import json

[mariaholland]

.@article{su2025agentfounder, title={Towards General Agentic Intelligence via Environment Scaling}, author={Runnan Fang, Shihao Cai, Baixuan Li, Jialong Wu, Guangyu Li, Wenbiao Yin, Xinyu Wang, Xiaobin Wang, Liangcai Su, Zhen Zhang, Shibin Wu, Zhengwei Tao, Yong Jiang, Pengjun Xie, Fei Huang, Jingren Zhou}, year={2025}, journal={arXiv preprint arXiv:2509.13311}, }.

///

.import json import json5 import os from typing import Dict, Iterator, List, Literal, Optional, Tuple, Union from qwen_agent.llm.schema import Message from qwen_agent.utils.utils import build_text_completion_prompt from openai import OpenAI, APIError, APIConnectionError, APITimeoutError import tiktoken from transformers import AutoTokenizer from datetime import datetime from qwen_agent.agents.fncall_agent import FnCallAgent from qwen_agent.llm import BaseChatModel from qwen_agent.llm.schema import ASSISTANT, DEFAULT_SYSTEM_MESSAGE, Message from qwen_agent.settings import MAX_LLM_CALL_PER_RUN from qwen_agent.tools import BaseTool from qwen_agent.utils.utils import format_as_text_message, merge_generate_cfgs from prompt import * import time import asyncio from tool_file import * from tool_scholar import * from tool_python import * from tool_search import * from tool_visit import * OBS_START = '<tool_response>' OBS_END = '\n</tool_response>' MAX_LLM_CALL_PER_RUN = int(os.getenv('MAX_LLM_CALL_PER_RUN', 100)) TOOL_CLASS = [ FileParser(), Scholar(), Visit(), Search(), PythonInterpreter(), ] TOOL_MAP = {tool.name: tool for tool in TOOL_CLASS} import random import datetime def today_date(): return datetime.date.today().strftime("%Y-%m-%d") class MultiTurnReactAgent(FnCallAgent): def init(self, function_list: Optional[List[Union[str, Dict, BaseTool]]] = None, llm: Optional[Union[Dict, BaseChatModel]] = None, **kwargs): self.llm_generate_cfg = llm["generate_cfg"] self.llm_local_path = llm["model"] def sanity_check_output(self, content): return "" in content and "" in content def call_server(self, msgs, planning_port, max_tries=10): openai_api_key = "EMPTY" openai_api_base = f"http://127.0.0.1:{planning_port}/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, timeout=600.0, ) base_sleep_time = 1 for attempt in range(max_tries): try: print(f"--- Attempting to call the service, try {attempt + 1}/{max_tries} ---") chat_response = client.chat.completions.create( model=self.model, messages=msgs, stop=["\n<tool_response>", "<tool_response>"], temperature=self.llm_generate_cfg.get('temperature', 0.6), top_p=self.llm_generate_cfg.get('top_p', 0.95), logprobs=True, max_tokens=10000, presence_penalty=self.llm_generate_cfg.get('presence_penalty', 1.1) ) content = chat_response.choices[0].message.content if content and content.strip(): print("--- Service call successful, received a valid response ---") return content.strip() else: print(f"Warning: Attempt {attempt + 1} received an empty response.") except (APIError, APIConnectionError, APITimeoutError) as e: print(f"Error: Attempt {attempt + 1} failed with an API or network error: {e}") except Exception as e: print(f"Error: Attempt {attempt + 1} failed with an unexpected error: {e}") if attempt < max_tries - 1: sleep_time = base_sleep_time * (2 ** attempt) + random.uniform(0, 1) sleep_time = min(sleep_time, 30) print(f"Retrying in {sleep_time:.2f} seconds...") time.sleep(sleep_time) else: print("Error: All retry attempts have been exhausted. The call has failed.") return f"vllm server error!!!" def count_tokens(self, messages, model="gpt-4o"): try: tokenizer = AutoTokenizer.from_pretrained(self.llm_local_path) except Exception as e: tokenizer = tiktoken.encoding_for_model(model) full_message = [Message(**x) for x in messages] full_prompt = build_text_completion_prompt(full_message, allow_special=True) return len(tokenizer.encode(full_prompt)) def _run(self, data: str, model: str, **kwargs) -> List[List[Message]]: self.model=model try: question = data['item']['question'] except: raw_msg = data['item']['messages'][1]["content"] question = raw_msg.split("User:")[1].strip() if "User:" in raw_msg else raw_msg start_time = time.time() planning_port = data['planning_port'] answer = data['item']['answer'] self.user_prompt = question system_prompt = SYSTEM_PROMPT cur_date = today_date() system_prompt = system_prompt + str(cur_date) messages = [{"role": "system", "content": system_prompt}, {"role": "user", "content": question}] num_llm_calls_available = MAX_LLM_CALL_PER_RUN round = 0 while num_llm_calls_available > 0: # Check whether time is reached if time.time() - start_time > 150 * 60: # 150 minutes in seconds prediction = 'No answer found after 2h30mins' termination = 'No answer found after 2h30mins' result = { "question": question, "answer": answer, "messages": messages, "prediction": prediction, "termination": termination } return result round += 1 num_llm_calls_available -= 1 content = self.call_server(messages, planning_port) print(f'Round {round}: {content}') if '<tool_response>' in content: pos = content.find('<tool_response>') content = content[:pos] messages.append({"role": "assistant", "content": content.strip()}) if '<tool_call>' in content and '</tool_call>' in content: tool_call = content.split('<tool_call>')[1].split('</tool_call>')[0] try: if "python" in tool_call.lower(): try: code_raw=content.split('<tool_call>')[1].split('</tool_call>')[0].split('')[1].split('')[0].strip() result = TOOL_MAP['PythonInterpreter'].call(code_raw) except: result = "[Python Interpreter Error]: Formatting error." else: tool_call = json5.loads(tool_call) tool_name = tool_call.get('name', '') tool_args = tool_call.get('arguments', {}) result = self.custom_call_tool(tool_name, tool_args) except: result = 'Error: Tool call is not a valid JSON. Tool call must contain a valid "name" and "arguments" field.' result = "<tool_response>\n" + result + "\n</tool_response>" # print(result) messages.append({"role": "user", "content": result}) if '' in content and '' in content: termination = 'answer' break if num_llm_calls_available <= 0 and '' not in content: messages[-1]['content'] = 'Sorry, the number of llm calls exceeds the limit.' max_tokens = 108 * 1024 token_count = self.count_tokens(messages) print(f"round: {round}, token count: {token_count}") if token_count > max_tokens: print(f"Token quantity exceeds the limit: {token_count} > {max_tokens}") messages[-1]['content'] = "You have now reached the maximum context length you can handle. You should stop making tool calls and, based on all the information above, think again and provide what you consider the most likely answer in the following format:your final thinking\nyour answer" content = self.call_server(messages, planning_port) messages.append({"role": "assistant", "content": content.strip()}) if '' in content and '' in content: prediction = messages[-1]['content'].split('')[1].split('')[0] termination = 'generate an answer as token limit reached' else: prediction = messages[-1]['content'] termination = 'format error: generate an answer as token limit reached' result = { "question": question, "answer": answer, "messages": messages, "prediction": prediction, "termination": termination } return result if '' in messages[-1]['content']: prediction = messages[-1]['content'].split('')[1].split('')[0] termination = 'answer' else: prediction = 'No answer found.' termination = 'answer not found' if num_llm_calls_available == 0: termination = 'exceed available llm calls' result = { "question": question, "answer": answer, "messages": messages, "prediction": prediction, "termination": termination } return result def custom_call_tool(self, tool_name: str, tool_args: dict, **kwargs): if tool_name in TOOL_MAP: tool_args["params"] = tool_args if "python" in tool_name.lower(): result = TOOL_MAP['PythonInterpreter'].call(tool_args) elif tool_name == "parse_file": params = {"files": tool_args["files"]} raw_result = asyncio.run(TOOL_MAP[tool_name].call(params, file_root_path="./eval_data/file_corpus")) result = raw_result if not isinstance(raw_result, str): result = str(raw_result) else: raw_result = TOOL_MAP[tool_name].call(tool_args, **kwargs) result = raw_result return result else: return f"Error: Tool {tool_name} not found" @misc{qiao2025webresearcherunleashingunboundedreasoning, title={WebResearcher: Unleashing unbounded reasoning capability in Long-Horizon Agents}, author={Zile Qiao and Guoxin Chen and Xuanzhong Chen and Donglei Yu and Wenbiao Yin and Xinyu Wang and Zhen Zhang and Baixuan Li and Huifeng Yin and Kuan Li and Rui Min and Minpeng Liao and Yong Jiang and Pengjun Xie and Fei Huang and Jingren Zhou}, year={2025}, eprint={2509.13309}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2509.13309}, } { // Place your snippets for clojure here. Each snippet is defined under a snippet name and has a prefix, body and // description. The prefix is what is used to trigger the snippet and the body will be expanded and inserted. Possible variables are: // $1, $2 for tab stops, $0 for the final cursor position, and ${1:label}, ${2:another} for placeholders. Placeholders with the // same ids are connected. // Example: // "Print to console": { // "prefix": "log", // "body": [ // "console.log('$1');", // "$2" // ], // "description": "Log output to console" // } } gitaly['configuration'] = { git: { config: [ { key: 'core.quotepath', value: 'false' } ] } } gitaly['configuration'] = { git: { config: [ { key: 'core.quotepath', value: 'false' } ] } } 3.