from __future__ import annotations import asyncio import dataclasses import inspect from collections.abc import Awaitable, Callable from dataclasses import dataclass, field from typing import TYPE_CHECKING, Any, Generic, Literal, TypeAlias, cast from openai.types.responses.response_prompt_param import ResponsePromptParam from pydantic import BaseModel, TypeAdapter, ValidationError from typing_extensions import NotRequired, TypedDict from ._tool_identity import get_function_tool_approval_keys from .agent_output import AgentOutputSchemaBase from .agent_tool_input import ( AgentAsToolInput, StructuredToolInputBuilder, build_structured_input_schema_info, resolve_agent_tool_input, ) from .agent_tool_state import ( consume_agent_tool_run_result, get_agent_tool_state_scope, peek_agent_tool_run_result, record_agent_tool_run_result, set_agent_tool_state_scope, ) from .exceptions import ModelBehaviorError, UserError from .guardrail import InputGuardrail, OutputGuardrail from .handoffs import Handoff from .logger import logger from .mcp import MCPUtil from .model_settings import ModelSettings from .models.default_models import ( get_default_model_settings, gpt_5_reasoning_settings_required, is_gpt_5_default, ) from .models.interface import Model from .prompts import DynamicPromptFunction, Prompt, PromptUtil from .run_context import RunContextWrapper, TContext from .strict_schema import ensure_strict_json_schema from .tool import ( FunctionTool, FunctionToolResult, Tool, ToolErrorFunction, ToolOrigin, ToolOriginType, _build_handled_function_tool_error_handler, _build_wrapped_function_tool, _log_function_tool_invocation, _parse_function_tool_json_input, default_tool_error_function, prune_orphaned_tool_search_tools, ) from .tool_context import ToolContext from .util import _transforms from .util._types import MaybeAwaitable if TYPE_CHECKING: from openai.types.responses.response_function_tool_call import ResponseFunctionToolCall from .items import ToolApprovalItem from .lifecycle import AgentHooks, RunHooks from .mcp import MCPServer from .memory.session import Session from .result import RunResult, RunResultStreaming from .run import RunConfig from .run_state import RunState from .stream_events import StreamEvent @dataclass class ToolsToFinalOutputResult: is_final_output: bool """Whether this is the final output. If False, the LLM will run again and receive the tool call output. """ final_output: Any | None = None """The final output. Can be None if `is_final_output` is False, otherwise must match the `output_type` of the agent. """ ToolsToFinalOutputFunction: TypeAlias = Callable[ [RunContextWrapper[TContext], list[FunctionToolResult]], MaybeAwaitable[ToolsToFinalOutputResult], ] """A function that takes a run context and a list of tool results, and returns a `ToolsToFinalOutputResult`. """ def _validate_codex_tool_name_collisions(tools: list[Tool]) -> None: codex_tool_names = { tool.name for tool in tools if isinstance(tool, FunctionTool) and bool(getattr(tool, "_is_codex_tool", False)) } if not codex_tool_names: return name_counts: dict[str, int] = {} for tool in tools: tool_name = getattr(tool, "name", None) if isinstance(tool_name, str) and tool_name: name_counts[tool_name] = name_counts.get(tool_name, 0) + 1 duplicate_codex_names = sorted( name for name in codex_tool_names if name_counts.get(name, 0) > 1 ) if duplicate_codex_names: raise UserError( "Duplicate Codex tool names found: " + ", ".join(duplicate_codex_names) + ". Provide a unique codex_tool(name=...) per tool instance." ) class AgentToolStreamEvent(TypedDict): """Streaming event emitted when an agent is invoked as a tool.""" event: StreamEvent """The streaming event from the nested agent run.""" agent: Agent[Any] """The nested agent emitting the event.""" tool_call: ResponseFunctionToolCall | None """The originating tool call, if available.""" class StopAtTools(TypedDict): stop_at_tool_names: list[str] """A list of tool names, any of which will stop the agent from running further.""" class MCPConfig(TypedDict): """Configuration for MCP servers.""" convert_schemas_to_strict: NotRequired[bool] """If True, we will attempt to convert the MCP schemas to strict-mode schemas. This is a best-effort conversion, so some schemas may not be convertible. Defaults to False. """ failure_error_function: NotRequired[ToolErrorFunction | None] """Optional function to convert MCP tool failures into model-visible messages. If explicitly set to None, tool errors will be raised instead. If unset, defaults to default_tool_error_function. """ @dataclass class AgentBase(Generic[TContext]): """Base class for `Agent` and `RealtimeAgent`.""" name: str """The name of the agent.""" handoff_description: str | None = None """A description of the agent. This is used when the agent is used as a handoff, so that an LLM knows what it does and when to invoke it. """ tools: list[Tool] = field(default_factory=list) """A list of tools that the agent can use.""" mcp_servers: list[MCPServer] = field(default_factory=list) """A list of [Model Context Protocol](https://modelcontextprotocol.io/) servers that the agent can use. Every time the agent runs, it will include tools from these servers in the list of available tools. NOTE: You are expected to manage the lifecycle of these servers. Specifically, you must call `server.connect()` before passing it to the agent, and `server.cleanup()` when the server is no longer needed. Consider using `MCPServerManager` from `agents.mcp` to keep connect/cleanup in the same task. """ mcp_config: MCPConfig = field(default_factory=lambda: MCPConfig()) """Configuration for MCP servers.""" async def get_mcp_tools(self, run_context: RunContextWrapper[TContext]) -> list[Tool]: """Fetches the available tools from the MCP servers.""" convert_schemas_to_strict = self.mcp_config.get("convert_schemas_to_strict", False) failure_error_function = self.mcp_config.get( "failure_error_function", default_tool_error_function ) return await MCPUtil.get_all_function_tools( self.mcp_servers, convert_schemas_to_strict, run_context, self, failure_error_function=failure_error_function, ) async def get_all_tools(self, run_context: RunContextWrapper[TContext]) -> list[Tool]: """All agent tools, including MCP tools and function tools.""" mcp_tools = await self.get_mcp_tools(run_context) async def _check_tool_enabled(tool: Tool) -> bool: if not isinstance(tool, FunctionTool): return True attr = tool.is_enabled if isinstance(attr, bool): return attr res = attr(run_context, self) if inspect.isawaitable(res): return bool(await res) return bool(res) results = await asyncio.gather(*(_check_tool_enabled(t) for t in self.tools)) enabled: list[Tool] = [t for t, ok in zip(self.tools, results, strict=False) if ok] all_tools: list[Tool] = prune_orphaned_tool_search_tools([*mcp_tools, *enabled]) _validate_codex_tool_name_collisions(all_tools) return all_tools @dataclass class Agent(AgentBase, Generic[TContext]): """An agent is an AI model configured with instructions, tools, guardrails, handoffs and more. We strongly recommend passing `instructions`, which is the "system prompt" for the agent. In addition, you can pass `handoff_description`, which is a human-readable description of the agent, used when the agent is used inside tools/handoffs. Agents are generic on the context type. The context is a (mutable) object you create. It is passed to tool functions, handoffs, guardrails, etc. See `AgentBase` for base parameters that are shared with `RealtimeAgent`s. """ instructions: ( str | Callable[ [RunContextWrapper[TContext], Agent[TContext]], MaybeAwaitable[str], ] | None ) = None """The instructions for the agent. Will be used as the "system prompt" when this agent is invoked. Describes what the agent should do, and how it responds. Can either be a string, or a function that dynamically generates instructions for the agent. If you provide a function, it will be called with the context and the agent instance. It must return a string. """ prompt: Prompt | DynamicPromptFunction | None = None """A prompt object (or a function that returns a Prompt). Prompts allow you to dynamically configure the instructions, tools and other config for an agent outside of your code. Only usable with OpenAI models, using the Responses API. """ handoffs: list[Agent[Any] | Handoff[TContext, Any]] = field(default_factory=list) """Handoffs are sub-agents that the agent can delegate to. You can provide a list of handoffs, and the agent can choose to delegate to them if relevant. Allows for separation of concerns and modularity. """ model: str | Model | None = None """The model implementation to use when invoking the LLM. By default, if not set, the agent will use the default model configured in `agents.models.get_default_model()` (currently "gpt-4.1"). """ model_settings: ModelSettings = field(default_factory=get_default_model_settings) """Configures model-specific tuning parameters (e.g. temperature, top_p). """ input_guardrails: list[InputGuardrail[TContext]] = field(default_factory=list) """A list of checks that run in parallel to the agent's execution, before generating a response. Runs only if the agent is the first agent in the chain. """ output_guardrails: list[OutputGuardrail[TContext]] = field(default_factory=list) """A list of checks that run on the final output of the agent, after generating a response. Runs only if the agent produces a final output. """ output_type: type[Any] | AgentOutputSchemaBase | None = None """The type of the output object. If not provided, the output will be `str`. In most cases, you should pass a regular Python type (e.g. a dataclass, Pydantic model, TypedDict, etc). You can customize this in two ways: 1. If you want non-strict schemas, pass `AgentOutputSchema(MyClass, strict_json_schema=False)`. 2. If you want to use a custom JSON schema (i.e. without using the SDK's automatic schema) creation, subclass and pass an `AgentOutputSchemaBase` subclass. """ hooks: AgentHooks[TContext] | None = None """A class that receives callbacks on various lifecycle events for this agent. """ tool_use_behavior: ( Literal["run_llm_again", "stop_on_first_tool"] | StopAtTools | ToolsToFinalOutputFunction ) = "run_llm_again" """ This lets you configure how tool use is handled. - "run_llm_again": The default behavior. Tools are run, and then the LLM receives the results and gets to respond. - "stop_on_first_tool": The output from the first tool call is treated as the final result. In other words, it isn’t sent back to the LLM for further processing but is used directly as the final output. - A StopAtTools object: The agent will stop running if any of the tools listed in `stop_at_tool_names` is called. The final output will be the output of the first matching tool call. The LLM does not process the result of the tool call. - A function: If you pass a function, it will be called with the run context and the list of tool results. It must return a `ToolsToFinalOutputResult`, which determines whether the tool calls result in a final output. NOTE: This configuration is specific to FunctionTools. Hosted tools, such as file search, web search, etc. are always processed by the LLM. """ reset_tool_choice: bool = True """Whether to reset the tool choice to the default value after a tool has been called. Defaults to True. This ensures that the agent doesn't enter an infinite loop of tool usage.""" def __post_init__(self): from typing import get_origin if not isinstance(self.name, str): raise TypeError(f"Agent name must be a string, got {type(self.name).__name__}") if self.handoff_description is not None and not isinstance(self.handoff_description, str): raise TypeError( f"Agent handoff_description must be a string or None, " f"got {type(self.handoff_description).__name__}" ) if not isinstance(self.tools, list): raise TypeError(f"Agent tools must be a list, got {type(self.tools).__name__}") if not isinstance(self.mcp_servers, list): raise TypeError( f"Agent mcp_servers must be a list, got {type(self.mcp_servers).__name__}" ) if not isinstance(self.mcp_config, dict): raise TypeError( f"Agent mcp_config must be a dict, got {type(self.mcp_config).__name__}" ) if ( self.instructions is not None and not isinstance(self.instructions, str) and not callable(self.instructions) ): raise TypeError( f"Agent instructions must be a string, callable, or None, " f"got {type(self.instructions).__name__}" ) if ( self.prompt is not None and not callable(self.prompt) and not hasattr(self.prompt, "get") ): raise TypeError( f"Agent prompt must be a Prompt, DynamicPromptFunction, or None, " f"got {type(self.prompt).__name__}" ) if not isinstance(self.handoffs, list): raise TypeError(f"Agent handoffs must be a list, got {type(self.handoffs).__name__}") if self.model is not None and not isinstance(self.model, str): from .models.interface import Model if not isinstance(self.model, Model): raise TypeError( f"Agent model must be a string, Model, or None, got {type(self.model).__name__}" ) if not isinstance(self.model_settings, ModelSettings): raise TypeError( f"Agent model_settings must be a ModelSettings instance, " f"got {type(self.model_settings).__name__}" ) if ( # The user sets a non-default model self.model is not None and ( # The default model is gpt-5 is_gpt_5_default() is True # However, the specified model is not a gpt-5 model and ( isinstance(self.model, str) is False or gpt_5_reasoning_settings_required(self.model) is False # type: ignore ) # The model settings are not customized for the specified model and self.model_settings == get_default_model_settings() ) ): # In this scenario, we should use a generic model settings # because non-gpt-5 models are not compatible with the default gpt-5 model settings. # This is a best-effort attempt to make the agent work with non-gpt-5 models. self.model_settings = ModelSettings() if not isinstance(self.input_guardrails, list): raise TypeError( f"Agent input_guardrails must be a list, got {type(self.input_guardrails).__name__}" ) if not isinstance(self.output_guardrails, list): raise TypeError( f"Agent output_guardrails must be a list, " f"got {type(self.output_guardrails).__name__}" ) if self.output_type is not None: from .agent_output import AgentOutputSchemaBase if not ( isinstance(self.output_type, type | AgentOutputSchemaBase) or get_origin(self.output_type) is not None ): raise TypeError( f"Agent output_type must be a type, AgentOutputSchemaBase, or None, " f"got {type(self.output_type).__name__}" ) if self.hooks is not None: from .lifecycle import AgentHooksBase if not isinstance(self.hooks, AgentHooksBase): raise TypeError( f"Agent hooks must be an AgentHooks instance or None, " f"got {type(self.hooks).__name__}" ) if ( not ( isinstance(self.tool_use_behavior, str) and self.tool_use_behavior in ["run_llm_again", "stop_on_first_tool"] ) and not isinstance(self.tool_use_behavior, dict) and not callable(self.tool_use_behavior) ): raise TypeError( f"Agent tool_use_behavior must be 'run_llm_again', 'stop_on_first_tool', " f"StopAtTools dict, or callable, got {type(self.tool_use_behavior).__name__}" ) if not isinstance(self.reset_tool_choice, bool): raise TypeError( f"Agent reset_tool_choice must be a boolean, " f"got {type(self.reset_tool_choice).__name__}" ) def clone(self, **kwargs: Any) -> Agent[TContext]: """Make a copy of the agent, with the given arguments changed. Notes: - Uses `dataclasses.replace`, which performs a **shallow copy**. - Mutable attributes like `tools` and `handoffs` are shallow-copied: new list objects are created only if overridden, but their contents (tool functions and handoff objects) are shared with the original. - To modify these independently, pass new lists when calling `clone()`. Example: ```python new_agent = agent.clone(instructions="New instructions") ``` """ return dataclasses.replace(self, **kwargs) def as_tool( self, tool_name: str | None, tool_description: str | None, custom_output_extractor: ( Callable[[RunResult | RunResultStreaming], Awaitable[str]] | None ) = None, is_enabled: bool | Callable[[RunContextWrapper[Any], AgentBase[Any]], MaybeAwaitable[bool]] = True, on_stream: Callable[[AgentToolStreamEvent], MaybeAwaitable[None]] | None = None, run_config: RunConfig | None = None, max_turns: int | None = None, hooks: RunHooks[TContext] | None = None, previous_response_id: str | None = None, conversation_id: str | None = None, session: Session | None = None, failure_error_function: ToolErrorFunction | None = default_tool_error_function, needs_approval: bool | Callable[[RunContextWrapper[Any], dict[str, Any], str], Awaitable[bool]] = False, parameters: type[Any] | None = None, input_builder: StructuredToolInputBuilder | None = None, include_input_schema: bool = False, ) -> FunctionTool: """Transform this agent into a tool, callable by other agents. This is different from handoffs in two ways: 1. In handoffs, the new agent receives the conversation history. In this tool, the new agent receives generated input. 2. In handoffs, the new agent takes over the conversation. In this tool, the new agent is called as a tool, and the conversation is continued by the original agent. Args: tool_name: The name of the tool. If not provided, the agent's name will be used. tool_description: The description of the tool, which should indicate what it does and when to use it. custom_output_extractor: A function that extracts the output from the agent. If not provided, the last message from the agent will be used. Nested run results expose `agent_tool_invocation` metadata when this agent is invoked via `as_tool()`. is_enabled: Whether the tool is enabled. Can be a bool or a callable that takes the run context and agent and returns whether the tool is enabled. Disabled tools are hidden from the LLM at runtime. on_stream: Optional callback (sync or async) to receive streaming events from the nested agent run. The callback receives an `AgentToolStreamEvent` containing the nested agent, the originating tool call (when available), and each stream event. When provided, the nested agent is executed in streaming mode. failure_error_function: If provided, generate an error message when the tool (agent) run fails. The message is sent to the LLM. If None, the exception is raised instead. needs_approval: Bool or callable to decide if this agent tool should pause for approval. parameters: Structured input type for the tool arguments (dataclass or Pydantic model). input_builder: Optional function to build the nested agent input from structured data. include_input_schema: Whether to include the full JSON schema in structured input. """ def _is_supported_parameters(value: Any) -> bool: if not isinstance(value, type): return False if dataclasses.is_dataclass(value): return True return issubclass(value, BaseModel) tool_name_resolved = tool_name or _transforms.transform_string_function_style(self.name) tool_description_resolved = tool_description or "" has_custom_parameters = parameters is not None include_schema = bool(include_input_schema and has_custom_parameters) should_capture_tool_input = bool( has_custom_parameters or include_schema or input_builder is not None ) if parameters is None: params_adapter = TypeAdapter(AgentAsToolInput) params_schema = ensure_strict_json_schema(params_adapter.json_schema()) else: if not _is_supported_parameters(parameters): raise TypeError("Agent tool parameters must be a dataclass or Pydantic model type.") params_adapter = TypeAdapter(parameters) params_schema = ensure_strict_json_schema(params_adapter.json_schema()) schema_info = build_structured_input_schema_info( params_schema, include_json_schema=include_schema, ) def _normalize_tool_input(parsed: Any, tool_name: str) -> Any: # Prefer JSON mode so structured params (datetime/UUID/Decimal, etc.) serialize cleanly. try: return params_adapter.dump_python(parsed, mode="json") except Exception as exc: raise ModelBehaviorError( f"Failed to serialize structured tool input for {tool_name}: {exc}" ) from exc async def _run_agent_impl(context: ToolContext, input_json: str) -> Any: from .run import DEFAULT_MAX_TURNS, Runner from .tool_context import ToolContext tool_name = ( context.tool_name if isinstance(context, ToolContext) else tool_name_resolved ) json_data = _parse_function_tool_json_input( tool_name=tool_name, input_json=input_json, ) _log_function_tool_invocation(tool_name=tool_name, input_json=input_json) try: parsed_params = params_adapter.validate_python(json_data) except ValidationError as exc: raise ModelBehaviorError(f"Invalid JSON input for tool {tool_name}: {exc}") from exc params_data = _normalize_tool_input(parsed_params, tool_name) resolved_input = await resolve_agent_tool_input( params=params_data, schema_info=schema_info if should_capture_tool_input else None, input_builder=input_builder, ) if not isinstance(resolved_input, str) and not isinstance(resolved_input, list): raise ModelBehaviorError("Agent tool called with invalid input") resolved_max_turns = max_turns if max_turns is not None else DEFAULT_MAX_TURNS resolved_run_config = run_config if resolved_run_config is None and isinstance(context, ToolContext): resolved_run_config = context.run_config tool_state_scope_id = get_agent_tool_state_scope(context) if isinstance(context, ToolContext): # Use a fresh ToolContext to avoid sharing approval state with parent runs. nested_context = ToolContext( context=context.context, usage=context.usage, tool_name=context.tool_name, tool_call_id=context.tool_call_id, tool_arguments=context.tool_arguments, tool_call=context.tool_call, tool_namespace=context.tool_namespace, agent=context.agent, run_config=resolved_run_config, ) set_agent_tool_state_scope(nested_context, tool_state_scope_id) if should_capture_tool_input: nested_context.tool_input = params_data elif isinstance(context, RunContextWrapper): if should_capture_tool_input: nested_context = RunContextWrapper(context=context.context) set_agent_tool_state_scope(nested_context, tool_state_scope_id) nested_context.tool_input = params_data else: nested_context = context.context else: if should_capture_tool_input: nested_context = RunContextWrapper(context=context) set_agent_tool_state_scope(nested_context, tool_state_scope_id) nested_context.tool_input = params_data else: nested_context = context run_result: RunResult | RunResultStreaming | None = None resume_state: RunState | None = None should_record_run_result = True def _nested_approvals_status( interruptions: list[ToolApprovalItem], ) -> Literal["approved", "pending", "rejected"]: has_pending = False has_decision = False for interruption in interruptions: call_id = interruption.call_id if not call_id: has_pending = True continue tool_namespace = RunContextWrapper._resolve_tool_namespace(interruption) status = context.get_approval_status( interruption.tool_name or "", call_id, tool_namespace=tool_namespace, existing_pending=interruption, ) if status is False: return "rejected" if status is True: has_decision = True if status is None: has_pending = True if has_decision: return "approved" if has_pending: return "pending" return "approved" def _apply_nested_approvals( nested_context: RunContextWrapper[Any], parent_context: RunContextWrapper[Any], interruptions: list[ToolApprovalItem], ) -> None: def _find_mirrored_approval_record( interruption: ToolApprovalItem, *, approved: bool, ) -> Any | None: candidate_keys = list(RunContextWrapper._resolve_approval_keys(interruption)) for candidate_key in get_function_tool_approval_keys( tool_name=RunContextWrapper._resolve_tool_name(interruption), tool_namespace=RunContextWrapper._resolve_tool_namespace(interruption), tool_lookup_key=RunContextWrapper._resolve_tool_lookup_key(interruption), include_legacy_deferred_key=True, ): if candidate_key not in candidate_keys: candidate_keys.append(candidate_key) fallback: Any | None = None for candidate_key in candidate_keys: candidate = parent_context._approvals.get(candidate_key) if candidate is None: continue if approved and candidate.approved is True: return candidate if not approved and candidate.rejected is True: return candidate if fallback is None: fallback = candidate return fallback for interruption in interruptions: call_id = interruption.call_id if not call_id: continue tool_name = RunContextWrapper._resolve_tool_name(interruption) tool_namespace = RunContextWrapper._resolve_tool_namespace(interruption) approval_key = RunContextWrapper._resolve_approval_key(interruption) status = parent_context.get_approval_status( tool_name, call_id, tool_namespace=tool_namespace, existing_pending=interruption, ) if status is None: continue approval_record = parent_context._approvals.get(approval_key) if approval_record is None: approval_record = _find_mirrored_approval_record( interruption, approved=status, ) if status is True: always_approve = bool(approval_record and approval_record.approved is True) nested_context.approve_tool( interruption, always_approve=always_approve, ) else: always_reject = bool(approval_record and approval_record.rejected is True) nested_context.reject_tool( interruption, always_reject=always_reject, ) if isinstance(context, ToolContext) and context.tool_call is not None: pending_run_result = peek_agent_tool_run_result( context.tool_call, scope_id=tool_state_scope_id, ) if pending_run_result and getattr(pending_run_result, "interruptions", None): status = _nested_approvals_status(pending_run_result.interruptions) if status == "pending": run_result = pending_run_result should_record_run_result = False elif status in ("approved", "rejected"): resume_state = pending_run_result.to_state() if resume_state._context is not None: # Apply only explicit parent approvals to the nested resumed run. _apply_nested_approvals( resume_state._context, context, pending_run_result.interruptions, ) consume_agent_tool_run_result( context.tool_call, scope_id=tool_state_scope_id, ) if run_result is None: if on_stream is not None: stream_handler = on_stream run_result_streaming = Runner.run_streamed( starting_agent=cast(Agent[Any], self), input=resume_state or resolved_input, context=None if resume_state is not None else cast(Any, nested_context), run_config=resolved_run_config, max_turns=resolved_max_turns, hooks=hooks, previous_response_id=None if resume_state is not None else previous_response_id, conversation_id=None if resume_state is not None else conversation_id, session=session, ) # Dispatch callbacks in the background so slow handlers do not block # event consumption. event_queue: asyncio.Queue[AgentToolStreamEvent | None] = asyncio.Queue() async def _run_handler(payload: AgentToolStreamEvent) -> None: """Execute the user callback while capturing exceptions.""" try: maybe_result = stream_handler(payload) if inspect.isawaitable(maybe_result): await maybe_result except Exception: logger.exception( "Error while handling on_stream event for agent tool %s.", self.name, ) async def dispatch_stream_events() -> None: while True: payload = await event_queue.get() is_sentinel = payload is None # None marks the end of the stream. try: if payload is not None: await _run_handler(payload) finally: event_queue.task_done() if is_sentinel: break dispatch_task = asyncio.create_task(dispatch_stream_events()) stream_iteration_cancelled = False try: from .stream_events import AgentUpdatedStreamEvent current_agent = run_result_streaming.current_agent try: async for event in run_result_streaming.stream_events(): if isinstance(event, AgentUpdatedStreamEvent): current_agent = event.new_agent payload: AgentToolStreamEvent = { "event": event, "agent": current_agent, "tool_call": context.tool_call, } await event_queue.put(payload) except asyncio.CancelledError: stream_iteration_cancelled = True raise finally: if stream_iteration_cancelled: dispatch_task.cancel() try: await dispatch_task except asyncio.CancelledError: pass else: await event_queue.put(None) await event_queue.join() await dispatch_task run_result = run_result_streaming else: run_result = await Runner.run( starting_agent=cast(Agent[Any], self), input=resume_state or resolved_input, context=None if resume_state is not None else cast(Any, nested_context), run_config=resolved_run_config, max_turns=resolved_max_turns, hooks=hooks, previous_response_id=None if resume_state is not None else previous_response_id, conversation_id=None if resume_state is not None else conversation_id, session=session, ) assert run_result is not None # Store the run result by tool call identity so nested interruptions can be read later. interruptions = getattr(run_result, "interruptions", None) if isinstance(context, ToolContext) and context.tool_call is not None and interruptions: if should_record_run_result: record_agent_tool_run_result( context.tool_call, run_result, scope_id=tool_state_scope_id, ) if custom_output_extractor: return await custom_output_extractor(run_result) if run_result.final_output is not None and ( not isinstance(run_result.final_output, str) or run_result.final_output != "" ): return run_result.final_output from .items import ItemHelpers, MessageOutputItem, ToolCallOutputItem for item in reversed(run_result.new_items): if isinstance(item, MessageOutputItem): text_output = ItemHelpers.text_message_output(item) if text_output: return text_output if ( isinstance(item, ToolCallOutputItem) and isinstance(item.output, str) and item.output ): return item.output return run_result.final_output run_agent_tool = _build_wrapped_function_tool( name=tool_name_resolved, description=tool_description_resolved, params_json_schema=params_schema, invoke_tool_impl=_run_agent_impl, on_handled_error=_build_handled_function_tool_error_handler( span_message="Error running tool (non-fatal)", span_message_for_json_decode_error="Error running tool", log_label="Tool", ), failure_error_function=failure_error_function, strict_json_schema=True, is_enabled=is_enabled, needs_approval=needs_approval, tool_origin=ToolOrigin( type=ToolOriginType.AGENT_AS_TOOL, agent_name=self.name, agent_tool_name=tool_name_resolved, ), ) run_agent_tool._is_agent_tool = True run_agent_tool._agent_instance = self return run_agent_tool async def get_system_prompt(self, run_context: RunContextWrapper[TContext]) -> str | None: if isinstance(self.instructions, str): return self.instructions elif callable(self.instructions): # Inspect the signature of the instructions function sig = inspect.signature(self.instructions) params = list(sig.parameters.values()) # Enforce exactly 2 parameters if len(params) != 2: raise TypeError( f"'instructions' callable must accept exactly 2 arguments (context, agent), " f"but got {len(params)}: {[p.name for p in params]}" ) # Call the instructions function properly if inspect.iscoroutinefunction(self.instructions): return await cast(Awaitable[str], self.instructions(run_context, self)) else: return cast(str, self.instructions(run_context, self)) elif self.instructions is not None: logger.error( f"Instructions must be a string or a callable function, " f"got {type(self.instructions).__name__}" ) return None async def get_prompt( self, run_context: RunContextWrapper[TContext] ) -> ResponsePromptParam | None: """Get the prompt for the agent.""" from ._public_agent import get_public_agent return await PromptUtil.to_model_input( self.prompt, run_context, cast(Agent[TContext], get_public_agent(self)), )