diff --git a/mm_agents/agent.py b/mm_agents/agent.py
index 744ee9c..cf140d8 100644
--- a/mm_agents/agent.py
+++ b/mm_agents/agent.py
@@ -549,55 +549,101 @@ class PromptAgent:
                 return response.json()['content'][0]['text']
 
 
-        # elif self.model.startswith("mistral"):
-        #     print("Call mistral")
-        #     messages = payload["messages"]
-        #     max_tokens = payload["max_tokens"]
-        #
-        #     misrtal_messages = []
-        #
-        #     for i, message in enumerate(messages):
-        #         mistral_message = {
-        #             "role": message["role"],
-        #             "content": []
-        #         }
-        #
-        #         for part in message["content"]:
-        #             mistral_message['content'] = part['text'] if part['type'] == "text" else None
-        #
-        #         misrtal_messages.append(mistral_message)
-        #
-        #     # the mistral not support system message in our endpoint, so we concatenate it at the first user message
-        #     if misrtal_messages[0]['role'] == "system":
-        #         misrtal_messages[1]['content'] = misrtal_messages[0]['content'] + "\n" + misrtal_messages[1]['content']
-        #         misrtal_messages.pop(0)
-        #
-        #     # openai.api_base = "http://localhost:8000/v1"
-        #     # openai.api_key = "test"
-        #     # response = openai.ChatCompletion.create(
-        #     #     messages=misrtal_messages,
-        #     #     model="Mixtral-8x7B-Instruct-v0.1"
-        #     # )
-        #
-        #     from openai import OpenAI
-        #     TOGETHER_API_KEY = "d011650e7537797148fb6170ec1e0be7ae75160375686fae02277136078e90d2"
-        #
-        #     client = OpenAI(api_key=TOGETHER_API_KEY,
-        #                     base_url='https://api.together.xyz',
-        #                     )
-        #     logger.info("Generating content with Mistral model: %s", self.model)
-        #     response = client.chat.completions.create(
-        #         messages=misrtal_messages,
-        #         model="mistralai/Mixtral-8x7B-Instruct-v0.1",
-        #         max_tokens=1024
-        #     )
-        #
-        #     try:
-        #         # return response['choices'][0]['message']['content']
-        #         return response.choices[0].message.content
-        #     except Exception as e:
-        #         print("Failed to call LLM: " + str(e))
-        #         return ""
+        elif self.model.startswith("mistral"):
+            print("Call mistral")
+            messages = payload["messages"]
+            max_tokens = payload["max_tokens"]
+            top_p = payload["top_p"]
+            temperature = payload["temperature"]
+        
+            misrtal_messages = []
+
+            for i, message in enumerate(messages):
+                mistral_message = {
+                    "role": message["role"],
+                    "content": ""
+                }
+
+                for part in message["content"]:
+                    mistral_message['content'] = part['text'] if part['type'] == "text" else ""
+                
+                
+                misrtal_messages.append(mistral_message)
+    
+        
+            # openai.api_base = "http://localhost:8000/v1"
+            # response = openai.ChatCompletion.create(
+            #     messages=misrtal_messages,
+            #     model="Mixtral-8x7B-Instruct-v0.1"
+            # )
+        
+            from openai import OpenAI
+
+            client = OpenAI(api_key=os.environ["TOGETHER_API_KEY"],
+                            base_url='https://api.together.xyz',
+                            )
+            logger.info("Generating content with Mistral model: %s", self.model)
+            
+            response = client.chat.completions.create(
+                messages=misrtal_messages,
+                model=self.model,
+                max_tokens=max_tokens
+            )
+        
+            try:
+                return response.choices[0].message.content
+            except Exception as e:
+                print("Failed to call LLM: " + str(e))
+                return ""
+            
+        elif self.model.startswith("THUDM"):
+            # THUDM/cogagent-chat-hf
+            print("Call CogAgent")
+            messages = payload["messages"]
+            max_tokens = payload["max_tokens"]
+            top_p = payload["top_p"]
+            temperature = payload["temperature"]
+            
+            cog_messages = []
+            
+            for i, message in enumerate(messages):
+                cog_message = {
+                    "role": message["role"],
+                    "content": []
+                }
+
+                for part in message["content"]:
+                    if part['type'] == "image_url":
+                        cog_message['content'].append({"type": "image_url", "image_url": {"url": part['image_url']['url'] }  })
+                        
+                    if part['type'] == "text":
+                        cog_message['content'].append({"type": "text", "text": part['text']})
+            
+                cog_messages.append(cog_message)
+
+            # the cogagent not support system message in our endpoint, so we concatenate it at the first user message
+            if cog_messages[0]['role'] == "system":
+                cog_system_message_item = cog_messages[0]['content'][0]
+                cog_messages[1]['content'].insert(0, cog_system_message_item)
+                cog_messages.pop(0)
+            
+            payload = {
+                "model": self.model,
+                "max_tokens": max_tokens,
+                "messages": cog_messages
+            }
+
+            base_url = "http://127.0.0.1:8000"
+            
+            response = requests.post(f"{base_url}/v1/chat/completions", json=payload, stream=False)
+            if response.status_code == 200:
+                decoded_line = response.json()
+                content = decoded_line.get("choices", [{}])[0].get("message", "").get("content", "")
+                return content
+            else:
+                print("Failed to call LLM: ", response.status_code)
+                return ""
+
 
         elif self.model.startswith("gemini"):
             def encoded_img_to_pil_img(data_str):