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from __future__ import annotations
import copy
import inspect
import logging
from abc import ABCMeta, abstractmethod
from typing import (
TYPE_CHECKING,
Any,
Callable,
Iterator,
Optional,
ParamSpec,
TypeVar,
overload,
)
from colorama import Fore
from pydantic import BaseModel, Field, validator
if TYPE_CHECKING:
from autogpt.core.resource.model_providers.schema import (
ChatModelInfo,
)
from autogpt.agents import protocols as _protocols
from autogpt.agents.components import (
AgentComponent,
ComponentEndpointError,
EndpointPipelineError,
)
from autogpt.config import ConfigBuilder
from autogpt.config.ai_directives import AIDirectives
from autogpt.config.ai_profile import AIProfile
from autogpt.core.configuration import (
Configurable,
SystemConfiguration,
SystemSettings,
UserConfigurable,
)
from autogpt.core.resource.model_providers.openai import (
OPEN_AI_CHAT_MODELS,
OpenAIModelName,
)
from autogpt.models.action_history import ActionResult, EpisodicActionHistory
from autogpt.prompts.prompt import DEFAULT_TRIGGERING_PROMPT
logger = logging.getLogger(__name__)
T = TypeVar("T")
P = ParamSpec("P")
CommandName = str
CommandArgs = dict[str, str]
AgentThoughts = dict[str, Any]
class BaseAgentConfiguration(SystemConfiguration):
allow_fs_access: bool = UserConfigurable(default=False)
fast_llm: OpenAIModelName = UserConfigurable(default=OpenAIModelName.GPT3_16k)
smart_llm: OpenAIModelName = UserConfigurable(default=OpenAIModelName.GPT4)
use_functions_api: bool = UserConfigurable(default=False)
default_cycle_instruction: str = DEFAULT_TRIGGERING_PROMPT
"""The default instruction passed to the AI for a thinking cycle."""
big_brain: bool = UserConfigurable(default=True)
"""
Whether this agent uses the configured smart LLM (default) to think,
as opposed to the configured fast LLM. Enabling this disables hybrid mode.
"""
cycle_budget: Optional[int] = 1
"""
The number of cycles that the agent is allowed to run unsupervised.
`None` for unlimited continuous execution,
`1` to require user approval for every step,
`0` to stop the agent.
"""
cycles_remaining = cycle_budget
"""The number of cycles remaining within the `cycle_budget`."""
cycle_count = 0
"""The number of cycles that the agent has run since its initialization."""
send_token_limit: Optional[int] = None
"""
The token limit for prompt construction. Should leave room for the completion;
defaults to 75% of `llm.max_tokens`.
"""
summary_max_tlength: Optional[int] = None
# TODO: move to ActionHistoryConfiguration
@validator("use_functions_api")
def validate_openai_functions(cls, v: bool, values: dict[str, Any]):
if v:
smart_llm = values["smart_llm"]
fast_llm = values["fast_llm"]
assert all(
[
not any(s in name for s in {"-0301", "-0314"})
for name in {smart_llm, fast_llm}
]
), (
f"Model {smart_llm} does not support OpenAI Functions. "
"Please disable OPENAI_FUNCTIONS or choose a suitable model."
)
return v
class BaseAgentSettings(SystemSettings):
agent_id: str = ""
ai_profile: AIProfile = Field(default_factory=lambda: AIProfile(ai_name="AutoGPT"))
"""The AI profile or "personality" of the agent."""
directives: AIDirectives = Field(
default_factory=lambda: AIDirectives.from_file(
ConfigBuilder.default_settings.prompt_settings_file
)
)
"""Directives (general instructional guidelines) for the agent."""
task: str = "Terminate immediately" # FIXME: placeholder for forge.sdk.schema.Task
"""The user-given task that the agent is working on."""
config: BaseAgentConfiguration = Field(default_factory=BaseAgentConfiguration)
"""The configuration for this BaseAgent subsystem instance."""
history: EpisodicActionHistory = Field(default_factory=EpisodicActionHistory)
"""(STATE) The action history of the agent."""
class AgentMeta(ABCMeta):
def __call__(cls, *args, **kwargs):
# Create instance of the class (Agent or BaseAgent)
instance = super().__call__(*args, **kwargs)
# Automatically collect modules after the instance is created
instance._collect_components()
return instance
class ThoughtProcessOutput(BaseModel):
command_name: str = ""
command_args: dict[str, Any] = Field(default_factory=dict)
thoughts: dict[str, Any] = Field(default_factory=dict)
def to_tuple(self) -> tuple[CommandName, CommandArgs, AgentThoughts]:
return self.command_name, self.command_args, self.thoughts
class BaseAgent(Configurable[BaseAgentSettings], metaclass=AgentMeta):
C = TypeVar("C", bound=AgentComponent)
default_settings = BaseAgentSettings(
name="BaseAgent",
description=__doc__ if __doc__ else "",
)
def __init__(
self,
settings: BaseAgentSettings,
):
self.state = settings
self.components: list[AgentComponent] = []
self.config = settings.config
# Execution data for debugging
self._trace: list[str] = []
logger.debug(f"Created {__class__} '{self.state.ai_profile.ai_name}'")
@property
def trace(self) -> list[str]:
return self._trace
@property
def llm(self) -> ChatModelInfo:
"""The LLM that the agent uses to think."""
llm_name = (
self.config.smart_llm if self.config.big_brain else self.config.fast_llm
)
return OPEN_AI_CHAT_MODELS[llm_name]
@property
def send_token_limit(self) -> int:
return self.config.send_token_limit or self.llm.max_tokens * 3 // 4
@abstractmethod
async def propose_action(self) -> ThoughtProcessOutput:
...
@abstractmethod
async def execute(
self,
command_name: str,
command_args: dict[str, str] = {},
user_input: str = "",
) -> ActionResult:
...
def reset_trace(self):
self._trace = []
@overload
async def run_pipeline(
self, protocol_method: Callable[P, Iterator[T]], *args, retry_limit: int = 3
) -> list[T]:
...
@overload
async def run_pipeline(
self, protocol_method: Callable[P, None], *args, retry_limit: int = 3
) -> list[None]:
...
async def run_pipeline(
self,
protocol_method: Callable[P, Iterator[T] | None],
*args,
retry_limit: int = 3,
) -> list[T] | list[None]:
method_name = protocol_method.__name__
protocol_name = protocol_method.__qualname__.split(".")[0]
protocol_class = getattr(_protocols, protocol_name)
if not issubclass(protocol_class, AgentComponent):
raise TypeError(f"{repr(protocol_method)} is not a protocol method")
# Clone parameters to revert on failure
original_args = self._selective_copy(args)
pipeline_attempts = 0
method_result: list[T] = []
self._trace.append(f"⬇️ {Fore.BLUE}{method_name}{Fore.RESET}")
while pipeline_attempts < retry_limit:
try:
for component in self.components:
# Skip other protocols
if not isinstance(component, protocol_class):
continue
# Skip disabled components
if not component.enabled:
self._trace.append(
f" {Fore.LIGHTBLACK_EX}"
f"{component.__class__.__name__}{Fore.RESET}"
)
continue
method = getattr(component, method_name, None)
if not callable(method):
continue
component_attempts = 0
while component_attempts < retry_limit:
try:
component_args = self._selective_copy(args)
if inspect.iscoroutinefunction(method):
result = await method(*component_args)
else:
result = method(*component_args)
if result is not None:
method_result.extend(result)
args = component_args
self._trace.append(f"✅ {component.__class__.__name__}")
except ComponentEndpointError:
self._trace.append(
f"❌ {Fore.YELLOW}{component.__class__.__name__}: "
f"ComponentEndpointError{Fore.RESET}"
)
# Retry the same component on ComponentEndpointError
component_attempts += 1
continue
# Successful component execution
break
# Successful pipeline execution
break
except EndpointPipelineError:
self._trace.append(
f"❌ {Fore.LIGHTRED_EX}{component.__class__.__name__}: "
f"EndpointPipelineError{Fore.RESET}"
)
# Restart from the beginning on EndpointPipelineError
# Revert to original parameters
args = self._selective_copy(original_args)
pipeline_attempts += 1
continue # Start the loop over
except Exception as e:
raise e
return method_result
def _collect_components(self):
components = [
getattr(self, attr)
for attr in dir(self)
if isinstance(getattr(self, attr), AgentComponent)
]
if self.components:
# Check if any coponent is missed (added to Agent but not to components)
for component in components:
if component not in self.components:
logger.warning(
f"Component {component.__class__.__name__} "
"is attached to an agent but not added to components list"
)
# Skip collecting anf sorting and sort if ordering is explicit
return
self.components = self._topological_sort(components)
def _topological_sort(
self, components: list[AgentComponent]
) -> list[AgentComponent]:
visited = set()
stack = []
def visit(node: AgentComponent):
if node in visited:
return
visited.add(node)
for neighbor_class in node.__class__.run_after:
# Find the instance of neighbor_class in components
neighbor = next(
(m for m in components if isinstance(m, neighbor_class)), None
)
if neighbor:
visit(neighbor)
stack.append(node)
for component in components:
visit(component)
return stack
def _selective_copy(self, args: tuple[Any, ...]) -> tuple[Any, ...]:
copied_args = []
for item in args:
if isinstance(item, list):
# Shallow copy for lists
copied_item = item[:]
elif isinstance(item, dict):
# Shallow copy for dicts
copied_item = item.copy()
elif isinstance(item, BaseModel):
# Deep copy for Pydantic models (deep=True to also copy nested models)
copied_item = item.copy(deep=True)
else:
# Deep copy for other objects
copied_item = copy.deepcopy(item)
copied_args.append(copied_item)
return tuple(copied_args)
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