update

app.py

@@ -1,4 +1,5 @@
 import sys
+
 import os
 from gradio_base import WebUI, UIHelper, PORT, HOST, Client
 from gradio_config import GradioConfig as gc
@@ -29,6 +30,11 @@ class CodeUI(WebUI):
         with gr.Blocks(css=gc.CSS) as demo:
             with gr.Row():
                 with gr.Column():
+                    self.text_api = gr.Textbox(
+                        value = self.cache["api_key"],
+                        placeholder="openai key",
+                        label="Please input valid openai key for gpt-3.5-turbo-16k."
+                    )
                     self.radio_mode = gr.Radio(
                         [Client.AUTO_MODE, Client.SINGLE_MODE],
                         value=Client.AUTO_MODE,
@@ -67,7 +73,7 @@ class CodeUI(WebUI):
                 
                 self.btn_start.click(
                     fn=self.btn_send_when_click,
-                    inputs=[self.chatbot, self.text_requirement, self.radio_mode],
+                    inputs=[self.chatbot, self.text_requirement, self.radio_mode, self.text_api],
                     outputs=[self.chatbot, self.btn_start, self.text_requirement, self.btn_reset]
                 ).then(
                     fn=self.btn_send_after_click,
@@ -76,7 +82,7 @@ class CodeUI(WebUI):
                 )
                 self.text_requirement.submit(
                     fn=self.btn_send_when_click,
-                    inputs=[self.chatbot, self.text_requirement],
+                    inputs=[self.chatbot, self.text_requirement, self.text_api],
                     outputs=[self.chatbot, self.btn_start, self.text_requirement, self.btn_reset]
                 ).then(
                     fn=self.btn_send_after_click,
@@ -126,17 +132,17 @@ class CodeUI(WebUI):
         render_data = self.render_bubble(history, self.data_history, node_name, render_node_name=True)
         return render_data
     
-    def btn_send_when_click(self, chatbot, text_requirement, mode):
+    def btn_send_when_click(self, chatbot, text_requirement, mode, api):
         """
-        inputs=[self.chatbot, self.text_requirement],
+        inputs=[self.chatbot, self.text_requirement, radio, text_api],
         outputs=[self.chatbot, self.btn_start, self.text_requirement, self.btn_reset]
         """
         chatbot = [[UIHelper.wrap_css(content=text_requirement, name="User"), None]]
         yield chatbot,\
             gr.Button.update(visible=True, interactive=False, value="Running"),\
             gr.Textbox.update(visible=True, interactive=False, value=""),\
-            gr.Button.update(visible=False, interactive=False) 
-        self.send_start_cmd({'requirement': text_requirement, "mode": mode})
+            gr.Button.update(visible=False, interactive=False)
+        self.send_start_cmd({'requirement': text_requirement, "mode": mode, "api_key": api})
         return
     
     def btn_send_after_click(
@@ -240,4 +246,4 @@ class CodeUI(WebUI):
 if __name__ == '__main__':
     ui = CodeUI(client_cmd=["python","gradio_backend.py"])
     ui.construct_ui()
-    ui.run()
+    ui.run()

config.json

@@ -1,6 +1,6 @@
 {
     "config": {
-        "API_KEY": "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",
+        "API_KEY": "",
         "PROXY": "",
         "MAX_CHAT_HISTORY": "3",
         "TOP_K": "0"

gradio_backend.py

@@ -1,13 +1,12 @@
 import os
 import argparse
 import sys
-sys.path.append("../../../src/agents")
-sys.path.append("../../Gradio_Config")
-from agents.utils import extract
-from agents.SOP import SOP
-from agents.Agent import Agent
-from agents.Environment import Environment
-from agents.Memory import Memory
+sys.path.append("src/agents")
+from utils import extract
+from SOP import SOP
+from Agent import Agent
+from Environment import Environment
+from Memory import Memory
 from gradio_base import Client, convert2list4agentname
 
 def process(action):
@@ -81,12 +80,12 @@ def block_when_next(current_agent, current_state):
 def run(agents,sop,environment):
     while True:      
         current_state,current_agent= sop.next(environment,agents)
-        block_when_next(current_agent, current_state)
         if sop.finished:
             print("finished!")
-            Client.send_server(str([99, ' ', ' ', current_state.name]))
+            Client.send_server(str([99, ' ', ' ', 'done']))
             os.environ.clear()
             break
+        block_when_next(current_agent, current_state)
         action = current_agent.step(current_state)   #component_dict = current_state[self.role[current_node.name]]   current_agent.compile(component_dict) 
         gradio_process(action,current_state)
         memory = process(action)
@@ -103,13 +102,15 @@ def prepare(agents, sop, environment):
             "agents_name": convert2list4agentname(sop)[0],
             # "only_name":  DebateUI.convert2list4agentname(sop)[1],
             "only_name":  convert2list4agentname(sop)[0],
-            "default_cos_play_id": -1
+            "default_cos_play_id": -1,
+            "api_key": os.environ["API_KEY"]
         }
     )
     # print(f"client: send {requirement_game_name}")
     client.listening_for_start_()
     client.mode = Client.mode = client.cache["mode"]
     new_requirement = Client.cache['requirement']
+    os.environ["API_KEY"] = client.cache["api_key"]
     for state in sop.states.values():
         state.environment_prompt = state.environment_prompt.replace("<target>a snake game with python</target>", f"<target>{new_requirement}</target>")
     # print(f"client: received {Client.cache['requirement']} from server.")
@@ -123,4 +124,4 @@ if __name__ == '__main__':
     # add================================
     prepare(agents, sop, environment)
     # ===================================
-    run(agents,sop,environment)
+    run(agents,sop,environment)

gradio_base.py

@@ -25,7 +25,22 @@ import socket
 import psutil
 import os
 from abc import abstractmethod
+import openai
 
+def test_apikey_connection(api_key=None, model="gpt-3.5-turbo"):
+    openai.api_key = api_key if api_key is not None else os.environ["API_KEY"]
+    if "PROXY" in os.environ:
+        openai.proxy = os.environ["PROXY"]
+    messages = [{"role": "user", "content": "what's your name?"}]
+    try:
+        response = openai.ChatCompletion.create(
+            model=model,
+            messages=messages,
+        )
+        return True
+    except:
+        return False
+    
 def convert2list4agentname(sop):
     """
     Extract the agent names of all states
@@ -556,4 +571,4 @@ class WebUI:
 
 
 if __name__ == '__main__':
-    pass
+    pass

gradio_config.py

@@ -90,7 +90,7 @@ class GradioConfig:
     </div>
         """,
 
-        # Background-color Font-size Font-color Name Content
+        # Backrgound-color Font-size Font-color Name Content
         "SYSTEM": """
     <div style="display: flex; align-items: center; justify-content: center;">
         <div style="background-color: {}; border-radius: 20px; padding: 1px; min-width: 200px; max-width: 1000px;">

logs/Architect/2023-09-20-10:19:36.json

@@ -0,0 +1,13 @@
+{
+  "input": [
+    {
+      "role": "system",
+      "content": "Imagine a scenario where the boss has presented a requirement. The architect is tasked with proposing a framework based on this requirement. The leader's role is to provide feedback on the architect's proposal, and another architect will finalize the framework based on the leader's comments.The target program is:<target>a snake game with python</target>\nNow your role is:\n<role>Architect</role>, your name is:\n<name>Bob</name>.             You need to follow the output style:\n<style>professional</style>.\n\nThe task you need to execute is: <task>Propose a Python framework based on the BOSS's requirements.</task>.\n\nThe rule you need to follow is:\n<rule>Thoroughly analyze the project requirements, evaluate potential technologies, and select suitable design principles to meet the project's needs.</rule>.\n\nHere are demonstrations you can refer to:\n<demonstrations>\nC\nr\ne\na\nt\ne\n \na\n \nd\ne\nt\na\ni\nl\ne\nd\n \nA\nr\nc\nh\ni\nt\ne\nc\nt\n \np\nr\no\np\no\ns\na\nl\n \nd\no\nc\nu\nm\ne\nn\nt\n,\n \ni\nn\nc\nl\nu\nd\ni\nn\ng\n \nt\nh\ne\n \nr\na\nt\ni\no\nn\na\nl\ne\n \nf\no\nr\n \nc\nh\no\no\ns\ni\nn\ng\n \nt\nh\ne\n \np\nr\no\np\no\ns\ne\nd\n \nf\nr\na\nm\ne\nw\no\nr\nk\n \na\nn\nd\n \na\nc\nc\no\nm\np\na\nn\ny\ni\nn\ng\n \nd\ne\ns\ni\ng\nn\n \nd\ni\na\ng\nr\na\nm\ns\n.\n \nF\no\nr\n \ni\nn\ns\nt\na\nn\nc\ne\n,\n \np\nr\no\nv\ni\nd\ne\n \na\nn\n \nA\nr\nc\nh\ni\nt\ne\nc\nt\n \nd\ni\na\ng\nr\na\nm\n \no\nu\nt\nl\ni\nn\ni\nn\ng\n \nt\nh\ne\n \nf\nr\na\nm\ne\nw\no\nr\nk\n'\ns\n \nh\ni\ng\nh\n-\nl\ne\nv\ne\nl\n \ns\nt\nr\nu\nc\nt\nu\nr\ne\n \na\nn\nd\n \na\n \nd\ne\nt\na\ni\nl\ne\nd\n \ne\nx\np\nl\na\nn\na\nt\ni\no\nn\n \no\nf\n \nw\nh\ny\n \nt\nh\ni\ns\n \na\nr\nc\nh\ni\nt\ne\nc\nt\nu\nr\ne\n \nw\na\ns\n \ns\ne\nl\ne\nc\nt\ne\nd\n.</demonstrations>\n,"
+    },
+    {
+      "role": "user",
+      "content": "Here's what you need to know(Remember, this is just information, Try not to repeat what's inside):\n<information>\nThe relevant chat history are as follows:\n<relevant_history> </relevant_history>\n;            The previous summary of chat history is as follows :<summary>\n\n</summary>.            The new chat history is as follows:\n<history> Alice said that :Please write code for the target game\n</history>\n            </information>;\nPlease continue the talk based on your known information,Make an effort to make the conversation more coherent and  try to respond differently from your existing knowledge, avoiding repeating what others have said."
+    }
+  ],
+  "output": "Bob: Thank you for providing the chat history. Based on the boss's requirement to create a snake game with Python, I have thoroughly analyzed the project requirements and evaluated potential technologies. After careful consideration, I propose using the Pygame framework for developing the game.\n\nPygame is a popular framework for building 2D games in Python. It provides a set of modules and functions that simplify game development, including graphics rendering, collision detection, and event handling. It is widely used and has a large community, which means there are plenty of resources and support available.\n\nThe reason for choosing Pygame is that it provides a good balance between simplicity and flexibility. It is easy to learn and use, making it suitable for beginners, but it also offers enough features and capabilities to create a complex game like the snake game.\n\nIn terms of architecture, I suggest following the Model-View-Controller (MVC) design pattern. This pattern separates the game logic (model) from the user interface (view) and the user input handling (controller). This separation allows for better code organization and maintainability.\n\nHere is a high-level overview of the proposed architecture:\n\n1. Model: This component will handle the game logic, including the movement of the snake, collision detection, and scoring. It will maintain the state of the game and provide methods for updating and querying the game state.\n\n2. View: This component will handle the rendering of the game graphics. It will display the game window, the snake, the food, and any other visual elements. It will receive updates from the model and redraw the screen accordingly.\n\n3. Controller: This component will handle user input, such as keyboard events. It will translate user input into actions that affect the game state, such as changing the direction of the snake. It will communicate with the model to update the game state based on user input.\n\nI believe this architecture will provide a solid foundation for developing the snake game. It separates concerns, making the code easier to understand and maintain. It also allows for flexibility and extensibility, making it possible to add new features or modify existing ones in the future.\n\nPlease let me know your thoughts and any feedback you have on this proposal."
+}

logs/god/2023-09-20-10:19:31.json

@@ -0,0 +1,33 @@
+{
+  "input": [
+    {
+      "role": "system",
+      "content": "The current scenario is as follows <environment> Imagine a scenario where the boss has presented a requirement. The architect is tasked with proposing a framework based on this requirement. The leader's role is to provide feedback on the architect's proposal, and another architect will finalize the framework based on the leader's comments.The target program is:<target>a snake game with python</target> </environment>;"
+    },
+    {
+      "role": "user",
+      "content": "None;The chat history is as follows:\n<chat> Alice said that :Please write code for the target game\n</chat>;You especially need to pay attention to the last query<query>\nAlice said that :Please write code for the target game\n</query> and the relevant conversation <relevant>\n \n</relevant>\n"
+    }
+  ],
+  "output": {
+    "id": "chatcmpl-80h6G1Ypra57k4w3tbGN2bYZYgeWk",
+    "object": "chat.completion",
+    "created": 1695176368,
+    "model": "gpt-3.5-turbo-16k-0613",
+    "choices": [
+      {
+        "index": 0,
+        "message": {
+          "role": "assistant",
+          "content": "Based on the given scenario, the boss has presented a requirement to develop a snake game using Python. The architect is tasked with proposing a framework for this game. The leader's role is to provide feedback on the architect's proposal, and another architect will finalize the framework based on the leader's comments.\n\nThe target program is a snake game implemented in Python."
+        },
+        "finish_reason": "stop"
+      }
+    ],
+    "usage": {
+      "prompt_tokens": 154,
+      "completion_tokens": 71,
+      "total_tokens": 225
+    }
+  }
+}

logs/god/2023-09-20-10:19:42.json

@@ -0,0 +1,33 @@
+{
+  "input": [
+    {
+      "role": "system",
+      "content": "The current scenario is as follows <environment> Imagine a scenario where the boss has presented a requirement. The architect is tasked with proposing a framework based on this requirement. The leader's role is to provide feedback on the architect's proposal, and another architect will finalize the framework based on the leader's comments.The target program is:<target>a snake game with python</target> </environment>;"
+    },
+    {
+      "role": "user",
+      "content": "None;The chat history is as follows:\n<chat> Alice said that :Please write code for the target gameBob said that :Bob: Thank you for providing the chat history. Based on the boss's requirement to create a snake game with Python, I have thoroughly analyzed the project requirements and evaluated potential technologies. After careful consideration, I propose using the Pygame framework for developing the game.\n\nPygame is a popular framework for building 2D games in Python. It provides a set of modules and functions that simplify game development, including graphics rendering, collision detection, and event handling. It is widely used and has a large community, which means there are plenty of resources and support available.\n\nThe reason for choosing Pygame is that it provides a good balance between simplicity and flexibility. It is easy to learn and use, making it suitable for beginners, but it also offers enough features and capabilities to create a complex game like the snake game.\n\nIn terms of architecture, I suggest following the Model-View-Controller (MVC) design pattern. This pattern separates the game logic (model) from the user interface (view) and the user input handling (controller). This separation allows for better code organization and maintainability.\n\nHere is a high-level overview of the proposed architecture:\n\n1. Model: This component will handle the game logic, including the movement of the snake, collision detection, and scoring. It will maintain the state of the game and provide methods for updating and querying the game state.\n\n2. View: This component will handle the rendering of the game graphics. It will display the game window, the snake, the food, and any other visual elements. It will receive updates from the model and redraw the screen accordingly.\n\n3. Controller: This component will handle user input, such as keyboard events. It will translate user input into actions that affect the game state, such as changing the direction of the snake. It will communicate with the model to update the game state based on user input.\n\nI believe this architecture will provide a solid foundation for developing the snake game. It separates concerns, making the code easier to understand and maintain. It also allows for flexibility and extensibility, making it possible to add new features or modify existing ones in the future.\n\nPlease let me know your thoughts and any feedback you have on this proposal.\n</chat>;You especially need to pay attention to the last query<query>\nBob said that :Bob: Thank you for providing the chat history. Based on the boss's requirement to create a snake game with Python, I have thoroughly analyzed the project requirements and evaluated potential technologies. After careful consideration, I propose using the Pygame framework for developing the game.\n\nPygame is a popular framework for building 2D games in Python. It provides a set of modules and functions that simplify game development, including graphics rendering, collision detection, and event handling. It is widely used and has a large community, which means there are plenty of resources and support available.\n\nThe reason for choosing Pygame is that it provides a good balance between simplicity and flexibility. It is easy to learn and use, making it suitable for beginners, but it also offers enough features and capabilities to create a complex game like the snake game.\n\nIn terms of architecture, I suggest following the Model-View-Controller (MVC) design pattern. This pattern separates the game logic (model) from the user interface (view) and the user input handling (controller). This separation allows for better code organization and maintainability.\n\nHere is a high-level overview of the proposed architecture:\n\n1. Model: This component will handle the game logic, including the movement of the snake, collision detection, and scoring. It will maintain the state of the game and provide methods for updating and querying the game state.\n\n2. View: This component will handle the rendering of the game graphics. It will display the game window, the snake, the food, and any other visual elements. It will receive updates from the model and redraw the screen accordingly.\n\n3. Controller: This component will handle user input, such as keyboard events. It will translate user input into actions that affect the game state, such as changing the direction of the snake. It will communicate with the model to update the game state based on user input.\n\nI believe this architecture will provide a solid foundation for developing the snake game. It separates concerns, making the code easier to understand and maintain. It also allows for flexibility and extensibility, making it possible to add new features or modify existing ones in the future.\n\nPlease let me know your thoughts and any feedback you have on this proposal.\n</query> and the relevant conversation <relevant>\n \n</relevant>\n"
+    }
+  ],
+  "output": {
+    "id": "chatcmpl-80h6ROa4nXy3RxxswTK1j4PFNfajI",
+    "object": "chat.completion",
+    "created": 1695176379,
+    "model": "gpt-3.5-turbo-16k-0613",
+    "choices": [
+      {
+        "index": 0,
+        "message": {
+          "role": "assistant",
+          "content": "<relevant>Leader: Thank you for your proposal, Bob. I appreciate your thorough analysis and consideration of potential technologies. Pygame seems like a suitable choice for developing the snake game.\n\nI agree with your suggestion to follow the Model-View-Controller (MVC) design pattern. It will help in organizing the code and separating the game logic from the user interface and input handling.\n\nHowever, I have a few suggestions for the proposed architecture:\n\n1. Model: In addition to handling the movement of the snake, collision detection, and scoring, I recommend including a method for generating food at random positions on the game screen. This will add an element of randomness and challenge to the game.\n\n2. View: Along with rendering the game graphics, I suggest implementing a method for displaying the current score on the screen. It will provide feedback to the player and enhance the gaming experience.\n\n3. Controller: In addition to handling keyboard events, I recommend implementing a method for handling game over conditions. This can include checking for collisions with the snake's body or with the game boundaries. It will allow for proper game termination and displaying a game over message.\n\nOverall, I believe these additions will improve the gameplay and user experience of the snake game. Please incorporate these suggestions into the final framework.\n\nOnce you have made the necessary changes, please share the updated framework for review and finalization.\n\nThank you again for your efforts in proposing the initial framework. I look forward to seeing the updated version.</relevant>"
+        },
+        "finish_reason": "stop"
+      }
+    ],
+    "usage": {
+      "prompt_tokens": 1033,
+      "completion_tokens": 298,
+      "total_tokens": 1331
+    }
+  }
+}

src/agents/Action/__init__.py

@@ -0,0 +1 @@
+from .base_action import Action

src/agents/Action/base_action.py

@@ -0,0 +1,48 @@
+from Memory import Memory
+class Action:
+    """
+    The basic action unit of agent
+    """
+    def __init__(self,**kwargs):
+        self.response = None
+        self.is_user = False
+        self.res_dict = {}
+        self.name = ""
+        self.role = ""
+        for key,value in kwargs.items():
+            setattr(self,key,value)
+    
+    
+    def process(self):
+        """
+        processing action
+        Rerutn : memory(Memory)
+        """
+        response = self.response
+        send_name = self.name
+        send_role = self.role
+        all = ""
+        for res in response:
+            all += res
+        parse = f"{send_name}:"
+        
+        # 将里面对话的第三人称删了
+        # The third person in the dialogue was deleted.
+        while parse in all:
+            index = all.index(parse) + len(parse)
+            all = all[index:]
+        
+        if not self.is_user:
+            print(f"{send_name}({send_role}):{all}")
+                # for software
+        if "<title>" in all:
+            title = extract(all,"title")
+            python = extract(all,"python")
+            os.makedirs("output_code", exist_ok=True)
+            file_name = "output_code/" + title
+            with open(file_name, "w", encoding="utf-8") as f:
+                f.write(python)
+        memory = Memory(send_role, send_name, all)
+        return memory
+    
+    

src/agents/Agent/Agent.py

@@ -0,0 +1,243 @@
+# coding=utf-8
+# Copyright 2023  The AIWaves Inc. team.
+
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""LLM autonoumous agent"""
+from LLM.base_LLM import *
+from Component import *
+from Action import Action
+from Prompt import *
+
+headers = {
+    "Content-Type": "text/event-stream",
+    "Cache-Control": "no-cache",
+    "X-Accel-Buffering": "no",
+}
+    
+    
+    
+
+class Agent:
+    """
+    Auto agent, input the JSON of SOP.
+    """
+
+    # Agent should have args: agents,states
+    def __init__(self, name, agent_state_roles, **kwargs) -> None:
+        self.state_roles = agent_state_roles
+        self.name = name
+        
+        self.style = kwargs["style"]
+        self.LLMs = kwargs["LLMs"]
+        self.LLM = None
+        self.is_user = kwargs["is_user"]
+        self.begins = kwargs["begins"] if "begins" in kwargs else False
+        self.current_role = ""
+        self.long_term_memory = []
+        self.short_term_memory = ""
+        self.current_state = None
+        self.first_speak = True
+        self.environment = None
+    
+
+    @classmethod
+    def from_config(cls, config_path):
+        """
+        Initialize agents based on json file
+        Return:
+        agents(dict) : key:agent_name;value:class(Agent) 
+        names_to_roles(dict) : key:state_name  value:(dict; (key:agent_name ; value:agent_role))
+        roles_to_names(dict) : key:state_name  value:(dict; (key:agent_role ; value:agent_name))
+        """
+        with open(config_path) as f:
+            config = json.load(f)
+        
+        roles_to_names = {}
+        names_to_roles = {}
+        agents = {}
+        user_names = json.loads(os.environ["User_Names"]) if "User_Names" in os.environ else []
+        for agent_name, agent_dict in config["agents"].items():
+            agent_state_roles = {}
+            agent_LLMs = {}
+            agent_begins = {}
+            for state_name, agent_role in agent_dict["roles"].items():
+                
+                agent_begins[state_name] = {}
+                
+                if state_name not in roles_to_names:
+                    roles_to_names[state_name] = {}
+                if state_name not in names_to_roles:
+                    names_to_roles[state_name] = {}
+                roles_to_names[state_name][agent_role] = agent_name
+                names_to_roles[state_name][agent_name] = agent_role
+                agent_state_roles[state_name] = agent_role
+                current_state = config["states"][state_name]
+                
+                current_state_begin_role = current_state["begin_role"] if "begin_role" in current_state else current_state["roles"][0]
+                agent_begins[state_name]["is_begin"] = current_state_begin_role==agent_role if "begin_role" in current_state else False
+                agent_begins[state_name]["begin_query"] = current_state["begin_query"] if "begin_query" in current_state else " "
+                agent_LLMs[state_name] = init_LLM(f"logs/{agent_name}",**current_state["agent_states"][agent_role])
+            agents[agent_name] = cls(
+                agent_name,
+                agent_state_roles,
+                LLMs=agent_LLMs,
+                is_user=agent_name in user_names,
+                style = agent_dict["style"],
+                begins = agent_begins
+            )
+        assert len(config["agents"].keys()) != 2 or (roles_to_names[config["root"]][config["states"][config["root"]]["begin_role"]] not in user_names and "begin_query"  in config["states"][config["root"]]),"In a single-agent scenario, there must be an opening statement and it must be the agent" 
+        return agents, roles_to_names, names_to_roles
+
+    def step(self, current_state,input=""):
+        """
+        return actions by current state and environment
+        Return: action(Action)
+        """
+        
+        current_state.chat_nums +=1
+        state_begin = current_state.is_begin
+        agent_begin = self.begins[current_state.name]["is_begin"]
+        self.begins[current_state.name]["is_begin"] = False
+        current_state.is_begin = False
+        environment = self.environment
+        
+        self.current_state = current_state
+        # 先根据当前环境更新信息
+        # First update the information according to the current environment
+        
+        response = " "
+        res_dict = {}
+        
+        if self.is_user:
+            response = f"{self.name}:{input}"
+        else:
+            if len(environment.shared_memory["long_term_memory"])>0:
+                current_history = self.observe()
+                self.long_term_memory.append(current_history)
+            if agent_begin:
+                response = (char for char in self.begins[current_state.name]["begin_query"])
+            else:
+                response,res_dict = self.act()
+        
+        
+        action_dict =  {
+            "response": response,
+            "res_dict": res_dict,
+            "role": self.state_roles[current_state.name],
+            "name": self.name,
+            "state_begin" : state_begin,
+            "agent_begin" : agent_begin,
+            "is_user" : self.is_user
+        }
+        return  Action(**action_dict)
+
+    def act(self):
+        """
+        return actions by the current state
+        """
+        current_state = self.current_state
+        chat_history = self.long_term_memory
+        current_LLM = self.LLMs[current_state.name]
+        
+        system_prompt, last_prompt, res_dict = self.compile()
+
+        
+
+        response = current_LLM.get_response(
+            chat_history, system_prompt, last_prompt, stream=True
+        )
+        return response,res_dict 
+
+    def update_memory(self, memory):
+        self.long_term_memory.append(
+            {"role": "assistant", "content": memory.content}
+        )
+        
+        MAX_CHAT_HISTORY = eval(os.environ["MAX_CHAT_HISTORY"])
+        environment = self.environment
+        current_chat_history_idx = environment.current_chat_history_idx if environment.environment_type == "competive" else 0
+        
+        current_long_term_memory = environment.shared_memory["long_term_memory"][current_chat_history_idx:]
+        last_conversation_idx = environment._get_agent_last_conversation_idx(self,current_long_term_memory)
+        if len(current_long_term_memory)-last_conversation_idx >= MAX_CHAT_HISTORY:
+            current_state = self.current_state
+            current_role = self.state_roles[current_state.name]
+            current_component_dict = current_state.components[current_role]
+                
+            # get chat history from new conversation
+            conversations = environment._get_agent_new_memory(self,current_long_term_memory)
+
+            # get summary
+            summary_prompt = (
+                current_state.summary_prompt[current_role]
+                if current_state.summary_prompt
+                else f"""your name is {self.name},your role is{current_component_dict["style"].role},your task is {current_component_dict["task"].task}.\n"""
+            )
+            summary_prompt =eval(Agent_summary_system_prompt)
+            summary = self.LLMs[current_state.name].get_response(None, summary_prompt,stream = False)
+            self.short_term_memory = summary
+            
+        
+    def compile(self):
+        """
+        get prompt from state depend on your role
+        Return:
+        system_prompt:system_prompt for agents's LLM
+        last_prompt:last_prompt for agents's LLM
+        res_dict(dict): Other return from tool component.For example: search engine results
+        """
+        current_state = self.current_state
+        self.current_roles = self.state_roles[current_state.name]
+        current_state_name = current_state.name
+        self.LLM = self.LLMs[current_state_name]
+        components = current_state.components[self.state_roles[current_state_name]]
+
+        system_prompt = self.current_state.environment_prompt
+        last_prompt = ""
+
+        res_dict = {}
+        for component in components.values():
+            if isinstance(component, (OutputComponent, LastComponent)):
+                last_prompt = last_prompt + "\n" + component.get_prompt(self)
+            elif isinstance(component, PromptComponent):
+                system_prompt = (
+                    system_prompt + "\n" + component.get_prompt(self)
+                )
+            elif isinstance(component, ToolComponent):
+                response = component.func(self)
+                if "prompt" in response and response["prompt"]:
+                    last_prompt = last_prompt + "\n" + response["prompt"]
+                res_dict.update(response)
+        
+        name = self.name
+        query = self.environment.shared_memory["long_term_memory"][-1]
+        last_prompt = eval(Agent_last_prompt)
+        system_prompt = eval(Agent_system_prompt)
+        return system_prompt, last_prompt, res_dict
+
+
+    def observe(self):
+        """
+        Update one's own memory according to the current environment, including: updating short-term memory; updating long-term memory
+        """
+        return self.environment._observe(self)
+        
+    
+    def generate_sop(self):
+        pass
+
+    def reflection(self):
+        pass
+
+

src/agents/Agent/__init__.py

@@ -0,0 +1 @@
+from .Agent import Agent

src/agents/Component/ExtraComponent.py

@@ -0,0 +1,128 @@
+from .ToolComponent import ToolComponent
+import json
+from utils import flatten_dict,get_embedding,matching_category,search_with_api,limit_keys,limit_values
+import os
+
+
+class CategoryRequirementsComponent(ToolComponent):
+    def __init__(self, information_path):
+        super().__init__()
+        self.information_dataset = []
+        self.leaf_name = []
+        for toy_path in information_path:
+            with open(toy_path, encoding="utf-8") as json_file:
+                data = json.load(json_file)
+            for d in data:
+                if "/" in d["cat_leaf_name"]:
+                    leaf_names = d["cat_leaf_name"].split("/") + [d["cat_leaf_name"]]
+                else:
+                    leaf_names = [d["cat_leaf_name"]]
+                for name in leaf_names:
+                    self.leaf_name.append(name)
+                    new_d = d.copy()
+                    new_d["cat_leaf_name"] = name
+                    new_d["information"] = flatten_dict(new_d["information"])
+                    self.information_dataset.append(new_d)
+
+        self.target_embbeding = get_embedding(
+            self.leaf_name
+        )
+
+    def search_information(self, category, information_dataset):
+        knowledge = {}
+        for d in information_dataset:
+            if category == d["cat_leaf_name"]:
+                knowledge = d["information"]
+                knowledge = {
+                    key: value
+                    for key, value in knowledge.items()
+                    if (value and key != "相关分类")
+                }
+                break
+        return knowledge
+
+    def func(self, agent):
+        prompt = ""
+        messages = agent.long_term_memory
+        outputdict = {}
+        functions = [
+            {
+                "name": "search_information",
+                "description": "根据用户所需要购买商品的种类跟用户的需求去寻找用户所需要的商品",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "category": {
+                            "type": "string",
+                            "description": "用户现在所需要的商品类别，比如纸尿布，笔记本电脑等，注意，只能有一个",
+                        },
+                        "requirements": {
+                            "type": "string",
+                            "description": "用户现在的需求，比如说便宜，安踏品牌等等，可以有多个需求，中间以“ ”分隔",
+                        },
+                    },
+                    "required": ["category", "requirements"],
+                },
+            }
+        ]
+        
+        response = agent.LLM.get_response(
+            messages,
+            None,
+            None,
+            functions=functions,
+            stream=False,
+            function_call={"name": "search_information"},
+        )
+        response_message = json.loads(response["function_call"]["arguments"])
+        category = (
+            response_message["category"] if response_message["category"] else None
+        )
+        requirements = (
+            response_message["requirements"]
+            if response_message["requirements"]
+            else category
+        )
+        if not (category or requirements):
+            return {}
+
+        topk_result = matching_category(
+            category, self.leaf_name, None, self.target_embbeding, top_k=3
+        )
+
+        top1_score = topk_result[1][0]
+        request_items, top_category = search_with_api(requirements, category)
+        
+
+        MIN_CATEGORY_SIM = eval(os.environ["MIN_CATEGORY_SIM"]
+                                ) if "MIN_CATEGORY_SIM" in os.environ else 0.7
+
+        if top1_score > MIN_CATEGORY_SIM:
+            agent.environment.shared_memory["category"] = topk_result[0][0]
+            category = topk_result[0][0]
+            information = self.search_information(
+                topk_result[0][0], self.information_dataset
+            )
+            information = limit_keys(information, 3)
+            information = limit_values(information, 2)
+            prompt += f"""你需要知道的是：用户目前选择的商品是{category}，该商品信息为{information}。你需要根据这些商品信息来详细介绍商品，比如详细介绍商品有哪些品牌，有哪些分类等等，并且询问用户是否有更多的需求。"""
+            if category in top_category:
+                top_category.remove(category)
+
+            recommend = "\n经过搜索后，推荐商品如下：\n"
+            prompt += "筛选出的商品如下：\n"
+            
+            for i, request_item in enumerate(request_items):
+                
+                itemTitle = request_item["itemTitle"]
+                itemPrice = request_item["itemPrice"]
+                itemPicUrl = request_item["itemPicUrl"]
+                recommend += f"[{i}.商品名称：{itemTitle},商品价格:{float(itemPrice)/100}]({itemPicUrl})\n"
+                prompt += f"[{i}.商品名称：{itemTitle},商品价格:{float(itemPrice)/100}]\n"
+            outputdict["recommend"] = recommend
+            print(recommend)
+        else:
+            prompt += f"""你需要知道的是：用户目前选择的商品是{category}，而我们店里没有这类商品，但是我们店里有一些近似商品，如{top_category},{topk_result[0][0]}，你需要对这些近似商品进行介绍，并引导用户购买"""
+        outputdict["prompt"] = prompt
+        return outputdict
+

src/agents/Component/PromptComponent.py

@@ -0,0 +1,133 @@
+from abc import abstractmethod
+
+
+class PromptComponent:
+    def __init__(self):
+        pass
+
+    @abstractmethod
+    def get_prompt(self, agent):
+        pass
+
+class TaskComponent(PromptComponent):
+    def __init__(self, task):
+        super().__init__()
+        self.task = task
+
+    def get_prompt(self, agent):
+        return f"""The task you need to execute is: <task>{self.task}</task>.\n"""
+
+
+class OutputComponent(PromptComponent):
+    def __init__(self, output):
+        super().__init__()
+        self.output = output
+
+    def get_prompt(self, agent):
+        return f"""Please contact the above to extract <{self.output}> and </{self.output}>, \
+            do not perform additional output, please output in strict accordance with the above format!\n"""
+
+
+class SystemComponent(PromptComponent):
+    def __init__(self,system_prompt):
+        super().__init__()
+        self.system_prompt = system_prompt
+    
+    def get_prompt(self, agent):
+        return self.system_prompt
+
+class LastComponent(PromptComponent):
+    def __init__(self, last_prompt):
+        super().__init__()
+        self.last_prompt = last_prompt
+
+    def get_prompt(self, agent):
+        return self.last_prompt
+
+
+class StyleComponent(PromptComponent):
+    """
+    角色、风格组件
+    """
+
+    def __init__(self, role):
+        super().__init__()
+        self.role = role
+
+    def get_prompt(self, agent):
+        name = agent.name
+        style = agent.style
+        return f"""Now your role is:\n<role>{self.role}</role>, your name is:\n<name>{name}</name>. \
+            You need to follow the output style:\n<style>{style}</style>.\n"""
+
+
+class RuleComponent(PromptComponent):
+    def __init__(self, rule):
+        super().__init__()
+        self.rule = rule
+
+    def get_prompt(self, agent):
+        return f"""The rule you need to follow is:\n<rule>{self.rule}</rule>.\n"""
+
+
+class DemonstrationComponent(PromptComponent):
+    """
+    input a list,the example of answer.
+    """
+
+    def __init__(self, demonstrations):
+        super().__init__()
+        self.demonstrations = demonstrations
+
+    def add_demonstration(self, demonstration):
+        self.demonstrations.append(demonstration)
+
+    def get_prompt(self, agent):
+        prompt = "Here are demonstrations you can refer to:\n<demonstrations>"
+        for demonstration in self.demonstrations:
+            prompt += "\n" + demonstration
+        prompt += "</demonstrations>\n"
+        return prompt
+
+
+class CoTComponent(PromptComponent):
+    """
+    input a list,the example of answer.
+    """
+
+    def __init__(self, demonstrations):
+        super().__init__()
+        self.demonstrations = demonstrations
+
+    def add_demonstration(self, demonstration):
+        self.demonstrations.append(demonstration)
+
+    def get_prompt(self, agent):
+        prompt = "You need to think in detail before outputting, the thinking case is as follows:\n<demonstrations>"
+        for demonstration in self.demonstrations:
+            prompt += "\n" + demonstration
+        prompt += "</demonstrations>\n"
+        return prompt
+
+
+class CustomizeComponent(PromptComponent):
+    """
+    Custom template
+    template(str) : example: "i am {}"
+    keywords(list) : example : ["name"]  
+    example : agent.environment.shared_memory["name"] = "Lilong"
+    the component will get the keyword attribute from the environment, and then add it to the template.
+    Return : "i am Lilong"
+    """
+    def __init__(self, template, keywords) -> None:
+        super().__init__()
+        self.template = template
+        self.keywords = keywords
+
+    def get_prompt(self, agent):
+        template_keyword = {}
+        for keyword in self.keywords:
+            
+            current_keyword = agent.environment.shared_memory[keyword]
+            template_keyword[keyword] = current_keyword
+        return self.template.format(**template_keyword)

src/agents/Component/ToolComponent.py

@@ -0,0 +1,887 @@
+from abc import abstractmethod
+import uuid
+from text2vec import semantic_search
+from utils import (
+    get_relevant_history,
+    load_knowledge_base_qa,
+    load_knowledge_base_UnstructuredFile,
+    get_embedding,
+    extract,
+)
+import json
+from typing import Dict, List
+import os
+from googleapiclient.discovery import build
+import requests
+from selenium import webdriver
+from selenium.webdriver.common.by import By
+from selenium.webdriver.support.ui import WebDriverWait
+from selenium.webdriver.support import expected_conditions as EC
+from bs4 import BeautifulSoup
+import base64
+import re
+from datetime import datetime, timedelta
+from typing import Tuple, List, Any, Dict
+from email.mime.text import MIMEText
+from email.mime.multipart import MIMEMultipart
+from google.auth.transport.requests import Request
+from google.oauth2.credentials import Credentials
+from google_auth_oauthlib.flow import InstalledAppFlow
+from googleapiclient.discovery import build
+from googleapiclient.errors import HttpError
+from tqdm import tqdm
+
+class ToolComponent:
+    def __init__(self):
+        pass
+
+    @abstractmethod
+    def func(self):
+        pass
+    
+class KnowledgeBaseComponent(ToolComponent):
+    """
+    Inject knowledge base
+    top_k : Top_k with the highest matching degree
+    type : "QA" or others
+    knowledge_base(json_path) : knowledge_base_path
+    """
+    def __init__(self, top_k, type, knowledge_base):
+        super().__init__()
+        self.top_k = top_k
+        self.type = type
+        self.knowledge_base = knowledge_base
+
+        if self.type == "QA":
+            (
+                self.kb_embeddings,
+                self.kb_questions,
+                self.kb_answers,
+                self.kb_chunks,
+            ) = load_knowledge_base_qa(self.knowledge_base)
+        else:
+            self.kb_embeddings, self.kb_chunks = load_knowledge_base_UnstructuredFile(
+                self.knowledge_base
+            )
+
+    def func(self, agent):
+        query = (
+            agent.long_term_memory[-1]["content"]
+            if len(agent.long_term_memory) > 0
+            else ""
+        )
+        knowledge = ""
+        query = extract(query, "query")
+        query_embedding = get_embedding(query)
+        hits = semantic_search(query_embedding, self.kb_embeddings, top_k=50)
+        hits = hits[0]
+        temp = []
+        if self.type == "QA":
+            for hit in hits:
+                matching_idx = hit["corpus_id"]
+                if self.kb_chunks[matching_idx] in temp:
+                    pass
+                else:
+                    knowledge = (
+                        knowledge
+                        + f"question:{self.kb_questions[matching_idx]},answer:{self.kb_answers[matching_idx]}\n\n"
+                    )
+                    temp.append(self.kb_answers[matching_idx])
+                    if len(temp) == 1:
+                        break
+            print(hits[0]["score"])
+            score = hits[0]["score"]
+            if score < 0.5:
+                return {"prompt": "No matching knowledge base"}
+            else:
+                return {"prompt": "The relevant content is: " + knowledge + "\n"}
+        else:
+            for hit in hits:
+                matching_idx = hit["corpus_id"]
+                if self.kb_chunks[matching_idx] in temp:
+                    pass
+                else:
+                    knowledge = knowledge + f"{self.kb_answers[matching_idx]}\n\n"
+                    temp.append(self.kb_answers[matching_idx])
+                    if len(temp) == self.top_k:
+                        break
+            print(hits[0]["score"])
+            score = hits[0]["score"]
+            if score < 0.5:
+                return {"prompt": "No matching knowledge base"}
+            else:
+                print(knowledge)
+                return {"prompt": "The relevant content is: " + knowledge + "\n"}
+
+
+class StaticComponent(ToolComponent):
+    "Return static response"
+    def __init__(self, output):
+        super().__init__()
+        self.output = output
+
+    def func(self, agent):
+        outputdict = {"response": self.output}
+        return outputdict
+
+
+class ExtractComponent(ToolComponent):
+    """
+    Extract keywords based on the current scene and store them in the environment
+    extract_words(list) : Keywords to be extracted
+    system_prompt & last_prompt : Prompt to extract keywords
+    """
+    def __init__(
+        self,
+        extract_words,
+        system_prompt,
+        last_prompt=None,
+    ):
+        super().__init__()
+        self.extract_words = extract_words
+        self.system_prompt = system_prompt
+        self.default_prompt = (
+            "Please strictly adhere to the following format for outputting:\n"
+        )
+        for extract_word in extract_words:
+            self.default_prompt += (
+                f"<{extract_word}> the content you need to extract </{extract_word}>"
+            )
+        self.last_prompt = last_prompt if last_prompt else self.default_prompt
+
+    def func(self, agent):
+        response = agent.LLM.get_response(
+            agent.long_term_memory,
+            self.system_prompt,
+            self.last_prompt,
+            stream=False,
+        )
+        for extract_word in self.extract_words:
+            key = extract(response, extract_word)
+            key = key if key else response
+            agent.environment.shared_memory[extract_word] = key
+
+        return {}
+
+
+"""Search sources: chatgpt/search engines/specific search sources/can even be multimodal (if it comes to clothing)"""
+
+
+class WebSearchComponent(ToolComponent):
+    """search engines"""
+
+    __ENGINE_NAME__: List = ["google", "bing"]
+
+    def __init__(self, engine_name: str, api: Dict):
+        """
+        :param engine_name: The name of the search engine used
+        :param api: Pass in a dictionary, such as {"bing":"key1", "google":"key2", ...}, of course each value can also be a list, or more complicated
+        """
+        super(WebSearchComponent, self).__init__()
+        """Determine whether the key and engine_name of the api are legal"""
+
+        assert engine_name in WebSearchComponent.__ENGINE_NAME__
+        for api_name in api:
+            assert api_name in WebSearchComponent.__ENGINE_NAME__
+
+        self.api = api
+        self.engine_name = engine_name
+
+        self.search: Dict = {"bing": self._bing_search, "google": self._google_search}
+
+    def _bing_search(self, query: str, **kwargs):
+        """Initialize search hyperparameters"""
+        subscription_key = self.api["bing"]
+        search_url = "https://api.bing.microsoft.com/v7.0/search"
+        headers = {"Ocp-Apim-Subscription-Key": subscription_key}
+        params = {
+            "q": query,
+            "textDecorations": True,
+            "textFormat": "HTML",
+            "count": 10,
+        }
+        """start searching"""
+        response = requests.get(search_url, headers=headers, params=params)
+        response.raise_for_status()
+        results = response.json()["webPages"]["value"]
+        """execute"""
+        metadata_results = []
+        for result in results:
+            metadata_result = {
+                "snippet": result["snippet"],
+                "title": result["name"],
+                "link": result["url"],
+            }
+            metadata_results.append(metadata_result)
+        return {"meta data": metadata_results}
+
+    def _google_search(self, query: str, **kwargs):
+        """Initialize search hyperparameters"""
+        api_key = self.api[self.engine_name]["api_key"]
+        cse_id = self.api[self.engine_name]["cse_id"]
+        service = build("customsearch", "v1", developerKey=api_key)
+        """start searching"""
+        results = (
+            service.cse().list(q=query, cx=cse_id, num=10, **kwargs).execute()["items"]
+        )
+        """execute"""
+        metadata_results = []
+        for result in results:
+            metadata_result = {
+                "snippet": result["snippet"],
+                "title": result["title"],
+                "link": result["link"],
+            }
+            metadata_results.append(metadata_result)
+        return {"meta data": metadata_results}
+
+    def func(self, agent, **kwargs) -> Dict:
+        query = (
+            agent.long_term_memory[-1]["content"]
+            if len(agent.long_term_memory) > 0
+            else " "
+        )
+        response = agent.LLM.get_response(
+            None,
+            system_prompt=f"Please analyze the provided conversation and identify keywords that can be used for a search engine query. Format the output as <keywords>extracted keywords</keywords>:\nConversation:\n{query}",
+            stream=False,
+        )
+        response = extract(response, "keywords")
+        query = response if response else query
+
+        search_results = self.search[self.engine_name](query=query, **kwargs)
+        information = ""
+        for i in search_results["meta data"][:5]:
+            information += i["snippet"]
+        return {
+            "prompt": "You can refer to the following information to reply:\n"
+            + information
+        }
+
+    def convert_search_engine_to(self, engine_name):
+        assert engine_name in WebSearchComponent.__ENGINE_NAME__
+        self.engine_name = engine_name
+
+
+class WebCrawlComponent(ToolComponent):
+    """Open a single web page for crawling"""
+
+    def __init__(self):
+        super(WebCrawlComponent, self).__init__()
+
+    def func(self, agent_dict) -> Dict:
+        url = agent_dict["url"]
+        print(f"crawling {url} ......")
+        content = ""
+        """Crawling content from url may need to be carried out according to different websites, such as wiki, baidu, zhihu, etc."""
+        driver = webdriver.Chrome()
+        try:
+            """open url"""
+            driver.get(url)
+
+            """wait 20 second"""
+            wait = WebDriverWait(driver, 20)
+            wait.until(EC.presence_of_element_located((By.TAG_NAME, "body")))
+
+            """crawl code"""
+            page_source = driver.page_source
+
+            """parse"""
+            soup = BeautifulSoup(page_source, "html.parser")
+
+            """concatenate"""
+            for paragraph in soup.find_all("p"):
+                content = f"{content}\n{paragraph.get_text()}"
+        except Exception as e:
+            print("Error:", e)
+        finally:
+            """quit"""
+            driver.quit()
+        return {"content": content.strip()}
+
+
+class MailComponent(ToolComponent):
+    __VALID_ACTION__ = ["read", "send"]
+
+    def __init__(
+        self, cfg_file: str, default_action: str = "read", name: str = "e-mail"
+    ):
+        """'../config/google_mail.json'"""
+        super(MailComponent, self).__init__(name)
+        self.name = name
+        assert (
+            default_action.lower() in self.__VALID_ACTION__
+        ), f"Action `{default_action}` is not allowed! The valid action is in `{self.__VALID_ACTION__}`"
+        self.action = default_action.lower()
+        self.credential = self._login(cfg_file)
+
+    def _login(self, cfg_file: str):
+        SCOPES = [
+            "https://www.googleapis.com/auth/gmail.readonly",
+            "https://www.googleapis.com/auth/gmail.send",
+        ]
+        creds = None
+        if os.path.exists("token.json"):
+            print("Login Successfully!")
+            creds = Credentials.from_authorized_user_file("token.json", SCOPES)
+        if not creds or not creds.valid:
+            print("Please authorize in an open browser.")
+            if creds and creds.expired and creds.refresh_token:
+                creds.refresh(Request())
+            else:
+                flow = InstalledAppFlow.from_client_secrets_file(cfg_file, SCOPES)
+                creds = flow.run_local_server(port=0)
+            # Save the credentials for the next run
+            with open("token.json", "w") as token:
+                token.write(creds.to_json())
+        return creds
+
+    def _read(self, mail_dict: dict):
+        credential = self.credential
+        state = mail_dict["state"] if "state" in mail_dict else None
+        time_between = (
+            mail_dict["time_between"] if "time_between" in mail_dict else None
+        )
+        sender_mail = mail_dict["sender_mail"] if "sender_mail" in mail_dict else None
+        only_both = mail_dict["only_both"] if "only_both" in mail_dict else False
+        order_by_time = (
+            mail_dict["order_by_time"] if "order_by_time" in mail_dict else "descend"
+        )
+        include_word = (
+            mail_dict["include_word"] if "include_word" in mail_dict else None
+        )
+        exclude_word = (
+            mail_dict["exclude_word"] if "exclude_word" in mail_dict else None
+        )
+        MAX_SEARCH_CNT = (
+            mail_dict["MAX_SEARCH_CNT"] if "MAX_SEARCH_CNT" in mail_dict else 50
+        )
+        number = mail_dict["number"] if "number" in mail_dict else 10
+        if state is None:
+            state = "all"
+        if time_between is not None:
+            assert isinstance(time_between, tuple)
+            assert len(time_between) == 2
+        assert state in ["all", "unread", "read", "sent"]
+        if only_both:
+            assert sender_mail is not None
+        if sender_mail is not None:
+            assert isinstance(sender_mail, str)
+        assert credential
+        assert order_by_time in ["descend", "ascend"]
+
+        def generate_query():
+            query = ""
+            if state in ["unread", "read"]:
+                query = f"is:{state}"
+            if state in ["sent"]:
+                query = f"in:{state}"
+            if only_both:
+                query = f"{query} from:{sender_mail} OR to:{sender_mail}"
+            if sender_mail is not None and not only_both:
+                query = f"{query} from:({sender_mail})"
+            if include_word is not None:
+                query = f"{query} {include_word}"
+            if exclude_word is not None:
+                query = f"{query} -{exclude_word}"
+            if time_between is not None:
+                TIME_FORMAT = "%Y/%m/%d"
+                t1, t2 = time_between
+                if t1 == "now":
+                    t1 = datetime.now().strftime(TIME_FORMAT)
+                if t2 == "now":
+                    t2 = datetime.now().strftime(TIME_FORMAT)
+                if isinstance(t1, str) and isinstance(t2, str):
+                    t1 = datetime.strptime(t1, TIME_FORMAT)
+                    t2 = datetime.strptime(t2, TIME_FORMAT)
+                elif isinstance(t1, str) and isinstance(t2, int):
+                    t1 = datetime.strptime(t1, TIME_FORMAT)
+                    t2 = t1 + timedelta(days=t2)
+                elif isinstance(t1, int) and isinstance(t2, str):
+                    t2 = datetime.strptime(t2, TIME_FORMAT)
+                    t1 = t2 + timedelta(days=t1)
+                else:
+                    assert False, "invalid time"
+                if t1 > t2:
+                    t1, t2 = t2, t1
+                query = f"{query} after:{t1.strftime(TIME_FORMAT)} before:{t2.strftime(TIME_FORMAT)}"
+            return query.strip()
+
+        def sort_by_time(data: List[Dict]):
+            if order_by_time == "descend":
+                reverse = True
+            else:
+                reverse = False
+            sorted_data = sorted(
+                data,
+                key=lambda x: datetime.strptime(x["time"], "%Y-%m-%d %H:%M:%S"),
+                reverse=reverse,
+            )
+            return sorted_data
+
+        try:
+            service = build("gmail", "v1", credentials=credential)
+            results = (
+                service.users()
+                .messages()
+                .list(userId="me", labelIds=["INBOX"], q=generate_query())
+                .execute()
+            )
+
+            messages = results.get("messages", [])
+            email_data = list()
+
+            if not messages:
+                print("No eligible emails.")
+                return None
+            else:
+                pbar = tqdm(total=min(MAX_SEARCH_CNT, len(messages)))
+                for cnt, message in enumerate(messages):
+                    pbar.update(1)
+                    if cnt >= MAX_SEARCH_CNT:
+                        break
+                    msg = (
+                        service.users()
+                        .messages()
+                        .get(
+                            userId="me",
+                            id=message["id"],
+                            format="full",
+                            metadataHeaders=None,
+                        )
+                        .execute()
+                    )
+
+                    subject = ""
+                    for header in msg["payload"]["headers"]:
+                        if header["name"] == "Subject":
+                            subject = header["value"]
+                            break
+
+                    sender = ""
+                    for header in msg["payload"]["headers"]:
+                        if header["name"] == "From":
+                            sender = re.findall(
+                                r"\b[\w\.-]+@[\w\.-]+\.\w+\b", header["value"]
+                            )[0]
+                            break
+                    body = ""
+                    if "parts" in msg["payload"]:
+                        for part in msg["payload"]["parts"]:
+                            if part["mimeType"] == "text/plain":
+                                data = part["body"]["data"]
+                                body = base64.urlsafe_b64decode(data).decode("utf-8")
+                                break
+
+                    email_info = {
+                        "sender": sender,
+                        "time": datetime.fromtimestamp(
+                            int(msg["internalDate"]) / 1000
+                        ).strftime("%Y-%m-%d %H:%M:%S"),
+                        "subject": subject,
+                        "body": body,
+                    }
+                    email_data.append(email_info)
+                pbar.close()
+            email_data = sort_by_time(email_data)[0:number]
+            return {"results": email_data}
+        except Exception as e:
+            print(e)
+            return None
+
+    def _send(self, mail_dict: dict):
+        recipient_mail = mail_dict["recipient_mail"]
+        subject = mail_dict["subject"]
+        body = mail_dict["body"]
+        credential = self.credential
+        service = build("gmail", "v1", credentials=credential)
+
+        message = MIMEMultipart()
+        message["to"] = recipient_mail
+        message["subject"] = subject
+
+        message.attach(MIMEText(body, "plain"))
+
+        raw_message = base64.urlsafe_b64encode(message.as_bytes()).decode("utf-8")
+        try:
+            message = (
+                service.users()
+                .messages()
+                .send(userId="me", body={"raw": raw_message})
+                .execute()
+            )
+            return {"state": True}
+        except HttpError as error:
+            print(error)
+            return {"state": False}
+
+    def func(self, mail_dict: dict):
+        if "action" in mail_dict:
+            assert mail_dict["action"].lower() in self.__VALID_ACTION__
+            self.action = mail_dict["action"]
+        functions = {"read": self._read, "send": self._send}
+        return functions[self.action](mail_dict)
+
+    def convert_action_to(self, action_name: str):
+        assert (
+            action_name.lower() in self.__VALID_ACTION__
+        ), f"Action `{action_name}` is not allowed! The valid action is in `{self.__VALID_ACTION__}`"
+        self.action = action_name.lower()
+
+
+class WeatherComponet(ToolComponent):
+    def __init__(self, api_key, name="weather", TIME_FORMAT="%Y-%m-%d"):
+        super(WeatherComponet, self).__init__(name)
+        self.name = name
+        self.TIME_FORMAT = TIME_FORMAT
+        self.api_key = api_key
+
+    def _parse(self, data):
+        dict_data: dict = {}
+        for item in data["data"]:
+            date = item["datetime"]
+            dict_data[date] = {}
+            if "weather" in item:
+                dict_data[date]["description"] = item["weather"]["description"]
+            mapping = {
+                "temp": "temperature",
+                "max_temp": "max_temperature",
+                "min_temp": "min_temperature",
+                "precip": "accumulated_precipitation",
+            }
+            for key in ["temp", "max_temp", "min_temp", "precip"]:
+                if key in item:
+                    dict_data[date][mapping[key]] = item[key]
+        return dict_data
+
+    def _query(self, city_name, country_code, start_date, end_date):
+        """https://www.weatherbit.io/api/historical-weather-daily"""
+        # print(datetime.strftime(start_date, self.TIME_FORMAT), datetime.strftime(datetime.now(), self.TIME_FORMAT), end_date, datetime.strftime(datetime.now()+timedelta(days=1), self.TIME_FORMAT))
+        if start_date == datetime.strftime(
+            datetime.now(), self.TIME_FORMAT
+        ) and end_date == datetime.strftime(
+            datetime.now() + timedelta(days=1), self.TIME_FORMAT
+        ):
+            """today"""
+            url = f"https://api.weatherbit.io/v2.0/current?city={city_name}&country={country_code}&key={self.api_key}"
+        else:
+            url = f"https://api.weatherbit.io/v2.0/history/daily?&city={city_name}&country={country_code}&start_date={start_date}&end_date={end_date}&key={self.api_key}"
+        response = requests.get(url)
+        data = response.json()
+        return self._parse(data)
+
+    def func(self, weather_dict: Dict) -> Dict:
+        TIME_FORMAT = self.TIME_FORMAT
+        # Beijing, Shanghai
+        city_name = weather_dict["city_name"]
+        # CN, US
+        country_code = weather_dict["country_code"]
+        # 2020-02-02
+        start_date = datetime.strftime(
+            datetime.strptime(weather_dict["start_date"], self.TIME_FORMAT),
+            self.TIME_FORMAT,
+        )
+        end_date = weather_dict["end_date"] if "end_date" in weather_dict else None
+        if end_date is None:
+            end_date = datetime.strftime(
+                datetime.strptime(start_date, TIME_FORMAT) + timedelta(days=-1),
+                TIME_FORMAT,
+            )
+        else:
+            end_date = datetime.strftime(
+                datetime.strptime(weather_dict["end_date"], self.TIME_FORMAT),
+                self.TIME_FORMAT,
+            )
+        if datetime.strptime(start_date, TIME_FORMAT) > datetime.strptime(
+            end_date, TIME_FORMAT
+        ):
+            start_date, end_date = end_date, start_date
+        assert start_date != end_date
+        return self._query(city_name, country_code, start_date, end_date)
+
+
+class TranslateComponent(ToolComponent):
+    __SUPPORT_LANGUAGE__ = [
+        "af",
+        "am",
+        "ar",
+        "as",
+        "az",
+        "ba",
+        "bg",
+        "bn",
+        "bo",
+        "bs",
+        "ca",
+        "cs",
+        "cy",
+        "da",
+        "de",
+        "dsb",
+        "dv",
+        "el",
+        "en",
+        "es",
+        "et",
+        "eu",
+        "fa",
+        "fi",
+        "fil",
+        "fj",
+        "fo",
+        "fr",
+        "fr-CA",
+        "ga",
+        "gl",
+        "gom",
+        "gu",
+        "ha",
+        "he",
+        "hi",
+        "hr",
+        "hsb",
+        "ht",
+        "hu",
+        "hy",
+        "id",
+        "ig",
+        "ikt",
+        "is",
+        "it",
+        "iu",
+        "iu-Latn",
+        "ja",
+        "ka",
+        "kk",
+        "km",
+        "kmr",
+        "kn",
+        "ko",
+        "ku",
+        "ky",
+        "ln",
+        "lo",
+        "lt",
+        "lug",
+        "lv",
+        "lzh",
+        "mai",
+        "mg",
+        "mi",
+        "mk",
+        "ml",
+        "mn-Cyrl",
+        "mn-Mong",
+        "mr",
+        "ms",
+        "mt",
+        "mww",
+        "my",
+        "nb",
+        "ne",
+        "nl",
+        "nso",
+        "nya",
+        "or",
+        "otq",
+        "pa",
+        "pl",
+        "prs",
+        "ps",
+        "pt",
+        "pt-PT",
+        "ro",
+        "ru",
+        "run",
+        "rw",
+        "sd",
+        "si",
+        "sk",
+        "sl",
+        "sm",
+        "sn",
+        "so",
+        "sq",
+        "sr-Cyrl",
+        "sr-Latn",
+        "st",
+        "sv",
+        "sw",
+        "ta",
+        "te",
+        "th",
+        "ti",
+        "tk",
+        "tlh-Latn",
+        "tlh-Piqd",
+        "tn",
+        "to",
+        "tr",
+        "tt",
+        "ty",
+        "ug",
+        "uk",
+        "ur",
+        "uz",
+        "vi",
+        "xh",
+        "yo",
+        "yua",
+        "yue",
+        "zh-Hans",
+        "zh-Hant",
+        "zu",
+    ]
+
+    def __init__(
+        self, api_key, location, default_target_language="zh-cn", name="translate"
+    ):
+        super(TranslateComponent, self).__init__(name)
+        self.name = name
+        self.api_key = api_key
+        self.location = location
+        self.default_target_language = default_target_language
+
+    def func(self, translate_dict: Dict) -> Dict:
+        content = translate_dict["content"]
+        target_language = self.default_target_language
+        if "target_language" in translate_dict:
+            target_language = translate_dict["target_language"]
+        assert (
+            target_language in self.__SUPPORT_LANGUAGE__
+        ), f"language `{target_language}` is not supported."
+
+        endpoint = "https://api.cognitive.microsofttranslator.com"
+
+        path = "/translate"
+        constructed_url = endpoint + path
+
+        params = {"api-version": "3.0", "to": target_language}
+
+        headers = {
+            "Ocp-Apim-Subscription-Key": self.api_key,
+            "Ocp-Apim-Subscription-Region": self.location,
+            "Content-type": "application/json",
+            "X-ClientTraceId": str(uuid.uuid4()),
+        }
+
+        body = [{"text": content}]
+
+        request = requests.post(
+            constructed_url, params=params, headers=headers, json=body
+        )
+        response = request.json()
+        response = json.dumps(
+            response,
+            sort_keys=True,
+            ensure_ascii=False,
+            indent=4,
+            separators=(",", ": "),
+        )
+        response = eval(response)
+        return {"result": response[0]["translations"][0]["text"]}
+
+
+class APIComponent(ToolComponent):
+    def __init__(self):
+        super(APIComponent, self).__init__()
+
+    def func(self, agent) -> Dict:
+        pass
+
+
+class FunctionComponent(ToolComponent):
+    def __init__(
+        self,
+        functions,
+        function_call="auto",
+        response_type="response",
+        your_function=None,
+    ):
+        super().__init__()
+        self.functions = functions
+        self.function_call = function_call
+        self.parameters = {}
+        self.available_functions = {}
+        self.response_type = response_type
+        if your_function:
+            function_name = your_function["name"]
+            function_content = your_function["content"]
+            exec(function_content)
+            self.available_functions[function_name] = eval(function_name)
+
+        for function in self.functions:
+            self.parameters[function["name"]] = list(
+                function["parameters"]["properties"].keys()
+            )
+            self.available_functions[function["name"]] = eval(function["name"])
+
+    def func(self, agent):
+        messages = agent.long_term_memory
+        outputdict = {}
+        query = agent.long_term_memory[-1].content if len(agent.long_term_memory) > 0 else " "
+        relevant_history = get_relevant_history(
+            query,
+            agent.long_term_memory[:-1],
+            agent.chat_embeddings[:-1],
+        )
+        response = agent.LLM.get_response(
+            messages,
+            None,
+            functions=self.functions,
+            stream=False,
+            function_call=self.function_call,
+            relevant_history=relevant_history,
+        )
+        response_message = response
+        if response_message.get("function_call"):
+            function_name = response_message["function_call"]["name"]
+            fuction_to_call = self.available_functions[function_name]
+            function_args = json.loads(response_message["function_call"]["arguments"])
+            input_args = {}
+            for args_name in self.parameters[function_name]:
+                input_args[args_name] = function_args.get(args_name)
+            function_response = fuction_to_call(**input_args)
+            if self.response_type == "response":
+                outputdict["response"] = function_response
+            elif self.response_type == "prompt":
+                outputdict["prompt"] = function_response
+
+        return outputdict
+
+
+class CodeComponent(ToolComponent):
+    def __init__(self, file_name, keyword) -> None:
+        super().__init__()
+        self.file_name = file_name
+        self.keyword = keyword
+        self.system_prompt = (
+            "you need to extract the modified code as completely as possible."
+        )
+        self.last_prompt = (
+            f"Please strictly adhere to the following format for outputting: \n"
+        )
+        self.last_prompt += (
+            f"<{self.keyword}> the content you need to extract </{self.keyword}>"
+        )
+
+    def func(self, agent):
+        response = agent.LLM.get_response(
+            agent.long_term_memory,
+            self.system_prompt,
+            self.last_prompt,
+            stream=False,
+        )
+        code = extract(response, self.keyword)
+        code = code if code else response
+        os.makedirs("output_code", exist_ok=True)
+        file_name = "output_code/" + self.file_name
+        codes = code.split("\n")
+        if codes[0] == "```python":
+            codes.remove(codes[0])
+        if codes[-1] == "```":
+            codes.remove(codes[-1])
+        code = "\n".join(codes)
+        with open(file_name, "w", encoding="utf-8") as f:
+            f.write(code)
+        return {}

src/agents/Component/__init__.py

@@ -0,0 +1,3 @@
+from .ExtraComponent import *
+from .PromptComponent import *
+from .ToolComponent import *

src/agents/Environment/__init__.py

@@ -0,0 +1 @@
+from .base_environment import Environment

src/agents/Environment/base_environment.py

@@ -0,0 +1,167 @@
+from utils import get_relevant_history, get_embedding
+import torch
+from LLM.base_LLM import *
+from Memory import Memory
+from Prompt import * 
+import json
+class Environment:
+    """
+    The place where the agent activities, responsible for storing some shared memories
+    """
+    def __init__(self, config) -> None:
+        self.shared_memory = {"long_term_memory": [], "short_term_memory": None}
+        self.agents = None
+
+        self.summary_system_prompt = {}
+        self.summary_last_prompt = {}
+        self.environment_prompt = {}
+        self.environment_type = config["environment_type"] if "environment_type" in config else "cooperative"
+        self.current_chat_history_idx = 0
+        self.LLMs = {}
+        
+        # 初始化每个state 的summary 方法
+        # Initialize the summary method for each state
+        for state_name, state_dict in config["states"].items():
+            if state_name != "end_state":
+                self.summary_system_prompt[state_name] = (
+                    state_dict["summary_system_prompt"]
+                    if "summary_system_prompt" in state_dict
+                    else eval(Default_environment_summary_system_prompt)
+                )
+
+                self.summary_last_prompt[state_name] = (
+                    state_dict["summary_last_prompt"]
+                    if "summary_last_prompt" in state_dict
+                    else eval(Default_environment_summary_last_prompt)
+                )
+
+                self.environment_prompt[state_name] = (
+                    state_dict["environment_prompt"]
+                    if "environment_prompt" in state_dict
+                    else " "
+                )
+                self.LLMs[state_name] = init_LLM(f"logs/{state_name}",**state_dict)
+        self.roles_to_names = None
+        self.names_to_roles = None
+
+    @classmethod
+    def from_config(cls, config_path):
+        with open(config_path) as f:
+            config = json.load(f)
+        return cls(config)
+
+    def summary(self, current_state):
+        """
+        Summarize the situation in the current environment every once in a while
+        """
+        MAX_CHAT_HISTORY = eval(os.environ["MAX_CHAT_HISTORY"])
+        current_state_name = current_state.name
+
+        query = self.shared_memory["long_term_memory"][-1].content
+        relevant_history = get_relevant_history(
+            query,
+            self.shared_memory["long_term_memory"][:-1],
+            self.shared_memory["chat_embeddings"][:-1],
+        )
+
+        relevant_history = Memory.get_chat_history(relevant_history)
+        chat_history = Memory.get_chat_history(
+            self.shared_memory["long_term_memory"][-MAX_CHAT_HISTORY + 1 :]
+        )
+        summary = self.shared_memory["short_term_memory"]
+        
+        
+        # system prompt = environment prompt + current memory + system prompt
+        # current_memory = summary + chat history + relevant history
+        current_memory = eval(Environment_summary_memory)
+        environment_prompt = self.environment_prompt[current_state_name]
+        summary_system_prompt = self.summary_system_prompt[current_state_name]
+        
+        environment_summary_system_prompt = eval(Environment_summary_system_prompt)
+        response = self.LLMs[current_state_name].get_response(None, environment_summary_system_prompt, stream=False)
+        return response
+
+    def update_memory(self, memory, current_state):
+        """
+        update chat embbedings and long term memory,short term memory,agents long term memory
+        """
+        MAX_CHAT_HISTORY = eval(os.environ["MAX_CHAT_HISTORY"])
+        self.shared_memory["long_term_memory"].append(memory)
+        current_embedding = get_embedding(memory.content)
+        if "chat_embeddings" not in self.shared_memory:
+            self.shared_memory["chat_embeddings"] = current_embedding
+        else:
+            self.shared_memory["chat_embeddings"] = torch.cat(
+                [self.shared_memory["chat_embeddings"], current_embedding], dim=0
+            )
+        if len(self.shared_memory["long_term_memory"]) % MAX_CHAT_HISTORY == 0:
+            summary = self.summary(current_state)
+            self.shared_memory["short_term_memory"] = summary
+
+        self.agents[memory.send_name].update_memory(memory)
+    
+    
+    def _get_agent_last_conversation_idx(self,agent,current_long_term_memory):
+        last_conversation_idx = -1
+        for i, history in enumerate(current_long_term_memory):
+            if history.send_name == agent.name:
+                last_conversation_idx = i
+        return last_conversation_idx
+    
+    
+    def _get_agent_new_memory(self,agent,current_long_term_memory):
+        # get new conversation
+        last_conversation_idx = self._get_agent_last_conversation_idx(agent,current_long_term_memory)
+
+        if last_conversation_idx == -1:
+            new_conversation =current_long_term_memory
+        elif (
+            last_conversation_idx
+            == len(current_long_term_memory) - 1
+        ):
+            new_conversation = []
+        else:
+            new_conversation = current_long_term_memory[
+                last_conversation_idx + 1 :
+            ]
+
+        # get chat history from new conversation
+        return Memory.get_chat_history(new_conversation)
+    
+    
+    def _observe(self,agent):
+        MAX_CHAT_HISTORY = eval(os.environ["MAX_CHAT_HISTORY"])
+        current_state = agent.current_state
+        current_role = agent.state_roles[current_state.name]
+        current_component_dict = current_state.components[current_role]
+        
+        # cooperative:Sharing information between different states ;  competive: No information is shared between different states
+        current_chat_history_idx = self.current_chat_history_idx if self.environment_type == "competive" else 0
+        current_long_term_memory = self.shared_memory["long_term_memory"][current_chat_history_idx:]
+        current_chat_embbedings = self.shared_memory["chat_embeddings"][current_chat_history_idx:]
+            
+        
+        # relevant_memory
+        query = current_long_term_memory[-1].content
+
+        relevant_memory = get_relevant_history(
+            query,
+            current_long_term_memory[:-1],
+            current_chat_embbedings[:-1],
+        )
+        relevant_memory = Memory.get_chat_history(relevant_memory,agent.name)
+        
+        relevant_memory = eval(Agent_observe_relevant_memory)
+        agent.relevant_memory = relevant_memory
+        
+        
+        # get chat history from new conversation
+        conversations = self._get_agent_new_memory(agent,current_long_term_memory)
+
+        # memory = relevant_memory + summary + history + query
+        query = current_long_term_memory[-1]
+        current_memory = eval(Agent_observe_memory)
+
+        return {"role": "user", "content": current_memory}
+
+

src/agents/LLM/base_LLM.py

@@ -0,0 +1,133 @@
+from abc import abstractclassmethod
+import openai
+import os
+import time
+from Memory import Memory
+from utils import save_logs
+
+class LLM:
+    def __init__(self) -> None:
+        pass
+
+    @abstractclassmethod 
+    def get_response():
+        pass
+
+
+class OpenAILLM(LLM):
+    def __init__(self,**kwargs) -> None:
+        super().__init__()
+        self.MAX_CHAT_HISTORY = eval(
+            os.environ["MAX_CHAT_HISTORY"]) if "MAX_CHAT_HISTORY" in os.environ else 10
+        
+        self.model = kwargs["model"] if "model" in kwargs else "gpt-3.5-turbo-16k-0613"
+        self.temperature = kwargs["temperature"] if "temperature" in  kwargs else 0.3
+        self.log_path = kwargs["log_path"] if "log_path" in kwargs else "logs"
+    
+
+    def get_stream(self,response, log_path, messages):
+        ans = ""
+        for res in response:
+            if res:
+                r = (res.choices[0]["delta"].get("content")
+                    if res.choices[0]["delta"].get("content") else "")
+                ans += r
+                yield r
+        
+        save_logs(log_path, messages, ans)
+        
+        
+        
+    def get_response(self,
+                    chat_history,
+                    system_prompt,
+                    last_prompt=None,
+                    stream=False,
+                    functions=None,
+                    function_call="auto",
+                    WAIT_TIME=20,
+                    **kwargs):
+        """
+        return LLM's response 
+        """
+        openai.api_key = os.environ["API_KEY"]
+        # if "PROXY" in os.environ:
+        #     assert "http:" in os.environ["PROXY"] or "socks" in os.environ["PROXY"],"PROXY error,PROXY must be http or socks"
+        #     openai.proxy = os.environ["PROXY"]
+        if "API_BASE" in os.environ:
+            openai.api_base = os.environ["API_BASE"]
+        active_mode = True if ("ACTIVE_MODE" in os.environ and os.environ["ACTIVE_MODE"] == "0") else False
+        model = self.model
+        temperature = self.temperature
+        
+        
+        if active_mode:
+            system_prompt = system_prompt + "Please keep your reply as concise as possible,Within three sentences, the total word count should not exceed 30"
+
+        messages = [{
+            "role": "system",
+            "content": system_prompt
+        }] if system_prompt else []
+        
+        if chat_history:
+            if len(chat_history) >  self.MAX_CHAT_HISTORY:
+                chat_history = chat_history[- self.MAX_CHAT_HISTORY:]
+            if isinstance(chat_history[0],dict):
+                messages += chat_history
+            elif isinstance(chat_history[0],Memory):
+                messages += [memory.get_gpt_message("user") for memory in chat_history]
+
+        if last_prompt:
+            if active_mode:
+                last_prompt = last_prompt + "Please keep your reply as concise as possible,Within three sentences, the total word count should not exceed 30"
+            # messages += [{"role": "system", "content": f"{last_prompt}"}]
+            messages[-1]["content"] += last_prompt
+        
+
+        while True:
+            try:
+                if functions:
+                    response = openai.ChatCompletion.create(
+                        model=model,
+                        messages=messages,
+                        functions=functions,
+                        function_call=function_call,
+                        temperature=temperature,
+                    )
+                else:
+                    response = openai.ChatCompletion.create(
+                        model=model,
+                        messages=messages,
+                        temperature=temperature,
+                        stream=stream)
+                break
+            except Exception as e:
+                print(e)
+                if "maximum context length is" in str(e):
+                    assert False, "exceed max length"
+                    break
+                else:
+                    print(f"Please wait {WAIT_TIME} seconds and resend later ...")
+                    time.sleep(WAIT_TIME)
+
+        if functions:
+            save_logs(self.log_path, messages, response)
+            return response.choices[0].message
+        elif stream:
+            return self.get_stream(response, self.log_path, messages)
+        else:
+            save_logs(self.log_path, messages, response)
+            return response.choices[0].message["content"]
+
+
+def init_LLM(default_log_path,**kwargs):
+    LLM_type = kwargs["LLM_type"] if "LLM_type" in kwargs else "OpenAI"
+    log_path = kwargs["log_path"] if "log_path" in kwargs else default_log_path
+    if LLM_type == "OpenAI":
+        LLM = (
+            OpenAILLM(**kwargs["LLM"])
+            if "LLM" in kwargs
+            else OpenAILLM(model = "gpt-3.5-turbo-16k-0613",temperature=0.3,log_path=log_path)
+        )
+        return LLM
+    

src/agents/Memory/__init__.py

@@ -0,0 +1 @@
+from .base_Memory import Memory

src/agents/Memory/base_Memory.py

@@ -0,0 +1,32 @@
+from Prompt import *
+class Memory:
+    def __init__(self,role,name,content) -> None:
+        self.send_role = role
+        self.send_name = name
+        self.content = content
+    
+    def get_gpt_message(self,role):
+        return {"role":role,"content":self.content}
+    
+    @classmethod
+    def get_chat_history(self,messages,agent_name =None):
+        """
+        Splice a memory list into a sentence
+        input : 
+        messages(list) : list of memory(Memory)
+        Return :
+        chat_history(str) : One sentence after integration
+        """
+        chat_history = ""
+        for message in messages:
+            name,role,content = message.send_name,message.send_role,message.content
+            if agent_name and agent_name==name:
+                name = "you"
+            chat_history += eval(Single_message)
+        chat_history = eval(Chat_total_message)
+        return chat_history
+    
+    def get_query(self):
+        "Return : query(str):last sentence"
+        name,role,content = self.send_name,self.send_role,self.content
+        return eval(Single_message)

src/agents/Prompt/__init__.py

@@ -0,0 +1 @@
+from .base_Prompts import *

src/agents/Prompt/base_Prompts.py

@@ -0,0 +1,83 @@
+
+# SOP========================================================================================================
+# "environment_prompt"
+# current_state , self(sop)
+Get_environment_prompt = "f\"The current scenario is as follows <environment> {self.current_state.environment_prompt} </environment>\""
+
+
+# sop.transit
+#================================================================
+Transit_system_prompt = "f\"{environment_prompt};{judge_system_prompt}\""
+
+#  transit chat message 
+#  "environment_prompt" is get from "Get_environment_prompt" ; "chat_history_message" if from Memory
+Transit_message = "f\"{environment_summary};The chat history is as follows:\\n<chat> {chat_history_message}\\n</chat>;You especially need to pay attention to the last query<query>\\n{query}\\n</query> and the relevant conversation <relevant>\\n{relevant_history} \\n</relevant>\\n\""
+
+
+Transit_last_prompt = "f\"{judge_last_prompt}\""
+#sop.transit================================================================
+
+# sop.call
+#================================================================
+# help controller to determine the next role to speak.(the {} is agent role)    call_prompt + allocate_component  
+Allocate_component = "f\"If it's currently supposed to be speaking for {role}, then output <end>{role}</end>.\\n\""
+
+# environment_prompt is get from "Get_environment_prompt" ; "chat_history_message" if from Memory
+Call_system_prompt = "f\"{environment_prompt};{call_system_prompt};{allocate_prompt}\""
+
+#
+Call_last_prompt = "f\"You especially need to pay attention to the last query<query>\\n{query}\\n</query> and the relevant conversation <relevant>\\n{relevant_history} \\n</relevant>\\n;Now please choose the person to speak according to the following rules :{allocate_prompt};Note: The person whose turn it is now cannot be the same as the person who spoke last time, so {last_name} cannot be output\\n.\""
+
+Call_message = "f\"The chat history is as follows:\\n<history>\\n{chat_history_message}</history>\\n;The last person to speak is: {last_name}\\n. \""
+#sop.call================================================================
+# SOP========================================================================================================
+
+
+
+
+
+
+# Memory========================================================================================================
+Single_message = "f\"{name} said that :{content}\""
+
+Chat_total_message = "f\"{chat_history}\""
+# Memory========================================================================================================
+
+
+
+
+
+
+# Environment========================================================================================================
+Default_environment_summary_system_prompt = "\"\\nYour task is to summarize the historical dialogue records according to the current scene, and summarize the most important information\""
+
+Default_environment_summary_last_prompt = "\"Please make a summary based on the historical chat records, the output format is history summary: \{your summary content\} \""
+
+Environment_summary_memory =  "f\"The information you need to know is as follows:\\n</information>\\n\
+            The summary of the previous dialogue history is:<summary>\\n{summary}\\n.</summary>\
+            The latest conversation record is as follows:\\n<hisroty> {chat_history}\\n</history>,\
+            the relevant chat history you may need is:<relevant>{relevant_history}</relevant>\""
+            
+Environment_summary_system_prompt = "f\"{environment_prompt};{current_memory};{summary_system_prompt};\""
+
+
+# observe
+Agent_observe_relevant_memory = "f\"The relevant chat history are as follows:\\n<relevant_history>{relevant_memory} </relevant_history>\\n\""
+
+
+Agent_observe_memory = "f\"Here's what you need to know(Remember, this is just information, Try not to repeat what's inside):\\n<information>\\n{relevant_memory};\
+            The previous summary of chat history is as follows :<summary>\\n{agent.short_term_memory}\\n</summary>.\
+            The new chat history is as follows:\\n<history> {conversations}\\n</history>\\n\
+            </information>\""
+# Environment========================================================================================================
+
+
+
+
+# Agent========================================================================================================
+Agent_summary_system_prompt = "f\"{summary_prompt};Please summarize past key summary \\n<summary>\\n {self.short_term_memory} </summary>and new chat_history as follows: <history>\\n{conversations}</history>\""          
+            
+Agent_last_prompt = "f\"{last_prompt};\\nPlease continue the talk based on your known information,Make an effort to make the conversation more coherent and  try to respond differently from your existing knowledge, avoiding repeating what others have said.\""
+
+Agent_system_prompt = "f\"{system_prompt},\""
+# Agent========================================================================================================

src/agents/SOP.py

@@ -0,0 +1,296 @@
+# coding=utf-8
+# Copyright 2023  The AIWaves Inc. team.
+
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""standard operation procedure of an LLM Autonomous agent"""
+import random
+from LLM.base_LLM import *
+from State import State
+from utils import extract, get_relevant_history
+from Memory import Memory
+from Prompt import *
+import json
+import os
+
+class SOP:
+    """
+    Responsible for managing the operational processes of all agents
+    """
+
+    # SOP should have args : "states" "relations" "root"
+
+    def __init__(self, **kwargs):
+        self.controller_dict = {}
+        self.LLM = init_LLM("logs/god",**kwargs)
+
+        self.states = {}
+        self.init_states(kwargs["states"])
+        self.init_relation(kwargs["relations"])
+        for state_name, states_dict in kwargs["states"].items():
+            if state_name != "end_state" and "controller" in states_dict:
+                self.controller_dict[state_name] = states_dict["controller"]
+
+        self.user_names = kwargs["user_names"] if "user_names" in kwargs else []
+        self.root = self.states[kwargs["root"]]
+        self.current_state = self.root
+        self.finish_state_name = (
+            kwargs["finish_state_name"]
+            if "finish_state_name" in kwargs
+            else "end_state"
+        )
+        self.roles_to_names = None
+        self.names_to_roles = None
+        self.finished = False
+
+    @classmethod
+    def from_config(cls, config_path):
+        with open(config_path) as f:
+            config = json.load(f)
+        os.environ.clear()
+        for key,value in config["config"].items():
+            if key == "API_BASE":
+                if value == "":
+                    pass
+            else:
+                os.environ[key] = value
+        # assert "API_KEY" in os.environ and os.environ["API_KEY"] != "API_KEY","Please go to config.json to set API_KEY"
+        
+        sop = SOP(**config)
+        return sop
+
+    def init_states(self, states_dict):
+        for state_name, state_dict in states_dict.items():
+            state_dict["name"] = state_name
+            self.states[state_name] = State(**state_dict)
+
+    def init_relation(self, relations):
+        for state_name, state_relation in relations.items():
+            for idx, next_state_name in state_relation.items():
+                self.states[state_name].next_states[idx] = self.states[next_state_name]
+
+    def transit(self, chat_history, **kwargs):
+        """
+        Determine the next state based on the current situation
+        Return : 
+        next_state(State) : the next state
+        """
+        # 如果是单一循环节点，则一直循环即可
+        # If it is a single loop node, just keep looping
+        if len(self.current_state.next_states) == 1:
+            next_state = "0"
+            
+        # 否则则需要controller去判断进入哪一节点
+        # Otherwise, the controller needs to determine which node to enter.   
+        else:
+            current_state = self.current_state
+            controller_dict = self.controller_dict[current_state.name]
+            relevant_history = kwargs["relevant_history"]
+            
+            max_chat_nums = controller_dict["max_chat_nums"] if "max_chat_nums" in controller_dict else 1000
+            if current_state.chat_nums>=max_chat_nums:
+                return self.current_state.next_states["1"]
+            
+
+            # 否则则让controller判断是否结束
+            # Otherwise, let the controller judge whether to end
+            judge_system_prompt = controller_dict["judge_system_prompt"]
+            environment_prompt = eval(Get_environment_prompt) if current_state.environment_prompt else ""
+            transit_system_prompt = eval(Transit_system_prompt)
+            
+            judge_last_prompt = controller_dict["judge_last_prompt"]
+            transit_last_prompt = eval(Transit_last_prompt)
+            
+
+            
+            environment = kwargs["environment"]
+            environment_summary = environment.shared_memory["short_term_memory"]
+            chat_history_message = Memory.get_chat_history(chat_history)
+            query = chat_history[-1].get_query()
+            
+            chat_messages = [
+                {
+                    "role": "user",
+                    "content": eval(Transit_message)
+                }
+            ]
+            
+            extract_words = controller_dict["judge_extract_words"] if "judge_extract_words" in controller_dict else "end"
+
+
+            response = self.LLM.get_response(
+                chat_messages, transit_system_prompt, transit_last_prompt, stream=False, **kwargs
+            )
+            next_state = (
+                response if response.isdigit() else extract(response, extract_words)
+            )
+            
+            # 如果没有parse出来则继续循环
+            # If no parse comes out, continue looping
+            if not next_state.isdigit():
+                next_state = "0"
+            
+        next_state = self.current_state.next_states[next_state]
+        return next_state
+
+
+    def route(self, chat_history, **kwargs):
+        """
+        Determine the role that needs action based on the current situation
+        Return : 
+        current_agent(Agent) : the next act agent
+        """
+        
+        agents = kwargs["agents"]
+        
+        # 知道进入哪一状态后开始分配角色，如果该状态下只有一个角色则直接分配给他
+        # Start assigning roles after knowing which state you have entered. If there is only one role in that state, assign it directly to him.
+        if len(self.current_state.roles) == 1:
+            next_role = self.current_state.roles[0]
+        
+        
+        
+        # 否则controller进行分配
+        # Otherwise the controller determines
+        else:
+            relevant_history = kwargs["relevant_history"]
+            controller_type = (
+                self.controller_dict[self.current_state.name]["controller_type"]
+                if "controller_type" in self.controller_dict[self.current_state.name]
+                else "order"
+            )
+
+
+            # 如果是rule 控制器，则交由LLM进行分配角色
+            # If  controller type is rule, it is left to LLM to assign roles.
+            if controller_type == "rule":
+                controller_dict = self.controller_dict[self.current_state.name]
+                
+                call_last_prompt = controller_dict["call_last_prompt"] if "call_last_prompt" in controller_dict else ""
+                
+                allocate_prompt = ""
+                roles = list(set(self.current_state.roles))
+                for role in roles:
+                    allocate_prompt += eval(Allocate_component)
+                    
+                call_system_prompt = controller_dict["call_system_prompt"]  if "call_system_prompt" in controller_dict else ""
+                environment_prompt = eval(Get_environment_prompt) if self.current_state.environment_prompt else ""    
+                # call_system_prompt + environment + allocate_prompt 
+                call_system_prompt = eval(Call_system_prompt)
+                
+                query = chat_history[-1].get_query()
+                last_name = chat_history[-1].send_name
+                # last_prompt: note + last_prompt + query
+                call_last_prompt =eval(Call_last_prompt)
+                
+                
+                chat_history_message = Memory.get_chat_history(chat_history)
+                # Intermediate historical conversation records
+                chat_messages = [
+                    {
+                        "role": "user",
+                        "content": eval(Call_message),
+                    }
+                ]
+
+                extract_words = controller_dict["call_extract_words"] if "call_extract_words" in controller_dict else "end"
+
+                response = self.LLM.get_response(
+                    chat_messages, call_system_prompt, call_last_prompt, stream=False, **kwargs
+                )
+
+                # get next role
+                next_role = extract(response, extract_words)
+
+            # Speak in order
+            elif controller_type == "order":
+                # If there is no begin role, it will be given directly to the first person.
+                if not self.current_state.current_role:
+                    next_role = self.current_state.roles[0]
+                # otherwise first
+                else:
+                    self.current_state.index += 1
+                    self.current_state.index =  (self.current_state.index) % len(self.current_state.roles)
+                    next_role = self.current_state.roles[self.current_state.index]
+            # random speak
+            elif controller_type == "random":
+                next_role = random.choice(self.current_state.roles)
+            
+        # 如果下一角色不在，则随机挑选一个
+        # If the next character is not available, pick one at random    
+        if next_role not in self.current_state.roles:
+            next_role = random.choice(self.current_state.roles)
+            
+        self.current_state.current_role = next_role 
+        
+        next_agent = agents[self.roles_to_names[self.current_state.name][next_role]]
+        
+        return next_agent
+    
+    def next(self, environment, agents):
+        """
+        Determine the next state and the agent that needs action based on the current situation
+        """
+        
+        # 如��是第一次进入该状态
+        # If it is the first time to enter this state
+        
+        if self.current_state.is_begin:
+            agent_name = self.roles_to_names[self.current_state.name][self.current_state.begin_role]
+            agent = agents[agent_name]
+            return self.current_state,agent
+    
+    
+        # get relevant history
+        query = environment.shared_memory["long_term_memory"][-1].content
+        relevant_history = get_relevant_history(
+            query,
+            environment.shared_memory["long_term_memory"][:-1],
+            environment.shared_memory["chat_embeddings"][:-1],
+        )
+        relevant_history = Memory.get_chat_history(relevant_history)
+        
+        
+        
+        next_state = self.transit(
+            chat_history=environment.shared_memory["long_term_memory"][
+                environment.current_chat_history_idx :
+            ],
+            relevant_history=relevant_history,
+            environment=environment,
+        )
+        # 如果进入终止节点，则直接终止
+        # If you enter the termination node, terminate directly
+        if next_state.name == self.finish_state_name:
+            self.finished = True
+            return None, None
+
+        self.current_state = next_state
+        
+        # 如果是首次进入该节点且有开场白，则直接分配给开场角色
+        # If it is the first time to enter the state and there is a begin query, it will be directly assigned to the begin role.
+        if self.current_state.is_begin and self.current_state.begin_role:
+            agent_name = self.roles_to_names[self.current_state.name][self.current_state.begin_role]
+            agent = agents[agent_name]
+            return self.current_state,agent
+           
+
+        next_agent = self.route(
+            chat_history=environment.shared_memory["long_term_memory"][
+                environment.current_chat_history_idx :
+            ],
+            agents = agents,
+            relevant_history=relevant_history,
+        )
+
+        return self.current_state, next_agent

src/agents/State.py

@@ -0,0 +1,142 @@
+from Component import *
+
+
+class State:
+    """
+    Sub-scenes of role activities, responsible for storing the tasks that each role needs to do
+    """
+    def __init__(self, **kwargs):
+        self.next_states = {}
+        self.name = kwargs["name"]
+
+        self.environment_prompt = (
+            kwargs["environment_prompt"] if "environment_prompt" in kwargs else ""
+        )
+
+        self.roles = kwargs["roles"] if "roles" in kwargs else (list(kwargs["agent_states"].keys()) if "agent_states" in kwargs else [0])
+        if len(self.roles) == 0:
+            self.roles = [0]
+        self.begin_role = (
+            kwargs["begin_role"] if "begin_role" in kwargs else self.roles[0]
+        )
+        self.begin_query = kwargs["begin_query"] if "begin_query" in kwargs else None
+
+        self.is_begin = True
+
+        self.summary_prompt = (
+            kwargs["summary_prompt"] if "summary_prompt" in kwargs else None
+        )
+        self.current_role = self.begin_role
+        self.components = (
+            self.init_components(kwargs["agent_states"])
+            if "agent_states" in kwargs
+            else {}
+        )
+        self.index = (
+            self.roles.index(self.begin_role) if self.begin_role in self.roles else 0
+        )
+        self.chat_nums = 0
+
+    def init_components(self, agent_states_dict: dict):
+        agent_states = {}
+        for role, components in agent_states_dict.items():
+            component_dict = {}
+            for component, component_args in components.items():
+                if component:
+                    # "role" "style"
+                    if component == "style":
+                        component_dict["style"] = StyleComponent(component_args["role"])
+
+                        # "task"
+                    elif component == "task":
+                        component_dict["task"] = TaskComponent(component_args["task"])
+
+                        # "rule"
+                    elif component == "rule":
+                        component_dict["rule"] = RuleComponent(component_args["rule"])
+
+                        # "demonstration"
+                    elif component == "demonstrations":
+                        component_dict["demonstrations"] = DemonstrationComponent(
+                            component_args["demonstrations"]
+                        )
+
+                    # "output"
+                    elif component == "output":
+                        component_dict["output"] = OutputComponent(
+                            component_args["output"]
+                        )
+
+                    elif component == "last":
+                        component_dict["last"] = LastComponent(
+                            component_args["last_prompt"]
+                        )
+
+                    # "demonstrations"
+                    elif component == "cot":
+                        component_dict["cot"] = CoTComponent(
+                            component_args["demonstrations"]
+                        )
+                    elif component == "CustomizeComponent":
+                        component_dict["CustomizeComponent"] = CustomizeComponent(
+                            component_args["template"], component_args["keywords"]
+                        )
+                    
+                    elif component == "system" : 
+                        component_dict["system"] = SystemComponent(
+                            component_args["system_prompt"]
+                        )
+
+                    # =================================================================================#
+
+                    # "output"
+                    elif component == "StaticComponent":
+                        component_dict["StaticComponent"] = StaticComponent(
+                            component_args["output"]
+                        )
+
+                    # "top_k"  "type" "knowledge_base" "system_prompt" "last_prompt"
+                    elif component == "KnowledgeBaseComponent":
+                        component_dict["tool"] = KnowledgeBaseComponent(
+                            component_args["top_k"],
+                            component_args["type"],
+                            component_args["knowledge_path"],
+                        )
+
+                    elif component == "CategoryRequirementsComponent":
+                        component_dict[
+                            "CategoryRequirementsComponent"
+                        ] = CategoryRequirementsComponent(
+                            component_args["information_path"]
+                        )
+
+                    elif component == "FunctionComponent":
+                        component_dict["FunctionComponent"] = FunctionComponent(component_args[""])
+                    # "short_memory_extract_words"  "long_memory_extract_words" "system_prompt" "last_prompt"
+                    elif component == "ExtractComponent":
+                        component_dict["ExtractComponent"] = ExtractComponent(
+                            component_args["extract_words"],
+                            component_args["system_prompt"],
+                            component_args["last_prompt"],
+                        )
+                    elif component == "WebSearchComponent":
+                        component_dict["WebSearchComponent"] = WebSearchComponent(
+                            component_args["engine_name"], component_args["api"]
+                        )
+                    elif component == "WebCrawlComponent":
+                        component_dict["WebCrawlComponent"] = WebCrawlComponent(
+                            component_args["name"]
+                        )
+
+                    elif component == "CodeComponent":
+                        component_dict["CodeComponent"] = CodeComponent(
+                            component_args["file_name"], component_args["keyword"]
+                        )
+
+                    # ====================================================
+                    else:
+                        continue
+
+            agent_states[role] = component_dict
+
+        return agent_states

src/agents/__init__.py

@@ -0,0 +1,4 @@
+from .evolve import *
+from .SOP import *
+from .State import *
+from .utils import *

src/agents/evolve.py

@@ -0,0 +1,17 @@
+# coding=utf-8
+# Copyright 2023  The AIWaves Inc. team.
+
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""self evolution of an LLM autonoumous agent"""

src/agents/template.py

@@ -0,0 +1,111 @@
+## default { "temperature": 0.3, "model": "gpt-3.5-turbo-16k-0613","log_path": "logs/{your name}"}
+LLM = {
+    "temperature": 0.0,
+    "model": "gpt-3.5-turbo-16k-0613",
+    "log_path": "logs/god"
+}
+
+
+Agents = {
+    "Lilong" : {
+        "style" : "professional",
+        "roles" : {
+            "company" : "coder",
+            "state2" : "role2",
+        },
+    "name2" : {   
+        "style" : "professional",
+            "roles" : {
+                "company" : "coder",
+                "state2" : "role2",
+            },
+        }
+    }
+}
+
+# indispensable parameter:  "controller_type"（"order","random","rule"）
+# default extract words: "end". You can choose not to fill in this parameter
+controller = {
+    "controller_type": "order",
+    "max_chat_nums" : 12,
+    "judge_system_prompt": "",
+    "judge_last_prompt": "",
+    "judge_extract_words": "end",
+    "call_system_prompt" : "",
+    "call_last_prompt": "",
+    "call_extract_words": ""
+}
+
+#
+Agent_state = {
+    "role": {
+        "LLM_type": "OpenAI",
+        "LLM": LLM,
+        "style": {
+            "role": "Opening Advocate for the Affirmative",
+            "style": "professional"
+        },
+        "task": {
+            "task": ""
+        },
+        "rule": {
+            "rule": ""
+        }
+    },
+}
+
+
+# indispensable parameter: "agent_states","controller"
+# "roles" determines the speaking order when the rule is order. If not set, it is the default order.
+# "begin_query" & "begin_role" determines the first speaker.It often determines the direction of the next speech. If you do not set it, it will default to the first agent.
+# "environment_prompt" : Responsible for setting the scene for the current environment
+State = {
+    "controller": controller,
+    "begin_role": "",
+    "begin_query": "",
+    "environment_prompt": "",
+    "roles": ["role1","role2"],
+    "LLM_type": "OpenAI",
+    "LLM": LLM,
+    "agent_state" : Agent_state,
+}
+
+
+
+States = {
+    "end_state":{
+            "agent_states":{}
+        },
+    "state1" : State
+    
+}
+
+
+# default finish_state_name is "end_state"
+# "environment_type" : "competive" : different states not share the memory; "cooperative":diffrent states share the memory
+SOP = {
+    "config" : {
+    "API_KEY" : "Your key",
+    "PROXY" : "Your PROXY",
+    "MAX_CHAT_HISTORY" : "5",
+    "User_Names" : "[\"alexander\"]"
+    },
+    "environment_type" : "competive",
+    "LLM_type": "OpenAI",
+    "LLM" :LLM,
+    "root": "state1",
+    "finish_state_name" : "end_state",
+    "relations": {
+        "state1": {
+            "0": "state1",
+            "1": "state2"
+        },
+        "state2":{
+            "0":"state2",
+            "1":"end_state"
+        }
+    },
+    "agents": Agents,
+    "states": States,
+}
+