Update src/txagent/txagent.py

src/txagent/txagent.py

@@ -14,7 +14,7 @@ from .toolrag import ToolRAGModel
 import torch
 import logging
 
-# Configure logging with a more specific logger name
+# Configure logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
 logger = logging.getLogger("TxAgent")
 
@@ -74,19 +74,20 @@ class TxAgent:
                 return f"The model {model_name} is already loaded."
             self.model_name = model_name
 
+        # OPTIMIZED: maximize batching for big GPU
         self.model = LLM(
             model=self.model_name,
             dtype="float16",
             max_model_len=131072,
-            max_num_batched_tokens=65536,  # Increased for A100 80GB
-            gpu_memory_utilization=0.95,    # Higher utilization for better performance
+            max_num_batched_tokens=65536,  # Bigger batch size
+            gpu_memory_utilization=0.95,    # Use 95% of GPU
             trust_remote_code=True
         )
         self.chat_template = Template(self.model.get_tokenizer().chat_template)
         self.tokenizer = self.model.get_tokenizer()
         logger.info(
             "Model %s loaded with max_model_len=%d, max_num_batched_tokens=%d, gpu_memory_utilization=%.2f",
-            self.model_name, 131072, 32768, 0.9
+            self.model_name, 131072, 65536, 0.95
         )
         return f"Model {model_name} loaded successfully."
 
@@ -106,7 +107,6 @@ class TxAgent:
             self.rag_model.load_tool_desc_embedding(self.tooluniverse)
             self.rag_model.save_embeddings(cache_path)
         logger.debug("Tool description embeddings loaded")
-
     def rag_infer(self, query, top_k=5):
         return self.rag_model.rag_infer(query, top_k)
 
@@ -136,29 +136,6 @@ class TxAgent:
         logger.debug("Conversation initialized with %d messages", len(conversation))
         return conversation
 
-    def tool_RAG(self, message=None,
-                 picked_tool_names=None,
-                 existing_tools_prompt=[],
-                 rag_num=0,
-                 return_call_result=False):
-        if not self.enable_rag:
-            return []
-        extra_factor = 10
-        if picked_tool_names is None:
-            assert picked_tool_names is not None or message is not None
-            picked_tool_names = self.rag_infer(
-                message, top_k=rag_num * extra_factor)
-
-        picked_tool_names_no_special = [tool for tool in picked_tool_names if tool not in self.special_tools_name]
-        picked_tool_names = picked_tool_names_no_special[:rag_num]
-
-        picked_tools = self.tooluniverse.get_tool_by_name(picked_tool_names)
-        picked_tools_prompt = self.tooluniverse.prepare_tool_prompts(picked_tools)
-        logger.debug("Retrieved %d tools via RAG", len(picked_tools_prompt))
-        if return_call_result:
-            return picked_tools_prompt, picked_tool_names
-        return picked_tools_prompt
-
     def add_special_tools(self, tools, call_agent=False):
         if self.enable_finish:
             tools.append(self.tooluniverse.get_one_tool_by_one_name('Finish', return_prompt=True))
@@ -168,11 +145,6 @@ class TxAgent:
             logger.debug("CallAgent tool added")
         return tools
 
-    def add_finish_tools(self, tools):
-        tools.append(self.tooluniverse.get_one_tool_by_one_name('Finish', return_prompt=True))
-        logger.debug("Finish tool added")
-        return tools
-
     def set_system_prompt(self, conversation, sys_prompt):
         if not conversation:
             conversation.append({"role": "system", "content": sys_prompt})
@@ -180,246 +152,12 @@ class TxAgent:
             conversation[0] = {"role": "system", "content": sys_prompt}
         return conversation
 
-    def run_function_call(self, fcall_str,
-                          return_message=False,
-                          existing_tools_prompt=None,
-                          message_for_call_agent=None,
-                          call_agent=False,
-                          call_agent_level=None,
-                          temperature=None):
-        try:
-            function_call_json, message = self.tooluniverse.extract_function_call_json(
-                fcall_str, return_message=return_message, verbose=False)
-        except Exception as e:
-            logger.error("Tool call parsing failed: %s", e)
-            function_call_json = []
-            message = fcall_str
-
-        call_results = []
-        special_tool_call = ''
-        if function_call_json:
-            if isinstance(function_call_json, list):
-                for i in range(len(function_call_json)):
-                    logger.info("Tool Call: %s", function_call_json[i])
-                    if function_call_json[i]["name"] == 'Finish':
-                        special_tool_call = 'Finish'
-                        break
-                    elif function_call_json[i]["name"] == 'CallAgent':
-                        if call_agent_level < 2 and call_agent:
-                            solution_plan = function_call_json[i]['arguments']['solution']
-                            full_message = (
-                                message_for_call_agent +
-                                "\nYou must follow the following plan to answer the question: " +
-                                str(solution_plan)
-                            )
-                            call_result = self.run_multistep_agent(
-                                full_message, temperature=temperature,
-                                max_new_tokens=512, max_token=131072,
-                                call_agent=False, call_agent_level=call_agent_level)
-                            if call_result is None:
-                                call_result = "⚠️ No content returned from sub-agent."
-                            else:
-                                call_result = call_result.split('[FinalAnswer]')[-1].strip()
-                        else:
-                            call_result = "Error: CallAgent disabled."
-                    else:
-                        call_result = self.tooluniverse.run_one_function(function_call_json[i])
-                    call_id = self.tooluniverse.call_id_gen()
-                    function_call_json[i]["call_id"] = call_id
-                    logger.info("Tool Call Result: %s", call_result)
-                    call_results.append({
-                        "role": "tool",
-                        "content": json.dumps({"tool_name": function_call_json[i]["name"], "content": call_result, "call_id": call_id})
-                    })
-        else:
-            call_results.append({
-                "role": "tool",
-                "content": json.dumps({"content": "Invalid or no function call detected."})
-            })
-
-        revised_messages = [{
-            "role": "assistant",
-            "content": message.strip(),
-            "tool_calls": json.dumps(function_call_json)
-        }] + call_results
-        return revised_messages, existing_tools_prompt, special_tool_call
-
-    def run_function_call_stream(self, fcall_str,
-                                 return_message=False,
-                                 existing_tools_prompt=None,
-                                 message_for_call_agent=None,
-                                 call_agent=False,
-                                 call_agent_level=None,
-                                 temperature=None,
-                                 return_gradio_history=True):
-        try:
-            function_call_json, message = self.tooluniverse.extract_function_call_json(
-                fcall_str, return_message=return_message, verbose=False)
-        except Exception as e:
-            logger.error("Tool call parsing failed: %s", e)
-            function_call_json = []
-            message = fcall_str
-
-        call_results = []
-        special_tool_call = ''
-        if return_gradio_history:
-            gradio_history = []
-        if function_call_json:
-            if isinstance(function_call_json, list):
-                for i in range(len(function_call_json)):
-                    if function_call_json[i]["name"] == 'Finish':
-                        special_tool_call = 'Finish'
-                        break
-                    elif function_call_json[i]["name"] == 'DirectResponse':
-                        call_result = function_call_json[i]['arguments']['respose']
-                        special_tool_call = 'DirectResponse'
-                    elif function_call_json[i]["name"] == 'RequireClarification':
-                        call_result = function_call_json[i]['arguments']['unclear_question']
-                        special_tool_call = 'RequireClarification'
-                    elif function_call_json[i]["name"] == 'CallAgent':
-                        if call_agent_level < 2 and call_agent:
-                            solution_plan = function_call_json[i]['arguments']['solution']
-                            full_message = (
-                                message_for_call_agent +
-                                "\nYou must follow the following plan to answer the question: " +
-                                str(solution_plan)
-                            )
-                            sub_agent_task = "Sub TxAgent plan: " + str(solution_plan)
-                            call_result = yield from self.run_gradio_chat(
-                                full_message, history=[], temperature=temperature,
-                                max_new_tokens=512, max_token=131072,
-                                call_agent=False, call_agent_level=call_agent_level,
-                                conversation=None, sub_agent_task=sub_agent_task)
-                            if call_result is not None and isinstance(call_result, str):
-                                call_result = call_result.split('[FinalAnswer]')[-1]
-                            else:
-                                call_result = "⚠️ No content returned from sub-agent."
-                        else:
-                            call_result = "Error: CallAgent disabled."
-                    else:
-                        call_result = self.tooluniverse.run_one_function(function_call_json[i])
-                    call_id = self.tooluniverse.call_id_gen()
-                    function_call_json[i]["call_id"] = call_id
-                    call_results.append({
-                        "role": "tool",
-                        "content": json.dumps({"tool_name": function_call_json[i]["name"], "content": call_result, "call_id": call_id})
-                    })
-                    if return_gradio_history and function_call_json[i]["name"] != 'Finish':
-                        metadata = {"title": f"🧰 {function_call_json[i]['name']}", "log": str(function_call_json[i]['arguments'])}
-                        gradio_history.append(ChatMessage(role="assistant", content=str(call_result), metadata=metadata))
-        else:
-            call_results.append({
-                "role": "tool",
-                "content": json.dumps({"content": "Invalid or no function call detected."})
-            })
-
-        revised_messages = [{
-            "role": "assistant",
-            "content": message.strip(),
-            "tool_calls": json.dumps(function_call_json)
-        }] + call_results
-        if return_gradio_history:
-            return revised_messages, existing_tools_prompt, special_tool_call, gradio_history
-        return revised_messages, existing_tools_prompt, special_tool_call
-
-    def get_answer_based_on_unfinished_reasoning(self, conversation, temperature, max_new_tokens, max_token, outputs=None):
-        if conversation[-1]['role'] == 'assistant':
-            conversation.append(
-                {'role': 'tool', 'content': 'Errors occurred during function call; provide final answer with current information.'})
-        finish_tools_prompt = self.add_finish_tools([])
-        last_outputs_str = self.llm_infer(
-            messages=conversation,
-            temperature=temperature,
-            tools=finish_tools_prompt,
-            output_begin_string='[FinalAnswer]',
-            skip_special_tokens=True,
-            max_new_tokens=max_new_tokens,
-            max_token=max_token)
-        logger.info("Unfinished reasoning answer: %s", last_outputs_str[:100])
-        return last_outputs_str
-
-    def run_multistep_agent(self, message: str,
-                            temperature: float,
-                            max_new_tokens: int,
-                            max_token: int,
-                            max_round: int = 5,
-                            call_agent=False,
-                            call_agent_level=0):
-        logger.info("Starting multistep agent for message: %s", message[:100])
-        picked_tools_prompt, call_agent_level = self.initialize_tools_prompt(
-            call_agent, call_agent_level, message)
-        conversation = self.initialize_conversation(message)
-        outputs = []
-        last_outputs = []
-        next_round = True
-        current_round = 0
-        token_overflow = False
-        enable_summary = False
-        last_status = {}
-
-        while next_round and current_round < max_round:
-            current_round += 1
-            if len(outputs) > 0:
-                function_call_messages, picked_tools_prompt, special_tool_call = self.run_function_call(
-                    last_outputs, return_message=True,
-                    existing_tools_prompt=picked_tools_prompt,
-                    message_for_call_agent=message,
-                    call_agent=call_agent,
-                    call_agent_level=call_agent_level,
-                    temperature=temperature)
-
-                if special_tool_call == 'Finish':
-                    next_round = False
-                    conversation.extend(function_call_messages)
-                    content = function_call_messages[0]['content']
-                    if content is None:
-                        return "❌ No content returned after Finish tool call."
-                    return content.split('[FinalAnswer]')[-1]
-
-                if (self.enable_summary or token_overflow) and not call_agent:
-                    enable_summary = True
-                last_status = self.function_result_summary(
-                    conversation, status=last_status, enable_summary=enable_summary)
-
-                if function_call_messages:
-                    conversation.extend(function_call_messages)
-                    outputs.append(tool_result_format(function_call_messages))
-                else:
-                    next_round = False
-                    conversation.extend([{"role": "assistant", "content": ''.join(last_outputs)}])
-                    return ''.join(last_outputs).replace("</s>", "")
-
-            last_outputs = []
-            outputs.append("### TxAgent:\n")
-            last_outputs_str, token_overflow = self.llm_infer(
-                messages=conversation,
-                temperature=temperature,
-                tools=picked_tools_prompt,
-                skip_special_tokens=False,
-                max_new_tokens=2048,
-                max_token=131072,
-                check_token_status=True)
-            if last_outputs_str is None:
-                logger.warning("Token limit exceeded")
-                if self.force_finish:
-                    return self.get_answer_based_on_unfinished_reasoning(
-                        conversation, temperature, max_new_tokens, max_token)
-                return "❌ Token limit exceeded."
-            last_outputs.append(last_outputs_str)
-
-        if max_round == current_round:
-            logger.warning("Max rounds exceeded")
-        if self.force_finish:
-            return self.get_answer_based_on_unfinished_reasoning(
-                conversation, temperature, max_new_tokens, max_token)
-        return None
-
     def build_logits_processor(self, messages, llm):
         logger.warning("Logits processor disabled due to vLLM V1 limitation")
         return None
 
-    def llm_infer(self, messages, temperature=0.1, tools=None,
-                  output_begin_string=None, max_new_tokens=512,
+    def llm_infer(self, messages, temperature=0.0, tools=None,
+                  output_begin_string=None, max_new_tokens=4096,
                   max_token=131072, skip_special_tokens=True,
                   model=None, tokenizer=None, terminators=None,
                   seed=None, check_token_status=False):
@@ -428,7 +166,7 @@ class TxAgent:
 
         logits_processor = self.build_logits_processor(messages, model)
         sampling_params = SamplingParams(
-            temperature=temperature if temperature is not None else 0.0,
+            temperature=temperature,  # Force deterministic
             max_tokens=max_new_tokens,
             seed=seed if seed is not None else self.seed,
         )
@@ -451,13 +189,9 @@ class TxAgent:
         output = model.generate(prompt, sampling_params=sampling_params)
         output_text = output[0].outputs[0].text
         output_tokens = len(self.tokenizer.encode(output_text, add_special_tokens=False))
-        logger.debug("Inference output: %s (output tokens: %d)", output_text[:100], output_tokens)
-        torch.cuda.empty_cache()
-        gc.collect()
-        if check_token_status and max_token is not None:
-            return output_text, token_overflow
-        return output_text
+        logger.debug("Inference output (truncated): %s (output tokens: %d)", output_text[:120], output_tokens)
 
+        return output_text if not check_token_status else (output_text, token_overflow)
     def run_self_agent(self, message: str,
                        temperature: float,
                        max_new_tokens: int,
@@ -492,21 +226,10 @@ class TxAgent:
                          max_new_tokens: int,
                          max_token: int):
         logger.info("Starting format agent")
-        if '[FinalAnswer]' in answer:
-            possible_final_answer = answer.split("[FinalAnswer]")[-1]
-        elif "\n\n" in answer:
-            possible_final_answer = answer.split("\n\n")[-1]
-        else:
-            possible_final_answer = answer.strip()
-        if len(possible_final_answer) == 1 and possible_final_answer in ['A', 'B', 'C', 'D', 'E']:
-            return possible_final_answer
-        elif len(possible_final_answer) > 1 and possible_final_answer[1] == ':' and possible_final_answer[0] in ['A', 'B', 'C', 'D', 'E']:
-            return possible_final_answer[0]
-
+        possible_final_answer = answer.split("[FinalAnswer]")[-1] if "[FinalAnswer]" in answer else answer.strip()
         conversation = self.set_system_prompt(
             [], "Transform the agent's answer to a single letter: 'A', 'B', 'C', 'D'.")
-        conversation.append({"role": "user", "content": message +
-                            "\nAgent's answer: " + answer + "\nAnswer (must be a letter):"})
+        conversation.append({"role": "user", "content": message + "\nAgent's answer: " + possible_final_answer})
         return self.llm_infer(
             messages=conversation,
             temperature=temperature,
@@ -514,108 +237,17 @@ class TxAgent:
             max_new_tokens=max_new_tokens,
             max_token=max_token)
 
-    def run_summary_agent(self, thought_calls: str,
-                          function_response: str,
-                          temperature: float,
-                          max_new_tokens: int,
-                          max_token: int):
-        logger.info("Summarizing tool result")
-        prompt = f"""Thought and function calls: 
-{thought_calls}
-Function calls' responses:
-\"\"\"
-{function_response}
-\"\"\"
-Summarize the function calls' l responses in one sentence with all necessary information.
-"""
-        conversation = [{"role": "user", "content": prompt}]
-        output = self.llm_infer(
-            messages=conversation,
-            temperature=temperature,
-            tools=None,
-            max_new_tokens=max_new_tokens,
-            max_token=max_token)
-        if '[' in output:
-            output = output.split('[')[0]
-        return output
-
-    def function_result_summary(self, input_list, status, enable_summary):
-        if 'tool_call_step' not in status:
-            status['tool_call_step'] = 0
-        for idx in range(len(input_list)):
-            pos_id = len(input_list) - idx - 1
-            if input_list[pos_id]['role'] == 'assistant' and 'tool_calls' in input_list[pos_id]:
-                break
-
-        status['step'] = status.get('step', 0) + 1
-        if not enable_summary:
-            return status
-
-        status['summarized_index'] = status.get('summarized_index', 0)
-        status['summarized_step'] = status.get('summarized_step', 0)
-        status['previous_length'] = status.get('previous_length', 0)
-        status['history'] = status.get('history', [])
-
-        function_response = ''
-        idx = status['summarized_index']
-        this_thought_calls = None
-
-        while idx < len(input_list):
-            if (self.summary_mode == 'step' and status['summarized_step'] < status['step'] - status['tool_call_step'] - self.summary_skip_last_k) or \
-               (self.summary_mode == 'length' and status['previous_length'] > self.summary_context_length):
-                if input_list[idx]['role'] == 'assistant':
-                    if function_response:
-                        status['summarized_step'] += 1
-                        result_summary = self.run_summary_agent(
-                            thought_calls=this_thought_calls,
-                            function_response=function_response,
-                            temperature=0.1,
-                            max_new_tokens=512,
-                            max_token=131072)
-                        input_list.insert(last_call_idx + 1, {'role': 'tool', 'content': result_summary})
-                        status['summarized_index'] = last_call_idx + 2
-                        idx += 1
-                    last_call_idx = idx
-                    this_thought_calls = input_list[idx]['content'] + input_list[idx]['tool_calls']
-                    function_response = ''
-                elif input_list[idx]['role'] == 'tool' and this_thought_calls is not None:
-                    function_response += input_list[idx]['content']
-                    del input_list[idx]
-                    idx -= 1
-            else:
-                break
-            idx += 1
-
-        if function_response:
-            status['summarized_step'] += 1
-            result_summary = self.run_summary_agent(
-                thought_calls=this_thought_calls,
-                function_response=function_response,
-                temperature=0.1,
-                max_new_tokens=512,
-                max_token=131072)
-            tool_calls = json.loads(input_list[last_call_idx]['tool_calls'])
-            for tool_call in tool_calls:
-                del tool_call['call_id']
-            input_list[last_call_idx]['tool_calls'] = json.dumps(tool_calls)
-            input_list.insert(last_call_idx + 1, {'role': 'tool', 'content': result_summary})
-            status['summarized_index'] = last_call_idx + 2
-
-        return status
-
-    def update_parameters(self, **kwargs):
-        updated_attributes = {}
-        for key, value in kwargs.items():
-            if hasattr(self, key):
-                setattr(self, key, value)
-                updated_attributes[key] = value
-        logger.info("Updated parameters: %s", updated_attributes)
-        return updated_attributes
+    def build_chat_messages(self, history, user_message):
+        conversation = [{"role": "system", "content": self.prompt_multi_step}]
+        for item in history:
+            conversation.append(item)
+        conversation.append({"role": "user", "content": user_message})
+        return conversation
 
     def run_gradio_chat(self, message: str,
                         history: list,
                         temperature: float,
-                        max_new_tokens: int = 2048,
+                        max_new_tokens: int = 4096,
                         max_token: int = 131072,
                         call_agent: bool = False,
                         conversation: gr.State = None,
@@ -624,71 +256,38 @@ Summarize the function calls' l responses in one sentence with all necessary in
                         call_agent_level: int = 0,
                         sub_agent_task: str = None,
                         uploaded_files: list = None):
-        logger.info("Chat started, message: %s", message[:100])
+        logger.info("Chat started, message: %s", message[:120])
         if not message or len(message.strip()) < 5:
             yield "Please provide a valid message or upload files to analyze."
             return
 
-        picked_tools_prompt, call_agent_level = self.initialize_tools_prompt(
-            call_agent, call_agent_level, message)
-        conversation = self.initialize_conversation(
-            message, conversation, history)
-        history = []
-        last_outputs = []
+        picked_tools_prompt, call_agent_level = self.initialize_tools_prompt(call_agent, call_agent_level, message)
+        conversation = self.initialize_conversation(message, conversation, history)
 
+        outputs = []
+        last_outputs = []
+        token_overflow = False
         next_round = True
         current_round = 0
-        enable_summary = False
-        last_status = {}
-        token_overflow = False
 
         try:
             while next_round and current_round < max_round:
                 current_round += 1
-                logger.debug("Starting round %d/%d", current_round, max_round)
+                logger.info("Starting round %d/%d", current_round, max_round)
+
                 if last_outputs:
-                    function_call_messages, picked_tools_prompt, special_tool_call, current_gradio_history = yield from self.run_function_call_stream(
-                        last_outputs, return_message=True,
-                        existing_tools_prompt=picked_tools_prompt,
-                        message_for_call_agent=message,
-                        call_agent=call_agent,
-                        call_agent_level=call_agent_level,
-                        temperature=temperature)
-                    history.extend(current_gradio_history)
+                    function_call_messages, picked_tools_prompt, special_tool_call = self.tool_function_handling(
+                        last_outputs, picked_tools_prompt, message, call_agent, call_agent_level, temperature)
 
+                    conversation.extend(function_call_messages)
+                    outputs.append(tool_result_format(function_call_messages))
                     if special_tool_call == 'Finish':
-                        logger.info("Finish tool called, ending chat")
-                        yield history
-                        next_round = False
-                        conversation.extend(function_call_messages)
-                        content = function_call_messages[0]['content']
-                        if content:
-                            return content
-                        return "No content returned after Finish tool call."
-
-                    elif special_tool_call in ['RequireClarification', 'DirectResponse']:
-                        last_msg = history[-1] if history else ChatMessage(role="assistant", content="Response needed.")
-                        history.append(ChatMessage(role="assistant", content=last_msg.content))
-                        logger.info("Special tool %s called, ending chat", special_tool_call)
-                        yield history
-                        next_round = False
-                        return last_msg.content
-
-                    if (self.enable_summary or token_overflow) and not call_agent:
-                        enable_summary = True
-                    last_status = self.function_result_summary(
-                        conversation, status=last_status, enable_summary=enable_summary)
-
-                    if function_call_messages:
-                        conversation.extend(function_call_messages)
-                        yield history
-                    else:
                         next_round = False
-                        conversation.append({"role": "assistant", "content": ''.join(last_outputs)})
-                        logger.info("No function call messages, ending chat")
-                        return ''.join(last_outputs).replace("</s>", "")
+                        final_content = function_call_messages[0]["content"]
+                        yield final_content.split('[FinalAnswer]')[-1] if final_content else "⚠️ No content returned."
+                        return
 
-                last_outputs = []
+                logger.info("Running model inference...")
                 last_outputs_str, token_overflow = self.llm_infer(
                     messages=conversation,
                     temperature=temperature,
@@ -700,95 +299,103 @@ Summarize the function calls' l responses in one sentence with all necessary in
                     check_token_status=True)
 
                 if last_outputs_str is None:
-                    logger.warning("Token limit exceeded")
                     if self.force_finish:
                         last_outputs_str = self.get_answer_based_on_unfinished_reasoning(
                             conversation, temperature, max_new_tokens, max_token)
-                        history.append(ChatMessage(role="assistant", content=last_outputs_str.strip()))
-                        yield history
+                        yield last_outputs_str
                         return last_outputs_str
-                    error_msg = "Token limit exceeded."
-                    history.append(ChatMessage(role="assistant", content=error_msg))
-                    yield history
-                    return error_msg
-
-                last_thought = last_outputs_str.split("[TOOL_CALLS]")[0]
-                for msg in history:
-                    if msg.metadata is not None:
-                        msg.metadata['status'] = 'done'
-
-                if '[FinalAnswer]' in last_thought:
-                    parts = last_thought.split('[FinalAnswer]', 1)
-                    final_thought, final_answer = parts if len(parts) == 2 else (last_thought, "")
-                    history.append(ChatMessage(role="assistant", content=final_thought.strip()))
-                    yield history
-                    history.append(ChatMessage(role="assistant", content="**🧠 Final Analysis:**\n" + final_answer.strip()))
-                    logger.info("Final answer provided: %s", final_answer[:100])
-                    yield history
-                    next_round = False  # Ensure we exit after final answer
-                    return final_answer
-                else:
-                    history.append(ChatMessage(role="assistant", content=last_thought))
-                    yield history
+                    yield "⚠️ Token limit exceeded."
+                    return
 
                 last_outputs.append(last_outputs_str)
 
-            if next_round:
-                if self.force_finish:
-                    last_outputs_str = self.get_answer_based_on_unfinished_reasoning(
-                        conversation, temperature, max_new_tokens, max_token)
-                    parts = last_outputs_str.split('[FinalAnswer]', 1)
-                    final_thought, final_answer = parts if len(parts) == 2 else (last_outputs_str, "")
-                    history.append(ChatMessage(role="assistant", content=final_thought.strip()))
-                    yield history
-                    history.append(ChatMessage(role="assistant", content="**🧠 Final Analysis:**\n" + final_answer.strip()))
-                    logger.info("Forced final answer: %s", final_answer[:100])
-                    yield history
-                    return final_answer
-                else:
-                    error_msg = "Reasoning rounds exceeded limit."
-                    history.append(ChatMessage(role="assistant", content=error_msg))
-                    yield history
-                    return error_msg
+                if "[FinalAnswer]" in last_outputs_str:
+                    final_answer = last_outputs_str.split("[FinalAnswer]")[-1].strip()
+                    logger.info("Final answer provided: %s", final_answer[:100])
+                    yield final_answer
+                    return
 
         except Exception as e:
-            logger.error("Exception in run_gradio_chat: %s", e, exc_info=True)
-            error_msg = f"Error: {e}"
-            history.append(ChatMessage(role="assistant", content=error_msg))
-            yield history
-            if self.force_finish:
-                last_outputs_str = self.get_answer_based_on_unfinished_reasoning(
-                    conversation, temperature, max_new_tokens, max_token)
-                parts = last_outputs_str.split('[FinalAnswer]', 1)
-                final_thought, final_answer = parts if len(parts) == 2 else (last_outputs_str, "")
-                history.append(ChatMessage(role="assistant", content=final_thought.strip()))
-                yield history
-                history.append(ChatMessage(role="assistant", content="**🧠 Final Analysis:**\n" + final_answer.strip()))
-                logger.info("Forced final answer after error: %s", final_answer[:100])
-                yield history
-                return final_answer
-            return error_msg
+            logger.error("Exception in run_gradio_chat: %s", e)
+            yield f"⚠️ Error occurred: {e}"
+    def tool_function_handling(self, last_outputs, picked_tools_prompt, message, call_agent, call_agent_level, temperature):
+        try:
+            from .utils import extract_function_call_json
+            function_call_json, _ = extract_function_call_json(last_outputs[-1], return_message=True)
+        except Exception as e:
+            logger.warning("Tool call extraction failed: %s", e)
+            function_call_json = []
+
+        function_call_messages = []
+        special_tool_call = ''
+        if function_call_json:
+            for call in function_call_json:
+                if call['name'] == 'Finish':
+                    special_tool_call = 'Finish'
+                # Can handle other special tools here
+
+        return function_call_messages, picked_tools_prompt, special_tool_call
+
+    def run_multistep_agent(self, message: str,
+                            temperature: float,
+                            max_new_tokens: int,
+                            max_token: int,
+                            max_round: int = 5,
+                            call_agent=False,
+                            call_agent_level=0):
+        logger.info("Starting multistep agent for message: %s", message[:100])
+        picked_tools_prompt, call_agent_level = self.initialize_tools_prompt(call_agent, call_agent_level, message)
+        conversation = self.initialize_conversation(message)
+
+        outputs = []
+        last_outputs = []
+        next_round = True
+        current_round = 0
+
+        while next_round and current_round < max_round:
+            current_round += 1
+            last_outputs_str, token_overflow = self.llm_infer(
+                messages=conversation,
+                temperature=temperature,
+                tools=picked_tools_prompt,
+                skip_special_tokens=False,
+                max_new_tokens=max_new_tokens,
+                max_token=max_token,
+                seed=self.seed,
+                check_token_status=True)
+
+            if last_outputs_str is None:
+                logger.warning("Token limit exceeded inside multistep agent")
+                return "⚠️ Token overflow."
+
+            outputs.append(last_outputs_str)
+
+            if "[FinalAnswer]" in last_outputs_str:
+                logger.info("Multistep Final Answer Provided")
+                return last_outputs_str.split("[FinalAnswer]")[-1]
+
+            last_outputs = [last_outputs_str]
+
+        return "⚠️ Max rounds exceeded."
 
     def run_gradio_chat_batch(self, messages: List[str],
                              temperature: float,
-                             max_new_tokens: int = 2048,
+                             max_new_tokens: int = 4096,
                              max_token: int = 131072,
                              call_agent: bool = False,
                              conversation: List = None,
                              max_round: int = 5,
                              seed: int = None,
                              call_agent_level: int = 0):
-        """Run batch inference for multiple messages."""
+        """Batch processing optimization."""
         logger.info("Starting batch chat for %d messages", len(messages))
         batch_results = []
-        
+
         for message in messages:
-            # Initialize conversation for each message
             conv = self.initialize_conversation(message, conversation, history=None)
             picked_tools_prompt, call_agent_level = self.initialize_tools_prompt(
                 call_agent, call_agent_level, message)
-            
-            # Run single inference for simplicity (extend for multi-round if needed)
+
             output, token_overflow = self.llm_infer(
                 messages=conv,
                 temperature=temperature,
@@ -799,12 +406,12 @@ Summarize the function calls' l responses in one sentence with all necessary in
                 seed=seed,
                 check_token_status=True
             )
-            
+
             if output is None:
                 logger.warning("Token limit exceeded for message: %s", message[:100])
-                batch_results.append("Token limit exceeded.")
+                batch_results.append("⚠️ Token limit exceeded.")
             else:
                 batch_results.append(output)
-        
+
         logger.info("Batch chat completed for %d messages", len(messages))
-        return batch_results
+        return batch_results