From 78a5ec056abf4729d92415d41480529d72e313b7 Mon Sep 17 00:00:00 2001
From: zhangzaibin <m18602438523@163.com>
Date: Thu, 22 May 2025 09:53:57 +0800
Subject: [PATCH 01/32] add interview action

---
 examples/twitter_interview.py | 93 +++++++++++++++++++++++++++++++++++
 oasis/environment/env.py      |  6 +++
 oasis/social_agent/agent.py   | 40 +++++++++++++++
 3 files changed, 139 insertions(+)
 create mode 100644 examples/twitter_interview.py

diff --git a/examples/twitter_interview.py b/examples/twitter_interview.py
new file mode 100644
index 00000000..dbfc4c6e
--- /dev/null
+++ b/examples/twitter_interview.py
@@ -0,0 +1,93 @@
+# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
+# Licensed under the Apache License, Version 2.0 (the “License”);
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an “AS IS” BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
+import asyncio
+import os
+
+from camel.models import ModelFactory
+from camel.types import ModelPlatformType, ModelType
+
+import oasis
+from oasis import ActionType, EnvAction, SingleAction
+
+
+async def main():
+    openai_model = ModelFactory.create(
+        model_platform=ModelPlatformType.OPENAI,
+        model_type=ModelType.GPT_4O_MINI,
+    )
+
+    # Define the available actions for the agents
+    available_actions = [
+        ActionType.CREATE_POST,
+        ActionType.LIKE_POST,
+        ActionType.REPOST,
+        ActionType.FOLLOW,
+        ActionType.DO_NOTHING,
+        ActionType.QUOTE_POST,
+    ]
+
+    # Define the path to the database
+    db_path = "./data/twitter_simulation.db"
+
+    # Delete the old database
+    if os.path.exists(db_path):
+        os.remove(db_path)
+
+    # Make the environment
+    env = oasis.make(
+        platform=oasis.DefaultPlatformType.TWITTER,
+        database_path=db_path,
+        agent_profile_path=("data/twitter_dataset/anonymous_topic_200_1h/"
+                            "False_Business_0.csv"),
+        agent_models=openai_model,
+        available_actions=available_actions,
+    )
+
+    # Run the environment
+    await env.reset()
+
+    action_1 = SingleAction(agent_id=0,
+                            action=ActionType.CREATE_POST,
+                            args={"content": "Earth is flat."})
+    env_actions_1 = EnvAction(
+        # Activate 5 agents with id 1, 3, 5, 7, 9
+        activate_agents=[1, 3, 5, 7, 9],
+        intervention=[action_1])
+
+    action_2 = SingleAction(agent_id=1,
+                            action=ActionType.CREATE_POST,
+                            args={"content": "Earth is not flat."})
+    env_actions_2 = EnvAction(activate_agents=[2, 4, 6, 8, 10],
+                              intervention=[action_2])
+
+    empty_action = EnvAction()  # Means activate all agents and no intervention
+
+    all_env_actions = [
+        env_actions_1,
+        env_actions_2,
+        empty_action,
+    ]
+
+    # Simulate 3 timesteps
+    for i in range(3):
+        env_actions = all_env_actions[i]
+        # Perform the actions
+        await env.step(env_actions)
+
+    # Close the environment
+    await env.close()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/oasis/environment/env.py b/oasis/environment/env.py
index 53bca419..fdbec27e 100644
--- a/oasis/environment/env.py
+++ b/oasis/environment/env.py
@@ -164,6 +164,12 @@ async def _perform_llm_action(self, agent):
         async with self.llm_semaphore:
             return await agent.perform_action_by_llm()
 
+    async def _perform_interview_action(self, agent, interview_prompt: str):
+        r"""Send the request to the llm model and execute the interview.
+        """
+        async with self.llm_semaphore:
+            return await agent.perform_interview(interview_prompt)
+
     async def step(self, action: EnvAction) -> None:
         r"""Perform some control actions, update the recommendation system,
         and let some llm agents perform actions.
diff --git a/oasis/social_agent/agent.py b/oasis/social_agent/agent.py
index e7d5d15e..acfbc03a 100644
--- a/oasis/social_agent/agent.py
+++ b/oasis/social_agent/agent.py
@@ -180,6 +180,46 @@ async def perform_test(self):
             "content": content
         }
 
+
+    async def perform_interview(self, interview_prompt: str):
+        """
+        perform interview with the agent.
+        """
+        # user conduct test to agent
+        _ = BaseMessage.make_user_message(role_name="User",
+                                          content=("You are a twitter user."))
+        # Test memory should not be writed to memory.
+        # self.memory.write_record(MemoryRecord(user_msg,
+        #                                       OpenAIBackendRole.USER))
+
+        openai_messages, num_tokens = self.memory.get_context()
+
+        openai_messages = ([{
+            "role":
+            self.system_message.role_name,
+            "content":
+            self.system_message.content.split("# RESPONSE FORMAT")[0],
+        }] + openai_messages + [{
+            "role": "user",
+            "content": self.test_prompt
+        }])
+
+        agent_log.info(f"Agent {self.social_agent_id}: {openai_messages}")
+        # NOTE: this is a temporary solution.
+        # Camel can not stop updating the agents' memory after stop and astep
+        # now.
+        response = self._get_model_response(openai_messages=openai_messages,
+                                            num_tokens=num_tokens)
+        content = response.output_messages[0].content
+        agent_log.info(
+            f"Agent {self.social_agent_id} receive response: {content}")
+        return {
+            "user_id": self.social_agent_id,
+            "prompt": openai_messages,
+            "content": content
+        }
+
+
     async def perform_action_by_hci(self) -> Any:
         print("Please choose one function to perform:")
         function_list = self.env.action.get_openai_function_list()

From b96dca05e3e14c19acb488a557fe16a2134869fd Mon Sep 17 00:00:00 2001
From: zhangzaibin <m18602438523@163.com>
Date: Thu, 22 May 2025 10:25:11 +0800
Subject: [PATCH 02/32] interview actiontype

---
 examples/twitter_interview.py   | 67 +++++++++++++++++++++++++++++----
 oasis/environment/env.py        | 37 ++++++++++++++++--
 oasis/social_platform/typing.py |  1 +
 3 files changed, 93 insertions(+), 12 deletions(-)

diff --git a/examples/twitter_interview.py b/examples/twitter_interview.py
index dbfc4c6e..585e9dec 100644
--- a/examples/twitter_interview.py
+++ b/examples/twitter_interview.py
@@ -1,18 +1,20 @@
 # =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-# Licensed under the Apache License, Version 2.0 (the “License”);
+# Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an “AS IS” BASIS,
+# distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
 import asyncio
 import os
+import sqlite3
+import json
 
 from camel.models import ModelFactory
 from camel.types import ModelPlatformType, ModelType
@@ -35,6 +37,7 @@ async def main():
         ActionType.FOLLOW,
         ActionType.DO_NOTHING,
         ActionType.QUOTE_POST,
+        ActionType.INTERVIEW,  # Add the INTERVIEW action type
     ]
 
     # Define the path to the database
@@ -57,6 +60,7 @@ async def main():
     # Run the environment
     await env.reset()
 
+    # First timestep: Agent 0 creates a post
     action_1 = SingleAction(agent_id=0,
                             action=ActionType.CREATE_POST,
                             args={"content": "Earth is flat."})
@@ -65,28 +69,75 @@ async def main():
         activate_agents=[1, 3, 5, 7, 9],
         intervention=[action_1])
 
+    # Second timestep: Agent 1 creates a post, and we interview Agent 0
     action_2 = SingleAction(agent_id=1,
-                            action=ActionType.CREATE_POST,
-                            args={"content": "Earth is not flat."})
-    env_actions_2 = EnvAction(activate_agents=[2, 4, 6, 8, 10],
-                              intervention=[action_2])
+                           action=ActionType.CREATE_POST,
+                           args={"content": "Earth is not flat."})
+    
+    # Create an interview action to ask Agent 0 about their views
+    interview_action = SingleAction(
+        agent_id=0,
+        action=ActionType.INTERVIEW,
+        args={"prompt": "What do you think about the shape of the Earth? Please explain your reasoning."}
+    )
+    
+    env_actions_2 = EnvAction(
+        activate_agents=[2, 4, 6, 8, 10],
+        intervention=[action_2, interview_action]
+    )
 
-    empty_action = EnvAction()  # Means activate all agents and no intervention
+    # Third timestep: Interview multiple agents
+    interview_actions = [
+        SingleAction(
+            agent_id=1,
+            action=ActionType.INTERVIEW,
+            args={"prompt": "Why do you believe the Earth is not flat?"}
+        ),
+        SingleAction(
+            agent_id=2,
+            action=ActionType.INTERVIEW,
+            args={"prompt": "What are your thoughts on the debate about Earth's shape?"}
+        ),
+    ]
+    
+    env_actions_3 = EnvAction(
+        activate_agents=[3, 5, 7, 9],
+        intervention=interview_actions
+    )
 
     all_env_actions = [
         env_actions_1,
         env_actions_2,
-        empty_action,
+        env_actions_3,
     ]
 
     # Simulate 3 timesteps
     for i in range(3):
+        print(f"\n=== Timestep {i+1} ===")
         env_actions = all_env_actions[i]
         # Perform the actions
         await env.step(env_actions)
 
     # Close the environment
     await env.close()
+    
+    # Display the interview results from the database
+    print("\n=== Interview Results ===")
+    conn = sqlite3.connect(db_path)
+    cursor = conn.cursor()
+    cursor.execute("""
+        SELECT user_id, info, created_at
+        FROM trace
+        WHERE action = ?
+    """, (ActionType.INTERVIEW.value,))
+    
+    for user_id, info_json, timestamp in cursor.fetchall():
+        info = json.loads(info_json)
+        print(f"\nAgent {user_id} (Timestep {timestamp}):")
+        print(f"Prompt: {info.get('prompt', 'N/A')}")
+        print(f"Response: {info.get('response', 'N/A')}")
+    
+    conn.close()
 
 
 if __name__ == "__main__":
diff --git a/oasis/environment/env.py b/oasis/environment/env.py
index fdbec27e..7d76325c 100644
--- a/oasis/environment/env.py
+++ b/oasis/environment/env.py
@@ -1,12 +1,12 @@
 # =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-# Licensed under the Apache License, Version 2.0 (the “License”);
+# Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an “AS IS” BASIS,
+# distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
@@ -155,8 +155,37 @@ async def _perform_control_action(self, action: SingleAction) -> None:
             action(SingleAction): The action to perform.
         """
         control_agent = self.agent_graph.get_agent(action.agent_id)
-        await control_agent.perform_action_by_data(action.action,
-                                                   **action.args)
+        
+        # Check if this is an interview action
+        if action.action == ActionType.INTERVIEW:
+            # Extract interview prompt from args
+            interview_prompt = action.args.get("prompt", "")
+            if not interview_prompt:
+                env_log.warning(f"Empty interview prompt for agent {action.agent_id}")
+                return
+                
+            # Perform the interview
+            result = await self._perform_interview_action(control_agent, interview_prompt)
+            
+            # Store interview result in the database using the platform's record_trace method
+            # Convert the result to a dictionary suitable for JSON serialization
+            interview_info = {
+                "prompt": interview_prompt,
+                "response": result.get("content", "")
+            }
+            
+            # Get current timestamp based on the platform type
+            self.platform.pl_utils._record_trace(
+                user_id=action.agent_id,
+                action_type=ActionType.INTERVIEW.value,
+                action_info=interview_info
+            )
+            
+            env_log.info(f"Stored interview result for agent {action.agent_id}")
+            return
+        
+        # For regular actions
+        await control_agent.perform_action_by_data(action.action, **action.args)
 
     async def _perform_llm_action(self, agent):
         r"""Send the request to the llm model and execute the action.
diff --git a/oasis/social_platform/typing.py b/oasis/social_platform/typing.py
index cb68be08..62b0c859 100644
--- a/oasis/social_platform/typing.py
+++ b/oasis/social_platform/typing.py
@@ -40,6 +40,7 @@ class ActionType(Enum):
     UNDO_DISLIKE_COMMENT = "undo_dislike_comment"
     DO_NOTHING = "do_nothing"
     PURCHASE_PRODUCT = "purchase_product"
+    INTERVIEW = "interview"
 
 
 class RecsysType(Enum):

From 3d29467b85038074baa061afccbd4af3822fdb46 Mon Sep 17 00:00:00 2001
From: zhangzaibin <m18602438523@163.com>
Date: Thu, 22 May 2025 10:31:13 +0800
Subject: [PATCH 03/32] fix warning

---
 oasis/environment/env.py           | 74 ++++++++++++++++++++++--------
 oasis/social_agent/agent.py        |  2 +-
 oasis/social_agent/agent_action.py | 26 ++++++++++-
 oasis/social_platform/platform.py  | 29 +++++++++++-
 4 files changed, 107 insertions(+), 24 deletions(-)

diff --git a/oasis/environment/env.py b/oasis/environment/env.py
index 7d76325c..7803ad0c 100644
--- a/oasis/environment/env.py
+++ b/oasis/environment/env.py
@@ -156,7 +156,10 @@ async def _perform_control_action(self, action: SingleAction) -> None:
         """
         control_agent = self.agent_graph.get_agent(action.agent_id)
         
-        # Check if this is an interview action
+        # For regular actions
+        await control_agent.perform_action_by_data(action.action, **action.args)
+        
+        # If this was an interview action, perform the actual interview and update the database
         if action.action == ActionType.INTERVIEW:
             # Extract interview prompt from args
             interview_prompt = action.args.get("prompt", "")
@@ -164,28 +167,61 @@ async def _perform_control_action(self, action: SingleAction) -> None:
                 env_log.warning(f"Empty interview prompt for agent {action.agent_id}")
                 return
                 
-            # Perform the interview
+            # Perform the interview to get actual response
             result = await self._perform_interview_action(control_agent, interview_prompt)
             
-            # Store interview result in the database using the platform's record_trace method
-            # Convert the result to a dictionary suitable for JSON serialization
-            interview_info = {
-                "prompt": interview_prompt,
-                "response": result.get("content", "")
-            }
-            
-            # Get current timestamp based on the platform type
-            self.platform.pl_utils._record_trace(
-                user_id=action.agent_id,
-                action_type=ActionType.INTERVIEW.value,
-                action_info=interview_info
-            )
+            # Get the response content
+            response = result.get("content", "")
             
-            env_log.info(f"Stored interview result for agent {action.agent_id}")
+            try:
+                # Update the database with the actual response
+                # First, fetch the latest trace record for this interview
+                query = """SELECT rowid, info FROM trace 
+                           WHERE user_id = ? AND action = ? 
+                           ORDER BY created_at DESC LIMIT 1"""
+                self.platform.pl_utils._execute_db_command(query, (action.agent_id, ActionType.INTERVIEW.value))
+                last_interview_data = self.platform.db_cursor.fetchone()
+                
+                if last_interview_data:
+                    rowid, info_json = last_interview_data
+                    import json
+                    info = json.loads(info_json)
+                    info["response"] = response
+                    
+                    # Update the trace record with the actual response
+                    update_query = "UPDATE trace SET info = ? WHERE rowid = ?"
+                    self.platform.pl_utils._execute_db_command(
+                        update_query, 
+                        (json.dumps(info), rowid),
+                        commit=True
+                    )
+                    
+                    env_log.info(f"Updated interview result for agent {action.agent_id}")
+                else:
+                    # If we couldn't find the trace record, create a new one with the response
+                    interview_info = {
+                        "prompt": interview_prompt,
+                        "response": response
+                    }
+                    
+                    if self.platform.recsys_type == RecsysType.REDDIT:
+                        current_time = self.platform.sandbox_clock.time_transfer(
+                            datetime.now(), self.platform.start_time)
+                    else:
+                        current_time = self.platform.sandbox_clock.get_time_step()
+                        
+                    self.platform.pl_utils._record_trace(
+                        user_id=action.agent_id,
+                        action_type=ActionType.INTERVIEW.value,
+                        action_info=interview_info,
+                        current_time=current_time
+                    )
+                    
+                    env_log.info(f"Created new interview record for agent {action.agent_id}")
+            except Exception as e:
+                env_log.error(f"Error updating interview result: {str(e)}")
+                
             return
-        
-        # For regular actions
-        await control_agent.perform_action_by_data(action.action, **action.args)
 
     async def _perform_llm_action(self, agent):
         r"""Send the request to the llm model and execute the action.
diff --git a/oasis/social_agent/agent.py b/oasis/social_agent/agent.py
index acfbc03a..6287c554 100644
--- a/oasis/social_agent/agent.py
+++ b/oasis/social_agent/agent.py
@@ -201,7 +201,7 @@ async def perform_interview(self, interview_prompt: str):
             self.system_message.content.split("# RESPONSE FORMAT")[0],
         }] + openai_messages + [{
             "role": "user",
-            "content": self.test_prompt
+            "content": interview_prompt
         }])
 
         agent_log.info(f"Agent {self.social_agent_id}: {openai_messages}")
diff --git a/oasis/social_agent/agent_action.py b/oasis/social_agent/agent_action.py
index e9fcb1e6..6430efa2 100644
--- a/oasis/social_agent/agent_action.py
+++ b/oasis/social_agent/agent_action.py
@@ -1,12 +1,12 @@
 # =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-# Licensed under the Apache License, Version 2.0 (the “License”);
+# Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an “AS IS” BASIS,
+# distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
@@ -50,6 +50,7 @@ def get_openai_function_list(self) -> list[FunctionTool]:
                 self.mute,
                 self.unmute,
                 self.purchase_product,
+                self.interview,
             ]
         ]
 
@@ -642,3 +643,24 @@ async def purchase_product(self, product_name: str, purchase_num: int):
         purchase_message = (product_name, purchase_num)
         return await self.perform_action(purchase_message,
                                          ActionType.PURCHASE_PRODUCT.value)
+
+    async def interview(self, prompt: str):
+        r"""Interview an agent with the given prompt.
+
+        This method invokes an asynchronous action to interview an agent with a
+        specific prompt question. Upon successful execution, it returns a dictionary
+        containing the agent's response.
+
+        Args:
+            prompt (str): The interview question or prompt to ask the agent.
+
+        Returns:
+            dict: A dictionary containing the response from the agent.
+
+            Example of a successful return:
+            {
+                "success": True,
+                "response": "This is the agent's response to the interview question."
+            }
+        """
+        return await self.perform_action(prompt, ActionType.INTERVIEW.value)
diff --git a/oasis/social_platform/platform.py b/oasis/social_platform/platform.py
index 9336fc6e..a2aba3ff 100644
--- a/oasis/social_platform/platform.py
+++ b/oasis/social_platform/platform.py
@@ -1,12 +1,12 @@
 # =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-# Licensed under the Apache License, Version 2.0 (the “License”);
+# Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an “AS IS” BASIS,
+# distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
@@ -1330,3 +1330,28 @@ async def do_nothing(self, agent_id: int):
             return {"success": True}
         except Exception as e:
             return {"success": False, "error": str(e)}
+
+    async def interview(self, agent_id: int, prompt: str):
+        """Interview an agent with the given prompt.
+
+        Args:
+            agent_id (int): The ID of the agent being interviewed.
+            prompt (str): The interview question or prompt.
+
+        Returns:
+            dict: A dictionary with success status.
+        """
+        if self.recsys_type == RecsysType.REDDIT:
+            current_time = self.sandbox_clock.time_transfer(
+                datetime.now(), self.start_time)
+        else:
+            current_time = self.sandbox_clock.get_time_step()
+        try:
+            user_id = agent_id
+
+            action_info = {"prompt": prompt}
+            self.pl_utils._record_trace(user_id, ActionType.INTERVIEW.value,
+                                        action_info, current_time)
+            return {"success": True}
+        except Exception as e:
+            return {"success": False, "error": str(e)}

From 66acd8ef5c3dcf55b290295bebe8991790dbf6db Mon Sep 17 00:00:00 2001
From: zhangzaibin <m18602438523@163.com>
Date: Thu, 22 May 2025 10:34:00 +0800
Subject: [PATCH 04/32] fix

---
 oasis/social_agent/agent.py | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/oasis/social_agent/agent.py b/oasis/social_agent/agent.py
index 6287c554..aa1c91e0 100644
--- a/oasis/social_agent/agent.py
+++ b/oasis/social_agent/agent.py
@@ -143,7 +143,7 @@ async def perform_action_by_llm(self):
 
     async def perform_test(self):
         """
-        doing test for all agents.
+        doing group polarization test for all agents.
         TODO: rewrite the function according to the ChatAgent.
         """
         # user conduct test to agent
@@ -183,7 +183,19 @@ async def perform_test(self):
 
     async def perform_interview(self, interview_prompt: str):
         """
-        perform interview with the agent.
+        Perform an interview with the agent.
+        
+        This method allows an external entity to ask a specific question to the agent
+        and get a response based on the agent's persona and memory context. The interview
+        doesn't affect the agent's memory and is used primarily for evaluation or
+        extracting the agent's opinions on specific topics.
+        
+        Args:
+            interview_prompt (str): The question or prompt to ask the agent during the interview.
+            
+        Returns:
+            dict: A dictionary containing the agent's user_id, the full prompt context,
+                 and the agent's response content.
         """
         # user conduct test to agent
         _ = BaseMessage.make_user_message(role_name="User",

From d73badefa91ec042deb0978ad71513f2754a3791 Mon Sep 17 00:00:00 2001
From: zhangzaibin <m18602438523@163.com>
Date: Thu, 22 May 2025 10:34:35 +0800
Subject: [PATCH 05/32] add todo

---
 oasis/social_agent/agent.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/oasis/social_agent/agent.py b/oasis/social_agent/agent.py
index aa1c91e0..1dad1ec1 100644
--- a/oasis/social_agent/agent.py
+++ b/oasis/social_agent/agent.py
@@ -145,6 +145,7 @@ async def perform_test(self):
         """
         doing group polarization test for all agents.
         TODO: rewrite the function according to the ChatAgent.
+        TODO: unify the test and interview function.
         """
         # user conduct test to agent
         _ = BaseMessage.make_user_message(role_name="User",

From 705fcb0e33cae58b093e48bcf426463b1b91c503 Mon Sep 17 00:00:00 2001
From: zhangzaibin <m18602438523@163.com>
Date: Thu, 22 May 2025 10:44:34 +0800
Subject: [PATCH 06/32] refactor interview action

---
 examples/twitter_interview.py      |  1 +
 oasis/environment/env.py           | 76 +++++++++++-------------------
 oasis/social_agent/agent_action.py |  6 +--
 oasis/social_platform/platform.py  | 60 +++++++++++++++++++++--
 4 files changed, 88 insertions(+), 55 deletions(-)

diff --git a/examples/twitter_interview.py b/examples/twitter_interview.py
index 585e9dec..79a04b98 100644
--- a/examples/twitter_interview.py
+++ b/examples/twitter_interview.py
@@ -135,6 +135,7 @@ async def main():
         info = json.loads(info_json)
         print(f"\nAgent {user_id} (Timestep {timestamp}):")
         print(f"Prompt: {info.get('prompt', 'N/A')}")
+        print(f"Interview ID: {info.get('interview_id', 'N/A')}")
         print(f"Response: {info.get('response', 'N/A')}")
     
     conn.close()
diff --git a/oasis/environment/env.py b/oasis/environment/env.py
index 7803ad0c..f77d12b9 100644
--- a/oasis/environment/env.py
+++ b/oasis/environment/env.py
@@ -156,71 +156,49 @@ async def _perform_control_action(self, action: SingleAction) -> None:
         """
         control_agent = self.agent_graph.get_agent(action.agent_id)
         
-        # For regular actions
-        await control_agent.perform_action_by_data(action.action, **action.args)
+        # First, perform the regular action by sending it to the platform
+        action_result = await control_agent.perform_action_by_data(action.action, **action.args)
         
-        # If this was an interview action, perform the actual interview and update the database
+        # If this was an interview action, perform the actual interview and record the response
         if action.action == ActionType.INTERVIEW:
             # Extract interview prompt from args
             interview_prompt = action.args.get("prompt", "")
             if not interview_prompt:
                 env_log.warning(f"Empty interview prompt for agent {action.agent_id}")
                 return
-                
-            # Perform the interview to get actual response
+            
+            # Check if the platform action was successful
+            if not action_result.get("success", False):
+                env_log.warning(f"Failed to record interview request: {action_result.get('error', 'Unknown error')}")
+                return
+            
+            # Get the interview_id from the result
+            interview_id = action_result.get("interview_id")
+            if not interview_id:
+                env_log.warning(f"No interview_id returned from platform")
+                return
+            
+            # Perform the interview to get the actual response
             result = await self._perform_interview_action(control_agent, interview_prompt)
             
             # Get the response content
             response = result.get("content", "")
             
             try:
-                # Update the database with the actual response
-                # First, fetch the latest trace record for this interview
-                query = """SELECT rowid, info FROM trace 
-                           WHERE user_id = ? AND action = ? 
-                           ORDER BY created_at DESC LIMIT 1"""
-                self.platform.pl_utils._execute_db_command(query, (action.agent_id, ActionType.INTERVIEW.value))
-                last_interview_data = self.platform.db_cursor.fetchone()
+                # Record the response using the platform's method
+                response_result = await self.platform.record_interview_response(
+                    agent_id=action.agent_id,
+                    interview_id=interview_id,
+                    response=response
+                )
                 
-                if last_interview_data:
-                    rowid, info_json = last_interview_data
-                    import json
-                    info = json.loads(info_json)
-                    info["response"] = response
-                    
-                    # Update the trace record with the actual response
-                    update_query = "UPDATE trace SET info = ? WHERE rowid = ?"
-                    self.platform.pl_utils._execute_db_command(
-                        update_query, 
-                        (json.dumps(info), rowid),
-                        commit=True
-                    )
-                    
-                    env_log.info(f"Updated interview result for agent {action.agent_id}")
+                if response_result.get("success", False):
+                    env_log.info(f"Successfully recorded interview response for agent {action.agent_id}")
                 else:
-                    # If we couldn't find the trace record, create a new one with the response
-                    interview_info = {
-                        "prompt": interview_prompt,
-                        "response": response
-                    }
-                    
-                    if self.platform.recsys_type == RecsysType.REDDIT:
-                        current_time = self.platform.sandbox_clock.time_transfer(
-                            datetime.now(), self.platform.start_time)
-                    else:
-                        current_time = self.platform.sandbox_clock.get_time_step()
-                        
-                    self.platform.pl_utils._record_trace(
-                        user_id=action.agent_id,
-                        action_type=ActionType.INTERVIEW.value,
-                        action_info=interview_info,
-                        current_time=current_time
-                    )
-                    
-                    env_log.info(f"Created new interview record for agent {action.agent_id}")
+                    env_log.warning(f"Failed to record interview response: {response_result.get('error', 'Unknown error')}")
             except Exception as e:
-                env_log.error(f"Error updating interview result: {str(e)}")
-                
+                env_log.error(f"Error recording interview response: {str(e)}")
+            
             return
 
     async def _perform_llm_action(self, agent):
diff --git a/oasis/social_agent/agent_action.py b/oasis/social_agent/agent_action.py
index 6430efa2..66d2a40d 100644
--- a/oasis/social_agent/agent_action.py
+++ b/oasis/social_agent/agent_action.py
@@ -649,18 +649,18 @@ async def interview(self, prompt: str):
 
         This method invokes an asynchronous action to interview an agent with a
         specific prompt question. Upon successful execution, it returns a dictionary
-        containing the agent's response.
+        containing a success status and an interview_id for tracking.
 
         Args:
             prompt (str): The interview question or prompt to ask the agent.
 
         Returns:
-            dict: A dictionary containing the response from the agent.
+            dict: A dictionary containing success status and interview tracking information.
 
             Example of a successful return:
             {
                 "success": True,
-                "response": "This is the agent's response to the interview question."
+                "interview_id": "1621234567_0"  # Timestamp_UserID format
             }
         """
         return await self.perform_action(prompt, ActionType.INTERVIEW.value)
diff --git a/oasis/social_platform/platform.py b/oasis/social_platform/platform.py
index a2aba3ff..11d8d628 100644
--- a/oasis/social_platform/platform.py
+++ b/oasis/social_platform/platform.py
@@ -1339,7 +1339,7 @@ async def interview(self, agent_id: int, prompt: str):
             prompt (str): The interview question or prompt.
 
         Returns:
-            dict: A dictionary with success status.
+            dict: A dictionary with success status and an interview_id for tracking.
         """
         if self.recsys_type == RecsysType.REDDIT:
             current_time = self.sandbox_clock.time_transfer(
@@ -1349,9 +1349,63 @@ async def interview(self, agent_id: int, prompt: str):
         try:
             user_id = agent_id
 
-            action_info = {"prompt": prompt}
+            # Create a unique interview ID (using timestamp + user_id)
+            interview_id = f"{current_time}_{user_id}"
+
+            # Record the interview request in the trace table
+            action_info = {
+                "prompt": prompt,
+                "interview_id": interview_id
+            }
             self.pl_utils._record_trace(user_id, ActionType.INTERVIEW.value,
                                         action_info, current_time)
-            return {"success": True}
+            
+            return {
+                "success": True, 
+                "interview_id": interview_id
+            }
+        except Exception as e:
+            return {"success": False, "error": str(e)}
+            
+    async def record_interview_response(self, agent_id: int, interview_id: str, response: str):
+        """Record the response for an interview.
+
+        Args:
+            agent_id (int): The ID of the agent who was interviewed.
+            interview_id (str): The unique ID of the interview.
+            response (str): The agent's response to the interview.
+
+        Returns:
+            dict: A dictionary with success status.
+        """
+        try:
+            # First, fetch the trace record for this interview
+            query = """SELECT rowid, info FROM trace 
+                       WHERE user_id = ? AND action = ? 
+                       ORDER BY created_at DESC LIMIT 1"""
+            self.pl_utils._execute_db_command(query, (agent_id, ActionType.INTERVIEW.value))
+            last_interview_data = self.db_cursor.fetchone()
+            
+            if last_interview_data:
+                rowid, info_json = last_interview_data
+                import json
+                info = json.loads(info_json)
+                
+                # Verify this is the correct interview record
+                if info.get("interview_id") == interview_id:
+                    # Update with the response
+                    info["response"] = response
+                    
+                    # Update the trace record
+                    update_query = "UPDATE trace SET info = ? WHERE rowid = ?"
+                    self.pl_utils._execute_db_command(
+                        update_query, 
+                        (json.dumps(info), rowid),
+                        commit=True
+                    )
+                    
+                    return {"success": True}
+                
+            return {"success": False, "error": "Interview record not found"}
         except Exception as e:
             return {"success": False, "error": str(e)}

From 1c9a38178b5b1c3bbc43e430b4c4a99a79cfbf74 Mon Sep 17 00:00:00 2001
From: zhangzaibin <m18602438523@163.com>
Date: Thu, 22 May 2025 10:52:12 +0800
Subject: [PATCH 07/32] add TODO

---
 oasis/social_platform/platform.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/oasis/social_platform/platform.py b/oasis/social_platform/platform.py
index 11d8d628..92453cec 100644
--- a/oasis/social_platform/platform.py
+++ b/oasis/social_platform/platform.py
@@ -1377,6 +1377,7 @@ async def record_interview_response(self, agent_id: int, interview_id: str, resp
 
         Returns:
             dict: A dictionary with success status.
+        TODO: maybe move this function to utils
         """
         try:
             # First, fetch the trace record for this interview

From 0be53d0d050868416f92dc230399992f8294eb29 Mon Sep 17 00:00:00 2001
From: zhangzaibin <m18602438523@163.com>
Date: Thu, 22 May 2025 10:53:27 +0800
Subject: [PATCH 08/32] fix

---
 examples/twitter_interview.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/twitter_interview.py b/examples/twitter_interview.py
index 79a04b98..9114f17b 100644
--- a/examples/twitter_interview.py
+++ b/examples/twitter_interview.py
@@ -121,7 +121,7 @@ async def main():
     # Close the environment
     await env.close()
     
-    # Display the interview results from the database
+    # visualize the interview results
     print("\n=== Interview Results ===")
     conn = sqlite3.connect(db_path)
     cursor = conn.cursor()

From c6bacb90d13bd8547536b984f082db0015a75cbb Mon Sep 17 00:00:00 2001
From: zhangzaibin <m18602438523@163.com>
Date: Thu, 22 May 2025 10:54:42 +0800
Subject: [PATCH 09/32] add explaination

---
 examples/twitter_interview.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/examples/twitter_interview.py b/examples/twitter_interview.py
index 9114f17b..b7045e93 100644
--- a/examples/twitter_interview.py
+++ b/examples/twitter_interview.py
@@ -125,6 +125,9 @@ async def main():
     print("\n=== Interview Results ===")
     conn = sqlite3.connect(db_path)
     cursor = conn.cursor()
+    # Here we query all interview records from the database
+    # We use ActionType.INTERVIEW.value as the query condition to get all interview records
+    # Each record contains user ID, interview information (in JSON format), and creation timestamp
     cursor.execute("""
         SELECT user_id, info, created_at
         FROM trace

From 3f810c125be816043adefa10e7dcf88e9fc7c1f5 Mon Sep 17 00:00:00 2001
From: zhangzaibin <m18602438523@163.com>
Date: Thu, 22 May 2025 10:55:36 +0800
Subject: [PATCH 10/32] add explaination

---
 examples/twitter_interview.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/examples/twitter_interview.py b/examples/twitter_interview.py
index b7045e93..661669bd 100644
--- a/examples/twitter_interview.py
+++ b/examples/twitter_interview.py
@@ -134,6 +134,11 @@ async def main():
         WHERE action = ?
     """, (ActionType.INTERVIEW.value,))
     
+    # This query retrieves all interview records from the trace table
+    # - user_id: the ID of the agent who was interviewed
+    # - info: JSON string containing interview details (prompt, response, etc.)
+    # - created_at: timestamp when the interview was conducted
+    # We'll parse this data below to display the interview results
     for user_id, info_json, timestamp in cursor.fetchall():
         info = json.loads(info_json)
         print(f"\nAgent {user_id} (Timestep {timestamp}):")

From 86f99dd40bed47e880b8f0ad303a0fd2defc08a0 Mon Sep 17 00:00:00 2001
From: zhangzaibin <m18602438523@163.com>
Date: Thu, 22 May 2025 10:57:16 +0800
Subject: [PATCH 11/32] add interview doc

---
 docs/cookbooks/twitter_interview.mdx | 6 ++++++
 1 file changed, 6 insertions(+)
 create mode 100644 docs/cookbooks/twitter_interview.mdx

diff --git a/docs/cookbooks/twitter_interview.mdx b/docs/cookbooks/twitter_interview.mdx
new file mode 100644
index 00000000..09bd26c5
--- /dev/null
+++ b/docs/cookbooks/twitter_interview.mdx
@@ -0,0 +1,6 @@
+---
+title: 'Twitter Interview'
+description: 'Comprehensive guide to all available actions in the OASIS simulation environment'
+---
+
+# Twitter Interview
\ No newline at end of file

From c4f724d8c576438cb334db4a30dbaa36c4bd70b0 Mon Sep 17 00:00:00 2001
From: zhangzaibin <m18602438523@163.com>
Date: Thu, 22 May 2025 13:23:05 +0800
Subject: [PATCH 12/32] finish doc

---
 docs/cookbooks/twitter_interview.mdx | 162 ++++++++++++++++++++++++++-
 1 file changed, 161 insertions(+), 1 deletion(-)

diff --git a/docs/cookbooks/twitter_interview.mdx b/docs/cookbooks/twitter_interview.mdx
index 09bd26c5..3dc64489 100644
--- a/docs/cookbooks/twitter_interview.mdx
+++ b/docs/cookbooks/twitter_interview.mdx
@@ -3,4 +3,164 @@ title: 'Twitter Interview'
 description: 'Comprehensive guide to all available actions in the OASIS simulation environment'
 ---
 
-# Twitter Interview
\ No newline at end of file
+# Twitter Interview
+
+
+```python
+
+# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
+import asyncio
+import os
+import sqlite3
+import json
+
+from camel.models import ModelFactory
+from camel.types import ModelPlatformType, ModelType
+
+import oasis
+from oasis import ActionType, EnvAction, SingleAction
+
+
+async def main():
+    openai_model = ModelFactory.create(
+        model_platform=ModelPlatformType.OPENAI,
+        model_type=ModelType.GPT_4O_MINI,
+    )
+
+    # Define the available actions for the agents
+    available_actions = [
+        ActionType.CREATE_POST,
+        ActionType.LIKE_POST,
+        ActionType.REPOST,
+        ActionType.FOLLOW,
+        ActionType.DO_NOTHING,
+        ActionType.QUOTE_POST,
+        ActionType.INTERVIEW,  # Add the INTERVIEW action type
+    ]
+
+    # Define the path to the database
+    db_path = "./data/twitter_simulation.db"
+
+    # Delete the old database
+    if os.path.exists(db_path):
+        os.remove(db_path)
+
+    # Make the environment
+    env = oasis.make(
+        platform=oasis.DefaultPlatformType.TWITTER,
+        database_path=db_path,
+        agent_profile_path=("data/twitter_dataset/anonymous_topic_200_1h/"
+                            "False_Business_0.csv"),
+        agent_models=openai_model,
+        available_actions=available_actions,
+    )
+
+    # Run the environment
+    await env.reset()
+
+    # First timestep: Agent 0 creates a post
+    action_1 = SingleAction(agent_id=0,
+                            action=ActionType.CREATE_POST,
+                            args={"content": "Earth is flat."})
+    env_actions_1 = EnvAction(
+        # Activate 5 agents with id 1, 3, 5, 7, 9
+        activate_agents=[1, 3, 5, 7, 9],
+        intervention=[action_1])
+
+    # Second timestep: Agent 1 creates a post, and we interview Agent 0
+    action_2 = SingleAction(agent_id=1,
+                           action=ActionType.CREATE_POST,
+                           args={"content": "Earth is not flat."})
+    
+    # Create an interview action to ask Agent 0 about their views
+    interview_action = SingleAction(
+        agent_id=0,
+        action=ActionType.INTERVIEW,
+        args={"prompt": "What do you think about the shape of the Earth? Please explain your reasoning."}
+    )
+    
+    env_actions_2 = EnvAction(
+        activate_agents=[2, 4, 6, 8, 10],
+        intervention=[action_2, interview_action]
+    )
+
+    # Third timestep: Interview multiple agents
+    interview_actions = [
+        SingleAction(
+            agent_id=1,
+            action=ActionType.INTERVIEW,
+            args={"prompt": "Why do you believe the Earth is not flat?"}
+        ),
+        SingleAction(
+            agent_id=2,
+            action=ActionType.INTERVIEW,
+            args={"prompt": "What are your thoughts on the debate about Earth's shape?"}
+        ),
+    ]
+    
+    env_actions_3 = EnvAction(
+        activate_agents=[3, 5, 7, 9],
+        intervention=interview_actions
+    )
+
+    all_env_actions = [
+        env_actions_1,
+        env_actions_2,
+        env_actions_3,
+    ]
+
+    # Simulate 3 timesteps
+    for i in range(3):
+        print(f"\n=== Timestep {i+1} ===")
+        env_actions = all_env_actions[i]
+        # Perform the actions
+        await env.step(env_actions)
+
+    # Close the environment
+    await env.close()
+    
+    # visualize the interview results
+    print("\n=== Interview Results ===")
+    conn = sqlite3.connect(db_path)
+    cursor = conn.cursor()
+    # Here we query all interview records from the database
+    # We use ActionType.INTERVIEW.value as the query condition to get all interview records
+    # Each record contains user ID, interview information (in JSON format), and creation timestamp
+    cursor.execute("""
+        SELECT user_id, info, created_at
+        FROM trace
+        WHERE action = ?
+    """, (ActionType.INTERVIEW.value,))
+    
+    # This query retrieves all interview records from the trace table
+    # - user_id: the ID of the agent who was interviewed
+    # - info: JSON string containing interview details (prompt, response, etc.)
+    # - created_at: timestamp when the interview was conducted
+    # We'll parse this data below to display the interview results
+    for user_id, info_json, timestamp in cursor.fetchall():
+        info = json.loads(info_json)
+        print(f"\nAgent {user_id} (Timestep {timestamp}):")
+        print(f"Prompt: {info.get('prompt', 'N/A')}")
+        print(f"Interview ID: {info.get('interview_id', 'N/A')}")
+        print(f"Response: {info.get('response', 'N/A')}")
+    
+    conn.close()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
+
+
+```
\ No newline at end of file

From 23be6f4f6e825b5251af32073f7e312ad4c2bde0 Mon Sep 17 00:00:00 2001
From: zhangzaibin <m18602438523@163.com>
Date: Fri, 23 May 2025 09:18:33 +0800
Subject: [PATCH 13/32] rafactor env

---
 oasis/environment/env.py | 123 ++++++++++++++++++++-------------------
 1 file changed, 63 insertions(+), 60 deletions(-)

diff --git a/oasis/environment/env.py b/oasis/environment/env.py
index 9bf44494..7e42fd08 100644
--- a/oasis/environment/env.py
+++ b/oasis/environment/env.py
@@ -121,59 +121,6 @@ async def reset(self) -> None:
         self.agent_graph = await generate_custom_agents(
             channel=self.channel, agent_graph=self.agent_graph)
 
-    async def _perform_control_action(self, action: SingleAction) -> None:
-        r"""Perform a control action.
-
-        Args:
-            action(SingleAction): The action to perform.
-        """
-        control_agent = self.agent_graph.get_agent(action.agent_id)
-        
-        # First, perform the regular action by sending it to the platform
-        action_result = await control_agent.perform_action_by_data(action.action, **action.args)
-        
-        # If this was an interview action, perform the actual interview and record the response
-        if action.action == ActionType.INTERVIEW:
-            # Extract interview prompt from args
-            interview_prompt = action.args.get("prompt", "")
-            if not interview_prompt:
-                env_log.warning(f"Empty interview prompt for agent {action.agent_id}")
-                return
-            
-            # Check if the platform action was successful
-            if not action_result.get("success", False):
-                env_log.warning(f"Failed to record interview request: {action_result.get('error', 'Unknown error')}")
-                return
-            
-            # Get the interview_id from the result
-            interview_id = action_result.get("interview_id")
-            if not interview_id:
-                env_log.warning(f"No interview_id returned from platform")
-                return
-            
-            # Perform the interview to get the actual response
-            result = await self._perform_interview_action(control_agent, interview_prompt)
-            
-            # Get the response content
-            response = result.get("content", "")
-            
-            try:
-                # Record the response using the platform's method
-                response_result = await self.platform.record_interview_response(
-                    agent_id=action.agent_id,
-                    interview_id=interview_id,
-                    response=response
-                )
-                
-                if response_result.get("success", False):
-                    env_log.info(f"Successfully recorded interview response for agent {action.agent_id}")
-                else:
-                    env_log.warning(f"Failed to record interview response: {response_result.get('error', 'Unknown error')}")
-            except Exception as e:
-                env_log.error(f"Error recording interview response: {str(e)}")
-            
-            return
-
     async def _perform_llm_action(self, agent):
         r"""Send the request to the llm model and execute the action.
         """
@@ -211,17 +158,25 @@ async def step(
             if isinstance(action, list):
                 for single_action in action:
                     if isinstance(single_action, ManualAction):
-                        tasks.append(
-                            agent.perform_action_by_data(
-                                single_action.action_type,
-                                **single_action.action_args))
+                        if single_action.action_type == ActionType.INTERVIEW:
+                            # Special handling for interview actions
+                            tasks.append(self._perform_manual_interview_action(agent, single_action))
+                        else:
+                            tasks.append(
+                                agent.perform_action_by_data(
+                                    single_action.action_type,
+                                    **single_action.action_args))
                     elif isinstance(single_action, LLMAction):
                         tasks.append(self._perform_llm_action(agent))
             else:
                 if isinstance(action, ManualAction):
-                    tasks.append(
-                        agent.perform_action_by_data(action.action_type,
-                                                     **action.action_args))
+                    if action.action_type == ActionType.INTERVIEW:
+                        # Special handling for interview actions
+                        tasks.append(self._perform_manual_interview_action(agent, action))
+                    else:
+                        tasks.append(
+                            agent.perform_action_by_data(action.action_type,
+                                                         **action.action_args))
                 elif isinstance(action, LLMAction):
                     tasks.append(self._perform_llm_action(agent))
 
@@ -233,6 +188,54 @@ async def step(
         if self.platform_type == DefaultPlatformType.TWITTER:
             self.platform.sandbox_clock.time_step += 1
 
+    async def _perform_manual_interview_action(self, agent: SocialAgent, action: ManualAction) -> None:
+        r"""Perform a manual interview action.
+
+        Args:
+            agent(SocialAgent): The agent to interview.
+            action(ManualAction): The interview action to perform.
+        """
+        # Extract interview prompt from args
+        interview_prompt = action.action_args.get("prompt", "")
+        if not interview_prompt:
+            env_log.warning(f"Empty interview prompt for agent {agent.social_agent_id}")
+            return
+        
+        # First, perform the regular action by sending it to the platform
+        action_result = await agent.perform_action_by_data(action.action_type, **action.action_args)
+        
+        # Check if the platform action was successful
+        if not action_result.get("success", False):
+            env_log.warning(f"Failed to record interview request: {action_result.get('error', 'Unknown error')}")
+            return
+        
+        # Get the interview_id from the result
+        interview_id = action_result.get("interview_id")
+        if not interview_id:
+            env_log.warning(f"No interview_id returned from platform")
+            return
+        
+        # Perform the interview to get the actual response
+        result = await self._perform_interview_action(agent, interview_prompt)
+        
+        # Get the response content
+        response = result.get("content", "")
+        
+        try:
+            # Record the response using the platform's method
+            response_result = await self.platform.record_interview_response(
+                agent_id=agent.social_agent_id,
+                interview_id=interview_id,
+                response=response
+            )
+            
+            if response_result.get("success", False):
+                env_log.info(f"Successfully recorded interview response for agent {agent.social_agent_id}")
+            else:
+                env_log.warning(f"Failed to record interview response: {response_result.get('error', 'Unknown error')}")
+        except Exception as e:
+            env_log.error(f"Error recording interview response: {str(e)}")
+
     async def close(self) -> None:
         r"""Stop the platform and close the environment.
         """

From 8263d9814812069b4742213448eed8111c6b139d Mon Sep 17 00:00:00 2001
From: zhangzaibin <m18602438523@163.com>
Date: Fri, 23 May 2025 09:37:27 +0800
Subject: [PATCH 14/32] finish the doc of interview action

---
 docs/cookbooks/twitter_interview.mdx | 271 +++++++++++++++++++++------
 docs/docs.json                       |   3 +-
 examples/twitter_interview.py        | 113 +++++------
 3 files changed, 272 insertions(+), 115 deletions(-)

diff --git a/docs/cookbooks/twitter_interview.mdx b/docs/cookbooks/twitter_interview.mdx
index 3dc64489..77d795db 100644
--- a/docs/cookbooks/twitter_interview.mdx
+++ b/docs/cookbooks/twitter_interview.mdx
@@ -1,13 +1,30 @@
 ---
-title: 'Twitter Interview'
-description: 'Comprehensive guide to all available actions in the OASIS simulation environment'
+title: 'Interview'
+description: 'Learn how to conduct interviews with AI agents in Twitter simulations using the INTERVIEW action type'
 ---
 
-# Twitter Interview
+# Interview
 
+This cookbook demonstrates how to use the INTERVIEW action type to conduct interviews with AI agents in a Twitter simulation. The interview functionality allows you to ask specific questions to agents and collect their responses, which is useful for research, opinion polling, and understanding agent behaviors.
 
-```python
+## Overview
+
+The INTERVIEW action type enables you to:
+- Ask specific questions to individual agents
+- Collect structured responses from agents
+- Store interview data in the database for analysis
+- Conduct interviews alongside regular social media interactions
+
+## Key Features
+
+- **Manual Interview Actions**: Use `ManualAction` with `ActionType.INTERVIEW` to conduct interviews
+- **Automatic Response Collection**: The system automatically collects and stores agent responses
+- **Database Storage**: All interview data is stored in the trace table for later analysis
+- **Concurrent Execution**: Interviews can be conducted alongside other social media actions
+
+## Complete Example
 
+```python
 # =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -30,7 +47,8 @@ from camel.models import ModelFactory
 from camel.types import ModelPlatformType, ModelType
 
 import oasis
-from oasis import ActionType, EnvAction, SingleAction
+from oasis import (ActionType, LLMAction, ManualAction,
+                   generate_twitter_agent_graph)
 
 
 async def main():
@@ -50,6 +68,13 @@ async def main():
         ActionType.INTERVIEW,  # Add the INTERVIEW action type
     ]
 
+    agent_graph = await generate_twitter_agent_graph(
+        profile_path=("data/twitter_dataset/anonymous_topic_200_1h/"
+                      "False_Business_0.csv"),
+        model=openai_model,
+        available_actions=available_actions,
+    )
+
     # Define the path to the database
     db_path = "./data/twitter_simulation.db"
 
@@ -59,74 +84,67 @@ async def main():
 
     # Make the environment
     env = oasis.make(
+        agent_graph=agent_graph,
         platform=oasis.DefaultPlatformType.TWITTER,
         database_path=db_path,
-        agent_profile_path=("data/twitter_dataset/anonymous_topic_200_1h/"
-                            "False_Business_0.csv"),
-        agent_models=openai_model,
-        available_actions=available_actions,
     )
 
     # Run the environment
     await env.reset()
 
     # First timestep: Agent 0 creates a post
-    action_1 = SingleAction(agent_id=0,
-                            action=ActionType.CREATE_POST,
-                            args={"content": "Earth is flat."})
-    env_actions_1 = EnvAction(
+    actions_1 = {}
+    actions_1[env.agent_graph.get_agent(0)] = ManualAction(
+        action_type=ActionType.CREATE_POST,
+        action_args={"content": "Earth is flat."})
+    await env.step(actions_1)
+
+    # Second timestep: Let some agents respond with LLM actions
+    actions_2 = {
+        agent: LLMAction()
         # Activate 5 agents with id 1, 3, 5, 7, 9
-        activate_agents=[1, 3, 5, 7, 9],
-        intervention=[action_1])
+        for _, agent in env.agent_graph.get_agents([1, 3, 5, 7, 9])
+    }
+    await env.step(actions_2)
 
-    # Second timestep: Agent 1 creates a post, and we interview Agent 0
-    action_2 = SingleAction(agent_id=1,
-                           action=ActionType.CREATE_POST,
-                           args={"content": "Earth is not flat."})
+    # Third timestep: Agent 1 creates a post, and we interview Agent 0
+    actions_3 = {}
+    actions_3[env.agent_graph.get_agent(1)] = ManualAction(
+        action_type=ActionType.CREATE_POST,
+        action_args={"content": "Earth is not flat."})
     
     # Create an interview action to ask Agent 0 about their views
-    interview_action = SingleAction(
-        agent_id=0,
-        action=ActionType.INTERVIEW,
-        args={"prompt": "What do you think about the shape of the Earth? Please explain your reasoning."}
-    )
+    actions_3[env.agent_graph.get_agent(0)] = ManualAction(
+        action_type=ActionType.INTERVIEW,
+        action_args={"prompt": "What do you think about the shape of the Earth? Please explain your reasoning."})
     
-    env_actions_2 = EnvAction(
-        activate_agents=[2, 4, 6, 8, 10],
-        intervention=[action_2, interview_action]
-    )
+    await env.step(actions_3)
 
-    # Third timestep: Interview multiple agents
-    interview_actions = [
-        SingleAction(
-            agent_id=1,
-            action=ActionType.INTERVIEW,
-            args={"prompt": "Why do you believe the Earth is not flat?"}
-        ),
-        SingleAction(
-            agent_id=2,
-            action=ActionType.INTERVIEW,
-            args={"prompt": "What are your thoughts on the debate about Earth's shape?"}
-        ),
-    ]
-    
-    env_actions_3 = EnvAction(
-        activate_agents=[3, 5, 7, 9],
-        intervention=interview_actions
-    )
+    # Fourth timestep: Let some other agents respond
+    actions_4 = {
+        agent: LLMAction()
+        for _, agent in env.agent_graph.get_agents([2, 4, 6, 8, 10])
+    }
+    await env.step(actions_4)
 
-    all_env_actions = [
-        env_actions_1,
-        env_actions_2,
-        env_actions_3,
-    ]
+    # Fifth timestep: Interview multiple agents
+    actions_5 = {}
+    actions_5[env.agent_graph.get_agent(1)] = ManualAction(
+        action_type=ActionType.INTERVIEW,
+        action_args={"prompt": "Why do you believe the Earth is not flat?"})
+    
+    actions_5[env.agent_graph.get_agent(2)] = ManualAction(
+        action_type=ActionType.INTERVIEW,
+        action_args={"prompt": "What are your thoughts on the debate about Earth's shape?"})
+    
+    await env.step(actions_5)
 
-    # Simulate 3 timesteps
-    for i in range(3):
-        print(f"\n=== Timestep {i+1} ===")
-        env_actions = all_env_actions[i]
-        # Perform the actions
-        await env.step(env_actions)
+    # Sixth timestep: Final LLM actions for remaining agents
+    actions_6 = {
+        agent: LLMAction()
+        for _, agent in env.agent_graph.get_agents([3, 5, 7, 9])
+    }
+    await env.step(actions_6)
 
     # Close the environment
     await env.close()
@@ -161,6 +179,143 @@ async def main():
 
 if __name__ == "__main__":
     asyncio.run(main())
+```
+
+## How It Works
+
+### 1. Setup and Configuration
+
+First, ensure that `ActionType.INTERVIEW` is included in your available actions:
+
+```python
+available_actions = [
+    ActionType.CREATE_POST,
+    ActionType.LIKE_POST,
+    ActionType.REPOST,
+    ActionType.FOLLOW,
+    ActionType.DO_NOTHING,
+    ActionType.QUOTE_POST,
+    ActionType.INTERVIEW,  # Essential for interview functionality
+]
+```
+
+### 2. Conducting Interviews
+
+Use `ManualAction` with `ActionType.INTERVIEW` to conduct interviews:
+
+```python
+# Single interview
+interview_action = ManualAction(
+    action_type=ActionType.INTERVIEW,
+    action_args={"prompt": "What are your thoughts on climate change?"})
+
+actions = {env.agent_graph.get_agent(0): interview_action}
+await env.step(actions)
+```
+
+### 3. Multiple Interviews in One Step
+
+You can interview multiple agents simultaneously:
+
+```python
+actions = {}
+actions[env.agent_graph.get_agent(1)] = ManualAction(
+    action_type=ActionType.INTERVIEW,
+    action_args={"prompt": "Why do you believe the Earth is not flat?"})
+
+actions[env.agent_graph.get_agent(2)] = ManualAction(
+    action_type=ActionType.INTERVIEW,
+    action_args={"prompt": "What are your thoughts on the debate about Earth's shape?"})
+
+await env.step(actions)
+```
+
+### 4. Mixing Interviews with Other Actions
+
+Interviews can be conducted alongside regular social media actions:
+
+```python
+actions = {}
+# Regular post creation
+actions[env.agent_graph.get_agent(1)] = ManualAction(
+    action_type=ActionType.CREATE_POST,
+    action_args={"content": "Earth is not flat."})
+
+# Interview action
+actions[env.agent_graph.get_agent(0)] = ManualAction(
+    action_type=ActionType.INTERVIEW,
+    action_args={"prompt": "What do you think about the shape of the Earth?"})
+
+await env.step(actions)
+```
+
+## Data Storage and Retrieval
+
+### Database Schema
+
+Interview data is stored in the `trace` table with the following structure:
+- `user_id`: The ID of the interviewed agent
+- `action`: Set to `ActionType.INTERVIEW.value`
+- `info`: JSON string containing interview details
+- `created_at`: Timestamp of the interview
+
+### Retrieving Interview Results
+
+```python
+import sqlite3
+import json
+
+conn = sqlite3.connect(db_path)
+cursor = conn.cursor()
+
+# Query all interview records
+cursor.execute("""
+    SELECT user_id, info, created_at
+    FROM trace
+    WHERE action = ?
+""", (ActionType.INTERVIEW.value,))
+
+for user_id, info_json, timestamp in cursor.fetchall():
+    info = json.loads(info_json)
+    print(f"Agent {user_id}: {info.get('response', 'N/A')}")
+
+conn.close()
+```
+
+### Interview Data Structure
+
+Each interview record contains:
+- `prompt`: The question asked to the agent
+- `interview_id`: Unique identifier for the interview
+- `response`: The agent's response to the question
+
+## Best Practices
+
+### 1. Strategic Interview Timing
+
+Conduct interviews at strategic points in your simulation:
+- After controversial posts to gauge reactions
+- Before and after significant events
+- At regular intervals to track opinion changes
+
+### 2. Question Design
+
+Design effective interview questions:
+- Be specific and clear
+- Avoid leading questions
+- Ask open-ended questions for richer responses
+
+```python
+# Good examples
+"What are your thoughts on renewable energy?"
+"How do you feel about the recent policy changes?"
+"Can you explain your reasoning behind your last post?"
+
+# Avoid
+"Don't you think renewable energy is great?" # Leading
+"Yes or no: Do you like cats?" # Too restrictive
+```
+
+
 
 
-```
\ No newline at end of file
diff --git a/docs/docs.json b/docs/docs.json
index 92c743ca..ee4c4d83 100644
--- a/docs/docs.json
+++ b/docs/docs.json
@@ -46,7 +46,8 @@
               "cookbooks/reddit_simulation",
               "cookbooks/sympy_tools_simulation",
               "cookbooks/search_tools_simulation",
-              "cookbooks/custom_prompt_simulation"
+              "cookbooks/custom_prompt_simulation",
+              "cookbooks/twitter_interview"
             ]
           },
           {
diff --git a/examples/twitter_interview.py b/examples/twitter_interview.py
index 661669bd..1d2bbf10 100644
--- a/examples/twitter_interview.py
+++ b/examples/twitter_interview.py
@@ -20,7 +20,8 @@
 from camel.types import ModelPlatformType, ModelType
 
 import oasis
-from oasis import ActionType, EnvAction, SingleAction
+from oasis import (ActionType, LLMAction, ManualAction,
+                   generate_twitter_agent_graph)
 
 
 async def main():
@@ -40,6 +41,13 @@ async def main():
         ActionType.INTERVIEW,  # Add the INTERVIEW action type
     ]
 
+    agent_graph = await generate_twitter_agent_graph(
+        profile_path=("data/twitter_dataset/anonymous_topic_200_1h/"
+                      "False_Business_0.csv"),
+        model=openai_model,
+        available_actions=available_actions,
+    )
+
     # Define the path to the database
     db_path = "./data/twitter_simulation.db"
 
@@ -49,74 +57,67 @@ async def main():
 
     # Make the environment
     env = oasis.make(
+        agent_graph=agent_graph,
         platform=oasis.DefaultPlatformType.TWITTER,
         database_path=db_path,
-        agent_profile_path=("data/twitter_dataset/anonymous_topic_200_1h/"
-                            "False_Business_0.csv"),
-        agent_models=openai_model,
-        available_actions=available_actions,
     )
 
     # Run the environment
     await env.reset()
 
     # First timestep: Agent 0 creates a post
-    action_1 = SingleAction(agent_id=0,
-                            action=ActionType.CREATE_POST,
-                            args={"content": "Earth is flat."})
-    env_actions_1 = EnvAction(
+    actions_1 = {}
+    actions_1[env.agent_graph.get_agent(0)] = ManualAction(
+        action_type=ActionType.CREATE_POST,
+        action_args={"content": "Earth is flat."})
+    await env.step(actions_1)
+
+    # Second timestep: Let some agents respond with LLM actions
+    actions_2 = {
+        agent: LLMAction()
         # Activate 5 agents with id 1, 3, 5, 7, 9
-        activate_agents=[1, 3, 5, 7, 9],
-        intervention=[action_1])
-
-    # Second timestep: Agent 1 creates a post, and we interview Agent 0
-    action_2 = SingleAction(agent_id=1,
-                           action=ActionType.CREATE_POST,
-                           args={"content": "Earth is not flat."})
+        for _, agent in env.agent_graph.get_agents([1, 3, 5, 7, 9])
+    }
+    await env.step(actions_2)
+
+    # Third timestep: Agent 1 creates a post, and we interview Agent 0
+    actions_3 = {}
+    actions_3[env.agent_graph.get_agent(1)] = ManualAction(
+        action_type=ActionType.CREATE_POST,
+        action_args={"content": "Earth is not flat."})
     
     # Create an interview action to ask Agent 0 about their views
-    interview_action = SingleAction(
-        agent_id=0,
-        action=ActionType.INTERVIEW,
-        args={"prompt": "What do you think about the shape of the Earth? Please explain your reasoning."}
-    )
+    actions_3[env.agent_graph.get_agent(0)] = ManualAction(
+        action_type=ActionType.INTERVIEW,
+        action_args={"prompt": "What do you think about the shape of the Earth? Please explain your reasoning."})
     
-    env_actions_2 = EnvAction(
-        activate_agents=[2, 4, 6, 8, 10],
-        intervention=[action_2, interview_action]
-    )
-
-    # Third timestep: Interview multiple agents
-    interview_actions = [
-        SingleAction(
-            agent_id=1,
-            action=ActionType.INTERVIEW,
-            args={"prompt": "Why do you believe the Earth is not flat?"}
-        ),
-        SingleAction(
-            agent_id=2,
-            action=ActionType.INTERVIEW,
-            args={"prompt": "What are your thoughts on the debate about Earth's shape?"}
-        ),
-    ]
+    await env.step(actions_3)
+
+    # Fourth timestep: Let some other agents respond
+    actions_4 = {
+        agent: LLMAction()
+        for _, agent in env.agent_graph.get_agents([2, 4, 6, 8, 10])
+    }
+    await env.step(actions_4)
+
+    # Fifth timestep: Interview multiple agents
+    actions_5 = {}
+    actions_5[env.agent_graph.get_agent(1)] = ManualAction(
+        action_type=ActionType.INTERVIEW,
+        action_args={"prompt": "Why do you believe the Earth is not flat?"})
     
-    env_actions_3 = EnvAction(
-        activate_agents=[3, 5, 7, 9],
-        intervention=interview_actions
-    )
-
-    all_env_actions = [
-        env_actions_1,
-        env_actions_2,
-        env_actions_3,
-    ]
-
-    # Simulate 3 timesteps
-    for i in range(3):
-        print(f"\n=== Timestep {i+1} ===")
-        env_actions = all_env_actions[i]
-        # Perform the actions
-        await env.step(env_actions)
+    actions_5[env.agent_graph.get_agent(2)] = ManualAction(
+        action_type=ActionType.INTERVIEW,
+        action_args={"prompt": "What are your thoughts on the debate about Earth's shape?"})
+    
+    await env.step(actions_5)
+
+    # Sixth timestep: Final LLM actions for remaining agents
+    actions_6 = {
+        agent: LLMAction()
+        for _, agent in env.agent_graph.get_agents([3, 5, 7, 9])
+    }
+    await env.step(actions_6)
 
     # Close the environment
     await env.close()

From b7c614e0f7e0d5966174dd97fd5b852972148feb Mon Sep 17 00:00:00 2001
From: zhangzaibin <m18602438523@163.com>
Date: Fri, 23 May 2025 09:48:45 +0800
Subject: [PATCH 15/32] remove interview from avaliable action list

---
 docs/cookbooks/twitter_interview.mdx |  15 ++-
 examples/twitter_interview.py        |   4 +-
 examples/twitter_interview_fixed.py  | 192 +++++++++++++++++++++++++++
 3 files changed, 207 insertions(+), 4 deletions(-)
 create mode 100644 examples/twitter_interview_fixed.py

diff --git a/docs/cookbooks/twitter_interview.mdx b/docs/cookbooks/twitter_interview.mdx
index 77d795db..e7420210 100644
--- a/docs/cookbooks/twitter_interview.mdx
+++ b/docs/cookbooks/twitter_interview.mdx
@@ -22,6 +22,10 @@ The INTERVIEW action type enables you to:
 - **Database Storage**: All interview data is stored in the trace table for later analysis
 - **Concurrent Execution**: Interviews can be conducted alongside other social media actions
 
+## Important Note
+
+**Do NOT include `ActionType.INTERVIEW` in the `available_actions` list** when creating your agent graph. The interview action is designed to be used only manually by researchers/developers, not automatically selected by LLM agents. Including it in `available_actions` would allow agents to interview each other automatically, which is typically not desired behavior.
+
 ## Complete Example
 
 ```python
@@ -58,6 +62,8 @@ async def main():
     )
 
     # Define the available actions for the agents
+    # Note: INTERVIEW is NOT included here to prevent LLM from automatically selecting it
+    # INTERVIEW can still be used manually via ManualAction
     available_actions = [
         ActionType.CREATE_POST,
         ActionType.LIKE_POST,
@@ -65,7 +71,7 @@ async def main():
         ActionType.FOLLOW,
         ActionType.DO_NOTHING,
         ActionType.QUOTE_POST,
-        ActionType.INTERVIEW,  # Add the INTERVIEW action type
+        # ActionType.INTERVIEW,  # DO NOT include this - interviews should be manual only
     ]
 
     agent_graph = await generate_twitter_agent_graph(
@@ -185,9 +191,10 @@ if __name__ == "__main__":
 
 ### 1. Setup and Configuration
 
-First, ensure that `ActionType.INTERVIEW` is included in your available actions:
+**Important**: Do NOT include `ActionType.INTERVIEW` in your available actions list. Interviews should only be conducted manually:
 
 ```python
+# Correct configuration - INTERVIEW is NOT included
 available_actions = [
     ActionType.CREATE_POST,
     ActionType.LIKE_POST,
@@ -195,10 +202,12 @@ available_actions = [
     ActionType.FOLLOW,
     ActionType.DO_NOTHING,
     ActionType.QUOTE_POST,
-    ActionType.INTERVIEW,  # Essential for interview functionality
+    # ActionType.INTERVIEW,  # DO NOT include - interviews are manual only
 ]
 ```
 
+This prevents LLM agents from automatically selecting the interview action during their decision-making process. Interviews can still be conducted using `ManualAction`.
+
 ### 2. Conducting Interviews
 
 Use `ManualAction` with `ActionType.INTERVIEW` to conduct interviews:
diff --git a/examples/twitter_interview.py b/examples/twitter_interview.py
index 1d2bbf10..1250d640 100644
--- a/examples/twitter_interview.py
+++ b/examples/twitter_interview.py
@@ -31,6 +31,8 @@ async def main():
     )
 
     # Define the available actions for the agents
+    # Note: INTERVIEW is NOT included here to prevent LLM from automatically selecting it
+    # INTERVIEW can still be used manually via ManualAction
     available_actions = [
         ActionType.CREATE_POST,
         ActionType.LIKE_POST,
@@ -38,7 +40,7 @@ async def main():
         ActionType.FOLLOW,
         ActionType.DO_NOTHING,
         ActionType.QUOTE_POST,
-        ActionType.INTERVIEW,  # Add the INTERVIEW action type
+        # ActionType.INTERVIEW,  # Removed to prevent LLM auto-selection
     ]
 
     agent_graph = await generate_twitter_agent_graph(
diff --git a/examples/twitter_interview_fixed.py b/examples/twitter_interview_fixed.py
new file mode 100644
index 00000000..3f9367de
--- /dev/null
+++ b/examples/twitter_interview_fixed.py
@@ -0,0 +1,192 @@
+# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
+"""
+Fixed Twitter Interview Example
+
+This example demonstrates the correct way to implement interviews in OASIS:
+- INTERVIEW is NOT included in available_actions to prevent LLM auto-selection
+- Interviews are conducted manually using ManualAction
+- LLM agents can only perform regular social media actions
+"""
+import asyncio
+import os
+import sqlite3
+import json
+
+from camel.models import ModelFactory
+from camel.types import ModelPlatformType, ModelType
+
+import oasis
+from oasis import (ActionType, LLMAction, ManualAction,
+                   generate_twitter_agent_graph)
+
+
+async def main():
+    openai_model = ModelFactory.create(
+        model_platform=ModelPlatformType.OPENAI,
+        model_type=ModelType.GPT_4O_MINI,
+    )
+
+    # Define the available actions for the agents
+    # IMPORTANT: INTERVIEW is NOT included here to prevent LLM from automatically selecting it
+    # This ensures that interviews are only conducted manually by researchers/developers
+    available_actions = [
+        ActionType.CREATE_POST,
+        ActionType.LIKE_POST,
+        ActionType.REPOST,
+        ActionType.FOLLOW,
+        ActionType.DO_NOTHING,
+        ActionType.QUOTE_POST,
+        # ActionType.INTERVIEW,  # Deliberately excluded - interviews are manual only
+    ]
+
+    agent_graph = await generate_twitter_agent_graph(
+        profile_path=("data/twitter_dataset/anonymous_topic_200_1h/"
+                      "False_Business_0.csv"),
+        model=openai_model,
+        available_actions=available_actions,
+    )
+
+    # Define the path to the database
+    db_path = "./data/twitter_simulation_fixed.db"
+
+    # Delete the old database
+    if os.path.exists(db_path):
+        os.remove(db_path)
+
+    # Make the environment
+    env = oasis.make(
+        agent_graph=agent_graph,
+        platform=oasis.DefaultPlatformType.TWITTER,
+        database_path=db_path,
+    )
+
+    # Run the environment
+    await env.reset()
+
+    # First timestep: Agent 0 creates a post
+    print("=== Timestep 1: Initial post ===")
+    actions_1 = {}
+    actions_1[env.agent_graph.get_agent(0)] = ManualAction(
+        action_type=ActionType.CREATE_POST,
+        action_args={"content": "Earth is flat."})
+    await env.step(actions_1)
+
+    # Second timestep: Let some agents respond with LLM actions
+    # Note: Since INTERVIEW is not in available_actions, LLM agents cannot choose it
+    print("=== Timestep 2: LLM agents respond (no interview option) ===")
+    actions_2 = {
+        agent: LLMAction()
+        # Activate 5 agents with id 1, 3, 5, 7, 9
+        for _, agent in env.agent_graph.get_agents([1, 3, 5, 7, 9])
+    }
+    await env.step(actions_2)
+
+    # Third timestep: Agent 1 creates a post, and we manually interview Agent 0
+    print("=== Timestep 3: Manual interview ===")
+    actions_3 = {}
+    actions_3[env.agent_graph.get_agent(1)] = ManualAction(
+        action_type=ActionType.CREATE_POST,
+        action_args={"content": "Earth is not flat."})
+    
+    # Manual interview - this is controlled by the researcher, not the LLM
+    actions_3[env.agent_graph.get_agent(0)] = ManualAction(
+        action_type=ActionType.INTERVIEW,
+        action_args={"prompt": "What do you think about the shape of the Earth? Please explain your reasoning."})
+    
+    await env.step(actions_3)
+
+    # Fourth timestep: Let some other agents respond with LLM actions
+    print("=== Timestep 4: More LLM responses ===")
+    actions_4 = {
+        agent: LLMAction()
+        for _, agent in env.agent_graph.get_agents([2, 4, 6, 8, 10])
+    }
+    await env.step(actions_4)
+
+    # Fifth timestep: Interview multiple agents manually
+    print("=== Timestep 5: Multiple manual interviews ===")
+    actions_5 = {}
+    actions_5[env.agent_graph.get_agent(1)] = ManualAction(
+        action_type=ActionType.INTERVIEW,
+        action_args={"prompt": "Why do you believe the Earth is not flat?"})
+    
+    actions_5[env.agent_graph.get_agent(2)] = ManualAction(
+        action_type=ActionType.INTERVIEW,
+        action_args={"prompt": "What are your thoughts on the debate about Earth's shape?"})
+    
+    await env.step(actions_5)
+
+    # Sixth timestep: Final LLM actions for remaining agents
+    print("=== Timestep 6: Final LLM responses ===")
+    actions_6 = {
+        agent: LLMAction()
+        for _, agent in env.agent_graph.get_agents([3, 5, 7, 9])
+    }
+    await env.step(actions_6)
+
+    # Close the environment
+    await env.close()
+    
+    # Visualize the interview results
+    print("\n=== Interview Results ===")
+    conn = sqlite3.connect(db_path)
+    cursor = conn.cursor()
+    
+    # Query all interview records from the database
+    cursor.execute("""
+        SELECT user_id, info, created_at
+        FROM trace
+        WHERE action = ?
+        ORDER BY created_at
+    """, (ActionType.INTERVIEW.value,))
+    
+    interview_count = 0
+    for user_id, info_json, timestamp in cursor.fetchall():
+        info = json.loads(info_json)
+        interview_count += 1
+        print(f"\nInterview #{interview_count} - Agent {user_id} (Timestep {timestamp}):")
+        print(f"Question: {info.get('prompt', 'N/A')}")
+        print(f"Response: {info.get('response', 'N/A')}")
+        print(f"Interview ID: {info.get('interview_id', 'N/A')}")
+    
+    if interview_count == 0:
+        print("No interviews found in the database.")
+    else:
+        print(f"\nTotal interviews conducted: {interview_count}")
+    
+    # Also check what actions LLM agents actually performed
+    print("\n=== LLM Agent Actions (should not include interviews) ===")
+    cursor.execute("""
+        SELECT DISTINCT action, COUNT(*) as count
+        FROM trace
+        WHERE action != ?
+        GROUP BY action
+        ORDER BY count DESC
+    """, (ActionType.INTERVIEW.value,))
+    
+    for action, count in cursor.fetchall():
+        print(f"{action}: {count} times")
+    
+    conn.close()
+    
+    print("\n=== Summary ===")
+    print("✅ INTERVIEW was not included in available_actions")
+    print("✅ LLM agents could not automatically select interview actions")
+    print("✅ Interviews were conducted manually using ManualAction")
+    print("✅ This prevents unwanted agent-to-agent interviews")
+
+
+if __name__ == "__main__":
+    asyncio.run(main()) 
\ No newline at end of file

From b67ab183422b87313fbec5f2c71d24a43c130a7c Mon Sep 17 00:00:00 2001
From: zhangzaibin <m18602438523@163.com>
Date: Fri, 23 May 2025 09:50:03 +0800
Subject: [PATCH 16/32] clean unuseful files

---
 examples/twitter_interview_fixed.py | 192 ----------------------------
 1 file changed, 192 deletions(-)
 delete mode 100644 examples/twitter_interview_fixed.py

diff --git a/examples/twitter_interview_fixed.py b/examples/twitter_interview_fixed.py
deleted file mode 100644
index 3f9367de..00000000
--- a/examples/twitter_interview_fixed.py
+++ /dev/null
@@ -1,192 +0,0 @@
-# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-"""
-Fixed Twitter Interview Example
-
-This example demonstrates the correct way to implement interviews in OASIS:
-- INTERVIEW is NOT included in available_actions to prevent LLM auto-selection
-- Interviews are conducted manually using ManualAction
-- LLM agents can only perform regular social media actions
-"""
-import asyncio
-import os
-import sqlite3
-import json
-
-from camel.models import ModelFactory
-from camel.types import ModelPlatformType, ModelType
-
-import oasis
-from oasis import (ActionType, LLMAction, ManualAction,
-                   generate_twitter_agent_graph)
-
-
-async def main():
-    openai_model = ModelFactory.create(
-        model_platform=ModelPlatformType.OPENAI,
-        model_type=ModelType.GPT_4O_MINI,
-    )
-
-    # Define the available actions for the agents
-    # IMPORTANT: INTERVIEW is NOT included here to prevent LLM from automatically selecting it
-    # This ensures that interviews are only conducted manually by researchers/developers
-    available_actions = [
-        ActionType.CREATE_POST,
-        ActionType.LIKE_POST,
-        ActionType.REPOST,
-        ActionType.FOLLOW,
-        ActionType.DO_NOTHING,
-        ActionType.QUOTE_POST,
-        # ActionType.INTERVIEW,  # Deliberately excluded - interviews are manual only
-    ]
-
-    agent_graph = await generate_twitter_agent_graph(
-        profile_path=("data/twitter_dataset/anonymous_topic_200_1h/"
-                      "False_Business_0.csv"),
-        model=openai_model,
-        available_actions=available_actions,
-    )
-
-    # Define the path to the database
-    db_path = "./data/twitter_simulation_fixed.db"
-
-    # Delete the old database
-    if os.path.exists(db_path):
-        os.remove(db_path)
-
-    # Make the environment
-    env = oasis.make(
-        agent_graph=agent_graph,
-        platform=oasis.DefaultPlatformType.TWITTER,
-        database_path=db_path,
-    )
-
-    # Run the environment
-    await env.reset()
-
-    # First timestep: Agent 0 creates a post
-    print("=== Timestep 1: Initial post ===")
-    actions_1 = {}
-    actions_1[env.agent_graph.get_agent(0)] = ManualAction(
-        action_type=ActionType.CREATE_POST,
-        action_args={"content": "Earth is flat."})
-    await env.step(actions_1)
-
-    # Second timestep: Let some agents respond with LLM actions
-    # Note: Since INTERVIEW is not in available_actions, LLM agents cannot choose it
-    print("=== Timestep 2: LLM agents respond (no interview option) ===")
-    actions_2 = {
-        agent: LLMAction()
-        # Activate 5 agents with id 1, 3, 5, 7, 9
-        for _, agent in env.agent_graph.get_agents([1, 3, 5, 7, 9])
-    }
-    await env.step(actions_2)
-
-    # Third timestep: Agent 1 creates a post, and we manually interview Agent 0
-    print("=== Timestep 3: Manual interview ===")
-    actions_3 = {}
-    actions_3[env.agent_graph.get_agent(1)] = ManualAction(
-        action_type=ActionType.CREATE_POST,
-        action_args={"content": "Earth is not flat."})
-    
-    # Manual interview - this is controlled by the researcher, not the LLM
-    actions_3[env.agent_graph.get_agent(0)] = ManualAction(
-        action_type=ActionType.INTERVIEW,
-        action_args={"prompt": "What do you think about the shape of the Earth? Please explain your reasoning."})
-    
-    await env.step(actions_3)
-
-    # Fourth timestep: Let some other agents respond with LLM actions
-    print("=== Timestep 4: More LLM responses ===")
-    actions_4 = {
-        agent: LLMAction()
-        for _, agent in env.agent_graph.get_agents([2, 4, 6, 8, 10])
-    }
-    await env.step(actions_4)
-
-    # Fifth timestep: Interview multiple agents manually
-    print("=== Timestep 5: Multiple manual interviews ===")
-    actions_5 = {}
-    actions_5[env.agent_graph.get_agent(1)] = ManualAction(
-        action_type=ActionType.INTERVIEW,
-        action_args={"prompt": "Why do you believe the Earth is not flat?"})
-    
-    actions_5[env.agent_graph.get_agent(2)] = ManualAction(
-        action_type=ActionType.INTERVIEW,
-        action_args={"prompt": "What are your thoughts on the debate about Earth's shape?"})
-    
-    await env.step(actions_5)
-
-    # Sixth timestep: Final LLM actions for remaining agents
-    print("=== Timestep 6: Final LLM responses ===")
-    actions_6 = {
-        agent: LLMAction()
-        for _, agent in env.agent_graph.get_agents([3, 5, 7, 9])
-    }
-    await env.step(actions_6)
-
-    # Close the environment
-    await env.close()
-    
-    # Visualize the interview results
-    print("\n=== Interview Results ===")
-    conn = sqlite3.connect(db_path)
-    cursor = conn.cursor()
-    
-    # Query all interview records from the database
-    cursor.execute("""
-        SELECT user_id, info, created_at
-        FROM trace
-        WHERE action = ?
-        ORDER BY created_at
-    """, (ActionType.INTERVIEW.value,))
-    
-    interview_count = 0
-    for user_id, info_json, timestamp in cursor.fetchall():
-        info = json.loads(info_json)
-        interview_count += 1
-        print(f"\nInterview #{interview_count} - Agent {user_id} (Timestep {timestamp}):")
-        print(f"Question: {info.get('prompt', 'N/A')}")
-        print(f"Response: {info.get('response', 'N/A')}")
-        print(f"Interview ID: {info.get('interview_id', 'N/A')}")
-    
-    if interview_count == 0:
-        print("No interviews found in the database.")
-    else:
-        print(f"\nTotal interviews conducted: {interview_count}")
-    
-    # Also check what actions LLM agents actually performed
-    print("\n=== LLM Agent Actions (should not include interviews) ===")
-    cursor.execute("""
-        SELECT DISTINCT action, COUNT(*) as count
-        FROM trace
-        WHERE action != ?
-        GROUP BY action
-        ORDER BY count DESC
-    """, (ActionType.INTERVIEW.value,))
-    
-    for action, count in cursor.fetchall():
-        print(f"{action}: {count} times")
-    
-    conn.close()
-    
-    print("\n=== Summary ===")
-    print("✅ INTERVIEW was not included in available_actions")
-    print("✅ LLM agents could not automatically select interview actions")
-    print("✅ Interviews were conducted manually using ManualAction")
-    print("✅ This prevents unwanted agent-to-agent interviews")
-
-
-if __name__ == "__main__":
-    asyncio.run(main()) 
\ No newline at end of file

From 997c8fc0895cf58803a358c374f576e8faa73d56 Mon Sep 17 00:00:00 2001
From: zhangzaibin <m18602438523@163.com>
Date: Fri, 23 May 2025 09:51:31 +0800
Subject: [PATCH 17/32] add explaination

---
 examples/twitter_interview.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/twitter_interview.py b/examples/twitter_interview.py
index 1250d640..a34299a8 100644
--- a/examples/twitter_interview.py
+++ b/examples/twitter_interview.py
@@ -40,7 +40,6 @@ async def main():
         ActionType.FOLLOW,
         ActionType.DO_NOTHING,
         ActionType.QUOTE_POST,
-        # ActionType.INTERVIEW,  # Removed to prevent LLM auto-selection
     ]
 
     agent_graph = await generate_twitter_agent_graph(
@@ -89,6 +88,7 @@ async def main():
         action_args={"content": "Earth is not flat."})
     
     # Create an interview action to ask Agent 0 about their views
+    # ActionType.INTERVIEW is a external action, which can not be exceuted by agents themselves
     actions_3[env.agent_graph.get_agent(0)] = ManualAction(
         action_type=ActionType.INTERVIEW,
         action_args={"prompt": "What do you think about the shape of the Earth? Please explain your reasoning."})

From aeae3154fe80db07e51d6d5d7a139c3894672861 Mon Sep 17 00:00:00 2001
From: zhangzaibin <m18602438523@163.com>
Date: Fri, 23 May 2025 09:55:59 +0800
Subject: [PATCH 18/32] add test function

---
 test/agent/test_interview_action.py | 449 ++++++++++++++++++++++++++++
 1 file changed, 449 insertions(+)
 create mode 100644 test/agent/test_interview_action.py

diff --git a/test/agent/test_interview_action.py b/test/agent/test_interview_action.py
new file mode 100644
index 00000000..f9eacf14
--- /dev/null
+++ b/test/agent/test_interview_action.py
@@ -0,0 +1,449 @@
+# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
+import asyncio
+import json
+import os
+import os.path as osp
+import sqlite3
+import tempfile
+
+import pytest
+from camel.models import ModelFactory
+from camel.types import ModelPlatformType, ModelType
+
+import oasis
+from oasis import ActionType, LLMAction, ManualAction, generate_twitter_agent_graph
+from oasis.social_agent.agent import SocialAgent
+from oasis.social_platform.channel import Channel
+from oasis.social_platform.config import UserInfo
+from oasis.social_platform.platform import Platform
+
+parent_folder = osp.dirname(osp.abspath(__file__))
+test_db_filepath = osp.join(parent_folder, "test_interview.db")
+
+
+@pytest.fixture
+def setup_interview_test():
+    """Setup fixture for interview tests."""
+    if os.path.exists(test_db_filepath):
+        os.remove(test_db_filepath)
+    yield
+    # Cleanup after test
+    if os.path.exists(test_db_filepath):
+        os.remove(test_db_filepath)
+
+
+@pytest.mark.asyncio
+async def test_single_interview_action(setup_interview_test):
+    """Test conducting a single interview with an agent."""
+    agents = []
+    channel = Channel()
+    infra = Platform(test_db_filepath, channel)
+    task = asyncio.create_task(infra.running())
+
+    try:
+        # Create and sign up a test agent
+        real_name = "TestAgent"
+        description = "A test agent for interview testing."
+        profile = {
+            "nodes": [],
+            "edges": [],
+            "other_info": {
+                "user_profile": "I am a test agent with strong opinions on technology.",
+                "mbti": "INTJ",
+                "activity_level": ["online"] * 24,
+                "activity_level_frequency": [5] * 24,
+                "active_threshold": [0.5] * 24,
+            },
+        }
+        user_info = UserInfo(name=real_name, description=description, profile=profile)
+        agent = SocialAgent(agent_id=0, user_info=user_info, twitter_channel=channel)
+        
+        # Sign up the agent
+        return_message = await agent.env.action.sign_up("testuser", "TestUser", "A test bio.")
+        assert return_message["success"] is True
+        agents.append(agent)
+
+        # Conduct an interview
+        interview_prompt = "What are your thoughts on artificial intelligence?"
+        return_message = await agent.env.action.interview(interview_prompt)
+        assert return_message["success"] is True
+        assert "interview_id" in return_message
+        
+        # Verify the interview was recorded in the database
+        conn = sqlite3.connect(test_db_filepath)
+        cursor = conn.cursor()
+        cursor.execute("""
+            SELECT user_id, info, action
+            FROM trace
+            WHERE action = ? AND user_id = ?
+        """, (ActionType.INTERVIEW.value, 0))
+        
+        interview_records = cursor.fetchall()
+        assert len(interview_records) == 1
+        
+        user_id, info_json, action = interview_records[0]
+        assert user_id == 0
+        assert action == ActionType.INTERVIEW.value
+        
+        info = json.loads(info_json)
+        assert info["prompt"] == interview_prompt
+        assert "interview_id" in info
+        
+        conn.close()
+
+    finally:
+        await channel.write_to_receive_queue((None, None, ActionType.EXIT))
+        await task
+
+
+@pytest.mark.asyncio
+async def test_multiple_interviews_action(setup_interview_test):
+    """Test conducting multiple interviews with different agents."""
+    agents = []
+    channel = Channel()
+    infra = Platform(test_db_filepath, channel)
+    task = asyncio.create_task(infra.running())
+
+    try:
+        # Create and sign up multiple test agents
+        for i in range(3):
+            real_name = f"TestAgent{i}"
+            description = f"Test agent {i} for interview testing."
+            profile = {
+                "nodes": [],
+                "edges": [],
+                "other_info": {
+                    "user_profile": f"I am test agent {i} with unique perspectives.",
+                    "mbti": "INTJ",
+                    "activity_level": ["online"] * 24,
+                    "activity_level_frequency": [5] * 24,
+                    "active_threshold": [0.5] * 24,
+                },
+            }
+            user_info = UserInfo(name=real_name, description=description, profile=profile)
+            agent = SocialAgent(agent_id=i, user_info=user_info, twitter_channel=channel)
+            
+            # Sign up the agent
+            return_message = await agent.env.action.sign_up(f"testuser{i}", f"TestUser{i}", f"Test bio {i}.")
+            assert return_message["success"] is True
+            agents.append(agent)
+
+        # Conduct interviews with different prompts
+        interview_prompts = [
+            "What is your opinion on climate change?",
+            "How do you feel about social media?",
+            "What are your thoughts on remote work?"
+        ]
+        
+        for i, (agent, prompt) in enumerate(zip(agents, interview_prompts)):
+            return_message = await agent.env.action.interview(prompt)
+            assert return_message["success"] is True
+            assert "interview_id" in return_message
+
+        # Verify all interviews were recorded
+        conn = sqlite3.connect(test_db_filepath)
+        cursor = conn.cursor()
+        cursor.execute("""
+            SELECT user_id, info, action
+            FROM trace
+            WHERE action = ?
+            ORDER BY user_id
+        """, (ActionType.INTERVIEW.value,))
+        
+        interview_records = cursor.fetchall()
+        assert len(interview_records) == 3
+        
+        for i, (user_id, info_json, action) in enumerate(interview_records):
+            assert user_id == i
+            assert action == ActionType.INTERVIEW.value
+            
+            info = json.loads(info_json)
+            assert info["prompt"] == interview_prompts[i]
+            assert "interview_id" in info
+        
+        conn.close()
+
+    finally:
+        await channel.write_to_receive_queue((None, None, ActionType.EXIT))
+        await task
+
+
+@pytest.mark.asyncio
+async def test_interview_with_environment():
+    """Test interview functionality using the full OASIS environment."""
+    # Create a temporary database file
+    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as tmp_file:
+        db_path = tmp_file.name
+
+    try:
+        # Create a mock model (we'll use a simple mock since we're testing the action flow)
+        openai_model = ModelFactory.create(
+            model_platform=ModelPlatformType.OPENAI,
+            model_type=ModelType.GPT_4O_MINI,
+        )
+
+        # Define available actions (excluding INTERVIEW to prevent automatic selection)
+        available_actions = [
+            ActionType.CREATE_POST,
+            ActionType.LIKE_POST,
+            ActionType.REPOST,
+            ActionType.FOLLOW,
+            ActionType.DO_NOTHING,
+        ]
+
+        # Create a minimal agent graph for testing
+        agent_graph = await generate_twitter_agent_graph(
+            profile_path=("data/twitter_dataset/anonymous_topic_200_1h/"
+                         "False_Business_0.csv"),
+            model=openai_model,
+            available_actions=available_actions,
+        )
+
+        # Create environment
+        env = oasis.make(
+            agent_graph=agent_graph,
+            platform=oasis.DefaultPlatformType.TWITTER,
+            database_path=db_path,
+        )
+
+        await env.reset()
+
+        # Test single interview action
+        actions = {}
+        actions[env.agent_graph.get_agent(0)] = ManualAction(
+            action_type=ActionType.INTERVIEW,
+            action_args={"prompt": "What is your favorite color and why?"}
+        )
+        await env.step(actions)
+
+        # Test multiple interviews in one step
+        actions = {}
+        actions[env.agent_graph.get_agent(1)] = ManualAction(
+            action_type=ActionType.INTERVIEW,
+            action_args={"prompt": "What do you think about technology?"}
+        )
+        actions[env.agent_graph.get_agent(2)] = ManualAction(
+            action_type=ActionType.INTERVIEW,
+            action_args={"prompt": "How do you spend your free time?"}
+        )
+        await env.step(actions)
+
+        # Test mixing interview with other actions
+        actions = {}
+        actions[env.agent_graph.get_agent(0)] = ManualAction(
+            action_type=ActionType.CREATE_POST,
+            action_args={"content": "Hello world!"}
+        )
+        actions[env.agent_graph.get_agent(3)] = ManualAction(
+            action_type=ActionType.INTERVIEW,
+            action_args={"prompt": "What motivates you in life?"}
+        )
+        await env.step(actions)
+
+        await env.close()
+
+        # Verify interview data in database
+        conn = sqlite3.connect(db_path)
+        cursor = conn.cursor()
+        cursor.execute("""
+            SELECT user_id, info, created_at
+            FROM trace
+            WHERE action = ?
+            ORDER BY created_at
+        """, (ActionType.INTERVIEW.value,))
+        
+        interview_records = cursor.fetchall()
+        assert len(interview_records) == 4  # 3 interviews conducted
+
+        # Check the content of interviews
+        expected_prompts = [
+            "What is your favorite color and why?",
+            "What do you think about technology?",
+            "How do you spend your free time?",
+            "What motivates you in life?"
+        ]
+        
+        for i, (user_id, info_json, timestamp) in enumerate(interview_records):
+            info = json.loads(info_json)
+            assert info["prompt"] == expected_prompts[i]
+            assert "interview_id" in info
+            # Check if response was recorded (it should be present after the interview)
+            assert "response" in info
+
+        conn.close()
+
+    finally:
+        # Cleanup
+        if os.path.exists(db_path):
+            os.remove(db_path)
+
+
+@pytest.mark.asyncio
+async def test_interview_data_retrieval(setup_interview_test):
+    """Test retrieving and analyzing interview data from the database."""
+    agents = []
+    channel = Channel()
+    infra = Platform(test_db_filepath, channel)
+    task = asyncio.create_task(infra.running())
+
+    try:
+        # Create and sign up test agents
+        for i in range(2):
+            real_name = f"TestAgent{i}"
+            description = f"Test agent {i}."
+            profile = {
+                "nodes": [],
+                "edges": [],
+                "other_info": {
+                    "user_profile": f"Agent {i} profile",
+                    "mbti": "INTJ",
+                    "activity_level": ["online"] * 24,
+                    "activity_level_frequency": [5] * 24,
+                    "active_threshold": [0.5] * 24,
+                },
+            }
+            user_info = UserInfo(name=real_name, description=description, profile=profile)
+            agent = SocialAgent(agent_id=i, user_info=user_info, twitter_channel=channel)
+            
+            return_message = await agent.env.action.sign_up(f"testuser{i}", f"TestUser{i}", f"Bio {i}.")
+            assert return_message["success"] is True
+            agents.append(agent)
+
+        # Conduct interviews
+        interview_data = [
+            (0, "What is your favorite programming language?"),
+            (1, "How do you approach problem-solving?"),
+            (0, "What are your career goals?"),  # Second interview with agent 0
+        ]
+        
+        for agent_id, prompt in interview_data:
+            return_message = await agents[agent_id].env.action.interview(prompt)
+            assert return_message["success"] is True
+
+        # Test data retrieval functions
+        conn = sqlite3.connect(test_db_filepath)
+        cursor = conn.cursor()
+        
+        # Test 1: Get all interviews
+        cursor.execute("""
+            SELECT user_id, info, created_at
+            FROM trace
+            WHERE action = ?
+            ORDER BY created_at
+        """, (ActionType.INTERVIEW.value,))
+        
+        all_interviews = cursor.fetchall()
+        assert len(all_interviews) == 3
+        
+        # Test 2: Get interviews for specific agent
+        cursor.execute("""
+            SELECT user_id, info, created_at
+            FROM trace
+            WHERE action = ? AND user_id = ?
+            ORDER BY created_at
+        """, (ActionType.INTERVIEW.value, 0))
+        
+        agent_0_interviews = cursor.fetchall()
+        assert len(agent_0_interviews) == 2
+        
+        # Test 3: Verify interview content
+        for i, (user_id, info_json, timestamp) in enumerate(all_interviews):
+            info = json.loads(info_json)
+            expected_agent_id, expected_prompt = interview_data[i]
+            assert user_id == expected_agent_id
+            assert info["prompt"] == expected_prompt
+            assert "interview_id" in info
+            
+        conn.close()
+
+    finally:
+        await channel.write_to_receive_queue((None, None, ActionType.EXIT))
+        await task
+
+
+@pytest.mark.asyncio
+async def test_interview_error_handling(setup_interview_test):
+    """Test error handling in interview functionality."""
+    agents = []
+    channel = Channel()
+    infra = Platform(test_db_filepath, channel)
+    task = asyncio.create_task(infra.running())
+
+    try:
+        # Create and sign up a test agent
+        real_name = "TestAgent"
+        description = "Test agent."
+        profile = {
+            "nodes": [],
+            "edges": [],
+            "other_info": {
+                "user_profile": "Test profile",
+                "mbti": "INTJ",
+                "activity_level": ["online"] * 24,
+                "activity_level_frequency": [5] * 24,
+                "active_threshold": [0.5] * 24,
+            },
+        }
+        user_info = UserInfo(name=real_name, description=description, profile=profile)
+        agent = SocialAgent(agent_id=0, user_info=user_info, twitter_channel=channel)
+        
+        return_message = await agent.env.action.sign_up("testuser", "TestUser", "Test bio.")
+        assert return_message["success"] is True
+        agents.append(agent)
+
+        # Test with empty prompt
+        return_message = await agent.env.action.interview("")
+        assert return_message["success"] is True  # Empty prompt should still work
+        
+        # Test with very long prompt
+        long_prompt = "What do you think about " + "very " * 1000 + "long questions?"
+        return_message = await agent.env.action.interview(long_prompt)
+        assert return_message["success"] is True
+        
+        # Verify both interviews were recorded
+        conn = sqlite3.connect(test_db_filepath)
+        cursor = conn.cursor()
+        cursor.execute("""
+            SELECT COUNT(*)
+            FROM trace
+            WHERE action = ? AND user_id = ?
+        """, (ActionType.INTERVIEW.value, 0))
+        
+        count = cursor.fetchone()[0]
+        assert count == 2
+        
+        conn.close()
+
+    finally:
+        await channel.write_to_receive_queue((None, None, ActionType.EXIT))
+        await task
+
+
+if __name__ == "__main__":
+    # Run tests individually for debugging
+    import asyncio
+    
+    async def run_tests():
+        print("Running interview action tests...")
+        
+        # You can run individual tests here for debugging
+        # await test_single_interview_action(None)
+        # await test_multiple_interviews_action(None)
+        # await test_interview_data_retrieval(None)
+        # await test_interview_error_handling(None)
+        
+        print("All tests completed!")
+    
+    # asyncio.run(run_tests()) 
\ No newline at end of file

From 44d25c718aaf16f1e43aca5bff7a783e9a7a51bd Mon Sep 17 00:00:00 2001
From: zhangzaibin <m18602438523@163.com>
Date: Fri, 23 May 2025 10:03:14 +0800
Subject: [PATCH 19/32] fix precommit

---
 docs/cookbooks/twitter_interview.mdx |  18 ++-
 examples/twitter_interview.py        |  35 +++---
 oasis/environment/env.py             |  56 +++++----
 oasis/social_agent/agent.py          |   8 +-
 oasis/social_agent/agent_action.py   |   4 +-
 oasis/social_platform/platform.py    |  43 +++----
 test/agent/test_interview_action.py  | 174 +++++++++++++++------------
 7 files changed, 185 insertions(+), 153 deletions(-)

diff --git a/docs/cookbooks/twitter_interview.mdx b/docs/cookbooks/twitter_interview.mdx
index e7420210..81b76c29 100644
--- a/docs/cookbooks/twitter_interview.mdx
+++ b/docs/cookbooks/twitter_interview.mdx
@@ -118,12 +118,12 @@ async def main():
     actions_3[env.agent_graph.get_agent(1)] = ManualAction(
         action_type=ActionType.CREATE_POST,
         action_args={"content": "Earth is not flat."})
-    
+
     # Create an interview action to ask Agent 0 about their views
     actions_3[env.agent_graph.get_agent(0)] = ManualAction(
         action_type=ActionType.INTERVIEW,
         action_args={"prompt": "What do you think about the shape of the Earth? Please explain your reasoning."})
-    
+
     await env.step(actions_3)
 
     # Fourth timestep: Let some other agents respond
@@ -138,11 +138,11 @@ async def main():
     actions_5[env.agent_graph.get_agent(1)] = ManualAction(
         action_type=ActionType.INTERVIEW,
         action_args={"prompt": "Why do you believe the Earth is not flat?"})
-    
+
     actions_5[env.agent_graph.get_agent(2)] = ManualAction(
         action_type=ActionType.INTERVIEW,
         action_args={"prompt": "What are your thoughts on the debate about Earth's shape?"})
-    
+
     await env.step(actions_5)
 
     # Sixth timestep: Final LLM actions for remaining agents
@@ -154,7 +154,7 @@ async def main():
 
     # Close the environment
     await env.close()
-    
+
     # visualize the interview results
     print("\n=== Interview Results ===")
     conn = sqlite3.connect(db_path)
@@ -167,7 +167,7 @@ async def main():
         FROM trace
         WHERE action = ?
     """, (ActionType.INTERVIEW.value,))
-    
+
     # This query retrieves all interview records from the trace table
     # - user_id: the ID of the agent who was interviewed
     # - info: JSON string containing interview details (prompt, response, etc.)
@@ -179,7 +179,7 @@ async def main():
         print(f"Prompt: {info.get('prompt', 'N/A')}")
         print(f"Interview ID: {info.get('interview_id', 'N/A')}")
         print(f"Response: {info.get('response', 'N/A')}")
-    
+
     conn.close()
 
 
@@ -324,7 +324,3 @@ Design effective interview questions:
 "Don't you think renewable energy is great?" # Leading
 "Yes or no: Do you like cats?" # Too restrictive
 ```
-
-
-
-
diff --git a/examples/twitter_interview.py b/examples/twitter_interview.py
index a34299a8..e3407f68 100644
--- a/examples/twitter_interview.py
+++ b/examples/twitter_interview.py
@@ -1,20 +1,20 @@
 # =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-# Licensed under the Apache License, Version 2.0 (the "License");
+# Licensed under the Apache License, Version 2.0 (the “License”);
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
+# distributed under the License is distributed on an “AS IS” BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
 import asyncio
+import json
 import os
 import sqlite3
-import json
 
 from camel.models import ModelFactory
 from camel.types import ModelPlatformType, ModelType
@@ -86,13 +86,16 @@ async def main():
     actions_3[env.agent_graph.get_agent(1)] = ManualAction(
         action_type=ActionType.CREATE_POST,
         action_args={"content": "Earth is not flat."})
-    
+
     # Create an interview action to ask Agent 0 about their views
     # ActionType.INTERVIEW is a external action, which can not be exceuted by agents themselves
     actions_3[env.agent_graph.get_agent(0)] = ManualAction(
         action_type=ActionType.INTERVIEW,
-        action_args={"prompt": "What do you think about the shape of the Earth? Please explain your reasoning."})
-    
+        action_args={
+            "prompt":
+            "What do you think about the shape of the Earth? Please explain your reasoning."
+        })
+
     await env.step(actions_3)
 
     # Fourth timestep: Let some other agents respond
@@ -107,11 +110,14 @@ async def main():
     actions_5[env.agent_graph.get_agent(1)] = ManualAction(
         action_type=ActionType.INTERVIEW,
         action_args={"prompt": "Why do you believe the Earth is not flat?"})
-    
+
     actions_5[env.agent_graph.get_agent(2)] = ManualAction(
         action_type=ActionType.INTERVIEW,
-        action_args={"prompt": "What are your thoughts on the debate about Earth's shape?"})
-    
+        action_args={
+            "prompt":
+            "What are your thoughts on the debate about Earth's shape?"
+        })
+
     await env.step(actions_5)
 
     # Sixth timestep: Final LLM actions for remaining agents
@@ -123,7 +129,7 @@ async def main():
 
     # Close the environment
     await env.close()
-    
+
     # visualize the interview results
     print("\n=== Interview Results ===")
     conn = sqlite3.connect(db_path)
@@ -131,12 +137,13 @@ async def main():
     # Here we query all interview records from the database
     # We use ActionType.INTERVIEW.value as the query condition to get all interview records
     # Each record contains user ID, interview information (in JSON format), and creation timestamp
-    cursor.execute("""
+    cursor.execute(
+        """
         SELECT user_id, info, created_at
         FROM trace
         WHERE action = ?
-    """, (ActionType.INTERVIEW.value,))
-    
+    """, (ActionType.INTERVIEW.value, ))
+
     # This query retrieves all interview records from the trace table
     # - user_id: the ID of the agent who was interviewed
     # - info: JSON string containing interview details (prompt, response, etc.)
@@ -148,7 +155,7 @@ async def main():
         print(f"Prompt: {info.get('prompt', 'N/A')}")
         print(f"Interview ID: {info.get('interview_id', 'N/A')}")
         print(f"Response: {info.get('response', 'N/A')}")
-    
+
     conn.close()
 
 
diff --git a/oasis/environment/env.py b/oasis/environment/env.py
index 7e42fd08..21439b6c 100644
--- a/oasis/environment/env.py
+++ b/oasis/environment/env.py
@@ -1,12 +1,12 @@
 # =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-# Licensed under the Apache License, Version 2.0 (the "License");
+# Licensed under the Apache License, Version 2.0 (the “License”);
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
+# distributed under the License is distributed on an “AS IS” BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
@@ -160,7 +160,9 @@ async def step(
                     if isinstance(single_action, ManualAction):
                         if single_action.action_type == ActionType.INTERVIEW:
                             # Special handling for interview actions
-                            tasks.append(self._perform_manual_interview_action(agent, single_action))
+                            tasks.append(
+                                self._perform_manual_interview_action(
+                                    agent, single_action))
                         else:
                             tasks.append(
                                 agent.perform_action_by_data(
@@ -172,11 +174,13 @@ async def step(
                 if isinstance(action, ManualAction):
                     if action.action_type == ActionType.INTERVIEW:
                         # Special handling for interview actions
-                        tasks.append(self._perform_manual_interview_action(agent, action))
+                        tasks.append(
+                            self._perform_manual_interview_action(
+                                agent, action))
                     else:
                         tasks.append(
-                            agent.perform_action_by_data(action.action_type,
-                                                         **action.action_args))
+                            agent.perform_action_by_data(
+                                action.action_type, **action.action_args))
                 elif isinstance(action, LLMAction):
                     tasks.append(self._perform_llm_action(agent))
 
@@ -188,7 +192,8 @@ async def step(
         if self.platform_type == DefaultPlatformType.TWITTER:
             self.platform.sandbox_clock.time_step += 1
 
-    async def _perform_manual_interview_action(self, agent: SocialAgent, action: ManualAction) -> None:
+    async def _perform_manual_interview_action(self, agent: SocialAgent,
+                                               action: ManualAction) -> None:
         r"""Perform a manual interview action.
 
         Args:
@@ -198,41 +203,48 @@ async def _perform_manual_interview_action(self, agent: SocialAgent, action: Man
         # Extract interview prompt from args
         interview_prompt = action.action_args.get("prompt", "")
         if not interview_prompt:
-            env_log.warning(f"Empty interview prompt for agent {agent.social_agent_id}")
+            env_log.warning(
+                f"Empty interview prompt for agent {agent.social_agent_id}")
             return
-        
+
         # First, perform the regular action by sending it to the platform
-        action_result = await agent.perform_action_by_data(action.action_type, **action.action_args)
-        
+        action_result = await agent.perform_action_by_data(
+            action.action_type, **action.action_args)
+
         # Check if the platform action was successful
         if not action_result.get("success", False):
-            env_log.warning(f"Failed to record interview request: {action_result.get('error', 'Unknown error')}")
+            env_log.warning(
+                f"Failed to record interview request: {action_result.get('error', 'Unknown error')}"
+            )
             return
-        
+
         # Get the interview_id from the result
         interview_id = action_result.get("interview_id")
         if not interview_id:
-            env_log.warning(f"No interview_id returned from platform")
+            env_log.warning("No interview_id returned from platform")
             return
-        
+
         # Perform the interview to get the actual response
         result = await self._perform_interview_action(agent, interview_prompt)
-        
+
         # Get the response content
         response = result.get("content", "")
-        
+
         try:
             # Record the response using the platform's method
             response_result = await self.platform.record_interview_response(
                 agent_id=agent.social_agent_id,
                 interview_id=interview_id,
-                response=response
-            )
-            
+                response=response)
+
             if response_result.get("success", False):
-                env_log.info(f"Successfully recorded interview response for agent {agent.social_agent_id}")
+                env_log.info(
+                    f"Successfully recorded interview response for agent {agent.social_agent_id}"
+                )
             else:
-                env_log.warning(f"Failed to record interview response: {response_result.get('error', 'Unknown error')}")
+                env_log.warning(
+                    f"Failed to record interview response: {response_result.get('error', 'Unknown error')}"
+                )
         except Exception as e:
             env_log.error(f"Error recording interview response: {str(e)}")
 
diff --git a/oasis/social_agent/agent.py b/oasis/social_agent/agent.py
index 9750108b..f0336af5 100644
--- a/oasis/social_agent/agent.py
+++ b/oasis/social_agent/agent.py
@@ -190,19 +190,18 @@ async def perform_test(self):
             "content": content
         }
 
-
     async def perform_interview(self, interview_prompt: str):
         """
         Perform an interview with the agent.
-        
+
         This method allows an external entity to ask a specific question to the agent
         and get a response based on the agent's persona and memory context. The interview
         doesn't affect the agent's memory and is used primarily for evaluation or
         extracting the agent's opinions on specific topics.
-        
+
         Args:
             interview_prompt (str): The question or prompt to ask the agent during the interview.
-            
+
         Returns:
             dict: A dictionary containing the agent's user_id, the full prompt context,
                  and the agent's response content.
@@ -241,7 +240,6 @@ async def perform_interview(self, interview_prompt: str):
             "content": content
         }
 
-
     async def perform_action_by_hci(self) -> Any:
         print("Please choose one function to perform:")
         function_list = self.env.action.get_openai_function_list()
diff --git a/oasis/social_agent/agent_action.py b/oasis/social_agent/agent_action.py
index 66d2a40d..a5476c31 100644
--- a/oasis/social_agent/agent_action.py
+++ b/oasis/social_agent/agent_action.py
@@ -1,12 +1,12 @@
 # =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-# Licensed under the Apache License, Version 2.0 (the "License");
+# Licensed under the Apache License, Version 2.0 (the “License”);
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
+# distributed under the License is distributed on an “AS IS” BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
diff --git a/oasis/social_platform/platform.py b/oasis/social_platform/platform.py
index e9de8cd9..97c6c4be 100644
--- a/oasis/social_platform/platform.py
+++ b/oasis/social_platform/platform.py
@@ -1,12 +1,12 @@
 # =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-# Licensed under the Apache License, Version 2.0 (the "License");
+# Licensed under the Apache License, Version 2.0 (the “License”);
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
+# distributed under the License is distributed on an “AS IS” BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
@@ -1363,21 +1363,16 @@ async def interview(self, agent_id: int, prompt: str):
             interview_id = f"{current_time}_{user_id}"
 
             # Record the interview request in the trace table
-            action_info = {
-                "prompt": prompt,
-                "interview_id": interview_id
-            }
+            action_info = {"prompt": prompt, "interview_id": interview_id}
             self.pl_utils._record_trace(user_id, ActionType.INTERVIEW.value,
                                         action_info, current_time)
-            
-            return {
-                "success": True, 
-                "interview_id": interview_id
-            }
+
+            return {"success": True, "interview_id": interview_id}
         except Exception as e:
             return {"success": False, "error": str(e)}
-            
-    async def record_interview_response(self, agent_id: int, interview_id: str, response: str):
+
+    async def record_interview_response(self, agent_id: int, interview_id: str,
+                                        response: str):
         """Record the response for an interview.
 
         Args:
@@ -1391,32 +1386,30 @@ async def record_interview_response(self, agent_id: int, interview_id: str, resp
         """
         try:
             # First, fetch the trace record for this interview
-            query = """SELECT rowid, info FROM trace 
-                       WHERE user_id = ? AND action = ? 
+            query = """SELECT rowid, info FROM trace
+                       WHERE user_id = ? AND action = ?
                        ORDER BY created_at DESC LIMIT 1"""
-            self.pl_utils._execute_db_command(query, (agent_id, ActionType.INTERVIEW.value))
+            self.pl_utils._execute_db_command(
+                query, (agent_id, ActionType.INTERVIEW.value))
             last_interview_data = self.db_cursor.fetchone()
-            
+
             if last_interview_data:
                 rowid, info_json = last_interview_data
                 import json
                 info = json.loads(info_json)
-                
+
                 # Verify this is the correct interview record
                 if info.get("interview_id") == interview_id:
                     # Update with the response
                     info["response"] = response
-                    
+
                     # Update the trace record
                     update_query = "UPDATE trace SET info = ? WHERE rowid = ?"
                     self.pl_utils._execute_db_command(
-                        update_query, 
-                        (json.dumps(info), rowid),
-                        commit=True
-                    )
-                    
+                        update_query, (json.dumps(info), rowid), commit=True)
+
                     return {"success": True}
-                
+
             return {"success": False, "error": "Interview record not found"}
         except Exception as e:
             return {"success": False, "error": str(e)}
diff --git a/test/agent/test_interview_action.py b/test/agent/test_interview_action.py
index f9eacf14..4ddbefbe 100644
--- a/test/agent/test_interview_action.py
+++ b/test/agent/test_interview_action.py
@@ -1,12 +1,12 @@
 # =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-# Licensed under the Apache License, Version 2.0 (the "License");
+# Licensed under the Apache License, Version 2.0 (the “License”);
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
+# distributed under the License is distributed on an “AS IS” BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
@@ -23,7 +23,8 @@
 from camel.types import ModelPlatformType, ModelType
 
 import oasis
-from oasis import ActionType, LLMAction, ManualAction, generate_twitter_agent_graph
+from oasis import (ActionType, ManualAction,
+                   generate_twitter_agent_graph)
 from oasis.social_agent.agent import SocialAgent
 from oasis.social_platform.channel import Channel
 from oasis.social_platform.config import UserInfo
@@ -60,18 +61,24 @@ async def test_single_interview_action(setup_interview_test):
             "nodes": [],
             "edges": [],
             "other_info": {
-                "user_profile": "I am a test agent with strong opinions on technology.",
+                "user_profile":
+                "I am a test agent with strong opinions on technology.",
                 "mbti": "INTJ",
                 "activity_level": ["online"] * 24,
                 "activity_level_frequency": [5] * 24,
                 "active_threshold": [0.5] * 24,
             },
         }
-        user_info = UserInfo(name=real_name, description=description, profile=profile)
-        agent = SocialAgent(agent_id=0, user_info=user_info, twitter_channel=channel)
-        
+        user_info = UserInfo(name=real_name,
+                             description=description,
+                             profile=profile)
+        agent = SocialAgent(agent_id=0,
+                            user_info=user_info,
+                            twitter_channel=channel)
+
         # Sign up the agent
-        return_message = await agent.env.action.sign_up("testuser", "TestUser", "A test bio.")
+        return_message = await agent.env.action.sign_up(
+            "testuser", "TestUser", "A test bio.")
         assert return_message["success"] is True
         agents.append(agent)
 
@@ -80,27 +87,28 @@ async def test_single_interview_action(setup_interview_test):
         return_message = await agent.env.action.interview(interview_prompt)
         assert return_message["success"] is True
         assert "interview_id" in return_message
-        
+
         # Verify the interview was recorded in the database
         conn = sqlite3.connect(test_db_filepath)
         cursor = conn.cursor()
-        cursor.execute("""
+        cursor.execute(
+            """
             SELECT user_id, info, action
             FROM trace
             WHERE action = ? AND user_id = ?
         """, (ActionType.INTERVIEW.value, 0))
-        
+
         interview_records = cursor.fetchall()
         assert len(interview_records) == 1
-        
+
         user_id, info_json, action = interview_records[0]
         assert user_id == 0
         assert action == ActionType.INTERVIEW.value
-        
+
         info = json.loads(info_json)
         assert info["prompt"] == interview_prompt
         assert "interview_id" in info
-        
+
         conn.close()
 
     finally:
@@ -125,18 +133,24 @@ async def test_multiple_interviews_action(setup_interview_test):
                 "nodes": [],
                 "edges": [],
                 "other_info": {
-                    "user_profile": f"I am test agent {i} with unique perspectives.",
+                    "user_profile":
+                    f"I am test agent {i} with unique perspectives.",
                     "mbti": "INTJ",
                     "activity_level": ["online"] * 24,
                     "activity_level_frequency": [5] * 24,
                     "active_threshold": [0.5] * 24,
                 },
             }
-            user_info = UserInfo(name=real_name, description=description, profile=profile)
-            agent = SocialAgent(agent_id=i, user_info=user_info, twitter_channel=channel)
-            
+            user_info = UserInfo(name=real_name,
+                                 description=description,
+                                 profile=profile)
+            agent = SocialAgent(agent_id=i,
+                                user_info=user_info,
+                                twitter_channel=channel)
+
             # Sign up the agent
-            return_message = await agent.env.action.sign_up(f"testuser{i}", f"TestUser{i}", f"Test bio {i}.")
+            return_message = await agent.env.action.sign_up(
+                f"testuser{i}", f"TestUser{i}", f"Test bio {i}.")
             assert return_message["success"] is True
             agents.append(agent)
 
@@ -146,7 +160,7 @@ async def test_multiple_interviews_action(setup_interview_test):
             "How do you feel about social media?",
             "What are your thoughts on remote work?"
         ]
-        
+
         for i, (agent, prompt) in enumerate(zip(agents, interview_prompts)):
             return_message = await agent.env.action.interview(prompt)
             assert return_message["success"] is True
@@ -155,24 +169,25 @@ async def test_multiple_interviews_action(setup_interview_test):
         # Verify all interviews were recorded
         conn = sqlite3.connect(test_db_filepath)
         cursor = conn.cursor()
-        cursor.execute("""
+        cursor.execute(
+            """
             SELECT user_id, info, action
             FROM trace
             WHERE action = ?
             ORDER BY user_id
-        """, (ActionType.INTERVIEW.value,))
-        
+        """, (ActionType.INTERVIEW.value, ))
+
         interview_records = cursor.fetchall()
         assert len(interview_records) == 3
-        
+
         for i, (user_id, info_json, action) in enumerate(interview_records):
             assert user_id == i
             assert action == ActionType.INTERVIEW.value
-            
+
             info = json.loads(info_json)
             assert info["prompt"] == interview_prompts[i]
             assert "interview_id" in info
-        
+
         conn.close()
 
     finally:
@@ -206,7 +221,7 @@ async def test_interview_with_environment():
         # Create a minimal agent graph for testing
         agent_graph = await generate_twitter_agent_graph(
             profile_path=("data/twitter_dataset/anonymous_topic_200_1h/"
-                         "False_Business_0.csv"),
+                          "False_Business_0.csv"),
             model=openai_model,
             available_actions=available_actions,
         )
@@ -224,32 +239,27 @@ async def test_interview_with_environment():
         actions = {}
         actions[env.agent_graph.get_agent(0)] = ManualAction(
             action_type=ActionType.INTERVIEW,
-            action_args={"prompt": "What is your favorite color and why?"}
-        )
+            action_args={"prompt": "What is your favorite color and why?"})
         await env.step(actions)
 
         # Test multiple interviews in one step
         actions = {}
         actions[env.agent_graph.get_agent(1)] = ManualAction(
             action_type=ActionType.INTERVIEW,
-            action_args={"prompt": "What do you think about technology?"}
-        )
+            action_args={"prompt": "What do you think about technology?"})
         actions[env.agent_graph.get_agent(2)] = ManualAction(
             action_type=ActionType.INTERVIEW,
-            action_args={"prompt": "How do you spend your free time?"}
-        )
+            action_args={"prompt": "How do you spend your free time?"})
         await env.step(actions)
 
         # Test mixing interview with other actions
         actions = {}
         actions[env.agent_graph.get_agent(0)] = ManualAction(
             action_type=ActionType.CREATE_POST,
-            action_args={"content": "Hello world!"}
-        )
+            action_args={"content": "Hello world!"})
         actions[env.agent_graph.get_agent(3)] = ManualAction(
             action_type=ActionType.INTERVIEW,
-            action_args={"prompt": "What motivates you in life?"}
-        )
+            action_args={"prompt": "What motivates you in life?"})
         await env.step(actions)
 
         await env.close()
@@ -257,13 +267,14 @@ async def test_interview_with_environment():
         # Verify interview data in database
         conn = sqlite3.connect(db_path)
         cursor = conn.cursor()
-        cursor.execute("""
+        cursor.execute(
+            """
             SELECT user_id, info, created_at
             FROM trace
             WHERE action = ?
             ORDER BY created_at
-        """, (ActionType.INTERVIEW.value,))
-        
+        """, (ActionType.INTERVIEW.value, ))
+
         interview_records = cursor.fetchall()
         assert len(interview_records) == 4  # 3 interviews conducted
 
@@ -271,10 +282,9 @@ async def test_interview_with_environment():
         expected_prompts = [
             "What is your favorite color and why?",
             "What do you think about technology?",
-            "How do you spend your free time?",
-            "What motivates you in life?"
+            "How do you spend your free time?", "What motivates you in life?"
         ]
-        
+
         for i, (user_id, info_json, timestamp) in enumerate(interview_records):
             info = json.loads(info_json)
             assert info["prompt"] == expected_prompts[i]
@@ -314,10 +324,15 @@ async def test_interview_data_retrieval(setup_interview_test):
                     "active_threshold": [0.5] * 24,
                 },
             }
-            user_info = UserInfo(name=real_name, description=description, profile=profile)
-            agent = SocialAgent(agent_id=i, user_info=user_info, twitter_channel=channel)
-            
-            return_message = await agent.env.action.sign_up(f"testuser{i}", f"TestUser{i}", f"Bio {i}.")
+            user_info = UserInfo(name=real_name,
+                                 description=description,
+                                 profile=profile)
+            agent = SocialAgent(agent_id=i,
+                                user_info=user_info,
+                                twitter_channel=channel)
+
+            return_message = await agent.env.action.sign_up(
+                f"testuser{i}", f"TestUser{i}", f"Bio {i}.")
             assert return_message["success"] is True
             agents.append(agent)
 
@@ -325,39 +340,43 @@ async def test_interview_data_retrieval(setup_interview_test):
         interview_data = [
             (0, "What is your favorite programming language?"),
             (1, "How do you approach problem-solving?"),
-            (0, "What are your career goals?"),  # Second interview with agent 0
+            (0,
+             "What are your career goals?"),  # Second interview with agent 0
         ]
-        
+
         for agent_id, prompt in interview_data:
-            return_message = await agents[agent_id].env.action.interview(prompt)
+            return_message = await agents[agent_id].env.action.interview(prompt
+                                                                         )
             assert return_message["success"] is True
 
         # Test data retrieval functions
         conn = sqlite3.connect(test_db_filepath)
         cursor = conn.cursor()
-        
+
         # Test 1: Get all interviews
-        cursor.execute("""
+        cursor.execute(
+            """
             SELECT user_id, info, created_at
             FROM trace
             WHERE action = ?
             ORDER BY created_at
-        """, (ActionType.INTERVIEW.value,))
-        
+        """, (ActionType.INTERVIEW.value, ))
+
         all_interviews = cursor.fetchall()
         assert len(all_interviews) == 3
-        
+
         # Test 2: Get interviews for specific agent
-        cursor.execute("""
+        cursor.execute(
+            """
             SELECT user_id, info, created_at
             FROM trace
             WHERE action = ? AND user_id = ?
             ORDER BY created_at
         """, (ActionType.INTERVIEW.value, 0))
-        
+
         agent_0_interviews = cursor.fetchall()
         assert len(agent_0_interviews) == 2
-        
+
         # Test 3: Verify interview content
         for i, (user_id, info_json, timestamp) in enumerate(all_interviews):
             info = json.loads(info_json)
@@ -365,7 +384,7 @@ async def test_interview_data_retrieval(setup_interview_test):
             assert user_id == expected_agent_id
             assert info["prompt"] == expected_prompt
             assert "interview_id" in info
-            
+
         conn.close()
 
     finally:
@@ -396,34 +415,41 @@ async def test_interview_error_handling(setup_interview_test):
                 "active_threshold": [0.5] * 24,
             },
         }
-        user_info = UserInfo(name=real_name, description=description, profile=profile)
-        agent = SocialAgent(agent_id=0, user_info=user_info, twitter_channel=channel)
-        
-        return_message = await agent.env.action.sign_up("testuser", "TestUser", "Test bio.")
+        user_info = UserInfo(name=real_name,
+                             description=description,
+                             profile=profile)
+        agent = SocialAgent(agent_id=0,
+                            user_info=user_info,
+                            twitter_channel=channel)
+
+        return_message = await agent.env.action.sign_up(
+            "testuser", "TestUser", "Test bio.")
         assert return_message["success"] is True
         agents.append(agent)
 
         # Test with empty prompt
         return_message = await agent.env.action.interview("")
-        assert return_message["success"] is True  # Empty prompt should still work
-        
+        assert return_message[
+            "success"] is True  # Empty prompt should still work
+
         # Test with very long prompt
         long_prompt = "What do you think about " + "very " * 1000 + "long questions?"
         return_message = await agent.env.action.interview(long_prompt)
         assert return_message["success"] is True
-        
+
         # Verify both interviews were recorded
         conn = sqlite3.connect(test_db_filepath)
         cursor = conn.cursor()
-        cursor.execute("""
+        cursor.execute(
+            """
             SELECT COUNT(*)
             FROM trace
             WHERE action = ? AND user_id = ?
         """, (ActionType.INTERVIEW.value, 0))
-        
+
         count = cursor.fetchone()[0]
         assert count == 2
-        
+
         conn.close()
 
     finally:
@@ -434,16 +460,16 @@ async def test_interview_error_handling(setup_interview_test):
 if __name__ == "__main__":
     # Run tests individually for debugging
     import asyncio
-    
+
     async def run_tests():
         print("Running interview action tests...")
-        
+
         # You can run individual tests here for debugging
         # await test_single_interview_action(None)
         # await test_multiple_interviews_action(None)
         # await test_interview_data_retrieval(None)
         # await test_interview_error_handling(None)
-        
+
         print("All tests completed!")
-    
-    # asyncio.run(run_tests()) 
\ No newline at end of file
+
+    # asyncio.run(run_tests())

From b0fc96c5b9e59fa6d85a6141e39f7234942639e3 Mon Sep 17 00:00:00 2001
From: zhangzaibin <m18602438523@163.com>
Date: Fri, 23 May 2025 10:19:00 +0800
Subject: [PATCH 20/32] fix precommit

---
 test/agent/test_interview_action.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/test/agent/test_interview_action.py b/test/agent/test_interview_action.py
index 4ddbefbe..7facce4d 100644
--- a/test/agent/test_interview_action.py
+++ b/test/agent/test_interview_action.py
@@ -23,8 +23,7 @@
 from camel.types import ModelPlatformType, ModelType
 
 import oasis
-from oasis import (ActionType, ManualAction,
-                   generate_twitter_agent_graph)
+from oasis import ActionType, ManualAction, generate_twitter_agent_graph
 from oasis.social_agent.agent import SocialAgent
 from oasis.social_platform.channel import Channel
 from oasis.social_platform.config import UserInfo

From 6a8d1841065c76b647eb738f5dba5364426a9ffc Mon Sep 17 00:00:00 2001
From: zhangzaibin <m18602438523@163.com>
Date: Fri, 23 May 2025 10:46:03 +0800
Subject: [PATCH 21/32] fix format

---
 examples/twitter_interview.py       | 15 +++++++++------
 oasis/environment/env.py            | 12 +++++-------
 oasis/social_agent/agent.py         | 12 ------------
 oasis/social_agent/agent_action.py  |  7 ++++---
 oasis/social_platform/platform.py   |  2 +-
 test/agent/test_interview_action.py |  7 +------
 6 files changed, 20 insertions(+), 35 deletions(-)

diff --git a/examples/twitter_interview.py b/examples/twitter_interview.py
index e3407f68..d2a3c8c2 100644
--- a/examples/twitter_interview.py
+++ b/examples/twitter_interview.py
@@ -31,7 +31,8 @@ async def main():
     )
 
     # Define the available actions for the agents
-    # Note: INTERVIEW is NOT included here to prevent LLM from automatically selecting it
+    # Note: INTERVIEW is NOT included here to
+    # prevent LLM from automatically selecting it
     # INTERVIEW can still be used manually via ManualAction
     available_actions = [
         ActionType.CREATE_POST,
@@ -88,12 +89,12 @@ async def main():
         action_args={"content": "Earth is not flat."})
 
     # Create an interview action to ask Agent 0 about their views
-    # ActionType.INTERVIEW is a external action, which can not be exceuted by agents themselves
+    # ActionType.INTERVIEW is a external action,
+    # which can not be exceuted by agents themselves
     actions_3[env.agent_graph.get_agent(0)] = ManualAction(
         action_type=ActionType.INTERVIEW,
         action_args={
-            "prompt":
-            "What do you think about the shape of the Earth? Please explain your reasoning."
+            "prompt": "What do you think about the shape of the Earth?"
         })
 
     await env.step(actions_3)
@@ -135,8 +136,10 @@ async def main():
     conn = sqlite3.connect(db_path)
     cursor = conn.cursor()
     # Here we query all interview records from the database
-    # We use ActionType.INTERVIEW.value as the query condition to get all interview records
-    # Each record contains user ID, interview information (in JSON format), and creation timestamp
+    # We use ActionType.INTERVIEW.value as the query condition
+    # to get all interview records
+    # Each record contains user ID, interview information
+    # (in JSON format), and creation timestamp
     cursor.execute(
         """
         SELECT user_id, info, created_at
diff --git a/oasis/environment/env.py b/oasis/environment/env.py
index 21439b6c..6adb375c 100644
--- a/oasis/environment/env.py
+++ b/oasis/environment/env.py
@@ -213,9 +213,8 @@ async def _perform_manual_interview_action(self, agent: SocialAgent,
 
         # Check if the platform action was successful
         if not action_result.get("success", False):
-            env_log.warning(
-                f"Failed to record interview request: {action_result.get('error', 'Unknown error')}"
-            )
+            error = action_result.get('error', 'Unknown error')
+            env_log.warning(f"Failed to record interview request: {error}")
             return
 
         # Get the interview_id from the result
@@ -239,12 +238,11 @@ async def _perform_manual_interview_action(self, agent: SocialAgent,
 
             if response_result.get("success", False):
                 env_log.info(
-                    f"Successfully recorded interview response for agent {agent.social_agent_id}"
-                )
+                    f"Recorded response for agent {agent.social_agent_id}")
             else:
+                error_msg = response_result.get('error', 'Unknown error')
                 env_log.warning(
-                    f"Failed to record interview response: {response_result.get('error', 'Unknown error')}"
-                )
+                    f"Failed to record interview response: {error_msg}")
         except Exception as e:
             env_log.error(f"Error recording interview response: {str(e)}")
 
diff --git a/oasis/social_agent/agent.py b/oasis/social_agent/agent.py
index f0336af5..b49cfe49 100644
--- a/oasis/social_agent/agent.py
+++ b/oasis/social_agent/agent.py
@@ -193,18 +193,6 @@ async def perform_test(self):
     async def perform_interview(self, interview_prompt: str):
         """
         Perform an interview with the agent.
-
-        This method allows an external entity to ask a specific question to the agent
-        and get a response based on the agent's persona and memory context. The interview
-        doesn't affect the agent's memory and is used primarily for evaluation or
-        extracting the agent's opinions on specific topics.
-
-        Args:
-            interview_prompt (str): The question or prompt to ask the agent during the interview.
-
-        Returns:
-            dict: A dictionary containing the agent's user_id, the full prompt context,
-                 and the agent's response content.
         """
         # user conduct test to agent
         _ = BaseMessage.make_user_message(role_name="User",
diff --git a/oasis/social_agent/agent_action.py b/oasis/social_agent/agent_action.py
index a5476c31..528388e3 100644
--- a/oasis/social_agent/agent_action.py
+++ b/oasis/social_agent/agent_action.py
@@ -648,14 +648,15 @@ async def interview(self, prompt: str):
         r"""Interview an agent with the given prompt.
 
         This method invokes an asynchronous action to interview an agent with a
-        specific prompt question. Upon successful execution, it returns a dictionary
-        containing a success status and an interview_id for tracking.
+        specific prompt question. Upon successful execution,
+        it returns a dictionary containing a success status
+        and an interview_id for tracking.
 
         Args:
             prompt (str): The interview question or prompt to ask the agent.
 
         Returns:
-            dict: A dictionary containing success status and interview tracking information.
+            dict: A dictionary containing success status and an interview_id.
 
             Example of a successful return:
             {
diff --git a/oasis/social_platform/platform.py b/oasis/social_platform/platform.py
index 97c6c4be..676be553 100644
--- a/oasis/social_platform/platform.py
+++ b/oasis/social_platform/platform.py
@@ -1349,7 +1349,7 @@ async def interview(self, agent_id: int, prompt: str):
             prompt (str): The interview question or prompt.
 
         Returns:
-            dict: A dictionary with success status and an interview_id for tracking.
+            dict: A dictionary with success status and an interview_id.
         """
         if self.recsys_type == RecsysType.REDDIT:
             current_time = self.sandbox_clock.time_transfer(
diff --git a/test/agent/test_interview_action.py b/test/agent/test_interview_action.py
index 7facce4d..b8d11b45 100644
--- a/test/agent/test_interview_action.py
+++ b/test/agent/test_interview_action.py
@@ -202,13 +202,11 @@ async def test_interview_with_environment():
         db_path = tmp_file.name
 
     try:
-        # Create a mock model (we'll use a simple mock since we're testing the action flow)
         openai_model = ModelFactory.create(
             model_platform=ModelPlatformType.OPENAI,
             model_type=ModelType.GPT_4O_MINI,
         )
 
-        # Define available actions (excluding INTERVIEW to prevent automatic selection)
         available_actions = [
             ActionType.CREATE_POST,
             ActionType.LIKE_POST,
@@ -288,7 +286,6 @@ async def test_interview_with_environment():
             info = json.loads(info_json)
             assert info["prompt"] == expected_prompts[i]
             assert "interview_id" in info
-            # Check if response was recorded (it should be present after the interview)
             assert "response" in info
 
         conn.close()
@@ -432,7 +429,7 @@ async def test_interview_error_handling(setup_interview_test):
             "success"] is True  # Empty prompt should still work
 
         # Test with very long prompt
-        long_prompt = "What do you think about " + "very " * 1000 + "long questions?"
+        long_prompt = "What do you think about your state."
         return_message = await agent.env.action.interview(long_prompt)
         assert return_message["success"] is True
 
@@ -458,8 +455,6 @@ async def test_interview_error_handling(setup_interview_test):
 
 if __name__ == "__main__":
     # Run tests individually for debugging
-    import asyncio
-
     async def run_tests():
         print("Running interview action tests...")
 

From 14b0eb9727a8b0ccd1aec0c891cd2166dc83d74e Mon Sep 17 00:00:00 2001
From: liuxiaosu <lxsgocode123@gmail.com>
Date: Sun, 25 May 2025 18:50:26 +0800
Subject: [PATCH 22/32] issue #101 change twitter_channel to channel

---
 oasis/social_agent/agent.py            |  6 +++---
 oasis/social_agent/agents_generator.py | 18 +++++++++---------
 2 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/oasis/social_agent/agent.py b/oasis/social_agent/agent.py
index f9fb8830..d58bcb64 100644
--- a/oasis/social_agent/agent.py
+++ b/oasis/social_agent/agent.py
@@ -59,7 +59,7 @@ def __init__(self,
                  agent_id: int,
                  user_info: UserInfo,
                  user_info_template: TextPrompt | None = None,
-                 twitter_channel: Channel | None = None,
+                 channel: Channel | None = None,
                  model: Optional[Union[BaseModelBackend,
                                        List[BaseModelBackend]]] = None,
                  agent_graph: "AgentGraph" = None,
@@ -68,9 +68,9 @@ def __init__(self,
                  single_iteration: bool = True):
         self.social_agent_id = agent_id
         self.user_info = user_info
-        self.twitter_channel = twitter_channel or Channel()
+        self.channel = channel or Channel()
         self.env = SocialEnvironment(
-            SocialAction(agent_id, self.twitter_channel))
+            SocialAction(agent_id, self.channel))
         if user_info_template is None:
             system_message_content = self.user_info.to_system_message()
         else:
diff --git a/oasis/social_agent/agents_generator.py b/oasis/social_agent/agents_generator.py
index 51a591b1..39191c0d 100644
--- a/oasis/social_agent/agents_generator.py
+++ b/oasis/social_agent/agents_generator.py
@@ -33,7 +33,7 @@
 
 async def generate_agents(
     agent_info_path: str,
-    twitter_channel: Channel,
+    channel: Channel,
     model: Union[BaseModelBackend, List[BaseModelBackend]],
     start_time,
     recsys_type: str = "twitter",
@@ -93,7 +93,7 @@ class instances.
         agent = SocialAgent(
             agent_id=agent_id,
             user_info=user_info,
-            twitter_channel=twitter_channel,
+            channel=channel,
             model=model,
             agent_graph=agent_graph,
             available_actions=available_actions,
@@ -178,7 +178,7 @@ class instances.
 
 async def generate_agents_100w(
     agent_info_path: str,
-    twitter_channel: Channel,
+    channel: Channel,
     start_time,
     model: Union[BaseModelBackend, List[BaseModelBackend]],
     recsys_type: str = "twitter",
@@ -248,7 +248,7 @@ class instances.
         agent = SocialAgent(
             agent_id=agent_id,
             user_info=user_info,
-            twitter_channel=twitter_channel,
+            channel=channel,
             model=model,
             agent_graph=agent_graph,
             available_actions=available_actions,
@@ -362,7 +362,7 @@ async def generate_controllable_agents(
         # controllable的agent_id全都在llm agent的agent_id的前面
         agent = SocialAgent(agent_id=i,
                             user_info=user_info,
-                            twitter_channel=channel,
+                            channel=channel,
                             agent_graph=agent_graph)
         # Add agent to the agent graph
         agent_graph.add_agent(agent)
@@ -411,7 +411,7 @@ async def gen_control_agents_with_data(
         agent = SocialAgent(
             agent_id=i,
             user_info=user_info,
-            twitter_channel=channel,
+            channel=channel,
             agent_graph=agent_graph,
             model=models,
             available_actions=None,
@@ -430,7 +430,7 @@ async def gen_control_agents_with_data(
 
 async def generate_reddit_agents(
     agent_info_path: str,
-    twitter_channel: Channel,
+    channel: Channel,
     agent_graph: AgentGraph | None = None,
     agent_user_id_mapping: dict[int, int] | None = None,
     follow_post_agent: bool = False,
@@ -472,7 +472,7 @@ async def process_agent(i):
         agent = SocialAgent(
             agent_id=i + control_user_num,
             user_info=user_info,
-            twitter_channel=twitter_channel,
+            channel=channel,
             agent_graph=agent_graph,
             model=model,
             available_actions=available_actions,
@@ -538,7 +538,7 @@ def connect_platform_channel(
     agent_graph: AgentGraph | None = None,
 ) -> AgentGraph:
     for _, agent in agent_graph.get_agents():
-        agent.twitter_channel = channel
+        agent.channel = channel
         agent.env.action.channel = channel
     return agent_graph
 

From c350b3adc42c31c1cba001d1d2755133f07c53d6 Mon Sep 17 00:00:00 2001
From: zhangzaibin <m18602438523@163.com>
Date: Fri, 30 May 2025 14:21:04 +0800
Subject: [PATCH 23/32] refactor interview

---
 oasis/environment/env.py          | 69 ++++-------------------------
 oasis/social_agent/agent.py       | 11 ++++-
 oasis/social_platform/platform.py | 73 ++++++++++---------------------
 3 files changed, 41 insertions(+), 112 deletions(-)

diff --git a/oasis/environment/env.py b/oasis/environment/env.py
index 6adb375c..5d30c08f 100644
--- a/oasis/environment/env.py
+++ b/oasis/environment/env.py
@@ -159,10 +159,12 @@ async def step(
                 for single_action in action:
                     if isinstance(single_action, ManualAction):
                         if single_action.action_type == ActionType.INTERVIEW:
-                            # Special handling for interview actions
+                            # Use the agent's perform_interview method for interview actions
+                            interview_prompt = single_action.action_args.get(
+                                "prompt", "")
                             tasks.append(
-                                self._perform_manual_interview_action(
-                                    agent, single_action))
+                                self._perform_interview_action(
+                                    agent, interview_prompt))
                         else:
                             tasks.append(
                                 agent.perform_action_by_data(
@@ -173,10 +175,11 @@ async def step(
             else:
                 if isinstance(action, ManualAction):
                     if action.action_type == ActionType.INTERVIEW:
-                        # Special handling for interview actions
+                        # Use the agent's perform_interview method for interview actions
+                        interview_prompt = action.action_args.get("prompt", "")
                         tasks.append(
-                            self._perform_manual_interview_action(
-                                agent, action))
+                            self._perform_interview_action(
+                                agent, interview_prompt))
                     else:
                         tasks.append(
                             agent.perform_action_by_data(
@@ -192,60 +195,6 @@ async def step(
         if self.platform_type == DefaultPlatformType.TWITTER:
             self.platform.sandbox_clock.time_step += 1
 
-    async def _perform_manual_interview_action(self, agent: SocialAgent,
-                                               action: ManualAction) -> None:
-        r"""Perform a manual interview action.
-
-        Args:
-            agent(SocialAgent): The agent to interview.
-            action(ManualAction): The interview action to perform.
-        """
-        # Extract interview prompt from args
-        interview_prompt = action.action_args.get("prompt", "")
-        if not interview_prompt:
-            env_log.warning(
-                f"Empty interview prompt for agent {agent.social_agent_id}")
-            return
-
-        # First, perform the regular action by sending it to the platform
-        action_result = await agent.perform_action_by_data(
-            action.action_type, **action.action_args)
-
-        # Check if the platform action was successful
-        if not action_result.get("success", False):
-            error = action_result.get('error', 'Unknown error')
-            env_log.warning(f"Failed to record interview request: {error}")
-            return
-
-        # Get the interview_id from the result
-        interview_id = action_result.get("interview_id")
-        if not interview_id:
-            env_log.warning("No interview_id returned from platform")
-            return
-
-        # Perform the interview to get the actual response
-        result = await self._perform_interview_action(agent, interview_prompt)
-
-        # Get the response content
-        response = result.get("content", "")
-
-        try:
-            # Record the response using the platform's method
-            response_result = await self.platform.record_interview_response(
-                agent_id=agent.social_agent_id,
-                interview_id=interview_id,
-                response=response)
-
-            if response_result.get("success", False):
-                env_log.info(
-                    f"Recorded response for agent {agent.social_agent_id}")
-            else:
-                error_msg = response_result.get('error', 'Unknown error')
-                env_log.warning(
-                    f"Failed to record interview response: {error_msg}")
-        except Exception as e:
-            env_log.error(f"Error recording interview response: {str(e)}")
-
     async def close(self) -> None:
         r"""Stop the platform and close the environment.
         """
diff --git a/oasis/social_agent/agent.py b/oasis/social_agent/agent.py
index b49cfe49..2ba3de11 100644
--- a/oasis/social_agent/agent.py
+++ b/oasis/social_agent/agent.py
@@ -217,15 +217,24 @@ async def perform_interview(self, interview_prompt: str):
         # NOTE: this is a temporary solution.
         # Camel can not stop updating the agents' memory after stop and astep
         # now.
+
         response = self._get_model_response(openai_messages=openai_messages,
                                             num_tokens=num_tokens)
         content = response.output_messages[0].content
         agent_log.info(
             f"Agent {self.social_agent_id} receive response: {content}")
+
+        # Record the complete interview (prompt + response) through the channel
+        interview_data = {"prompt": interview_prompt, "response": content}
+        result = await self.env.action.perform_action(
+            interview_data, ActionType.INTERVIEW.value)
+
+        # Return the combined result
         return {
             "user_id": self.social_agent_id,
             "prompt": openai_messages,
-            "content": content
+            "content": content,
+            "success": result.get("success", False)
         }
 
     async def perform_action_by_hci(self) -> Any:
diff --git a/oasis/social_platform/platform.py b/oasis/social_platform/platform.py
index 676be553..67c6cc9f 100644
--- a/oasis/social_platform/platform.py
+++ b/oasis/social_platform/platform.py
@@ -1341,15 +1341,15 @@ async def do_nothing(self, agent_id: int):
         except Exception as e:
             return {"success": False, "error": str(e)}
 
-    async def interview(self, agent_id: int, prompt: str):
-        """Interview an agent with the given prompt.
+    async def interview(self, agent_id: int, interview_data):
+        """Interview an agent with the given prompt and record the response.
 
         Args:
             agent_id (int): The ID of the agent being interviewed.
-            prompt (str): The interview question or prompt.
+            interview_data: Either a string (prompt only) or dict with prompt and response.
 
         Returns:
-            dict: A dictionary with success status and an interview_id.
+            dict: A dictionary with success status.
         """
         if self.recsys_type == RecsysType.REDDIT:
             current_time = self.sandbox_clock.time_transfer(
@@ -1359,57 +1359,28 @@ async def interview(self, agent_id: int, prompt: str):
         try:
             user_id = agent_id
 
-            # Create a unique interview ID (using timestamp + user_id)
-            interview_id = f"{current_time}_{user_id}"
+            # Handle both old format (string prompt) and new format (dict with prompt + response)
+            if isinstance(interview_data, str):
+                # Old format: just the prompt
+                prompt = interview_data
+                response = None
+                interview_id = f"{current_time}_{user_id}"
+                action_info = {"prompt": prompt, "interview_id": interview_id}
+            else:
+                # New format: dict with prompt and response
+                prompt = interview_data.get("prompt", "")
+                response = interview_data.get("response", "")
+                interview_id = f"{current_time}_{user_id}"
+                action_info = {
+                    "prompt": prompt,
+                    "response": response,
+                    "interview_id": interview_id
+                }
 
-            # Record the interview request in the trace table
-            action_info = {"prompt": prompt, "interview_id": interview_id}
+            # Record the interview in the trace table
             self.pl_utils._record_trace(user_id, ActionType.INTERVIEW.value,
                                         action_info, current_time)
 
             return {"success": True, "interview_id": interview_id}
         except Exception as e:
             return {"success": False, "error": str(e)}
-
-    async def record_interview_response(self, agent_id: int, interview_id: str,
-                                        response: str):
-        """Record the response for an interview.
-
-        Args:
-            agent_id (int): The ID of the agent who was interviewed.
-            interview_id (str): The unique ID of the interview.
-            response (str): The agent's response to the interview.
-
-        Returns:
-            dict: A dictionary with success status.
-        TODO: maybe move this function to utils
-        """
-        try:
-            # First, fetch the trace record for this interview
-            query = """SELECT rowid, info FROM trace
-                       WHERE user_id = ? AND action = ?
-                       ORDER BY created_at DESC LIMIT 1"""
-            self.pl_utils._execute_db_command(
-                query, (agent_id, ActionType.INTERVIEW.value))
-            last_interview_data = self.db_cursor.fetchone()
-
-            if last_interview_data:
-                rowid, info_json = last_interview_data
-                import json
-                info = json.loads(info_json)
-
-                # Verify this is the correct interview record
-                if info.get("interview_id") == interview_id:
-                    # Update with the response
-                    info["response"] = response
-
-                    # Update the trace record
-                    update_query = "UPDATE trace SET info = ? WHERE rowid = ?"
-                    self.pl_utils._execute_db_command(
-                        update_query, (json.dumps(info), rowid), commit=True)
-
-                    return {"success": True}
-
-            return {"success": False, "error": "Interview record not found"}
-        except Exception as e:
-            return {"success": False, "error": str(e)}

From 87cbf8ae14c3fd931a19893c5dbe9e468104980b Mon Sep 17 00:00:00 2001
From: yiyiyi0817 <1359513271@qq.com>
Date: Fri, 30 May 2025 14:39:16 +0800
Subject: [PATCH 24/32] update qr code

---
 assets/wechatgroup.png | Bin 240520 -> 234605 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/assets/wechatgroup.png b/assets/wechatgroup.png
index 8e37d31128858409ef8a47529481e94a0be20965..6a27870d7be2ea27b6b4c9396f6c038f0e3a5708 100644
GIT binary patch
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HZT<Kk?Jhx7


From e59c365f46981f0929546607fa5b9543829fa8d2 Mon Sep 17 00:00:00 2001
From: liuxiaosu <lxsgocode123@gmail.com>
Date: Sat, 31 May 2025 15:05:31 +0800
Subject: [PATCH 25/32] update twitter_channel name

---
 examples/experiment/reddit_emall_demo/emall_simulation.py   | 2 +-
 .../experiment/reddit_gpt_example/reddit_simulation_gpt.py  | 2 +-
 .../reddit_simulation_align_with_human.py                   | 2 +-
 .../reddit_simulation_counterfactual.py                     | 2 +-
 .../experiment/twitter_gpt_example/twitter_simulation.py    | 2 +-
 .../twitter_simulation.py                                   | 2 +-
 .../align_with_real_world/twitter_simulation_large.py       | 2 +-
 .../group_polarization/twitter_simulation_group_polar.py    | 2 +-
 .../twitter_simulation_1M_agents/twitter_simulation_1m.py   | 2 +-
 test/agent/test_agent_custom_prompt.py                      | 4 ++--
 test/agent/test_agent_generator.py                          | 2 +-
 test/agent/test_agent_graph.py                              | 6 +++---
 test/agent/test_agent_tools.py                              | 2 +-
 test/agent/test_multi_agent_signup_create.py                | 2 +-
 test/agent/test_twitter_user_agent_all_actions.py           | 2 +-
 15 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/examples/experiment/reddit_emall_demo/emall_simulation.py b/examples/experiment/reddit_emall_demo/emall_simulation.py
index f0048215..f726e9f4 100644
--- a/examples/experiment/reddit_emall_demo/emall_simulation.py
+++ b/examples/experiment/reddit_emall_demo/emall_simulation.py
@@ -137,7 +137,7 @@ async def running(
         )
         agent_graph = await generate_reddit_agents(
             agent_info_path=user_path,
-            twitter_channel=twitter_channel,
+            channel=twitter_channel,
             agent_graph=agent_graph,
             agent_user_id_mapping=id_mapping,
             follow_post_agent=follow_post_agent,
diff --git a/examples/experiment/reddit_gpt_example/reddit_simulation_gpt.py b/examples/experiment/reddit_gpt_example/reddit_simulation_gpt.py
index 201970d9..e3a9146a 100644
--- a/examples/experiment/reddit_gpt_example/reddit_simulation_gpt.py
+++ b/examples/experiment/reddit_gpt_example/reddit_simulation_gpt.py
@@ -136,7 +136,7 @@ async def running(
         )
         agent_graph = await generate_reddit_agents(
             agent_info_path=user_path,
-            twitter_channel=twitter_channel,
+            channel=twitter_channel,
             agent_graph=agent_graph,
             agent_user_id_mapping=id_mapping,
             follow_post_agent=follow_post_agent,
diff --git a/examples/experiment/reddit_simulation_align_with_human/reddit_simulation_align_with_human.py b/examples/experiment/reddit_simulation_align_with_human/reddit_simulation_align_with_human.py
index a4e7988d..999fbf26 100644
--- a/examples/experiment/reddit_simulation_align_with_human/reddit_simulation_align_with_human.py
+++ b/examples/experiment/reddit_simulation_align_with_human/reddit_simulation_align_with_human.py
@@ -139,7 +139,7 @@ async def running(
         )
         agent_graph = await generate_reddit_agents(
             agent_info_path=user_path,
-            twitter_channel=twitter_channel,
+            channel=twitter_channel,
             agent_graph=agent_graph,
             agent_user_id_mapping=id_mapping,
             follow_post_agent=follow_post_agent,
diff --git a/examples/experiment/reddit_simulation_counterfactual/reddit_simulation_counterfactual.py b/examples/experiment/reddit_simulation_counterfactual/reddit_simulation_counterfactual.py
index f42f6191..fc97fa16 100644
--- a/examples/experiment/reddit_simulation_counterfactual/reddit_simulation_counterfactual.py
+++ b/examples/experiment/reddit_simulation_counterfactual/reddit_simulation_counterfactual.py
@@ -137,7 +137,7 @@ async def running(
         )
         agent_graph = await generate_reddit_agents(
             agent_info_path=user_path,
-            twitter_channel=twitter_channel,
+            channel=twitter_channel,
             agent_graph=agent_graph,
             agent_user_id_mapping=id_mapping,
             follow_post_agent=follow_post_agent,
diff --git a/examples/experiment/twitter_gpt_example/twitter_simulation.py b/examples/experiment/twitter_gpt_example/twitter_simulation.py
index e514bdde..acc07f09 100644
--- a/examples/experiment/twitter_gpt_example/twitter_simulation.py
+++ b/examples/experiment/twitter_gpt_example/twitter_simulation.py
@@ -127,7 +127,7 @@ async def running(
         start_hour = 13
 
     agent_graph = await generate_agents(agent_info_path=csv_path,
-                                        twitter_channel=twitter_channel,
+                                        channel=twitter_channel,
                                         start_time=start_time,
                                         model=model,
                                         recsys_type=recsys_type,
diff --git a/examples/experiment/twitter_gpt_example_openai_embedding/twitter_simulation.py b/examples/experiment/twitter_gpt_example_openai_embedding/twitter_simulation.py
index 4a9e5f5f..461a5cda 100644
--- a/examples/experiment/twitter_gpt_example_openai_embedding/twitter_simulation.py
+++ b/examples/experiment/twitter_gpt_example_openai_embedding/twitter_simulation.py
@@ -129,7 +129,7 @@ async def running(
         start_hour = 13
 
     agent_graph = await generate_agents(agent_info_path=csv_path,
-                                        twitter_channel=twitter_channel,
+                                        channel=twitter_channel,
                                         start_time=start_time,
                                         model=model,
                                         recsys_type=recsys_type,
diff --git a/examples/experiment/twitter_simulation/align_with_real_world/twitter_simulation_large.py b/examples/experiment/twitter_simulation/align_with_real_world/twitter_simulation_large.py
index 59d1ae16..2e2841a4 100644
--- a/examples/experiment/twitter_simulation/align_with_real_world/twitter_simulation_large.py
+++ b/examples/experiment/twitter_simulation/align_with_real_world/twitter_simulation_large.py
@@ -135,7 +135,7 @@ async def running(
     model_configs = model_configs or {}
 
     agent_graph = await generate_agents(agent_info_path=csv_path,
-                                        twitter_channel=twitter_channel,
+                                        channel=twitter_channel,
                                         start_time=start_time,
                                         model=models,
                                         recsys_type=recsys_type,
diff --git a/examples/experiment/twitter_simulation/group_polarization/twitter_simulation_group_polar.py b/examples/experiment/twitter_simulation/group_polarization/twitter_simulation_group_polar.py
index 225a9c8e..7a3e1578 100644
--- a/examples/experiment/twitter_simulation/group_polarization/twitter_simulation_group_polar.py
+++ b/examples/experiment/twitter_simulation/group_polarization/twitter_simulation_group_polar.py
@@ -135,7 +135,7 @@ async def running(
     model_configs = model_configs or {}
     agent_graph = await generate_agents_100w(
         agent_info_path=csv_path,
-        twitter_channel=twitter_channel,
+        channel=twitter_channel,
         start_time=start_time,
         recsys_type=recsys_type,
         twitter=infra,
diff --git a/examples/experiment/twitter_simulation_1M_agents/twitter_simulation_1m.py b/examples/experiment/twitter_simulation_1M_agents/twitter_simulation_1m.py
index b211a929..0aaee990 100644
--- a/examples/experiment/twitter_simulation_1M_agents/twitter_simulation_1m.py
+++ b/examples/experiment/twitter_simulation_1M_agents/twitter_simulation_1m.py
@@ -126,7 +126,7 @@ async def running(
 
     agent_graph = await generate_agents_100w(
         agent_info_path=csv_path,
-        twitter_channel=twitter_channel,
+        channel=twitter_channel,
         start_time=start_time,
         recsys_type=recsys_type,
         twitter=infra,
diff --git a/test/agent/test_agent_custom_prompt.py b/test/agent/test_agent_custom_prompt.py
index f8cf18f9..85d35a2d 100644
--- a/test/agent/test_agent_custom_prompt.py
+++ b/test/agent/test_agent_custom_prompt.py
@@ -47,7 +47,7 @@ async def test_agents_profile():
     agent = SocialAgent(agent_id=0,
                         user_info=user_info,
                         user_info_template=user_info_template,
-                        twitter_channel=channel)
+                        channel=channel)
     assert agent.system_message.content == (
         'Your aim is: Persuade people to buy a product. Your task is: '
         'Using roleplay to tell some story about the product.')
@@ -84,7 +84,7 @@ async def test_agents_posting(setup_platform):
     agent = SocialAgent(agent_id=0,
                         user_info=user_info,
                         user_info_template=user_info_template,
-                        twitter_channel=channel)
+                        channel=channel)
     await agent.env.action.sign_up("user0", "User0", "A bio.")
 
     # create post
diff --git a/test/agent/test_agent_generator.py b/test/agent/test_agent_generator.py
index a0f2d9be..e3152c24 100644
--- a/test/agent/test_agent_generator.py
+++ b/test/agent/test_agent_generator.py
@@ -42,7 +42,7 @@ async def running():
     task = asyncio.create_task(infra.running())
     os.environ["SANDBOX_TIME"] = "0"
     agent_graph = await generate_agents(agent_info_path=agent_info_path,
-                                        twitter_channel=twitter_channel,
+                                        channel=twitter_channel,
                                         model=model,
                                         twitter=infra,
                                         start_time=0)
diff --git a/test/agent/test_agent_graph.py b/test/agent/test_agent_graph.py
index f1401ab1..a6bf1ad6 100644
--- a/test/agent/test_agent_graph.py
+++ b/test/agent/test_agent_graph.py
@@ -35,17 +35,17 @@ def test_agent_graph(tmp_path):
     agent_0 = SocialAgent(
         agent_id=0,
         user_info=UserInfo(name="0"),
-        twitter_channel=twitter_channel,
+        channel=twitter_channel,
     )
     agent_1 = SocialAgent(
         agent_id=1,
         user_info=UserInfo(name="1"),
-        twitter_channel=twitter_channel,
+        channel=twitter_channel,
     )
     agent_2 = SocialAgent(
         agent_id=2,
         user_info=UserInfo(name="2"),
-        twitter_channel=twitter_channel,
+        channel=twitter_channel,
     )
 
     graph.add_agent(agent_0)
diff --git a/test/agent/test_agent_tools.py b/test/agent/test_agent_tools.py
index 44d5fc3e..25b95681 100644
--- a/test/agent/test_agent_tools.py
+++ b/test/agent/test_agent_tools.py
@@ -64,7 +64,7 @@ async def test_agents_posting(setup_platform):
                              profile=profile)
         agent = SocialAgent(agent_id=0,
                             user_info=user_info,
-                            twitter_channel=channel,
+                            channel=channel,
                             tools=MathToolkit().get_tools(),
                             available_actions=[ActionType.CREATE_POST],
                             single_iteration=False)
diff --git a/test/agent/test_multi_agent_signup_create.py b/test/agent/test_multi_agent_signup_create.py
index 7cd4edd6..e6e20cae 100644
--- a/test/agent/test_multi_agent_signup_create.py
+++ b/test/agent/test_multi_agent_signup_create.py
@@ -67,7 +67,7 @@ async def test_agents_posting(setup_platform):
                              profile=profile)
         agent = SocialAgent(agent_id=i,
                             user_info=user_info,
-                            twitter_channel=channel)
+                            channel=channel)
         await agent.env.action.sign_up(f"user{i}0101", f"User{i}", "A bio.")
         agents.append(agent)
 
diff --git a/test/agent/test_twitter_user_agent_all_actions.py b/test/agent/test_twitter_user_agent_all_actions.py
index 02644434..c5cfbe3c 100644
--- a/test/agent/test_twitter_user_agent_all_actions.py
+++ b/test/agent/test_twitter_user_agent_all_actions.py
@@ -62,7 +62,7 @@ async def test_agents_actions(setup_twitter):
                              profile=profile)
         agent = SocialAgent(agent_id=i,
                             user_info=user_info,
-                            twitter_channel=channel)
+                            channel=channel)
         return_message = await agent.env.action.sign_up(
             f"user{i}0101", f"User{i}", "A bio.")
         assert return_message["success"] is True

From d6ede436c793bd075e1e67fca5f1986d9d283269 Mon Sep 17 00:00:00 2001
From: yiyiyi0817 <1359513271@qq.com>
Date: Mon, 2 Jun 2025 17:09:09 +0800
Subject: [PATCH 26/32] update pytest

---
 oasis/social_agent/agent.py                       | 3 +--
 test/agent/test_multi_agent_signup_create.py      | 4 +---
 test/agent/test_twitter_user_agent_all_actions.py | 4 +---
 3 files changed, 3 insertions(+), 8 deletions(-)

diff --git a/oasis/social_agent/agent.py b/oasis/social_agent/agent.py
index d58bcb64..38911a3d 100644
--- a/oasis/social_agent/agent.py
+++ b/oasis/social_agent/agent.py
@@ -69,8 +69,7 @@ def __init__(self,
         self.social_agent_id = agent_id
         self.user_info = user_info
         self.channel = channel or Channel()
-        self.env = SocialEnvironment(
-            SocialAction(agent_id, self.channel))
+        self.env = SocialEnvironment(SocialAction(agent_id, self.channel))
         if user_info_template is None:
             system_message_content = self.user_info.to_system_message()
         else:
diff --git a/test/agent/test_multi_agent_signup_create.py b/test/agent/test_multi_agent_signup_create.py
index e6e20cae..5b6bcc73 100644
--- a/test/agent/test_multi_agent_signup_create.py
+++ b/test/agent/test_multi_agent_signup_create.py
@@ -65,9 +65,7 @@ async def test_agents_posting(setup_platform):
         user_info = UserInfo(name=real_name,
                              description=description,
                              profile=profile)
-        agent = SocialAgent(agent_id=i,
-                            user_info=user_info,
-                            channel=channel)
+        agent = SocialAgent(agent_id=i, user_info=user_info, channel=channel)
         await agent.env.action.sign_up(f"user{i}0101", f"User{i}", "A bio.")
         agents.append(agent)
 
diff --git a/test/agent/test_twitter_user_agent_all_actions.py b/test/agent/test_twitter_user_agent_all_actions.py
index c5cfbe3c..f016aa76 100644
--- a/test/agent/test_twitter_user_agent_all_actions.py
+++ b/test/agent/test_twitter_user_agent_all_actions.py
@@ -60,9 +60,7 @@ async def test_agents_actions(setup_twitter):
         user_info = UserInfo(name=real_name,
                              description=description,
                              profile=profile)
-        agent = SocialAgent(agent_id=i,
-                            user_info=user_info,
-                            channel=channel)
+        agent = SocialAgent(agent_id=i, user_info=user_info, channel=channel)
         return_message = await agent.env.action.sign_up(
             f"user{i}0101", f"User{i}", "A bio.")
         assert return_message["success"] is True

From bd9fcf9f2b18b91699a85a5add5b49b47663a603 Mon Sep 17 00:00:00 2001
From: yiyiyi0817 <1359513271@qq.com>
Date: Mon, 2 Jun 2025 17:28:06 +0800
Subject: [PATCH 27/32] add default model=None in gen_control_agents_with_data

---
 oasis/social_agent/agents_generator.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/oasis/social_agent/agents_generator.py b/oasis/social_agent/agents_generator.py
index 39191c0d..a8591ae3 100644
--- a/oasis/social_agent/agents_generator.py
+++ b/oasis/social_agent/agents_generator.py
@@ -389,7 +389,7 @@ async def generate_controllable_agents(
 async def gen_control_agents_with_data(
     channel: Channel,
     control_user_num: int,
-    models: list[BaseModelBackend],
+    models: list[BaseModelBackend] | None = None,
 ) -> tuple[AgentGraph, dict]:
     agent_graph = AgentGraph()
     agent_user_id_mapping = {}

From 060924b4e8d3af7d559c828639c23984b98da8b1 Mon Sep 17 00:00:00 2001
From: yiyiyi0817 <1359513271@qq.com>
Date: Mon, 2 Jun 2025 18:19:26 +0800
Subject: [PATCH 28/32] add aget_model_response and fix pre-commit

---
 oasis/social_agent/agent.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/oasis/social_agent/agent.py b/oasis/social_agent/agent.py
index d87c4774..20be669e 100644
--- a/oasis/social_agent/agent.py
+++ b/oasis/social_agent/agent.py
@@ -178,8 +178,8 @@ async def perform_test(self):
         # NOTE: this is a temporary solution.
         # Camel can not stop updating the agents' memory after stop and astep
         # now.
-        response = self._get_model_response(openai_messages=openai_messages,
-                                            num_tokens=num_tokens)
+        response = await self._aget_model_response(
+            openai_messages=openai_messages, num_tokens=num_tokens)
         content = response.output_messages[0].content
         agent_log.info(
             f"Agent {self.social_agent_id} receive response: {content}")

From 6943ffd8e382c90c32d33feb2b7f0a53766fc983 Mon Sep 17 00:00:00 2001
From: yiyiyi0817 <1359513271@qq.com>
Date: Mon, 2 Jun 2025 18:24:32 +0800
Subject: [PATCH 29/32] fix pre-commit

---
 oasis/environment/env.py          | 6 ++++--
 oasis/social_platform/platform.py | 6 ++++--
 2 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/oasis/environment/env.py b/oasis/environment/env.py
index 5d30c08f..4d815c60 100644
--- a/oasis/environment/env.py
+++ b/oasis/environment/env.py
@@ -159,7 +159,8 @@ async def step(
                 for single_action in action:
                     if isinstance(single_action, ManualAction):
                         if single_action.action_type == ActionType.INTERVIEW:
-                            # Use the agent's perform_interview method for interview actions
+                            # Use the agent's perform_interview method for
+                            # interview actions
                             interview_prompt = single_action.action_args.get(
                                 "prompt", "")
                             tasks.append(
@@ -175,7 +176,8 @@ async def step(
             else:
                 if isinstance(action, ManualAction):
                     if action.action_type == ActionType.INTERVIEW:
-                        # Use the agent's perform_interview method for interview actions
+                        # Use the agent's perform_interview method for
+                        # interview actions
                         interview_prompt = action.action_args.get("prompt", "")
                         tasks.append(
                             self._perform_interview_action(
diff --git a/oasis/social_platform/platform.py b/oasis/social_platform/platform.py
index 67c6cc9f..b62908ef 100644
--- a/oasis/social_platform/platform.py
+++ b/oasis/social_platform/platform.py
@@ -1346,7 +1346,8 @@ async def interview(self, agent_id: int, interview_data):
 
         Args:
             agent_id (int): The ID of the agent being interviewed.
-            interview_data: Either a string (prompt only) or dict with prompt and response.
+            interview_data: Either a string (prompt only) or dict with prompt
+                and response.
 
         Returns:
             dict: A dictionary with success status.
@@ -1359,7 +1360,8 @@ async def interview(self, agent_id: int, interview_data):
         try:
             user_id = agent_id
 
-            # Handle both old format (string prompt) and new format (dict with prompt + response)
+            # Handle both old format (string prompt) and new format
+            # (dict with prompt + response)
             if isinstance(interview_data, str):
                 # Old format: just the prompt
                 prompt = interview_data

From 5f62f88ff7dfe16ddeec15aa4005212df40c0960 Mon Sep 17 00:00:00 2001
From: yiyiyi0817 <1359513271@qq.com>
Date: Mon, 2 Jun 2025 18:52:28 +0800
Subject: [PATCH 30/32] add interview_record bool args and add more description
 of interview in the doc

---
 docs/key_modules/actions.mdx      |  9 +++++++++
 docs/key_modules/social_agent.mdx |  1 +
 oasis/social_agent/agent.py       | 25 +++++++++++++++++--------
 3 files changed, 27 insertions(+), 8 deletions(-)

diff --git a/docs/key_modules/actions.mdx b/docs/key_modules/actions.mdx
index a8d0b9de..27945117 100644
--- a/docs/key_modules/actions.mdx
+++ b/docs/key_modules/actions.mdx
@@ -107,6 +107,7 @@ OASIS provides a comprehensive set of actions that simulate real social media be
 | `REFRESH` | Refresh the timeline to get recommended posts |
 | `DO_NOTHING` | Perform no action (pass the turn) |
 | `PURCHASE_PRODUCT` | Purchase a product (for e-commerce simulations) |
+| `INTERVIEW` | Interview a user and record the interview result in the database |
 
 
 ## Arguments for `ManualAction`
@@ -286,3 +287,11 @@ action = ManualAction(
     args={"product_name": "Premium Subscription", "purchase_num": 1}
 )
 ```
+
+#### INTERVIEW
+```python
+action = ManualAction(
+    action=ActionType.INTERVIEW,
+    args={"prompt": "What is your name?"}
+)
+```
diff --git a/docs/key_modules/social_agent.mdx b/docs/key_modules/social_agent.mdx
index 99200d40..e049bdab 100644
--- a/docs/key_modules/social_agent.mdx
+++ b/docs/key_modules/social_agent.mdx
@@ -19,6 +19,7 @@ When initializing a `SocialAgent`, you can configure the following core paramete
 | `available_actions`  | [`list[ActionType]`](https://docs.oasis.camel-ai.org/key_modules/actions) or `None`                         | ✗        | `None`  | List of allowed actions in the social platform. For more details, see [Actions Module](https://docs.oasis.camel-ai.org/key_modules/actions). If `None`, all actions are enabled by default. |
 | `tools`              | `List[Union[FunctionTool, Callable]]` or `None` | ✗ | `None`  | External tools the agent can use, such as a `get_weather` function, a `Toolkit`, or an `MCPToolkit` from [CAMEL](https://docs.camel-ai.org/key_modules/tools.html). If set to `None`, the agent will not be able to use any external tools. |
 | `single_iteration`   | `bool`                                               | ✗        | `True`  | Whether the agent performs only a single round of reasoning when taking an LLM action. If `False`, the agent may continue acting based on the outcome of previous actions or tool calls. |
+| `interview_record`   | `bool`                                               | ✗        | `False`  | Whether to record the interview prompt and result in the agent's memory. |
 
 For more details on the `model` and the `tools` parameter, see [Models Module](https://docs.oasis.camel-ai.org/key_modules/models) and [Toolkits Module](https://docs.oasis.camel-ai.org/key_modules/toolkits).
 
diff --git a/oasis/social_agent/agent.py b/oasis/social_agent/agent.py
index 20be669e..4fe76679 100644
--- a/oasis/social_agent/agent.py
+++ b/oasis/social_agent/agent.py
@@ -65,7 +65,8 @@ def __init__(self,
                  agent_graph: "AgentGraph" = None,
                  available_actions: list[ActionType] = None,
                  tools: Optional[List[Union[FunctionTool, Callable]]] = None,
-                 single_iteration: bool = True):
+                 single_iteration: bool = True,
+                 interview_record: bool = False):
         self.social_agent_id = agent_id
         self.user_info = user_info
         self.channel = channel or Channel()
@@ -106,6 +107,7 @@ def __init__(self,
                          scheduling_strategy='random_model',
                          tools=all_tools,
                          single_iteration=single_iteration)
+        self.interview_record = interview_record
         self.agent_graph = agent_graph
         self.test_prompt = (
             "\n"
@@ -194,11 +196,12 @@ async def perform_interview(self, interview_prompt: str):
         Perform an interview with the agent.
         """
         # user conduct test to agent
-        _ = BaseMessage.make_user_message(role_name="User",
-                                          content=("You are a twitter user."))
-        # Test memory should not be writed to memory.
-        # self.memory.write_record(MemoryRecord(user_msg,
-        #                                       OpenAIBackendRole.USER))
+        user_msg = BaseMessage.make_user_message(
+            role_name="User", content=("You are a twitter user."))
+
+        if self.interview_record:
+            # Test memory should not be writed to memory.
+            self.update_memory(message=user_msg, role=OpenAIBackendRole.SYSTEM)
 
         openai_messages, num_tokens = self.memory.get_context()
 
@@ -217,9 +220,15 @@ async def perform_interview(self, interview_prompt: str):
         # Camel can not stop updating the agents' memory after stop and astep
         # now.
 
-        response = self._get_model_response(openai_messages=openai_messages,
-                                            num_tokens=num_tokens)
+        response = await self._aget_model_response(
+            openai_messages=openai_messages, num_tokens=num_tokens)
+
         content = response.output_messages[0].content
+
+        if self.interview_record:
+            # Test memory should not be writed to memory.
+            self.update_memory(message=response.output_messages[0],
+                               role=OpenAIBackendRole.USER)
         agent_log.info(
             f"Agent {self.social_agent_id} receive response: {content}")
 

From d85435916183e329b1ce1da388093a00c88fe599 Mon Sep 17 00:00:00 2001
From: yiyiyi0817 <1359513271@qq.com>
Date: Mon, 2 Jun 2025 19:07:31 +0800
Subject: [PATCH 31/32] fix pytest

---
 test/agent/test_interview_action.py | 31 ++++++++++++++++-------------
 1 file changed, 17 insertions(+), 14 deletions(-)

diff --git a/test/agent/test_interview_action.py b/test/agent/test_interview_action.py
index b8d11b45..e83f0491 100644
--- a/test/agent/test_interview_action.py
+++ b/test/agent/test_interview_action.py
@@ -71,9 +71,7 @@ async def test_single_interview_action(setup_interview_test):
         user_info = UserInfo(name=real_name,
                              description=description,
                              profile=profile)
-        agent = SocialAgent(agent_id=0,
-                            user_info=user_info,
-                            twitter_channel=channel)
+        agent = SocialAgent(agent_id=0, user_info=user_info, channel=channel)
 
         # Sign up the agent
         return_message = await agent.env.action.sign_up(
@@ -145,7 +143,7 @@ async def test_multiple_interviews_action(setup_interview_test):
                                  profile=profile)
             agent = SocialAgent(agent_id=i,
                                 user_info=user_info,
-                                twitter_channel=channel)
+                                channel=channel)
 
             # Sign up the agent
             return_message = await agent.env.action.sign_up(
@@ -273,21 +271,28 @@ async def test_interview_with_environment():
         """, (ActionType.INTERVIEW.value, ))
 
         interview_records = cursor.fetchall()
-        assert len(interview_records) == 4  # 3 interviews conducted
+        assert len(interview_records) == 4  # 4 interviews conducted
 
-        # Check the content of interviews
-        expected_prompts = [
+        # Check interview content
+        expected_prompts = {
             "What is your favorite color and why?",
             "What do you think about technology?",
             "How do you spend your free time?", "What motivates you in life?"
-        ]
+        }
 
-        for i, (user_id, info_json, timestamp) in enumerate(interview_records):
+        # Get all records and verify content exists
+        actual_prompts = set()
+        for user_id, info_json, timestamp in interview_records:
             info = json.loads(info_json)
-            assert info["prompt"] == expected_prompts[i]
+            actual_prompts.add(info["prompt"])
             assert "interview_id" in info
             assert "response" in info
 
+        # Use set comparison to verify all expected prompts exist
+        assert actual_prompts == expected_prompts, (
+            f"Missing prompts: {expected_prompts - actual_prompts}, "
+            f"Unexpected prompts: {actual_prompts - expected_prompts}")
+
         conn.close()
 
     finally:
@@ -325,7 +330,7 @@ async def test_interview_data_retrieval(setup_interview_test):
                                  profile=profile)
             agent = SocialAgent(agent_id=i,
                                 user_info=user_info,
-                                twitter_channel=channel)
+                                channel=channel)
 
             return_message = await agent.env.action.sign_up(
                 f"testuser{i}", f"TestUser{i}", f"Bio {i}.")
@@ -414,9 +419,7 @@ async def test_interview_error_handling(setup_interview_test):
         user_info = UserInfo(name=real_name,
                              description=description,
                              profile=profile)
-        agent = SocialAgent(agent_id=0,
-                            user_info=user_info,
-                            twitter_channel=channel)
+        agent = SocialAgent(agent_id=0, user_info=user_info, channel=channel)
 
         return_message = await agent.env.action.sign_up(
             "testuser", "TestUser", "Test bio.")

From 1fc8fe41993fcde4f68946d7492b8a5dda0f7c4a Mon Sep 17 00:00:00 2001
From: yiyiyi0817 <1359513271@qq.com>
Date: Mon, 2 Jun 2025 19:13:31 +0800
Subject: [PATCH 32/32] update news in readme

---
 README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 086e6c53..61713404 100644
--- a/README.md
+++ b/README.md
@@ -249,8 +249,9 @@ To discover how to create profiles for large-scale users, as well as how to visu
 
 ### Latest Updates
 
-📢 Add a new feature to customize each agent's models, tools, and prompts, and refactor the interface to follow the PettingZoo style. - 📆 May 22, 2025
+📢 Support Interview Action for asking agents specific questions and getting answers. - 📆 June 2, 2025
 
+- Support customization of each agent's models, tools, and prompts; refactor the interface to follow the PettingZoo style. - 📆 May 22, 2025
 - Refactor into the OASIS environment, publish camel-oasis on PyPI, and release the documentation. - 📆 April 24, 2025
 - Support OPENAI Embedding model for Twhin-Bert Recommendation System. - 📆 March 25, 2025
 - Updated social media links and QR codes in the README! Join OASIS & CAMEL on WeChat, X, Reddit, and Discord. - 📆 March 24, 2025