From fb656d32ca72cd787936578b231e8395fec26b6a Mon Sep 17 00:00:00 2001 From: Abhijeet Prasad Date: Mon, 9 Nov 2026 15:05:03 -0500 Subject: [PATCH] feat(strands): trace decision models as decision spans MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit > 🤖 **This text was AI-generated.** strands-agents 1.63 added `DecisionModel`, and 1.64 added two hosted implementations, `DecisionsAPIDecisionModel` and `TypeSafeDecisionModel`. None of them go through `strands.telemetry.Tracer`, which is the only thing the Strands integration patched, so decision calls produced no Braintrust spans: ```python result = await TypeSafeDecisionModel().ask( "I was charged twice, please refund one charge.", {"department": {"question": "Which team?", "choices": ["billing", "support"]}}, ) # before: no span ``` The integration now patches `DecisionModel.ask`. Every implementation inherits it, so the one patch covers `LLMDecisionModel`, `TypeSafeDecisionModel` and `DecisionsAPIDecisionModel`. Each call becomes a `decision` span that follows the decision-spans spec: https://github.com/braintrustdata/braintrust-spec/blob/main/skills/instrumentation-spec/references/features/decision-spans.md ``` bt-agent.invoke └── event_loop.cycle └── route_ticket.execute (tool) ├── LLMDecisionModel.ask (decision) │ └── └── TypeSafeDecisionModel.ask (decision) ``` - input: `{"state": ..., "questions": [{"id", "type", "instructions", "criteria"}]}`. Yes/no questions are `boolean`; choice lists become `choice` with criteria. - output: `{"answers": [...]}`, linked to questions by `id`. Boolean answers carry `probability`; choice answers carry `choice` plus the provider's `probabilities` and `confidence` when it returns them. `LLMDecisionModel` forces a true/false answer, recorded as probability 1.0 or 0.0. When strands returns `Uncertain` because a System One answer fell below its confidence cutoff, the provider's actual answer is logged; when the model itself declined, the answer is a `refusal`. - metadata: `provider`, `model` (the provider-reported model, e.g. `jev-1.14.0` for a requested `jev-latest`), `decision_model`, `latency_ms`. - metrics: TypeSafe and DecisionsAPI calls go over raw HTTP, so their token usage is recorded as standard token metrics. `LLMDecisionModel` keeps usage in `metadata.strands_usage`, because the wrapped model's own provider integration already records those tokens on a child LLM span. - attachments: inline image/document/video bytes in `state` are logged as Braintrust attachments in place; the request sent to the model is unchanged. Decision spans nest under the active Strands span, for example the tool that called `ask()`. Without an OTEL SDK, Strands' spans are invalid and the integration stands in a key object that `get_current_span()` rejects, so the parent is read from the raw OTEL context value (`opentelemetry.trace.propagation._SPAN_KEY`, a private name, guarded). Instrumentation failures are caught and never break the `ask()` call. Limitations: users running an OTEL SDK with Braintrust's OTEL processor will also see Strands' own OTEL `decision` span. Inline media is only converted to attachments on decision spans, not on the other Strands spans. Closes #1087 --- ...n_image_state_is_logged_as_attachment.yaml | 123 ++++ ...n_models_in_tool_trace_decision_spans.yaml | 529 ++++++++++++++++++ .../integrations/strands/integration.py | 4 +- .../integrations/strands/patchers.py | 15 + .../integrations/strands/test_strands.py | 130 ++++- .../integrations/strands/tracing.py | 155 ++++- 6 files changed, 952 insertions(+), 4 deletions(-) create mode 100644 py/src/braintrust/integrations/strands/cassettes/latest/test_strands_decision_image_state_is_logged_as_attachment.yaml create mode 100644 py/src/braintrust/integrations/strands/cassettes/latest/test_strands_decision_models_in_tool_trace_decision_spans.yaml diff --git a/py/src/braintrust/integrations/strands/cassettes/latest/test_strands_decision_image_state_is_logged_as_attachment.yaml b/py/src/braintrust/integrations/strands/cassettes/latest/test_strands_decision_image_state_is_logged_as_attachment.yaml new file mode 100644 index 000000000..001ef4803 --- /dev/null +++ b/py/src/braintrust/integrations/strands/cassettes/latest/test_strands_decision_image_state_is_logged_as_attachment.yaml @@ -0,0 +1,123 @@ +interactions: +- request: + body: '{"messages":[{"role":"system","content":"You answer closed questions about + the user content below. 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preload + transfer-encoding: + - chunked + x-content-type-options: + - nosniff + x-openai-proxy-wasm: + - v0.1 + x-ratelimit-limit-requests: + - '30000' + x-ratelimit-limit-tokens: + - '150000000' + x-ratelimit-remaining-requests: + - '29999' + x-ratelimit-remaining-tokens: + - '149999925' + x-ratelimit-reset-requests: + - 2ms + x-ratelimit-reset-tokens: + - 0s + x-request-id: + - req_74d8122cda464b58b578ddba5840977a + status: + code: 200 + message: OK +version: 1 diff --git a/py/src/braintrust/integrations/strands/integration.py b/py/src/braintrust/integrations/strands/integration.py index 38bc468b3..c9dc2e7d7 100644 --- a/py/src/braintrust/integrations/strands/integration.py +++ b/py/src/braintrust/integrations/strands/integration.py @@ -2,7 +2,7 @@ from braintrust.integrations.base import BaseIntegration -from .patchers import TracerPatcher +from .patchers import DecisionModelPatcher, TracerPatcher class StrandsIntegration(BaseIntegration): @@ -11,4 +11,4 @@ class StrandsIntegration(BaseIntegration): name = "strands" import_names = ("strands",) min_version = "1.20.0" - patchers = (TracerPatcher,) + patchers = (TracerPatcher, DecisionModelPatcher) diff --git a/py/src/braintrust/integrations/strands/patchers.py b/py/src/braintrust/integrations/strands/patchers.py index 5695fa5ea..c5a2d750c 100644 --- a/py/src/braintrust/integrations/strands/patchers.py +++ b/py/src/braintrust/integrations/strands/patchers.py @@ -5,6 +5,7 @@ from braintrust.integrations.base import CompositeFunctionWrapperPatcher, FunctionWrapperPatcher from .tracing import ( + _decision_model_ask_wrapper, _end_agent_span_wrapper, _end_event_loop_cycle_span_wrapper, _end_model_invoke_span_wrapper, @@ -88,6 +89,20 @@ class TracerPatcher(CompositeFunctionWrapperPatcher): ) +class DecisionModelPatcher(FunctionWrapperPatcher): + """Trace ``DecisionModel.ask`` (strands-agents >= 1.63.0). + + Every decision model (``LLMDecisionModel``, ``DecisionsAPIDecisionModel``, + ``TypeSafeDecisionModel``) inherits ``ask`` from this base class and none + of them go through ``strands.telemetry.Tracer``. + """ + + name = "strands.decision_model.ask" + target_module = "strands.decisions.decision_model" + target_path = "DecisionModel.ask" + wrapper = _decision_model_ask_wrapper + + def wrap_strands_tracer(Tracer: Any) -> Any: """Manually patch a Strands ``Tracer`` class for Braintrust tracing.""" return TracerPatcher.wrap_target(Tracer) diff --git a/py/src/braintrust/integrations/strands/test_strands.py b/py/src/braintrust/integrations/strands/test_strands.py index 471dbfe11..de9b2883a 100644 --- a/py/src/braintrust/integrations/strands/test_strands.py +++ b/py/src/braintrust/integrations/strands/test_strands.py @@ -1,7 +1,9 @@ # pylint: disable=import-error +from pathlib import Path + import pytest -from braintrust import logger +from braintrust import Attachment, logger from braintrust.integrations.strands import StrandsIntegration from braintrust.span_types import SpanTypeAttribute from braintrust.test_helpers import find_spans_by_type, init_test_logger @@ -61,3 +63,129 @@ async def test_strands_openai_agent_traces_native_otel_lifecycle(memory_logger): assert "tokens" not in llm_span.get("metrics", {}) for span in spans: assert span["context"]["span_origin"]["instrumentation"]["name"] == "strands-auto" + + +@pytest.mark.asyncio +@pytest.mark.vcr +async def test_strands_decision_models_in_tool_trace_decision_spans(memory_logger): + decisions = pytest.importorskip("strands.decisions", reason="DecisionModel requires strands-agents >= 1.63.0") + if not hasattr(decisions, "TypeSafeDecisionModel"): + pytest.skip("TypeSafeDecisionModel requires strands-agents >= 1.64.0") + from strands import Agent, tool + from strands.models.openai import OpenAIModel + + assert StrandsIntegration.setup() + + questions = { + "department": {"question": "Which team should handle this?", "choices": ["billing", "support"]}, + "urgent": {"question": "Is this urgent?", "choices": "boolean"}, + } + llm_decision_model = decisions.LLMDecisionModel( + model=OpenAIModel(model_id="gpt-4o-mini", params={"temperature": 0}) + ) + typesafe_decision_model = decisions.TypeSafeDecisionModel() + + @tool + async def route_ticket(ticket: str) -> str: + """Route a customer support ticket to the right team. + + Args: + ticket: The customer's message. + """ + llm_result = await llm_decision_model.ask(ticket, questions) + typesafe_result = await typesafe_decision_model.ask(ticket, questions) + return f"llm={llm_result.answers} typesafe={typesafe_result.answers}" + + agent = Agent( + model=OpenAIModel(model_id="gpt-4o-mini", params={"temperature": 0}), + tools=[route_ticket], + system_prompt="Always call route_ticket exactly once with the user's message, then report its result.", + ) + + await agent.invoke_async("I was charged twice for my subscription this month, please refund one charge.") + + spans = memory_logger.pop() + names = [span["span_attributes"]["name"] for span in spans] + tool_spans = [ + span + for span in find_spans_by_type(spans, SpanTypeAttribute.TOOL) + if span["span_attributes"]["name"] == "route_ticket.execute" + ] + assert len(tool_spans) == 1, names + tool_span = tool_spans[0] + decision_spans = { + span["span_attributes"]["name"]: span for span in find_spans_by_type(spans, SpanTypeAttribute.DECISION) + } + assert set(decision_spans) == {"LLMDecisionModel.ask", "TypeSafeDecisionModel.ask"}, names + + expected_input = { + "state": "I was charged twice for my subscription this month, please refund one charge.", + "questions": [ + { + "id": "department", + "type": "choice", + "instructions": "Which team should handle this?", + "criteria": {"billing": None, "support": None}, + }, + {"id": "urgent", "type": "boolean", "instructions": "Is this urgent?"}, + ], + } + for span in decision_spans.values(): + assert span["span_parents"] == [tool_span["span_id"]] + assert span["input"] == expected_input + assert span["context"]["span_origin"]["instrumentation"]["name"] == "strands-auto" + assert span["metadata"]["latency_ms"] >= 0 + + # LLMDecisionModel: answers only; the vended model's provider integration owns token metrics. + llm_span = decision_spans["LLMDecisionModel.ask"] + llm_answers = {answer["id"]: answer for answer in llm_span["output"]["answers"]} + assert llm_answers["department"] == {"id": "department", "type": "choice", "choice": "billing"} + # A forced categorical boolean is recorded as probability 1.0 / 0.0. + assert llm_answers["urgent"]["type"] == "boolean" + assert llm_answers["urgent"]["probability"] in (0.0, 1.0) + assert llm_span["metadata"]["decision_model"] == "LLMDecisionModel" + assert llm_span["metadata"]["provider"] == "openai" + assert llm_span["metadata"]["model"] == "gpt-4o-mini" + assert "tokens" not in llm_span.get("metrics", {}) + + # TypeSafeDecisionModel: System One probabilities and confidence over raw HTTP. + typesafe_span = decision_spans["TypeSafeDecisionModel.ask"] + typesafe_answers = {answer["id"]: answer for answer in typesafe_span["output"]["answers"]} + assert typesafe_answers["department"]["choice"] == "billing" + assert set(typesafe_answers["department"]["probabilities"]) == {"billing", "support"} + assert 0 <= typesafe_answers["department"]["confidence"] <= 1 + assert set(typesafe_answers["urgent"]) == {"id", "type", "probability"} + assert typesafe_answers["urgent"]["type"] == "boolean" + assert 0 <= typesafe_answers["urgent"]["probability"] <= 1 + assert typesafe_span["metadata"]["decision_model"] == "TypeSafeDecisionModel" + assert typesafe_span["metadata"]["provider"] == "typesafe" + # The provider-reported model replaces the requested ``jev-latest`` alias. + assert typesafe_span["metadata"]["model"] == "jev-1.14.0" + assert typesafe_span["metrics"]["tokens"] == ( + typesafe_span["metrics"]["prompt_tokens"] + typesafe_span["metrics"]["completion_tokens"] + ) + + +@pytest.mark.asyncio +@pytest.mark.vcr +async def test_strands_decision_image_state_is_logged_as_attachment(memory_logger): + decisions = pytest.importorskip("strands.decisions", reason="DecisionModel requires strands-agents >= 1.63.0") + from strands.models.openai import OpenAIModel + + assert StrandsIntegration.setup() + + image = (Path(__file__).parent.parent.parent / "fixtures" / "test-image.png").read_bytes() + state = [{"text": "Describe the attached image."}, {"image": {"format": "png", "source": {"bytes": image}}}] + decision_model = decisions.LLMDecisionModel(model=OpenAIModel(model_id="gpt-4o-mini", params={"temperature": 0})) + + await decision_model.ask(state, {"has_image": {"question": "Is an image attached?", "choices": "boolean"}}) + + (span,) = find_spans_by_type(memory_logger.pop(), SpanTypeAttribute.DECISION) + logged_state = span["input"]["state"] + assert logged_state[0] == {"text": "Describe the attached image."} + attachment = logged_state[1]["image"]["source"]["bytes"] + assert isinstance(attachment, Attachment) + assert attachment.reference["content_type"] == "image/png" + assert logged_state[1]["image"]["format"] == "png" + # The model request keeps the original bytes. + assert state[1]["image"]["source"]["bytes"] == image diff --git a/py/src/braintrust/integrations/strands/tracing.py b/py/src/braintrust/integrations/strands/tracing.py index 4890b4453..4240dd626 100644 --- a/py/src/braintrust/integrations/strands/tracing.py +++ b/py/src/braintrust/integrations/strands/tracing.py @@ -1,16 +1,19 @@ """Strands Agents tracing helpers.""" +import logging import uuid import weakref from typing import Any -from braintrust.integrations.utils import _is_supported_metric_value +from braintrust.integrations.question import _question_span_input, _question_span_output +from braintrust.integrations.utils import _is_supported_metric_value, _materialize_attachment from braintrust.logger import Span from braintrust.logger import start_span as _bt_start_span from braintrust.span_types import SpanTypeAttribute _INSTRUMENTATION = "strands-auto" +_log = logging.getLogger(__name__) _SPANS_BY_OTEL_SPAN: "weakref.WeakKeyDictionary[Any, Span]" = weakref.WeakKeyDictionary() _SPANS_BY_INVALID_OTEL_KEY: dict[uuid.UUID, Span] = {} @@ -292,3 +295,153 @@ def _end_tool_call_span_wrapper(wrapped: Any, instance: Any, args: Any, kwargs: metadata={"status": tool_result.get("status") if isinstance(tool_result, dict) else None}, error=error, ) + + +def _current_parent_span() -> Span | None: + """Return the Braintrust span mirroring the active Strands OTEL span, if any.""" + try: + from opentelemetry import context as context_api + from opentelemetry.trace.propagation import _SPAN_KEY + except ImportError: + return None + # Read the raw context value: without an OTEL SDK, Strands' spans are invalid and we hand back + # an ``_InvalidOtelSpanKey`` in their place, which ``get_current_span()`` rejects (not a ``Span``). + return _span_for_otel(context_api.get_value(_SPAN_KEY)) + + +def _decision_questions(questions: dict[str, Any]) -> dict[str, dict[str, Any]]: + """Map Strands questions to the shared decision question shape, keyed by question ID.""" + normalized = {} + for question_id, question in questions.items(): + choices = question["choices"] + if choices == "boolean": + normalized[question_id] = {"type": "boolean", "instructions": question["question"]} + else: + normalized[question_id] = { + "type": "choice", + "instructions": question["question"], + "criteria": {choice: None for choice in choices}, + } + return normalized + + +def _decision_answers(result: Any, questions: dict[str, Any]) -> dict[str, dict[str, Any]]: + """Map a Strands ``DecisionResult`` to the shared decision answer shape, keyed by question ID. + + Boolean answers carry ``probability`` and choice answers carry ``choice``. System One + providers return probabilities, which are preserved. ``LLMDecisionModel`` forces a + categorical answer, so a boolean is recorded with probability 1.0 or 0.0. ``Uncertain`` is + recorded as the provider's underlying answer when probabilities are available, since + Strands only applied a confidence cutoff. Otherwise the model declined, which is a ``refusal``. + """ + probabilities = result.metadata.probabilities or {} + confidence = result.metadata.confidence or {} + normalized: dict[str, dict[str, Any]] = {} + for question_id, answer in result.answers.items(): + question_probabilities = probabilities.get(question_id) + payload: dict[str, Any] | None + if questions[question_id]["choices"] == "boolean": + probability = question_probabilities["true"] if question_probabilities else None + if probability is None and isinstance(answer, bool): + probability = 1.0 if answer else 0.0 + payload = None if probability is None else {"type": "boolean", "probability": probability} + else: + choice = answer if isinstance(answer, str) else None + if choice is None and question_probabilities: + # Strands validated that the provider's choice has the highest probability. + choice = max(question_probabilities, key=question_probabilities.__getitem__) + payload = None if choice is None else {"type": "choice", "choice": choice} + if payload is not None and question_probabilities: + payload["probabilities"] = question_probabilities + if payload is not None and question_id in confidence: + payload["confidence"] = confidence[question_id] + normalized[question_id] = payload or {"type": "refusal"} + return normalized + + +def _decision_media_transform(value: Any) -> Any: + """Convert inline bytes in Strands ``image``/``document``/``video`` content blocks to attachments.""" + if not isinstance(value, dict) or len(value) != 1: + return value + ((kind, media),) = value.items() + if kind not in ("image", "document", "video") or not isinstance(media, dict): + return value + source = media.get("source") + if not isinstance(source, dict) or not isinstance(source.get("bytes"), (bytes, bytearray)): + return value + resolved = _materialize_attachment(source["bytes"], filename=f"{kind}.{media.get('format')}") + if resolved is None or resolved.attachment.reference.get("content_type") == "application/octet-stream": + return value + return {kind: {**media, "source": {**source, "bytes": resolved.attachment}}} + + +def _decision_model_metadata(instance: Any) -> dict[str, Any]: + metadata: dict[str, Any] = {"decision_model": type(instance).__name__} + provider_config = getattr(instance, "_provider_config", None) + if provider_config is not None: + # System One models (TypeSafeDecisionModel, DecisionsAPIDecisionModel). + metadata["provider"] = provider_config.provider_name + metadata["model"] = provider_config.model_id + model = getattr(instance, "_model", None) + if model is not None: + # LLMDecisionModel wraps a vended Strands ``Model`` such as ``OpenAIModel``. + model_class = type(model).__name__ + metadata["provider"] = model_class.removesuffix("Model").lower() or model_class + metadata["model"] = model.get_config().get("model_id") + return {key: value for key, value in metadata.items() if value is not None} + + +def _log_decision_result(span: Span, instance: Any, result: Any, questions: dict[str, Any]) -> None: + metadata: dict[str, Any] = {"latency_ms": result.metadata.latency_ms} + if result.metadata.model_id is not None: + # The provider-reported model, e.g. ``jev-1.14.0`` for a requested ``jev-latest``. + metadata["model"] = result.metadata.model_id + + metrics: dict[str, Any] = {} + usage = _pick_numeric(result.usage, _STRANDS_USAGE_KEYS) + if getattr(instance, "_model", None) is not None: + # LLMDecisionModel calls a vended model whose provider integration already records these + # tokens on a child LLM span; recording them as metrics here would double count. + if usage: + metadata["strands_usage"] = usage + else: + # System One providers are called over raw HTTP, so this span is the only one with these tokens. + for src_key, dst_key in ( + ("input_tokens", "prompt_tokens"), + ("output_tokens", "completion_tokens"), + ("total_tokens", "tokens"), + ): + if src_key in usage: + metrics[dst_key] = usage[src_key] + + span.log(output=_question_span_output(_decision_answers(result, questions)), metadata=metadata) + if metrics: + span.log(metrics=metrics) + + +async def _decision_model_ask_wrapper(wrapped: Any, instance: Any, args: Any, kwargs: Any) -> Any: + try: + state = _arg(args, kwargs, 0, "state") + questions = _arg(args, kwargs, 1, "questions") + parent = _current_parent_span() + start = parent.start_span if parent is not None else start_span + span = start( + name=f"{type(instance).__name__}.ask", + type=SpanTypeAttribute.DECISION, + input=_question_span_input( + state, _decision_questions(questions), media_transform=_decision_media_transform + ), + metadata=_decision_model_metadata(instance), + internal={"instrumentation": _INSTRUMENTATION}, + ) + except Exception: + _log.debug("Failed to start Strands decision span", exc_info=True) + return await wrapped(*args, **kwargs) + + with span: + result = await wrapped(*args, **kwargs) + try: + _log_decision_result(span, instance, result, questions) + except Exception: + _log.debug("Failed to log Strands decision result", exc_info=True) + return result