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270 | class SimpleAgent(BaseAgent):
"""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,
output_type: type[Any] | AgentOutputSchemaBase | None = None,
tool_use_behavior: Literal["run_llm_again", "stop_on_first_tool"] | StopAtTools = "run_llm_again",
):
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.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.current_agent: Agent[TContext] = None # move to task recorder?
self.input_items: list[TResponseInputItem] = []
self._run_hooks: RunHooks = None
self._mcp_servers: list[MCPServer] = []
self._toolkits: list[AsyncBaseToolkit] = []
self._mcps_exit_stack = AsyncExitStack()
self._tools_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 "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):
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...")
await self._tools_exit_stack.aclose()
self._toolkits = []
logger.info("Cleaning up env...")
await self.env.cleanup()
self._initialized = False
async def get_tools(self) -> list[Tool]:
if self.tools:
return self.tools
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():
if toolkit_config.mode == "mcp":
await self._load_mcp_server(toolkit_config)
elif toolkit_config.mode == "builtin":
toolkit = await self._load_toolkit(toolkit_config)
tools_list.extend(await toolkit.get_tools_in_agents())
else:
raise ValueError(f"Unknown toolkit mode: {toolkit_config.mode}")
tool_names = [tool.name for tool in tools_list]
logger.info(f"Loaded {len(tool_names)} tools: {tool_names}")
self.tools = tools_list
return tools_list
async def _load_toolkit(self, toolkit_config: ToolkitConfig) -> AsyncBaseToolkit:
logger.info(f"Loading builtin toolkit `{toolkit_config.name}` with config {toolkit_config}")
toolkit = await self._tools_exit_stack.enter_async_context(TOOLKIT_MAP[toolkit_config.name](toolkit_config))
self._toolkits.append(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}")
server = await self._mcps_exit_stack.enter_async_context(
MCPServerStdio( # FIXME: support other types of servers
name=toolkit_config.name,
params=toolkit_config.config,
client_session_timeout_seconds=20,
)
)
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,
}
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 use history (use `input_items`)
"""
if not self._initialized:
await self.build(trace_id)
trace_id = trace_id or AgentsUtils.gen_trace_id()
logger.info(f"> trace_id: {trace_id}")
if isinstance(input, str):
input = self.input_items + [{"content": input, "role": "user"}]
run_kwargs = self._prepare_run_kwargs(input)
if AgentsUtils.get_current_trace():
run_result = await Runner.run(**run_kwargs)
else:
with trace(workflow_name="simple_agent", trace_id=trace_id):
run_result = await Runner.run(**run_kwargs)
task_recorder = TaskRecorder(input, trace_id)
task_recorder.add_run_result(run_result)
task_recorder.set_final_output(run_result.final_output)
if save:
self.input_items = run_result.to_input_list()
self.current_agent = run_result.last_agent # NOTE: acturally, there are only one agent in SimpleAgent
return task_recorder
def run_streamed(self, input: str | list[TResponseInputItem], trace_id: str = None) -> RunResultStreaming:
"""Entrypoint for running the agent streamly
Notes:
- do not support `save` option for now
Args:
trace_id: str to identify the run
"""
if not self._initialized:
raise RuntimeError("Agent is not initialized. Please call `build` first.")
trace_id = trace_id or AgentsUtils.gen_trace_id()
logger.info(f"> trace_id: {trace_id}")
run_kwargs = self._prepare_run_kwargs(input)
if AgentsUtils.get_current_trace():
return Runner.run_streamed(**run_kwargs)
else:
with trace(workflow_name="simple_agent", trace_id=trace_id):
return Runner.run_streamed(**run_kwargs)
# util apis
async def chat(self, input: str) -> RunResult:
# TODO: set "session-level" tracing for multi-turn chat
self.input_items.append({"content": input, "role": "user"})
recorder = await self.run(self.input_items, 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) -> RunResultStreaming:
self.input_items.append({"content": input, "role": "user"})
run_result_streaming = self.run_streamed(self.input_items)
await AgentsUtils.print_stream_events(run_result_streaming.stream_events())
self.input_items = run_result_streaming.to_input_list()
self.current_agent = run_result_streaming.last_agent
return run_result_streaming
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 = []
def set_run_hooks(self, run_hooks: RunHooks):
# WIP
self._run_hooks = run_hooks
|