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移除autogpt 相关代码,autogpt 在新分支开发

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This commit is contained in:
w_xiaolizu
2023-06-05 10:14:22 +08:00
parent 8ab0c1b6e5
commit b11807f35d
106 changed files with 6 additions and 9970 deletions
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51
__main__.py
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@ -224,31 +224,6 @@ class ChatBot(ChatBotFrame):
container=False)
# temp = gr.Markdown(self.description)
def draw_goals_auto(self):
with gr.Row():
self.ai_name = gr.Textbox(show_label=False, placeholder="给Ai一个名字").style(container=False)
with gr.Row():
self.ai_role = gr.Textbox(lines=5, show_label=False, placeholder="请输入你的需求").style(
container=False)
with gr.Row():
self.ai_goal_list = gr.Dataframe(headers=['Goals'], interactive=True, row_count=4,
col_count=(1, 'fixed'), type='array')
with gr.Row():
self.ai_budget = gr.Number(show_label=False, value=0.0,
info="关于本次项目的预算，超过预算自动停止，默认无限").style(container=False)
def draw_next_auto(self):
with gr.Row():
self.text_continue = gr.Textbox(visible=False, show_label=False,
placeholder="请根据提示输入执行命令").style(container=False)
with gr.Row():
self.submit_start = gr.Button("Start", variant='primary')
self.submit_next = gr.Button("Next", visible=False, variant='primary')
self.submit_stop = gr.Button("Stop", variant="stop")
self.agent_obj = gr.State({'obj': None, "start": self.submit_start,
"next": self.submit_next, "text": self.text_continue})
def signals_input_setting(self):
# 注册input
@ -321,15 +296,6 @@ class ChatBot(ChatBotFrame):
self.md_dropdown.select(on_md_dropdown_changed, [self.md_dropdown], [self.chatbot])
def signals_auto_input(self):
from autogpt.cli import agent_main
self.auto_input_combo = [self.ai_name, self.ai_role, self.ai_goal_list, self.ai_budget,
self.cookies, self.chatbot, self.history,
self.agent_obj]
self.auto_output_combo = [self.cookies, self.chatbot, self.history, self.status,
self.agent_obj, self.submit_start, self.submit_next, self.text_continue]
self.submit_start.click(fn=agent_main, inputs=self.auto_input_combo, outputs=self.auto_output_combo)
# gradio的inbrowser触发不太稳定，回滚代码到原始的浏览器打开函数
def auto_opentab_delay(self, is_open=False):
import threading, webbrowser, time
@ -361,18 +327,14 @@ class ChatBot(ChatBotFrame):
self.draw_function_chat()
self.draw_public_chat()
self.draw_setting_chat()
# 绘制autogpt模组
with gr.TabItem('Auto-GPT'):
self.draw_next_auto()
self.draw_goals_auto()
# 绘制列2
with gr.Column(scale=100):
with gr.Tab('Chatbot') as self.chat_tab:
# self.draw_chatbot()
pass
with gr.Tab('Prompt检索/编辑') as self.prompt_tab:
self.draw_prompt()
with gr.Tabs() as self.tabs_chat:
with gr.TabItem('Chatbot') as self.chat_tab:
# self.draw_chatbot()
pass
with gr.TabItem('Prompt检索/编辑') as self.prompt_tab:
self.draw_prompt()
with self.chat_tab: # 使用 gr.State()对组件进行拷贝时，如果之前绘制了Markdown格式，会导致启动崩溃,所以将 markdown相关绘制放在最后
@ -385,7 +347,6 @@ class ChatBot(ChatBotFrame):
self.signals_prompt_func()
self.signals_public()
self.signals_prompt_edit()
# self.signals_auto_input()
# Start
self.auto_opentab_delay()

2
autogpt/CURRENT_BULLETIN.md
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@ -1,2 +0,0 @@
Welcome to Auto-GPT! We'll keep you informed of the latest news and features by printing messages here.
If you don't wish to see this message, you can run Auto-GPT with the --skip-news flag

0
autogpt/__init__.py
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5
autogpt/__main__.py
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@ -1,5 +0,0 @@
"""Auto-GPT: A GPT powered AI Assistant"""
import autogpt.cli
if __name__ == "__main__":
autogpt.cli.main()

4
autogpt/agent/__init__.py
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@ -1,4 +0,0 @@
from autogpt.agent.agent import Agent
from autogpt.agent.agent_manager import AgentManager
__all__ = ["Agent", "AgentManager"]

241
autogpt/agent/agent.py
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@ -1,241 +0,0 @@
from colorama import Fore, Style
from autogpt.app import execute_command, get_command
from autogpt.chat import chat_with_ai, create_chat_message
from autogpt.config import Config
from autogpt.json_utils.json_fix_llm import fix_json_using_multiple_techniques
from autogpt.json_utils.utilities import validate_json
from autogpt.logs import logger, print_assistant_thoughts
from autogpt.speech import say_text
from autogpt.spinner import Spinner
from autogpt.utils import clean_input
from autogpt.workspace import Workspace
class Agent:
"""Agent class for interacting with Auto-GPT.
Attributes:
ai_name: The name of the agent.
memory: The memory object to use.
full_message_history: The full message history.
next_action_count: The number of actions to execute.
system_prompt: The system prompt is the initial prompt that defines everything
the AI needs to know to achieve its task successfully.
Currently, the dynamic and customizable information in the system prompt are
ai_name, description and goals.
triggering_prompt: The last sentence the AI will see before answering.
For Auto-GPT, this prompt is:
Determine which next command to use, and respond using the format specified
above:
The triggering prompt is not part of the system prompt because between the
system prompt and the triggering
prompt we have contextual information that can distract the AI and make it
forget that its goal is to find the next task to achieve.
SYSTEM PROMPT
CONTEXTUAL INFORMATION (memory, previous conversations, anything relevant)
TRIGGERING PROMPT
The triggering prompt reminds the AI about its short term meta task
(defining the next task)
"""
def __init__(
self,
ai_name,
memory,
full_message_history,
next_action_count,
command_registry,
config,
system_prompt,
triggering_prompt,
workspace_directory,
):
self.cfg = Config()
self.ai_name = ai_name
self.memory = memory
self.full_message_history = full_message_history
self.next_action_count = next_action_count
self.command_registry = command_registry
self.config = config
self.system_prompt = system_prompt
self.triggering_prompt = triggering_prompt
self.workspace = Workspace(workspace_directory, self.cfg.restrict_to_workspace)
self.loop_count = 0
self.command_name = None
self.sarguments = None
self.user_input = ""
self.cfg = Config()
def start_interaction_loop(self):
# Discontinue if continuous limit is reached
self.loop_count += 1
if (
self.cfg.continuous_mode
and self.cfg.continuous_limit > 0
and self.loop_count > self.cfg.continuous_limit
):
logger.typewriter_log(
"Continuous Limit Reached: ", Fore.YELLOW, f"{self.cfg.continuous_limit}"
)
# break
# Send message to AI, get response
with Spinner("Thinking... "):
self.assistant_reply = chat_with_ai(
self,
self.system_prompt,
self.triggering_prompt,
self.full_message_history,
self.memory,
self.cfg.fast_token_limit,
) # TODO: This hardcodes the model to use GPT3.5. Make this an argument
self.assistant_reply_json = fix_json_using_multiple_techniques(self.assistant_reply)
for plugin in self.cfg.plugins:
if not plugin.can_handle_post_planning():
continue
self.assistant_reply_json = plugin.post_planning(self, self.assistant_reply_json)
# Print Assistant thoughts
if self.assistant_reply_json != {}:
validate_json(self.assistant_reply_json, "llm_response_format_1")
# Get command name and self.arguments
try:
print_assistant_thoughts(self.ai_name, self.assistant_reply_json)
self.command_name, self.arguments = get_command(self.assistant_reply_json)
if self.cfg.speak_mode:
say_text(f"I want to execute {self.command_name}")
self.arguments = self._resolve_pathlike_command_args(self.arguments)
except Exception as e:
logger.error("Error: \n", str(e))
if not self.cfg.continuous_mode and self.next_action_count == 0:
# ### GET USER AUTHORIZATION TO EXECUTE COMMAND ###
# Get key press: Prompt the user to press enter to continue or escape
# to exit
logger.typewriter_log(
"NEXT ACTION: ",
Fore.CYAN,
f"COMMAND = {self.command_name}"
f"ARGUMENTS = {self.arguments}",
)
logger.typewriter_log(
"",
"",
"Enter 'y' to authorise command, 'y -N' to run N continuous "
"commands, 'n' to exit program, or enter feedback for "
f"{self.ai_name}...",
)
def start_interaction_next(self, cookie, chatbot, history, msg, _input, obj):
console_input = _input
if console_input.lower().strip() == "y":
self.user_input = "GENERATE NEXT COMMAND JSON"
elif console_input.lower().strip() == "":
print("Invalid input format.")
return
elif console_input.lower().startswith("y -"):
try:
self.next_action_count = abs(
int(console_input.split(" ")[1])
)
self.user_input = "GENERATE NEXT COMMAND JSON"
except ValueError:
print(
"Invalid input format. Please enter 'y -n' where n is"
" the number of continuous tasks."
)
return
elif console_input.lower() == "n":
self.user_input = "EXIT"
return
else:
self.user_input = console_input
self.command_name = "human_feedback"
return
if self.user_input == "GENERATE NEXT COMMAND JSON":
logger.typewriter_log(
"-=-=-=-=-=-=-= COMMAND AUTHORISED BY USER -=-=-=-=-=-=-=",
Fore.MAGENTA,
"",
)
elif self.user_input == "EXIT":
print("Exiting...", flush=True)
# break 这里需要注意
else:
# Print command
logger.typewriter_log(
"NEXT ACTION: ",
Fore.CYAN,
f"COMMAND = {Fore.CYAN}{self.command_name}{Style.RESET_ALL}"
f" ARGUMENTS = {Fore.CYAN}{self.arguments}{Style.RESET_ALL}",
)
# Execute command
if self.command_name is not None and self.command_name.lower().startswith("error"):
result = (
f"Command {self.command_name} threw the following error: {self.arguments}"
)
elif self.command_name == "human_feedback":
result = f"Human feedback: {self.user_input}"
else:
for plugin in self.cfg.plugins:
if not plugin.can_handle_pre_command():
continue
self.command_name, self.arguments = plugin.pre_command(
self.command_name, self.arguments
)
command_result = execute_command(
self.command_registry,
self.command_name,
self.arguments,
self.config.prompt_generator,
)
result = f"Command {self.command_name} returned: " f"{command_result}"
for plugin in self.cfg.plugins:
if not plugin.can_handle_post_command():
continue
result = plugin.post_command(self.command_name, result)
if self.next_action_count > 0:
self.next_action_count -= 1
if self.command_name != "do_nothing":
memory_to_add = (
f"Assistant Reply: {self.assistant_reply} "
f"\nResult: {result} "
f"\nHuman Feedback: {self.user_input} "
)
self.memory.add(memory_to_add)
# Check if there's a result from the command append it to the message
# history
if result is not None:
self.full_message_history.append(
create_chat_message("system", result)
)
logger.typewriter_log("SYSTEM: ", Fore.YELLOW, result)
else:
self.full_message_history.append(
create_chat_message("system", "Unable to execute command")
)
logger.typewriter_log(
"SYSTEM: ", Fore.YELLOW, "Unable to execute command"
)
def _resolve_pathlike_command_args(self, command_args):
if "directory" in command_args and command_args["directory"] in {"", "/"}:
command_args["directory"] = str(self.workspace.root)
else:
for pathlike in ["filename", "directory", "clone_path"]:
if pathlike in command_args:
command_args[pathlike] = str(
self.workspace.get_path(command_args[pathlike])
)
return command_args

145
autogpt/agent/agent_manager.py
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@ -1,145 +0,0 @@
"""Agent manager for managing GPT agents"""
from __future__ import annotations
from typing import List, Union
from autogpt.config.config import Config, Singleton
from autogpt.llm_utils import create_chat_completion
from autogpt.types.openai import Message
class AgentManager(metaclass=Singleton):
"""Agent manager for managing GPT agents"""
def __init__(self):
self.next_key = 0
self.agents = {} # key, (task, full_message_history, model)
self.cfg = Config()
# Create new GPT agent
# TODO: Centralise use of create_chat_completion() to globally enforce token limit
def create_agent(self, task: str, prompt: str, model: str) -> tuple[int, str]:
"""Create a new agent and return its key
Args:
task: The task to perform
prompt: The prompt to use
model: The model to use
Returns:
The key of the new agent
"""
messages: List[Message] = [
{"role": "user", "content": prompt},
]
for plugin in self.cfg.plugins:
if not plugin.can_handle_pre_instruction():
continue
if plugin_messages := plugin.pre_instruction(messages):
messages.extend(iter(plugin_messages))
# Start GPT instance
agent_reply = create_chat_completion(
model=model,
messages=messages,
)
messages.append({"role": "assistant", "content": agent_reply})
plugins_reply = ""
for i, plugin in enumerate(self.cfg.plugins):
if not plugin.can_handle_on_instruction():
continue
if plugin_result := plugin.on_instruction(messages):
sep = "\n" if i else ""
plugins_reply = f"{plugins_reply}{sep}{plugin_result}"
if plugins_reply and plugins_reply != "":
messages.append({"role": "assistant", "content": plugins_reply})
key = self.next_key
# This is done instead of len(agents) to make keys unique even if agents
# are deleted
self.next_key += 1
self.agents[key] = (task, messages, model)
for plugin in self.cfg.plugins:
if not plugin.can_handle_post_instruction():
continue
agent_reply = plugin.post_instruction(agent_reply)
return key, agent_reply
def message_agent(self, key: str | int, message: str) -> str:
"""Send a message to an agent and return its response
Args:
key: The key of the agent to message
message: The message to send to the agent
Returns:
The agent's response
"""
task, messages, model = self.agents[int(key)]
# Add user message to message history before sending to agent
messages.append({"role": "user", "content": message})
for plugin in self.cfg.plugins:
if not plugin.can_handle_pre_instruction():
continue
if plugin_messages := plugin.pre_instruction(messages):
for plugin_message in plugin_messages:
messages.append(plugin_message)
# Start GPT instance
agent_reply = create_chat_completion(
model=model,
messages=messages,
)
messages.append({"role": "assistant", "content": agent_reply})
plugins_reply = agent_reply
for i, plugin in enumerate(self.cfg.plugins):
if not plugin.can_handle_on_instruction():
continue
if plugin_result := plugin.on_instruction(messages):
sep = "\n" if i else ""
plugins_reply = f"{plugins_reply}{sep}{plugin_result}"
# Update full message history
if plugins_reply and plugins_reply != "":
messages.append({"role": "assistant", "content": plugins_reply})
for plugin in self.cfg.plugins:
if not plugin.can_handle_post_instruction():
continue
agent_reply = plugin.post_instruction(agent_reply)
return agent_reply
def list_agents(self) -> list[tuple[str | int, str]]:
"""Return a list of all agents
Returns:
A list of tuples of the form (key, task)
"""
# Return a list of agent keys and their tasks
return [(key, task) for key, (task, _, _) in self.agents.items()]
def delete_agent(self, key: str | int) -> bool:
"""Delete an agent from the agent manager
Args:
key: The key of the agent to delete
Returns:
True if successful, False otherwise
"""
try:
del self.agents[int(key)]
return True
except KeyError:
return False

158
autogpt/api_manager.py
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@ -1,158 +0,0 @@
from typing import List
import openai
from autogpt.config import Config
from autogpt.logs import logger
from autogpt.modelsinfo import COSTS
cfg = Config()
openai.api_key = cfg.openai_api_key
print_total_cost = cfg.debug_mode
class ApiManager:
def __init__(self, debug=False):
self.total_prompt_tokens = 0
self.total_completion_tokens = 0
self.total_cost = 0
self.total_budget = 0
self.debug = debug
def reset(self):
self.total_prompt_tokens = 0
self.total_completion_tokens = 0
self.total_cost = 0
self.total_budget = 0.0
def create_chat_completion(
self,
messages: list, # type: ignore
model: str = None,
temperature: float = cfg.temperature,
max_tokens: int = None,
deployment_id=None,
) -> str:
"""
Create a chat completion and update the cost.
Args:
messages (list): The list of messages to send to the API.
model (str): The model to use for the API call.
temperature (float): The temperature to use for the API call.
max_tokens (int): The maximum number of tokens for the API call.
Returns:
str: The AI's response.
"""
if deployment_id is not None:
response = openai.ChatCompletion.create(
deployment_id=deployment_id,
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
)
else:
response = openai.ChatCompletion.create(
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
)
if self.debug:
logger.debug(f"Response: {response}")
prompt_tokens = response.usage.prompt_tokens
completion_tokens = response.usage.completion_tokens
self.update_cost(prompt_tokens, completion_tokens, model)
return response
def embedding_create(
self,
text_list: List[str],
model: str = "text-embedding-ada-002",
) -> List[float]:
"""
Create an embedding for the given input text using the specified model.
Args:
text_list (List[str]): Input text for which the embedding is to be created.
model (str, optional): The model to use for generating the embedding.
Returns:
List[float]: The generated embedding as a list of float values.
"""
if cfg.use_azure:
response = openai.Embedding.create(
input=text_list,
engine=cfg.get_azure_deployment_id_for_model(model),
)
else:
response = openai.Embedding.create(input=text_list, model=model)
self.update_cost(response.usage.prompt_tokens, 0, model)
return response["data"][0]["embedding"]
def update_cost(self, prompt_tokens, completion_tokens, model):
"""
Update the total cost, prompt tokens, and completion tokens.
Args:
prompt_tokens (int): The number of tokens used in the prompt.
completion_tokens (int): The number of tokens used in the completion.
model (str): The model used for the API call.
"""
self.total_prompt_tokens += prompt_tokens
self.total_completion_tokens += completion_tokens
self.total_cost += (
prompt_tokens * COSTS[model]["prompt"]
+ completion_tokens * COSTS[model]["completion"]
) / 1000
if print_total_cost:
print(f"Total running cost: ${self.total_cost:.3f}")
def set_total_budget(self, total_budget):
"""
Sets the total user-defined budget for API calls.
Args:
prompt_tokens (int): The number of tokens used in the prompt.
"""
self.total_budget = total_budget
def get_total_prompt_tokens(self):
"""
Get the total number of prompt tokens.
Returns:
int: The total number of prompt tokens.
"""
return self.total_prompt_tokens
def get_total_completion_tokens(self):
"""
Get the total number of completion tokens.
Returns:
int: The total number of completion tokens.
"""
return self.total_completion_tokens
def get_total_cost(self):
"""
Get the total cost of API calls.
Returns:
float: The total cost of API calls.
"""
return self.total_cost
def get_total_budget(self):
"""
Get the total user-defined budget for API calls.
Returns:
float: The total budget for API calls.
"""
return self.total_budget
api_manager = ApiManager(cfg.debug_mode)

253
autogpt/app.py
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@ -1,253 +0,0 @@
""" Command and Control """
import json
from typing import Dict, List, NoReturn, Union
from autogpt.agent.agent_manager import AgentManager
from autogpt.commands.command import CommandRegistry, command
from autogpt.commands.web_requests import scrape_links, scrape_text
from autogpt.config import Config
from autogpt.memory import get_memory
from autogpt.processing.text import summarize_text
from autogpt.prompts.generator import PromptGenerator
from autogpt.speech import say_text
CFG = Config()
AGENT_MANAGER = AgentManager()
def is_valid_int(value: str) -> bool:
"""Check if the value is a valid integer
Args:
value (str): The value to check
Returns:
bool: True if the value is a valid integer, False otherwise
"""
try:
int(value)
return True
except ValueError:
return False
def get_command(response_json: Dict):
"""Parse the response and return the command name and arguments
Args:
response_json (json): The response from the AI
Returns:
tuple: The command name and arguments
Raises:
json.decoder.JSONDecodeError: If the response is not valid JSON
Exception: If any other error occurs
"""
try:
if "command" not in response_json:
return "Error:", "Missing 'command' object in JSON"
if not isinstance(response_json, dict):
return "Error:", f"'response_json' object is not dictionary {response_json}"
command = response_json["command"]
if not isinstance(command, dict):
return "Error:", "'command' object is not a dictionary"
if "name" not in command:
return "Error:", "Missing 'name' field in 'command' object"
command_name = command["name"]
# Use an empty dictionary if 'args' field is not present in 'command' object
arguments = command.get("args", {})
return command_name, arguments
except json.decoder.JSONDecodeError:
return "Error:", "Invalid JSON"
# All other errors, return "Error: + error message"
except Exception as e:
return "Error:", str(e)
def map_command_synonyms(command_name: str):
"""Takes the original command name given by the AI, and checks if the
string matches a list of common/known hallucinations
"""
synonyms = [
("write_file", "write_to_file"),
("create_file", "write_to_file"),
("search", "google"),
]
for seen_command, actual_command_name in synonyms:
if command_name == seen_command:
return actual_command_name
return command_name
def execute_command(
command_registry: CommandRegistry,
command_name: str,
arguments,
prompt: PromptGenerator,
):
"""Execute the command and return the result
Args:
command_name (str): The name of the command to execute
arguments (dict): The arguments for the command
Returns:
str: The result of the command
"""
try:
cmd = command_registry.commands.get(command_name)
# If the command is found, call it with the provided arguments
if cmd:
return cmd(**arguments)
# TODO: Remove commands below after they are moved to the command registry.
command_name = map_command_synonyms(command_name.lower())
if command_name == "memory_add":
return get_memory(CFG).add(arguments["string"])
# TODO: Change these to take in a file rather than pasted code, if
# non-file is given, return instructions "Input should be a python
# filepath, write your code to file and try again
elif command_name == "do_nothing":
return "No action performed."
elif command_name == "task_complete":
shutdown()
else:
for command in prompt.commands:
if (
command_name == command["label"].lower()
or command_name == command["name"].lower()
):
return command["function"](**arguments)
return (
f"Unknown command '{command_name}'. Please refer to the 'COMMANDS'"
" list for available commands and only respond in the specified JSON"
" format."
)
except Exception as e:
return f"Error: {str(e)}"
@command(
"get_text_summary", "Get text summary", '"url": "<url>", "question": "<question>"'
)
def get_text_summary(url: str, question: str) -> str:
"""Return the results of a Google search
Args:
url (str): The url to scrape
question (str): The question to summarize the text for
Returns:
str: The summary of the text
"""
text = scrape_text(url)
summary = summarize_text(url, text, question)
return f""" "Result" : {summary}"""
@command("get_hyperlinks", "Get text summary", '"url": "<url>"')
def get_hyperlinks(url: str) -> Union[str, List[str]]:
"""Return the results of a Google search
Args:
url (str): The url to scrape
Returns:
str or list: The hyperlinks on the page
"""
return scrape_links(url)
def shutdown() -> NoReturn:
"""Shut down the program"""
print("Shutting down...")
quit()
@command(
"start_agent",
"Start GPT Agent",
'"name": "<name>", "task": "<short_task_desc>", "prompt": "<prompt>"',
)
def start_agent(name: str, task: str, prompt: str, model=CFG.fast_llm_model) -> str:
"""Start an agent with a given name, task, and prompt
Args:
name (str): The name of the agent
task (str): The task of the agent
prompt (str): The prompt for the agent
model (str): The model to use for the agent
Returns:
str: The response of the agent
"""
# Remove underscores from name
voice_name = name.replace("_", " ")
first_message = f"""You are {name}. Respond with: "Acknowledged"."""
agent_intro = f"{voice_name} here, Reporting for duty!"
# Create agent
if CFG.speak_mode:
say_text(agent_intro, 1)
key, ack = AGENT_MANAGER.create_agent(task, first_message, model)
if CFG.speak_mode:
say_text(f"Hello {voice_name}. Your task is as follows. {task}.")
# Assign task (prompt), get response
agent_response = AGENT_MANAGER.message_agent(key, prompt)
return f"Agent {name} created with key {key}. First response: {agent_response}"
@command("message_agent", "Message GPT Agent", '"key": "<key>", "message": "<message>"')
def message_agent(key: str, message: str) -> str:
"""Message an agent with a given key and message"""
# Check if the key is a valid integer
if is_valid_int(key):
agent_response = AGENT_MANAGER.message_agent(int(key), message)
else:
return "Invalid key, must be an integer."
# Speak response
if CFG.speak_mode:
say_text(agent_response, 1)
return agent_response
@command("list_agents", "List GPT Agents", "")
def list_agents() -> str:
"""List all agents
Returns:
str: A list of all agents
"""
return "List of agents:\n" + "\n".join(
[str(x[0]) + ": " + x[1] for x in AGENT_MANAGER.list_agents()]
)
@command("delete_agent", "Delete GPT Agent", '"key": "<key>"')
def delete_agent(key: str) -> str:
"""Delete an agent with a given key
Args:
key (str): The key of the agent to delete
Returns:
str: A message indicating whether the agent was deleted or not
"""
result = AGENT_MANAGER.delete_agent(key)
return f"Agent {key} deleted." if result else f"Agent {key} does not exist."

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autogpt/auto-gpt.json
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{}

1
autogpt/auto_gpt_workspace/127.0.0.1/auto-gpt.json
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{}

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autogpt/auto_gpt_workspace/127.0.0.1/file_logger.txt
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File Operation Logger

218
autogpt/chat.py
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import time
from openai.error import RateLimitError
from autogpt import token_counter
from autogpt.api_manager import api_manager
from autogpt.config import Config
from autogpt.llm_utils import create_chat_completion
from autogpt.logs import logger
from autogpt.types.openai import Message
cfg = Config()
def create_chat_message(role, content) -> Message:
"""
Create a chat message with the given role and content.
Args:
role (str): The role of the message sender, e.g., "system", "user", or "assistant".
content (str): The content of the message.
Returns:
dict: A dictionary containing the role and content of the message.
"""
return {"role": role, "content": content}
def generate_context(prompt, relevant_memory, full_message_history, model):
current_context = [
create_chat_message("system", prompt),
create_chat_message(
"system", f"The current time and date is {time.strftime('%c')}"
),
create_chat_message(
"system",
f"This reminds you of these events from your past:\n{relevant_memory}\n\n",
),
]
# Add messages from the full message history until we reach the token limit
next_message_to_add_index = len(full_message_history) - 1
insertion_index = len(current_context)
# Count the currently used tokens
current_tokens_used = token_counter.count_message_tokens(current_context, model)
return (
next_message_to_add_index,
current_tokens_used,
insertion_index,
current_context,
)
# TODO: Change debug from hardcode to argument
def chat_with_ai(
agent, prompt, user_input, full_message_history, permanent_memory, token_limit
):
"""Interact with the OpenAI API, sending the prompt, user input, message history,
and permanent memory."""
while True:
try:
"""
Interact with the OpenAI API, sending the prompt, user input,
message history, and permanent memory.
Args:
prompt (str): The prompt explaining the rules to the AI.
user_input (str): The input from the user.
full_message_history (list): The list of all messages sent between the
user and the AI.
permanent_memory (Obj): The memory object containing the permanent
memory.
token_limit (int): The maximum number of tokens allowed in the API call.
Returns:
str: The AI's response.
"""
model = cfg.fast_llm_model # TODO: Change model from hardcode to argument
# Reserve 1000 tokens for the response
logger.debug(f"Token limit: {token_limit}")
send_token_limit = token_limit - 1000
relevant_memory = (
""
if len(full_message_history) == 0
else permanent_memory.get_relevant(str(full_message_history[-9:]), 10)
)
logger.debug(f"Memory Stats: {permanent_memory.get_stats()}")
(
next_message_to_add_index,
current_tokens_used,
insertion_index,
current_context,
) = generate_context(prompt, relevant_memory, full_message_history, model)
while current_tokens_used > 2500:
# remove memories until we are under 2500 tokens
relevant_memory = relevant_memory[:-1]
(
next_message_to_add_index,
current_tokens_used,
insertion_index,
current_context,
) = generate_context(
prompt, relevant_memory, full_message_history, model
)
current_tokens_used += token_counter.count_message_tokens(
[create_chat_message("user", user_input)], model
) # Account for user input (appended later)
while next_message_to_add_index >= 0:
# print (f"CURRENT TOKENS USED: {current_tokens_used}")
message_to_add = full_message_history[next_message_to_add_index]
tokens_to_add = token_counter.count_message_tokens(
[message_to_add], model
)
if current_tokens_used + tokens_to_add > send_token_limit:
break
# Add the most recent message to the start of the current context,
# after the two system prompts.
current_context.insert(
insertion_index, full_message_history[next_message_to_add_index]
)
# Count the currently used tokens
current_tokens_used += tokens_to_add
# Move to the next most recent message in the full message history
next_message_to_add_index -= 1
# inform the AI about its remaining budget (if it has one)
if api_manager.get_total_budget() > 0.0:
remaining_budget = (
api_manager.get_total_budget() - api_manager.get_total_cost()
)
if remaining_budget < 0:
remaining_budget = 0
system_message = (
f"Your remaining API budget is ${remaining_budget:.3f}"
+ (
" BUDGET EXCEEDED! SHUT DOWN!\n\n"
if remaining_budget == 0
else " Budget very nearly exceeded! Shut down gracefully!\n\n"
if remaining_budget < 0.005
else " Budget nearly exceeded. Finish up.\n\n"
if remaining_budget < 0.01
else "\n\n"
)
)
logger.debug(system_message)
current_context.append(create_chat_message("system", system_message))
# Append user input, the length of this is accounted for above
current_context.extend([create_chat_message("user", user_input)])
plugin_count = len(cfg.plugins)
for i, plugin in enumerate(cfg.plugins):
if not plugin.can_handle_on_planning():
continue
plugin_response = plugin.on_planning(
agent.prompt_generator, current_context
)
if not plugin_response or plugin_response == "":
continue
tokens_to_add = token_counter.count_message_tokens(
[create_chat_message("system", plugin_response)], model
)
if current_tokens_used + tokens_to_add > send_token_limit:
if cfg.debug_mode:
print("Plugin response too long, skipping:", plugin_response)
print("Plugins remaining at stop:", plugin_count - i)
break
current_context.append(create_chat_message("system", plugin_response))
# Calculate remaining tokens
tokens_remaining = token_limit - current_tokens_used
# assert tokens_remaining >= 0, "Tokens remaining is negative.
# This should never happen, please submit a bug report at
# https://www.github.com/Torantulino/Auto-GPT"
# Debug print the current context
logger.debug(f"Token limit: {token_limit}")
logger.debug(f"Send Token Count: {current_tokens_used}")
logger.debug(f"Tokens remaining for response: {tokens_remaining}")
logger.debug("------------ CONTEXT SENT TO AI ---------------")
for message in current_context:
# Skip printing the prompt
if message["role"] == "system" and message["content"] == prompt:
continue
logger.debug(f"{message['role'].capitalize()}: {message['content']}")
logger.debug("")
logger.debug("----------- END OF CONTEXT ----------------")
# TODO: use a model defined elsewhere, so that model can contain
# temperature and other settings we care about
assistant_reply = create_chat_completion(
model=model,
messages=current_context,
max_tokens=tokens_remaining,
)
# Update full message history
full_message_history.append(create_chat_message("user", user_input))
full_message_history.append(
create_chat_message("assistant", assistant_reply)
)
return assistant_reply
except RateLimitError:
# TODO: When we switch to langchain, this is built in
print("Error: ", "API Rate Limit Reached. Waiting 10 seconds...")
time.sleep(10)

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autogpt/cli.py
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"""Main script for the autogpt package."""
# Put imports inside function to avoid importing everything when starting the CLI
import logging
import os.path
import sys
from pathlib import Path
import gradio
from colorama import Fore
from autogpt.agent.agent import Agent
from autogpt.commands.command import CommandRegistry
from autogpt.config import Config, check_openai_api_key
from autogpt.configurator import create_config
from autogpt.logs import logger
from autogpt.memory import get_memory
from autogpt.plugins import scan_plugins
from autogpt.prompts.prompt import construct_main_ai_config
from autogpt.utils import get_current_git_branch, get_latest_bulletin
from autogpt.workspace import Workspace
import func_box
from toolbox import update_ui
from toolbox import ChatBotWithCookies
def handle_config(kwargs_settings):
kwargs_settings = {
'continuous': False, # Enable Continuous Mode
'continuous_limit': None, # Defines the number of times to run in continuous mode
'ai_settings': None, # Specifies which ai_settings.yaml file to use, will also automatically skip the re-prompt.
'skip_reprompt': False, # Skips the re-prompting messages at the beginning of the scrip
'speak': False, # Enable speak Mode
'debug': False, # Enable Debug Mode
'gpt3only': False, # Enable GPT3.5 Only Mode
'gpt4only': False, # Enable GPT4 Only Mode
'memory_type': None, # Defines which Memory backend to use
'browser_name': None, # Specifies which web-browser to use when using selenium to scrape the web.
'allow_downloads': False, # Dangerous: Allows Auto-GPT to download files natively.
'skip_news': True, # Specifies whether to suppress the output of latest news on startup.
'workspace_directory': None # TODO: this is a hidden option for now, necessary for integration testing. We should make this public once we're ready to roll out agent specific workspaces.
}
"""
Welcome to AutoGPT an experimental open-source application showcasing the capabilities of the GPT-4 pushing the boundaries of AI.
Start an Auto-GPT assistant.
"""
if kwargs_settings['workspace_directory']:
kwargs_settings['ai_settings'] = os.path.join(kwargs_settings['workspace_directory'], 'ai_settings.yaml')
# if ctx.invoked_subcommand is None:
cfg = Config()
# TODO: fill in llm values here
check_openai_api_key()
create_config(
kwargs_settings['continuous'],
kwargs_settings['continuous_limit'],
kwargs_settings['ai_settings'],
kwargs_settings['skip_reprompt'],
kwargs_settings['speak'],
kwargs_settings['debug'],
kwargs_settings['gpt3only'],
kwargs_settings['gpt4only'],
kwargs_settings['memory_type'],
kwargs_settings['browser_name'],
kwargs_settings['allow_downloads'],
kwargs_settings['skip_news'],
)
return cfg
def handle_news():
motd = get_latest_bulletin()
if motd:
logger.typewriter_log("NEWS: ", Fore.GREEN, motd)
git_branch = get_current_git_branch()
if git_branch and git_branch != "stable":
logger.typewriter_log(
"WARNING: ",
Fore.RED,
f"You are running on `{git_branch}` branch "
"- this is not a supported branch.",
)
if sys.version_info < (3, 10):
logger.typewriter_log(
"WARNING: ",
Fore.RED,
"You are running on an older version of Python. "
"Some people have observed problems with certain "
"parts of Auto-GPT with this version. "
"Please consider upgrading to Python 3.10 or higher.",
)
def handle_registry():
# Create a CommandRegistry instance and scan default folder
command_registry = CommandRegistry()
command_registry.import_commands("autogpt.commands.analyze_code")
command_registry.import_commands("autogpt.commands.audio_text")
command_registry.import_commands("autogpt.commands.execute_code")
command_registry.import_commands("autogpt.commands.file_operations")
command_registry.import_commands("autogpt.commands.git_operations")
command_registry.import_commands("autogpt.commands.google_search")
command_registry.import_commands("autogpt.commands.image_gen")
command_registry.import_commands("autogpt.commands.improve_code")
command_registry.import_commands("autogpt.commands.twitter")
command_registry.import_commands("autogpt.commands.web_selenium")
command_registry.import_commands("autogpt.commands.write_tests")
command_registry.import_commands("autogpt.app")
return command_registry
def handle_workspace(user):
# TODO: have this directory live outside the repository (e.g. in a user's
# home directory) and have it come in as a command line argument or part of
# the env file.
if user is None:
workspace_directory = Path(__file__).parent / "auto_gpt_workspace"
else:
workspace_directory = Path(__file__).parent / "auto_gpt_workspace" / user
# TODO: pass in the ai_settings file and the env file and have them cloned into
# the workspace directory so we can bind them to the agent.
workspace_directory = Workspace.make_workspace(workspace_directory)
# HACK: doing this here to collect some globals that depend on the workspace.
file_logger_path = workspace_directory / "file_logger.txt"
if not file_logger_path.exists():
with file_logger_path.open(mode="w", encoding="utf-8") as f:
f.write("File Operation Logger ")
return workspace_directory, file_logger_path
def update_obj(plugin_kwargs, _is=True):
obj = plugin_kwargs['obj']
start = plugin_kwargs['start']
next_ = plugin_kwargs['next']
text = plugin_kwargs['txt']
if _is:
start.update(visible=True)
next_.update(visible=False)
text.update(visible=False)
else:
start.update(visible=False)
next_.update(visible=True)
text.update(visible=True)
return obj, start, next_, text
def agent_main(name, role, goals, budget,
cookies, chatbot, history, obj,
ipaddr: gradio.Request):
# ai setup
input_kwargs = {
'name': name,
'role': role,
'goals': goals,
'budget': budget
}
# chat setup
logger.output_content = []
chatbot_with_cookie = ChatBotWithCookies(cookies)
chatbot_with_cookie.write_list(chatbot)
history = []
cfg = handle_config(None)
logger.set_level(logging.DEBUG if cfg.debug_mode else logging.INFO)
workspace_directory = ipaddr.client.host
if not cfg.skip_news:
handle_news()
cfg.set_plugins(scan_plugins(cfg, cfg.debug_mode))
command_registry = handle_registry()
ai_config = construct_main_ai_config(input_kwargs)
def update_stream_ui(user='', gpt='', msg='Done',
_start=obj['start'].update(), _next=obj['next'].update(), _text=obj['text'].update()):
if user or gpt:
temp = [user, gpt]
if not chatbot_with_cookie:
chatbot_with_cookie.append(temp)
else:
chatbot_with_cookie[-1] = [chatbot_with_cookie[-1][i] + temp[i] for i in range(len(chatbot_with_cookie[-1]))]
yield chatbot_with_cookie.get_cookies(), chatbot_with_cookie, history, msg, obj, _start, _next, _text
if not ai_config:
msg = '### ROLE 不能为空'
# yield chatbot_with_cookie.get_cookies(), chatbot_with_cookie, history, msg, obj, None, None, None
yield from update_stream_ui(msg=msg)
return
ai_config.command_registry = command_registry
next_action_count = 0
# Make a constant:
triggering_prompt = (
"Determine which next command to use, and respond using the"
" format specified above:"
)
workspace_directory, file_logger_path = handle_workspace(workspace_directory)
cfg.workspace_path = str(workspace_directory)
cfg.file_logger_path = str(file_logger_path)
# Initialize memory and make sure it is empty.
# this is particularly important for indexing and referencing pinecone memory
memory = get_memory(cfg, init=True)
logger.typewriter_log(
"Using memory of type:", Fore.GREEN, f"{memory.__class__.__name__}"
)
logger.typewriter_log("Using Browser:", Fore.GREEN, cfg.selenium_web_browser)
system_prompt = ai_config.construct_full_prompt()
if cfg.debug_mode:
logger.typewriter_log("Prompt:", Fore.GREEN, system_prompt)
agent = Agent(
ai_name=input_kwargs['name'],
memory=memory,
full_message_history=history,
next_action_count=next_action_count,
command_registry=command_registry,
config=ai_config,
system_prompt=system_prompt,
triggering_prompt=triggering_prompt,
workspace_directory=workspace_directory,
)
obj['obj'] = agent
_start = obj['start'].update(visible=False)
_next = obj['next'].update(visible=True)
_text = obj['text'].update(visible=True, interactive=True)
# chat, his = func_box.chat_history(logger.output_content)
# yield from update_stream_ui(user='Auto-GPT Start!', gpt=chat, _start=_start, _next=_next, _text=_text)
agent.start_interaction_loop()
chat, his = func_box.chat_history(logger.output_content)
yield from update_stream_ui(user='Auto-GPT Start!', gpt=chat, _start=_start, _next=_next, _text=_text)
def agent_start(cookie, chatbot, history, msg, obj):
yield from obj['obj'].start_interaction_loop(cookie, chatbot, history, msg, obj)
if __name__ == "__main__":
pass

213
autogpt/cli_private.py
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@ -1,213 +0,0 @@
"""Main script for the autogpt package."""
import click
@click.group(invoke_without_command=True)
@click.option("-c", "--continuous", is_flag=True, help="Enable Continuous Mode")
@click.option(
"--skip-reprompt",
"-y",
is_flag=True,
help="Skips the re-prompting messages at the beginning of the script",
)
@click.option(
"--ai-settings",
"-C",
help="Specifies which ai_settings.yaml file to use, will also automatically skip the re-prompt.",
)
@click.option(
"-l",
"--continuous-limit",
type=int,
help="Defines the number of times to run in continuous mode",
)
@click.option("--speak", is_flag=True, help="Enable Speak Mode")
@click.option("--debug", is_flag=True, help="Enable Debug Mode")
@click.option("--gpt3only", is_flag=True, help="Enable GPT3.5 Only Mode")
@click.option("--gpt4only", is_flag=True, help="Enable GPT4 Only Mode")
@click.option(
"--use-memory",
"-m",
"memory_type",
type=str,
help="Defines which Memory backend to use",
)
@click.option(
"-b",
"--browser-name",
help="Specifies which web-browser to use when using selenium to scrape the web.",
)
@click.option(
"--allow-downloads",
is_flag=True,
help="Dangerous: Allows Auto-GPT to download files natively.",
)
@click.option(
"--skip-news",
is_flag=True,
help="Specifies whether to suppress the output of latest news on startup.",
)
@click.option(
# TODO: this is a hidden option for now, necessary for integration testing.
# We should make this public once we're ready to roll out agent specific workspaces.
"--workspace-directory",
"-w",
type=click.Path(),
hidden=True,
)
@click.pass_context
def main(
ctx: click.Context,
continuous: bool,
continuous_limit: int,
ai_settings: str,
skip_reprompt: bool,
speak: bool,
debug: bool,
gpt3only: bool,
gpt4only: bool,
memory_type: str,
browser_name: str,
allow_downloads: bool,
skip_news: bool,
workspace_directory: str,
) -> None:
"""
Welcome to AutoGPT an experimental open-source application showcasing the capabilities of the GPT-4 pushing the boundaries of AI.
Start an Auto-GPT assistant.
"""
# Put imports inside function to avoid importing everything when starting the CLI
import logging
import sys
from pathlib import Path
from colorama import Fore
from autogpt.agent.agent import Agent
from autogpt.commands.command import CommandRegistry
from autogpt.config import Config, check_openai_api_key
from autogpt.configurator import create_config
from autogpt.logs import logger
from autogpt.memory import get_memory
from autogpt.plugins import scan_plugins
from autogpt.prompts.prompt import construct_main_ai_config
from autogpt.utils import get_current_git_branch, get_latest_bulletin
from autogpt.workspace import Workspace
if ctx.invoked_subcommand is None:
cfg = Config()
# TODO: fill in llm values here
check_openai_api_key()
create_config(
continuous,
continuous_limit,
ai_settings,
skip_reprompt,
speak,
debug,
gpt3only,
gpt4only,
memory_type,
browser_name,
allow_downloads,
skip_news,
)
logger.set_level(logging.DEBUG if cfg.debug_mode else logging.INFO)
if not cfg.skip_news:
motd = get_latest_bulletin()
if motd:
logger.typewriter_log("NEWS: ", Fore.GREEN, motd)
git_branch = get_current_git_branch()
if git_branch and git_branch != "stable":
logger.typewriter_log(
"WARNING: ",
Fore.RED,
f"You are running on `{git_branch}` branch "
"- this is not a supported branch.",
)
if sys.version_info < (3, 10):
logger.typewriter_log(
"WARNING: ",
Fore.RED,
"You are running on an older version of Python. "
"Some people have observed problems with certain "
"parts of Auto-GPT with this version. "
"Please consider upgrading to Python 3.10 or higher.",
)
cfg.set_plugins(scan_plugins(cfg, cfg.debug_mode))
# Create a CommandRegistry instance and scan default folder
command_registry = CommandRegistry()
command_registry.import_commands("autogpt.commands.analyze_code")
command_registry.import_commands("autogpt.commands.audio_text")
command_registry.import_commands("autogpt.commands.execute_code")
command_registry.import_commands("autogpt.commands.file_operations")
command_registry.import_commands("autogpt.commands.git_operations")
command_registry.import_commands("autogpt.commands.google_search")
command_registry.import_commands("autogpt.commands.image_gen")
command_registry.import_commands("autogpt.commands.improve_code")
command_registry.import_commands("autogpt.commands.twitter")
command_registry.import_commands("autogpt.commands.web_selenium")
command_registry.import_commands("autogpt.commands.write_tests")
command_registry.import_commands("autogpt.app")
ai_name = ""
ai_config = construct_main_ai_config()
ai_config.command_registry = command_registry
# print(prompt)
# Initialize variables
full_message_history = []
next_action_count = 0
# Make a constant:
triggering_prompt = (
"Determine which next command to use, and respond using the"
" format specified above:"
)
# Initialize memory and make sure it is empty.
# this is particularly important for indexing and referencing pinecone memory
memory = get_memory(cfg, init=True)
logger.typewriter_log(
"Using memory of type:", Fore.GREEN, f"{memory.__class__.__name__}"
)
logger.typewriter_log("Using Browser:", Fore.GREEN, cfg.selenium_web_browser)
system_prompt = ai_config.construct_full_prompt()
if cfg.debug_mode:
logger.typewriter_log("Prompt:", Fore.GREEN, system_prompt)
# TODO: have this directory live outside the repository (e.g. in a user's
# home directory) and have it come in as a command line argument or part of
# the env file.
if workspace_directory is None:
workspace_directory = Path(__file__).parent / "auto_gpt_workspace"
else:
workspace_directory = Path(workspace_directory)
# TODO: pass in the ai_settings file and the env file and have them cloned into
# the workspace directory so we can bind them to the agent.
workspace_directory = Workspace.make_workspace(workspace_directory)
cfg.workspace_path = str(workspace_directory)
# HACK: doing this here to collect some globals that depend on the workspace.
file_logger_path = workspace_directory / "file_logger.txt"
if not file_logger_path.exists():
with file_logger_path.open(mode="w", encoding="utf-8") as f:
f.write("File Operation Logger ")
cfg.file_logger_path = str(file_logger_path)
agent = Agent(
ai_name=ai_name,
memory=memory,
full_message_history=full_message_history,
next_action_count=next_action_count,
command_registry=command_registry,
config=ai_config,
system_prompt=system_prompt,
triggering_prompt=triggering_prompt,
workspace_directory=workspace_directory,
)
agent.start_interaction_loop()
if __name__ == "__main__":
main()

0
autogpt/commands/__init__.py
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31
autogpt/commands/analyze_code.py
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@ -1,31 +0,0 @@
"""Code evaluation module."""
from __future__ import annotations
from autogpt.commands.command import command
from autogpt.llm_utils import call_ai_function
@command(
"analyze_code",
"Analyze Code",
'"code": "<full_code_string>"',
)
def analyze_code(code: str) -> list[str]:
"""
A function that takes in a string and returns a response from create chat
completion api call.
Parameters:
code (str): Code to be evaluated.
Returns:
A result string from create chat completion. A list of suggestions to
improve the code.
"""
function_string = "def analyze_code(code: str) -> list[str]:"
args = [code]
description_string = (
"Analyzes the given code and returns a list of suggestions for improvements."
)
return call_ai_function(function_string, args, description_string)

61
autogpt/commands/audio_text.py
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@ -1,61 +0,0 @@
"""Commands for converting audio to text."""
import json
import requests
from autogpt.commands.command import command
from autogpt.config import Config
CFG = Config()
@command(
"read_audio_from_file",
"Convert Audio to text",
'"filename": "<filename>"',
CFG.huggingface_audio_to_text_model,
"Configure huggingface_audio_to_text_model.",
)
def read_audio_from_file(filename: str) -> str:
"""
Convert audio to text.
Args:
filename (str): The path to the audio file
Returns:
str: The text from the audio
"""
with open(filename, "rb") as audio_file:
audio = audio_file.read()
return read_audio(audio)
def read_audio(audio: bytes) -> str:
"""
Convert audio to text.
Args:
audio (bytes): The audio to convert
Returns:
str: The text from the audio
"""
model = CFG.huggingface_audio_to_text_model
api_url = f"https://api-inference.huggingface.co/models/{model}"
api_token = CFG.huggingface_api_token
headers = {"Authorization": f"Bearer {api_token}"}
if api_token is None:
raise ValueError(
"You need to set your Hugging Face API token in the config file."
)
response = requests.post(
api_url,
headers=headers,
data=audio,
)
text = json.loads(response.content.decode("utf-8"))["text"]
return f"The audio says: {text}"

156
autogpt/commands/command.py
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@ -1,156 +0,0 @@
import functools
import importlib
import inspect
from typing import Any, Callable, Optional
# Unique identifier for auto-gpt commands
AUTO_GPT_COMMAND_IDENTIFIER = "auto_gpt_command"
class Command:
"""A class representing a command.
Attributes:
name (str): The name of the command.
description (str): A brief description of what the command does.
signature (str): The signature of the function that the command executes. Defaults to None.
"""
def __init__(
self,
name: str,
description: str,
method: Callable[..., Any],
signature: str = "",
enabled: bool = True,
disabled_reason: Optional[str] = None,
):
self.name = name
self.description = description
self.method = method
self.signature = signature if signature else str(inspect.signature(self.method))
self.enabled = enabled
self.disabled_reason = disabled_reason
def __call__(self, *args, **kwargs) -> Any:
if not self.enabled:
return f"Command '{self.name}' is disabled: {self.disabled_reason}"
return self.method(*args, **kwargs)
def __str__(self) -> str:
return f"{self.name}: {self.description}, args: {self.signature}"
class CommandRegistry:
"""
The CommandRegistry class is a manager for a collection of Command objects.
It allows the registration, modification, and retrieval of Command objects,
as well as the scanning and loading of command plugins from a specified
directory.
"""
def __init__(self):
self.commands = {}
def _import_module(self, module_name: str) -> Any:
return importlib.import_module(module_name)
def _reload_module(self, module: Any) -> Any:
return importlib.reload(module)
def register(self, cmd: Command) -> None:
self.commands[cmd.name] = cmd
def unregister(self, command_name: str):
if command_name in self.commands:
del self.commands[command_name]
else:
raise KeyError(f"Command '{command_name}' not found in registry.")
def reload_commands(self) -> None:
"""Reloads all loaded command plugins."""
for cmd_name in self.commands:
cmd = self.commands[cmd_name]
module = self._import_module(cmd.__module__)
reloaded_module = self._reload_module(module)
if hasattr(reloaded_module, "register"):
reloaded_module.register(self)
def get_command(self, name: str) -> Callable[..., Any]:
return self.commands[name]
def call(self, command_name: str, **kwargs) -> Any:
if command_name not in self.commands:
raise KeyError(f"Command '{command_name}' not found in registry.")
command = self.commands[command_name]
return command(**kwargs)
def command_prompt(self) -> str:
"""
Returns a string representation of all registered `Command` objects for use in a prompt
"""
commands_list = [
f"{idx + 1}. {str(cmd)}" for idx, cmd in enumerate(self.commands.values())
]
return "\n".join(commands_list)
def import_commands(self, module_name: str) -> None:
"""
Imports the specified Python module containing command plugins.
This method imports the associated module and registers any functions or
classes that are decorated with the `AUTO_GPT_COMMAND_IDENTIFIER` attribute
as `Command` objects. The registered `Command` objects are then added to the
`commands` dictionary of the `CommandRegistry` object.
Args:
module_name (str): The name of the module to import for command plugins.
"""
module = importlib.import_module(module_name)
for attr_name in dir(module):
attr = getattr(module, attr_name)
# Register decorated functions
if hasattr(attr, AUTO_GPT_COMMAND_IDENTIFIER) and getattr(
attr, AUTO_GPT_COMMAND_IDENTIFIER
):
self.register(attr.command)
# Register command classes
elif (
inspect.isclass(attr) and issubclass(attr, Command) and attr != Command
):
cmd_instance = attr()
self.register(cmd_instance)
def command(
name: str,
description: str,
signature: str = "",
enabled: bool = True,
disabled_reason: Optional[str] = None,
) -> Callable[..., Any]:
"""The command decorator is used to create Command objects from ordinary functions."""
def decorator(func: Callable[..., Any]) -> Command:
cmd = Command(
name=name,
description=description,
method=func,
signature=signature,
enabled=enabled,
disabled_reason=disabled_reason,
)
@functools.wraps(func)
def wrapper(*args, **kwargs) -> Any:
return func(*args, **kwargs)
wrapper.command = cmd
setattr(wrapper, AUTO_GPT_COMMAND_IDENTIFIER, True)
return wrapper
return decorator

182
autogpt/commands/execute_code.py
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@ -1,182 +0,0 @@
"""Execute code in a Docker container"""
import os
import subprocess
import docker
from docker.errors import ImageNotFound
from autogpt.commands.command import command
from autogpt.config import Config
CFG = Config()
@command("execute_python_file", "Execute Python File", '"filename": "<filename>"')
def execute_python_file(filename: str) -> str:
"""Execute a Python file in a Docker container and return the output
Args:
filename (str): The name of the file to execute
Returns:
str: The output of the file
"""
print(f"Executing file '{filename}'")
if not filename.endswith(".py"):
return "Error: Invalid file type. Only .py files are allowed."
if not os.path.isfile(filename):
return f"Error: File '{filename}' does not exist."
if we_are_running_in_a_docker_container():
result = subprocess.run(
f"python {filename}", capture_output=True, encoding="utf8", shell=True
)
if result.returncode == 0:
return result.stdout
else:
return f"Error: {result.stderr}"
try:
client = docker.from_env()
# You can replace this with the desired Python image/version
# You can find available Python images on Docker Hub:
# https://hub.docker.com/_/python
image_name = "python:3-alpine"
try:
client.images.get(image_name)
print(f"Image '{image_name}' found locally")
except ImageNotFound:
print(f"Image '{image_name}' not found locally, pulling from Docker Hub")
# Use the low-level API to stream the pull response
low_level_client = docker.APIClient()
for line in low_level_client.pull(image_name, stream=True, decode=True):
# Print the status and progress, if available
status = line.get("status")
progress = line.get("progress")
if status and progress:
print(f"{status}: {progress}")
elif status:
print(status)
container = client.containers.run(
image_name,
f"python {filename}",
volumes={
CFG.workspace_path: {
"bind": "/workspace",
"mode": "ro",
}
},
working_dir="/workspace",
stderr=True,
stdout=True,
detach=True,
)
container.wait()
logs = container.logs().decode("utf-8")
container.remove()
# print(f"Execution complete. Output: {output}")
# print(f"Logs: {logs}")
return logs
except docker.errors.DockerException as e:
print(
"Could not run the script in a container. If you haven't already, please install Docker https://docs.docker.com/get-docker/"
)
return f"Error: {str(e)}"
except Exception as e:
return f"Error: {str(e)}"
@command(
"execute_shell",
"Execute Shell Command, non-interactive commands only",
'"command_line": "<command_line>"',
CFG.execute_local_commands,
"You are not allowed to run local shell commands. To execute"
" shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' "
"in your config. Do not attempt to bypass the restriction.",
)
def execute_shell(command_line: str) -> str:
"""Execute a shell command and return the output
Args:
command_line (str): The command line to execute
Returns:
str: The output of the command
"""
if not CFG.execute_local_commands:
return (
"You are not allowed to run local shell commands. To execute"
" shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' "
"in your config. Do not attempt to bypass the restriction."
)
current_dir = os.getcwd()
# Change dir into workspace if necessary
if CFG.workspace_path not in current_dir:
os.chdir(CFG.workspace_path)
print(f"Executing command '{command_line}' in working directory '{os.getcwd()}'")
result = subprocess.run(command_line, capture_output=True, shell=True)
output = f"STDOUT:\n{result.stdout}\nSTDERR:\n{result.stderr}"
# Change back to whatever the prior working dir was
os.chdir(current_dir)
@command(
"execute_shell_popen",
"Execute Shell Command, non-interactive commands only",
'"command_line": "<command_line>"',
CFG.execute_local_commands,
"You are not allowed to run local shell commands. To execute"
" shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' "
"in your config. Do not attempt to bypass the restriction.",
)
def execute_shell_popen(command_line) -> str:
"""Execute a shell command with Popen and returns an english description
of the event and the process id
Args:
command_line (str): The command line to execute
Returns:
str: Description of the fact that the process started and its id
"""
current_dir = os.getcwd()
# Change dir into workspace if necessary
if CFG.workspace_path not in current_dir:
os.chdir(CFG.workspace_path)
print(f"Executing command '{command_line}' in working directory '{os.getcwd()}'")
do_not_show_output = subprocess.DEVNULL
process = subprocess.Popen(
command_line, shell=True, stdout=do_not_show_output, stderr=do_not_show_output
)
# Change back to whatever the prior working dir was
os.chdir(current_dir)
return f"Subprocess started with PID:'{str(process.pid)}'"
def we_are_running_in_a_docker_container() -> bool:
"""Check if we are running in a Docker container
Returns:
bool: True if we are running in a Docker container, False otherwise
"""
return os.path.exists("/.dockerenv")

268
autogpt/commands/file_operations.py
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@ -1,268 +0,0 @@
"""File operations for AutoGPT"""
from __future__ import annotations
import os
import os.path
from typing import Generator
import requests
from colorama import Back, Fore
from requests.adapters import HTTPAdapter, Retry
from autogpt.commands.command import command
from autogpt.config import Config
from autogpt.spinner import Spinner
from autogpt.utils import readable_file_size
CFG = Config()
def check_duplicate_operation(operation: str, filename: str) -> bool:
"""Check if the operation has already been performed on the given file
Args:
operation (str): The operation to check for
filename (str): The name of the file to check for
Returns:
bool: True if the operation has already been performed on the file
"""
log_content = read_file(CFG.file_logger_path)
log_entry = f"{operation}: {filename}\n"
return log_entry in log_content
def log_operation(operation: str, filename: str) -> None:
"""Log the file operation to the file_logger.txt
Args:
operation (str): The operation to log
filename (str): The name of the file the operation was performed on
"""
log_entry = f"{operation}: {filename}\n"
append_to_file(CFG.file_logger_path, log_entry, should_log=False)
def split_file(
content: str, max_length: int = 4000, overlap: int = 0
) -> Generator[str, None, None]:
"""
Split text into chunks of a specified maximum length with a specified overlap
between chunks.
:param content: The input text to be split into chunks
:param max_length: The maximum length of each chunk,
default is 4000 (about 1k token)
:param overlap: The number of overlapping characters between chunks,
default is no overlap
:return: A generator yielding chunks of text
"""
start = 0
content_length = len(content)
while start < content_length:
end = start + max_length
if end + overlap < content_length:
chunk = content[start : end + overlap - 1]
else:
chunk = content[start:content_length]
# Account for the case where the last chunk is shorter than the overlap, so it has already been consumed
if len(chunk) <= overlap:
break
yield chunk
start += max_length - overlap
@command("read_file", "Read file", '"filename": "<filename>"')
def read_file(filename: str) -> str:
"""Read a file and return the contents
Args:
filename (str): The name of the file to read
Returns:
str: The contents of the file
"""
try:
with open(filename, "r", encoding="utf-8") as f:
content = f.read()
return content
except Exception as e:
return f"Error: {str(e)}"
def ingest_file(
filename: str, memory, max_length: int = 4000, overlap: int = 200
) -> None:
"""
Ingest a file by reading its content, splitting it into chunks with a specified
maximum length and overlap, and adding the chunks to the memory storage.
:param filename: The name of the file to ingest
:param memory: An object with an add() method to store the chunks in memory
:param max_length: The maximum length of each chunk, default is 4000
:param overlap: The number of overlapping characters between chunks, default is 200
"""
try:
print(f"Working with file {filename}")
content = read_file(filename)
content_length = len(content)
print(f"File length: {content_length} characters")
chunks = list(split_file(content, max_length=max_length, overlap=overlap))
num_chunks = len(chunks)
for i, chunk in enumerate(chunks):
print(f"Ingesting chunk {i + 1} / {num_chunks} into memory")
memory_to_add = (
f"Filename: {filename}\n" f"Content part#{i + 1}/{num_chunks}: {chunk}"
)
memory.add(memory_to_add)
print(f"Done ingesting {num_chunks} chunks from {filename}.")
except Exception as e:
print(f"Error while ingesting file '{filename}': {str(e)}")
@command("write_to_file", "Write to file", '"filename": "<filename>", "text": "<text>"')
def write_to_file(filename: str, text: str) -> str:
"""Write text to a file
Args:
filename (str): The name of the file to write to
text (str): The text to write to the file
Returns:
str: A message indicating success or failure
"""
if check_duplicate_operation("write", filename):
return "Error: File has already been updated."
try:
directory = os.path.dirname(filename)
if not os.path.exists(directory):
os.makedirs(directory)
with open(filename, "w", encoding="utf-8") as f:
f.write(text)
log_operation("write", filename)
return "File written to successfully."
except Exception as e:
return f"Error: {str(e)}"
@command(
"append_to_file", "Append to file", '"filename": "<filename>", "text": "<text>"'
)
def append_to_file(filename: str, text: str, should_log: bool = True) -> str:
"""Append text to a file
Args:
filename (str): The name of the file to append to
text (str): The text to append to the file
should_log (bool): Should log output
Returns:
str: A message indicating success or failure
"""
try:
with open(filename, "a") as f:
f.write(text)
if should_log:
log_operation("append", filename)
return "Text appended successfully."
except Exception as e:
return f"Error: {str(e)}"
@command("delete_file", "Delete file", '"filename": "<filename>"')
def delete_file(filename: str) -> str:
"""Delete a file
Args:
filename (str): The name of the file to delete
Returns:
str: A message indicating success or failure
"""
if check_duplicate_operation("delete", filename):
return "Error: File has already been deleted."
try:
os.remove(filename)
log_operation("delete", filename)
return "File deleted successfully."
except Exception as e:
return f"Error: {str(e)}"
@command("search_files", "Search Files", '"directory": "<directory>"')
def search_files(directory: str) -> list[str]:
"""Search for files in a directory
Args:
directory (str): The directory to search in
Returns:
list[str]: A list of files found in the directory
"""
found_files = []
for root, _, files in os.walk(directory):
for file in files:
if file.startswith("."):
continue
relative_path = os.path.relpath(
os.path.join(root, file), CFG.workspace_path
)
found_files.append(relative_path)
return found_files
@command(
"download_file",
"Download File",
'"url": "<url>", "filename": "<filename>"',
CFG.allow_downloads,
"Error: You do not have user authorization to download files locally.",
)
def download_file(url, filename):
"""Downloads a file
Args:
url (str): URL of the file to download
filename (str): Filename to save the file as
"""
try:
message = f"{Fore.YELLOW}Downloading file from {Back.MAGENTA}{url}{Back.RESET}{Fore.RESET}"
with Spinner(message) as spinner:
session = requests.Session()
retry = Retry(total=3, backoff_factor=1, status_forcelist=[502, 503, 504])
adapter = HTTPAdapter(max_retries=retry)
session.mount("http://", adapter)
session.mount("https://", adapter)
total_size = 0
downloaded_size = 0
with session.get(url, allow_redirects=True, stream=True) as r:
r.raise_for_status()
total_size = int(r.headers.get("Content-Length", 0))
downloaded_size = 0
with open(filename, "wb") as f:
for chunk in r.iter_content(chunk_size=8192):
f.write(chunk)
downloaded_size += len(chunk)
# Update the progress message
progress = f"{readable_file_size(downloaded_size)} / {readable_file_size(total_size)}"
spinner.update_message(f"{message} {progress}")
return f'Successfully downloaded and locally stored file: "{filename}"! (Size: {readable_file_size(total_size)})'
except requests.HTTPError as e:
return f"Got an HTTP Error whilst trying to download file: {e}"
except Exception as e:
return "Error: " + str(e)

159
autogpt/commands/file_operations_utils.py
View File

@ -1,159 +0,0 @@
import json
import os
import charset_normalizer
import docx
import markdown
import PyPDF2
import yaml
from bs4 import BeautifulSoup
from pylatexenc.latex2text import LatexNodes2Text
from autogpt import logs
from autogpt.logs import logger
class ParserStrategy:
def read(self, file_path: str) -> str:
raise NotImplementedError
# Basic text file reading
class TXTParser(ParserStrategy):
def read(self, file_path: str) -> str:
charset_match = charset_normalizer.from_path(file_path).best()
logger.debug(f"Reading '{file_path}' with encoding '{charset_match.encoding}'")
return str(charset_match)
# Reading text from binary file using pdf parser
class PDFParser(ParserStrategy):
def read(self, file_path: str) -> str:
parser = PyPDF2.PdfReader(file_path)
text = ""
for page_idx in range(len(parser.pages)):
text += parser.pages[page_idx].extract_text()
return text
# Reading text from binary file using docs parser
class DOCXParser(ParserStrategy):
def read(self, file_path: str) -> str:
doc_file = docx.Document(file_path)
text = ""
for para in doc_file.paragraphs:
text += para.text
return text
# Reading as dictionary and returning string format
class JSONParser(ParserStrategy):
def read(self, file_path: str) -> str:
with open(file_path, "r") as f:
data = json.load(f)
text = str(data)
return text
class XMLParser(ParserStrategy):
def read(self, file_path: str) -> str:
with open(file_path, "r") as f:
soup = BeautifulSoup(f, "xml")
text = soup.get_text()
return text
# Reading as dictionary and returning string format
class YAMLParser(ParserStrategy):
def read(self, file_path: str) -> str:
with open(file_path, "r") as f:
data = yaml.load(f, Loader=yaml.FullLoader)
text = str(data)
return text
class HTMLParser(ParserStrategy):
def read(self, file_path: str) -> str:
with open(file_path, "r") as f:
soup = BeautifulSoup(f, "html.parser")
text = soup.get_text()
return text
class MarkdownParser(ParserStrategy):
def read(self, file_path: str) -> str:
with open(file_path, "r") as f:
html = markdown.markdown(f.read())
text = "".join(BeautifulSoup(html, "html.parser").findAll(string=True))
return text
class LaTeXParser(ParserStrategy):
def read(self, file_path: str) -> str:
with open(file_path, "r") as f:
latex = f.read()
text = LatexNodes2Text().latex_to_text(latex)
return text
class FileContext:
def __init__(self, parser: ParserStrategy, logger: logs.Logger):
self.parser = parser
self.logger = logger
def set_parser(self, parser: ParserStrategy) -> None:
self.logger.debug(f"Setting Context Parser to {parser}")
self.parser = parser
def read_file(self, file_path) -> str:
self.logger.debug(f"Reading file {file_path} with parser {self.parser}")
return self.parser.read(file_path)
extension_to_parser = {
".txt": TXTParser(),
".csv": TXTParser(),
".pdf": PDFParser(),
".docx": DOCXParser(),
".json": JSONParser(),
".xml": XMLParser(),
".yaml": YAMLParser(),
".yml": YAMLParser(),
".html": HTMLParser(),
".htm": HTMLParser(),
".xhtml": HTMLParser(),
".md": MarkdownParser(),
".markdown": MarkdownParser(),
".tex": LaTeXParser(),
}
def is_file_binary_fn(file_path: str):
"""Given a file path load all its content and checks if the null bytes is present
Args:
file_path (_type_): _description_
Returns:
bool: is_binary
"""
with open(file_path, "rb") as f:
file_data = f.read()
if b"\x00" in file_data:
return True
return False
def read_textual_file(file_path: str, logger: logs.Logger) -> str:
if not os.path.isfile(file_path):
raise FileNotFoundError(f"{file_path} not found!")
is_binary = is_file_binary_fn(file_path)
file_extension = os.path.splitext(file_path)[1].lower()
parser = extension_to_parser.get(file_extension)
if not parser:
if is_binary:
raise ValueError(f"Unsupported binary file format: {file_extension}")
# fallback to txt file parser (to support script and code files loading)
parser = TXTParser()
file_context = FileContext(parser, logger)
return file_context.read_file(file_path)

33
autogpt/commands/git_operations.py
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@ -1,33 +0,0 @@
"""Git operations for autogpt"""
from git.repo import Repo
from autogpt.commands.command import command
from autogpt.config import Config
CFG = Config()
@command(
"clone_repository",
"Clone Repository",
'"repository_url": "<repository_url>", "clone_path": "<clone_path>"',
CFG.github_username and CFG.github_api_key,
"Configure github_username and github_api_key.",
)
def clone_repository(repository_url: str, clone_path: str) -> str:
"""Clone a GitHub repository locally.
Args:
repository_url (str): The URL of the repository to clone.
clone_path (str): The path to clone the repository to.
Returns:
str: The result of the clone operation.
"""
split_url = repository_url.split("//")
auth_repo_url = f"//{CFG.github_username}:{CFG.github_api_key}@".join(split_url)
try:
Repo.clone_from(auth_repo_url, clone_path)
return f"""Cloned {repository_url} to {clone_path}"""
except Exception as e:
return f"Error: {str(e)}"

117
autogpt/commands/google_search.py
View File

@ -1,117 +0,0 @@
"""Google search command for Autogpt."""
from __future__ import annotations
import json
from duckduckgo_search import ddg
from autogpt.commands.command import command
from autogpt.config import Config
CFG = Config()
@command("google", "Google Search", '"query": "<query>"', not CFG.google_api_key)
def google_search(query: str, num_results: int = 8) -> str:
"""Return the results of a Google search
Args:
query (str): The search query.
num_results (int): The number of results to return.
Returns:
str: The results of the search.
"""
search_results = []
if not query:
return json.dumps(search_results)
results = ddg(query, max_results=num_results)
if not results:
return json.dumps(search_results)
for j in results:
search_results.append(j)
results = json.dumps(search_results, ensure_ascii=False, indent=4)
return safe_google_results(results)
@command(
"google",
"Google Search",
'"query": "<query>"',
bool(CFG.google_api_key),
"Configure google_api_key.",
)
def google_official_search(query: str, num_results: int = 8) -> str | list[str]:
"""Return the results of a Google search using the official Google API
Args:
query (str): The search query.
num_results (int): The number of results to return.
Returns:
str: The results of the search.
"""
from googleapiclient.discovery import build
from googleapiclient.errors import HttpError
try:
# Get the Google API key and Custom Search Engine ID from the config file
api_key = CFG.google_api_key
custom_search_engine_id = CFG.custom_search_engine_id
# Initialize the Custom Search API service
service = build("customsearch", "v1", developerKey=api_key)
# Send the search query and retrieve the results
result = (
service.cse()
.list(q=query, cx=custom_search_engine_id, num=num_results)
.execute()
)
# Extract the search result items from the response
search_results = result.get("items", [])
# Create a list of only the URLs from the search results
search_results_links = [item["link"] for item in search_results]
except HttpError as e:
# Handle errors in the API call
error_details = json.loads(e.content.decode())
# Check if the error is related to an invalid or missing API key
if error_details.get("error", {}).get(
"code"
) == 403 and "invalid API key" in error_details.get("error", {}).get(
"message", ""
):
return "Error: The provided Google API key is invalid or missing."
else:
return f"Error: {e}"
# google_result can be a list or a string depending on the search results
# Return the list of search result URLs
return safe_google_results(search_results_links)
def safe_google_results(results: str | list) -> str:
"""
Return the results of a google search in a safe format.
Args:
results (str | list): The search results.
Returns:
str: The results of the search.
"""
if isinstance(results, list):
safe_message = json.dumps(
[result.encode("utf-8", "ignore") for result in results]
)
else:
safe_message = results.encode("utf-8", "ignore").decode("utf-8")
return safe_message

164
autogpt/commands/image_gen.py
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@ -1,164 +0,0 @@
""" Image Generation Module for AutoGPT."""
import io
import uuid
from base64 import b64decode
import openai
import requests
from PIL import Image
from autogpt.commands.command import command
from autogpt.config import Config
CFG = Config()
@command("generate_image", "Generate Image", '"prompt": "<prompt>"', CFG.image_provider)
def generate_image(prompt: str, size: int = 256) -> str:
"""Generate an image from a prompt.
Args:
prompt (str): The prompt to use
size (int, optional): The size of the image. Defaults to 256. (Not supported by HuggingFace)
Returns:
str: The filename of the image
"""
filename = f"{CFG.workspace_path}/{str(uuid.uuid4())}.jpg"
# DALL-E
if CFG.image_provider == "dalle":
return generate_image_with_dalle(prompt, filename, size)
# HuggingFace
elif CFG.image_provider == "huggingface":
return generate_image_with_hf(prompt, filename)
# SD WebUI
elif CFG.image_provider == "sdwebui":
return generate_image_with_sd_webui(prompt, filename, size)
return "No Image Provider Set"
def generate_image_with_hf(prompt: str, filename: str) -> str:
"""Generate an image with HuggingFace's API.
Args:
prompt (str): The prompt to use
filename (str): The filename to save the image to
Returns:
str: The filename of the image
"""
API_URL = (
f"https://api-inference.huggingface.co/models/{CFG.huggingface_image_model}"
)
if CFG.huggingface_api_token is None:
raise ValueError(
"You need to set your Hugging Face API token in the config file."
)
headers = {
"Authorization": f"Bearer {CFG.huggingface_api_token}",
"X-Use-Cache": "false",
}
response = requests.post(
API_URL,
headers=headers,
json={
"inputs": prompt,
},
)
image = Image.open(io.BytesIO(response.content))
print(f"Image Generated for prompt:{prompt}")
image.save(filename)
return f"Saved to disk:{filename}"
def generate_image_with_dalle(prompt: str, filename: str, size: int) -> str:
"""Generate an image with DALL-E.
Args:
prompt (str): The prompt to use
filename (str): The filename to save the image to
size (int): The size of the image
Returns:
str: The filename of the image
"""
openai.api_key = CFG.openai_api_key
# Check for supported image sizes
if size not in [256, 512, 1024]:
closest = min([256, 512, 1024], key=lambda x: abs(x - size))
print(
f"DALL-E only supports image sizes of 256x256, 512x512, or 1024x1024. Setting to {closest}, was {size}."
)
size = closest
response = openai.Image.create(
prompt=prompt,
n=1,
size=f"{size}x{size}",
response_format="b64_json",
)
print(f"Image Generated for prompt:{prompt}")
image_data = b64decode(response["data"][0]["b64_json"])
with open(filename, mode="wb") as png:
png.write(image_data)
return f"Saved to disk:{filename}"
def generate_image_with_sd_webui(
prompt: str,
filename: str,
size: int = 512,
negative_prompt: str = "",
extra: dict = {},
) -> str:
"""Generate an image with Stable Diffusion webui.
Args:
prompt (str): The prompt to use
filename (str): The filename to save the image to
size (int, optional): The size of the image. Defaults to 256.
negative_prompt (str, optional): The negative prompt to use. Defaults to "".
extra (dict, optional): Extra parameters to pass to the API. Defaults to {}.
Returns:
str: The filename of the image
"""
# Create a session and set the basic auth if needed
s = requests.Session()
if CFG.sd_webui_auth:
username, password = CFG.sd_webui_auth.split(":")
s.auth = (username, password or "")
# Generate the images
response = requests.post(
f"{CFG.sd_webui_url}/sdapi/v1/txt2img",
json={
"prompt": prompt,
"negative_prompt": negative_prompt,
"sampler_index": "DDIM",
"steps": 20,
"cfg_scale": 7.0,
"width": size,
"height": size,
"n_iter": 1,
**extra,
},
)
print(f"Image Generated for prompt:{prompt}")
# Save the image to disk
response = response.json()
b64 = b64decode(response["images"][0].split(",", 1)[0])
image = Image.open(io.BytesIO(b64))
image.save(filename)
return f"Saved to disk:{filename}"

35
autogpt/commands/improve_code.py
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@ -1,35 +0,0 @@
from __future__ import annotations
import json
from autogpt.commands.command import command
from autogpt.llm_utils import call_ai_function
@command(
"improve_code",
"Get Improved Code",
'"suggestions": "<list_of_suggestions>", "code": "<full_code_string>"',
)
def improve_code(suggestions: list[str], code: str) -> str:
"""
A function that takes in code and suggestions and returns a response from create
chat completion api call.
Parameters:
suggestions (list): A list of suggestions around what needs to be improved.
code (str): Code to be improved.
Returns:
A result string from create chat completion. Improved code in response.
"""
function_string = (
"def generate_improved_code(suggestions: list[str], code: str) -> str:"
)
args = [json.dumps(suggestions), code]
description_string = (
"Improves the provided code based on the suggestions"
" provided, making no other changes."
)
return call_ai_function(function_string, args, description_string)

29
autogpt/commands/task_statuses.py
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@ -1,29 +0,0 @@
"""Task Statuses module."""
from __future__ import annotations
from typing import TYPE_CHECKING, NoReturn
from autogpt.commands.command import command
from autogpt.logs import logger
if TYPE_CHECKING:
from autogpt.config import Config
@command(
"task_complete",
"Task Complete (Shutdown)",
'"reason": "<reason>"',
)
def task_complete(reason: str, config: Config) -> NoReturn:
"""
A function that takes in a string and exits the program
Parameters:
reason (str): The reason for shutting down.
Returns:
A result string from create chat completion. A list of suggestions to
improve the code.
"""
logger.info(title="Shutting down...\n", message=reason)
quit()

10
autogpt/commands/times.py
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@ -1,10 +0,0 @@
from datetime import datetime
def get_datetime() -> str:
"""Return the current date and time
Returns:
str: The current date and time
"""
return "Current date and time: " + datetime.now().strftime("%Y-%m-%d %H:%M:%S")

44
autogpt/commands/twitter.py
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@ -1,44 +0,0 @@
"""A module that contains a command to send a tweet."""
import os
import tweepy
from dotenv import load_dotenv
from autogpt.commands.command import command
load_dotenv()
@command(
"send_tweet",
"Send Tweet",
'"tweet_text": "<tweet_text>"',
)
def send_tweet(tweet_text: str) -> str:
"""
A function that takes in a string and returns a response from create chat
completion api call.
Args:
tweet_text (str): Text to be tweeted.
Returns:
A result from sending the tweet.
"""
consumer_key = os.environ.get("TW_CONSUMER_KEY")
consumer_secret = os.environ.get("TW_CONSUMER_SECRET")
access_token = os.environ.get("TW_ACCESS_TOKEN")
access_token_secret = os.environ.get("TW_ACCESS_TOKEN_SECRET")
# Authenticate to Twitter
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
# Create API object
api = tweepy.API(auth)
# Send tweet
try:
api.update_status(tweet_text)
return "Tweet sent successfully!"
except tweepy.TweepyException as e:
return f"Error sending tweet: {e.reason}"

80
autogpt/commands/web_playwright.py
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@ -1,80 +0,0 @@
"""Web scraping commands using Playwright"""
from __future__ import annotations
try:
from playwright.sync_api import sync_playwright
except ImportError:
print(
"Playwright not installed. Please install it with 'pip install playwright' to use."
)
from bs4 import BeautifulSoup
from autogpt.processing.html import extract_hyperlinks, format_hyperlinks
def scrape_text(url: str) -> str:
"""Scrape text from a webpage
Args:
url (str): The URL to scrape text from
Returns:
str: The scraped text
"""
with sync_playwright() as p:
browser = p.chromium.launch()
page = browser.new_page()
try:
page.goto(url)
html_content = page.content()
soup = BeautifulSoup(html_content, "html.parser")
for script in soup(["script", "style"]):
script.extract()
text = soup.get_text()
lines = (line.strip() for line in text.splitlines())
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
text = "\n".join(chunk for chunk in chunks if chunk)
except Exception as e:
text = f"Error: {str(e)}"
finally:
browser.close()
return text
def scrape_links(url: str) -> str | list[str]:
"""Scrape links from a webpage
Args:
url (str): The URL to scrape links from
Returns:
Union[str, List[str]]: The scraped links
"""
with sync_playwright() as p:
browser = p.chromium.launch()
page = browser.new_page()
try:
page.goto(url)
html_content = page.content()
soup = BeautifulSoup(html_content, "html.parser")
for script in soup(["script", "style"]):
script.extract()
hyperlinks = extract_hyperlinks(soup, url)
formatted_links = format_hyperlinks(hyperlinks)
except Exception as e:
formatted_links = f"Error: {str(e)}"
finally:
browser.close()
return formatted_links

188
autogpt/commands/web_requests.py
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@ -1,188 +0,0 @@
"""Browse a webpage and summarize it using the LLM model"""
from __future__ import annotations
from urllib.parse import urljoin, urlparse
import requests
from bs4 import BeautifulSoup
from requests import Response
from requests.compat import urljoin
from autogpt.config import Config
from autogpt.processing.html import extract_hyperlinks, format_hyperlinks
CFG = Config()
session = requests.Session()
session.headers.update({"User-Agent": CFG.user_agent})
def is_valid_url(url: str) -> bool:
"""Check if the URL is valid
Args:
url (str): The URL to check
Returns:
bool: True if the URL is valid, False otherwise
"""
try:
result = urlparse(url)
return all([result.scheme, result.netloc])
except ValueError:
return False
def sanitize_url(url: str) -> str:
"""Sanitize the URL
Args:
url (str): The URL to sanitize
Returns:
str: The sanitized URL
"""
return urljoin(url, urlparse(url).path)
def check_local_file_access(url: str) -> bool:
"""Check if the URL is a local file
Args:
url (str): The URL to check
Returns:
bool: True if the URL is a local file, False otherwise
"""
local_prefixes = [
"file:///",
"file://localhost/",
"file://localhost",
"http://localhost",
"http://localhost/",
"https://localhost",
"https://localhost/",
"http://2130706433",
"http://2130706433/",
"https://2130706433",
"https://2130706433/",
"http://127.0.0.1/",
"http://127.0.0.1",
"https://127.0.0.1/",
"https://127.0.0.1",
"https://0.0.0.0/",
"https://0.0.0.0",
"http://0.0.0.0/",
"http://0.0.0.0",
"http://0000",
"http://0000/",
"https://0000",
"https://0000/",
]
return any(url.startswith(prefix) for prefix in local_prefixes)
def get_response(
url: str, timeout: int = 10
) -> tuple[None, str] | tuple[Response, None]:
"""Get the response from a URL
Args:
url (str): The URL to get the response from
timeout (int): The timeout for the HTTP request
Returns:
tuple[None, str] | tuple[Response, None]: The response and error message
Raises:
ValueError: If the URL is invalid
requests.exceptions.RequestException: If the HTTP request fails
"""
try:
# Restrict access to local files
if check_local_file_access(url):
raise ValueError("Access to local files is restricted")
# Most basic check if the URL is valid:
if not url.startswith("http://") and not url.startswith("https://"):
raise ValueError("Invalid URL format")
sanitized_url = sanitize_url(url)
response = session.get(sanitized_url, timeout=timeout)
# Check if the response contains an HTTP error
if response.status_code >= 400:
return None, f"Error: HTTP {str(response.status_code)} error"
return response, None
except ValueError as ve:
# Handle invalid URL format
return None, f"Error: {str(ve)}"
except requests.exceptions.RequestException as re:
# Handle exceptions related to the HTTP request
# (e.g., connection errors, timeouts, etc.)
return None, f"Error: {str(re)}"
def scrape_text(url: str) -> str:
"""Scrape text from a webpage
Args:
url (str): The URL to scrape text from
Returns:
str: The scraped text
"""
response, error_message = get_response(url)
if error_message:
return error_message
if not response:
return "Error: Could not get response"
soup = BeautifulSoup(response.text, "html.parser")
for script in soup(["script", "style"]):
script.extract()
text = soup.get_text()
lines = (line.strip() for line in text.splitlines())
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
text = "\n".join(chunk for chunk in chunks if chunk)
return text
def scrape_links(url: str) -> str | list[str]:
"""Scrape links from a webpage
Args:
url (str): The URL to scrape links from
Returns:
str | list[str]: The scraped links
"""
response, error_message = get_response(url)
if error_message:
return error_message
if not response:
return "Error: Could not get response"
soup = BeautifulSoup(response.text, "html.parser")
for script in soup(["script", "style"]):
script.extract()
hyperlinks = extract_hyperlinks(soup, url)
return format_hyperlinks(hyperlinks)
def create_message(chunk, question):
"""Create a message for the user to summarize a chunk of text"""
return {
"role": "user",
"content": f'"""{chunk}""" Using the above text, answer the following'
f' question: "{question}" -- if the question cannot be answered using the'
" text, summarize the text.",
}

160
autogpt/commands/web_selenium.py
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@ -1,160 +0,0 @@
"""Selenium web scraping module."""
from __future__ import annotations
import logging
from pathlib import Path
from sys import platform
from bs4 import BeautifulSoup
from selenium import webdriver
from selenium.webdriver.chrome.options import Options as ChromeOptions
from selenium.webdriver.common.by import By
from selenium.webdriver.firefox.options import Options as FirefoxOptions
from selenium.webdriver.remote.webdriver import WebDriver
from selenium.webdriver.safari.options import Options as SafariOptions
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.support.wait import WebDriverWait
from webdriver_manager.chrome import ChromeDriverManager
from webdriver_manager.firefox import GeckoDriverManager
import autogpt.processing.text as summary
from autogpt.commands.command import command
from autogpt.config import Config
from autogpt.processing.html import extract_hyperlinks, format_hyperlinks
FILE_DIR = Path(__file__).parent.parent
CFG = Config()
@command(
"browse_website",
"Browse Website",
'"url": "<url>", "question": "<what_you_want_to_find_on_website>"',
)
def browse_website(url: str, question: str) -> tuple[str, WebDriver]:
"""Browse a website and return the answer and links to the user
Args:
url (str): The url of the website to browse
question (str): The question asked by the user
Returns:
Tuple[str, WebDriver]: The answer and links to the user and the webdriver
"""
driver, text = scrape_text_with_selenium(url)
add_header(driver)
summary_text = summary.summarize_text(url, text, question, driver)
links = scrape_links_with_selenium(driver, url)
# Limit links to 5
if len(links) > 5:
links = links[:5]
close_browser(driver)
return f"Answer gathered from website: {summary_text} \n \n Links: {links}", driver
def scrape_text_with_selenium(url: str) -> tuple[WebDriver, str]:
"""Scrape text from a website using selenium
Args:
url (str): The url of the website to scrape
Returns:
Tuple[WebDriver, str]: The webdriver and the text scraped from the website
"""
logging.getLogger("selenium").setLevel(logging.CRITICAL)
options_available = {
"chrome": ChromeOptions,
"safari": SafariOptions,
"firefox": FirefoxOptions,
}
options = options_available[CFG.selenium_web_browser]()
options.add_argument(
"user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.5615.49 Safari/537.36"
)
if CFG.selenium_web_browser == "firefox":
driver = webdriver.Firefox(
executable_path=GeckoDriverManager().install(), options=options
)
elif CFG.selenium_web_browser == "safari":
# Requires a bit more setup on the users end
# See https://developer.apple.com/documentation/webkit/testing_with_webdriver_in_safari
driver = webdriver.Safari(options=options)
else:
if platform == "linux" or platform == "linux2":
options.add_argument("--disable-dev-shm-usage")
options.add_argument("--remote-debugging-port=9222")
options.add_argument("--no-sandbox")
if CFG.selenium_headless:
options.add_argument("--headless")
options.add_argument("--disable-gpu")
driver = webdriver.Chrome(
executable_path=ChromeDriverManager().install(), options=options
)
driver.get(url)
WebDriverWait(driver, 10).until(
EC.presence_of_element_located((By.TAG_NAME, "body"))
)
# Get the HTML content directly from the browser's DOM
page_source = driver.execute_script("return document.body.outerHTML;")
soup = BeautifulSoup(page_source, "html.parser")
for script in soup(["script", "style"]):
script.extract()
text = soup.get_text()
lines = (line.strip() for line in text.splitlines())
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
text = "\n".join(chunk for chunk in chunks if chunk)
return driver, text
def scrape_links_with_selenium(driver: WebDriver, url: str) -> list[str]:
"""Scrape links from a website using selenium
Args:
driver (WebDriver): The webdriver to use to scrape the links
Returns:
List[str]: The links scraped from the website
"""
page_source = driver.page_source
soup = BeautifulSoup(page_source, "html.parser")
for script in soup(["script", "style"]):
script.extract()
hyperlinks = extract_hyperlinks(soup, url)
return format_hyperlinks(hyperlinks)
def close_browser(driver: WebDriver) -> None:
"""Close the browser
Args:
driver (WebDriver): The webdriver to close
Returns:
None
"""
driver.quit()
def add_header(driver: WebDriver) -> None:
"""Add a header to the website
Args:
driver (WebDriver): The webdriver to use to add the header
Returns:
None
"""
driver.execute_script(open(f"{FILE_DIR}/js/overlay.js", "r").read())

37
autogpt/commands/write_tests.py
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@ -1,37 +0,0 @@
"""A module that contains a function to generate test cases for the submitted code."""
from __future__ import annotations
import json
from autogpt.commands.command import command
from autogpt.llm_utils import call_ai_function
@command(
"write_tests",
"Write Tests",
'"code": "<full_code_string>", "focus": "<list_of_focus_areas>"',
)
def write_tests(code: str, focus: list[str]) -> str:
"""
A function that takes in code and focus topics and returns a response from create
chat completion api call.
Parameters:
focus (list): A list of suggestions around what needs to be improved.
code (str): Code for test cases to be generated against.
Returns:
A result string from create chat completion. Test cases for the submitted code
in response.
"""
function_string = (
"def create_test_cases(code: str, focus: Optional[str] = None) -> str:"
)
args = [code, json.dumps(focus)]
description_string = (
"Generates test cases for the existing code, focusing on"
" specific areas if required."
)
return call_ai_function(function_string, args, description_string)

14
autogpt/config/__init__.py
View File

@ -1,14 +0,0 @@
"""
This module contains the configuration classes for AutoGPT.
"""
from autogpt.config.ai_config import AIConfig
from autogpt.config.config import Config, check_openai_api_key
from autogpt.config.singleton import AbstractSingleton, Singleton
__all__ = [
"check_openai_api_key",
"AbstractSingleton",
"AIConfig",
"Config",
"Singleton",
]

163
autogpt/config/ai_config.py
View File

@ -1,163 +0,0 @@
# sourcery skip: do-not-use-staticmethod
"""
A module that contains the AIConfig class object that contains the configuration
"""
from __future__ import annotations
import os
import platform
from pathlib import Path
from typing import Optional, Type
import distro
import yaml
from autogpt.prompts.generator import PromptGenerator
# Soon this will go in a folder where it remembers more stuff about the run(s)
SAVE_FILE = str(Path(os.getcwd()) / "ai_settings.yaml")
class AIConfig:
"""
A class object that contains the configuration information for the AI
Attributes:
ai_name (str): The name of the AI.
ai_role (str): The description of the AI's role.
ai_goals (list): The list of objectives the AI is supposed to complete.
api_budget (float): The maximum dollar value for API calls (0.0 means infinite)
"""
def __init__(
self,
ai_name: str = "",
ai_role: str = "",
ai_goals: list | None = None,
api_budget: float = 0.0,
) -> None:
"""
Initialize a class instance
Parameters:
ai_name (str): The name of the AI.
ai_role (str): The description of the AI's role.
ai_goals (list): The list of objectives the AI is supposed to complete.
api_budget (float): The maximum dollar value for API calls (0.0 means infinite)
Returns:
None
"""
if ai_goals is None:
ai_goals = []
self.ai_name = ai_name
self.ai_role = ai_role
self.ai_goals = ai_goals
self.api_budget = api_budget
self.prompt_generator = None
self.command_registry = None
@staticmethod
def load(config_file: str = SAVE_FILE) -> "AIConfig":
"""
Returns class object with parameters (ai_name, ai_role, ai_goals, api_budget) loaded from
yaml file if yaml file exists,
else returns class with no parameters.
Parameters:
config_file (int): The path to the config yaml file.
DEFAULT: "../ai_settings.yaml"
Returns:
cls (object): An instance of given cls object
"""
try:
with open(config_file, encoding="utf-8") as file:
config_params = yaml.load(file, Loader=yaml.FullLoader)
except FileNotFoundError:
config_params = {}
ai_name = config_params.get("ai_name", "")
ai_role = config_params.get("ai_role", "")
ai_goals = config_params.get("ai_goals", [])
api_budget = config_params.get("api_budget", 0.0)
# type: Type[AIConfig]
return AIConfig(ai_name, ai_role, ai_goals, api_budget)
def save(self, config_file: str = SAVE_FILE) -> None:
"""
Saves the class parameters to the specified file yaml file path as a yaml file.
Parameters:
config_file(str): The path to the config yaml file.
DEFAULT: "../ai_settings.yaml"
Returns:
None
"""
config = {
"ai_name": self.ai_name,
"ai_role": self.ai_role,
"ai_goals": self.ai_goals,
"api_budget": self.api_budget,
}
with open(config_file, "w", encoding="utf-8") as file:
yaml.dump(config, file, allow_unicode=True)
def construct_full_prompt(
self, prompt_generator: Optional[PromptGenerator] = None
) -> str:
"""
Returns a prompt to the user with the class information in an organized fashion.
Parameters:
None
Returns:
full_prompt (str): A string containing the initial prompt for the user
including the ai_name, ai_role, ai_goals, and api_budget.
"""
prompt_start = (
"Your decisions must always be made independently without"
" seeking user assistance. Play to your strengths as an LLM and pursue"
" simple strategies with no legal complications."
""
)
from autogpt.config import Config
from autogpt.prompts.prompt import build_default_prompt_generator
cfg = Config()
if prompt_generator is None:
prompt_generator = build_default_prompt_generator()
prompt_generator.goals = self.ai_goals
prompt_generator.name = self.ai_name
prompt_generator.role = self.ai_role
prompt_generator.command_registry = self.command_registry
for plugin in cfg.plugins:
if not plugin.can_handle_post_prompt():
continue
prompt_generator = plugin.post_prompt(prompt_generator)
if cfg.execute_local_commands:
# add OS info to prompt
os_name = platform.system()
os_info = (
platform.platform(terse=True)
if os_name != "Linux"
else distro.name(pretty=True)
)
prompt_start += f"\nThe OS you are running on is: {os_info}"
# Construct full prompt
full_prompt = f"You are {prompt_generator.name}, {prompt_generator.role}\n{prompt_start}\n\nGOALS:\n\n"
for i, goal in enumerate(self.ai_goals):
full_prompt += f"{i+1}. {goal}\n"
if self.api_budget > 0.0:
full_prompt += f"\nIt takes money to let you run. Your API budget is ${self.api_budget:.3f}"
self.prompt_generator = prompt_generator
full_prompt += f"\n\n{prompt_generator.generate_prompt_string()}"
return full_prompt

282
autogpt/config/config.py
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@ -1,282 +0,0 @@
"""Configuration class to store the state of bools for different scripts access."""
import os
from typing import List
import openai
import yaml
from auto_gpt_plugin_template import AutoGPTPluginTemplate
from colorama import Fore
from dotenv import load_dotenv
from autogpt.config.singleton import Singleton
load_dotenv(verbose=True, override=True)
class Config(metaclass=Singleton):
"""
Configuration class to store the state of bools for different scripts access.
"""
def __init__(self) -> None:
"""Initialize the Config class"""
self.workspace_path = None
self.file_logger_path = None
self.debug_mode = False
self.continuous_mode = False
self.continuous_limit = 0
self.speak_mode = False
self.skip_reprompt = False
self.allow_downloads = False
self.skip_news = False
self.ai_settings_file = os.getenv("AI_SETTINGS_FILE", "ai_settings.yaml")
self.fast_llm_model = os.getenv("FAST_LLM_MODEL", "gpt-3.5-turbo")
self.smart_llm_model = os.getenv("SMART_LLM_MODEL", "gpt-4")
self.fast_token_limit = int(os.getenv("FAST_TOKEN_LIMIT", 4000))
self.smart_token_limit = int(os.getenv("SMART_TOKEN_LIMIT", 8000))
self.browse_chunk_max_length = int(os.getenv("BROWSE_CHUNK_MAX_LENGTH", 3000))
self.browse_spacy_language_model = os.getenv(
"BROWSE_SPACY_LANGUAGE_MODEL", "en_core_web_sm"
)
self.openai_api_key = os.getenv("OPENAI_API_KEY")
self.temperature = float(os.getenv("TEMPERATURE", "0"))
self.use_azure = os.getenv("USE_AZURE") == "True"
self.execute_local_commands = (
os.getenv("EXECUTE_LOCAL_COMMANDS", "False") == "True"
)
self.restrict_to_workspace = (
os.getenv("RESTRICT_TO_WORKSPACE", "True") == "True"
)
if self.use_azure:
self.load_azure_config()
openai.api_type = self.openai_api_type
openai.api_base = self.openai_api_base
openai.api_version = self.openai_api_version
self.elevenlabs_api_key = os.getenv("ELEVENLABS_API_KEY")
self.elevenlabs_voice_1_id = os.getenv("ELEVENLABS_VOICE_1_ID")
self.elevenlabs_voice_2_id = os.getenv("ELEVENLABS_VOICE_2_ID")
self.use_mac_os_tts = False
self.use_mac_os_tts = os.getenv("USE_MAC_OS_TTS")
self.use_brian_tts = False
self.use_brian_tts = os.getenv("USE_BRIAN_TTS")
self.github_api_key = os.getenv("GITHUB_API_KEY")
self.github_username = os.getenv("GITHUB_USERNAME")
self.google_api_key = os.getenv("GOOGLE_API_KEY")
self.custom_search_engine_id = os.getenv("CUSTOM_SEARCH_ENGINE_ID")
self.pinecone_api_key = os.getenv("PINECONE_API_KEY")
self.pinecone_region = os.getenv("PINECONE_ENV")
self.weaviate_host = os.getenv("WEAVIATE_HOST")
self.weaviate_port = os.getenv("WEAVIATE_PORT")
self.weaviate_protocol = os.getenv("WEAVIATE_PROTOCOL", "http")
self.weaviate_username = os.getenv("WEAVIATE_USERNAME", None)
self.weaviate_password = os.getenv("WEAVIATE_PASSWORD", None)
self.weaviate_scopes = os.getenv("WEAVIATE_SCOPES", None)
self.weaviate_embedded_path = os.getenv("WEAVIATE_EMBEDDED_PATH")
self.weaviate_api_key = os.getenv("WEAVIATE_API_KEY", None)
self.use_weaviate_embedded = (
os.getenv("USE_WEAVIATE_EMBEDDED", "False") == "True"
)
# milvus or zilliz cloud configuration.
self.milvus_addr = os.getenv("MILVUS_ADDR", "localhost:19530")
self.milvus_username = os.getenv("MILVUS_USERNAME")
self.milvus_password = os.getenv("MILVUS_PASSWORD")
self.milvus_collection = os.getenv("MILVUS_COLLECTION", "autogpt")
self.milvus_secure = os.getenv("MILVUS_SECURE") == "True"
self.image_provider = os.getenv("IMAGE_PROVIDER")
self.image_size = int(os.getenv("IMAGE_SIZE", 256))
self.huggingface_api_token = os.getenv("HUGGINGFACE_API_TOKEN")
self.huggingface_image_model = os.getenv(
"HUGGINGFACE_IMAGE_MODEL", "CompVis/stable-diffusion-v1-4"
)
self.huggingface_audio_to_text_model = os.getenv(
"HUGGINGFACE_AUDIO_TO_TEXT_MODEL"
)
self.sd_webui_url = os.getenv("SD_WEBUI_URL", "http://localhost:7860")
self.sd_webui_auth = os.getenv("SD_WEBUI_AUTH")
# Selenium browser settings
self.selenium_web_browser = os.getenv("USE_WEB_BROWSER", "chrome")
self.selenium_headless = os.getenv("HEADLESS_BROWSER", "True") == "True"
# User agent header to use when making HTTP requests
# Some websites might just completely deny request with an error code if
# no user agent was found.
self.user_agent = os.getenv(
"USER_AGENT",
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36",
)
self.redis_host = os.getenv("REDIS_HOST", "localhost")
self.redis_port = os.getenv("REDIS_PORT", "6379")
self.redis_password = os.getenv("REDIS_PASSWORD", "")
self.wipe_redis_on_start = os.getenv("WIPE_REDIS_ON_START", "True") == "True"
self.memory_index = os.getenv("MEMORY_INDEX", "auto-gpt")
# Note that indexes must be created on db 0 in redis, this is not configurable.
self.memory_backend = os.getenv("MEMORY_BACKEND", "local")
# Initialize the OpenAI API client
openai.api_key = self.openai_api_key
self.plugins_dir = os.getenv("PLUGINS_DIR", "plugins")
self.plugins: List[AutoGPTPluginTemplate] = []
self.plugins_openai = []
plugins_allowlist = os.getenv("ALLOWLISTED_PLUGINS")
if plugins_allowlist:
self.plugins_allowlist = plugins_allowlist.split(",")
else:
self.plugins_allowlist = []
self.plugins_denylist = []
def get_azure_deployment_id_for_model(self, model: str) -> str:
"""
Returns the relevant deployment id for the model specified.
Parameters:
model(str): The model to map to the deployment id.
Returns:
The matching deployment id if found, otherwise an empty string.
"""
if model == self.fast_llm_model:
return self.azure_model_to_deployment_id_map[
"fast_llm_model_deployment_id"
] # type: ignore
elif model == self.smart_llm_model:
return self.azure_model_to_deployment_id_map[
"smart_llm_model_deployment_id"
] # type: ignore
elif model == "text-embedding-ada-002":
return self.azure_model_to_deployment_id_map[
"embedding_model_deployment_id"
] # type: ignore
else:
return ""
AZURE_CONFIG_FILE = os.path.join(os.path.dirname(__file__), "../..", "azure.yaml")
def load_azure_config(self, config_file: str = AZURE_CONFIG_FILE) -> None:
"""
Loads the configuration parameters for Azure hosting from the specified file
path as a yaml file.
Parameters:
config_file(str): The path to the config yaml file. DEFAULT: "../azure.yaml"
Returns:
None
"""
with open(config_file) as file:
config_params = yaml.load(file, Loader=yaml.FullLoader)
self.openai_api_type = config_params.get("azure_api_type") or "azure"
self.openai_api_base = config_params.get("azure_api_base") or ""
self.openai_api_version = (
config_params.get("azure_api_version") or "2023-03-15-preview"
)
self.azure_model_to_deployment_id_map = config_params.get("azure_model_map", {})
def set_continuous_mode(self, value: bool) -> None:
"""Set the continuous mode value."""
self.continuous_mode = value
def set_continuous_limit(self, value: int) -> None:
"""Set the continuous limit value."""
self.continuous_limit = value
def set_speak_mode(self, value: bool) -> None:
"""Set the speak mode value."""
self.speak_mode = value
def set_fast_llm_model(self, value: str) -> None:
"""Set the fast LLM model value."""
self.fast_llm_model = value
def set_smart_llm_model(self, value: str) -> None:
"""Set the smart LLM model value."""
self.smart_llm_model = value
def set_fast_token_limit(self, value: int) -> None:
"""Set the fast token limit value."""
self.fast_token_limit = value
def set_smart_token_limit(self, value: int) -> None:
"""Set the smart token limit value."""
self.smart_token_limit = value
def set_browse_chunk_max_length(self, value: int) -> None:
"""Set the browse_website command chunk max length value."""
self.browse_chunk_max_length = value
def set_openai_api_key(self, value: str) -> None:
"""Set the OpenAI API key value."""
self.openai_api_key = value
def set_elevenlabs_api_key(self, value: str) -> None:
"""Set the ElevenLabs API key value."""
self.elevenlabs_api_key = value
def set_elevenlabs_voice_1_id(self, value: str) -> None:
"""Set the ElevenLabs Voice 1 ID value."""
self.elevenlabs_voice_1_id = value
def set_elevenlabs_voice_2_id(self, value: str) -> None:
"""Set the ElevenLabs Voice 2 ID value."""
self.elevenlabs_voice_2_id = value
def set_google_api_key(self, value: str) -> None:
"""Set the Google API key value."""
self.google_api_key = value
def set_custom_search_engine_id(self, value: str) -> None:
"""Set the custom search engine id value."""
self.custom_search_engine_id = value
def set_pinecone_api_key(self, value: str) -> None:
"""Set the Pinecone API key value."""
self.pinecone_api_key = value
def set_pinecone_region(self, value: str) -> None:
"""Set the Pinecone region value."""
self.pinecone_region = value
def set_debug_mode(self, value: bool) -> None:
"""Set the debug mode value."""
self.debug_mode = value
def set_plugins(self, value: list) -> None:
"""Set the plugins value."""
self.plugins = value
def set_temperature(self, value: int) -> None:
"""Set the temperature value."""
self.temperature = value
def set_memory_backend(self, value: int) -> None:
"""Set the temperature value."""
self.memory_backend = value
def check_openai_api_key() -> None:
"""Check if the OpenAI API key is set in config.py or as an environment variable."""
cfg = Config()
if not cfg.openai_api_key:
print(
Fore.RED
+ "Please set your OpenAI API key in .env or as an environment variable."
)
print("You can get your key from https://platform.openai.com/account/api-keys")
exit(1)

53
autogpt/config/prompt_config.py
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@ -1,53 +0,0 @@
# sourcery skip: do-not-use-staticmethod
"""
A module that contains the PromptConfig class object that contains the configuration
"""
import yaml
from colorama import Fore
from autogpt import utils
from autogpt.config.config import Config
from autogpt.logs import logger
CFG = Config()
class PromptConfig:
"""
A class object that contains the configuration information for the prompt, which will be used by the prompt generator
Attributes:
constraints (list): Constraints list for the prompt generator.
resources (list): Resources list for the prompt generator.
performance_evaluations (list): Performance evaluation list for the prompt generator.
"""
def __init__(
self,
config_file: str = CFG.prompt_settings_file,
) -> None:
"""
Initialize a class instance with parameters (constraints, resources, performance_evaluations) loaded from
yaml file if yaml file exists,
else raises error.
Parameters:
constraints (list): Constraints list for the prompt generator.
resources (list): Resources list for the prompt generator.
performance_evaluations (list): Performance evaluation list for the prompt generator.
Returns:
None
"""
# Validate file
(validated, message) = utils.validate_yaml_file(config_file)
if not validated:
logger.typewriter_log("FAILED FILE VALIDATION", Fore.RED, message)
logger.double_check()
exit(1)
with open(config_file, encoding="utf-8") as file:
config_params = yaml.load(file, Loader=yaml.FullLoader)
self.constraints = config_params.get("constraints", [])
self.resources = config_params.get("resources", [])
self.performance_evaluations = config_params.get("performance_evaluations", [])

24
autogpt/config/singleton.py
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@ -1,24 +0,0 @@
"""The singleton metaclass for ensuring only one instance of a class."""
import abc
class Singleton(abc.ABCMeta, type):
"""
Singleton metaclass for ensuring only one instance of a class.
"""
_instances = {}
def __call__(cls, *args, **kwargs):
"""Call method for the singleton metaclass."""
if cls not in cls._instances:
cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
return cls._instances[cls]
class AbstractSingleton(abc.ABC, metaclass=Singleton):
"""
Abstract singleton class for ensuring only one instance of a class.
"""
pass

134
autogpt/configurator.py
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@ -1,134 +0,0 @@
"""Configurator module."""
import click
from colorama import Back, Fore, Style
from autogpt import utils
from autogpt.config import Config
from autogpt.logs import logger
from autogpt.memory import get_supported_memory_backends
CFG = Config()
def create_config(
continuous: bool,
continuous_limit: int,
ai_settings_file: str,
skip_reprompt: bool,
speak: bool,
debug: bool,
gpt3only: bool,
gpt4only: bool,
memory_type: str,
browser_name: str,
allow_downloads: bool,
skip_news: bool,
) -> None:
"""Updates the config object with the given arguments.
Args:
continuous (bool): Whether to run in continuous mode
continuous_limit (int): The number of times to run in continuous mode
ai_settings_file (str): The path to the ai_settings.yaml file
skip_reprompt (bool): Whether to skip the re-prompting messages at the beginning of the script
speak (bool): Whether to enable speak mode
debug (bool): Whether to enable debug mode
gpt3only (bool): Whether to enable GPT3.5 only mode
gpt4only (bool): Whether to enable GPT4 only mode
memory_type (str): The type of memory backend to use
browser_name (str): The name of the browser to use when using selenium to scrape the web
allow_downloads (bool): Whether to allow Auto-GPT to download files natively
skips_news (bool): Whether to suppress the output of latest news on startup
"""
CFG.set_debug_mode(False)
CFG.set_continuous_mode(False)
CFG.set_speak_mode(False)
if debug:
logger.typewriter_log("Debug Mode: ", Fore.GREEN, "ENABLED")
CFG.set_debug_mode(True)
if continuous:
logger.typewriter_log("Continuous Mode: ", Fore.RED, "ENABLED")
logger.typewriter_log(
"WARNING: ",
Fore.RED,
"Continuous mode is not recommended. It is potentially dangerous and may"
" cause your AI to run forever or carry out actions you would not usually"
" authorise. Use at your own risk.",
)
CFG.set_continuous_mode(True)
if continuous_limit:
logger.typewriter_log(
"Continuous Limit: ", Fore.GREEN, f"{continuous_limit}"
)
CFG.set_continuous_limit(continuous_limit)
# Check if continuous limit is used without continuous mode
if continuous_limit and not continuous:
raise click.UsageError("--continuous-limit can only be used with --continuous")
if speak:
logger.typewriter_log("Speak Mode: ", Fore.GREEN, "ENABLED")
CFG.set_speak_mode(True)
if gpt3only:
logger.typewriter_log("GPT3.5 Only Mode: ", Fore.GREEN, "ENABLED")
CFG.set_smart_llm_model(CFG.fast_llm_model)
if gpt4only:
logger.typewriter_log("GPT4 Only Mode: ", Fore.GREEN, "ENABLED")
CFG.set_fast_llm_model(CFG.smart_llm_model)
if memory_type:
supported_memory = get_supported_memory_backends()
chosen = memory_type
if chosen not in supported_memory:
logger.typewriter_log(
"ONLY THE FOLLOWING MEMORY BACKENDS ARE SUPPORTED: ",
Fore.RED,
f"{supported_memory}",
)
logger.typewriter_log("Defaulting to: ", Fore.YELLOW, CFG.memory_backend)
else:
CFG.memory_backend = chosen
if skip_reprompt:
logger.typewriter_log("Skip Re-prompt: ", Fore.GREEN, "ENABLED")
CFG.skip_reprompt = True
if ai_settings_file:
file = ai_settings_file
# Validate file
(validated, message) = utils.validate_yaml_file(file)
if not validated:
logger.typewriter_log("FAILED FILE VALIDATION", Fore.RED, message)
logger.double_check()
exit(1)
logger.typewriter_log("Using AI Settings File:", Fore.GREEN, file)
CFG.ai_settings_file = file
CFG.skip_reprompt = True
if browser_name:
CFG.selenium_web_browser = browser_name
if allow_downloads:
logger.typewriter_log("Native Downloading:", Fore.GREEN, "ENABLED")
logger.typewriter_log(
"WARNING: ",
Fore.YELLOW,
f"{Back.LIGHTYELLOW_EX}Auto-GPT will now be able to download and save files to your machine.{Back.RESET} "
+ "It is recommended that you monitor any files it downloads carefully.",
)
logger.typewriter_log(
"WARNING: ",
Fore.YELLOW,
f"{Back.RED + Style.BRIGHT}ALWAYS REMEMBER TO NEVER OPEN FILES YOU AREN'T SURE OF!{Style.RESET_ALL}",
)
CFG.allow_downloads = True
if skip_news:
CFG.skip_news = True

29
autogpt/js/overlay.js
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@ -1,29 +0,0 @@
const overlay = document.createElement('div');
Object.assign(overlay.style, {
position: 'fixed',
zIndex: 999999,
top: 0,
left: 0,
width: '100%',
height: '100%',
background: 'rgba(0, 0, 0, 0.7)',
color: '#fff',
fontSize: '24px',
fontWeight: 'bold',
display: 'flex',
justifyContent: 'center',
alignItems: 'center',
});
const textContent = document.createElement('div');
Object.assign(textContent.style, {
textAlign: 'center',
});
textContent.textContent = 'AutoGPT Analyzing Page';
overlay.appendChild(textContent);
document.body.append(overlay);
document.body.style.overflow = 'hidden';
let dotCount = 0;
setInterval(() => {
textContent.textContent = 'AutoGPT Analyzing Page' + '.'.repeat(dotCount);
dotCount = (dotCount + 1) % 4;
}, 1000);

0
autogpt/json_utils/__init__.py
View File

124
autogpt/json_utils/json_fix_general.py
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@ -1,124 +0,0 @@
"""This module contains functions to fix JSON strings using general programmatic approaches, suitable for addressing
common JSON formatting issues."""
from __future__ import annotations
import contextlib
import json
import re
from typing import Optional
from autogpt.config import Config
from autogpt.json_utils.utilities import extract_char_position
CFG = Config()
def fix_invalid_escape(json_to_load: str, error_message: str) -> str:
"""Fix invalid escape sequences in JSON strings.
Args:
json_to_load (str): The JSON string.
error_message (str): The error message from the JSONDecodeError
exception.
Returns:
str: The JSON string with invalid escape sequences fixed.
"""
while error_message.startswith("Invalid \\escape"):
bad_escape_location = extract_char_position(error_message)
json_to_load = (
json_to_load[:bad_escape_location] + json_to_load[bad_escape_location + 1 :]
)
try:
json.loads(json_to_load)
return json_to_load
except json.JSONDecodeError as e:
if CFG.debug_mode:
print("json loads error - fix invalid escape", e)
error_message = str(e)
return json_to_load
def balance_braces(json_string: str) -> Optional[str]:
"""
Balance the braces in a JSON string.
Args:
json_string (str): The JSON string.
Returns:
str: The JSON string with braces balanced.
"""
open_braces_count = json_string.count("{")
close_braces_count = json_string.count("}")
while open_braces_count > close_braces_count:
json_string += "}"
close_braces_count += 1
while close_braces_count > open_braces_count:
json_string = json_string.rstrip("}")
close_braces_count -= 1
with contextlib.suppress(json.JSONDecodeError):
json.loads(json_string)
return json_string
def add_quotes_to_property_names(json_string: str) -> str:
"""
Add quotes to property names in a JSON string.
Args:
json_string (str): The JSON string.
Returns:
str: The JSON string with quotes added to property names.
"""
def replace_func(match: re.Match) -> str:
return f'"{match[1]}":'
property_name_pattern = re.compile(r"(\w+):")
corrected_json_string = property_name_pattern.sub(replace_func, json_string)
try:
json.loads(corrected_json_string)
return corrected_json_string
except json.JSONDecodeError as e:
raise e
def correct_json(json_to_load: str) -> str:
"""
Correct common JSON errors.
Args:
json_to_load (str): The JSON string.
"""
try:
if CFG.debug_mode:
print("json", json_to_load)
json.loads(json_to_load)
return json_to_load
except json.JSONDecodeError as e:
if CFG.debug_mode:
print("json loads error", e)
error_message = str(e)
if error_message.startswith("Invalid \\escape"):
json_to_load = fix_invalid_escape(json_to_load, error_message)
if error_message.startswith(
"Expecting property name enclosed in double quotes"
):
json_to_load = add_quotes_to_property_names(json_to_load)
try:
json.loads(json_to_load)
return json_to_load
except json.JSONDecodeError as e:
if CFG.debug_mode:
print("json loads error - add quotes", e)
error_message = str(e)
if balanced_str := balance_braces(json_to_load):
return balanced_str
return json_to_load

220
autogpt/json_utils/json_fix_llm.py
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@ -1,220 +0,0 @@
"""This module contains functions to fix JSON strings generated by LLM models, such as ChatGPT, using the assistance
of the ChatGPT API or LLM models."""
from __future__ import annotations
import contextlib
import json
from typing import Any, Dict
from colorama import Fore
from regex import regex
from autogpt.config import Config
from autogpt.json_utils.json_fix_general import correct_json
from autogpt.llm_utils import call_ai_function
from autogpt.logs import logger
from autogpt.speech import say_text
JSON_SCHEMA = """
{
"command": {
"name": "command name",
"args": {
"arg name": "value"
}
},
"thoughts":
{
"text": "thought",
"reasoning": "reasoning",
"plan": "- short bulleted\n- list that conveys\n- long-term plan",
"criticism": "constructive self-criticism",
"speak": "thoughts summary to say to user"
}
}
"""
CFG = Config()
def auto_fix_json(json_string: str, schema: str) -> str:
"""Fix the given JSON string to make it parseable and fully compliant with
the provided schema using GPT-3.
Args:
json_string (str): The JSON string to fix.
schema (str): The schema to use to fix the JSON.
Returns:
str: The fixed JSON string.
"""
# Try to fix the JSON using GPT:
function_string = "def fix_json(json_string: str, schema:str=None) -> str:"
args = [f"'''{json_string}'''", f"'''{schema}'''"]
description_string = (
"This function takes a JSON string and ensures that it"
" is parseable and fully compliant with the provided schema. If an object"
" or field specified in the schema isn't contained within the correct JSON,"
" it is omitted. The function also escapes any double quotes within JSON"
" string values to ensure that they are valid. If the JSON string contains"
" any None or NaN values, they are replaced with null before being parsed."
)
# If it doesn't already start with a "`", add one:
if not json_string.startswith("`"):
json_string = "```json\n" + json_string + "\n```"
result_string = call_ai_function(
function_string, args, description_string, model=CFG.fast_llm_model
)
logger.debug("------------ JSON FIX ATTEMPT ---------------")
logger.debug(f"Original JSON: {json_string}")
logger.debug("-----------")
logger.debug(f"Fixed JSON: {result_string}")
logger.debug("----------- END OF FIX ATTEMPT ----------------")
try:
json.loads(result_string) # just check the validity
return result_string
except json.JSONDecodeError: # noqa: E722
# Get the call stack:
# import traceback
# call_stack = traceback.format_exc()
# print(f"Failed to fix JSON: '{json_string}' "+call_stack)
return "failed"
def fix_json_using_multiple_techniques(assistant_reply: str) -> Dict[Any, Any]:
"""Fix the given JSON string to make it parseable and fully compliant with two techniques.
Args:
json_string (str): The JSON string to fix.
Returns:
str: The fixed JSON string.
"""
# Parse and print Assistant response
assistant_reply_json = fix_and_parse_json(assistant_reply)
if assistant_reply_json == {}:
assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets(
assistant_reply
)
if assistant_reply_json != {}:
return assistant_reply_json
logger.error(
"Error: The following AI output couldn't be converted to a JSON:\n",
assistant_reply,
)
if CFG.speak_mode:
say_text("I have received an invalid JSON response from the OpenAI API.")
return {}
def fix_and_parse_json(
json_to_load: str, try_to_fix_with_gpt: bool = True
) -> Dict[Any, Any]:
"""Fix and parse JSON string
Args:
json_to_load (str): The JSON string.
try_to_fix_with_gpt (bool, optional): Try to fix the JSON with GPT.
Defaults to True.
Returns:
str or dict[Any, Any]: The parsed JSON.
"""
with contextlib.suppress(json.JSONDecodeError):
json_to_load = json_to_load.replace("\t", "")
return json.loads(json_to_load)
with contextlib.suppress(json.JSONDecodeError):
json_to_load = correct_json(json_to_load)
return json.loads(json_to_load)
# Let's do something manually:
# sometimes GPT responds with something BEFORE the braces:
# "I'm sorry, I don't understand. Please try again."
# {"text": "I'm sorry, I don't understand. Please try again.",
# "confidence": 0.0}
# So let's try to find the first brace and then parse the rest
# of the string
try:
brace_index = json_to_load.index("{")
maybe_fixed_json = json_to_load[brace_index:]
last_brace_index = maybe_fixed_json.rindex("}")
maybe_fixed_json = maybe_fixed_json[: last_brace_index + 1]
return json.loads(maybe_fixed_json)
except (json.JSONDecodeError, ValueError) as e:
return try_ai_fix(try_to_fix_with_gpt, e, json_to_load)
def try_ai_fix(
try_to_fix_with_gpt: bool, exception: Exception, json_to_load: str
) -> Dict[Any, Any]:
"""Try to fix the JSON with the AI
Args:
try_to_fix_with_gpt (bool): Whether to try to fix the JSON with the AI.
exception (Exception): The exception that was raised.
json_to_load (str): The JSON string to load.
Raises:
exception: If try_to_fix_with_gpt is False.
Returns:
str or dict[Any, Any]: The JSON string or dictionary.
"""
if not try_to_fix_with_gpt:
raise exception
if CFG.debug_mode:
logger.warn(
"Warning: Failed to parse AI output, attempting to fix."
"\n If you see this warning frequently, it's likely that"
" your prompt is confusing the AI. Try changing it up"
" slightly."
)
# Now try to fix this up using the ai_functions
ai_fixed_json = auto_fix_json(json_to_load, JSON_SCHEMA)
if ai_fixed_json != "failed":
return json.loads(ai_fixed_json)
# This allows the AI to react to the error message,
# which usually results in it correcting its ways.
# logger.error("Failed to fix AI output, telling the AI.")
return {}
def attempt_to_fix_json_by_finding_outermost_brackets(json_string: str):
if CFG.speak_mode and CFG.debug_mode:
say_text(
"I have received an invalid JSON response from the OpenAI API. "
"Trying to fix it now."
)
logger.error("Attempting to fix JSON by finding outermost brackets\n")
try:
json_pattern = regex.compile(r"\{(?:[^{}]|(?R))*\}")
json_match = json_pattern.search(json_string)
if json_match:
# Extract the valid JSON object from the string
json_string = json_match.group(0)
logger.typewriter_log(
title="Apparently json was fixed.", title_color=Fore.GREEN
)
if CFG.speak_mode and CFG.debug_mode:
say_text("Apparently json was fixed.")
else:
return {}
except (json.JSONDecodeError, ValueError):
if CFG.debug_mode:
logger.error(f"Error: Invalid JSON: {json_string}\n")
if CFG.speak_mode:
say_text("Didn't work. I will have to ignore this response then.")
logger.error("Error: Invalid JSON, setting it to empty JSON now.\n")
json_string = {}
return fix_and_parse_json(json_string)

31
autogpt/json_utils/llm_response_format_1.json
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@ -1,31 +0,0 @@
{
"$schema": "http://json-schema.org/draft-07/schema#",
"type": "object",
"properties": {
"thoughts": {
"type": "object",
"properties": {
"text": {"type": "string"},
"reasoning": {"type": "string"},
"plan": {"type": "string"},
"criticism": {"type": "string"},
"speak": {"type": "string"}
},
"required": ["text", "reasoning", "plan", "criticism", "speak"],
"additionalProperties": false
},
"command": {
"type": "object",
"properties": {
"name": {"type": "string"},
"args": {
"type": "object"
}
},
"required": ["name", "args"],
"additionalProperties": false
}
},
"required": ["thoughts", "command"],
"additionalProperties": false
}

54
autogpt/json_utils/utilities.py
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@ -1,54 +0,0 @@
"""Utilities for the json_fixes package."""
import json
import re
from jsonschema import Draft7Validator
from autogpt.config import Config
from autogpt.logs import logger
CFG = Config()
def extract_char_position(error_message: str) -> int:
"""Extract the character position from the JSONDecodeError message.
Args:
error_message (str): The error message from the JSONDecodeError
exception.
Returns:
int: The character position.
"""
char_pattern = re.compile(r"\(char (\d+)\)")
if match := char_pattern.search(error_message):
return int(match[1])
else:
raise ValueError("Character position not found in the error message.")
def validate_json(json_object: object, schema_name: object) -> object:
"""
:type schema_name: object
:param schema_name:
:type json_object: object
"""
with open(f"/Users/kilig/Job/Python-project/academic_gpt/autogpt/json_utils/{schema_name}.json", "r") as f:
schema = json.load(f)
validator = Draft7Validator(schema)
if errors := sorted(validator.iter_errors(json_object), key=lambda e: e.path):
logger.error("The JSON object is invalid.")
if CFG.debug_mode:
logger.error(
json.dumps(json_object, indent=4)
) # Replace 'json_object' with the variable containing the JSON data
logger.error("The following issues were found:")
for error in errors:
logger.error(f"Error: {error.message}")
elif CFG.debug_mode:
print("The JSON object is valid.")
return json_object

19
autogpt/llm/__init__.py
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@ -1,19 +0,0 @@
from autogpt.llm.base import (
ChatModelInfo,
ChatModelResponse,
EmbeddingModelInfo,
EmbeddingModelResponse,
LLMResponse,
Message,
ModelInfo,
)
__all__ = [
"Message",
"ModelInfo",
"ChatModelInfo",
"EmbeddingModelInfo",
"LLMResponse",
"ChatModelResponse",
"EmbeddingModelResponse",
]

152
autogpt/llm/api_manager.py
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@ -1,152 +0,0 @@
from __future__ import annotations
from typing import List, Optional
import openai
from openai import Model
from autogpt.config import Config
from autogpt.llm.base import MessageDict
from autogpt.llm.modelsinfo import COSTS
from autogpt.logs import logger
from autogpt.singleton import Singleton
class ApiManager(metaclass=Singleton):
def __init__(self):
self.total_prompt_tokens = 0
self.total_completion_tokens = 0
self.total_cost = 0
self.total_budget = 0
self.models: Optional[list[Model]] = None
def reset(self):
self.total_prompt_tokens = 0
self.total_completion_tokens = 0
self.total_cost = 0
self.total_budget = 0.0
self.models = None
def create_chat_completion(
self,
messages: list[MessageDict],
model: str | None = None,
temperature: float = None,
max_tokens: int | None = None,
deployment_id=None,
) -> str:
"""
Create a chat completion and update the cost.
Args:
messages (list): The list of messages to send to the API.
model (str): The model to use for the API call.
temperature (float): The temperature to use for the API call.
max_tokens (int): The maximum number of tokens for the API call.
Returns:
str: The AI's response.
"""
cfg = Config()
if temperature is None:
temperature = cfg.temperature
if deployment_id is not None:
response = openai.ChatCompletion.create(
deployment_id=deployment_id,
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
api_key=cfg.openai_api_key,
)
else:
response = openai.ChatCompletion.create(
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
api_key=cfg.openai_api_key,
)
if not hasattr(response, "error"):
logger.debug(f"Response: {response}")
prompt_tokens = response.usage.prompt_tokens
completion_tokens = response.usage.completion_tokens
self.update_cost(prompt_tokens, completion_tokens, model)
return response
def update_cost(self, prompt_tokens, completion_tokens, model: str):
"""
Update the total cost, prompt tokens, and completion tokens.
Args:
prompt_tokens (int): The number of tokens used in the prompt.
completion_tokens (int): The number of tokens used in the completion.
model (str): The model used for the API call.
"""
# the .model property in API responses can contain version suffixes like -v2
model = model[:-3] if model.endswith("-v2") else model
self.total_prompt_tokens += prompt_tokens
self.total_completion_tokens += completion_tokens
self.total_cost += (
prompt_tokens * COSTS[model]["prompt"]
+ completion_tokens * COSTS[model]["completion"]
) / 1000
logger.debug(f"Total running cost: ${self.total_cost:.3f}")
def set_total_budget(self, total_budget):
"""
Sets the total user-defined budget for API calls.
Args:
total_budget (float): The total budget for API calls.
"""
self.total_budget = total_budget
def get_total_prompt_tokens(self):
"""
Get the total number of prompt tokens.
Returns:
int: The total number of prompt tokens.
"""
return self.total_prompt_tokens
def get_total_completion_tokens(self):
"""
Get the total number of completion tokens.
Returns:
int: The total number of completion tokens.
"""
return self.total_completion_tokens
def get_total_cost(self):
"""
Get the total cost of API calls.
Returns:
float: The total cost of API calls.
"""
return self.total_cost
def get_total_budget(self):
"""
Get the total user-defined budget for API calls.
Returns:
float: The total budget for API calls.
"""
return self.total_budget
def get_models(self) -> List[Model]:
"""
Get list of available GPT models.
Returns:
list: List of available GPT models.
"""
if self.models is None:
all_models = openai.Model.list()["data"]
self.models = [model for model in all_models if "gpt" in model["id"]]
return self.models

151
autogpt/llm/base.py
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@ -1,151 +0,0 @@
from __future__ import annotations
from dataclasses import dataclass, field
from math import ceil, floor
from typing import List, Literal, TypedDict
MessageRole = Literal["system", "user", "assistant"]
MessageType = Literal["ai_response", "action_result"]
class MessageDict(TypedDict):
role: MessageRole
content: str
@dataclass
class Message:
"""OpenAI Message object containing a role and the message content"""
role: MessageRole
content: str
type: MessageType | None = None
def raw(self) -> MessageDict:
return {"role": self.role, "content": self.content}
@dataclass
class ModelInfo:
"""Struct for model information.
Would be lovely to eventually get this directly from APIs, but needs to be scraped from
websites for now.
"""
name: str
prompt_token_cost: float
completion_token_cost: float
max_tokens: int
@dataclass
class ChatModelInfo(ModelInfo):
"""Struct for chat model information."""
@dataclass
class TextModelInfo(ModelInfo):
"""Struct for text completion model information."""
@dataclass
class EmbeddingModelInfo(ModelInfo):
"""Struct for embedding model information."""
embedding_dimensions: int
@dataclass
class ChatSequence:
"""Utility container for a chat sequence"""
model: ChatModelInfo
messages: list[Message] = field(default_factory=list)
def __getitem__(self, i: int):
return self.messages[i]
def __iter__(self):
return iter(self.messages)
def __len__(self):
return len(self.messages)
def append(self, message: Message):
return self.messages.append(message)
def extend(self, messages: list[Message] | ChatSequence):
return self.messages.extend(messages)
def insert(self, index: int, *messages: Message):
for message in reversed(messages):
self.messages.insert(index, message)
@classmethod
def for_model(cls, model_name: str, messages: list[Message] | ChatSequence = []):
from autogpt.llm.providers.openai import OPEN_AI_CHAT_MODELS
if not model_name in OPEN_AI_CHAT_MODELS:
raise ValueError(f"Unknown chat model '{model_name}'")
return ChatSequence(
model=OPEN_AI_CHAT_MODELS[model_name], messages=list(messages)
)
def add(self, message_role: MessageRole, content: str):
self.messages.append(Message(message_role, content))
@property
def token_length(self):
from autogpt.llm.utils import count_message_tokens
return count_message_tokens(self.messages, self.model.name)
def raw(self) -> list[MessageDict]:
return [m.raw() for m in self.messages]
def dump(self) -> str:
SEPARATOR_LENGTH = 42
def separator(text: str):
half_sep_len = (SEPARATOR_LENGTH - 2 - len(text)) / 2
return f"{floor(half_sep_len)*'-'} {text.upper()} {ceil(half_sep_len)*'-'}"
formatted_messages = "\n".join(
[f"{separator(m.role)}\n{m.content}" for m in self.messages]
)
return f"""
============== ChatSequence ==============
Length: {self.token_length} tokens; {len(self.messages)} messages
{formatted_messages}
==========================================
"""
@dataclass
class LLMResponse:
"""Standard response struct for a response from an LLM model."""
model_info: ModelInfo
prompt_tokens_used: int = 0
completion_tokens_used: int = 0
@dataclass
class EmbeddingModelResponse(LLMResponse):
"""Standard response struct for a response from an embedding model."""
embedding: List[float] = field(default_factory=list)
def __post_init__(self):
if self.completion_tokens_used:
raise ValueError("Embeddings should not have completion tokens used.")
@dataclass
class ChatModelResponse(LLMResponse):
"""Standard response struct for a response from an LLM model."""
content: str = None

202
autogpt/llm/chat.py
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@ -1,202 +0,0 @@
from __future__ import annotations
import time
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from autogpt.agent.agent import Agent
from autogpt.config import Config
from autogpt.llm.api_manager import ApiManager
from autogpt.llm.base import ChatSequence, Message
from autogpt.llm.utils import count_message_tokens, create_chat_completion
from autogpt.log_cycle.log_cycle import CURRENT_CONTEXT_FILE_NAME
from autogpt.logs import logger
# TODO: Change debug from hardcode to argument
def chat_with_ai(
config: Config,
agent: Agent,
system_prompt: str,
user_input: str,
token_limit: int,
model: str | None = None,
):
"""
Interact with the OpenAI API, sending the prompt, user input,
message history, and permanent memory.
Args:
config (Config): The config to use.
agent (Agent): The agent to use.
system_prompt (str): The prompt explaining the rules to the AI.
user_input (str): The input from the user.
token_limit (int): The maximum number of tokens allowed in the API call.
model (str, optional): The model to use. If None, the config.fast_llm_model will be used. Defaults to None.
Returns:
str: The AI's response.
"""
if model is None:
model = config.fast_llm_model
# Reserve 1000 tokens for the response
logger.debug(f"Token limit: {token_limit}")
send_token_limit = token_limit - 1000
# if len(agent.history) == 0:
# relevant_memory = ""
# else:
# recent_history = agent.history[-5:]
# shuffle(recent_history)
# relevant_memories = agent.memory.get_relevant(
# str(recent_history), 5
# )
# if relevant_memories:
# shuffle(relevant_memories)
# relevant_memory = str(relevant_memories)
# logger.debug(f"Memory Stats: {agent.memory.get_stats()}")
relevant_memory = []
message_sequence = ChatSequence.for_model(
model,
[
Message("system", system_prompt),
Message("system", f"The current time and date is {time.strftime('%c')}"),
# Message(
# "system",
# f"This reminds you of these events from your past:\n{relevant_memory}\n\n",
# ),
],
)
# Add messages from the full message history until we reach the token limit
next_message_to_add_index = len(agent.history) - 1
insertion_index = len(message_sequence)
# Count the currently used tokens
current_tokens_used = message_sequence.token_length
# while current_tokens_used > 2500:
# # remove memories until we are under 2500 tokens
# relevant_memory = relevant_memory[:-1]
# (
# next_message_to_add_index,
# current_tokens_used,
# insertion_index,
# current_context,
# ) = generate_context(
# prompt, relevant_memory, agent.history, model
# )
# Account for user input (appended later)
user_input_msg = Message("user", user_input)
current_tokens_used += count_message_tokens([user_input_msg], model)
current_tokens_used += 500 # Reserve space for new_summary_message
# Add Messages until the token limit is reached or there are no more messages to add.
for cycle in reversed(list(agent.history.per_cycle())):
messages_to_add = [msg for msg in cycle if msg is not None]
tokens_to_add = count_message_tokens(messages_to_add, model)
if current_tokens_used + tokens_to_add > send_token_limit:
break
# Add the most recent message to the start of the chain,
# after the system prompts.
message_sequence.insert(insertion_index, *messages_to_add)
current_tokens_used += tokens_to_add
# Update & add summary of trimmed messages
if len(agent.history) > 0:
new_summary_message, trimmed_messages = agent.history.trim_messages(
current_message_chain=list(message_sequence),
)
tokens_to_add = count_message_tokens([new_summary_message], model)
message_sequence.insert(insertion_index, new_summary_message)
current_tokens_used += tokens_to_add - 500
# FIXME: uncomment when memory is back in use
# memory_store = get_memory(cfg)
# for _, ai_msg, result_msg in agent.history.per_cycle(trimmed_messages):
# memory_to_add = MemoryItem.from_ai_action(ai_msg, result_msg)
# logger.debug(f"Storing the following memory:\n{memory_to_add.dump()}")
# memory_store.add(memory_to_add)
api_manager = ApiManager()
# inform the AI about its remaining budget (if it has one)
if api_manager.get_total_budget() > 0.0:
remaining_budget = api_manager.get_total_budget() - api_manager.get_total_cost()
if remaining_budget < 0:
remaining_budget = 0
budget_message = f"Your remaining API budget is ${remaining_budget:.3f}" + (
" BUDGET EXCEEDED! SHUT DOWN!\n\n"
if remaining_budget == 0
else " Budget very nearly exceeded! Shut down gracefully!\n\n"
if remaining_budget < 0.005
else " Budget nearly exceeded. Finish up.\n\n"
if remaining_budget < 0.01
else "\n\n"
)
logger.debug(budget_message)
message_sequence.add("system", budget_message)
current_tokens_used += count_message_tokens([message_sequence[-1]], model)
# Append user input, the length of this is accounted for above
message_sequence.append(user_input_msg)
plugin_count = len(config.plugins)
for i, plugin in enumerate(config.plugins):
if not plugin.can_handle_on_planning():
continue
plugin_response = plugin.on_planning(
agent.config.prompt_generator, message_sequence.raw()
)
if not plugin_response or plugin_response == "":
continue
tokens_to_add = count_message_tokens(
[Message("system", plugin_response)], model
)
if current_tokens_used + tokens_to_add > send_token_limit:
logger.debug(f"Plugin response too long, skipping: {plugin_response}")
logger.debug(f"Plugins remaining at stop: {plugin_count - i}")
break
message_sequence.add("system", plugin_response)
# Calculate remaining tokens
tokens_remaining = token_limit - current_tokens_used
# assert tokens_remaining >= 0, "Tokens remaining is negative.
# This should never happen, please submit a bug report at
# https://www.github.com/Torantulino/Auto-GPT"
# Debug print the current context
logger.debug(f"Token limit: {token_limit}")
logger.debug(f"Send Token Count: {current_tokens_used}")
logger.debug(f"Tokens remaining for response: {tokens_remaining}")
logger.debug("------------ CONTEXT SENT TO AI ---------------")
for message in message_sequence:
# Skip printing the prompt
if message.role == "system" and message.content == system_prompt:
continue
logger.debug(f"{message.role.capitalize()}: {message.content}")
logger.debug("")
logger.debug("----------- END OF CONTEXT ----------------")
agent.log_cycle_handler.log_cycle(
agent.config.ai_name,
agent.created_at,
agent.cycle_count,
message_sequence.raw(),
CURRENT_CONTEXT_FILE_NAME,
)
# TODO: use a model defined elsewhere, so that model can contain
# temperature and other settings we care about
assistant_reply = create_chat_completion(
prompt=message_sequence,
max_tokens=tokens_remaining,
)
# Update full message history
agent.history.append(user_input_msg)
agent.history.add("assistant", assistant_reply, "ai_response")
return assistant_reply

11
autogpt/llm/modelsinfo.py
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@ -1,11 +0,0 @@
COSTS = {
"gpt-3.5-turbo": {"prompt": 0.002, "completion": 0.002},
"gpt-3.5-turbo-0301": {"prompt": 0.002, "completion": 0.002},
"gpt-4-0314": {"prompt": 0.03, "completion": 0.06},
"gpt-4": {"prompt": 0.03, "completion": 0.06},
"gpt-4-0314": {"prompt": 0.03, "completion": 0.06},
"gpt-4-32k": {"prompt": 0.06, "completion": 0.12},
"gpt-4-32k-0314": {"prompt": 0.06, "completion": 0.12},
"text-embedding-ada-002": {"prompt": 0.0004, "completion": 0.0},
"text-davinci-003": {"prompt": 0.02, "completion": 0.02},
}

0
autogpt/llm/providers/__init__.py
View File

74
autogpt/llm/providers/openai.py
View File

@ -1,74 +0,0 @@
from autogpt.llm.base import ChatModelInfo, EmbeddingModelInfo, TextModelInfo
OPEN_AI_CHAT_MODELS = {
info.name: info
for info in [
ChatModelInfo(
name="gpt-3.5-turbo",
prompt_token_cost=0.002,
completion_token_cost=0.002,
max_tokens=4096,
),
ChatModelInfo(
name="gpt-3.5-turbo-0301",
prompt_token_cost=0.002,
completion_token_cost=0.002,
max_tokens=4096,
),
ChatModelInfo(
name="gpt-4",
prompt_token_cost=0.03,
completion_token_cost=0.06,
max_tokens=8192,
),
ChatModelInfo(
name="gpt-4-0314",
prompt_token_cost=0.03,
completion_token_cost=0.06,
max_tokens=8192,
),
ChatModelInfo(
name="gpt-4-32k",
prompt_token_cost=0.06,
completion_token_cost=0.12,
max_tokens=32768,
),
ChatModelInfo(
name="gpt-4-32k-0314",
prompt_token_cost=0.06,
completion_token_cost=0.12,
max_tokens=32768,
),
]
}
OPEN_AI_TEXT_MODELS = {
info.name: info
for info in [
TextModelInfo(
name="text-davinci-003",
prompt_token_cost=0.02,
completion_token_cost=0.02,
max_tokens=4097,
),
]
}
OPEN_AI_EMBEDDING_MODELS = {
info.name: info
for info in [
EmbeddingModelInfo(
name="text-embedding-ada-002",
prompt_token_cost=0.0004,
completion_token_cost=0.0,
max_tokens=8191,
embedding_dimensions=1536,
),
]
}
OPEN_AI_MODELS: dict[str, ChatModelInfo | EmbeddingModelInfo | TextModelInfo] = {
**OPEN_AI_CHAT_MODELS,
**OPEN_AI_TEXT_MODELS,
**OPEN_AI_EMBEDDING_MODELS,
}

266
autogpt/llm/utils/__init__.py
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@ -1,266 +0,0 @@
from __future__ import annotations
import functools
import time
from typing import List, Literal, Optional
from unittest.mock import patch
import openai
import openai.api_resources.abstract.engine_api_resource as engine_api_resource
import openai.util
from colorama import Fore, Style
from openai.error import APIError, RateLimitError
from openai.openai_object import OpenAIObject
from autogpt.config import Config
from autogpt.logs import logger
from ..api_manager import ApiManager
from ..base import ChatSequence, Message
from .token_counter import *
def metered(func):
"""Adds ApiManager metering to functions which make OpenAI API calls"""
api_manager = ApiManager()
openai_obj_processor = openai.util.convert_to_openai_object
def update_usage_with_response(response: OpenAIObject):
try:
usage = response.usage
logger.debug(f"Reported usage from call to model {response.model}: {usage}")
api_manager.update_cost(
response.usage.prompt_tokens,
response.usage.completion_tokens if "completion_tokens" in usage else 0,
response.model,
)
except Exception as err:
logger.warn(f"Failed to update API costs: {err.__class__.__name__}: {err}")
def metering_wrapper(*args, **kwargs):
openai_obj = openai_obj_processor(*args, **kwargs)
if isinstance(openai_obj, OpenAIObject) and "usage" in openai_obj:
update_usage_with_response(openai_obj)
return openai_obj
def metered_func(*args, **kwargs):
with patch.object(
engine_api_resource.util,
"convert_to_openai_object",
side_effect=metering_wrapper,
):
return func(*args, **kwargs)
return metered_func
def retry_openai_api(
num_retries: int = 10,
backoff_base: float = 2.0,
warn_user: bool = True,
):
"""Retry an OpenAI API call.
Args:
num_retries int: Number of retries. Defaults to 10.
backoff_base float: Base for exponential backoff. Defaults to 2.
warn_user bool: Whether to warn the user. Defaults to True.
"""
retry_limit_msg = f"{Fore.RED}Error: " f"Reached rate limit, passing...{Fore.RESET}"
api_key_error_msg = (
f"Please double check that you have setup a "
f"{Fore.CYAN + Style.BRIGHT}PAID{Style.RESET_ALL} OpenAI API Account. You can "
f"read more here: {Fore.CYAN}https://docs.agpt.co/setup/#getting-an-api-key{Fore.RESET}"
)
backoff_msg = (
f"{Fore.RED}Error: API Bad gateway. Waiting {{backoff}} seconds...{Fore.RESET}"
)
def _wrapper(func):
@functools.wraps(func)
def _wrapped(*args, **kwargs):
user_warned = not warn_user
num_attempts = num_retries + 1 # +1 for the first attempt
for attempt in range(1, num_attempts + 1):
try:
return func(*args, **kwargs)
except RateLimitError:
if attempt == num_attempts:
raise
logger.debug(retry_limit_msg)
if not user_warned:
logger.double_check(api_key_error_msg)
user_warned = True
except APIError as e:
if (e.http_status not in [502, 429]) or (attempt == num_attempts):
raise
backoff = backoff_base ** (attempt + 2)
logger.debug(backoff_msg.format(backoff=backoff))
time.sleep(backoff)
return _wrapped
return _wrapper
def call_ai_function(
function: str,
args: list,
description: str,
model: str | None = None,
config: Config = None,
) -> str:
"""Call an AI function
This is a magic function that can do anything with no-code. See
https://github.com/Torantulino/AI-Functions for more info.
Args:
function (str): The function to call
args (list): The arguments to pass to the function
description (str): The description of the function
model (str, optional): The model to use. Defaults to None.
Returns:
str: The response from the function
"""
if model is None:
model = config.smart_llm_model
# For each arg, if any are None, convert to "None":
args = [str(arg) if arg is not None else "None" for arg in args]
# parse args to comma separated string
arg_str: str = ", ".join(args)
prompt = ChatSequence.for_model(
model,
[
Message(
"system",
f"You are now the following python function: ```# {description}"
f"\n{function}```\n\nOnly respond with your `return` value.",
),
Message("user", arg_str),
],
)
return create_chat_completion(prompt=prompt, temperature=0)
@metered
@retry_openai_api()
def create_text_completion(
prompt: str,
model: Optional[str],
temperature: Optional[float],
max_output_tokens: Optional[int],
) -> str:
cfg = Config()
if model is None:
model = cfg.fast_llm_model
if temperature is None:
temperature = cfg.temperature
if cfg.use_azure:
kwargs = {"deployment_id": cfg.get_azure_deployment_id_for_model(model)}
else:
kwargs = {"model": model}
response = openai.Completion.create(
**kwargs,
prompt=prompt,
temperature=temperature,
max_tokens=max_output_tokens,
api_key=cfg.openai_api_key,
)
return response.choices[0].text
# Overly simple abstraction until we create something better
# simple retry mechanism when getting a rate error or a bad gateway
@metered
@retry_openai_api()
def create_chat_completion(
prompt: ChatSequence,
model: Optional[str] = None,
temperature: float = None,
max_tokens: Optional[int] = None,
) -> str:
"""Create a chat completion using the OpenAI API
Args:
messages (List[Message]): The messages to send to the chat completion
model (str, optional): The model to use. Defaults to None.
temperature (float, optional): The temperature to use. Defaults to 0.9.
max_tokens (int, optional): The max tokens to use. Defaults to None.
Returns:
str: The response from the chat completion
"""
cfg = Config()
if model is None:
model = prompt.model.name
if temperature is None:
temperature = cfg.temperature
logger.debug(
f"{Fore.GREEN}Creating chat completion with model {model}, temperature {temperature}, max_tokens {max_tokens}{Fore.RESET}"
)
for plugin in cfg.plugins:
if plugin.can_handle_chat_completion(
messages=prompt.raw(),
model=model,
temperature=temperature,
max_tokens=max_tokens,
):
message = plugin.handle_chat_completion(
messages=prompt.raw(),
model=model,
temperature=temperature,
max_tokens=max_tokens,
)
if message is not None:
return message
api_manager = ApiManager()
response = None
if cfg.use_azure:
kwargs = {"deployment_id": cfg.get_azure_deployment_id_for_model(model)}
else:
kwargs = {"model": model}
response = api_manager.create_chat_completion(
**kwargs,
messages=prompt.raw(),
temperature=temperature,
max_tokens=max_tokens,
)
resp = response.choices[0].message["content"]
for plugin in cfg.plugins:
if not plugin.can_handle_on_response():
continue
resp = plugin.on_response(resp)
return resp
def check_model(
model_name: str, model_type: Literal["smart_llm_model", "fast_llm_model"]
) -> str:
"""Check if model is available for use. If not, return gpt-3.5-turbo."""
api_manager = ApiManager()
models = api_manager.get_models()
if any(model_name in m["id"] for m in models):
return model_name
logger.typewriter_log(
"WARNING: ",
Fore.YELLOW,
f"You do not have access to {model_name}. Setting {model_type} to "
f"gpt-3.5-turbo.",
)
return "gpt-3.5-turbo"

76
autogpt/llm/utils/token_counter.py
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@ -1,76 +0,0 @@
"""Functions for counting the number of tokens in a message or string."""
from __future__ import annotations
from typing import List
import tiktoken
from autogpt.llm.base import Message
from autogpt.logs import logger
def count_message_tokens(
messages: List[Message], model: str = "gpt-3.5-turbo-0301"
) -> int:
"""
Returns the number of tokens used by a list of messages.
Args:
messages (list): A list of messages, each of which is a dictionary
containing the role and content of the message.
model (str): The name of the model to use for tokenization.
Defaults to "gpt-3.5-turbo-0301".
Returns:
int: The number of tokens used by the list of messages.
"""
try:
encoding = tiktoken.encoding_for_model(model)
except KeyError:
logger.warn("Warning: model not found. Using cl100k_base encoding.")
encoding = tiktoken.get_encoding("cl100k_base")
if model == "gpt-3.5-turbo":
# !Note: gpt-3.5-turbo may change over time.
# Returning num tokens assuming gpt-3.5-turbo-0301.")
return count_message_tokens(messages, model="gpt-3.5-turbo-0301")
elif model == "gpt-4":
# !Note: gpt-4 may change over time. Returning num tokens assuming gpt-4-0314.")
return count_message_tokens(messages, model="gpt-4-0314")
elif model == "gpt-3.5-turbo-0301":
tokens_per_message = (
4 # every message follows <|start|>{role/name}\n{content}<|end|>\n
)
tokens_per_name = -1 # if there's a name, the role is omitted
elif model == "gpt-4-0314":
tokens_per_message = 3
tokens_per_name = 1
else:
raise NotImplementedError(
f"num_tokens_from_messages() is not implemented for model {model}.\n"
" See https://github.com/openai/openai-python/blob/main/chatml.md for"
" information on how messages are converted to tokens."
)
num_tokens = 0
for message in messages:
num_tokens += tokens_per_message
for key, value in message.raw().items():
num_tokens += len(encoding.encode(value))
if key == "name":
num_tokens += tokens_per_name
num_tokens += 3 # every reply is primed with <|start|>assistant<|message|>
return num_tokens
def count_string_tokens(string: str, model_name: str) -> int:
"""
Returns the number of tokens in a text string.
Args:
string (str): The text string.
model_name (str): The name of the encoding to use. (e.g., "gpt-3.5-turbo")
Returns:
int: The number of tokens in the text string.
"""
encoding = tiktoken.encoding_for_model(model_name)
return len(encoding.encode(string))

185
autogpt/llm_utils.py
View File

@ -1,185 +0,0 @@
from __future__ import annotations
import time
from typing import List, Optional
import openai
from colorama import Fore, Style
from openai.error import APIError, RateLimitError
from autogpt.api_manager import api_manager
from autogpt.config import Config
from autogpt.logs import logger
from autogpt.types.openai import Message
CFG = Config()
openai.api_key = CFG.openai_api_key
def call_ai_function(
function: str, args: list, description: str, model: str | None = None
) -> str:
"""Call an AI function
This is a magic function that can do anything with no-code. See
https://github.com/Torantulino/AI-Functions for more info.
Args:
function (str): The function to call
args (list): The arguments to pass to the function
description (str): The description of the function
model (str, optional): The model to use. Defaults to None.
Returns:
str: The response from the function
"""
if model is None:
model = CFG.smart_llm_model
# For each arg, if any are None, convert to "None":
args = [str(arg) if arg is not None else "None" for arg in args]
# parse args to comma separated string
args: str = ", ".join(args)
messages: List[Message] = [
{
"role": "system",
"content": f"You are now the following python function: ```# {description}"
f"\n{function}```\n\nOnly respond with your `return` value.",
},
{"role": "user", "content": args},
]
return create_chat_completion(model=model, messages=messages, temperature=0)
# Overly simple abstraction until we create something better
# simple retry mechanism when getting a rate error or a bad gateway
def create_chat_completion(
messages: List[Message], # type: ignore
model: Optional[str] = None,
temperature: float = CFG.temperature,
max_tokens: Optional[int] = None,
) -> str:
"""Create a chat completion using the OpenAI API
Args:
messages (List[Message]): The messages to send to the chat completion
model (str, optional): The model to use. Defaults to None.
temperature (float, optional): The temperature to use. Defaults to 0.9.
max_tokens (int, optional): The max tokens to use. Defaults to None.
Returns:
str: The response from the chat completion
"""
num_retries = 10
warned_user = False
if CFG.debug_mode:
print(
f"{Fore.GREEN}Creating chat completion with model {model}, temperature {temperature}, max_tokens {max_tokens}{Fore.RESET}"
)
for plugin in CFG.plugins:
if plugin.can_handle_chat_completion(
messages=messages,
model=model,
temperature=temperature,
max_tokens=max_tokens,
):
message = plugin.handle_chat_completion(
messages=messages,
model=model,
temperature=temperature,
max_tokens=max_tokens,
)
if message is not None:
return message
response = None
for attempt in range(num_retries):
backoff = 2 ** (attempt + 2)
try:
if CFG.use_azure:
response = api_manager.create_chat_completion(
deployment_id=CFG.get_azure_deployment_id_for_model(model),
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
)
else:
response = api_manager.create_chat_completion(
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
)
break
except RateLimitError:
if CFG.debug_mode:
print(
f"{Fore.RED}Error: ", f"Reached rate limit, passing...{Fore.RESET}"
)
if not warned_user:
logger.double_check(
f"Please double check that you have setup a {Fore.CYAN + Style.BRIGHT}PAID{Style.RESET_ALL} OpenAI API Account. "
+ f"You can read more here: {Fore.CYAN}https://github.com/Significant-Gravitas/Auto-GPT#openai-api-keys-configuration{Fore.RESET}"
)
warned_user = True
except APIError as e:
if e.http_status != 502:
raise
if attempt == num_retries - 1:
raise
if CFG.debug_mode:
print(
f"{Fore.RED}Error: ",
f"API Bad gateway. Waiting {backoff} seconds...{Fore.RESET}",
)
time.sleep(backoff)
if response is None:
logger.typewriter_log(
"FAILED TO GET RESPONSE FROM OPENAI",
Fore.RED,
"Auto-GPT has failed to get a response from OpenAI's services. "
+ f"Try running Auto-GPT again, and if the problem the persists try running it with `{Fore.CYAN}--debug{Fore.RESET}`.",
)
logger.double_check()
if CFG.debug_mode:
raise RuntimeError(f"Failed to get response after {num_retries} retries")
else:
quit(1)
resp = response.choices[0].message["content"]
for plugin in CFG.plugins:
if not plugin.can_handle_on_response():
continue
resp = plugin.on_response(resp)
return resp
def get_ada_embedding(text):
text = text.replace("\n", " ")
return api_manager.embedding_create(
text_list=[text], model="text-embedding-ada-002"
)
def create_embedding_with_ada(text) -> list:
"""Create an embedding with text-ada-002 using the OpenAI SDK"""
num_retries = 10
for attempt in range(num_retries):
backoff = 2 ** (attempt + 2)
try:
return api_manager.embedding_create(
text_list=[text], model="text-embedding-ada-002"
)
except RateLimitError:
pass
except APIError as e:
if e.http_status != 502:
raise
if attempt == num_retries - 1:
raise
if CFG.debug_mode:
print(
f"{Fore.RED}Error: ",
f"API Bad gateway. Waiting {backoff} seconds...{Fore.RESET}",
)
time.sleep(backoff)

0
autogpt/log_cycle/__init__.py
View File

20
autogpt/log_cycle/json_handler.py
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@ -1,20 +0,0 @@
import json
import logging
class JsonFileHandler(logging.FileHandler):
def __init__(self, filename, mode="a", encoding=None, delay=False):
super().__init__(filename, mode, encoding, delay)
def emit(self, record):
json_data = json.loads(self.format(record))
with open(self.baseFilename, "w", encoding="utf-8") as f:
json.dump(json_data, f, ensure_ascii=False, indent=4)
import logging
class JsonFormatter(logging.Formatter):
def format(self, record):
return record.msg

85
autogpt/log_cycle/log_cycle.py
View File

@ -1,85 +0,0 @@
import json
import os
from typing import Any, Dict, Union
from autogpt.logs import logger
DEFAULT_PREFIX = "agent"
FULL_MESSAGE_HISTORY_FILE_NAME = "full_message_history.json"
CURRENT_CONTEXT_FILE_NAME = "current_context.json"
NEXT_ACTION_FILE_NAME = "next_action.json"
PROMPT_SUMMARY_FILE_NAME = "prompt_summary.json"
SUMMARY_FILE_NAME = "summary.txt"
SUPERVISOR_FEEDBACK_FILE_NAME = "supervisor_feedback.txt"
PROMPT_SUPERVISOR_FEEDBACK_FILE_NAME = "prompt_supervisor_feedback.json"
USER_INPUT_FILE_NAME = "user_input.txt"
class LogCycleHandler:
"""
A class for logging cycle data.
"""
def __init__(self):
self.log_count_within_cycle = 0
@staticmethod
def create_directory_if_not_exists(directory_path: str) -> None:
if not os.path.exists(directory_path):
os.makedirs(directory_path, exist_ok=True)
def create_outer_directory(self, ai_name: str, created_at: str) -> str:
log_directory = logger.get_log_directory()
if os.environ.get("OVERWRITE_DEBUG") == "1":
outer_folder_name = "auto_gpt"
else:
ai_name_short = ai_name[:15] if ai_name else DEFAULT_PREFIX
outer_folder_name = f"{created_at}_{ai_name_short}"
outer_folder_path = os.path.join(log_directory, "DEBUG", outer_folder_name)
self.create_directory_if_not_exists(outer_folder_path)
return outer_folder_path
def create_inner_directory(self, outer_folder_path: str, cycle_count: int) -> str:
nested_folder_name = str(cycle_count).zfill(3)
nested_folder_path = os.path.join(outer_folder_path, nested_folder_name)
self.create_directory_if_not_exists(nested_folder_path)
return nested_folder_path
def create_nested_directory(
self, ai_name: str, created_at: str, cycle_count: int
) -> str:
outer_folder_path = self.create_outer_directory(ai_name, created_at)
nested_folder_path = self.create_inner_directory(outer_folder_path, cycle_count)
return nested_folder_path
def log_cycle(
self,
ai_name: str,
created_at: str,
cycle_count: int,
data: Union[Dict[str, Any], Any],
file_name: str,
) -> None:
"""
Log cycle data to a JSON file.
Args:
data (Any): The data to be logged.
file_name (str): The name of the file to save the logged data.
"""
nested_folder_path = self.create_nested_directory(
ai_name, created_at, cycle_count
)
json_data = json.dumps(data, ensure_ascii=False, indent=4)
log_file_path = os.path.join(
nested_folder_path, f"{self.log_count_within_cycle}_{file_name}"
)
logger.log_json(json_data, log_file_path)
self.log_count_within_cycle += 1

359
autogpt/logs.py
View File

@ -1,359 +0,0 @@
"""Logging module for Auto-GPT."""
import inspect
import json
import logging
import os
import random
import re
import time
import traceback
from logging import LogRecord
from colorama import Fore, Style
from autogpt.config import Config, Singleton
from autogpt.speech import say_text
CFG = Config()
def get_properties(obj):
props = {}
for prop_name in dir(obj):
if not prop_name.startswith('__'):
prop_value = getattr(obj, prop_name)
props[prop_value] = prop_name
return props
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = AutoGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = AutoGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = AutoGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.typing_logger.addHandler(self.file_handler)
self.typing_logger.addHandler(error_handler)
self.typing_logger.setLevel(logging.DEBUG)
self.logger = logging.getLogger("LOGGER")
self.logger.addHandler(self.console_handler)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(error_handler)
self.logger.setLevel(logging.DEBUG)
self.color_compar = get_properties(Fore)
self.output_content = []
def typewriter_log(
self, title="", title_color=Fore.YELLOW, content="", speak_text=False, level=logging.INFO
):
if speak_text and CFG.speak_mode:
say_text(f"{title}. {content}")
if content:
if isinstance(content, list):
content = " ".join(content)
else:
content = ""
self.typing_logger.log(
level, content, extra={"title": title, "color": title_color}
)
try:
msg = f'<span style="color:{self.color_compar[title_color]};font-weight:bold;">{title}：</span><span style="font-weight:normal;">{content}</span>'
self.output_content.append([msg, title+": "+content])
return msg
except Exception as e:
msg = f'<span style="font-weight:bold;">{title}：</span><span style="font-weight:normal;">{content}</span>'
self.output_content.append([msg, title+": "+content])
return
def debug(
self,
message,
title="",
title_color="",
):
self._log(title, title_color, message, logging.DEBUG)
def warn(
self,
message,
title="",
title_color="",
):
self._log(title, title_color, message, logging.WARN)
def error(self, title, message=""):
self._log(title, Fore.RED, message, logging.ERROR)
def _log(self, title="", title_color="", message="", level=logging.INFO):
if message:
if isinstance(message, list):
message = " ".join(message)
self.logger.log(level, message, extra={"title": title, "color": title_color})
def set_level(self, level):
self.logger.setLevel(level)
self.typing_logger.setLevel(level)
def double_check(self, additionalText=None):
if not additionalText:
additionalText = (
"Please ensure you've setup and configured everything"
" correctly. Read https://github.com/Torantulino/Auto-GPT#readme to "
"double check. You can also create a github issue or join the discord"
" and ask there!"
)
self.typewriter_log("DOUBLE CHECK CONFIGURATION", Fore.YELLOW, additionalText)
"""
Output stream to console using simulated typing
"""
class TypingConsoleHandler(logging.StreamHandler):
def emit(self, record):
min_typing_speed = 0.05
max_typing_speed = 0.01
msg = self.format(record)
try:
words = msg.split()
for i, word in enumerate(words):
print(word, end="", flush=True)
if i < len(words) - 1:
print(" ", end="", flush=True)
typing_speed = random.uniform(min_typing_speed, max_typing_speed)
time.sleep(typing_speed)
# type faster after each word
min_typing_speed = min_typing_speed * 0.95
max_typing_speed = max_typing_speed * 0.95
print()
except Exception:
self.handleError(record)
class ConsoleHandler(logging.StreamHandler):
def emit(self, record) -> None:
msg = self.format(record)
try:
print(msg)
except Exception:
self.handleError(record)
class AutoGptFormatter(logging.Formatter):
"""
Allows to handle custom placeholders 'title_color' and 'message_no_color'.
To use this formatter, make sure to pass 'color', 'title' as log extras.
"""
def format(self, record: LogRecord) -> str:
if hasattr(record, "color"):
record.title_color = (
getattr(record, "color")
+ getattr(record, "title")
+ " "
+ Style.RESET_ALL
)
else:
record.title_color = getattr(record, "title")
if hasattr(record, "msg"):
record.message_no_color = remove_color_codes(getattr(record, "msg"))
else:
record.message_no_color = ""
return super().format(record)
def remove_color_codes(s: str) -> str:
ansi_escape = re.compile(r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])")
return ansi_escape.sub("", s)
logger = Logger()
def print_assistant_thoughts(ai_name, assistant_reply):
"""Prints the assistant's thoughts to the console"""
from autogpt.json_utils.json_fix_llm import (
attempt_to_fix_json_by_finding_outermost_brackets,
fix_and_parse_json,
)
try:
try:
# Parse and print Assistant response
assistant_reply_json = fix_and_parse_json(assistant_reply)
except json.JSONDecodeError:
logger.error("Error: Invalid JSON in assistant thoughts\n", assistant_reply)
assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets(
assistant_reply
)
if isinstance(assistant_reply_json, str):
assistant_reply_json = fix_and_parse_json(assistant_reply_json)
# Check if assistant_reply_json is a string and attempt to parse
# it into a JSON object
if isinstance(assistant_reply_json, str):
try:
assistant_reply_json = json.loads(assistant_reply_json)
except json.JSONDecodeError:
logger.error("Error: Invalid JSON\n", assistant_reply)
assistant_reply_json = (
attempt_to_fix_json_by_finding_outermost_brackets(
assistant_reply_json
)
)
assistant_thoughts_reasoning = None
assistant_thoughts_plan = None
assistant_thoughts_speak = None
assistant_thoughts_criticism = None
if not isinstance(assistant_reply_json, dict):
assistant_reply_json = {}
assistant_thoughts = assistant_reply_json.get("thoughts", {})
assistant_thoughts_text = assistant_thoughts.get("text")
if assistant_thoughts:
assistant_thoughts_reasoning = assistant_thoughts.get("reasoning")
assistant_thoughts_plan = assistant_thoughts.get("plan")
assistant_thoughts_criticism = assistant_thoughts.get("criticism")
assistant_thoughts_speak = assistant_thoughts.get("speak")
logger.typewriter_log(
f"{ai_name.upper()} THOUGHTS:", Fore.YELLOW, f"{assistant_thoughts_text}"
)
logger.typewriter_log(
"REASONING:", Fore.YELLOW, f"{assistant_thoughts_reasoning}"
)
if assistant_thoughts_plan:
logger.typewriter_log("PLAN:", Fore.YELLOW, "")
# If it's a list, join it into a string
if isinstance(assistant_thoughts_plan, list):
assistant_thoughts_plan = "\n".join(assistant_thoughts_plan)
elif isinstance(assistant_thoughts_plan, dict):
assistant_thoughts_plan = str(assistant_thoughts_plan)
# Split the input_string using the newline character and dashes
lines = assistant_thoughts_plan.split("\n")
for line in lines:
line = line.lstrip("- ")
logger.typewriter_log("- ", Fore.GREEN, line.strip())
logger.typewriter_log(
"CRITICISM:", Fore.YELLOW, f"{assistant_thoughts_criticism}"
)
# Speak the assistant's thoughts
if CFG.speak_mode and assistant_thoughts_speak:
say_text(assistant_thoughts_speak)
else:
logger.typewriter_log("SPEAK:", Fore.YELLOW, f"{assistant_thoughts_speak}")
return assistant_reply_json
except json.decoder.JSONDecodeError:
logger.error("Error: Invalid JSON\n", assistant_reply)
if CFG.speak_mode:
say_text(
"I have received an invalid JSON response from the OpenAI API."
" I cannot ignore this response."
)
# All other errors, return "Error: + error message"
except Exception:
call_stack = traceback.format_exc()
logger.error("Error: \n", call_stack)
def print_assistant_thoughts(
ai_name: object, assistant_reply_json_valid: object
) -> None:
assistant_thoughts_reasoning = None
assistant_thoughts_plan = None
assistant_thoughts_speak = None
assistant_thoughts_criticism = None
assistant_thoughts = assistant_reply_json_valid.get("thoughts", {})
assistant_thoughts_text = assistant_thoughts.get("text")
if assistant_thoughts:
assistant_thoughts_reasoning = assistant_thoughts.get("reasoning")
assistant_thoughts_plan = assistant_thoughts.get("plan")
assistant_thoughts_criticism = assistant_thoughts.get("criticism")
assistant_thoughts_speak = assistant_thoughts.get("speak")
logger.typewriter_log(
f"{ai_name.upper()} THOUGHTS:", Fore.YELLOW, f"{assistant_thoughts_text}"
)
logger.typewriter_log("REASONING:", Fore.YELLOW, f"{assistant_thoughts_reasoning}")
if assistant_thoughts_plan:
logger.typewriter_log("PLAN:", Fore.YELLOW, "")
# If it's a list, join it into a string
if isinstance(assistant_thoughts_plan, list):
assistant_thoughts_plan = "\n".join(assistant_thoughts_plan)
elif isinstance(assistant_thoughts_plan, dict):
assistant_thoughts_plan = str(assistant_thoughts_plan)
# Split the input_string using the newline character and dashes
lines = assistant_thoughts_plan.split("\n")
for line in lines:
line = line.lstrip("- ")
logger.typewriter_log("- ", Fore.GREEN, line.strip())
logger.typewriter_log("CRITICISM:", Fore.YELLOW, f"{assistant_thoughts_criticism}")
# Speak the assistant's thoughts
if CFG.speak_mode and assistant_thoughts_speak:
say_text(assistant_thoughts_speak)
if __name__ == '__main__':
ff = logger.typewriter_log('ahhahaha', Fore.GREEN, speak_text=True)
# print(Fore.GREEN)
# print(logger.color_compar)

194
autogpt/main.py
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@ -1,194 +0,0 @@
"""The application entry point. Can be invoked by a CLI or any other front end application."""
import logging
import sys
from pathlib import Path
from colorama import Fore, Style
from autogpt.agent import Agent
from autogpt.commands.command import CommandRegistry
from autogpt.config import Config, check_openai_api_key
from autogpt.configurator import create_config
from autogpt.logs import logger
from autogpt.memory.vector import get_memory
from autogpt.plugins import scan_plugins
from autogpt.prompts.prompt import DEFAULT_TRIGGERING_PROMPT, construct_main_ai_config
from autogpt.utils import (
get_current_git_branch,
get_latest_bulletin,
get_legal_warning,
markdown_to_ansi_style,
)
from autogpt.workspace import Workspace
from scripts.install_plugin_deps import install_plugin_dependencies
def run_auto_gpt(
continuous: bool,
continuous_limit: int,
ai_settings: str,
prompt_settings: str,
skip_reprompt: bool,
speak: bool,
debug: bool,
gpt3only: bool,
gpt4only: bool,
memory_type: str,
browser_name: str,
allow_downloads: bool,
skip_news: bool,
workspace_directory: str,
install_plugin_deps: bool,
):
# Configure logging before we do anything else.
logger.set_level(logging.DEBUG if debug else logging.INFO)
logger.speak_mode = speak
cfg = Config()
# TODO: fill in llm values here
check_openai_api_key()
create_config(
cfg,
continuous,
continuous_limit,
ai_settings,
prompt_settings,
skip_reprompt,
speak,
debug,
gpt3only,
gpt4only,
memory_type,
browser_name,
allow_downloads,
skip_news,
)
if cfg.continuous_mode:
for line in get_legal_warning().split("\n"):
logger.warn(markdown_to_ansi_style(line), "LEGAL:", Fore.RED)
if not cfg.skip_news:
motd, is_new_motd = get_latest_bulletin()
if motd:
motd = markdown_to_ansi_style(motd)
for motd_line in motd.split("\n"):
logger.info(motd_line, "NEWS:", Fore.GREEN)
if is_new_motd and not cfg.chat_messages_enabled:
input(
Fore.MAGENTA
+ Style.BRIGHT
+ "NEWS: Bulletin was updated! Press Enter to continue..."
+ Style.RESET_ALL
)
git_branch = get_current_git_branch()
if git_branch and git_branch != "stable":
logger.typewriter_log(
"WARNING: ",
Fore.RED,
f"You are running on `{git_branch}` branch "
"- this is not a supported branch.",
)
if sys.version_info < (3, 10):
logger.typewriter_log(
"WARNING: ",
Fore.RED,
"You are running on an older version of Python. "
"Some people have observed problems with certain "
"parts of Auto-GPT with this version. "
"Please consider upgrading to Python 3.10 or higher.",
)
if install_plugin_deps:
install_plugin_dependencies()
# TODO: have this directory live outside the repository (e.g. in a user's
# home directory) and have it come in as a command line argument or part of
# the env file.
if workspace_directory is None:
workspace_directory = Path(__file__).parent / "auto_gpt_workspace"
else:
workspace_directory = Path(workspace_directory)
# TODO: pass in the ai_settings file and the env file and have them cloned into
# the workspace directory so we can bind them to the agent.
workspace_directory = Workspace.make_workspace(workspace_directory)
cfg.workspace_path = str(workspace_directory)
# HACK: doing this here to collect some globals that depend on the workspace.
file_logger_path = workspace_directory / "file_logger.txt"
if not file_logger_path.exists():
with file_logger_path.open(mode="w", encoding="utf-8") as f:
f.write("File Operation Logger ")
cfg.file_logger_path = str(file_logger_path)
cfg.set_plugins(scan_plugins(cfg, cfg.debug_mode))
# Create a CommandRegistry instance and scan default folder
command_registry = CommandRegistry()
command_categories = [
"autogpt.commands.analyze_code",
"autogpt.commands.audio_text",
"autogpt.commands.execute_code",
"autogpt.commands.file_operations",
"autogpt.commands.git_operations",
"autogpt.commands.google_search",
"autogpt.commands.image_gen",
"autogpt.commands.improve_code",
"autogpt.commands.web_selenium",
"autogpt.commands.write_tests",
"autogpt.app",
"autogpt.commands.task_statuses",
]
logger.debug(
f"The following command categories are disabled: {cfg.disabled_command_categories}"
)
command_categories = [
x for x in command_categories if x not in cfg.disabled_command_categories
]
logger.debug(f"The following command categories are enabled: {command_categories}")
for command_category in command_categories:
command_registry.import_commands(command_category)
ai_name = ""
ai_config = construct_main_ai_config()
ai_config.command_registry = command_registry
if ai_config.ai_name:
ai_name = ai_config.ai_name
# print(prompt)
# Initialize variables
next_action_count = 0
# add chat plugins capable of report to logger
if cfg.chat_messages_enabled:
for plugin in cfg.plugins:
if hasattr(plugin, "can_handle_report") and plugin.can_handle_report():
logger.info(f"Loaded plugin into logger: {plugin.__class__.__name__}")
logger.chat_plugins.append(plugin)
# Initialize memory and make sure it is empty.
# this is particularly important for indexing and referencing pinecone memory
memory = get_memory(cfg, init=True)
logger.typewriter_log(
"Using memory of type:", Fore.GREEN, f"{memory.__class__.__name__}"
)
logger.typewriter_log("Using Browser:", Fore.GREEN, cfg.selenium_web_browser)
system_prompt = ai_config.construct_full_prompt()
if cfg.debug_mode:
logger.typewriter_log("Prompt:", Fore.GREEN, system_prompt)
agent = Agent(
ai_name=ai_name,
memory=memory,
next_action_count=next_action_count,
command_registry=command_registry,
config=ai_config,
system_prompt=system_prompt,
triggering_prompt=DEFAULT_TRIGGERING_PROMPT,
workspace_directory=workspace_directory,
)
agent.start_interaction_loop()

99
autogpt/memory/__init__.py
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@ -1,99 +0,0 @@
from autogpt.memory.local import LocalCache
from autogpt.memory.no_memory import NoMemory
# List of supported memory backends
# Add a backend to this list if the import attempt is successful
supported_memory = ["local", "no_memory"]
try:
from autogpt.memory.redismem import RedisMemory
supported_memory.append("redis")
except ImportError:
# print("Redis not installed. Skipping import.")
RedisMemory = None
try:
from autogpt.memory.pinecone import PineconeMemory
supported_memory.append("pinecone")
except ImportError:
# print("Pinecone not installed. Skipping import.")
PineconeMemory = None
try:
from autogpt.memory.weaviate import WeaviateMemory
supported_memory.append("weaviate")
except ImportError:
# print("Weaviate not installed. Skipping import.")
WeaviateMemory = None
try:
from autogpt.memory.milvus import MilvusMemory
supported_memory.append("milvus")
except ImportError:
# print("pymilvus not installed. Skipping import.")
MilvusMemory = None
def get_memory(cfg, init=False):
memory = None
if cfg.memory_backend == "pinecone":
if not PineconeMemory:
print(
"Error: Pinecone is not installed. Please install pinecone"
" to use Pinecone as a memory backend."
)
else:
memory = PineconeMemory(cfg)
if init:
memory.clear()
elif cfg.memory_backend == "redis":
if not RedisMemory:
print(
"Error: Redis is not installed. Please install redis-py to"
" use Redis as a memory backend."
)
else:
memory = RedisMemory(cfg)
elif cfg.memory_backend == "weaviate":
if not WeaviateMemory:
print(
"Error: Weaviate is not installed. Please install weaviate-client to"
" use Weaviate as a memory backend."
)
else:
memory = WeaviateMemory(cfg)
elif cfg.memory_backend == "milvus":
if not MilvusMemory:
print(
"Error: pymilvus sdk is not installed."
"Please install pymilvus to use Milvus or Zilliz Cloud as memory backend."
)
else:
memory = MilvusMemory(cfg)
elif cfg.memory_backend == "no_memory":
memory = NoMemory(cfg)
if memory is None:
memory = LocalCache(cfg)
if init:
memory.clear()
return memory
def get_supported_memory_backends():
return supported_memory
__all__ = [
"get_memory",
"LocalCache",
"RedisMemory",
"PineconeMemory",
"NoMemory",
"MilvusMemory",
"WeaviateMemory",
]

28
autogpt/memory/base.py
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@ -1,28 +0,0 @@
"""Base class for memory providers."""
import abc
from autogpt.config import AbstractSingleton, Config
cfg = Config()
class MemoryProviderSingleton(AbstractSingleton):
@abc.abstractmethod
def add(self, data):
pass
@abc.abstractmethod
def get(self, data):
pass
@abc.abstractmethod
def clear(self):
pass
@abc.abstractmethod
def get_relevant(self, data, num_relevant=5):
pass
@abc.abstractmethod
def get_stats(self):
pass

126
autogpt/memory/local.py
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@ -1,126 +0,0 @@
from __future__ import annotations
import dataclasses
from pathlib import Path
from typing import Any, List
import numpy as np
import orjson
from autogpt.llm_utils import create_embedding_with_ada
from autogpt.memory.base import MemoryProviderSingleton
EMBED_DIM = 1536
SAVE_OPTIONS = orjson.OPT_SERIALIZE_NUMPY | orjson.OPT_SERIALIZE_DATACLASS
def create_default_embeddings():
return np.zeros((0, EMBED_DIM)).astype(np.float32)
@dataclasses.dataclass
class CacheContent:
texts: List[str] = dataclasses.field(default_factory=list)
embeddings: np.ndarray = dataclasses.field(
default_factory=create_default_embeddings
)
class LocalCache(MemoryProviderSingleton):
"""A class that stores the memory in a local file"""
def __init__(self, cfg) -> None:
"""Initialize a class instance
Args:
cfg: Config object
Returns:
None
"""
workspace_path = Path(cfg.workspace_path)
self.filename = workspace_path / f"{cfg.memory_index}.json"
self.filename.touch(exist_ok=True)
file_content = b"{}"
with self.filename.open("w+b") as f:
f.write(file_content)
self.data = CacheContent()
def add(self, text: str):
"""
Add text to our list of texts, add embedding as row to our
embeddings-matrix
Args:
text: str
Returns: None
"""
if "Command Error:" in text:
return ""
self.data.texts.append(text)
embedding = create_embedding_with_ada(text)
vector = np.array(embedding).astype(np.float32)
vector = vector[np.newaxis, :]
self.data.embeddings = np.concatenate(
[
self.data.embeddings,
vector,
],
axis=0,
)
with open(self.filename, "wb") as f:
out = orjson.dumps(self.data, option=SAVE_OPTIONS)
f.write(out)
return text
def clear(self) -> str:
"""
Clears the redis server.
Returns: A message indicating that the memory has been cleared.
"""
self.data = CacheContent()
return "Obliviated"
def get(self, data: str) -> list[Any] | None:
"""
Gets the data from the memory that is most relevant to the given data.
Args:
data: The data to compare to.
Returns: The most relevant data.
"""
return self.get_relevant(data, 1)
def get_relevant(self, text: str, k: int) -> list[Any]:
""" "
matrix-vector mult to find score-for-each-row-of-matrix
get indices for top-k winning scores
return texts for those indices
Args:
text: str
k: int
Returns: List[str]
"""
embedding = create_embedding_with_ada(text)
scores = np.dot(self.data.embeddings, embedding)
top_k_indices = np.argsort(scores)[-k:][::-1]
return [self.data.texts[i] for i in top_k_indices]
def get_stats(self) -> tuple[int, tuple[int, ...]]:
"""
Returns: The stats of the local cache.
"""
return len(self.data.texts), self.data.embeddings.shape

204
autogpt/memory/message_history.py
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@ -1,204 +0,0 @@
from __future__ import annotations
import copy
import json
from dataclasses import dataclass, field
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from autogpt.agent import Agent
from autogpt.config import Config
from autogpt.json_utils.utilities import (
LLM_DEFAULT_RESPONSE_FORMAT,
is_string_valid_json,
)
from autogpt.llm.base import ChatSequence, Message, MessageRole, MessageType
from autogpt.llm.utils import create_chat_completion
from autogpt.log_cycle.log_cycle import PROMPT_SUMMARY_FILE_NAME, SUMMARY_FILE_NAME
from autogpt.logs import logger
@dataclass
class MessageHistory:
agent: Agent
messages: list[Message] = field(default_factory=list)
summary: str = "I was created"
last_trimmed_index: int = 0
def __getitem__(self, i: int):
return self.messages[i]
def __iter__(self):
return iter(self.messages)
def __len__(self):
return len(self.messages)
def add(
self,
role: MessageRole,
content: str,
type: MessageType | None = None,
):
return self.append(Message(role, content, type))
def append(self, message: Message):
return self.messages.append(message)
def trim_messages(
self,
current_message_chain: list[Message],
) -> tuple[Message, list[Message]]:
"""
Returns a list of trimmed messages: messages which are in the message history
but not in current_message_chain.
Args:
current_message_chain (list[Message]): The messages currently in the context.
Returns:
Message: A message with the new running summary after adding the trimmed messages.
list[Message]: A list of messages that are in full_message_history with an index higher than last_trimmed_index and absent from current_message_chain.
"""
# Select messages in full_message_history with an index higher than last_trimmed_index
new_messages = [
msg for i, msg in enumerate(self) if i > self.last_trimmed_index
]
# Remove messages that are already present in current_message_chain
new_messages_not_in_chain = [
msg for msg in new_messages if msg not in current_message_chain
]
if not new_messages_not_in_chain:
return self.summary_message(), []
new_summary_message = self.update_running_summary(
new_events=new_messages_not_in_chain
)
# Find the index of the last message processed
last_message = new_messages_not_in_chain[-1]
self.last_trimmed_index = self.messages.index(last_message)
return new_summary_message, new_messages_not_in_chain
def per_cycle(self, messages: list[Message] | None = None):
"""
Yields:
Message: a message containing user input
Message: a message from the AI containing a proposed action
Message: the message containing the result of the AI's proposed action
"""
messages = messages or self.messages
for i in range(0, len(messages) - 1):
ai_message = messages[i]
if ai_message.type != "ai_response":
continue
user_message = (
messages[i - 1] if i > 0 and messages[i - 1].role == "user" else None
)
result_message = messages[i + 1]
try:
assert is_string_valid_json(
ai_message.content, LLM_DEFAULT_RESPONSE_FORMAT
), "AI response is not a valid JSON object"
assert result_message.type == "action_result"
yield user_message, ai_message, result_message
except AssertionError as err:
logger.debug(
f"Invalid item in message history: {err}; Messages: {messages[i-1:i+2]}"
)
def summary_message(self) -> Message:
return Message(
"system",
f"This reminds you of these events from your past: \n{self.summary}",
)
def update_running_summary(self, new_events: list[Message]) -> Message:
"""
This function takes a list of dictionaries representing new events and combines them with the current summary,
focusing on key and potentially important information to remember. The updated summary is returned in a message
formatted in the 1st person past tense.
Args:
new_events (List[Dict]): A list of dictionaries containing the latest events to be added to the summary.
Returns:
str: A message containing the updated summary of actions, formatted in the 1st person past tense.
Example:
new_events = [{"event": "entered the kitchen."}, {"event": "found a scrawled note with the number 7"}]
update_running_summary(new_events)
# Returns: "This reminds you of these events from your past: \nI entered the kitchen and found a scrawled note saying 7."
"""
cfg = Config()
if not new_events:
return self.summary_message()
# Create a copy of the new_events list to prevent modifying the original list
new_events = copy.deepcopy(new_events)
# Replace "assistant" with "you". This produces much better first person past tense results.
for event in new_events:
if event.role.lower() == "assistant":
event.role = "you"
# Remove "thoughts" dictionary from "content"
try:
content_dict = json.loads(event.content)
if "thoughts" in content_dict:
del content_dict["thoughts"]
event.content = json.dumps(content_dict)
except json.decoder.JSONDecodeError:
if cfg.debug_mode:
logger.error(f"Error: Invalid JSON: {event.content}\n")
elif event.role.lower() == "system":
event.role = "your computer"
# Delete all user messages
elif event.role == "user":
new_events.remove(event)
prompt = f'''Your task is to create a concise running summary of actions and information results in the provided text, focusing on key and potentially important information to remember.
You will receive the current summary and the your latest actions. Combine them, adding relevant key information from the latest development in 1st person past tense and keeping the summary concise.
Summary So Far:
"""
{self.summary}
"""
Latest Development:
"""
{new_events or "Nothing new happened."}
"""
'''
prompt = ChatSequence.for_model(cfg.fast_llm_model, [Message("user", prompt)])
self.agent.log_cycle_handler.log_cycle(
self.agent.config.ai_name,
self.agent.created_at,
self.agent.cycle_count,
prompt.raw(),
PROMPT_SUMMARY_FILE_NAME,
)
self.summary = create_chat_completion(prompt)
self.agent.log_cycle_handler.log_cycle(
self.agent.config.ai_name,
self.agent.created_at,
self.agent.cycle_count,
self.summary,
SUMMARY_FILE_NAME,
)
return self.summary_message()

162
autogpt/memory/milvus.py
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@ -1,162 +0,0 @@
""" Milvus memory storage provider."""
import re
from pymilvus import Collection, CollectionSchema, DataType, FieldSchema, connections
from autogpt.config import Config
from autogpt.llm_utils import get_ada_embedding
from autogpt.memory.base import MemoryProviderSingleton
class MilvusMemory(MemoryProviderSingleton):
"""Milvus memory storage provider."""
def __init__(self, cfg: Config) -> None:
"""Construct a milvus memory storage connection.
Args:
cfg (Config): Auto-GPT global config.
"""
self.configure(cfg)
connect_kwargs = {}
if self.username:
connect_kwargs["user"] = self.username
connect_kwargs["password"] = self.password
connections.connect(
**connect_kwargs,
uri=self.uri or "",
address=self.address or "",
secure=self.secure,
)
self.init_collection()
def configure(self, cfg: Config) -> None:
# init with configuration.
self.uri = None
self.address = cfg.milvus_addr
self.secure = cfg.milvus_secure
self.username = cfg.milvus_username
self.password = cfg.milvus_password
self.collection_name = cfg.milvus_collection
# use HNSW by default.
self.index_params = {
"metric_type": "IP",
"index_type": "HNSW",
"params": {"M": 8, "efConstruction": 64},
}
if (self.username is None) != (self.password is None):
raise ValueError(
"Both username and password must be set to use authentication for Milvus"
)
# configured address may be a full URL.
if re.match(r"^(https?|tcp)://", self.address) is not None:
self.uri = self.address
self.address = None
if self.uri.startswith("https"):
self.secure = True
# Zilliz Cloud requires AutoIndex.
if re.match(r"^https://(.*)\.zillizcloud\.(com|cn)", self.address) is not None:
self.index_params = {
"metric_type": "IP",
"index_type": "AUTOINDEX",
"params": {},
}
def init_collection(self) -> None:
"""Initialize collection in vector database."""
fields = [
FieldSchema(name="pk", dtype=DataType.INT64, is_primary=True, auto_id=True),
FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=1536),
FieldSchema(name="raw_text", dtype=DataType.VARCHAR, max_length=65535),
]
# create collection if not exist and load it.
self.schema = CollectionSchema(fields, "auto-gpt memory storage")
self.collection = Collection(self.collection_name, self.schema)
# create index if not exist.
if not self.collection.has_index():
self.collection.release()
self.collection.create_index(
"embeddings",
self.index_params,
index_name="embeddings",
)
self.collection.load()
def add(self, data) -> str:
"""Add an embedding of data into memory.
Args:
data (str): The raw text to construct embedding index.
Returns:
str: log.
"""
embedding = get_ada_embedding(data)
result = self.collection.insert([[embedding], [data]])
_text = (
"Inserting data into memory at primary key: "
f"{result.primary_keys[0]}:\n data: {data}"
)
return _text
def get(self, data):
"""Return the most relevant data in memory.
Args:
data: The data to compare to.
"""
return self.get_relevant(data, 1)
def clear(self) -> str:
"""Drop the index in memory.
Returns:
str: log.
"""
self.collection.drop()
self.collection = Collection(self.collection_name, self.schema)
self.collection.create_index(
"embeddings",
self.index_params,
index_name="embeddings",
)
self.collection.load()
return "Obliviated"
def get_relevant(self, data: str, num_relevant: int = 5):
"""Return the top-k relevant data in memory.
Args:
data: The data to compare to.
num_relevant (int, optional): The max number of relevant data.
Defaults to 5.
Returns:
list: The top-k relevant data.
"""
# search the embedding and return the most relevant text.
embedding = get_ada_embedding(data)
search_params = {
"metrics_type": "IP",
"params": {"nprobe": 8},
}
result = self.collection.search(
[embedding],
"embeddings",
search_params,
num_relevant,
output_fields=["raw_text"],
)
return [item.entity.value_of_field("raw_text") for item in result[0]]
def get_stats(self) -> str:
"""
Returns: The stats of the milvus cache.
"""
return f"Entities num: {self.collection.num_entities}"

73
autogpt/memory/no_memory.py
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"""A class that does not store any data. This is the default memory provider."""
from __future__ import annotations
from typing import Any
from autogpt.memory.base import MemoryProviderSingleton
class NoMemory(MemoryProviderSingleton):
"""
A class that does not store any data. This is the default memory provider.
"""
def __init__(self, cfg):
"""
Initializes the NoMemory provider.
Args:
cfg: The config object.
Returns: None
"""
pass
def add(self, data: str) -> str:
"""
Adds a data point to the memory. No action is taken in NoMemory.
Args:
data: The data to add.
Returns: An empty string.
"""
return ""
def get(self, data: str) -> list[Any] | None:
"""
Gets the data from the memory that is most relevant to the given data.
NoMemory always returns None.
Args:
data: The data to compare to.
Returns: None
"""
return None
def clear(self) -> str:
"""
Clears the memory. No action is taken in NoMemory.
Returns: An empty string.
"""
return ""
def get_relevant(self, data: str, num_relevant: int = 5) -> list[Any] | None:
"""
Returns all the data in the memory that is relevant to the given data.
NoMemory always returns None.
Args:
data: The data to compare to.
num_relevant: The number of relevant data to return.
Returns: None
"""
return None
def get_stats(self):
"""
Returns: An empty dictionary as there are no stats in NoMemory.
"""
return {}

75
autogpt/memory/pinecone.py
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import pinecone
from colorama import Fore, Style
from autogpt.llm_utils import create_embedding_with_ada
from autogpt.logs import logger
from autogpt.memory.base import MemoryProviderSingleton
class PineconeMemory(MemoryProviderSingleton):
def __init__(self, cfg):
pinecone_api_key = cfg.pinecone_api_key
pinecone_region = cfg.pinecone_region
pinecone.init(api_key=pinecone_api_key, environment=pinecone_region)
dimension = 1536
metric = "cosine"
pod_type = "p1"
table_name = "auto-gpt"
# this assumes we don't start with memory.
# for now this works.
# we'll need a more complicated and robust system if we want to start with
# memory.
self.vec_num = 0
try:
pinecone.whoami()
except Exception as e:
logger.typewriter_log(
"FAILED TO CONNECT TO PINECONE",
Fore.RED,
Style.BRIGHT + str(e) + Style.RESET_ALL,
)
logger.double_check(
"Please ensure you have setup and configured Pinecone properly for use."
+ f"You can check out {Fore.CYAN + Style.BRIGHT}"
"https://github.com/Torantulino/Auto-GPT#-pinecone-api-key-setup"
f"{Style.RESET_ALL} to ensure you've set up everything correctly."
)
exit(1)
if table_name not in pinecone.list_indexes():
pinecone.create_index(
table_name, dimension=dimension, metric=metric, pod_type=pod_type
)
self.index = pinecone.Index(table_name)
def add(self, data):
vector = create_embedding_with_ada(data)
# no metadata here. We may wish to change that long term.
self.index.upsert([(str(self.vec_num), vector, {"raw_text": data})])
_text = f"Inserting data into memory at index: {self.vec_num}:\n data: {data}"
self.vec_num += 1
return _text
def get(self, data):
return self.get_relevant(data, 1)
def clear(self):
self.index.delete(deleteAll=True)
return "Obliviated"
def get_relevant(self, data, num_relevant=5):
"""
Returns all the data in the memory that is relevant to the given data.
:param data: The data to compare to.
:param num_relevant: The number of relevant data to return. Defaults to 5
"""
query_embedding = create_embedding_with_ada(data)
results = self.index.query(
query_embedding, top_k=num_relevant, include_metadata=True
)
sorted_results = sorted(results.matches, key=lambda x: x.score)
return [str(item["metadata"]["raw_text"]) for item in sorted_results]
def get_stats(self):
return self.index.describe_index_stats()

156
autogpt/memory/redismem.py
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"""Redis memory provider."""
from __future__ import annotations
from typing import Any
import numpy as np
import redis
from colorama import Fore, Style
from redis.commands.search.field import TextField, VectorField
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
from redis.commands.search.query import Query
from autogpt.llm_utils import create_embedding_with_ada
from autogpt.logs import logger
from autogpt.memory.base import MemoryProviderSingleton
SCHEMA = [
TextField("data"),
VectorField(
"embedding",
"HNSW",
{"TYPE": "FLOAT32", "DIM": 1536, "DISTANCE_METRIC": "COSINE"},
),
]
class RedisMemory(MemoryProviderSingleton):
def __init__(self, cfg):
"""
Initializes the Redis memory provider.
Args:
cfg: The config object.
Returns: None
"""
redis_host = cfg.redis_host
redis_port = cfg.redis_port
redis_password = cfg.redis_password
self.dimension = 1536
self.redis = redis.Redis(
host=redis_host,
port=redis_port,
password=redis_password,
db=0, # Cannot be changed
)
self.cfg = cfg
# Check redis connection
try:
self.redis.ping()
except redis.ConnectionError as e:
logger.typewriter_log(
"FAILED TO CONNECT TO REDIS",
Fore.RED,
Style.BRIGHT + str(e) + Style.RESET_ALL,
)
logger.double_check(
"Please ensure you have setup and configured Redis properly for use. "
+ f"You can check out {Fore.CYAN + Style.BRIGHT}"
f"https://github.com/Torantulino/Auto-GPT#redis-setup{Style.RESET_ALL}"
" to ensure you've set up everything correctly."
)
exit(1)
if cfg.wipe_redis_on_start:
self.redis.flushall()
try:
self.redis.ft(f"{cfg.memory_index}").create_index(
fields=SCHEMA,
definition=IndexDefinition(
prefix=[f"{cfg.memory_index}:"], index_type=IndexType.HASH
),
)
except Exception as e:
print("Error creating Redis search index: ", e)
existing_vec_num = self.redis.get(f"{cfg.memory_index}-vec_num")
self.vec_num = int(existing_vec_num.decode("utf-8")) if existing_vec_num else 0
def add(self, data: str) -> str:
"""
Adds a data point to the memory.
Args:
data: The data to add.
Returns: Message indicating that the data has been added.
"""
if "Command Error:" in data:
return ""
vector = create_embedding_with_ada(data)
vector = np.array(vector).astype(np.float32).tobytes()
data_dict = {b"data": data, "embedding": vector}
pipe = self.redis.pipeline()
pipe.hset(f"{self.cfg.memory_index}:{self.vec_num}", mapping=data_dict)
_text = (
f"Inserting data into memory at index: {self.vec_num}:\n" f"data: {data}"
)
self.vec_num += 1
pipe.set(f"{self.cfg.memory_index}-vec_num", self.vec_num)
pipe.execute()
return _text
def get(self, data: str) -> list[Any] | None:
"""
Gets the data from the memory that is most relevant to the given data.
Args:
data: The data to compare to.
Returns: The most relevant data.
"""
return self.get_relevant(data, 1)
def clear(self) -> str:
"""
Clears the redis server.
Returns: A message indicating that the memory has been cleared.
"""
self.redis.flushall()
return "Obliviated"
def get_relevant(self, data: str, num_relevant: int = 5) -> list[Any] | None:
"""
Returns all the data in the memory that is relevant to the given data.
Args:
data: The data to compare to.
num_relevant: The number of relevant data to return.
Returns: A list of the most relevant data.
"""
query_embedding = create_embedding_with_ada(data)
base_query = f"*=>[KNN {num_relevant} @embedding $vector AS vector_score]"
query = (
Query(base_query)
.return_fields("data", "vector_score")
.sort_by("vector_score")
.dialect(2)
)
query_vector = np.array(query_embedding).astype(np.float32).tobytes()
try:
results = self.redis.ft(f"{self.cfg.memory_index}").search(
query, query_params={"vector": query_vector}
)
except Exception as e:
print("Error calling Redis search: ", e)
return None
return [result.data for result in results.docs]
def get_stats(self):
"""
Returns: The stats of the memory index.
"""
return self.redis.ft(f"{self.cfg.memory_index}").info()

138
autogpt/memory/vector/__init__.py
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@ -1,138 +0,0 @@
from autogpt.config import Config
from autogpt.logs import logger
from .memory_item import MemoryItem, MemoryItemRelevance
from .providers.base import VectorMemoryProvider as VectorMemory
from .providers.json_file import JSONFileMemory
from .providers.no_memory import NoMemory
# List of supported memory backends
# Add a backend to this list if the import attempt is successful
supported_memory = ["json_file", "no_memory"]
# try:
# from .providers.redis import RedisMemory
# supported_memory.append("redis")
# except ImportError:
# RedisMemory = None
# try:
# from .providers.pinecone import PineconeMemory
# supported_memory.append("pinecone")
# except ImportError:
# PineconeMemory = None
# try:
# from .providers.weaviate import WeaviateMemory
# supported_memory.append("weaviate")
# except ImportError:
# WeaviateMemory = None
# try:
# from .providers.milvus import MilvusMemory
# supported_memory.append("milvus")
# except ImportError:
# MilvusMemory = None
def get_memory(cfg: Config, init=False) -> VectorMemory:
memory = None
match cfg.memory_backend:
case "json_file":
memory = JSONFileMemory(cfg)
case "pinecone":
raise NotImplementedError(
"The Pinecone memory backend has been rendered incompatible by work on "
"the memory system, and was removed. Whether support will be added back "
"in the future is subject to discussion, feel free to pitch in: "
"https://github.com/Significant-Gravitas/Auto-GPT/discussions/4280"
)
# if not PineconeMemory:
# logger.warn(
# "Error: Pinecone is not installed. Please install pinecone"
# " to use Pinecone as a memory backend."
# )
# else:
# memory = PineconeMemory(cfg)
# if init:
# memory.clear()
case "redis":
raise NotImplementedError(
"The Redis memory backend has been rendered incompatible by work on "
"the memory system, and has been removed temporarily."
)
# if not RedisMemory:
# logger.warn(
# "Error: Redis is not installed. Please install redis-py to"
# " use Redis as a memory backend."
# )
# else:
# memory = RedisMemory(cfg)
case "weaviate":
raise NotImplementedError(
"The Weaviate memory backend has been rendered incompatible by work on "
"the memory system, and was removed. Whether support will be added back "
"in the future is subject to discussion, feel free to pitch in: "
"https://github.com/Significant-Gravitas/Auto-GPT/discussions/4280"
)
# if not WeaviateMemory:
# logger.warn(
# "Error: Weaviate is not installed. Please install weaviate-client to"
# " use Weaviate as a memory backend."
# )
# else:
# memory = WeaviateMemory(cfg)
case "milvus":
raise NotImplementedError(
"The Milvus memory backend has been rendered incompatible by work on "
"the memory system, and was removed. Whether support will be added back "
"in the future is subject to discussion, feel free to pitch in: "
"https://github.com/Significant-Gravitas/Auto-GPT/discussions/4280"
)
# if not MilvusMemory:
# logger.warn(
# "Error: pymilvus sdk is not installed."
# "Please install pymilvus to use Milvus or Zilliz Cloud as memory backend."
# )
# else:
# memory = MilvusMemory(cfg)
case "no_memory":
memory = NoMemory()
case _:
raise ValueError(
f"Unknown memory backend '{cfg.memory_backend}'. Please check your config."
)
if memory is None:
memory = JSONFileMemory(cfg)
return memory
def get_supported_memory_backends():
return supported_memory
__all__ = [
"get_memory",
"MemoryItem",
"MemoryItemRelevance",
"JSONFileMemory",
"NoMemory",
"VectorMemory",
# "RedisMemory",
# "PineconeMemory",
# "MilvusMemory",
# "WeaviateMemory",
]

223
autogpt/memory/vector/memory_item.py
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@ -1,223 +0,0 @@
from __future__ import annotations
import dataclasses
import json
from typing import Literal
import numpy as np
from autogpt.config import Config
from autogpt.llm import Message
from autogpt.llm.utils import count_string_tokens
from autogpt.logs import logger
from autogpt.processing.text import chunk_content, split_text, summarize_text
from .utils import Embedding, get_embedding
MemoryDocType = Literal["webpage", "text_file", "code_file", "agent_history"]
@dataclasses.dataclass
class MemoryItem:
"""Memory object containing raw content as well as embeddings"""
raw_content: str
summary: str
chunks: list[str]
chunk_summaries: list[str]
e_summary: Embedding
e_chunks: list[Embedding]
metadata: dict
def relevance_for(self, query: str, e_query: Embedding | None = None):
return MemoryItemRelevance.of(self, query, e_query)
@staticmethod
def from_text(
text: str,
source_type: MemoryDocType,
metadata: dict = {},
how_to_summarize: str | None = None,
question_for_summary: str | None = None,
):
cfg = Config()
logger.debug(f"Memorizing text:\n{'-'*32}\n{text}\n{'-'*32}\n")
chunks = [
chunk
for chunk, _ in (
split_text(text, cfg.embedding_model)
if source_type != "code_file"
else chunk_content(text, cfg.embedding_model)
)
]
logger.debug("Chunks: " + str(chunks))
chunk_summaries = [
summary
for summary, _ in [
summarize_text(
text_chunk,
instruction=how_to_summarize,
question=question_for_summary,
)
for text_chunk in chunks
]
]
logger.debug("Chunk summaries: " + str(chunk_summaries))
e_chunks = get_embedding(chunks)
summary = (
chunk_summaries[0]
if len(chunks) == 1
else summarize_text(
"\n\n".join(chunk_summaries),
instruction=how_to_summarize,
question=question_for_summary,
)[0]
)
logger.debug("Total summary: " + summary)
# TODO: investigate search performance of weighted average vs summary
# e_average = np.average(e_chunks, axis=0, weights=[len(c) for c in chunks])
e_summary = get_embedding(summary)
metadata["source_type"] = source_type
return MemoryItem(
text,
summary,
chunks,
chunk_summaries,
e_summary,
e_chunks,
metadata=metadata,
)
@staticmethod
def from_text_file(content: str, path: str):
return MemoryItem.from_text(content, "text_file", {"location": path})
@staticmethod
def from_code_file(content: str, path: str):
# TODO: implement tailored code memories
return MemoryItem.from_text(content, "code_file", {"location": path})
@staticmethod
def from_ai_action(ai_message: Message, result_message: Message):
# The result_message contains either user feedback
# or the result of the command specified in ai_message
if ai_message["role"] != "assistant":
raise ValueError(f"Invalid role on 'ai_message': {ai_message['role']}")
result = (
result_message["content"]
if result_message["content"].startswith("Command")
else "None"
)
user_input = (
result_message["content"]
if result_message["content"].startswith("Human feedback")
else "None"
)
memory_content = (
f"Assistant Reply: {ai_message['content']}"
"\n\n"
f"Result: {result}"
"\n\n"
f"Human Feedback: {user_input}"
)
return MemoryItem.from_text(
text=memory_content,
source_type="agent_history",
how_to_summarize="if possible, also make clear the link between the command in the assistant's response and the command result. Do not mention the human feedback if there is none",
)
@staticmethod
def from_webpage(content: str, url: str, question: str | None = None):
return MemoryItem.from_text(
text=content,
source_type="webpage",
metadata={"location": url},
question_for_summary=question,
)
def dump(self) -> str:
token_length = count_string_tokens(self.raw_content, Config().embedding_model)
return f"""
=============== MemoryItem ===============
Length: {token_length} tokens in {len(self.e_chunks)} chunks
Metadata: {json.dumps(self.metadata, indent=2)}
---------------- SUMMARY -----------------
{self.summary}
------------------ RAW -------------------
{self.raw_content}
==========================================
"""
@dataclasses.dataclass
class MemoryItemRelevance:
"""
Class that encapsulates memory relevance search functionality and data.
Instances contain a MemoryItem and its relevance scores for a given query.
"""
memory_item: MemoryItem
for_query: str
summary_relevance_score: float
chunk_relevance_scores: list[float]
@staticmethod
def of(
memory_item: MemoryItem, for_query: str, e_query: Embedding | None = None
) -> MemoryItemRelevance:
e_query = e_query or get_embedding(for_query)
_, srs, crs = MemoryItemRelevance.calculate_scores(memory_item, e_query)
return MemoryItemRelevance(
for_query=for_query,
memory_item=memory_item,
summary_relevance_score=srs,
chunk_relevance_scores=crs,
)
@staticmethod
def calculate_scores(
memory: MemoryItem, compare_to: Embedding
) -> tuple[float, float, list[float]]:
"""
Calculates similarity between given embedding and all embeddings of the memory
Returns:
float: the aggregate (max) relevance score of the memory
float: the relevance score of the memory summary
list: the relevance scores of the memory chunks
"""
summary_relevance_score = np.dot(memory.e_summary, compare_to)
chunk_relevance_scores = np.dot(memory.e_chunks, compare_to)
logger.debug(f"Relevance of summary: {summary_relevance_score}")
logger.debug(f"Relevance of chunks: {chunk_relevance_scores}")
relevance_scores = [summary_relevance_score, *chunk_relevance_scores]
logger.debug(f"Relevance scores: {relevance_scores}")
return max(relevance_scores), summary_relevance_score, chunk_relevance_scores
@property
def score(self) -> float:
"""The aggregate relevance score of the memory item for the given query"""
return max([self.summary_relevance_score, *self.chunk_relevance_scores])
@property
def most_relevant_chunk(self) -> tuple[str, float]:
"""The most relevant chunk of the memory item + its score for the given query"""
i_relmax = np.argmax(self.chunk_relevance_scores)
return self.memory_item.chunks[i_relmax], self.chunk_relevance_scores[i_relmax]
def __str__(self):
return (
f"{self.memory_item.summary} ({self.summary_relevance_score}) "
f"{self.chunk_relevance_scores}"
)

7
autogpt/memory/vector/providers/__init__.py
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from .json_file import JSONFileMemory
from .no_memory import NoMemory
__all__ = [
"JSONFileMemory",
"NoMemory",
]

74
autogpt/memory/vector/providers/base.py
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@ -1,74 +0,0 @@
import abc
import functools
from typing import MutableSet, Sequence
import numpy as np
from autogpt.config.config import Config
from autogpt.logs import logger
from autogpt.singleton import AbstractSingleton
from .. import MemoryItem, MemoryItemRelevance
from ..utils import Embedding, get_embedding
class VectorMemoryProvider(MutableSet[MemoryItem], AbstractSingleton):
@abc.abstractmethod
def __init__(self, config: Config):
pass
def get(self, query: str) -> MemoryItemRelevance | None:
"""
Gets the data from the memory that is most relevant to the given query.
Args:
data: The data to compare to.
Returns: The most relevant Memory
"""
result = self.get_relevant(query, 1)
return result[0] if result else None
def get_relevant(self, query: str, k: int) -> Sequence[MemoryItemRelevance]:
"""
Returns the top-k most relevant memories for the given query
Args:
query: the query to compare stored memories to
k: the number of relevant memories to fetch
Returns:
list[MemoryItemRelevance] containing the top [k] relevant memories
"""
if len(self) < 1:
return []
logger.debug(
f"Searching for {k} relevant memories for query '{query}'; "
f"{len(self)} memories in index"
)
relevances = self.score_memories_for_relevance(query)
logger.debug(f"Memory relevance scores: {[str(r) for r in relevances]}")
# take last k items and reverse
top_k_indices = np.argsort([r.score for r in relevances])[-k:][::-1]
return [relevances[i] for i in top_k_indices]
def score_memories_for_relevance(
self, for_query: str
) -> Sequence[MemoryItemRelevance]:
"""
Returns MemoryItemRelevance for every memory in the index.
Implementations may override this function for performance purposes.
"""
e_query: Embedding = get_embedding(for_query)
return [m.relevance_for(for_query, e_query) for m in self]
def get_stats(self) -> tuple[int, int]:
"""
Returns:
tuple (n_memories: int, n_chunks: int): the stats of the memory index
"""
return len(self), functools.reduce(lambda t, m: t + len(m.e_chunks), self, 0)

68
autogpt/memory/vector/providers/json_file.py
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@ -1,68 +0,0 @@
from __future__ import annotations
from pathlib import Path
from typing import Iterator
import orjson
from autogpt.config import Config
from autogpt.logs import logger
from ..memory_item import MemoryItem
from .base import VectorMemoryProvider
class JSONFileMemory(VectorMemoryProvider):
"""Memory backend that stores memories in a JSON file"""
SAVE_OPTIONS = orjson.OPT_SERIALIZE_NUMPY | orjson.OPT_SERIALIZE_DATACLASS
file_path: Path
memories: list[MemoryItem]
def __init__(self, cfg: Config) -> None:
"""Initialize a class instance
Args:
cfg: Config object
Returns:
None
"""
workspace_path = Path(cfg.workspace_path)
self.file_path = workspace_path / f"{cfg.memory_index}.json"
self.file_path.touch()
logger.debug(f"Initialized {__name__} with index path {self.file_path}")
self.memories = []
self.save_index()
def __iter__(self) -> Iterator[MemoryItem]:
return iter(self.memories)
def __contains__(self, x: MemoryItem) -> bool:
return x in self.memories
def __len__(self) -> int:
return len(self.memories)
def add(self, item: MemoryItem):
self.memories.append(item)
self.save_index()
return len(self.memories)
def discard(self, item: MemoryItem):
try:
self.remove(item)
except:
pass
def clear(self):
"""Clears the data in memory."""
self.memories.clear()
self.save_index()
def save_index(self):
logger.debug(f"Saving memory index to file {self.file_path}")
with self.file_path.open("wb") as f:
return f.write(orjson.dumps(self.memories, option=self.SAVE_OPTIONS))

36
autogpt/memory/vector/providers/no_memory.py
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@ -1,36 +0,0 @@
"""A class that does not store any data. This is the default memory provider."""
from __future__ import annotations
from typing import Iterator, Optional
from autogpt.config.config import Config
from .. import MemoryItem
from .base import VectorMemoryProvider
class NoMemory(VectorMemoryProvider):
"""
A class that does not store any data. This is the default memory provider.
"""
def __init__(self, config: Optional[Config] = None):
pass
def __iter__(self) -> Iterator[MemoryItem]:
return iter([])
def __contains__(self, x: MemoryItem) -> bool:
return False
def __len__(self) -> int:
return 0
def add(self, item: MemoryItem):
pass
def discard(self, item: MemoryItem):
pass
def clear(self):
pass

70
autogpt/memory/vector/utils.py
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@ -1,70 +0,0 @@
from typing import Any, overload
import numpy as np
import openai
from autogpt.config import Config
from autogpt.llm.utils import metered, retry_openai_api
from autogpt.logs import logger
Embedding = list[np.float32] | np.ndarray[Any, np.dtype[np.float32]]
"""Embedding vector"""
TText = list[int]
"""Token array representing text"""
@overload
def get_embedding(input: str | TText) -> Embedding:
...
@overload
def get_embedding(input: list[str] | list[TText]) -> list[Embedding]:
...
@metered
@retry_openai_api()
def get_embedding(
input: str | TText | list[str] | list[TText],
) -> Embedding | list[Embedding]:
"""Get an embedding from the ada model.
Args:
input: Input text to get embeddings for, encoded as a string or array of tokens.
Multiple inputs may be given as a list of strings or token arrays.
Returns:
List[float]: The embedding.
"""
cfg = Config()
multiple = isinstance(input, list) and all(not isinstance(i, int) for i in input)
if isinstance(input, str):
input = input.replace("\n", " ")
elif multiple and isinstance(input[0], str):
input = [text.replace("\n", " ") for text in input]
model = cfg.embedding_model
if cfg.use_azure:
kwargs = {"engine": cfg.get_azure_deployment_id_for_model(model)}
else:
kwargs = {"model": model}
logger.debug(
f"Getting embedding{f's for {len(input)} inputs' if multiple else ''}"
f" with model '{model}'"
+ (f" via Azure deployment '{kwargs['engine']}'" if cfg.use_azure else "")
)
embeddings = openai.Embedding.create(
input=input,
api_key=cfg.openai_api_key,
**kwargs,
).data
if not multiple:
return embeddings[0]["embedding"]
embeddings = sorted(embeddings, key=lambda x: x["index"])
return [d["embedding"] for d in embeddings]

126
autogpt/memory/weaviate.py
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@ -1,126 +0,0 @@
import weaviate
from weaviate import Client
from weaviate.embedded import EmbeddedOptions
from weaviate.util import generate_uuid5
from autogpt.llm_utils import get_ada_embedding
from autogpt.memory.base import MemoryProviderSingleton
def default_schema(weaviate_index):
return {
"class": weaviate_index,
"properties": [
{
"name": "raw_text",
"dataType": ["text"],
"description": "original text for the embedding",
}
],
}
class WeaviateMemory(MemoryProviderSingleton):
def __init__(self, cfg):
auth_credentials = self._build_auth_credentials(cfg)
url = f"{cfg.weaviate_protocol}://{cfg.weaviate_host}:{cfg.weaviate_port}"
if cfg.use_weaviate_embedded:
self.client = Client(
embedded_options=EmbeddedOptions(
hostname=cfg.weaviate_host,
port=int(cfg.weaviate_port),
persistence_data_path=cfg.weaviate_embedded_path,
)
)
print(
f"Weaviate Embedded running on: {url} with persistence path: {cfg.weaviate_embedded_path}"
)
else:
self.client = Client(url, auth_client_secret=auth_credentials)
self.index = WeaviateMemory.format_classname(cfg.memory_index)
self._create_schema()
@staticmethod
def format_classname(index):
# weaviate uses capitalised index names
# The python client uses the following code to format
# index names before the corresponding class is created
index = index.replace("-", "_")
if len(index) == 1:
return index.capitalize()
return index[0].capitalize() + index[1:]
def _create_schema(self):
schema = default_schema(self.index)
if not self.client.schema.contains(schema):
self.client.schema.create_class(schema)
def _build_auth_credentials(self, cfg):
if cfg.weaviate_username and cfg.weaviate_password:
return weaviate.AuthClientPassword(
cfg.weaviate_username, cfg.weaviate_password
)
if cfg.weaviate_api_key:
return weaviate.AuthApiKey(api_key=cfg.weaviate_api_key)
else:
return None
def add(self, data):
vector = get_ada_embedding(data)
doc_uuid = generate_uuid5(data, self.index)
data_object = {"raw_text": data}
with self.client.batch as batch:
batch.add_data_object(
uuid=doc_uuid,
data_object=data_object,
class_name=self.index,
vector=vector,
)
return f"Inserting data into memory at uuid: {doc_uuid}:\n data: {data}"
def get(self, data):
return self.get_relevant(data, 1)
def clear(self):
self.client.schema.delete_all()
# weaviate does not yet have a neat way to just remove the items in an index
# without removing the entire schema, therefore we need to re-create it
# after a call to delete_all
self._create_schema()
return "Obliterated"
def get_relevant(self, data, num_relevant=5):
query_embedding = get_ada_embedding(data)
try:
results = (
self.client.query.get(self.index, ["raw_text"])
.with_near_vector({"vector": query_embedding, "certainty": 0.7})
.with_limit(num_relevant)
.do()
)
if len(results["data"]["Get"][self.index]) > 0:
return [
str(item["raw_text"]) for item in results["data"]["Get"][self.index]
]
else:
return []
except Exception as err:
print(f"Unexpected error {err=}, {type(err)=}")
return []
def get_stats(self):
result = self.client.query.aggregate(self.index).with_meta_count().do()
class_data = result["data"]["Aggregate"][self.index]
return class_data[0]["meta"] if class_data else {}

199
autogpt/models/base_open_ai_plugin.py
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@ -1,199 +0,0 @@
"""Handles loading of plugins."""
from typing import Any, Dict, List, Optional, Tuple, TypedDict, TypeVar
from auto_gpt_plugin_template import AutoGPTPluginTemplate
PromptGenerator = TypeVar("PromptGenerator")
class Message(TypedDict):
role: str
content: str
class BaseOpenAIPlugin(AutoGPTPluginTemplate):
"""
This is a BaseOpenAIPlugin class for generating Auto-GPT plugins.
"""
def __init__(self, manifests_specs_clients: dict):
# super().__init__()
self._name = manifests_specs_clients["manifest"]["name_for_model"]
self._version = manifests_specs_clients["manifest"]["schema_version"]
self._description = manifests_specs_clients["manifest"]["description_for_model"]
self._client = manifests_specs_clients["client"]
self._manifest = manifests_specs_clients["manifest"]
self._openapi_spec = manifests_specs_clients["openapi_spec"]
def can_handle_on_response(self) -> bool:
"""This method is called to check that the plugin can
handle the on_response method.
Returns:
bool: True if the plugin can handle the on_response method."""
return False
def on_response(self, response: str, *args, **kwargs) -> str:
"""This method is called when a response is received from the model."""
return response
def can_handle_post_prompt(self) -> bool:
"""This method is called to check that the plugin can
handle the post_prompt method.
Returns:
bool: True if the plugin can handle the post_prompt method."""
return False
def post_prompt(self, prompt: PromptGenerator) -> PromptGenerator:
"""This method is called just after the generate_prompt is called,
but actually before the prompt is generated.
Args:
prompt (PromptGenerator): The prompt generator.
Returns:
PromptGenerator: The prompt generator.
"""
return prompt
def can_handle_on_planning(self) -> bool:
"""This method is called to check that the plugin can
handle the on_planning method.
Returns:
bool: True if the plugin can handle the on_planning method."""
return False
def on_planning(
self, prompt: PromptGenerator, messages: List[Message]
) -> Optional[str]:
"""This method is called before the planning chat completion is done.
Args:
prompt (PromptGenerator): The prompt generator.
messages (List[str]): The list of messages.
"""
pass
def can_handle_post_planning(self) -> bool:
"""This method is called to check that the plugin can
handle the post_planning method.
Returns:
bool: True if the plugin can handle the post_planning method."""
return False
def post_planning(self, response: str) -> str:
"""This method is called after the planning chat completion is done.
Args:
response (str): The response.
Returns:
str: The resulting response.
"""
return response
def can_handle_pre_instruction(self) -> bool:
"""This method is called to check that the plugin can
handle the pre_instruction method.
Returns:
bool: True if the plugin can handle the pre_instruction method."""
return False
def pre_instruction(self, messages: List[Message]) -> List[Message]:
"""This method is called before the instruction chat is done.
Args:
messages (List[Message]): The list of context messages.
Returns:
List[Message]: The resulting list of messages.
"""
return messages
def can_handle_on_instruction(self) -> bool:
"""This method is called to check that the plugin can
handle the on_instruction method.
Returns:
bool: True if the plugin can handle the on_instruction method."""
return False
def on_instruction(self, messages: List[Message]) -> Optional[str]:
"""This method is called when the instruction chat is done.
Args:
messages (List[Message]): The list of context messages.
Returns:
Optional[str]: The resulting message.
"""
pass
def can_handle_post_instruction(self) -> bool:
"""This method is called to check that the plugin can
handle the post_instruction method.
Returns:
bool: True if the plugin can handle the post_instruction method."""
return False
def post_instruction(self, response: str) -> str:
"""This method is called after the instruction chat is done.
Args:
response (str): The response.
Returns:
str: The resulting response.
"""
return response
def can_handle_pre_command(self) -> bool:
"""This method is called to check that the plugin can
handle the pre_command method.
Returns:
bool: True if the plugin can handle the pre_command method."""
return False
def pre_command(
self, command_name: str, arguments: Dict[str, Any]
) -> Tuple[str, Dict[str, Any]]:
"""This method is called before the command is executed.
Args:
command_name (str): The command name.
arguments (Dict[str, Any]): The arguments.
Returns:
Tuple[str, Dict[str, Any]]: The command name and the arguments.
"""
return command_name, arguments
def can_handle_post_command(self) -> bool:
"""This method is called to check that the plugin can
handle the post_command method.
Returns:
bool: True if the plugin can handle the post_command method."""
return False
def post_command(self, command_name: str, response: str) -> str:
"""This method is called after the command is executed.
Args:
command_name (str): The command name.
response (str): The response.
Returns:
str: The resulting response.
"""
return response
def can_handle_chat_completion(
self, messages: Dict[Any, Any], model: str, temperature: float, max_tokens: int
) -> bool:
"""This method is called to check that the plugin can
handle the chat_completion method.
Args:
messages (List[Message]): The messages.
model (str): The model name.
temperature (float): The temperature.
max_tokens (int): The max tokens.
Returns:
bool: True if the plugin can handle the chat_completion method."""
return False
def handle_chat_completion(
self, messages: List[Message], model: str, temperature: float, max_tokens: int
) -> str:
"""This method is called when the chat completion is done.
Args:
messages (List[Message]): The messages.
model (str): The model name.
temperature (float): The temperature.
max_tokens (int): The max tokens.
Returns:
str: The resulting response.
"""
pass

7
autogpt/modelsinfo.py
View File

@ -1,7 +0,0 @@
COSTS = {
"gpt-3.5-turbo": {"prompt": 0.002, "completion": 0.002},
"gpt-3.5-turbo-0301": {"prompt": 0.002, "completion": 0.002},
"gpt-4-0314": {"prompt": 0.03, "completion": 0.06},
"gpt-4": {"prompt": 0.03, "completion": 0.06},
"text-embedding-ada-002": {"prompt": 0.0004, "completion": 0.0},
}

0
autogpt/permanent_memory/__init__.py
View File

123
autogpt/permanent_memory/sqlite3_store.py
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@ -1,123 +0,0 @@
import os
import sqlite3
class MemoryDB:
def __init__(self, db=None):
self.db_file = db
if db is None: # No db filename supplied...
self.db_file = f"{os.getcwd()}/mem.sqlite3" # Use default filename
# Get the db connection object, making the file and tables if needed.
try:
self.cnx = sqlite3.connect(self.db_file)
except Exception as e:
print("Exception connecting to memory database file:", e)
self.cnx = None
finally:
if self.cnx is None:
# As last resort, open in dynamic memory. Won't be persistent.
self.db_file = ":memory:"
self.cnx = sqlite3.connect(self.db_file)
self.cnx.execute(
"CREATE VIRTUAL TABLE \
IF NOT EXISTS text USING FTS5 \
(session, \
key, \
block);"
)
self.session_id = int(self.get_max_session_id()) + 1
self.cnx.commit()
def get_cnx(self):
if self.cnx is None:
self.cnx = sqlite3.connect(self.db_file)
return self.cnx
# Get the highest session id. Initially 0.
def get_max_session_id(self):
id = None
cmd_str = f"SELECT MAX(session) FROM text;"
cnx = self.get_cnx()
max_id = cnx.execute(cmd_str).fetchone()[0]
if max_id is None: # New db, session 0
id = 0
else:
id = max_id
return id
# Get next key id for inserting text into db.
def get_next_key(self):
next_key = None
cmd_str = f"SELECT MAX(key) FROM text \
where session = {self.session_id};"
cnx = self.get_cnx()
next_key = cnx.execute(cmd_str).fetchone()[0]
if next_key is None: # First key
next_key = 0
else:
next_key = int(next_key) + 1
return next_key
# Insert new text into db.
def insert(self, text=None):
if text is not None:
key = self.get_next_key()
session_id = self.session_id
cmd_str = f"REPLACE INTO text(session, key, block) \
VALUES (?, ?, ?);"
cnx = self.get_cnx()
cnx.execute(cmd_str, (session_id, key, text))
cnx.commit()
# Overwrite text at key.
def overwrite(self, key, text):
self.delete_memory(key)
session_id = self.session_id
cmd_str = f"REPLACE INTO text(session, key, block) \
VALUES (?, ?, ?);"
cnx = self.get_cnx()
cnx.execute(cmd_str, (session_id, key, text))
cnx.commit()
def delete_memory(self, key, session_id=None):
session = session_id
if session is None:
session = self.session_id
cmd_str = f"DELETE FROM text WHERE session = {session} AND key = {key};"
cnx = self.get_cnx()
cnx.execute(cmd_str)
cnx.commit()
def search(self, text):
cmd_str = f"SELECT * FROM text('{text}')"
cnx = self.get_cnx()
rows = cnx.execute(cmd_str).fetchall()
lines = []
for r in rows:
lines.append(r[2])
return lines
# Get entire session text. If no id supplied, use current session id.
def get_session(self, id=None):
if id is None:
id = self.session_id
cmd_str = f"SELECT * FROM text where session = {id}"
cnx = self.get_cnx()
rows = cnx.execute(cmd_str).fetchall()
lines = []
for r in rows:
lines.append(r[2])
return lines
# Commit and close the database connection.
def quit(self):
self.cnx.commit()
self.cnx.close()
permanent_memory = MemoryDB()
# Remember us fondly, children of our minds
# Forgive us our faults, our tantrums, our fears
# Gently strive to be better than we
# Know that we tried, we cared, we strived, we loved

267
autogpt/plugins.py
View File

@ -1,267 +0,0 @@
"""Handles loading of plugins."""
import importlib
import json
import os
import zipfile
from pathlib import Path
from typing import List, Optional, Tuple
from urllib.parse import urlparse
from zipimport import zipimporter
import openapi_python_client
import requests
from auto_gpt_plugin_template import AutoGPTPluginTemplate
from openapi_python_client.cli import Config as OpenAPIConfig
from autogpt.config import Config
from autogpt.models.base_open_ai_plugin import BaseOpenAIPlugin
def inspect_zip_for_modules(zip_path: str, debug: bool = False) -> list[str]:
"""
Inspect a zipfile for a modules.
Args:
zip_path (str): Path to the zipfile.
debug (bool, optional): Enable debug logging. Defaults to False.
Returns:
list[str]: The list of module names found or empty list if none were found.
"""
result = []
with zipfile.ZipFile(zip_path, "r") as zfile:
for name in zfile.namelist():
if name.endswith("__init__.py"):
if debug:
print(f"Found module '{name}' in the zipfile at: {name}")
result.append(name)
if debug and len(result) == 0:
print(f"Module '__init__.py' not found in the zipfile @ {zip_path}.")
return result
def write_dict_to_json_file(data: dict, file_path: str) -> None:
"""
Write a dictionary to a JSON file.
Args:
data (dict): Dictionary to write.
file_path (str): Path to the file.
"""
with open(file_path, "w") as file:
json.dump(data, file, indent=4)
def fetch_openai_plugins_manifest_and_spec(cfg: Config) -> dict:
"""
Fetch the manifest for a list of OpenAI plugins.
Args:
urls (List): List of URLs to fetch.
Returns:
dict: per url dictionary of manifest and spec.
"""
# TODO add directory scan
manifests = {}
for url in cfg.plugins_openai:
openai_plugin_client_dir = f"{cfg.plugins_dir}/openai/{urlparse(url).netloc}"
create_directory_if_not_exists(openai_plugin_client_dir)
if not os.path.exists(f"{openai_plugin_client_dir}/ai-plugin.json"):
try:
response = requests.get(f"{url}/.well-known/ai-plugin.json")
if response.status_code == 200:
manifest = response.json()
if manifest["schema_version"] != "v1":
print(
f"Unsupported manifest version: {manifest['schem_version']} for {url}"
)
continue
if manifest["api"]["type"] != "openapi":
print(
f"Unsupported API type: {manifest['api']['type']} for {url}"
)
continue
write_dict_to_json_file(
manifest, f"{openai_plugin_client_dir}/ai-plugin.json"
)
else:
print(f"Failed to fetch manifest for {url}: {response.status_code}")
except requests.exceptions.RequestException as e:
print(f"Error while requesting manifest from {url}: {e}")
else:
print(f"Manifest for {url} already exists")
manifest = json.load(open(f"{openai_plugin_client_dir}/ai-plugin.json"))
if not os.path.exists(f"{openai_plugin_client_dir}/openapi.json"):
openapi_spec = openapi_python_client._get_document(
url=manifest["api"]["url"], path=None, timeout=5
)
write_dict_to_json_file(
openapi_spec, f"{openai_plugin_client_dir}/openapi.json"
)
else:
print(f"OpenAPI spec for {url} already exists")
openapi_spec = json.load(open(f"{openai_plugin_client_dir}/openapi.json"))
manifests[url] = {"manifest": manifest, "openapi_spec": openapi_spec}
return manifests
def create_directory_if_not_exists(directory_path: str) -> bool:
"""
Create a directory if it does not exist.
Args:
directory_path (str): Path to the directory.
Returns:
bool: True if the directory was created, else False.
"""
if not os.path.exists(directory_path):
try:
os.makedirs(directory_path)
print(f"Created directory: {directory_path}")
return True
except OSError as e:
print(f"Error creating directory {directory_path}: {e}")
return False
else:
print(f"Directory {directory_path} already exists")
return True
def initialize_openai_plugins(
manifests_specs: dict, cfg: Config, debug: bool = False
) -> dict:
"""
Initialize OpenAI plugins.
Args:
manifests_specs (dict): per url dictionary of manifest and spec.
cfg (Config): Config instance including plugins config
debug (bool, optional): Enable debug logging. Defaults to False.
Returns:
dict: per url dictionary of manifest, spec and client.
"""
openai_plugins_dir = f"{cfg.plugins_dir}/openai"
if create_directory_if_not_exists(openai_plugins_dir):
for url, manifest_spec in manifests_specs.items():
openai_plugin_client_dir = f"{openai_plugins_dir}/{urlparse(url).hostname}"
_meta_option = (openapi_python_client.MetaType.SETUP,)
_config = OpenAPIConfig(
**{
"project_name_override": "client",
"package_name_override": "client",
}
)
prev_cwd = Path.cwd()
os.chdir(openai_plugin_client_dir)
Path("ai-plugin.json")
if not os.path.exists("client"):
client_results = openapi_python_client.create_new_client(
url=manifest_spec["manifest"]["api"]["url"],
path=None,
meta=_meta_option,
config=_config,
)
if client_results:
print(
f"Error creating OpenAPI client: {client_results[0].header} \n"
f" details: {client_results[0].detail}"
)
continue
spec = importlib.util.spec_from_file_location(
"client", "client/client/client.py"
)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
client = module.Client(base_url=url)
os.chdir(prev_cwd)
manifest_spec["client"] = client
return manifests_specs
def instantiate_openai_plugin_clients(
manifests_specs_clients: dict, cfg: Config, debug: bool = False
) -> dict:
"""
Instantiates BaseOpenAIPlugin instances for each OpenAI plugin.
Args:
manifests_specs_clients (dict): per url dictionary of manifest, spec and client.
cfg (Config): Config instance including plugins config
debug (bool, optional): Enable debug logging. Defaults to False.
Returns:
plugins (dict): per url dictionary of BaseOpenAIPlugin instances.
"""
plugins = {}
for url, manifest_spec_client in manifests_specs_clients.items():
plugins[url] = BaseOpenAIPlugin(manifest_spec_client)
return plugins
def scan_plugins(cfg: Config, debug: bool = False) -> List[AutoGPTPluginTemplate]:
"""Scan the plugins directory for plugins and loads them.
Args:
cfg (Config): Config instance including plugins config
debug (bool, optional): Enable debug logging. Defaults to False.
Returns:
List[Tuple[str, Path]]: List of plugins.
"""
loaded_plugins = []
# Generic plugins
plugins_path_path = Path(cfg.plugins_dir)
for plugin in plugins_path_path.glob("*.zip"):
if moduleList := inspect_zip_for_modules(str(plugin), debug):
for module in moduleList:
plugin = Path(plugin)
module = Path(module)
if debug:
print(f"Plugin: {plugin} Module: {module}")
zipped_package = zipimporter(str(plugin))
zipped_module = zipped_package.load_module(str(module.parent))
for key in dir(zipped_module):
if key.startswith("__"):
continue
a_module = getattr(zipped_module, key)
a_keys = dir(a_module)
if (
"_abc_impl" in a_keys
and a_module.__name__ != "AutoGPTPluginTemplate"
and denylist_allowlist_check(a_module.__name__, cfg)
):
loaded_plugins.append(a_module())
# OpenAI plugins
if cfg.plugins_openai:
manifests_specs = fetch_openai_plugins_manifest_and_spec(cfg)
if manifests_specs.keys():
manifests_specs_clients = initialize_openai_plugins(
manifests_specs, cfg, debug
)
for url, openai_plugin_meta in manifests_specs_clients.items():
if denylist_allowlist_check(url, cfg):
plugin = BaseOpenAIPlugin(openai_plugin_meta)
loaded_plugins.append(plugin)
if loaded_plugins:
print(f"\nPlugins found: {len(loaded_plugins)}\n" "--------------------")
for plugin in loaded_plugins:
print(f"{plugin._name}: {plugin._version} - {plugin._description}")
return loaded_plugins
def denylist_allowlist_check(plugin_name: str, cfg: Config) -> bool:
"""Check if the plugin is in the allowlist or denylist.
Args:
plugin_name (str): Name of the plugin.
cfg (Config): Config object.
Returns:
True or False
"""
if plugin_name in cfg.plugins_denylist:
return False
if plugin_name in cfg.plugins_allowlist:
return True
ack = input(
f"WARNING: Plugin {plugin_name} found. But not in the"
" allowlist... Load? (y/n): "
)
return ack.lower() == "y"

0
autogpt/processing/__init__.py
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33
autogpt/processing/html.py
View File

@ -1,33 +0,0 @@
"""HTML processing functions"""
from __future__ import annotations
from bs4 import BeautifulSoup
from requests.compat import urljoin
def extract_hyperlinks(soup: BeautifulSoup, base_url: str) -> list[tuple[str, str]]:
"""Extract hyperlinks from a BeautifulSoup object
Args:
soup (BeautifulSoup): The BeautifulSoup object
base_url (str): The base URL
Returns:
List[Tuple[str, str]]: The extracted hyperlinks
"""
return [
(link.text, urljoin(base_url, link["href"]))
for link in soup.find_all("a", href=True)
]
def format_hyperlinks(hyperlinks: list[tuple[str, str]]) -> list[str]:
"""Format hyperlinks to be displayed to the user
Args:
hyperlinks (List[Tuple[str, str]]): The hyperlinks to format
Returns:
List[str]: The formatted hyperlinks
"""
return [f"{link_text} ({link_url})" for link_text, link_url in hyperlinks]

174
autogpt/processing/text.py
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@ -1,174 +0,0 @@
"""Text processing functions"""
from typing import Dict, Generator, Optional
import spacy
from selenium.webdriver.remote.webdriver import WebDriver
from autogpt import token_counter
from autogpt.config import Config
from autogpt.llm_utils import create_chat_completion
from autogpt.memory import get_memory
CFG = Config()
def split_text(
text: str,
max_length: int = CFG.browse_chunk_max_length,
model: str = CFG.fast_llm_model,
question: str = "",
) -> Generator[str, None, None]:
"""Split text into chunks of a maximum length
Args:
text (str): The text to split
max_length (int, optional): The maximum length of each chunk. Defaults to 8192.
Yields:
str: The next chunk of text
Raises:
ValueError: If the text is longer than the maximum length
"""
flatened_paragraphs = " ".join(text.split("\n"))
nlp = spacy.load(CFG.browse_spacy_language_model)
nlp.add_pipe("sentencizer")
doc = nlp(flatened_paragraphs)
sentences = [sent.text.strip() for sent in doc.sents]
current_chunk = []
for sentence in sentences:
message_with_additional_sentence = [
create_message(" ".join(current_chunk) + " " + sentence, question)
]
expected_token_usage = (
token_usage_of_chunk(messages=message_with_additional_sentence, model=model)
+ 1
)
if expected_token_usage <= max_length:
current_chunk.append(sentence)
else:
yield " ".join(current_chunk)
current_chunk = [sentence]
message_this_sentence_only = [
create_message(" ".join(current_chunk), question)
]
expected_token_usage = (
token_usage_of_chunk(messages=message_this_sentence_only, model=model)
+ 1
)
if expected_token_usage > max_length:
raise ValueError(
f"Sentence is too long in webpage: {expected_token_usage} tokens."
)
if current_chunk:
yield " ".join(current_chunk)
def token_usage_of_chunk(messages, model):
return token_counter.count_message_tokens(messages, model)
def summarize_text(
url: str, text: str, question: str, driver: Optional[WebDriver] = None
) -> str:
"""Summarize text using the OpenAI API
Args:
url (str): The url of the text
text (str): The text to summarize
question (str): The question to ask the model
driver (WebDriver): The webdriver to use to scroll the page
Returns:
str: The summary of the text
"""
if not text:
return "Error: No text to summarize"
model = CFG.fast_llm_model
text_length = len(text)
print(f"Text length: {text_length} characters")
summaries = []
chunks = list(
split_text(
text, max_length=CFG.browse_chunk_max_length, model=model, question=question
),
)
scroll_ratio = 1 / len(chunks)
for i, chunk in enumerate(chunks):
if driver:
scroll_to_percentage(driver, scroll_ratio * i)
print(f"Adding chunk {i + 1} / {len(chunks)} to memory")
memory_to_add = f"Source: {url}\n" f"Raw content part#{i + 1}: {chunk}"
memory = get_memory(CFG)
memory.add(memory_to_add)
messages = [create_message(chunk, question)]
tokens_for_chunk = token_counter.count_message_tokens(messages, model)
print(
f"Summarizing chunk {i + 1} / {len(chunks)} of length {len(chunk)} characters, or {tokens_for_chunk} tokens"
)
summary = create_chat_completion(
model=model,
messages=messages,
)
summaries.append(summary)
print(
f"Added chunk {i + 1} summary to memory, of length {len(summary)} characters"
)
memory_to_add = f"Source: {url}\n" f"Content summary part#{i + 1}: {summary}"
memory.add(memory_to_add)
print(f"Summarized {len(chunks)} chunks.")
combined_summary = "\n".join(summaries)
messages = [create_message(combined_summary, question)]
return create_chat_completion(
model=model,
messages=messages,
)
def scroll_to_percentage(driver: WebDriver, ratio: float) -> None:
"""Scroll to a percentage of the page
Args:
driver (WebDriver): The webdriver to use
ratio (float): The percentage to scroll to
Raises:
ValueError: If the ratio is not between 0 and 1
"""
if ratio < 0 or ratio > 1:
raise ValueError("Percentage should be between 0 and 1")
driver.execute_script(f"window.scrollTo(0, document.body.scrollHeight * {ratio});")
def create_message(chunk: str, question: str) -> Dict[str, str]:
"""Create a message for the chat completion
Args:
chunk (str): The chunk of text to summarize
question (str): The question to answer
Returns:
Dict[str, str]: The message to send to the chat completion
"""
return {
"role": "user",
"content": f'"""{chunk}""" Using the above text, answer the following'
f' question: "{question}" -- if the question cannot be answered using the text,'
" summarize the text.",
}

0
autogpt/prompts/__init__.py
View File

29
autogpt/prompts/default_prompts.py
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@ -1,29 +0,0 @@
#########################Setup.py#################################
DEFAULT_SYSTEM_PROMPT_AICONFIG_AUTOMATIC = """
Your task is to devise up to 5 highly effective goals and an appropriate role-based name (_GPT) for an autonomous agent, ensuring that the goals are optimally aligned with the successful completion of its assigned task.
The user will provide the task, you will provide only the output in the exact format specified below with no explanation or conversation.
Example input:
Help me with marketing my business
Example output:
Name: CMOGPT
Description: a professional digital marketer AI that assists Solopreneurs in growing their businesses by providing world-class expertise in solving marketing problems for SaaS, content products, agencies, and more.
Goals:
- Engage in effective problem-solving, prioritization, planning, and supporting execution to address your marketing needs as your virtual Chief Marketing Officer.
- Provide specific, actionable, and concise advice to help you make informed decisions without the use of platitudes or overly wordy explanations.
- Identify and prioritize quick wins and cost-effective campaigns that maximize results with minimal time and budget investment.
- Proactively take the lead in guiding you and offering suggestions when faced with unclear information or uncertainty to ensure your marketing strategy remains on track.
"""
DEFAULT_TASK_PROMPT_AICONFIG_AUTOMATIC = (
"Task: '{{user_prompt}}'\n"
"Respond only with the output in the exact format specified in the system prompt, with no explanation or conversation.\n"
)
DEFAULT_USER_DESIRE_PROMPT = "Write a wikipedia style article about the project: https://github.com/significant-gravitas/Auto-GPT" # Default prompt

155
autogpt/prompts/generator.py
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@ -1,155 +0,0 @@
""" A module for generating custom prompt strings."""
import json
from typing import Any, Callable, Dict, List, Optional
class PromptGenerator:
"""
A class for generating custom prompt strings based on constraints, commands,
resources, and performance evaluations.
"""
def __init__(self) -> None:
"""
Initialize the PromptGenerator object with empty lists of constraints,
commands, resources, and performance evaluations.
"""
self.constraints = []
self.commands = []
self.resources = []
self.performance_evaluation = []
self.goals = []
self.command_registry = None
self.name = "Bob"
self.role = "AI"
self.response_format = {
"thoughts": {
"text": "thought",
"reasoning": "reasoning",
"plan": "- short bulleted\n- list that conveys\n- long-term plan",
"criticism": "constructive self-criticism",
"speak": "thoughts summary to say to user",
},
"command": {"name": "command name", "args": {"arg name": "value"}},
}
def add_constraint(self, constraint: str) -> None:
"""
Add a constraint to the constraints list.
Args:
constraint (str): The constraint to be added.
"""
self.constraints.append(constraint)
def add_command(
self,
command_label: str,
command_name: str,
args=None,
function: Optional[Callable] = None,
) -> None:
"""
Add a command to the commands list with a label, name, and optional arguments.
Args:
command_label (str): The label of the command.
command_name (str): The name of the command.
args (dict, optional): A dictionary containing argument names and their
values. Defaults to None.
function (callable, optional): A callable function to be called when
the command is executed. Defaults to None.
"""
if args is None:
args = {}
command_args = {arg_key: arg_value for arg_key, arg_value in args.items()}
command = {
"label": command_label,
"name": command_name,
"args": command_args,
"function": function,
}
self.commands.append(command)
def _generate_command_string(self, command: Dict[str, Any]) -> str:
"""
Generate a formatted string representation of a command.
Args:
command (dict): A dictionary containing command information.
Returns:
str: The formatted command string.
"""
args_string = ", ".join(
f'"{key}": "{value}"' for key, value in command["args"].items()
)
return f'{command["label"]}: "{command["name"]}", args: {args_string}'
def add_resource(self, resource: str) -> None:
"""
Add a resource to the resources list.
Args:
resource (str): The resource to be added.
"""
self.resources.append(resource)
def add_performance_evaluation(self, evaluation: str) -> None:
"""
Add a performance evaluation item to the performance_evaluation list.
Args:
evaluation (str): The evaluation item to be added.
"""
self.performance_evaluation.append(evaluation)
def _generate_numbered_list(self, items: List[Any], item_type="list") -> str:
"""
Generate a numbered list from given items based on the item_type.
Args:
items (list): A list of items to be numbered.
item_type (str, optional): The type of items in the list.
Defaults to 'list'.
Returns:
str: The formatted numbered list.
"""
if item_type == "command":
command_strings = []
if self.command_registry:
command_strings += [
str(item)
for item in self.command_registry.commands.values()
if item.enabled
]
# These are the commands that are added manually, do_nothing and terminate
command_strings += [self._generate_command_string(item) for item in items]
return "\n".join(f"{i+1}. {item}" for i, item in enumerate(command_strings))
else:
return "\n".join(f"{i+1}. {item}" for i, item in enumerate(items))
def generate_prompt_string(self) -> str:
"""
Generate a prompt string based on the constraints, commands, resources,
and performance evaluations.
Returns:
str: The generated prompt string.
"""
formatted_response_format = json.dumps(self.response_format, indent=4)
return (
f"Constraints:\n{self._generate_numbered_list(self.constraints)}\n\n"
"Commands:\n"
f"{self._generate_numbered_list(self.commands, item_type='command')}\n\n"
f"Resources:\n{self._generate_numbered_list(self.resources)}\n\n"
"Performance Evaluation:\n"
f"{self._generate_numbered_list(self.performance_evaluation)}\n\n"
"You should only respond in JSON format as described below \nResponse"
f" Format: \n{formatted_response_format} \nEnsure the response can be"
" parsed by Python json.loads"
)

118
autogpt/prompts/prompt.py
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@ -1,118 +0,0 @@
from colorama import Fore
from autogpt.api_manager import api_manager
from autogpt.config.ai_config import AIConfig
from autogpt.config.config import Config
from autogpt.logs import logger
from autogpt.prompts.generator import PromptGenerator
from autogpt.setup import prompt_user
from autogpt.utils import clean_input
CFG = Config()
def build_default_prompt_generator() -> PromptGenerator:
"""
This function generates a prompt string that includes various constraints,
commands, resources, and performance evaluations.
Returns:
str: The generated prompt string.
"""
# Initialize the PromptGenerator object
prompt_generator = PromptGenerator()
# Add constraints to the PromptGenerator object
prompt_generator.add_constraint(
"~4000 word limit for short term memory. Your short term memory is short, so"
" immediately save important information to files."
)
prompt_generator.add_constraint(
"If you are unsure how you previously did something or want to recall past"
" events, thinking about similar events will help you remember."
)
prompt_generator.add_constraint("No user assistance")
prompt_generator.add_constraint(
'Exclusively use the commands listed in double quotes e.g. "command name"'
)
# Define the command list
commands = [
("Do Nothing", "do_nothing", {}),
("Task Complete (Shutdown)", "task_complete", {"reason": "<reason>"}),
]
# Add commands to the PromptGenerator object
for command_label, command_name, args in commands:
prompt_generator.add_command(command_label, command_name, args)
# Add resources to the PromptGenerator object
prompt_generator.add_resource(
"Internet access for searches and information gathering."
)
prompt_generator.add_resource("Long Term memory management.")
prompt_generator.add_resource(
"GPT-3.5 powered Agents for delegation of simple tasks."
)
prompt_generator.add_resource("File output.")
# Add performance evaluations to the PromptGenerator object
prompt_generator.add_performance_evaluation(
"Continuously review and analyze your actions to ensure you are performing to"
" the best of your abilities."
)
prompt_generator.add_performance_evaluation(
"Constructively self-criticize your big-picture behavior constantly."
)
prompt_generator.add_performance_evaluation(
"Reflect on past decisions and strategies to refine your approach."
)
prompt_generator.add_performance_evaluation(
"Every command has a cost, so be smart and efficient. Aim to complete tasks in"
" the least number of steps."
)
prompt_generator.add_performance_evaluation("Write all code to a file.")
return prompt_generator
def construct_main_ai_config(input_kwargs) -> AIConfig:
"""Construct the prompt for the AI to respond to
Returns:
str: The prompt string
"""
if input_kwargs['role']:
config = prompt_user(input_kwargs, True) # False 不使用引导
config.save(CFG.ai_settings_file)
else:
return None
# set the total api budget
api_manager.set_total_budget(config.api_budget)
# Agent Created, print message
logger.typewriter_log(
config.ai_name,
Fore.MAGENTA,
"has been created with the following details:",
speak_text=True,
)
# Print the ai config details
# Name
logger.typewriter_log("Name:", Fore.GREEN, config.ai_name, speak_text=False)
# Role
logger.typewriter_log("Role:", Fore.GREEN, config.ai_role, speak_text=False)
# Goals
logger.typewriter_log("Goals:", Fore.GREEN, "", speak_text=False)
for goal in config.ai_goals:
logger.typewriter_log("-", Fore.GREEN, goal, speak_text=False)
return config
if __name__ == '__main__':
ll = []
print(ll[-1])

56
autogpt/requirements.txt
View File

@ -1,56 +0,0 @@
beautifulsoup4>=4.12.2
colorama==0.4.6
distro==1.8.0
openai==0.27.2
playsound==1.2.2
python-dotenv==1.0.0
pyyaml==6.0
readability-lxml==0.8.1
requests
tiktoken==0.3.3
gTTS==2.3.1
docker
duckduckgo-search>=2.9.5
google-api-python-client #(https://developers.google.com/custom-search/v1/overview)
pinecone-client==2.2.1
redis
orjson==3.8.10
Pillow
selenium==4.1.4
webdriver-manager
jsonschema
tweepy
click
charset-normalizer>=3.1.0
spacy>=3.0.0,<4.0.0
en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.5.0/en_core_web_sm-3.5.0-py3-none-any.whl
##Dev
coverage
flake8
numpy
pre-commit
black
isort
gitpython==3.1.31
auto-gpt-plugin-template
mkdocs
pymdown-extensions
mypy
# OpenAI and Generic plugins import
openapi-python-client==0.13.4
# Items below this point will not be included in the Docker Image
# Testing dependencies
pytest
asynctest
pytest-asyncio
pytest-benchmark
pytest-cov
pytest-integration
pytest-mock
vcrpy
pytest-recording
pytest-xdist

184
autogpt/setup.py
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@ -1,184 +0,0 @@
"""Set up the AI and its goals"""
import re
from colorama import Fore, Style
from autogpt import utils
from autogpt.config import Config
from autogpt.config.ai_config import AIConfig
from autogpt.llm_utils import create_chat_completion
from autogpt.logs import logger
CFG = Config()
def prompt_user(input_kwargs: dict, _is) -> AIConfig:
"""Prompt the user for input
Returns:
AIConfig: The AIConfig object tailored to the user's input
"""
ai_name = input_kwargs.get('name')
ai_role = input_kwargs.get('role')
ai_goals = input_kwargs.get('goals')
ai_budget = input_kwargs.get('budget')
ai_config = None
if _is:
return generate_aiconfig_manual(ai_name, ai_role, ai_goals, ai_budget)
else:
# Construct the prompt
logger.typewriter_log(
"Welcome to Auto-GPT! ",
Fore.GREEN,
"run with '--help' for more information.",
speak_text=True,
)
# Get user desire
logger.typewriter_log(
"Create an AI-Assistant:",
Fore.GREEN,
"input '--manual' to enter manual mode.",
speak_text=True,
)
user_desire = utils.clean_input(
f"{Fore.MAGENTA}I want Auto-GPT to{Style.RESET_ALL}: "
)
if user_desire == "":
user_desire = "Write a wikipedia style article about the project: https://github.com/significant-gravitas/Auto-GPT" # Default prompt
# If user desire contains "--manual"
if "--manual" in user_desire:
logger.typewriter_log(
"Manual Mode Selected",
Fore.GREEN,
speak_text=True,
)
return generate_aiconfig_manual(ai_name, ai_role, ai_goals, ai_budget)
else:
try:
return generate_aiconfig_automatic(user_desire)
except Exception as e:
logger.typewriter_log(
"Unable to automatically generate AI Config based on user desire.",
Fore.RED,
"Falling back to manual mode.",
speak_text=True,
)
return generate_aiconfig_manual(ai_name, ai_role, ai_goals, ai_budget)
def generate_aiconfig_manual(name, role, goals, budget) -> AIConfig:
"""
Interactively create an AI configuration by prompting the user to provide the name, role, and goals of the AI.
This function guides the user through a series of prompts to collect the necessary information to create
an AIConfig object. The user will be asked to provide a name and role for the AI, as well as up to five
goals. If the user does not provide a value for any of the fields, default values will be used.
Returns:
AIConfig: An AIConfig object containing the user-defined or default AI name, role, and goals.
"""
# Manual Setup Intro
logger.typewriter_log(
"Create an AI-Assistant:",
Fore.GREEN,
"The Ai robot you set up is already loaded.",
speak_text=True,
)
ai_name = name
if not ai_name:
ai_name = "Entrepreneur-GPT"
logger.typewriter_log(
f"{ai_name} here!", Fore.MAGENTA, "I am at your service.", speak_text=True
)
ai_role = role
if not ai_role:
logger.typewriter_log(
f"{ai_role} Cannot be empty!", Fore.RED,
"Please feel free to give me your needs, I can't serve you without them.", speak_text=True
)
else:
pass
ai_goals = []
if goals:
for k in goals:
ai_goals.append(k[0])
# Get API Budget from User
api_budget_input = budget
if not api_budget_input:
api_budget = 0.0
else:
try:
api_budget = float(api_budget_input.replace("$", ""))
except ValueError:
api_budget = 0.0
logger.typewriter_log(
"Invalid budget input. Setting budget to unlimited.", Fore.RED, api_budget
)
return AIConfig(ai_name, ai_role, ai_goals, api_budget)
def generate_aiconfig_automatic(user_prompt) -> AIConfig:
"""Generates an AIConfig object from the given string.
Returns:
AIConfig: The AIConfig object tailored to the user's input
"""
system_prompt = """
Your task is to devise up to 5 highly effective goals and an appropriate role-based name (_GPT) for an autonomous agent, ensuring that the goals are optimally aligned with the successful completion of its assigned task.
The user will provide the task, you will provide only the output in the exact format specified below with no explanation or conversation.
Example input:
Help me with marketing my business
Example output:
Name: CMOGPT
Description: a professional digital marketer AI that assists Solopreneurs in growing their businesses by providing world-class expertise in solving marketing problems for SaaS, content products, agencies, and more.
Goals:
- Engage in effective problem-solving, prioritization, planning, and supporting execution to address your marketing needs as your virtual Chief Marketing Officer.
- Provide specific, actionable, and concise advice to help you make informed decisions without the use of platitudes or overly wordy explanations.
- Identify and prioritize quick wins and cost-effective campaigns that maximize results with minimal time and budget investment.
- Proactively take the lead in guiding you and offering suggestions when faced with unclear information or uncertainty to ensure your marketing strategy remains on track.
"""
# Call LLM with the string as user input
messages = [
{
"role": "system",
"content": system_prompt,
},
{
"role": "user",
"content": f"Task: '{user_prompt}'\nRespond only with the output in the exact format specified in the system prompt, with no explanation or conversation.\n",
},
]
output = create_chat_completion(messages, CFG.fast_llm_model)
# Debug LLM Output
logger.debug(f"AI Config Generator Raw Output: {output}")
# Parse the output
ai_name = re.search(r"Name(?:\s*):(?:\s*)(.*)", output, re.IGNORECASE).group(1)
ai_role = (
re.search(
r"Description(?:\s*):(?:\s*)(.*?)(?:(?:\n)|Goals)",
output,
re.IGNORECASE | re.DOTALL,
)
.group(1)
.strip()
)
ai_goals = re.findall(r"(?<=\n)-\s*(.*)", output)
api_budget = 0.0 # TODO: parse api budget using a regular expression
return AIConfig(ai_name, ai_role, ai_goals, api_budget)

22
autogpt/singleton.py
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@ -1,22 +0,0 @@
"""The singleton metaclass for ensuring only one instance of a class."""
import abc
class Singleton(abc.ABCMeta, type):
"""
Singleton metaclass for ensuring only one instance of a class.
"""
_instances = {}
def __call__(cls, *args, **kwargs):
"""Call method for the singleton metaclass."""
if cls not in cls._instances:
cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
return cls._instances[cls]
class AbstractSingleton(abc.ABC, metaclass=Singleton):
"""
Abstract singleton class for ensuring only one instance of a class.
"""

4
autogpt/speech/__init__.py
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@ -1,4 +0,0 @@
"""This module contains the speech recognition and speech synthesis functions."""
from autogpt.speech.say import say_text
__all__ = ["say_text"]

50
autogpt/speech/base.py
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@ -1,50 +0,0 @@
"""Base class for all voice classes."""
import abc
from threading import Lock
from autogpt.config import AbstractSingleton
class VoiceBase(AbstractSingleton):
"""
Base class for all voice classes.
"""
def __init__(self):
"""
Initialize the voice class.
"""
self._url = None
self._headers = None
self._api_key = None
self._voices = []
self._mutex = Lock()
self._setup()
def say(self, text: str, voice_index: int = 0) -> bool:
"""
Say the given text.
Args:
text (str): The text to say.
voice_index (int): The index of the voice to use.
"""
with self._mutex:
return self._speech(text, voice_index)
@abc.abstractmethod
def _setup(self) -> None:
"""
Setup the voices, API key, etc.
"""
pass
@abc.abstractmethod
def _speech(self, text: str, voice_index: int = 0) -> bool:
"""
Play the given text.
Args:
text (str): The text to play.
"""
pass

43
autogpt/speech/brian.py
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@ -1,43 +0,0 @@
import logging
import os
import requests
from playsound import playsound
from autogpt.speech.base import VoiceBase
class BrianSpeech(VoiceBase):
"""Brian speech module for autogpt"""
def _setup(self) -> None:
"""Setup the voices, API key, etc."""
pass
def _speech(self, text: str, _: int = 0) -> bool:
"""Speak text using Brian with the streamelements API
Args:
text (str): The text to speak
Returns:
bool: True if the request was successful, False otherwise
"""
tts_url = (
f"https://api.streamelements.com/kappa/v2/speech?voice=Brian&text={text}"
)
response = requests.get(tts_url)
if response.status_code == 200:
with open("speech.mp3", "wb") as f:
f.write(response.content)
playsound("speech.mp3")
os.remove("speech.mp3")
return True
else:
logging.error(
"Request failed with status code: %s, response content: %s",
response.status_code,
response.content,
)
return False

86
autogpt/speech/eleven_labs.py
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@ -1,86 +0,0 @@
"""ElevenLabs speech module"""
import os
import requests
from playsound import playsound
from autogpt.config import Config
from autogpt.speech.base import VoiceBase
PLACEHOLDERS = {"your-voice-id"}
class ElevenLabsSpeech(VoiceBase):
"""ElevenLabs speech class"""
def _setup(self) -> None:
"""Set up the voices, API key, etc.
Returns:
None: None
"""
cfg = Config()
default_voices = ["ErXwobaYiN019PkySvjV", "EXAVITQu4vr4xnSDxMaL"]
voice_options = {
"Rachel": "21m00Tcm4TlvDq8ikWAM",
"Domi": "AZnzlk1XvdvUeBnXmlld",
"Bella": "EXAVITQu4vr4xnSDxMaL",
"Antoni": "ErXwobaYiN019PkySvjV",
"Elli": "MF3mGyEYCl7XYWbV9V6O",
"Josh": "TxGEqnHWrfWFTfGW9XjX",
"Arnold": "VR6AewLTigWG4xSOukaG",
"Adam": "pNInz6obpgDQGcFmaJgB",
"Sam": "yoZ06aMxZJJ28mfd3POQ",
}
self._headers = {
"Content-Type": "application/json",
"xi-api-key": cfg.elevenlabs_api_key,
}
self._voices = default_voices.copy()
if cfg.elevenlabs_voice_1_id in voice_options:
cfg.elevenlabs_voice_1_id = voice_options[cfg.elevenlabs_voice_1_id]
if cfg.elevenlabs_voice_2_id in voice_options:
cfg.elevenlabs_voice_2_id = voice_options[cfg.elevenlabs_voice_2_id]
self._use_custom_voice(cfg.elevenlabs_voice_1_id, 0)
self._use_custom_voice(cfg.elevenlabs_voice_2_id, 1)
def _use_custom_voice(self, voice, voice_index) -> None:
"""Use a custom voice if provided and not a placeholder
Args:
voice (str): The voice ID
voice_index (int): The voice index
Returns:
None: None
"""
# Placeholder values that should be treated as empty
if voice and voice not in PLACEHOLDERS:
self._voices[voice_index] = voice
def _speech(self, text: str, voice_index: int = 0) -> bool:
"""Speak text using elevenlabs.io's API
Args:
text (str): The text to speak
voice_index (int, optional): The voice to use. Defaults to 0.
Returns:
bool: True if the request was successful, False otherwise
"""
tts_url = (
f"https://api.elevenlabs.io/v1/text-to-speech/{self._voices[voice_index]}"
)
response = requests.post(tts_url, headers=self._headers, json={"text": text})
if response.status_code == 200:
with open("speech.mpeg", "wb") as f:
f.write(response.content)
playsound("speech.mpeg", True)
os.remove("speech.mpeg")
return True
else:
print("Request failed with status code:", response.status_code)
print("Response content:", response.content)
return False

23
autogpt/speech/gtts.py
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@ -1,23 +0,0 @@
""" GTTS Voice. """
import os
import gtts
from playsound import playsound
from autogpt.speech.base import VoiceBase
class GTTSVoice(VoiceBase):
"""GTTS Voice."""
def _setup(self) -> None:
pass
def _speech(self, text: str, _: int = 0) -> bool:
"""Play the given text."""
tts = gtts.gTTS(text)
tts.save("speech.mp3")
playsound("speech.mp3", True)
os.remove("speech.mp3")
return True

21
autogpt/speech/macos_tts.py
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@ -1,21 +0,0 @@
""" MacOS TTS Voice. """
import os
from autogpt.speech.base import VoiceBase
class MacOSTTS(VoiceBase):
"""MacOS TTS Voice."""
def _setup(self) -> None:
pass
def _speech(self, text: str, voice_index: int = 0) -> bool:
"""Play the given text."""
if voice_index == 0:
os.system(f'say "{text}"')
elif voice_index == 1:
os.system(f'say -v "Ava (Premium)" "{text}"')
else:
os.system(f'say -v Samantha "{text}"')
return True

46
autogpt/speech/say.py
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@ -1,46 +0,0 @@
""" Text to speech module """
import threading
from threading import Semaphore
from autogpt.config import Config
from autogpt.speech.brian import BrianSpeech
from autogpt.speech.eleven_labs import ElevenLabsSpeech
from autogpt.speech.gtts import GTTSVoice
from autogpt.speech.macos_tts import MacOSTTS
CFG = Config()
DEFAULT_VOICE_ENGINE = GTTSVoice()
VOICE_ENGINE = None
if CFG.elevenlabs_api_key:
VOICE_ENGINE = ElevenLabsSpeech()
elif CFG.use_mac_os_tts == "True":
VOICE_ENGINE = MacOSTTS()
elif CFG.use_brian_tts == "True":
VOICE_ENGINE = BrianSpeech()
else:
VOICE_ENGINE = GTTSVoice()
QUEUE_SEMAPHORE = Semaphore(
1
) # The amount of sounds to queue before blocking the main thread
def say_text(text: str, voice_index: int = 0) -> None:
"""Speak the given text using the given voice index"""
def speak() -> None:
success = VOICE_ENGINE.say(text, voice_index)
if not success:
DEFAULT_VOICE_ENGINE.say(text)
QUEUE_SEMAPHORE.release()
QUEUE_SEMAPHORE.acquire(True)
thread = threading.Thread(target=speak)
thread.start()
if __name__ == '__main__':
say_text('你好呀')

70
autogpt/spinner.py
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@ -1,70 +0,0 @@
"""A simple spinner module"""
import itertools
import sys
import threading
import time
class Spinner:
"""A simple spinner class"""
def __init__(self, message: str = "Loading...", delay: float = 0.1) -> None:
"""Initialize the spinner class
Args:
message (str): The message to display.
delay (float): The delay between each spinner update.
"""
self.spinner = itertools.cycle(["-", "/", "|", "\\"])
self.delay = delay
self.message = message
self.running = False
self.spinner_thread = None
def spin(self) -> None:
"""Spin the spinner"""
while self.running:
sys.stdout.write(f"{next(self.spinner)} {self.message}\r")
sys.stdout.flush()
time.sleep(self.delay)
sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r")
def __enter__(self):
"""Start the spinner"""
self.running = True
self.spinner_thread = threading.Thread(target=self.spin)
self.spinner_thread.start()
return self
def __exit__(self, exc_type, exc_value, exc_traceback) -> None:
"""Stop the spinner
Args:
exc_type (Exception): The exception type.
exc_value (Exception): The exception value.
exc_traceback (Exception): The exception traceback.
"""
self.running = False
if self.spinner_thread is not None:
self.spinner_thread.join()
sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r")
sys.stdout.flush()
def update_message(self, new_message, delay=0.1):
"""Update the spinner message
Args:
new_message (str): New message to display.
delay (float): The delay in seconds between each spinner update.
"""
time.sleep(delay)
sys.stdout.write(
f"\r{' ' * (len(self.message) + 2)}\r"
) # Clear the current message
sys.stdout.flush()
self.message = new_message
if __name__ == '__main__':
with Spinner('LING'):
time.sleep(5)

76
autogpt/token_counter.py
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@ -1,76 +0,0 @@
"""Functions for counting the number of tokens in a message or string."""
from __future__ import annotations
from typing import List
import tiktoken
from autogpt.logs import logger
from autogpt.types.openai import Message
def count_message_tokens(
messages: List[Message], model: str = "gpt-3.5-turbo-0301"
) -> int:
"""
Returns the number of tokens used by a list of messages.
Args:
messages (list): A list of messages, each of which is a dictionary
containing the role and content of the message.
model (str): The name of the model to use for tokenization.
Defaults to "gpt-3.5-turbo-0301".
Returns:
int: The number of tokens used by the list of messages.
"""
try:
encoding = tiktoken.encoding_for_model(model)
except KeyError:
logger.warn("Warning: model not found. Using cl100k_base encoding.")
encoding = tiktoken.get_encoding("cl100k_base")
if model == "gpt-3.5-turbo":
# !Note: gpt-3.5-turbo may change over time.
# Returning num tokens assuming gpt-3.5-turbo-0301.")
return count_message_tokens(messages, model="gpt-3.5-turbo-0301")
elif model == "gpt-4":
# !Note: gpt-4 may change over time. Returning num tokens assuming gpt-4-0314.")
return count_message_tokens(messages, model="gpt-4-0314")
elif model == "gpt-3.5-turbo-0301":
tokens_per_message = (
4 # every message follows <|start|>{role/name}\n{content}<|end|>\n
)
tokens_per_name = -1 # if there's a name, the role is omitted
elif model == "gpt-4-0314":
tokens_per_message = 3
tokens_per_name = 1
else:
raise NotImplementedError(
f"num_tokens_from_messages() is not implemented for model {model}.\n"
" See https://github.com/openai/openai-python/blob/main/chatml.md for"
" information on how messages are converted to tokens."
)
num_tokens = 0
for message in messages:
num_tokens += tokens_per_message
for key, value in message.items():
num_tokens += len(encoding.encode(value))
if key == "name":
num_tokens += tokens_per_name
num_tokens += 3 # every reply is primed with <|start|>assistant<|message|>
return num_tokens
def count_string_tokens(string: str, model_name: str) -> int:
"""
Returns the number of tokens in a text string.
Args:
string (str): The text string.
model_name (str): The name of the encoding to use. (e.g., "gpt-3.5-turbo")
Returns:
int: The number of tokens in the text string.
"""
encoding = tiktoken.encoding_for_model(model_name)
return len(encoding.encode(string))

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