Spaces:
Running
Running
File size: 46,004 Bytes
d631808 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 |
from __future__ import annotations
import asyncio
import copy
from dataclasses import dataclass, field
from typing import Any, Generic, cast
from openai.types.responses import ResponseCompletedEvent
from openai.types.responses.response_prompt_param import (
ResponsePromptParam,
)
from typing_extensions import NotRequired, TypedDict, Unpack
from ._run_impl import (
AgentToolUseTracker,
NextStepFinalOutput,
NextStepHandoff,
NextStepRunAgain,
QueueCompleteSentinel,
RunImpl,
SingleStepResult,
TraceCtxManager,
get_model_tracing_impl,
)
from .agent import Agent
from .agent_output import AgentOutputSchema, AgentOutputSchemaBase
from .exceptions import (
AgentsException,
InputGuardrailTripwireTriggered,
MaxTurnsExceeded,
ModelBehaviorError,
OutputGuardrailTripwireTriggered,
RunErrorDetails,
)
from .guardrail import (
InputGuardrail,
InputGuardrailResult,
OutputGuardrail,
OutputGuardrailResult,
)
from .handoffs import Handoff, HandoffInputFilter, handoff
from .items import ItemHelpers, ModelResponse, RunItem, TResponseInputItem
from .lifecycle import RunHooks
from .logger import logger
from .model_settings import ModelSettings
from .models.interface import Model, ModelProvider
from .models.multi_provider import MultiProvider
from .result import RunResult, RunResultStreaming
from .run_context import RunContextWrapper, TContext
from .stream_events import AgentUpdatedStreamEvent, RawResponsesStreamEvent
from .tool import Tool
from .tracing import Span, SpanError, agent_span, get_current_trace, trace
from .tracing.span_data import AgentSpanData
from .usage import Usage
from .util import _coro, _error_tracing
DEFAULT_MAX_TURNS = 10
DEFAULT_AGENT_RUNNER: AgentRunner = None # type: ignore
# the value is set at the end of the module
def set_default_agent_runner(runner: AgentRunner | None) -> None:
"""
WARNING: this class is experimental and not part of the public API
It should not be used directly.
"""
global DEFAULT_AGENT_RUNNER
DEFAULT_AGENT_RUNNER = runner or AgentRunner()
def get_default_agent_runner() -> AgentRunner:
"""
WARNING: this class is experimental and not part of the public API
It should not be used directly.
"""
global DEFAULT_AGENT_RUNNER
return DEFAULT_AGENT_RUNNER
@dataclass
class RunConfig:
"""Configures settings for the entire agent run."""
model: str | Model | None = None
"""The model to use for the entire agent run. If set, will override the model set on every
agent. The model_provider passed in below must be able to resolve this model name.
"""
model_provider: ModelProvider = field(default_factory=MultiProvider)
"""The model provider to use when looking up string model names. Defaults to OpenAI."""
model_settings: ModelSettings | None = None
"""Configure global model settings. Any non-null values will override the agent-specific model
settings.
"""
handoff_input_filter: HandoffInputFilter | None = None
"""A global input filter to apply to all handoffs. If `Handoff.input_filter` is set, then that
will take precedence. The input filter allows you to edit the inputs that are sent to the new
agent. See the documentation in `Handoff.input_filter` for more details.
"""
input_guardrails: list[InputGuardrail[Any]] | None = None
"""A list of input guardrails to run on the initial run input."""
output_guardrails: list[OutputGuardrail[Any]] | None = None
"""A list of output guardrails to run on the final output of the run."""
tracing_disabled: bool = False
"""Whether tracing is disabled for the agent run. If disabled, we will not trace the agent run.
"""
trace_include_sensitive_data: bool = True
"""Whether we include potentially sensitive data (for example: inputs/outputs of tool calls or
LLM generations) in traces. If False, we'll still create spans for these events, but the
sensitive data will not be included.
"""
workflow_name: str = "Agent workflow"
"""The name of the run, used for tracing. Should be a logical name for the run, like
"Code generation workflow" or "Customer support agent".
"""
trace_id: str | None = None
"""A custom trace ID to use for tracing. If not provided, we will generate a new trace ID."""
group_id: str | None = None
"""
A grouping identifier to use for tracing, to link multiple traces from the same conversation
or process. For example, you might use a chat thread ID.
"""
trace_metadata: dict[str, Any] | None = None
"""
An optional dictionary of additional metadata to include with the trace.
"""
class RunOptions(TypedDict, Generic[TContext]):
"""Arguments for ``AgentRunner`` methods."""
context: NotRequired[TContext | None]
"""The context for the run."""
max_turns: NotRequired[int]
"""The maximum number of turns to run for."""
hooks: NotRequired[RunHooks[TContext] | None]
"""Lifecycle hooks for the run."""
run_config: NotRequired[RunConfig | None]
"""Run configuration."""
previous_response_id: NotRequired[str | None]
"""The ID of the previous response, if any."""
class Runner:
@classmethod
async def run(
cls,
starting_agent: Agent[TContext],
input: str | list[TResponseInputItem],
*,
context: TContext | None = None,
max_turns: int = DEFAULT_MAX_TURNS,
hooks: RunHooks[TContext] | None = None,
run_config: RunConfig | None = None,
previous_response_id: str | None = None,
) -> RunResult:
"""Run a workflow starting at the given agent. The agent will run in a loop until a final
output is generated. The loop runs like so:
1. The agent is invoked with the given input.
2. If there is a final output (i.e. the agent produces something of type
`agent.output_type`, the loop terminates.
3. If there's a handoff, we run the loop again, with the new agent.
4. Else, we run tool calls (if any), and re-run the loop.
In two cases, the agent may raise an exception:
1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised.
2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered exception is raised.
Note that only the first agent's input guardrails are run.
Args:
starting_agent: The starting agent to run.
input: The initial input to the agent. You can pass a single string for a user message,
or a list of input items.
context: The context to run the agent with.
max_turns: The maximum number of turns to run the agent for. A turn is defined as one
AI invocation (including any tool calls that might occur).
hooks: An object that receives callbacks on various lifecycle events.
run_config: Global settings for the entire agent run.
previous_response_id: The ID of the previous response, if using OpenAI models via the
Responses API, this allows you to skip passing in input from the previous turn.
Returns:
A run result containing all the inputs, guardrail results and the output of the last
agent. Agents may perform handoffs, so we don't know the specific type of the output.
"""
runner = DEFAULT_AGENT_RUNNER
return await runner.run(
starting_agent,
input,
context=context,
max_turns=max_turns,
hooks=hooks,
run_config=run_config,
previous_response_id=previous_response_id,
)
@classmethod
def run_sync(
cls,
starting_agent: Agent[TContext],
input: str | list[TResponseInputItem],
*,
context: TContext | None = None,
max_turns: int = DEFAULT_MAX_TURNS,
hooks: RunHooks[TContext] | None = None,
run_config: RunConfig | None = None,
previous_response_id: str | None = None,
) -> RunResult:
"""Run a workflow synchronously, starting at the given agent. Note that this just wraps the
`run` method, so it will not work if there's already an event loop (e.g. inside an async
function, or in a Jupyter notebook or async context like FastAPI). For those cases, use
the `run` method instead.
The agent will run in a loop until a final output is generated. The loop runs like so:
1. The agent is invoked with the given input.
2. If there is a final output (i.e. the agent produces something of type
`agent.output_type`, the loop terminates.
3. If there's a handoff, we run the loop again, with the new agent.
4. Else, we run tool calls (if any), and re-run the loop.
In two cases, the agent may raise an exception:
1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised.
2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered exception is raised.
Note that only the first agent's input guardrails are run.
Args:
starting_agent: The starting agent to run.
input: The initial input to the agent. You can pass a single string for a user message,
or a list of input items.
context: The context to run the agent with.
max_turns: The maximum number of turns to run the agent for. A turn is defined as one
AI invocation (including any tool calls that might occur).
hooks: An object that receives callbacks on various lifecycle events.
run_config: Global settings for the entire agent run.
previous_response_id: The ID of the previous response, if using OpenAI models via the
Responses API, this allows you to skip passing in input from the previous turn.
Returns:
A run result containing all the inputs, guardrail results and the output of the last
agent. Agents may perform handoffs, so we don't know the specific type of the output.
"""
runner = DEFAULT_AGENT_RUNNER
return runner.run_sync(
starting_agent,
input,
context=context,
max_turns=max_turns,
hooks=hooks,
run_config=run_config,
previous_response_id=previous_response_id,
)
@classmethod
def run_streamed(
cls,
starting_agent: Agent[TContext],
input: str | list[TResponseInputItem],
context: TContext | None = None,
max_turns: int = DEFAULT_MAX_TURNS,
hooks: RunHooks[TContext] | None = None,
run_config: RunConfig | None = None,
previous_response_id: str | None = None,
) -> RunResultStreaming:
"""Run a workflow starting at the given agent in streaming mode. The returned result object
contains a method you can use to stream semantic events as they are generated.
The agent will run in a loop until a final output is generated. The loop runs like so:
1. The agent is invoked with the given input.
2. If there is a final output (i.e. the agent produces something of type
`agent.output_type`, the loop terminates.
3. If there's a handoff, we run the loop again, with the new agent.
4. Else, we run tool calls (if any), and re-run the loop.
In two cases, the agent may raise an exception:
1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised.
2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered exception is raised.
Note that only the first agent's input guardrails are run.
Args:
starting_agent: The starting agent to run.
input: The initial input to the agent. You can pass a single string for a user message,
or a list of input items.
context: The context to run the agent with.
max_turns: The maximum number of turns to run the agent for. A turn is defined as one
AI invocation (including any tool calls that might occur).
hooks: An object that receives callbacks on various lifecycle events.
run_config: Global settings for the entire agent run.
previous_response_id: The ID of the previous response, if using OpenAI models via the
Responses API, this allows you to skip passing in input from the previous turn.
Returns:
A result object that contains data about the run, as well as a method to stream events.
"""
runner = DEFAULT_AGENT_RUNNER
return runner.run_streamed(
starting_agent,
input,
context=context,
max_turns=max_turns,
hooks=hooks,
run_config=run_config,
previous_response_id=previous_response_id,
)
class AgentRunner:
"""
WARNING: this class is experimental and not part of the public API
It should not be used directly or subclassed.
"""
async def run(
self,
starting_agent: Agent[TContext],
input: str | list[TResponseInputItem],
**kwargs: Unpack[RunOptions[TContext]],
) -> RunResult:
context = kwargs.get("context")
max_turns = kwargs.get("max_turns", DEFAULT_MAX_TURNS)
hooks = kwargs.get("hooks")
run_config = kwargs.get("run_config")
previous_response_id = kwargs.get("previous_response_id")
if hooks is None:
hooks = RunHooks[Any]()
if run_config is None:
run_config = RunConfig()
tool_use_tracker = AgentToolUseTracker()
with TraceCtxManager(
workflow_name=run_config.workflow_name,
trace_id=run_config.trace_id,
group_id=run_config.group_id,
metadata=run_config.trace_metadata,
disabled=run_config.tracing_disabled,
):
current_turn = 0
original_input: str | list[TResponseInputItem] = copy.deepcopy(input)
generated_items: list[RunItem] = []
model_responses: list[ModelResponse] = []
context_wrapper: RunContextWrapper[TContext] = RunContextWrapper(
context=context, # type: ignore
)
input_guardrail_results: list[InputGuardrailResult] = []
current_span: Span[AgentSpanData] | None = None
current_agent = starting_agent
should_run_agent_start_hooks = True
try:
while True:
all_tools = await AgentRunner._get_all_tools(current_agent, context_wrapper)
# Start an agent span if we don't have one. This span is ended if the current
# agent changes, or if the agent loop ends.
if current_span is None:
handoff_names = [
h.agent_name for h in AgentRunner._get_handoffs(current_agent)
]
if output_schema := AgentRunner._get_output_schema(current_agent):
output_type_name = output_schema.name()
else:
output_type_name = "str"
current_span = agent_span(
name=current_agent.name,
handoffs=handoff_names,
output_type=output_type_name,
)
current_span.start(mark_as_current=True)
current_span.span_data.tools = [t.name for t in all_tools]
current_turn += 1
if current_turn > max_turns:
_error_tracing.attach_error_to_span(
current_span,
SpanError(
message="Max turns exceeded",
data={"max_turns": max_turns},
),
)
raise MaxTurnsExceeded(f"Max turns ({max_turns}) exceeded")
logger.debug(
f"Running agent {current_agent.name} (turn {current_turn})",
)
if current_turn == 1:
input_guardrail_results, turn_result = await asyncio.gather(
self._run_input_guardrails(
starting_agent,
starting_agent.input_guardrails
+ (run_config.input_guardrails or []),
copy.deepcopy(input),
context_wrapper,
),
self._run_single_turn(
agent=current_agent,
all_tools=all_tools,
original_input=original_input,
generated_items=generated_items,
hooks=hooks,
context_wrapper=context_wrapper,
run_config=run_config,
should_run_agent_start_hooks=should_run_agent_start_hooks,
tool_use_tracker=tool_use_tracker,
previous_response_id=previous_response_id,
),
)
else:
turn_result = await self._run_single_turn(
agent=current_agent,
all_tools=all_tools,
original_input=original_input,
generated_items=generated_items,
hooks=hooks,
context_wrapper=context_wrapper,
run_config=run_config,
should_run_agent_start_hooks=should_run_agent_start_hooks,
tool_use_tracker=tool_use_tracker,
previous_response_id=previous_response_id,
)
should_run_agent_start_hooks = False
model_responses.append(turn_result.model_response)
original_input = turn_result.original_input
generated_items = turn_result.generated_items
if isinstance(turn_result.next_step, NextStepFinalOutput):
output_guardrail_results = await self._run_output_guardrails(
current_agent.output_guardrails + (run_config.output_guardrails or []),
current_agent,
turn_result.next_step.output,
context_wrapper,
)
return RunResult(
input=original_input,
new_items=generated_items,
raw_responses=model_responses,
final_output=turn_result.next_step.output,
_last_agent=current_agent,
input_guardrail_results=input_guardrail_results,
output_guardrail_results=output_guardrail_results,
context_wrapper=context_wrapper,
)
elif isinstance(turn_result.next_step, NextStepHandoff):
current_agent = cast(Agent[TContext], turn_result.next_step.new_agent)
current_span.finish(reset_current=True)
current_span = None
should_run_agent_start_hooks = True
elif isinstance(turn_result.next_step, NextStepRunAgain):
pass
else:
raise AgentsException(
f"Unknown next step type: {type(turn_result.next_step)}"
)
except AgentsException as exc:
exc.run_data = RunErrorDetails(
input=original_input,
new_items=generated_items,
raw_responses=model_responses,
last_agent=current_agent,
context_wrapper=context_wrapper,
input_guardrail_results=input_guardrail_results,
output_guardrail_results=[],
)
raise
finally:
if current_span:
current_span.finish(reset_current=True)
def run_sync(
self,
starting_agent: Agent[TContext],
input: str | list[TResponseInputItem],
**kwargs: Unpack[RunOptions[TContext]],
) -> RunResult:
context = kwargs.get("context")
max_turns = kwargs.get("max_turns", DEFAULT_MAX_TURNS)
hooks = kwargs.get("hooks")
run_config = kwargs.get("run_config")
previous_response_id = kwargs.get("previous_response_id")
return asyncio.get_event_loop().run_until_complete(
self.run(
starting_agent,
input,
context=context,
max_turns=max_turns,
hooks=hooks,
run_config=run_config,
previous_response_id=previous_response_id,
)
)
def run_streamed(
self,
starting_agent: Agent[TContext],
input: str | list[TResponseInputItem],
**kwargs: Unpack[RunOptions[TContext]],
) -> RunResultStreaming:
context = kwargs.get("context")
max_turns = kwargs.get("max_turns", DEFAULT_MAX_TURNS)
hooks = kwargs.get("hooks")
run_config = kwargs.get("run_config")
previous_response_id = kwargs.get("previous_response_id")
if hooks is None:
hooks = RunHooks[Any]()
if run_config is None:
run_config = RunConfig()
# If there's already a trace, we don't create a new one. In addition, we can't end the
# trace here, because the actual work is done in `stream_events` and this method ends
# before that.
new_trace = (
None
if get_current_trace()
else trace(
workflow_name=run_config.workflow_name,
trace_id=run_config.trace_id,
group_id=run_config.group_id,
metadata=run_config.trace_metadata,
disabled=run_config.tracing_disabled,
)
)
output_schema = AgentRunner._get_output_schema(starting_agent)
context_wrapper: RunContextWrapper[TContext] = RunContextWrapper(
context=context # type: ignore
)
streamed_result = RunResultStreaming(
input=copy.deepcopy(input),
new_items=[],
current_agent=starting_agent,
raw_responses=[],
final_output=None,
is_complete=False,
current_turn=0,
max_turns=max_turns,
input_guardrail_results=[],
output_guardrail_results=[],
_current_agent_output_schema=output_schema,
trace=new_trace,
context_wrapper=context_wrapper,
)
# Kick off the actual agent loop in the background and return the streamed result object.
streamed_result._run_impl_task = asyncio.create_task(
self._start_streaming(
starting_input=input,
streamed_result=streamed_result,
starting_agent=starting_agent,
max_turns=max_turns,
hooks=hooks,
context_wrapper=context_wrapper,
run_config=run_config,
previous_response_id=previous_response_id,
)
)
return streamed_result
@classmethod
async def _run_input_guardrails_with_queue(
cls,
agent: Agent[Any],
guardrails: list[InputGuardrail[TContext]],
input: str | list[TResponseInputItem],
context: RunContextWrapper[TContext],
streamed_result: RunResultStreaming,
parent_span: Span[Any],
):
queue = streamed_result._input_guardrail_queue
# We'll run the guardrails and push them onto the queue as they complete
guardrail_tasks = [
asyncio.create_task(
RunImpl.run_single_input_guardrail(agent, guardrail, input, context)
)
for guardrail in guardrails
]
guardrail_results = []
try:
for done in asyncio.as_completed(guardrail_tasks):
result = await done
if result.output.tripwire_triggered:
_error_tracing.attach_error_to_span(
parent_span,
SpanError(
message="Guardrail tripwire triggered",
data={
"guardrail": result.guardrail.get_name(),
"type": "input_guardrail",
},
),
)
queue.put_nowait(result)
guardrail_results.append(result)
except Exception:
for t in guardrail_tasks:
t.cancel()
raise
streamed_result.input_guardrail_results = guardrail_results
@classmethod
async def _start_streaming(
cls,
starting_input: str | list[TResponseInputItem],
streamed_result: RunResultStreaming,
starting_agent: Agent[TContext],
max_turns: int,
hooks: RunHooks[TContext],
context_wrapper: RunContextWrapper[TContext],
run_config: RunConfig,
previous_response_id: str | None,
):
if streamed_result.trace:
streamed_result.trace.start(mark_as_current=True)
current_span: Span[AgentSpanData] | None = None
current_agent = starting_agent
current_turn = 0
should_run_agent_start_hooks = True
tool_use_tracker = AgentToolUseTracker()
streamed_result._event_queue.put_nowait(AgentUpdatedStreamEvent(new_agent=current_agent))
try:
while True:
if streamed_result.is_complete:
break
all_tools = await cls._get_all_tools(current_agent, context_wrapper)
# Start an agent span if we don't have one. This span is ended if the current
# agent changes, or if the agent loop ends.
if current_span is None:
handoff_names = [h.agent_name for h in cls._get_handoffs(current_agent)]
if output_schema := cls._get_output_schema(current_agent):
output_type_name = output_schema.name()
else:
output_type_name = "str"
current_span = agent_span(
name=current_agent.name,
handoffs=handoff_names,
output_type=output_type_name,
)
current_span.start(mark_as_current=True)
tool_names = [t.name for t in all_tools]
current_span.span_data.tools = tool_names
current_turn += 1
streamed_result.current_turn = current_turn
if current_turn > max_turns:
_error_tracing.attach_error_to_span(
current_span,
SpanError(
message="Max turns exceeded",
data={"max_turns": max_turns},
),
)
streamed_result._event_queue.put_nowait(QueueCompleteSentinel())
break
if current_turn == 1:
# Run the input guardrails in the background and put the results on the queue
streamed_result._input_guardrails_task = asyncio.create_task(
cls._run_input_guardrails_with_queue(
starting_agent,
starting_agent.input_guardrails + (run_config.input_guardrails or []),
copy.deepcopy(ItemHelpers.input_to_new_input_list(starting_input)),
context_wrapper,
streamed_result,
current_span,
)
)
try:
turn_result = await cls._run_single_turn_streamed(
streamed_result,
current_agent,
hooks,
context_wrapper,
run_config,
should_run_agent_start_hooks,
tool_use_tracker,
all_tools,
previous_response_id,
)
should_run_agent_start_hooks = False
streamed_result.raw_responses = streamed_result.raw_responses + [
turn_result.model_response
]
streamed_result.input = turn_result.original_input
streamed_result.new_items = turn_result.generated_items
if isinstance(turn_result.next_step, NextStepHandoff):
current_agent = turn_result.next_step.new_agent
current_span.finish(reset_current=True)
current_span = None
should_run_agent_start_hooks = True
streamed_result._event_queue.put_nowait(
AgentUpdatedStreamEvent(new_agent=current_agent)
)
elif isinstance(turn_result.next_step, NextStepFinalOutput):
streamed_result._output_guardrails_task = asyncio.create_task(
cls._run_output_guardrails(
current_agent.output_guardrails
+ (run_config.output_guardrails or []),
current_agent,
turn_result.next_step.output,
context_wrapper,
)
)
try:
output_guardrail_results = await streamed_result._output_guardrails_task
except Exception:
# Exceptions will be checked in the stream_events loop
output_guardrail_results = []
streamed_result.output_guardrail_results = output_guardrail_results
streamed_result.final_output = turn_result.next_step.output
streamed_result.is_complete = True
streamed_result._event_queue.put_nowait(QueueCompleteSentinel())
elif isinstance(turn_result.next_step, NextStepRunAgain):
pass
except AgentsException as exc:
streamed_result.is_complete = True
streamed_result._event_queue.put_nowait(QueueCompleteSentinel())
exc.run_data = RunErrorDetails(
input=streamed_result.input,
new_items=streamed_result.new_items,
raw_responses=streamed_result.raw_responses,
last_agent=current_agent,
context_wrapper=context_wrapper,
input_guardrail_results=streamed_result.input_guardrail_results,
output_guardrail_results=streamed_result.output_guardrail_results,
)
raise
except Exception as e:
if current_span:
_error_tracing.attach_error_to_span(
current_span,
SpanError(
message="Error in agent run",
data={"error": str(e)},
),
)
streamed_result.is_complete = True
streamed_result._event_queue.put_nowait(QueueCompleteSentinel())
raise
streamed_result.is_complete = True
finally:
if current_span:
current_span.finish(reset_current=True)
if streamed_result.trace:
streamed_result.trace.finish(reset_current=True)
@classmethod
async def _run_single_turn_streamed(
cls,
streamed_result: RunResultStreaming,
agent: Agent[TContext],
hooks: RunHooks[TContext],
context_wrapper: RunContextWrapper[TContext],
run_config: RunConfig,
should_run_agent_start_hooks: bool,
tool_use_tracker: AgentToolUseTracker,
all_tools: list[Tool],
previous_response_id: str | None,
) -> SingleStepResult:
if should_run_agent_start_hooks:
await asyncio.gather(
hooks.on_agent_start(context_wrapper, agent),
(
agent.hooks.on_start(context_wrapper, agent)
if agent.hooks
else _coro.noop_coroutine()
),
)
output_schema = cls._get_output_schema(agent)
streamed_result.current_agent = agent
streamed_result._current_agent_output_schema = output_schema
system_prompt, prompt_config = await asyncio.gather(
agent.get_system_prompt(context_wrapper),
agent.get_prompt(context_wrapper),
)
handoffs = cls._get_handoffs(agent)
model = cls._get_model(agent, run_config)
model_settings = agent.model_settings.resolve(run_config.model_settings)
model_settings = RunImpl.maybe_reset_tool_choice(agent, tool_use_tracker, model_settings)
final_response: ModelResponse | None = None
input = ItemHelpers.input_to_new_input_list(streamed_result.input)
input.extend([item.to_input_item() for item in streamed_result.new_items])
# 1. Stream the output events
async for event in model.stream_response(
system_prompt,
input,
model_settings,
all_tools,
output_schema,
handoffs,
get_model_tracing_impl(
run_config.tracing_disabled, run_config.trace_include_sensitive_data
),
previous_response_id=previous_response_id,
prompt=prompt_config,
):
if isinstance(event, ResponseCompletedEvent):
usage = (
Usage(
requests=1,
input_tokens=event.response.usage.input_tokens,
output_tokens=event.response.usage.output_tokens,
total_tokens=event.response.usage.total_tokens,
input_tokens_details=event.response.usage.input_tokens_details,
output_tokens_details=event.response.usage.output_tokens_details,
)
if event.response.usage
else Usage()
)
final_response = ModelResponse(
output=event.response.output,
usage=usage,
response_id=event.response.id,
)
context_wrapper.usage.add(usage)
streamed_result._event_queue.put_nowait(RawResponsesStreamEvent(data=event))
# 2. At this point, the streaming is complete for this turn of the agent loop.
if not final_response:
raise ModelBehaviorError("Model did not produce a final response!")
# 3. Now, we can process the turn as we do in the non-streaming case
single_step_result = await cls._get_single_step_result_from_response(
agent=agent,
original_input=streamed_result.input,
pre_step_items=streamed_result.new_items,
new_response=final_response,
output_schema=output_schema,
all_tools=all_tools,
handoffs=handoffs,
hooks=hooks,
context_wrapper=context_wrapper,
run_config=run_config,
tool_use_tracker=tool_use_tracker,
)
RunImpl.stream_step_result_to_queue(single_step_result, streamed_result._event_queue)
return single_step_result
@classmethod
async def _run_single_turn(
cls,
*,
agent: Agent[TContext],
all_tools: list[Tool],
original_input: str | list[TResponseInputItem],
generated_items: list[RunItem],
hooks: RunHooks[TContext],
context_wrapper: RunContextWrapper[TContext],
run_config: RunConfig,
should_run_agent_start_hooks: bool,
tool_use_tracker: AgentToolUseTracker,
previous_response_id: str | None,
) -> SingleStepResult:
# Ensure we run the hooks before anything else
if should_run_agent_start_hooks:
await asyncio.gather(
hooks.on_agent_start(context_wrapper, agent),
(
agent.hooks.on_start(context_wrapper, agent)
if agent.hooks
else _coro.noop_coroutine()
),
)
system_prompt, prompt_config = await asyncio.gather(
agent.get_system_prompt(context_wrapper),
agent.get_prompt(context_wrapper),
)
output_schema = cls._get_output_schema(agent)
handoffs = cls._get_handoffs(agent)
input = ItemHelpers.input_to_new_input_list(original_input)
input.extend([generated_item.to_input_item() for generated_item in generated_items])
new_response = await cls._get_new_response(
agent,
system_prompt,
input,
output_schema,
all_tools,
handoffs,
context_wrapper,
run_config,
tool_use_tracker,
previous_response_id,
prompt_config,
)
return await cls._get_single_step_result_from_response(
agent=agent,
original_input=original_input,
pre_step_items=generated_items,
new_response=new_response,
output_schema=output_schema,
all_tools=all_tools,
handoffs=handoffs,
hooks=hooks,
context_wrapper=context_wrapper,
run_config=run_config,
tool_use_tracker=tool_use_tracker,
)
@classmethod
async def _get_single_step_result_from_response(
cls,
*,
agent: Agent[TContext],
all_tools: list[Tool],
original_input: str | list[TResponseInputItem],
pre_step_items: list[RunItem],
new_response: ModelResponse,
output_schema: AgentOutputSchemaBase | None,
handoffs: list[Handoff],
hooks: RunHooks[TContext],
context_wrapper: RunContextWrapper[TContext],
run_config: RunConfig,
tool_use_tracker: AgentToolUseTracker,
) -> SingleStepResult:
processed_response = RunImpl.process_model_response(
agent=agent,
all_tools=all_tools,
response=new_response,
output_schema=output_schema,
handoffs=handoffs,
)
tool_use_tracker.add_tool_use(agent, processed_response.tools_used)
return await RunImpl.execute_tools_and_side_effects(
agent=agent,
original_input=original_input,
pre_step_items=pre_step_items,
new_response=new_response,
processed_response=processed_response,
output_schema=output_schema,
hooks=hooks,
context_wrapper=context_wrapper,
run_config=run_config,
)
@classmethod
async def _run_input_guardrails(
cls,
agent: Agent[Any],
guardrails: list[InputGuardrail[TContext]],
input: str | list[TResponseInputItem],
context: RunContextWrapper[TContext],
) -> list[InputGuardrailResult]:
if not guardrails:
return []
guardrail_tasks = [
asyncio.create_task(
RunImpl.run_single_input_guardrail(agent, guardrail, input, context)
)
for guardrail in guardrails
]
guardrail_results = []
for done in asyncio.as_completed(guardrail_tasks):
result = await done
if result.output.tripwire_triggered:
# Cancel all guardrail tasks if a tripwire is triggered.
for t in guardrail_tasks:
t.cancel()
_error_tracing.attach_error_to_current_span(
SpanError(
message="Guardrail tripwire triggered",
data={"guardrail": result.guardrail.get_name()},
)
)
raise InputGuardrailTripwireTriggered(result)
else:
guardrail_results.append(result)
return guardrail_results
@classmethod
async def _run_output_guardrails(
cls,
guardrails: list[OutputGuardrail[TContext]],
agent: Agent[TContext],
agent_output: Any,
context: RunContextWrapper[TContext],
) -> list[OutputGuardrailResult]:
if not guardrails:
return []
guardrail_tasks = [
asyncio.create_task(
RunImpl.run_single_output_guardrail(guardrail, agent, agent_output, context)
)
for guardrail in guardrails
]
guardrail_results = []
for done in asyncio.as_completed(guardrail_tasks):
result = await done
if result.output.tripwire_triggered:
# Cancel all guardrail tasks if a tripwire is triggered.
for t in guardrail_tasks:
t.cancel()
_error_tracing.attach_error_to_current_span(
SpanError(
message="Guardrail tripwire triggered",
data={"guardrail": result.guardrail.get_name()},
)
)
raise OutputGuardrailTripwireTriggered(result)
else:
guardrail_results.append(result)
return guardrail_results
@classmethod
async def _get_new_response(
cls,
agent: Agent[TContext],
system_prompt: str | None,
input: list[TResponseInputItem],
output_schema: AgentOutputSchemaBase | None,
all_tools: list[Tool],
handoffs: list[Handoff],
context_wrapper: RunContextWrapper[TContext],
run_config: RunConfig,
tool_use_tracker: AgentToolUseTracker,
previous_response_id: str | None,
prompt_config: ResponsePromptParam | None,
) -> ModelResponse:
model = cls._get_model(agent, run_config)
model_settings = agent.model_settings.resolve(run_config.model_settings)
model_settings = RunImpl.maybe_reset_tool_choice(agent, tool_use_tracker, model_settings)
new_response = await model.get_response(
system_instructions=system_prompt,
input=input,
model_settings=model_settings,
tools=all_tools,
output_schema=output_schema,
handoffs=handoffs,
tracing=get_model_tracing_impl(
run_config.tracing_disabled, run_config.trace_include_sensitive_data
),
previous_response_id=previous_response_id,
prompt=prompt_config,
)
context_wrapper.usage.add(new_response.usage)
return new_response
@classmethod
def _get_output_schema(cls, agent: Agent[Any]) -> AgentOutputSchemaBase | None:
if agent.output_type is None or agent.output_type is str:
return None
elif isinstance(agent.output_type, AgentOutputSchemaBase):
return agent.output_type
return AgentOutputSchema(agent.output_type)
@classmethod
def _get_handoffs(cls, agent: Agent[Any]) -> list[Handoff]:
handoffs = []
for handoff_item in agent.handoffs:
if isinstance(handoff_item, Handoff):
handoffs.append(handoff_item)
elif isinstance(handoff_item, Agent):
handoffs.append(handoff(handoff_item))
return handoffs
@classmethod
async def _get_all_tools(
cls, agent: Agent[Any], context_wrapper: RunContextWrapper[Any]
) -> list[Tool]:
return await agent.get_all_tools(context_wrapper)
@classmethod
def _get_model(cls, agent: Agent[Any], run_config: RunConfig) -> Model:
if isinstance(run_config.model, Model):
return run_config.model
elif isinstance(run_config.model, str):
return run_config.model_provider.get_model(run_config.model)
elif isinstance(agent.model, Model):
return agent.model
return run_config.model_provider.get_model(agent.model)
DEFAULT_AGENT_RUNNER = AgentRunner()
|