🤖AI Autonomous Agents

Brief Introduction

🤖 AI Autonomous Agents are intelligent entities capable of perceiving their environment, making decisions, and taking actions without direct human intervention. As human society rapidly develops, AI Autonomous Agents find applications in diverse circumstances.

Demonstrations & Attributes

Agent

class Agent:
    def __init__(self, config: AgentConfig):
        self.config = config
        self.agent_name = self.config.agent_name
        self.agent_roles = self.config.agent_roles
        self.agent_style = self.config.agent_style
        self.agent_description = self.config.agent_description
        llm_config = (
            LLMConfig(self.config.LLM_config) if self.config.LLM_config else None
        )
        self.LLM = OpenAILLM(llm_config) if llm_config else None
        if self.config.memory:
            self.short_term_memory = (
                ShortTermMemory(
                    config=self.config.memory["short_term_memory"], messages=[]
                )
                if "short_term_memory" in self.config.memory
                else ShortTermMemory(config={}, messages=[])
            )
            self.long_term_memory = (
                LongTermMemory(
                    config=self.config.memory["long_term_memory"],
                    json_path=self.config.memory["long_term_memory"].get(
                        "json_path", f"memory/{self.agent_name}.jsonl"
                    ),
                    chunk_list=[],
                )
                if "long_term_memory" in self.config.memory
                else LongTermMemory(
                    config={},
                    json_path=f"memory/{self.agent_name}.jsonl",
                    chunk_list=[],
                )
            )
        else:
            self.short_term_memory = ShortTermMemory(config={}, messages=[])
            self.long_term_memory = LongTermMemory(
                config={},
                json_path=f"memory/{self.agent_name}.jsonl",
                chunk_list=[],
            )
        self.toolkit = (
            Toolkit.from_config(self.config.toolkit) if self.config.toolkit else None
        )
        self.is_user = self.config.is_user

# Remark:
# state_roles(dict): The agent’s role in different state.
# is_user: True if the agent is a user operation, False otherwise.
# long_term_memory: The Agent's historical conversation records, in which the conversations of other agents are informed in the form of historical records.
# short_term_memory: The Agent’s short-term memory is a summary of its past historical memory.

Act

def act(self):
    """
    return actions by the current state
    """
    current_state = self.current_state
    system_prompt, last_prompt, res_dict = self.compile()
    chat_history = self.long_term_memory

    current_LLM = self.LLMs[current_state.name]

    response = current_LLM.get_response(
        chat_history, system_prompt, last_prompt, stream=True
    )
    return response, res_dict

# Remark:
# The act method generates and outputs the response of the Agent. Detailed explanations on particular attributes will be shown afterwards.

Step

def step(self, current_state, environment, input):
    """
    return actions by current state and environment
    """
    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

    self.current_state = current_state
    # 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(environment)
            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)

# Remark:
# Closely related to the act method, the step method updates the current circumstance and then returns the response of an Agent. Detailed explanations on particular attributes will be shown afterwards.

Compile

    def compile(self):
    """
    get prompt from state depend on your role
    Return:
    system_prompt: system_prompt for agent's LLM
    last_prompt: last_prompt for agent'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
    last_prompt = eval(Agent_last_prompt)
    return system_prompt, last_prompt, res_dict

# Remark:
# The Compile method reaches for the current role and returns the action of a certain agent state.

Observe

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)

# Remark:
# The Observe method is the core method of an agent. It updates and reads the current environment, including the chatting history and the basic information, and returns particular actions for the agent.

Update_memory

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

# Remark:
# The update_memory method is responsible for updating its long-term memory and short-term memory according to the environment. Every time it is more than a certain number of rounds since the last speech of the agent, it will be summarized to obtain short-term memory.