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319 | class SimpleAgent:
"""A simple agent with env, tools, mcps, and context manager, wrapped on openai-agents."""
def __init__(
self,
*,
config: AgentConfig | str | None = None, # use config to pass agent configs
name: str | None = None,
instructions: str | Callable | None = None,
model: str | Model | None = None,
model_settings: ModelSettings | None = None,
tools: list[Tool] = None, # config tools
toolkits: list[str] | None = None, # load tools from toolkit configs
output_type: type[Any] | AgentOutputSchemaBase | None = None,
tool_use_behavior: Literal["run_llm_again", "stop_on_first_tool"] | StopAtTools = "run_llm_again",
):
assert not (tools and toolkits), "You can't pass both tools and toolkits."
self.config = self._get_config(config)
if name:
self.config.agent.name = name
if instructions:
self.config.agent.instructions = instructions
self.model = self._get_model(self.config, model)
self.model_settings = self._get_model_settings(self.config, model_settings)
self.tools: list[Tool] = tools or []
self.toolkits: list[str] = toolkits or []
self.output_type: type[Any] | AgentOutputSchemaBase | None = output_type
self.tool_use_behavior: Literal["run_llm_again", "stop_on_first_tool"] | StopAtTools = tool_use_behavior
self.context_manager: BaseContextManager = None
self.env: BaseEnv = None
self.workspace_dir: str = ""
self.current_agent: Agent[TContext] = None # move to task recorder?
self.input_items: list[TResponseInputItem] = []
self.run_hooks: RunHooks = get_run_hooks(self.config)
self._mcp_servers: list[MCPServer] = []
self._toolkits: dict[str, AsyncBaseToolkit] = {}
self._mcps_exit_stack = AsyncExitStack()
self._initialized = False
def _get_config(self, config: AgentConfig | str | None) -> AgentConfig:
if isinstance(config, AgentConfig):
return config
return ConfigLoader.load_agent_config(config or "simple/base")
def _get_model(self, config: AgentConfig, model: str | Model | None = None) -> Model:
if isinstance(model, Model):
return model
model_provider_config = config.model.model_provider.model_dump()
if isinstance(model, str):
model_provider_config["model"] = model
return AgentsUtils.get_agents_model(**model_provider_config)
def _get_model_settings(self, config: AgentConfig, model_settings: ModelSettings | None = None) -> ModelSettings:
if isinstance(model_settings, ModelSettings):
return model_settings
return config.model.model_settings
async def __aenter__(self) -> "SimpleAgent":
await self.build()
return self
async def __aexit__(self, exc_type, exc_val, exc_tb):
await self.cleanup()
async def build(self, trace_id: str = None):
"""Build the agent"""
if self._initialized:
logger.info("Agent already initialized! Skipping build.")
return
self.env = await get_env(self.config, trace_id or AgentsUtils.gen_trace_id()) # Pass trace_id
await self.env.build()
self.current_agent = Agent(
name=self.config.agent.name,
instructions=self.config.agent.instructions,
model=self.model,
model_settings=self.model_settings,
tools=await self.get_tools(),
output_type=self.output_type,
tool_use_behavior=self.tool_use_behavior,
mcp_servers=self._mcp_servers,
)
self.context_manager = build_context_manager(self.config)
self._initialized = True
async def cleanup(self):
"""Cleanup"""
logger.info("Cleaning up MCP servers...")
await self._mcps_exit_stack.aclose()
self._mcp_servers = []
logger.info("Cleaning up tools...")
self._toolkits = {}
logger.info("Cleaning up env...")
await self.env.cleanup()
self._initialized = False
def setup_workspace(self, workspace_dir: str | pathlib.Path):
"""Setup workspace for toolkits that need it"""
assert pathlib.Path(workspace_dir).exists()
self.workspace_dir = str(workspace_dir)
self._setup_workspace_for_toolkits()
def _setup_workspace_for_toolkits(self):
for toolkit in self._toolkits.values():
if hasattr(toolkit, "setup_workspace"):
toolkit.setup_workspace(self.workspace_dir)
async def get_tools(self) -> list[Tool]:
if self.tools:
return self.tools
if self.toolkits:
await self._load_toolkits_config()
else:
tools_list: list[Tool] = []
tools_list += await self.env.get_tools() # add env tools
# TODO: handle duplicate tool names
for _, toolkit_config in self.config.toolkits.items():
toolkit = await self._load_toolkit(toolkit_config)
if toolkit_config.mode in ["customized", "builtin"]:
tools_list.extend(toolkit.get_tools_in_agents())
tool_names = [tool.name for tool in tools_list]
logger.info(f"Loaded {len(tool_names)} tools: {tool_names}")
self.tools = tools_list
# setup workspace if needed
if self.workspace_dir:
self._setup_workspace_for_toolkits()
return self.tools
async def _load_toolkits_config(self):
assert isinstance(self.toolkits, list) and all(isinstance(tool, str) for tool in self.toolkits)
parsed_tools = []
for tool_name in self.toolkits:
config = ConfigLoader.load_toolkit_config(tool_name)
toolkit = await self._load_toolkit(config)
if config.mode in ["customized", "builtin"]:
parsed_tools.extend(toolkit.get_tools_in_agents())
self.tools = parsed_tools
async def _load_toolkit(self, toolkit_config: ToolkitConfig) -> AsyncBaseToolkit | MCPServer:
if toolkit_config.mode == "builtin":
return await self._load_builtin_toolkit(toolkit_config)
elif toolkit_config.mode == "customized":
return await self._load_customized_toolkit(toolkit_config)
elif toolkit_config.mode == "mcp":
return await self._load_mcp_server(toolkit_config)
else:
raise ValueError(f"Unknown toolkit mode: {toolkit_config.mode}")
async def _load_builtin_toolkit(self, toolkit_config: ToolkitConfig) -> AsyncBaseToolkit:
logger.info(f"Loading builtin toolkit `{toolkit_config.name}` with config {toolkit_config}")
toolkit = TOOLKIT_MAP[toolkit_config.name](toolkit_config)
self._toolkits[toolkit_config.name] = toolkit
return toolkit
async def _load_customized_toolkit(self, toolkit_config: ToolkitConfig) -> AsyncBaseToolkit:
logger.info(f"Loading customized toolkit `{toolkit_config.name}` with config {toolkit_config}")
assert toolkit_config.customized_filepath is not None and toolkit_config.customized_classname is not None
toolkit_class = load_class_from_file(toolkit_config.customized_filepath, toolkit_config.customized_classname)
toolkit = toolkit_class(toolkit_config)
self._toolkits[toolkit_config.name] = toolkit
return toolkit
async def _load_mcp_server(self, toolkit_config: ToolkitConfig) -> MCPServer:
logger.info(f"Loading MCP server `{toolkit_config.name}` with params {toolkit_config.config}")
mcp_server = get_mcp_server(toolkit_config)
server = await self._mcps_exit_stack.enter_async_context(mcp_server)
self._mcp_servers.append(server)
return server
def _get_run_config(self) -> RunConfig:
run_config = RunConfig(
model=self.current_agent.model,
model_settings=self.config.model.model_settings,
workflow_name=self.config.agent.name,
)
return run_config
def _get_context(self) -> dict:
return {
"context_manager": self.context_manager,
"env": self.env,
"agent_config": self.config,
}
def _prepare_run_kwargs(self, input: str | list[TResponseInputItem]) -> dict:
return {
"starting_agent": self.current_agent,
"input": input,
"context": self._get_context(),
"max_turns": self.config.max_turns,
"hooks": self.run_hooks,
"run_config": self._get_run_config(),
}
# wrap `Runner` apis in @openai-agents
async def run(
self, input: str | list[TResponseInputItem], trace_id: str = None, save: bool = False
) -> TaskRecorder:
"""Entrypoint for running the agent
Args:
trace_id: str to identify the run
save: whether to update massage history (use `input_items`)
"""
recorder = self.run_streamed(input, trace_id)
async for _ in recorder.stream_events():
pass
return recorder
def run_streamed(
self, input: str | list[TResponseInputItem], trace_id: str = None, save: bool = False, log_to_db: bool = True
) -> TaskRecorder:
"""Entrypoint for running the agent streamly
Args:
trace_id: str to identify the run
"""
trace_id = trace_id or AgentsUtils.gen_trace_id()
logger.info(f"> trace_id: {trace_id}")
if isinstance(input, list):
assert isinstance(input[-1], dict) and "content" in input[-1], "invalid input format!"
task = input[-1]["content"]
else:
assert isinstance(input, str), "input should be str or list of TResponseInputItem!"
task = input
recorder = TaskRecorder(task=task, input=input, trace_id=trace_id)
recorder._run_impl_task = asyncio.create_task(self._start_streaming(recorder, save, log_to_db))
return recorder
async def _start_streaming(self, recorder: TaskRecorder, save: bool = False, log_to_db: bool = True):
if not self._initialized:
await self.build(recorder.trace_id)
try:
input = recorder.input
if isinstance(input, str): # only add history when input is str?
input = self.input_items + [{"content": input, "role": "user"}]
run_kwargs = self._prepare_run_kwargs(input)
if AgentsUtils.get_current_trace():
run_streamed_result = Runner.run_streamed(**run_kwargs)
else:
with trace(workflow_name="simple_agent", trace_id=recorder.trace_id):
run_streamed_result = Runner.run_streamed(**run_kwargs)
async for event in run_streamed_result.stream_events():
recorder._event_queue.put_nowait(event)
# save final output and trajectory
recorder.add_run_result(run_streamed_result)
if save:
self.input_items = run_streamed_result.to_input_list()
# NOTE: acturally, there are only one agent in SimpleAgent
self.current_agent = run_streamed_result.last_agent
# log to db
if log_to_db:
DBService.add(TrajectoryModel.from_task_recorder(recorder))
except Exception as e:
logger.error(f"Error processing task: {str(e)}")
recorder._event_queue.put_nowait(QueueCompleteSentinel())
recorder._is_complete = True
raise e
finally:
recorder._event_queue.put_nowait(QueueCompleteSentinel())
recorder._is_complete = True
# util apis
async def chat(self, input: str) -> TaskRecorder:
# TODO: set "session-level" tracing for multi-turn chat
recorder = await self.run(input, save=True)
run_result = recorder.get_run_result()
AgentsUtils.print_new_items(run_result.new_items)
return run_result
async def chat_streamed(self, input: str) -> TaskRecorder:
recorder = self.run_streamed(input, save=True)
await AgentsUtils.print_stream_events(recorder.stream_events())
return recorder
def set_instructions(self, instructions: str):
logger.warning("WARNING: reset instructions is dangerous!")
self.current_agent.instructions = instructions
def clear_input_items(self):
# reset chat history
self.input_items = []
|