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import click
from autogpt.core.agent import AgentSettings, SimpleAgent
from autogpt.core.runner.client_lib.logging import get_client_logger
async def run_auto_gpt(user_configuration: dict):
"""Run the Auto-GPT CLI client."""
client_logger = get_client_logger()
client_logger.debug("Getting agent settings")
agent_workspace = (
user_configuration.get("workspace", {}).get("configuration", {}).get("root", "")
)
if not agent_workspace: # We don't have an agent yet.
#################
# Bootstrapping #
#################
# Step 1. Collate the user's settings with the default system settings.
agent_settings: AgentSettings = SimpleAgent.compile_settings(
client_logger,
user_configuration,
)
# Step 2. Get a name and goals for the agent.
# First we need to figure out what the user wants to do with the agent.
# We'll do this by asking the user for a prompt.
user_objective = click.prompt("What do you want Auto-GPT to do?")
# Ask a language model to determine a name and goals for a suitable agent.
name_and_goals = await SimpleAgent.determine_agent_name_and_goals(
user_objective,
agent_settings,
client_logger,
)
print(parse_agent_name_and_goals(name_and_goals))
# Finally, update the agent settings with the name and goals.
agent_settings.update_agent_name_and_goals(name_and_goals)
# Step 3. Provision the agent.
agent_workspace = SimpleAgent.provision_agent(agent_settings, client_logger)
print("agent is provisioned")
# launch agent interaction loop
agent = SimpleAgent.from_workspace(
agent_workspace,
client_logger,
)
print("agent is loaded")
plan = await agent.build_initial_plan()
print(parse_agent_plan(plan))
while True:
current_task, next_ability = await agent.determine_next_ability(plan)
print(parse_next_ability(current_task, next_ability))
user_input = click.prompt(
"Should the agent proceed with this ability?",
default="y",
)
ability_result = await agent.execute_next_ability(user_input)
print(parse_ability_result(ability_result))
def parse_agent_name_and_goals(name_and_goals: dict) -> str:
parsed_response = f"Agent Name: {name_and_goals['agent_name']}\n"
parsed_response += f"Agent Role: {name_and_goals['agent_role']}\n"
parsed_response += "Agent Goals:\n"
for i, goal in enumerate(name_and_goals["agent_goals"]):
parsed_response += f"{i+1}. {goal}\n"
return parsed_response
def parse_agent_plan(plan: dict) -> str:
parsed_response = f"Agent Plan:\n"
for i, task in enumerate(plan["task_list"]):
parsed_response += f"{i+1}. {task['objective']}\n"
parsed_response += f"Task type: {task['type']} "
parsed_response += f"Priority: {task['priority']}\n"
parsed_response += f"Ready Criteria:\n"
for j, criteria in enumerate(task["ready_criteria"]):
parsed_response += f" {j+1}. {criteria}\n"
parsed_response += f"Acceptance Criteria:\n"
for j, criteria in enumerate(task["acceptance_criteria"]):
parsed_response += f" {j+1}. {criteria}\n"
parsed_response += "\n"
return parsed_response
def parse_next_ability(current_task, next_ability: dict) -> str:
parsed_response = f"Current Task: {current_task.objective}\n"
ability_args = ", ".join(
f"{k}={v}" for k, v in next_ability["ability_arguments"].items()
)
parsed_response += f"Next Ability: {next_ability['next_ability']}({ability_args})\n"
parsed_response += f"Motivation: {next_ability['motivation']}\n"
parsed_response += f"Self-criticism: {next_ability['self_criticism']}\n"
parsed_response += f"Reasoning: {next_ability['reasoning']}\n"
return parsed_response
def parse_ability_result(ability_result) -> str:
parsed_response = f"Ability: {ability_result['ability_name']}\n"
parsed_response += f"Ability Arguments: {ability_result['ability_args']}\n"
parsed_response = f"Ability Result: {ability_result['success']}\n"
parsed_response += f"Message: {ability_result['message']}\n"
parsed_response += f"Data: {ability_result['new_knowledge']}\n"
return parsed_response
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