_data.py
+        task_desc = self.task_desc
+        task_config = json.loads(task_desc)
+
+        rollout_n = self.repeat_times
+        # TODO: Make parallel envs
+        try:
+            from scienceworld import ScienceWorldEnv
+
+            def create_environment(task_config):
+                var_num = task_config["var_num"]
+                task_name = task_config["task_name"]
+                jar_path = task_config["jar_path"]
+                simplificationStr = "easy"
+                env = ScienceWorldEnv("", jar_path, envStepLimit=100)
+                env.load(task_name, var_num, simplificationStr, generateGoldPath=True)
+                return env
+
+        except Exception as e:
+            print("Please make sure you have installed the sciworld package.")
+            error_message = f"Error importing SciWorldTWEnv {str(e)}. Please make sure you have installed the sciworld package successfully, following the instructions in https://github.com/allenai/ScienceWorld"
+            raise ImportError(error_message)
+        env = create_environment(task_config)
+        return self.generate_env_inference_samples(env, rollout_n)