diff --git a/sources/agents/planner.tmp b/sources/agents/planner.tmp
deleted file mode 100644
index bd07db9..0000000
--- a/sources/agents/planner.tmp
+++ /dev/null
@@ -1,142 +0,0 @@
-import json
-from typing import List, Tuple, Type, Dict, Tuple
-from sources.utility import pretty_print, animate_thinking
-from sources.agents.agent import Agent
-from sources.agents.code_agent import CoderAgent
-from sources.agents.file_agent import FileAgent
-from sources.agents.browser_agent import BrowserAgent
-from sources.tools.tools import Tools
-
-
-class PlannerAgent(Agent):
-    def __init__(self, name, prompt_path, provider, verbose=False, browser=None):
-        """
-        The planner agent is a special agent that divides and conquers the task.
-        """
-        super().__init__(name, prompt_path, provider, verbose, None)
-        self.tools = {
-            "json": Tools()
-        }
-        self.tools['json'].tag = "json"
-        self.browser = browser
-        self.agents = {
-            "coder": CoderAgent(name, "prompts/base/coder_agent.txt", provider, verbose=False),
-            "file": FileAgent(name, "prompts/base/file_agent.txt", provider, verbose=False),
-            "web": BrowserAgent(name, "prompts/base/browser_agent.txt", provider, verbose=False, browser=browser)
-        }
-        self.role = {
-            "en": "Research, setup and code",
-            "fr": "Recherche, configuration et codage",
-            "zh": "研究，设置和编码",
-        }
-        self.type = "planner_agent"
-
-    def parse_agent_tasks(self, text: str) -> Tuple[list | None, list | None]:
-        """
-        Parse the agent tasks from the given text into json.
-        """
-        tasks = []
-        tasks_names = []
-
-        lines = text.strip().split('\n')
-        for line in lines:
-            if line is None or len(line) == 0:
-                continue
-            line = line.strip()
-            if '##' in line or line[0].isdigit():
-                tasks_names.append(line)
-                continue
-        blocks, _ = self.tools["json"].load_exec_block(text)
-        if blocks == None:
-            return (None, None)
-        for block in blocks:
-            line_json = json.loads(block)
-            if 'plan' in line_json:
-                for task in line_json['plan']:
-                    agent = {
-                        'agent': task['agent'],
-                        'id': task['id'],
-                        'task': task['task']
-                    }
-                    if 'need' in task:
-                        agent['need'] = task['need']
-                    tasks.append(agent)
-        if len(tasks_names) != len(tasks):
-            names = [task['task'] for task in tasks]
-            return zip(names, tasks)
-        return zip(tasks_names, tasks)
-    
-    def make_prompt(self, task: str, needed_infos: str) -> str:
-        """
-        Make a prompt for the agent.
-        """
-        if needed_infos is None:
-            needed_infos = "No needed informations."
-        prompt = f"""
-        You are given the following informations:
-        {needed_infos}
-        Your task is:
-        {task}
-        """
-        return prompt
-    
-    def show_plan(self, agents_tasks):
-        if agents_tasks == (None, None):
-            return
-        pretty_print("▂▘ P L A N ▝▂", color="output")
-        for task_name, task in agents_tasks:
-                    pretty_print(f"{task['agent']} -> {task['task']}", color="info")
-        pretty_print("▔▗ E N D ▖▔", color="output")
-    
-    def process(self, prompt: str, speech_module: type) -> str:
-        """
-        Process the prompt and divide the task between the agents.
-        Args:
-            prompt (str): The prompt to process.
-            speech_module (type): The speech module to use.
-        Returns:
-            str: The final answer resulting from successive task.
-        """
-        ok = False
-        agents_tasks = (None, None)
-        while not ok:
-            self.wait_message(speech_module)
-            animate_thinking("Thinking...", color="status")
-            self.memory.push('user', prompt)
-            answer, _ = self.llm_request()
-            pretty_print(answer.split('\n')[0], color="output")
-            agents_tasks = self.parse_agent_tasks(answer)
-            if agents_tasks == (None, None):
-                pretty_print("Failed to parse the tasks, retrying", color="failure")
-                prompt = "Task parsing failed. Please retry with proper json."
-                continue
-            self.show_plan(agents_tasks)
-            ok_str = input("Is the plan ok? (y/n): ")
-            if ok_str == 'y':
-                ok = True
-            else:
-                prompt = input("Please reformulate: ")
-
-        if agents_tasks == (None, None):
-            return "Failed to parse the tasks", reasoning
-        prev_agent_answer = None
-        for task_name, task in agents_tasks:
-            pretty_print(f"I will {task_name}.", color="info")
-            agent_prompt = self.make_prompt(task['task'], prev_agent_answer)
-            pretty_print(f"Assigned agent {task['agent']} to {task_name}", color="info")
-            if speech_module: speech_module.speak(f"I will {task_name}. I assigned the {task['agent']} agent to the task.")
-            try:
-                prev_agent_answer, _ = self.agents[task['agent'].lower()].process(agent_prompt, speech_module)
-                pretty_print(f"-- Agent answer ---\n\n", color="output")
-                self.agents[task['agent'].lower()].show_answer()
-                pretty_print(f"\n\n", color="output")
-            except Exception as e:
-                raise e
-        self.last_answer = prev_agent_answer
-        return prev_agent_answer, ""
-
-if __name__ == "__main__":
-    from llm_provider import Provider
-    server_provider = Provider("server", "deepseek-r1:14b", "192.168.1.100:5000")
-    agent = PlannerAgent("deepseek-r1:14b", "jarvis", "prompts/planner_agent.txt", server_provider)
-    ans = agent.process("Do a deep search of AI startup in Osaka and Tokyo, find at least 5, then save in the research_japan.txt file")
\ No newline at end of file