add reflection mechanism

MANIFEST.in

@@ -0,0 +1,2 @@
+include agentverse/tasks/*/*
+include agentverse/tasks/*/*/*

README.md

@@ -119,6 +119,16 @@ https://github.com/OpenBMB/AgentVerse/assets/11704492/4d07da68-f942-4205-b558-f1
 
 ## Contents
 
+- [✨ Features](#-features)
+- [📰 What's New](#-whats-new)
+- [🗓 Coming Soon](#-coming-soon)
+- [👾 Simple Demo Video](#-simple-demo-video)
+    - [NLP Classroom](#nlp-classroom)
+    - [Prisoner Dilemma](#prisoner-dilemma)
+    - [Software Design](#software-design)
+    - [Database Administrator (DBA)](#database-administrator-dba)
+    - [Pokemon](#pokemon)
+- [Contents](#contents)
 - [🚀 Getting Started](#-getting-started)
   - [Installation](#installation)
   - [CLI Example](#cli-example)
@@ -141,21 +151,30 @@ https://github.com/OpenBMB/AgentVerse/assets/11704492/4d07da68-f942-4205-b558-f1
 ### Installation
 
 ```bash
-# Install the dependencies
+pip install -U agentverse
+```
+Or you can install the package by manually cloning the latest repository
+```bash
 git clone https://github.com/OpenBMB/AgentVerse.git --depth 1
+cd AgentVerse
 pip install -r requirements.txt
+```
+Some users have reported problems installing the `orjson` required by `gradio`. One simple workaround is to install it with Anaconda `conda install -c conda-forge orjson`.
 
-cd ../
+You also need to export your OpenAI API key as follows
+```bash
+# Export your OpenAI API key
+export OPENAI_API_KEY="your_api_key_here"
+```
+
+If you want to use the tools provided by BMTools, you need to install BMTools as follows:
+```bash
 git clone git+https://github.com/OpenBMB/BMTools.git
 cd BMTools
 pip install -r requirements.txt
 python setup.py develop
-
-# Export your OpenAI API key
-export OPENAI_API_KEY="your_api_key_here"
 ```
 
-Some users have reported problems installing the `orjson` required by `gradio`. One simple workaround is to install it with Anaconda `conda install -c conda-forge orjson`.
 
 <!--
 # Install BMTools

agentverse/agents/__init__.py

@@ -6,4 +6,6 @@
 from .base import BaseAgent
 from .conversation_agent import ConversationAgent
 from .tool_agent import ToolAgent
+
 from .prisoner_dilemma_agent import PoliceAgent, PrisonerAgent
+from .reflection_agent import ReflectionAgent

agentverse/agents/base.py

@@ -8,7 +8,7 @@
 from agentverse.memory import BaseMemory, ChatHistoryMemory
 from agentverse.message import Message
 from agentverse.parser import OutputParser
-
+from agentverse.memory_manipulator import BaseMemoryManipulator
 
 class BaseAgent(BaseModel):
     name: str
@@ -17,6 +17,7 @@ class BaseAgent(BaseModel):
     prompt_template: str
     role_description: str = Field(default="")
     memory: BaseMemory = Field(default_factory=ChatHistoryMemory)
+    memory_manipulator: BaseMemoryManipulator = Field(default_factory=BaseMemoryManipulator)
     max_retry: int = Field(default=3)
     receiver: Set[str] = Field(default=set({"all"}))
     async_mode: bool = Field(default=True)

agentverse/agents/reflection_agent.py

@@ -0,0 +1,212 @@
+from __future__ import annotations
+
+"""
+An agent based upon Observation-Planning-Reflection architecture.
+"""
+
+from logging import getLogger
+
+from abc import abstractmethod
+from typing import List, Set, Union, NamedTuple, TYPE_CHECKING
+
+from pydantic import BaseModel, Field, validator
+
+from agentverse.llms import BaseLLM
+from agentverse.memory import BaseMemory, ChatHistoryMemory
+from agentverse.message import Message
+from agentverse.parser import OutputParser
+
+from agentverse.message import Message
+from agentverse.agents.base import BaseAgent
+
+from datetime import datetime as dt
+import datetime
+
+from . import agent_registry
+from string import Template
+
+
+logger = getLogger(__file__)
+
+if TYPE_CHECKING:
+    from agentverse.environments.base import BaseEnvironment
+
+
+@agent_registry.register("reflection")
+class ReflectionAgent(BaseAgent):
+    async_mode: bool = True,
+    current_time: str = None,
+    environment: BaseEnvironment = None
+    step_cnt: int = 0
+
+    manipulated_memory: str = Field(default="", description="one fragment used in prompt construction")
+
+    @validator('current_time')
+    def convert_str_to_dt(cls, current_time):
+        if not isinstance(current_time, str):
+            raise ValueError('current_time should be str')
+        return dt.strptime(current_time, "%Y-%m-%d %H:%M:%S")
+
+    def step(self, current_time: dt, env_description: str = "") -> Message:
+        """
+        Call this method at each time frame
+        """
+        self.current_time = current_time
+
+        self.manipulated_memory = self.memory_manipulator.manipulate_memory()
+
+        prompt = self._fill_prompt_template(env_description)
+
+        parsed_response, reaction, target = None, None, None
+        for i in range(self.max_retry):
+            try:
+                response = self.llm.agenerate_response(prompt)
+                parsed_response = self.output_parser.parse(response)
+
+                if 'say(' in parsed_response.return_values["output"]:
+                    reaction, target = eval("self._" + parsed_response.return_values["output"].strip())
+                elif 'act(' in parsed_response.return_values["output"]:
+                    reaction, target = eval("self._" + parsed_response.return_values["output"].strip())
+                elif 'do_nothing(' in parsed_response.return_values["output"]:
+                    reaction, target = None, None
+                else:
+                    raise Exception(f"no valid parsed_response detected, "
+                                    f"cur response {parsed_response.return_values['output']}")
+                break
+
+            except Exception as e:
+                logger.error(e)
+                logger.warning("Retrying...")
+                continue
+
+        if parsed_response is None:
+            logger.error(f"{self.name} failed to generate valid response.")
+
+        if reaction is None:
+            reaction = "Keep doing last action ..."
+
+        message = Message(
+            content=""
+            if reaction is None
+            else reaction,
+            sender=self.name,
+            receiver=self.get_receiver() if target is None else self.get_valid_receiver(target),
+        )
+
+        self.step_cnt += 1
+
+        return message
+
+    async def astep(self, current_time: dt, env_description: str = "") -> Message:
+        """Asynchronous version of step"""
+        #use environment's time to update agent's time
+        self.current_time = current_time
+        # Before the agent step, we check current status,
+        # TODO add this func after
+        # self.check_status_passive()
+
+        self.manipulated_memory = self.memory_manipulator.manipulate_memory()
+
+        prompt = self._fill_prompt_template(env_description)
+
+        parsed_response, reaction, target = None, None, None
+        for i in range(self.max_retry):
+            try:
+                response = await self.llm.agenerate_response(prompt)
+                parsed_response = self.output_parser.parse(response)
+
+                if 'say(' in parsed_response.return_values["output"]:
+                    reaction, target = eval("self._" + parsed_response.return_values["output"].strip())
+                elif 'act(' in parsed_response.return_values["output"]:
+                    reaction, target = eval("self._" + parsed_response.return_values["output"].strip())
+                elif 'do_nothing(' in parsed_response.return_values["output"]:
+                    reaction, target = None, None
+                else:
+                    raise Exception(f"no valid parsed_response detected, "
+                                    f"cur response {parsed_response.return_values['output']}")
+
+                break
+
+            except Exception as e:
+                logger.error(e)
+                logger.warning("Retrying...")
+                continue
+
+        if parsed_response is None:
+            logger.error(f"{self.name} failed to generate valid response.")
+
+        if reaction is None:
+            reaction = "Keep doing last action ..."
+
+        message = Message(
+            content=""
+            if reaction is None
+            else reaction,
+            sender=self.name,
+            receiver=self.get_receiver() if target is None else self.get_valid_receiver(target),
+        )
+
+        self.step_cnt += 1
+
+        return message
+
+    def _act(self, description=None, target=None):
+        if description is None:
+            return ""
+        if target is None:
+            reaction_content = f"{self.name} performs action: '{description}'."
+        else:
+            reaction_content = f"{self.name} performs action to {target}: '{description}'."
+        # self.environment.broadcast_observations(self, target, reaction_content)
+        return reaction_content, target
+
+    def _say(self, description, target=None):
+        if description is None:
+            return ""
+        if target is None:
+            reaction_content = f"{self.name} says: '{description}'."
+        else:
+            reaction_content = f"{self.name} says to {target}: '{description}'."
+        # self.environment.broadcast_observations(self, target, reaction_content)
+        return reaction_content, target
+
+    def get_valid_receiver(self, target: str) -> set():
+
+        all_agents_name = []
+        for agent in self.environment.agents:
+            all_agents_name.append(agent.name)
+
+        if not (target in all_agents_name):
+            return {"all"}
+        else:
+            return {target}
+
+    def _fill_prompt_template(self, env_description: str = "") -> str:
+        """Fill the placeholders in the prompt template
+
+        In the conversation agent, three placeholders are supported:
+        - ${agent_name}: the name of the agent
+        - ${env_description}: the description of the environment
+        - ${role_description}: the description of the role of the agent
+        - ${chat_history}: the chat history of the agent
+        """
+        input_arguments = {
+            "agent_name": self.name,
+            "role_description": self.role_description,
+            "chat_history": self.memory.to_string(add_sender_prefix=True),
+            "current_time": self.current_time,
+            "env_description": env_description,
+        }
+        return Template(self.prompt_template).safe_substitute(input_arguments)
+
+    def add_message_to_memory(self, messages: List[Message]) -> None:
+        self.memory.add_message(messages)
+
+    def reset(self, environment: BaseEnvironment) -> None:
+        """Reset the agent"""
+        self.environment = environment
+        self.memory.reset()
+        self.memory_manipulator.agent = self
+        self.memory_manipulator.memory = self.memory
+
+

agentverse/environments/__init__.py

@@ -6,6 +6,11 @@
 from .base import BaseEnvironment
 from .basic import BasicEnvironment
 from .pokemon import PokemonEnvironment
+
 from .prisoner_dilemma import PrisonerDilemmaEnvironment
+
 from .sde_team import SdeTeamEnvironment
 from .sde_team_given_tests import SdeTeamGivenTestsEnvironment
+
+from .reflection import ReflectionEnvironment
+