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import logging
import os
import pathlib
from collections import defaultdict
from io import BytesIO
from uuid import uuid4
import orjson
from fastapi import APIRouter, FastAPI, UploadFile
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import RedirectResponse, StreamingResponse
from fastapi.staticfiles import StaticFiles
from forge.sdk.db import AgentDB
from forge.sdk.errors import NotFoundError
from forge.sdk.middlewares import AgentMiddleware
from forge.sdk.model import (
Artifact,
Step,
StepRequestBody,
Task,
TaskArtifactsListResponse,
TaskListResponse,
TaskRequestBody,
TaskStepsListResponse,
)
from forge.sdk.routes.agent_protocol import base_router
from hypercorn.asyncio import serve as hypercorn_serve
from hypercorn.config import Config as HypercornConfig
from sentry_sdk import set_user
from autogpt.agent_factory.configurators import configure_agent_with_state
from autogpt.agent_factory.generators import generate_agent_for_task
from autogpt.agent_manager import AgentManager
from autogpt.app.utils import is_port_free
from autogpt.config import Config
from autogpt.core.resource.model_providers import ChatModelProvider, ModelProviderBudget
from autogpt.core.resource.model_providers.openai import OpenAIProvider
from autogpt.file_storage import FileStorage
from autogpt.models.action_history import ActionErrorResult, ActionSuccessResult
from autogpt.utils.exceptions import AgentFinished
from autogpt.utils.utils import DEFAULT_ASK_COMMAND, DEFAULT_FINISH_COMMAND
logger = logging.getLogger(__name__)
class AgentProtocolServer:
_task_budgets: dict[str, ModelProviderBudget]
def __init__(
self,
app_config: Config,
database: AgentDB,
file_storage: FileStorage,
llm_provider: ChatModelProvider,
):
self.app_config = app_config
self.db = database
self.file_storage = file_storage
self.llm_provider = llm_provider
self.agent_manager = AgentManager(file_storage)
self._task_budgets = defaultdict(ModelProviderBudget)
async def start(self, port: int = 8000, router: APIRouter = base_router):
"""Start the agent server."""
logger.debug("Starting the agent server...")
if not is_port_free(port):
logger.error(f"Port {port} is already in use.")
logger.info(
"You can specify a port by either setting the AP_SERVER_PORT "
"environment variable or defining AP_SERVER_PORT in the .env file."
)
return
config = HypercornConfig()
config.bind = [f"localhost:{port}"]
app = FastAPI(
title="AutoGPT Server",
description="Forked from AutoGPT Forge; "
"Modified version of The Agent Protocol.",
version="v0.4",
)
# Configure CORS middleware
default_origins = [f"http://localhost:{port}"] # Default only local access
configured_origins = [
origin
for origin in os.getenv("AP_SERVER_CORS_ALLOWED_ORIGINS", "").split(",")
if origin # Empty list if not configured
]
origins = configured_origins or default_origins
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
app.include_router(router, prefix="/ap/v1")
script_dir = os.path.dirname(os.path.realpath(__file__))
frontend_path = (
pathlib.Path(script_dir)
.joinpath("../../../../frontend/build/web")
.resolve()
)
if os.path.exists(frontend_path):
app.mount("/app", StaticFiles(directory=frontend_path), name="app")
@app.get("/", include_in_schema=False)
async def root():
return RedirectResponse(url="/app/index.html", status_code=307)
else:
logger.warning(
f"Frontend not found. {frontend_path} does not exist. "
"The frontend will not be available."
)
# Used to access the methods on this class from API route handlers
app.add_middleware(AgentMiddleware, agent=self)
config.loglevel = "ERROR"
config.bind = [f"0.0.0.0:{port}"]
logger.info(f"AutoGPT server starting on http://localhost:{port}")
await hypercorn_serve(app, config)
async def create_task(self, task_request: TaskRequestBody) -> Task:
"""
Create a task for the agent.
"""
if user_id := (task_request.additional_input or {}).get("user_id"):
set_user({"id": user_id})
task = await self.db.create_task(
input=task_request.input,
additional_input=task_request.additional_input,
)
logger.debug(f"Creating agent for task: '{task.input}'")
task_agent = await generate_agent_for_task(
agent_id=task_agent_id(task.task_id),
task=task.input,
app_config=self.app_config,
file_storage=self.file_storage,
llm_provider=self._get_task_llm_provider(task),
)
await task_agent.file_manager.save_state()
return task
async def list_tasks(self, page: int = 1, pageSize: int = 10) -> TaskListResponse:
"""
List all tasks that the agent has created.
"""
logger.debug("Listing all tasks...")
tasks, pagination = await self.db.list_tasks(page, pageSize)
response = TaskListResponse(tasks=tasks, pagination=pagination)
return response
async def get_task(self, task_id: str) -> Task:
"""
Get a task by ID.
"""
logger.debug(f"Getting task with ID: {task_id}...")
task = await self.db.get_task(task_id)
return task
async def list_steps(
self, task_id: str, page: int = 1, pageSize: int = 10
) -> TaskStepsListResponse:
"""
List the IDs of all steps that the task has created.
"""
logger.debug(f"Listing all steps created by task with ID: {task_id}...")
steps, pagination = await self.db.list_steps(task_id, page, pageSize)
response = TaskStepsListResponse(steps=steps, pagination=pagination)
return response
async def execute_step(self, task_id: str, step_request: StepRequestBody) -> Step:
"""Create a step for the task."""
logger.debug(f"Creating a step for task with ID: {task_id}...")
# Restore Agent instance
task = await self.get_task(task_id)
agent = configure_agent_with_state(
state=self.agent_manager.load_agent_state(task_agent_id(task_id)),
app_config=self.app_config,
file_storage=self.file_storage,
llm_provider=self._get_task_llm_provider(task),
)
if user_id := (task.additional_input or {}).get("user_id"):
set_user({"id": user_id})
# According to the Agent Protocol spec, the first execute_step request contains
# the same task input as the parent create_task request.
# To prevent this from interfering with the agent's process, we ignore the input
# of this first step request, and just generate the first step proposal.
is_init_step = not bool(agent.event_history)
last_proposal, tool_result = None, None
execute_approved = False
# HACK: only for compatibility with AGBenchmark
if step_request.input == "y":
step_request.input = ""
user_input = step_request.input if not is_init_step else ""
if (
not is_init_step
and agent.event_history.current_episode
and not agent.event_history.current_episode.result
):
last_proposal = agent.event_history.current_episode.action
execute_approved = not user_input
logger.debug(
f"Agent proposed command {last_proposal.use_tool}."
f" User input/feedback: {repr(user_input)}"
)
# Save step request
step = await self.db.create_step(
task_id=task_id,
input=step_request,
is_last=(
last_proposal is not None
and last_proposal.use_tool.name == DEFAULT_FINISH_COMMAND
and execute_approved
),
)
agent.llm_provider = self._get_task_llm_provider(task, step.step_id)
# Execute previously proposed action
if last_proposal:
agent.file_manager.workspace.on_write_file = (
lambda path: self._on_agent_write_file(
task=task, step=step, relative_path=path
)
)
if last_proposal.use_tool.name == DEFAULT_ASK_COMMAND:
tool_result = ActionSuccessResult(outputs=user_input)
agent.event_history.register_result(tool_result)
elif execute_approved:
step = await self.db.update_step(
task_id=task_id,
step_id=step.step_id,
status="running",
)
try:
# Execute previously proposed action
tool_result = await agent.execute(last_proposal)
except AgentFinished:
additional_output = {}
task_total_cost = agent.llm_provider.get_incurred_cost()
if task_total_cost > 0:
additional_output["task_total_cost"] = task_total_cost
logger.info(
f"Total LLM cost for task {task_id}: "
f"${round(task_total_cost, 2)}"
)
step = await self.db.update_step(
task_id=task_id,
step_id=step.step_id,
output=last_proposal.use_tool.arguments["reason"],
additional_output=additional_output,
)
await agent.file_manager.save_state()
return step
else:
assert user_input
tool_result = await agent.do_not_execute(last_proposal, user_input)
# Propose next action
try:
assistant_response = await agent.propose_action()
next_tool_to_use = assistant_response.use_tool
logger.debug(f"AI output: {assistant_response.thoughts}")
except Exception as e:
step = await self.db.update_step(
task_id=task_id,
step_id=step.step_id,
status="completed",
output=f"An error occurred while proposing the next action: {e}",
)
return step
# Format step output
output = (
(
f"`{last_proposal.use_tool}` returned:"
+ ("\n\n" if "\n" in str(tool_result) else " ")
+ f"{tool_result}\n\n"
)
if last_proposal and last_proposal.use_tool.name != DEFAULT_ASK_COMMAND
else ""
)
output += f"{assistant_response.thoughts.speak}\n\n"
output += (
f"Next Command: {next_tool_to_use}"
if next_tool_to_use.name != DEFAULT_ASK_COMMAND
else next_tool_to_use.arguments["question"]
)
additional_output = {
**(
{
"last_action": {
"name": last_proposal.use_tool.name,
"args": last_proposal.use_tool.arguments,
"result": (
""
if tool_result is None
else (
orjson.loads(tool_result.json())
if not isinstance(tool_result, ActionErrorResult)
else {
"error": str(tool_result.error),
"reason": tool_result.reason,
}
)
),
},
}
if last_proposal and tool_result
else {}
),
**assistant_response.dict(),
}
task_cumulative_cost = agent.llm_provider.get_incurred_cost()
if task_cumulative_cost > 0:
additional_output["task_cumulative_cost"] = task_cumulative_cost
logger.debug(
f"Running total LLM cost for task {task_id}: "
f"${round(task_cumulative_cost, 3)}"
)
step = await self.db.update_step(
task_id=task_id,
step_id=step.step_id,
status="completed",
output=output,
additional_output=additional_output,
)
await agent.file_manager.save_state()
return step
async def _on_agent_write_file(
self, task: Task, step: Step, relative_path: pathlib.Path
) -> None:
"""
Creates an Artifact for the written file, or updates the Artifact if it exists.
"""
if relative_path.is_absolute():
raise ValueError(f"File path '{relative_path}' is not relative")
for a in task.artifacts or []:
if a.relative_path == str(relative_path):
logger.debug(f"Updating Artifact after writing to existing file: {a}")
if not a.agent_created:
await self.db.update_artifact(a.artifact_id, agent_created=True)
break
else:
logger.debug(f"Creating Artifact for new file '{relative_path}'")
await self.db.create_artifact(
task_id=step.task_id,
step_id=step.step_id,
file_name=relative_path.parts[-1],
agent_created=True,
relative_path=str(relative_path),
)
async def get_step(self, task_id: str, step_id: str) -> Step:
"""
Get a step by ID.
"""
step = await self.db.get_step(task_id, step_id)
return step
async def list_artifacts(
self, task_id: str, page: int = 1, pageSize: int = 10
) -> TaskArtifactsListResponse:
"""
List the artifacts that the task has created.
"""
artifacts, pagination = await self.db.list_artifacts(task_id, page, pageSize)
return TaskArtifactsListResponse(artifacts=artifacts, pagination=pagination)
async def create_artifact(
self, task_id: str, file: UploadFile, relative_path: str
) -> Artifact:
"""
Create an artifact for the task.
"""
file_name = file.filename or str(uuid4())
data = b""
while contents := file.file.read(1024 * 1024):
data += contents
# Check if relative path ends with filename
if relative_path.endswith(file_name):
file_path = relative_path
else:
file_path = os.path.join(relative_path, file_name)
workspace = self._get_task_agent_file_workspace(task_id)
await workspace.write_file(file_path, data)
artifact = await self.db.create_artifact(
task_id=task_id,
file_name=file_name,
relative_path=relative_path,
agent_created=False,
)
return artifact
async def get_artifact(self, task_id: str, artifact_id: str) -> StreamingResponse:
"""
Download a task artifact by ID.
"""
try:
workspace = self._get_task_agent_file_workspace(task_id)
artifact = await self.db.get_artifact(artifact_id)
if artifact.file_name not in artifact.relative_path:
file_path = os.path.join(artifact.relative_path, artifact.file_name)
else:
file_path = artifact.relative_path
retrieved_artifact = workspace.read_file(file_path, binary=True)
except NotFoundError:
raise
except FileNotFoundError:
raise
return StreamingResponse(
BytesIO(retrieved_artifact),
media_type="application/octet-stream",
headers={
"Content-Disposition": f'attachment; filename="{artifact.file_name}"'
},
)
def _get_task_agent_file_workspace(self, task_id: str | int) -> FileStorage:
agent_id = task_agent_id(task_id)
return self.file_storage.clone_with_subroot(f"agents/{agent_id}/workspace")
def _get_task_llm_provider(
self, task: Task, step_id: str = ""
) -> ChatModelProvider:
"""
Configures the LLM provider with headers to link outgoing requests to the task.
"""
task_llm_budget = self._task_budgets[task.task_id]
task_llm_provider_config = self.llm_provider._configuration.copy(deep=True)
_extra_request_headers = task_llm_provider_config.extra_request_headers
_extra_request_headers["AP-TaskID"] = task.task_id
if step_id:
_extra_request_headers["AP-StepID"] = step_id
if task.additional_input and (user_id := task.additional_input.get("user_id")):
_extra_request_headers["AutoGPT-UserID"] = user_id
task_llm_provider = None
if isinstance(self.llm_provider, OpenAIProvider):
settings = self.llm_provider._settings.copy()
settings.budget = task_llm_budget
settings.configuration = task_llm_provider_config # type: ignore
task_llm_provider = OpenAIProvider(
settings=settings,
logger=logger.getChild(f"Task-{task.task_id}_OpenAIProvider"),
)
if task_llm_provider and task_llm_provider._budget:
self._task_budgets[task.task_id] = task_llm_provider._budget
return task_llm_provider or self.llm_provider
def task_agent_id(task_id: str | int) -> str:
return f"AutoGPT-{task_id}"
|