Initial project setup

sources/agents/agent.py

@@ -133,6 +133,8 @@ class Agent():
         Show the answer in a pretty way.
         Show code blocks and their respective feedback by inserting them in the ressponse.
         """
+        if self.last_answer is None:
+            return
         lines = self.last_answer.split("\n")
         for line in lines:
             if "block:" in line:
@@ -190,5 +192,5 @@ class Agent():
                 self.memory.push('user', feedback)
                 if save_path != None:
                     tool.save_block(blocks, save_path)
-        self.blocks_result = list(reversed(self.blocks_result))
+        self.blocks_result = self.blocks_result
         return True, feedback

sources/agents/browser_agent.py

@@ -93,7 +93,7 @@ class BrowserAgent(Agent):
         Your task:
         1. Decide if the current page answers the user’s query: {user_prompt}
           - If it does, take notes of the useful information, write down source, link or reference, then move to a new page.
-          - If it does and you completed use request, say REQUEST_EXIT
+          - If it does and you completed user request, say REQUEST_EXIT
           - If it doesn’t, say: Error: This page does not answer the user’s query then go back or navigate to another link.
         2. Navigate by either: 
           - Navigate to a navigation links (write the full URL, e.g., www.example.com/cats).
@@ -144,10 +144,10 @@ class BrowserAgent(Agent):
         animate_thinking("Thinking...", color="status")
         self.memory.push('user', prompt)
         answer, reasoning = self.llm_request()
-        output = f"Answer: {answer}" if len(answer) > 16 else f"Action: {answer}\nReasoning: {reasoning}"
-        pretty_print("-"*100)
+        output = answer if len(answer) > 16 else f"Action: {answer}\nReasoning: {reasoning}"
+        print()
         pretty_print(output, color="output")
-        pretty_print("-"*100)
+        print()
         return answer, reasoning
     
     def select_unvisited(self, search_result: List[str]) -> List[str]:
@@ -235,7 +235,8 @@ class BrowserAgent(Agent):
     def show_search_results(self, search_result: List[str]):
         pretty_print("\nSearch results:", color="output")
         for res in search_result:
-            pretty_print(f"Title: {res['title']} - Link: {res['link']}", color="output")
+            pretty_print(f"Title: {res['title']} - ", color="info", no_newline=True)
+            pretty_print(f"Link: {res['link']}", color="status")
 
     def process(self, user_prompt: str, speech_module: type) -> Tuple[str, str]:
         """

sources/agents/planner.tmp

@@ -0,0 +1,142 @@
+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")

sources/agents/planner_agent.py

@@ -76,10 +76,10 @@ class PlannerAgent(Agent):
         agents_tasks = self.parse_agent_tasks(json_plan)
         if agents_tasks == (None, None):
             return
-        pretty_print(f"--- Plan ---", color="output")
+        pretty_print("▂▘ P L A N ▝▂", color="output")
         for task_name, task in agents_tasks:
-            pretty_print(f"{task}", color="output")
-        pretty_print(f"--- End of Plan ---", color="output")
+                    pretty_print(f"{task['agent']} -> {task['task']}", color="info")
+        pretty_print("▔▗ E N D ▖▔", color="output")
     
     def process(self, prompt, speech_module) -> str:
         ok = False