diff --git a/examples/agno/README.md b/examples/agno/README.md
index 0d726fc20..2fe957171 100644
--- a/examples/agno/README.md
+++ b/examples/agno/README.md
@@ -9,6 +9,8 @@ Each script calls `braintrust.auto_instrument()` before importing `agno`, so all
| `async_simple_agent_stream.py` | one agent, async + streamed |
| `team_agent.py` | research + advisor team, sync |
| `async_team_agent.py` | research + advisor team, async + streamed |
+| `accuracy_eval.py` | `AccuracyEval` over one agent, scored in Braintrust |
+| `eval_suite.py` | an `agno.eval` suite; each `Case` becomes an experiment row |
## Run
diff --git a/examples/agno/accuracy_eval.py b/examples/agno/accuracy_eval.py
new file mode 100644
index 000000000..d1419fc16
--- /dev/null
+++ b/examples/agno/accuracy_eval.py
@@ -0,0 +1,33 @@
+import braintrust
+
+
+braintrust.auto_instrument()
+
+# An eval run logs to whatever is current. Swap init_logger for
+# braintrust.init(project=..., experiment=...) to score it as an experiment row instead.
+braintrust.init_logger(project="agno-evals-project")
+
+from agno.agent import Agent
+from agno.eval.accuracy import AccuracyEval
+from agno.models.openai import OpenAIChat
+from agno.tools.yfinance import YFinanceTools
+
+
+agent = Agent(
+ name="Stock Price Agent",
+ model=OpenAIChat(id="gpt-4o-mini"),
+ tools=[YFinanceTools()],
+ instructions="You are a stock price agent. Answer with the ticker and nothing else.",
+)
+
+evaluation = AccuracyEval(
+ name="Ticker Lookup",
+ model=OpenAIChat(id="gpt-4o-mini"),
+ agent=agent,
+ input="Which ticker does Figma trade under?",
+ expected_output="FIG",
+ num_iterations=2,
+)
+
+result = evaluation.run(print_summary=True)
+print(f"average score: {result.avg_score}/10")
diff --git a/examples/agno/eval_suite.py b/examples/agno/eval_suite.py
new file mode 100644
index 000000000..44cd8f135
--- /dev/null
+++ b/examples/agno/eval_suite.py
@@ -0,0 +1,51 @@
+# agno.eval lazy-imports its submodules through a module-level __getattr__, which
+# static analysis cannot see.
+# pylint: disable=no-name-in-module
+
+import sys
+
+import braintrust
+
+
+braintrust.auto_instrument()
+
+# A suite run opens a Braintrust experiment of its own, so each Case lands as a
+# scored experiment row. Pass eval_experiments=False to setup_agno() (or set
+# BRAINTRUST_AGNO_EVAL_EXPERIMENTS=false) to keep suite runs in logs instead.
+braintrust.init_logger(project="agno-evals-project")
+
+from agno.agent import Agent
+from agno.eval import Case, cli
+from agno.models.openai import OpenAIChat
+from agno.tools.yfinance import YFinanceTools
+
+
+agent = Agent(
+ id="stock-agent",
+ name="Stock Price Agent",
+ model=OpenAIChat(id="gpt-4o-mini"),
+ tools=[YFinanceTools()],
+ instructions="Use your tools for any market data question.",
+)
+
+CASES = (
+ Case(
+ name="looks_up_current_price",
+ agent=agent,
+ input="What is the current price of FIG?",
+ tags=("smoke",),
+ criteria="Reports a current share price for Figma.",
+ expected_tool_calls=("get_current_stock_price",),
+ ),
+ Case(
+ name="explains_pe_ratio",
+ agent=agent,
+ input="Explain the P/E ratio in one sentence.",
+ criteria="Explains that the P/E ratio compares share price to earnings per share.",
+ ),
+)
+
+if __name__ == "__main__":
+ # python eval_suite.py --tag smoke # run a tagged subset
+ # python eval_suite.py --list # list cases without running them
+ sys.exit(cli(CASES))
diff --git a/py/src/braintrust/integrations/agno/__init__.py b/py/src/braintrust/integrations/agno/__init__.py
index 8860a67a6..c9410b7f2 100644
--- a/py/src/braintrust/integrations/agno/__init__.py
+++ b/py/src/braintrust/integrations/agno/__init__.py
@@ -2,13 +2,19 @@
import logging
-from braintrust.logger import NOOP_SPAN, current_span, init_logger
+from braintrust.logger import NOOP_SPAN, current_experiment, current_span, init_logger
+from .eval_experiments import configure as _configure_eval_experiments
from .integration import AgnoIntegration
from .patchers import (
+ wrap_accuracy_eval,
wrap_agent,
+ wrap_agent_as_judge_eval,
+ wrap_eval_suite,
wrap_function_call,
wrap_model,
+ wrap_performance_eval,
+ wrap_reliability_eval,
wrap_team,
wrap_workflow,
)
@@ -19,9 +25,14 @@
__all__ = [
"AgnoIntegration",
"setup_agno",
+ "wrap_accuracy_eval",
"wrap_agent",
+ "wrap_agent_as_judge_eval",
+ "wrap_eval_suite",
"wrap_function_call",
"wrap_model",
+ "wrap_performance_eval",
+ "wrap_reliability_eval",
"wrap_team",
"wrap_workflow",
]
@@ -31,20 +42,30 @@ def setup_agno(
api_key: str | None = None,
project_id: str | None = None,
project_name: str | None = None,
+ eval_experiments: bool | None = None,
) -> bool:
"""
- Setup Braintrust integration with Agno. Will automatically patch Agno agents, models, and function calls for tracing.
+ Setup Braintrust integration with Agno. Will automatically patch Agno agents, models,
+ function calls, and evals (``agno.eval``) for tracing.
Args:
api_key: Braintrust API key (optional, can use env var BRAINTRUST_API_KEY)
project_id: Braintrust project ID (optional)
- project_name: Braintrust project name (optional, can use env var BRAINTRUST_PROJECT)
+ project_name: Braintrust project name (optional; defaults to the Global project)
+ eval_experiments: Whether an eval suite run should open a Braintrust experiment,
+ so its cases land as experiment rows rather than logs. Defaults to the
+ BRAINTRUST_AGNO_EVAL_EXPERIMENTS env var, which itself defaults to true.
+ Individual evals (AccuracyEval and friends) always log to whatever is
+ current, so pass eval_experiments=False to keep suite runs in logs too.
Returns:
True if setup was successful, False otherwise
"""
- span = current_span()
- if span == NOOP_SPAN:
+ _configure_eval_experiments(eval_experiments)
+
+ # An experiment opened by the caller is the destination for eval rows, so don't
+ # install a logger that would only shadow it for non-eval tracing.
+ if current_span() == NOOP_SPAN and current_experiment() is None:
init_logger(project=project_name, api_key=api_key, project_id=project_id)
return AgnoIntegration.setup()
diff --git a/py/src/braintrust/integrations/agno/cassettes/2.1.0/test_accuracy_eval_arun_logs_score.yaml b/py/src/braintrust/integrations/agno/cassettes/2.1.0/test_accuracy_eval_arun_logs_score.yaml
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diff --git a/py/src/braintrust/integrations/agno/cassettes/2.1.0/test_accuracy_eval_logs_score_and_nests_agent.yaml b/py/src/braintrust/integrations/agno/cassettes/2.1.0/test_accuracy_eval_logs_score_and_nests_agent.yaml
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new file mode 100644
index 000000000..82ea7fa72
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new file mode 100644
index 000000000..3c5b99c84
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new file mode 100644
index 000000000..a6b8ea048
--- /dev/null
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new file mode 100644
index 000000000..587467fb6
--- /dev/null
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new file mode 100644
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diff --git a/py/src/braintrust/integrations/agno/eval_experiments.py b/py/src/braintrust/integrations/agno/eval_experiments.py
new file mode 100644
index 000000000..8393399cc
--- /dev/null
+++ b/py/src/braintrust/integrations/agno/eval_experiments.py
@@ -0,0 +1,113 @@
+"""Experiment routing for Agno eval suite runs.
+
+An agno suite run is the natural analogue of a Braintrust experiment: a fixed set of
+cases, run together, scored, and worth comparing against the previous run. When a
+suite runs in a process that has a Braintrust project in scope but no experiment,
+this module opens one for the duration of the suite so each case lands as an
+experiment row instead of a log.
+
+Individual evals (``AccuracyEval`` and friends) deliberately do not get this
+treatment: a script running five of them would produce five one-row experiments.
+They log to whatever is already current, which is an experiment if the caller opened
+one themselves.
+"""
+
+import contextvars
+import logging
+from contextlib import contextmanager
+from typing import Any
+
+from braintrust.env import EnvParser, EnvVar
+from braintrust.logger import current_experiment, current_logger, init
+
+
+logger = logging.getLogger(__name__)
+
+# Declared here rather than in braintrust.env: this is one integration's behavior, and
+# the knob should live next to the code that reads it.
+_EVAL_EXPERIMENTS_ENV = EnvVar("BRAINTRUST_AGNO_EVAL_EXPERIMENTS", EnvParser.BOOL)
+
+# Set by ``setup_agno(eval_experiments=...)``; ``None`` defers to the environment.
+_override: bool | None = None
+
+_active: contextvars.ContextVar[Any | None] = contextvars.ContextVar("braintrust_agno_eval_experiment", default=None)
+
+
+def configure(eval_experiments: bool | None) -> None:
+ """Record the caller's opt in/out of per-suite experiments."""
+ global _override # pylint: disable=global-statement
+ _override = eval_experiments
+
+
+def _enabled() -> bool:
+ if _override is not None:
+ return _override
+ return _EVAL_EXPERIMENTS_ENV.get(True)
+
+
+def active_experiment() -> Any | None:
+ """Return the experiment opened for the running suite, if any."""
+ return _active.get()
+
+
+def _logger_project() -> dict[str, Any] | None:
+ """Return ``init()`` project kwargs taken from the current logger.
+
+ Read off the logger's stored metadata args rather than its ``project`` property,
+ which would force a login and a project lookup just to answer the question. Core
+ has no non-resolving accessor for this today.
+ """
+ active_logger = current_logger()
+ if active_logger is None:
+ return None
+ args = getattr(active_logger, "_compute_metadata_args", None) or {}
+ project_id = args.get("project_id")
+ if project_id:
+ return {"project_id": project_id}
+ project_name = args.get("project_name")
+ if project_name:
+ return {"project": project_name}
+ return None
+
+
+@contextmanager
+def suite_experiment(metadata: dict[str, Any] | None = None):
+ """Open an experiment for a suite run, or leave rows to be routed as usual.
+
+ Does nothing — so cases land wherever any other span would — when per-suite
+ experiments are turned off, when the caller already opened an experiment, when no
+ project is in scope, or when opening one fails. An eval suite must still run when
+ Braintrust is misconfigured or offline. Callers read the result through
+ ``active_experiment()``.
+ """
+ if not _enabled() or current_experiment() is not None:
+ yield
+ return
+
+ project_args = _logger_project()
+ if project_args is None:
+ # Reachable when the logger was built without a project (init_logger() with no
+ # arguments logs to the Global project), so say why nothing happened.
+ logger.debug("No Braintrust project in scope; agno eval suite rows will be logged, not run as an experiment")
+ yield
+ return
+
+ try:
+ # set_current=False: ``state.current_experiment`` is process-global, and rows are
+ # routed explicitly through ``experiment.start_span()`` instead, so a suite cannot
+ # leak its experiment into unrelated tracing.
+ experiment = init(**project_args, set_current=False, metadata=metadata or None)
+ except Exception:
+ logger.warning("Failed to open a Braintrust experiment for the agno eval suite", exc_info=True)
+ yield
+ return
+
+ token = _active.set(experiment)
+ try:
+ yield
+ finally:
+ _active.reset(token)
+ try:
+ experiment.flush()
+ except Exception:
+ logger.warning("Failed to flush the agno eval suite experiment", exc_info=True)
diff --git a/py/src/braintrust/integrations/agno/eval_tracing.py b/py/src/braintrust/integrations/agno/eval_tracing.py
new file mode 100644
index 000000000..90bbff36a
--- /dev/null
+++ b/py/src/braintrust/integrations/agno/eval_tracing.py
@@ -0,0 +1,651 @@
+"""Braintrust tracing for Agno's eval framework (``agno.eval``).
+
+Each eval run becomes a span shaped like a Braintrust eval row — ``input``,
+``expected``, ``output``, ``scores`` — so an agno eval reads the same way whether it
+lands in logs or, when the caller (or :mod:`.eval_experiments`) has an experiment in
+scope, as an experiment row.
+
+Agno grades on a 1-10 scale (accuracy, numeric judge) or a pass/fail verdict
+(binary judge, reliability); Braintrust scores are 0-1, so numeric scores are divided
+by their scale and verdicts become 1.0/0.0. The raw agno values stay in ``metadata``.
+
+Sub-evals that the suite runner creates per case (``AgentAsJudgeEval``,
+``ReliabilityEval``) render as scorer spans named for the check they perform, so a
+case's row shows *what* graded it rather than repeating the case name three times.
+
+The eval span carries the common metadata (which eval, which agent, which model) from
+the moment it starts; the payload builders below add only what the result reveals.
+"""
+
+import contextvars
+from contextlib import contextmanager
+from typing import Any
+
+from braintrust.logger import NOOP_SPAN, current_span
+from braintrust.span_types import SpanTypeAttribute
+from braintrust.util import clean_nones, is_numeric
+
+from .eval_experiments import active_experiment, suite_experiment
+from .tracing import bound_args, extract_metadata, omit, start_span, suppress_spans
+
+
+# Agno's numeric scales: 1-10 for accuracy and a numeric judge, 0-100 for a pass rate.
+_AGNO_SCORE_SCALE = 10.0
+_PASS_RATE_SCALE = 100.0
+
+_EVAL_SPAN_ATTRIBUTES = {"type": SpanTypeAttribute.EVAL}
+_SCORER_SPAN_ATTRIBUTES = {"type": SpanTypeAttribute.SCORE, "purpose": "scorer"}
+
+_EVAL_SPAN_NAMES = {
+ "accuracy": "AccuracyEval",
+ "agent_as_judge": "AgentAsJudgeEval",
+ "reliability": "ReliabilityEval",
+ "performance": "PerformanceEval",
+}
+
+# Inside a suite case the eval's own name is the case name (the suite passes it
+# through), which would repeat the row name on every child. Name them for the check
+# they perform instead — only the judge's eval type needs spelling differently.
+_IN_CASE_SPAN_NAMES = {"agent_as_judge": "judge"}
+
+# Set while a suite case is running, so the judge/reliability evals the suite runs for
+# that case render as scorer spans instead of top-level eval rows.
+_IN_CASE: contextvars.ContextVar[bool] = contextvars.ContextVar("braintrust_agno_in_eval_case", default=False)
+
+# Set while an AgentAsJudgeEval grades a single pair, so the ``_evaluate`` span — which
+# would carry exactly the same input, output and score as the eval row wrapping it — is
+# skipped. Batch runs keep their per-case spans.
+_JUDGE_SINGLE: contextvars.ContextVar[bool] = contextvars.ContextVar("braintrust_agno_judge_single", default=False)
+
+# The span a judge verdict should also be logged onto: set while ``post_check`` runs an
+# AgentAsJudgeEval as an agent post-hook, so the verdict lands on the agent's own row
+# and not only on the nested scorer span.
+_SCORE_TARGET: contextvars.ContextVar[Any] = contextvars.ContextVar("braintrust_agno_score_target", default=NOOP_SPAN)
+
+
+# ---------------------------------------------------------------------------
+# Scores
+# ---------------------------------------------------------------------------
+
+
+def _clamp01(value: float) -> float:
+ return max(0.0, min(1.0, value))
+
+
+def _score_from_scale(value: Any, scale: float = _AGNO_SCORE_SCALE) -> float | None:
+ """Convert an agno score to a Braintrust 0-1 score."""
+ if not is_numeric(value):
+ return None
+ return _clamp01(float(value) / scale)
+
+
+def _score_from_flag(value: Any) -> float | None:
+ """Convert a pass/fail verdict to a Braintrust 0-1 score."""
+ if value is None:
+ return None
+ return 1.0 if value else 0.0
+
+
+# ---------------------------------------------------------------------------
+# Spans
+# ---------------------------------------------------------------------------
+
+
+def _eval_metadata(instance: Any, eval_type: str) -> dict[str, Any]:
+ """Metadata common to every eval span: what ran, and against which component."""
+ metadata: dict[str, Any] = {
+ "component": "eval",
+ "eval_type": eval_type,
+ "eval_name": getattr(instance, "name", None),
+ }
+
+ agent = getattr(instance, "agent", None)
+ team = getattr(instance, "team", None)
+ if agent is not None:
+ metadata.update(omit(extract_metadata(agent, "agent"), ["component"]))
+ metadata["agent_id"] = getattr(agent, "id", None)
+ elif team is not None:
+ metadata.update(omit(extract_metadata(team, "team"), ["component"]))
+ metadata["team_id"] = getattr(team, "id", None)
+
+ evaluator_model = getattr(instance, "model", None)
+ if evaluator_model is not None:
+ evaluator = extract_metadata(evaluator_model, "model")
+ metadata["evaluator_model"] = evaluator.get("model") or evaluator.get("model_class")
+
+ return clean_nones(metadata)
+
+
+def _start_scorer_span(name: str, **event: Any):
+ return start_span(name=name, span_attributes=dict(_SCORER_SPAN_ATTRIBUTES), **clean_nones(event))
+
+
+def _start_eval_span(instance: Any, eval_type: str, **event: Any):
+ """Start the span for one eval run, as a row or as a scorer span inside a case."""
+ metadata = _eval_metadata(instance, eval_type)
+ if _IN_CASE.get():
+ return _start_scorer_span(_IN_CASE_SPAN_NAMES.get(eval_type, eval_type), metadata=metadata, **event)
+ return start_span(
+ name=getattr(instance, "name", None) or _EVAL_SPAN_NAMES[eval_type],
+ span_attributes=dict(_EVAL_SPAN_ATTRIBUTES),
+ metadata=metadata,
+ **clean_nones(event),
+ )
+
+
+def _log_to_score_target(scores: dict[str, float] | None) -> None:
+ """Mirror a post-hook judge's verdict onto the span of the run being judged."""
+ target = _SCORE_TARGET.get()
+ if scores and target is not NOOP_SPAN:
+ target.log(scores=scores)
+
+
+@contextmanager
+def _score_the_enclosing_span():
+ """Aim the next judge verdict at the span we are running inside."""
+ token = _SCORE_TARGET.set(current_span())
+ try:
+ yield
+ finally:
+ _SCORE_TARGET.reset(token)
+
+
+# ---------------------------------------------------------------------------
+# AccuracyEval
+# ---------------------------------------------------------------------------
+
+
+def _accuracy_payload(result: Any) -> dict[str, Any]:
+ """Row fields for an ``AccuracyResult``.
+
+ ``input``/``expected_output`` may be callables that agno invokes once per run, so
+ they are read back off the evaluations rather than called a second time here.
+ """
+ evaluations = list(getattr(result, "results", None) or [])
+ payload: dict[str, Any] = {}
+ if evaluations:
+ last = evaluations[-1]
+ payload["input"] = getattr(last, "input", None)
+ payload["expected"] = getattr(last, "expected_output", None)
+ payload["output"] = getattr(last, "output", None)
+
+ avg_score = getattr(result, "avg_score", None)
+ score = _score_from_scale(avg_score)
+ if score is not None:
+ payload["scores"] = {"accuracy": score}
+
+ payload["metadata"] = clean_nones(
+ {
+ "eval_run_id": getattr(result, "run_id", None),
+ "num_iterations": len(evaluations) or None,
+ "avg_score": avg_score,
+ "min_score": getattr(result, "min_score", None),
+ "max_score": getattr(result, "max_score", None),
+ "std_dev_score": getattr(result, "std_dev_score", None),
+ "iteration_scores": [getattr(item, "score", None) for item in evaluations] or None,
+ }
+ )
+ return clean_nones(payload)
+
+
+def _accuracy_run_wrapper(wrapped: Any, instance: Any, args: Any, kwargs: Any):
+ """Wrapper for ``AccuracyEval.run`` and ``AccuracyEval.run_with_output``."""
+ with _start_eval_span(instance, "accuracy") as span:
+ result = wrapped(*args, **kwargs)
+ span.log(**_accuracy_payload(result))
+ return result
+
+
+async def _accuracy_arun_wrapper(wrapped: Any, instance: Any, args: Any, kwargs: Any):
+ """Wrapper for ``AccuracyEval.arun`` and ``AccuracyEval.arun_with_output``."""
+ with _start_eval_span(instance, "accuracy") as span:
+ result = await wrapped(*args, **kwargs)
+ span.log(**_accuracy_payload(result))
+ return result
+
+
+_ACCURACY_EVALUATE_ARGS = (
+ "input",
+ "evaluator_agent",
+ "evaluation_input",
+ "evaluator_expected_output",
+ "agent_output",
+ "run_metrics",
+)
+
+
+@contextmanager
+def _accuracy_iteration_span(args: Any, kwargs: Any):
+ """Scorer span for one iteration's judge call, with that iteration's fields."""
+ bound = bound_args(args, kwargs, _ACCURACY_EVALUATE_ARGS)
+ with _start_scorer_span(
+ "accuracy",
+ input=clean_nones({"input": bound.get("input"), "expected": bound.get("evaluator_expected_output")}),
+ ) as span:
+ yield span, bound
+
+
+def _accuracy_evaluation_payload(bound: dict[str, Any], evaluation: Any) -> dict[str, Any]:
+ """Row fields for one iteration's ``AccuracyEvaluation``."""
+ if evaluation is None:
+ return clean_nones({"output": bound.get("agent_output")})
+
+ payload: dict[str, Any] = {
+ "expected": bound.get("evaluator_expected_output"),
+ "output": {
+ "score": getattr(evaluation, "score", None),
+ "reason": getattr(evaluation, "reason", None),
+ "output": getattr(evaluation, "output", None),
+ },
+ }
+ score = _score_from_scale(getattr(evaluation, "score", None))
+ if score is not None:
+ payload["scores"] = {"accuracy": score}
+ return clean_nones(payload)
+
+
+def _accuracy_evaluate_answer_wrapper(wrapped: Any, instance: Any, args: Any, kwargs: Any):
+ """Wrapper for ``AccuracyEval.evaluate_answer`` — one iteration's judge call."""
+ with _accuracy_iteration_span(args, kwargs) as (span, bound):
+ evaluation = wrapped(*args, **kwargs)
+ span.log(**_accuracy_evaluation_payload(bound, evaluation))
+ return evaluation
+
+
+async def _accuracy_aevaluate_answer_wrapper(wrapped: Any, instance: Any, args: Any, kwargs: Any):
+ """Wrapper for ``AccuracyEval.aevaluate_answer``."""
+ with _accuracy_iteration_span(args, kwargs) as (span, bound):
+ evaluation = await wrapped(*args, **kwargs)
+ span.log(**_accuracy_evaluation_payload(bound, evaluation))
+ return evaluation
+
+
+# ---------------------------------------------------------------------------
+# AgentAsJudgeEval
+# ---------------------------------------------------------------------------
+
+
+def _judge_evaluation_score(evaluation: Any) -> float | None:
+ """Numeric score if the judge graded one, else its pass/fail verdict."""
+ score = _score_from_scale(getattr(evaluation, "score", None))
+ if score is not None:
+ return score
+ return _score_from_flag(getattr(evaluation, "passed", None))
+
+
+def _judge_output(evaluation: Any) -> dict[str, Any]:
+ return {
+ "score": getattr(evaluation, "score", None),
+ "passed": getattr(evaluation, "passed", None),
+ "reason": getattr(evaluation, "reason", None),
+ }
+
+
+def _judge_scores(result: Any, evaluations: list) -> dict[str, float] | None:
+ if not evaluations:
+ return None
+
+ if len(evaluations) > 1:
+ # Batch mode: the per-case verdicts are scored on their own spans, so the batch
+ # row carries the pass rate.
+ pass_rate = _score_from_scale(getattr(result, "pass_rate", None), scale=_PASS_RATE_SCALE)
+ return {"judge": pass_rate} if pass_rate is not None else None
+
+ score = _judge_evaluation_score(evaluations[0])
+ return {"judge": score} if score is not None else None
+
+
+_JUDGE_RUN_ARGS = ("input", "output", "cases")
+
+
+def _judge_payload(instance: Any, bound: dict[str, Any], result: Any) -> dict[str, Any]:
+ evaluations = list(getattr(result, "results", None) or [])
+ scoring_strategy = getattr(instance, "scoring_strategy", None)
+
+ payload: dict[str, Any] = {
+ "input": bound.get("input"),
+ "output": _judge_output(evaluations[0]) if len(evaluations) == 1 else bound.get("output"),
+ "metadata": clean_nones(
+ {
+ "eval_run_id": getattr(result, "run_id", None),
+ "criteria": getattr(instance, "criteria", None),
+ "scoring_strategy": scoring_strategy,
+ "threshold": getattr(instance, "threshold", None) if scoring_strategy == "numeric" else None,
+ "pass_rate": getattr(result, "pass_rate", None),
+ "num_cases": len(evaluations) or None,
+ }
+ ),
+ }
+ scores = _judge_scores(result, evaluations)
+ if scores:
+ payload["scores"] = scores
+ return clean_nones(payload)
+
+
+@contextmanager
+def _judge_run_span(instance: Any, args: Any, kwargs: Any):
+ """Eval span for one ``AgentAsJudgeEval`` run, flagging single- vs batch-mode."""
+ bound = bound_args(args, kwargs, _JUDGE_RUN_ARGS)
+ token = _JUDGE_SINGLE.set(bound.get("cases") is None)
+ try:
+ with _start_eval_span(instance, "agent_as_judge", input=bound.get("input")) as span:
+ yield span, bound
+ finally:
+ _JUDGE_SINGLE.reset(token)
+
+
+def _finish_judge_run(span: Any, instance: Any, bound: dict[str, Any], result: Any) -> None:
+ payload = _judge_payload(instance, bound, result)
+ span.log(**payload)
+ _log_to_score_target(payload.get("scores"))
+
+
+def _judge_run_wrapper(wrapped: Any, instance: Any, args: Any, kwargs: Any):
+ """Wrapper for ``AgentAsJudgeEval.run`` (single and batch modes)."""
+ with _judge_run_span(instance, args, kwargs) as (span, bound):
+ result = wrapped(*args, **kwargs)
+ _finish_judge_run(span, instance, bound, result)
+ return result
+
+
+async def _judge_arun_wrapper(wrapped: Any, instance: Any, args: Any, kwargs: Any):
+ """Wrapper for ``AgentAsJudgeEval.arun``."""
+ with _judge_run_span(instance, args, kwargs) as (span, bound):
+ result = await wrapped(*args, **kwargs)
+ _finish_judge_run(span, instance, bound, result)
+ return result
+
+
+_JUDGE_EVALUATE_ARGS = ("input", "output", "evaluator_agent", "run_metrics")
+
+
+@contextmanager
+def _judge_pair_span(args: Any, kwargs: Any):
+ bound = bound_args(args, kwargs, _JUDGE_EVALUATE_ARGS)
+ with _start_scorer_span(
+ "judge",
+ input=clean_nones({"input": bound.get("input"), "output": bound.get("output")}),
+ ) as span:
+ yield span, bound
+
+
+def _judge_evaluation_payload(evaluation: Any) -> dict[str, Any]:
+ if evaluation is None:
+ return {}
+ payload: dict[str, Any] = {
+ "output": _judge_output(evaluation),
+ "metadata": clean_nones({"criteria": getattr(evaluation, "criteria", None)}),
+ }
+ score = _judge_evaluation_score(evaluation)
+ if score is not None:
+ payload["scores"] = {"judge": score}
+ return payload
+
+
+def _judge_evaluate_wrapper(wrapped: Any, instance: Any, args: Any, kwargs: Any):
+ """Wrapper for ``AgentAsJudgeEval._evaluate`` — one graded input/output pair."""
+ if _JUDGE_SINGLE.get():
+ return wrapped(*args, **kwargs)
+ with _judge_pair_span(args, kwargs) as (span, _bound):
+ evaluation = wrapped(*args, **kwargs)
+ span.log(**_judge_evaluation_payload(evaluation))
+ return evaluation
+
+
+async def _judge_aevaluate_wrapper(wrapped: Any, instance: Any, args: Any, kwargs: Any):
+ """Wrapper for ``AgentAsJudgeEval._aevaluate``."""
+ if _JUDGE_SINGLE.get():
+ return await wrapped(*args, **kwargs)
+ with _judge_pair_span(args, kwargs) as (span, _bound):
+ evaluation = await wrapped(*args, **kwargs)
+ span.log(**_judge_evaluation_payload(evaluation))
+ return evaluation
+
+
+def _judge_post_check_wrapper(wrapped: Any, instance: Any, args: Any, kwargs: Any):
+ """Wrapper for ``AgentAsJudgeEval.post_check`` (agent ``post_hooks`` usage).
+
+ ``post_check`` runs inside the agent's own span and discards the judge result, so
+ rather than starting a span here, point the nested ``run()`` at the enclosing span
+ and let it mirror the verdict onto the row being judged.
+ """
+ with _score_the_enclosing_span():
+ return wrapped(*args, **kwargs)
+
+
+async def _judge_async_post_check_wrapper(wrapped: Any, instance: Any, args: Any, kwargs: Any):
+ """Wrapper for ``AgentAsJudgeEval.async_post_check``."""
+ with _score_the_enclosing_span():
+ return await wrapped(*args, **kwargs)
+
+
+# ---------------------------------------------------------------------------
+# ReliabilityEval
+# ---------------------------------------------------------------------------
+
+
+def _reliability_input(instance: Any) -> dict[str, Any]:
+ return clean_nones(
+ {
+ "expected_tool_calls": getattr(instance, "expected_tool_calls", None),
+ "expected_tool_call_arguments": getattr(instance, "expected_tool_call_arguments", None),
+ "allow_additional_tool_calls": getattr(instance, "allow_additional_tool_calls", None),
+ }
+ )
+
+
+def _reliability_payload(result: Any) -> dict[str, Any]:
+ payload: dict[str, Any] = {"metadata": clean_nones({"eval_run_id": getattr(result, "run_id", None)})}
+
+ eval_status = getattr(result, "eval_status", None)
+ if eval_status is not None:
+ payload["output"] = clean_nones(
+ {
+ "eval_status": eval_status,
+ "passed_tool_calls": getattr(result, "passed_tool_calls", None),
+ "failed_tool_calls": getattr(result, "failed_tool_calls", None),
+ "missing_tool_calls": getattr(result, "missing_tool_calls", None),
+ "additional_tool_calls": getattr(result, "additional_tool_calls", None),
+ "passed_argument_checks": getattr(result, "passed_argument_checks", None),
+ "failed_argument_checks": getattr(result, "failed_argument_checks", None),
+ }
+ )
+ payload["scores"] = {"reliability": 1.0 if eval_status == "PASSED" else 0.0}
+
+ return payload
+
+
+def _reliability_run_wrapper(wrapped: Any, instance: Any, args: Any, kwargs: Any):
+ """Wrapper for ``ReliabilityEval.run``."""
+ with _start_eval_span(instance, "reliability", input=_reliability_input(instance)) as span:
+ result = wrapped(*args, **kwargs)
+ span.log(**_reliability_payload(result))
+ return result
+
+
+async def _reliability_arun_wrapper(wrapped: Any, instance: Any, args: Any, kwargs: Any):
+ """Wrapper for ``ReliabilityEval.arun``."""
+ with _start_eval_span(instance, "reliability", input=_reliability_input(instance)) as span:
+ result = await wrapped(*args, **kwargs)
+ span.log(**_reliability_payload(result))
+ return result
+
+
+# ---------------------------------------------------------------------------
+# PerformanceEval
+# ---------------------------------------------------------------------------
+
+_PERFORMANCE_METRICS = (
+ "avg_run_time",
+ "min_run_time",
+ "max_run_time",
+ "median_run_time",
+ "p95_run_time",
+ "avg_memory_usage",
+ "min_memory_usage",
+ "max_memory_usage",
+ "median_memory_usage",
+ "p95_memory_usage",
+)
+
+
+def _performance_payload(instance: Any, result: Any) -> dict[str, Any]:
+ summary: dict[str, Any] = {}
+ metrics: dict[str, float] = {}
+ for key in _PERFORMANCE_METRICS:
+ value = getattr(result, key, None)
+ if value is None:
+ continue
+ summary[key] = value
+ if is_numeric(value):
+ metrics[key] = float(value)
+
+ return clean_nones(
+ {
+ "output": summary or None,
+ "metrics": metrics or None,
+ "metadata": clean_nones(
+ {
+ "eval_run_id": getattr(result, "run_id", None),
+ "func": getattr(getattr(instance, "func", None), "__name__", None),
+ "warmup_runs": getattr(instance, "warmup_runs", None),
+ "num_iterations": getattr(instance, "num_iterations", None),
+ "measure_runtime": getattr(instance, "measure_runtime", None),
+ "measure_memory": getattr(instance, "measure_memory", None),
+ }
+ ),
+ }
+ )
+
+
+def _performance_run_wrapper(wrapped: Any, instance: Any, args: Any, kwargs: Any):
+ """Wrapper for ``PerformanceEval.run``.
+
+ The measured function is called ``warmup_runs + num_iterations`` times, so agno
+ instrumentation stands down for the duration (see ``suppress_spans``) and the eval
+ keeps one row.
+ """
+ with _start_eval_span(instance, "performance") as span, suppress_spans():
+ result = wrapped(*args, **kwargs)
+ span.log(**_performance_payload(instance, result))
+ return result
+
+
+async def _performance_arun_wrapper(wrapped: Any, instance: Any, args: Any, kwargs: Any):
+ """Wrapper for ``PerformanceEval.arun``."""
+ with _start_eval_span(instance, "performance") as span, suppress_spans():
+ result = await wrapped(*args, **kwargs)
+ span.log(**_performance_payload(instance, result))
+ return result
+
+
+# ---------------------------------------------------------------------------
+# Eval suites (``agno.eval.suite``)
+# ---------------------------------------------------------------------------
+
+# ``agno.eval.suite`` only exists from agno 2.9, where ``Case`` and ``CaseResult`` are
+# dataclasses declaring every field read below — so these read attributes directly. A
+# rename upstream should fail a test, not silently drop a field.
+
+
+def _case_metadata(case: Any, result: Any) -> dict[str, Any]:
+ judge_mode = case.judge_mode
+ return clean_nones(
+ {
+ "component": "eval",
+ "eval_type": "suite_case",
+ "case_name": case.name,
+ "criteria": case.criteria,
+ "expected_tool_calls": list(case.expected_tool_calls) if case.expected_tool_calls else None,
+ "judge_mode": getattr(judge_mode, "value", judge_mode),
+ "agent_id": result.agent_id,
+ "team_id": result.team_id,
+ "session_id": result.session_id or None,
+ "passed": result.passed,
+ "judge_passed": result.judge_passed,
+ "judge_score": result.judge_score,
+ "judge_reason": result.judge_reason,
+ "reliability_passed": result.reliability_passed,
+ "tools_called": list(result.tools_called) if result.tools_called else None,
+ "timed_out": result.timed_out or None,
+ "skipped": result.skipped or None,
+ "error": result.error,
+ "duration_seconds": result.duration_seconds,
+ }
+ )
+
+
+def _case_scores(result: Any) -> dict[str, float]:
+ """Score a ``CaseResult`` from whichever of its checks were configured."""
+ scores: dict[str, float] = {}
+
+ if result.judge_passed is not None:
+ judge_score = _score_from_scale(result.judge_score)
+ scores["judge"] = judge_score if judge_score is not None else float(bool(result.judge_passed))
+
+ if result.reliability_passed is not None:
+ scores["reliability"] = float(bool(result.reliability_passed))
+
+ # ``Case.scorer`` returns an ``agno.scorer.Score`` already in 0-1.
+ value = getattr(result.score, "value", None)
+ if is_numeric(value):
+ scores["scorer"] = _clamp01(float(value))
+
+ return scores
+
+
+def _case_payload(case: Any, result: Any) -> dict[str, Any]:
+ payload: dict[str, Any] = {"output": result.output, "metadata": _case_metadata(case, result)}
+ scores = _case_scores(result)
+ if scores:
+ payload["scores"] = scores
+ return clean_nones(payload)
+
+
+def _start_case_span(case: Any):
+ """Start the row span for one suite case, on the suite's experiment if there is one."""
+ return start_span(
+ name=case.name or "case",
+ span_attributes=dict(_EVAL_SPAN_ATTRIBUTES),
+ parent_object=active_experiment(),
+ **clean_nones(
+ {
+ "input": case.input,
+ "expected": case.expected,
+ "tags": list(case.tags) if case.tags else None,
+ }
+ ),
+ )
+
+
+async def _arun_cases_wrapper(wrapped: Any, instance: Any, args: Any, kwargs: Any):
+ """Wrapper for ``agno.eval.suite.arun_cases`` — the whole suite run.
+
+ Reached from every entry point (``cli``, ``acli``, ``run_cases``, ``arun_cases``),
+ because each resolves the next call through the module globals at call time.
+ """
+ cases = args[0] if args else kwargs.get("cases", ())
+ metadata = clean_nones(
+ {
+ "source": "agno",
+ "eval_type": "suite",
+ "num_cases": len(cases),
+ "tag": kwargs.get("tag"),
+ "case_name_filter": kwargs.get("name"),
+ }
+ )
+ with suite_experiment(metadata):
+ return await wrapped(*args, **kwargs)
+
+
+async def _arun_case_wrapper(wrapped: Any, instance: Any, args: Any, kwargs: Any):
+ """Wrapper for ``agno.eval.suite._arun_case`` — one case, one row."""
+ case = args[0] if args else kwargs["case"]
+ token = _IN_CASE.set(True)
+ try:
+ with _start_case_span(case) as span:
+ result = await wrapped(*args, **kwargs)
+ span.log(**_case_payload(case, result))
+ return result
+ finally:
+ _IN_CASE.reset(token)
diff --git a/py/src/braintrust/integrations/agno/integration.py b/py/src/braintrust/integrations/agno/integration.py
index 94d0d9c43..96c0ac8a8 100644
--- a/py/src/braintrust/integrations/agno/integration.py
+++ b/py/src/braintrust/integrations/agno/integration.py
@@ -5,9 +5,14 @@
from braintrust.integrations.base import BaseIntegration
from .patchers import (
+ AccuracyEvalPatcher,
+ AgentAsJudgeEvalPatcher,
AgentPatcher,
+ EvalSuitePatcher,
FunctionCallPatcher,
ModelPatcher,
+ PerformanceEvalPatcher,
+ ReliabilityEvalPatcher,
TeamPatcher,
WorkflowPatcher,
)
@@ -28,4 +33,9 @@ class AgnoIntegration(BaseIntegration):
ModelPatcher,
FunctionCallPatcher,
WorkflowPatcher,
+ AccuracyEvalPatcher,
+ AgentAsJudgeEvalPatcher,
+ ReliabilityEvalPatcher,
+ PerformanceEvalPatcher,
+ EvalSuitePatcher,
)
diff --git a/py/src/braintrust/integrations/agno/patchers.py b/py/src/braintrust/integrations/agno/patchers.py
index 112c947f1..a405e5c2c 100644
--- a/py/src/braintrust/integrations/agno/patchers.py
+++ b/py/src/braintrust/integrations/agno/patchers.py
@@ -2,6 +2,24 @@
from braintrust.integrations.base import CompositeFunctionWrapperPatcher, FunctionWrapperPatcher
+from .eval_tracing import (
+ _accuracy_aevaluate_answer_wrapper,
+ _accuracy_arun_wrapper,
+ _accuracy_evaluate_answer_wrapper,
+ _accuracy_run_wrapper,
+ _arun_case_wrapper,
+ _arun_cases_wrapper,
+ _judge_aevaluate_wrapper,
+ _judge_arun_wrapper,
+ _judge_async_post_check_wrapper,
+ _judge_evaluate_wrapper,
+ _judge_post_check_wrapper,
+ _judge_run_wrapper,
+ _performance_arun_wrapper,
+ _performance_run_wrapper,
+ _reliability_arun_wrapper,
+ _reliability_run_wrapper,
+)
from .tracing import (
_agent_arun_private_wrapper,
_agent_arun_public_wrapper,
@@ -31,9 +49,28 @@
_workflow_execute_stream_wrapper,
_workflow_execute_workflow_agent_wrapper,
_workflow_execute_wrapper,
+ spans_suppressed,
)
+class _AgnoFunctionWrapperPatcher(FunctionWrapperPatcher):
+ """Base for every agno patcher: hands through untouched while spans are suppressed.
+
+ ``PerformanceEval`` measures a function that is usually an agent run, so without
+ this the wrappers would still build span payloads — and retain every stream chunk —
+ for each of the 60 default iterations, charging instrumentation cost to the very
+ measurement being taken and then discarding the result. Returning ``wrapped(...)``
+ serves sync and async targets alike: an async target's coroutine is simply handed
+ back to the caller that awaits it.
+ """
+
+ @classmethod
+ def _wrapper(cls, wrapped: Any, instance: Any, args: Any, kwargs: Any) -> Any:
+ if spans_suppressed():
+ return wrapped(*args, **kwargs)
+ return cls.wrapper(wrapped, instance, args, kwargs)
+
+
# ---------------------------------------------------------------------------
# Agent patchers
# ---------------------------------------------------------------------------
@@ -43,7 +80,7 @@
# variant exists.
-class _AgentRunPrivatePatcher(FunctionWrapperPatcher):
+class _AgentRunPrivatePatcher(_AgnoFunctionWrapperPatcher):
name = "agno.agent.run.private"
target_module = "agno.agent"
target_path = "Agent._run"
@@ -51,7 +88,7 @@ class _AgentRunPrivatePatcher(FunctionWrapperPatcher):
priority: ClassVar[int] = 50
-class _AgentRunPublicPatcher(FunctionWrapperPatcher):
+class _AgentRunPublicPatcher(_AgnoFunctionWrapperPatcher):
"""Fallback: wrap ``Agent.run`` only when ``Agent._run`` does not exist."""
name = "agno.agent.run.public"
@@ -62,7 +99,7 @@ class _AgentRunPublicPatcher(FunctionWrapperPatcher):
superseded_by = (_AgentRunPrivatePatcher,)
-class _AgentArunPrivatePatcher(FunctionWrapperPatcher):
+class _AgentArunPrivatePatcher(_AgnoFunctionWrapperPatcher):
name = "agno.agent.arun.private"
target_module = "agno.agent"
target_path = "Agent._arun"
@@ -70,14 +107,14 @@ class _AgentArunPrivatePatcher(FunctionWrapperPatcher):
priority: ClassVar[int] = 50
-class _AgentRunStreamPatcher(FunctionWrapperPatcher):
+class _AgentRunStreamPatcher(_AgnoFunctionWrapperPatcher):
name = "agno.agent.run_stream"
target_module = "agno.agent"
target_path = "Agent._run_stream"
wrapper = _agent_run_stream_wrapper
-class _AgentArunStreamPatcher(FunctionWrapperPatcher):
+class _AgentArunStreamPatcher(_AgnoFunctionWrapperPatcher):
name = "agno.agent.arun_stream"
target_module = "agno.agent"
target_path = "Agent._arun_stream"
@@ -85,7 +122,7 @@ class _AgentArunStreamPatcher(FunctionWrapperPatcher):
priority: ClassVar[int] = 50
-class _AgentArunPublicPatcher(FunctionWrapperPatcher):
+class _AgentArunPublicPatcher(_AgnoFunctionWrapperPatcher):
"""Fallback: wrap ``Agent.arun`` only when neither ``_arun`` nor ``_arun_stream`` exist."""
name = "agno.agent.arun.public"
@@ -115,7 +152,7 @@ class AgentPatcher(CompositeFunctionWrapperPatcher):
# ---------------------------------------------------------------------------
-class _TeamRunPrivatePatcher(FunctionWrapperPatcher):
+class _TeamRunPrivatePatcher(_AgnoFunctionWrapperPatcher):
name = "agno.team.run.private"
target_module = "agno.team"
target_path = "Team._run"
@@ -123,7 +160,7 @@ class _TeamRunPrivatePatcher(FunctionWrapperPatcher):
priority: ClassVar[int] = 50
-class _TeamRunPublicPatcher(FunctionWrapperPatcher):
+class _TeamRunPublicPatcher(_AgnoFunctionWrapperPatcher):
"""Fallback: wrap ``Team.run`` only when ``Team._run`` does not exist."""
name = "agno.team.run.public"
@@ -134,7 +171,7 @@ class _TeamRunPublicPatcher(FunctionWrapperPatcher):
superseded_by = (_TeamRunPrivatePatcher,)
-class _TeamArunPrivatePatcher(FunctionWrapperPatcher):
+class _TeamArunPrivatePatcher(_AgnoFunctionWrapperPatcher):
name = "agno.team.arun.private"
target_module = "agno.team"
target_path = "Team._arun"
@@ -142,14 +179,14 @@ class _TeamArunPrivatePatcher(FunctionWrapperPatcher):
priority: ClassVar[int] = 50
-class _TeamRunStreamPatcher(FunctionWrapperPatcher):
+class _TeamRunStreamPatcher(_AgnoFunctionWrapperPatcher):
name = "agno.team.run_stream"
target_module = "agno.team"
target_path = "Team._run_stream"
wrapper = _team_run_stream_wrapper
-class _TeamArunStreamPatcher(FunctionWrapperPatcher):
+class _TeamArunStreamPatcher(_AgnoFunctionWrapperPatcher):
name = "agno.team.arun_stream"
target_module = "agno.team"
target_path = "Team._arun_stream"
@@ -157,7 +194,7 @@ class _TeamArunStreamPatcher(FunctionWrapperPatcher):
priority: ClassVar[int] = 50
-class _TeamArunPublicPatcher(FunctionWrapperPatcher):
+class _TeamArunPublicPatcher(_AgnoFunctionWrapperPatcher):
"""Fallback: wrap ``Team.arun`` only when neither ``_arun`` nor ``_arun_stream`` exist."""
name = "agno.team.arun.public"
@@ -187,56 +224,56 @@ class TeamPatcher(CompositeFunctionWrapperPatcher):
# ---------------------------------------------------------------------------
-class _ModelInvokePatcher(FunctionWrapperPatcher):
+class _ModelInvokePatcher(_AgnoFunctionWrapperPatcher):
name = "agno.model.invoke"
target_module = "agno.models.base"
target_path = "Model.invoke"
wrapper = _model_invoke_wrapper
-class _ModelAinvokePatcher(FunctionWrapperPatcher):
+class _ModelAinvokePatcher(_AgnoFunctionWrapperPatcher):
name = "agno.model.ainvoke"
target_module = "agno.models.base"
target_path = "Model.ainvoke"
wrapper = _model_ainvoke_wrapper
-class _ModelInvokeStreamPatcher(FunctionWrapperPatcher):
+class _ModelInvokeStreamPatcher(_AgnoFunctionWrapperPatcher):
name = "agno.model.invoke_stream"
target_module = "agno.models.base"
target_path = "Model.invoke_stream"
wrapper = _model_invoke_stream_wrapper
-class _ModelAinvokeStreamPatcher(FunctionWrapperPatcher):
+class _ModelAinvokeStreamPatcher(_AgnoFunctionWrapperPatcher):
name = "agno.model.ainvoke_stream"
target_module = "agno.models.base"
target_path = "Model.ainvoke_stream"
wrapper = _model_ainvoke_stream_wrapper
-class _ModelResponsePatcher(FunctionWrapperPatcher):
+class _ModelResponsePatcher(_AgnoFunctionWrapperPatcher):
name = "agno.model.response"
target_module = "agno.models.base"
target_path = "Model.response"
wrapper = _model_response_wrapper
-class _ModelAresponsePatcher(FunctionWrapperPatcher):
+class _ModelAresponsePatcher(_AgnoFunctionWrapperPatcher):
name = "agno.model.aresponse"
target_module = "agno.models.base"
target_path = "Model.aresponse"
wrapper = _model_aresponse_wrapper
-class _ModelResponseStreamPatcher(FunctionWrapperPatcher):
+class _ModelResponseStreamPatcher(_AgnoFunctionWrapperPatcher):
name = "agno.model.response_stream"
target_module = "agno.models.base"
target_path = "Model.response_stream"
wrapper = _model_response_stream_wrapper
-class _ModelAresponseStreamPatcher(FunctionWrapperPatcher):
+class _ModelAresponseStreamPatcher(_AgnoFunctionWrapperPatcher):
name = "agno.model.aresponse_stream"
target_module = "agno.models.base"
target_path = "Model.aresponse_stream"
@@ -264,14 +301,14 @@ class ModelPatcher(CompositeFunctionWrapperPatcher):
# ---------------------------------------------------------------------------
-class _FunctionCallExecutePatcher(FunctionWrapperPatcher):
+class _FunctionCallExecutePatcher(_AgnoFunctionWrapperPatcher):
name = "agno.function_call.execute"
target_module = "agno.tools.function"
target_path = "FunctionCall.execute"
wrapper = _function_call_execute_wrapper
-class _FunctionCallAexecutePatcher(FunctionWrapperPatcher):
+class _FunctionCallAexecutePatcher(_AgnoFunctionWrapperPatcher):
name = "agno.function_call.aexecute"
target_module = "agno.tools.function"
target_path = "FunctionCall.aexecute"
@@ -293,42 +330,42 @@ class FunctionCallPatcher(CompositeFunctionWrapperPatcher):
# ---------------------------------------------------------------------------
-class _WorkflowExecutePatcher(FunctionWrapperPatcher):
+class _WorkflowExecutePatcher(_AgnoFunctionWrapperPatcher):
name = "agno.workflow.execute"
target_module = "agno.workflow"
target_path = "Workflow._execute"
wrapper = _workflow_execute_wrapper
-class _WorkflowExecuteStreamPatcher(FunctionWrapperPatcher):
+class _WorkflowExecuteStreamPatcher(_AgnoFunctionWrapperPatcher):
name = "agno.workflow.execute_stream"
target_module = "agno.workflow"
target_path = "Workflow._execute_stream"
wrapper = _workflow_execute_stream_wrapper
-class _WorkflowAexecutePatcher(FunctionWrapperPatcher):
+class _WorkflowAexecutePatcher(_AgnoFunctionWrapperPatcher):
name = "agno.workflow.aexecute"
target_module = "agno.workflow"
target_path = "Workflow._aexecute"
wrapper = _workflow_aexecute_wrapper
-class _WorkflowAexecuteStreamPatcher(FunctionWrapperPatcher):
+class _WorkflowAexecuteStreamPatcher(_AgnoFunctionWrapperPatcher):
name = "agno.workflow.aexecute_stream"
target_module = "agno.workflow"
target_path = "Workflow._aexecute_stream"
wrapper = _workflow_aexecute_stream_wrapper
-class _WorkflowExecuteWorkflowAgentPatcher(FunctionWrapperPatcher):
+class _WorkflowExecuteWorkflowAgentPatcher(_AgnoFunctionWrapperPatcher):
name = "agno.workflow.execute_workflow_agent"
target_module = "agno.workflow"
target_path = "Workflow._execute_workflow_agent"
wrapper = _workflow_execute_workflow_agent_wrapper
-class _WorkflowAexecuteWorkflowAgentPatcher(FunctionWrapperPatcher):
+class _WorkflowAexecuteWorkflowAgentPatcher(_AgnoFunctionWrapperPatcher):
name = "agno.workflow.aexecute_workflow_agent"
target_module = "agno.workflow"
target_path = "Workflow._aexecute_workflow_agent"
@@ -349,6 +386,221 @@ class WorkflowPatcher(CompositeFunctionWrapperPatcher):
)
+# ---------------------------------------------------------------------------
+# Eval patchers (``agno.eval``)
+# ---------------------------------------------------------------------------
+
+# Every target lives in an ``agno.eval`` submodule that the eval package imports
+# lazily, so each patcher names its own ``target_module``. Submodules that a given
+# agno release does not ship (``agent_as_judge`` before 2.4, ``suite`` before 2.9)
+# fail to import, ``resolve_root()`` returns None, and the patcher simply does not
+# apply — no explicit version gate needed.
+
+
+class _AccuracyEvalRunPatcher(_AgnoFunctionWrapperPatcher):
+ name = "agno.eval.accuracy.run"
+ target_module = "agno.eval.accuracy"
+ target_path = "AccuracyEval.run"
+ wrapper = _accuracy_run_wrapper
+
+
+class _AccuracyEvalArunPatcher(_AgnoFunctionWrapperPatcher):
+ name = "agno.eval.accuracy.arun"
+ target_module = "agno.eval.accuracy"
+ target_path = "AccuracyEval.arun"
+ wrapper = _accuracy_arun_wrapper
+
+
+class _AccuracyEvalRunWithOutputPatcher(_AgnoFunctionWrapperPatcher):
+ name = "agno.eval.accuracy.run_with_output"
+ target_module = "agno.eval.accuracy"
+ target_path = "AccuracyEval.run_with_output"
+ wrapper = _accuracy_run_wrapper
+
+
+class _AccuracyEvalArunWithOutputPatcher(_AgnoFunctionWrapperPatcher):
+ name = "agno.eval.accuracy.arun_with_output"
+ target_module = "agno.eval.accuracy"
+ target_path = "AccuracyEval.arun_with_output"
+ wrapper = _accuracy_arun_wrapper
+
+
+class _AccuracyEvalEvaluateAnswerPatcher(_AgnoFunctionWrapperPatcher):
+ name = "agno.eval.accuracy.evaluate_answer"
+ target_module = "agno.eval.accuracy"
+ target_path = "AccuracyEval.evaluate_answer"
+ wrapper = _accuracy_evaluate_answer_wrapper
+
+
+class _AccuracyEvalAevaluateAnswerPatcher(_AgnoFunctionWrapperPatcher):
+ name = "agno.eval.accuracy.aevaluate_answer"
+ target_module = "agno.eval.accuracy"
+ target_path = "AccuracyEval.aevaluate_answer"
+ wrapper = _accuracy_aevaluate_answer_wrapper
+
+
+class AccuracyEvalPatcher(CompositeFunctionWrapperPatcher):
+ """Patch ``agno.eval.accuracy.AccuracyEval`` for tracing."""
+
+ name = "agno.eval.accuracy"
+ sub_patchers = (
+ _AccuracyEvalRunPatcher,
+ _AccuracyEvalArunPatcher,
+ _AccuracyEvalRunWithOutputPatcher,
+ _AccuracyEvalArunWithOutputPatcher,
+ _AccuracyEvalEvaluateAnswerPatcher,
+ _AccuracyEvalAevaluateAnswerPatcher,
+ )
+
+
+class _AgentAsJudgeEvalRunPatcher(_AgnoFunctionWrapperPatcher):
+ name = "agno.eval.agent_as_judge.run"
+ target_module = "agno.eval.agent_as_judge"
+ target_path = "AgentAsJudgeEval.run"
+ wrapper = _judge_run_wrapper
+
+
+class _AgentAsJudgeEvalArunPatcher(_AgnoFunctionWrapperPatcher):
+ name = "agno.eval.agent_as_judge.arun"
+ target_module = "agno.eval.agent_as_judge"
+ target_path = "AgentAsJudgeEval.arun"
+ wrapper = _judge_arun_wrapper
+
+
+class _AgentAsJudgeEvalEvaluatePatcher(_AgnoFunctionWrapperPatcher):
+ name = "agno.eval.agent_as_judge.evaluate"
+ target_module = "agno.eval.agent_as_judge"
+ target_path = "AgentAsJudgeEval._evaluate"
+ wrapper = _judge_evaluate_wrapper
+
+
+class _AgentAsJudgeEvalAevaluatePatcher(_AgnoFunctionWrapperPatcher):
+ name = "agno.eval.agent_as_judge.aevaluate"
+ target_module = "agno.eval.agent_as_judge"
+ target_path = "AgentAsJudgeEval._aevaluate"
+ wrapper = _judge_aevaluate_wrapper
+
+
+class _AgentAsJudgeEvalPostCheckPatcher(_AgnoFunctionWrapperPatcher):
+ name = "agno.eval.agent_as_judge.post_check"
+ target_module = "agno.eval.agent_as_judge"
+ target_path = "AgentAsJudgeEval.post_check"
+ wrapper = _judge_post_check_wrapper
+
+
+class _AgentAsJudgeEvalAsyncPostCheckPatcher(_AgnoFunctionWrapperPatcher):
+ name = "agno.eval.agent_as_judge.async_post_check"
+ target_module = "agno.eval.agent_as_judge"
+ target_path = "AgentAsJudgeEval.async_post_check"
+ wrapper = _judge_async_post_check_wrapper
+
+
+class AgentAsJudgeEvalPatcher(CompositeFunctionWrapperPatcher):
+ """Patch ``agno.eval.agent_as_judge.AgentAsJudgeEval`` for tracing (agno >= 2.4)."""
+
+ name = "agno.eval.agent_as_judge"
+ sub_patchers = (
+ _AgentAsJudgeEvalRunPatcher,
+ _AgentAsJudgeEvalArunPatcher,
+ _AgentAsJudgeEvalEvaluatePatcher,
+ _AgentAsJudgeEvalAevaluatePatcher,
+ _AgentAsJudgeEvalPostCheckPatcher,
+ _AgentAsJudgeEvalAsyncPostCheckPatcher,
+ )
+
+
+class _ReliabilityEvalRunPatcher(_AgnoFunctionWrapperPatcher):
+ name = "agno.eval.reliability.run"
+ target_module = "agno.eval.reliability"
+ target_path = "ReliabilityEval.run"
+ wrapper = _reliability_run_wrapper
+
+
+class _ReliabilityEvalArunPatcher(_AgnoFunctionWrapperPatcher):
+ name = "agno.eval.reliability.arun"
+ target_module = "agno.eval.reliability"
+ target_path = "ReliabilityEval.arun"
+ wrapper = _reliability_arun_wrapper
+
+
+class ReliabilityEvalPatcher(CompositeFunctionWrapperPatcher):
+ """Patch ``agno.eval.reliability.ReliabilityEval`` for tracing."""
+
+ name = "agno.eval.reliability"
+ sub_patchers = (
+ _ReliabilityEvalRunPatcher,
+ _ReliabilityEvalArunPatcher,
+ )
+
+
+class _PerformanceEvalRunPatcher(_AgnoFunctionWrapperPatcher):
+ name = "agno.eval.performance.run"
+ target_module = "agno.eval.performance"
+ target_path = "PerformanceEval.run"
+ wrapper = _performance_run_wrapper
+
+
+class _PerformanceEvalArunPatcher(_AgnoFunctionWrapperPatcher):
+ name = "agno.eval.performance.arun"
+ target_module = "agno.eval.performance"
+ target_path = "PerformanceEval.arun"
+ wrapper = _performance_arun_wrapper
+
+
+class PerformanceEvalPatcher(CompositeFunctionWrapperPatcher):
+ """Patch ``agno.eval.performance.PerformanceEval`` for tracing."""
+
+ name = "agno.eval.performance"
+ sub_patchers = (
+ _PerformanceEvalRunPatcher,
+ _PerformanceEvalArunPatcher,
+ )
+
+
+class _EvalSuiteRunCasesPatcher(_AgnoFunctionWrapperPatcher):
+ """Patch the suite runner, not ``cli``/``run_cases``.
+
+ ``cli``, ``acli`` and ``run_cases`` all resolve the next call through the module
+ globals at call time, so wrapping ``arun_cases`` covers every entry point — including
+ the common ``from agno.eval import cli`` layout, where the caller's name is bound
+ before ``setup_agno()`` gets a chance to patch anything.
+ """
+
+ name = "agno.eval.suite.arun_cases"
+ target_module = "agno.eval.suite"
+ target_path = "arun_cases"
+ wrapper = _arun_cases_wrapper
+
+
+class _EvalSuiteRunCasePatcher(_AgnoFunctionWrapperPatcher):
+ """Patch the private per-case runner: it is the only per-case seam.
+
+ The public presentation hooks (``on_case_start``/``on_case_end``) are two separate
+ callbacks, so a span cannot be held current across the case body, and ``cli()``
+ passes its own renderer into them.
+
+ ``applies()`` fails open (an unresolvable target simply does not apply), so if agno
+ renames this, per-case rows disappear silently while ``arun_cases`` above keeps
+ opening an experiment — an empty experiment with no error. The suite tests assert
+ one row per case, and are what catches that.
+ """
+
+ name = "agno.eval.suite.arun_case"
+ target_module = "agno.eval.suite"
+ target_path = "_arun_case"
+ wrapper = _arun_case_wrapper
+
+
+class EvalSuitePatcher(CompositeFunctionWrapperPatcher):
+ """Patch ``agno.eval.suite`` for tracing (agno >= 2.9)."""
+
+ name = "agno.eval.suite"
+ sub_patchers = (
+ _EvalSuiteRunCasesPatcher,
+ _EvalSuiteRunCasePatcher,
+ )
+
+
# ---------------------------------------------------------------------------
# Public wrap_*() helpers — thin wrappers around patcher.wrap_target()
# ---------------------------------------------------------------------------
@@ -377,3 +629,28 @@ def wrap_function_call(FunctionCall: Any) -> Any:
def wrap_workflow(Workflow: Any) -> Any:
"""Manually patch a Workflow class for tracing."""
return WorkflowPatcher.wrap_target(Workflow)
+
+
+def wrap_accuracy_eval(AccuracyEval: Any) -> Any:
+ """Manually patch an AccuracyEval class for tracing."""
+ return AccuracyEvalPatcher.wrap_target(AccuracyEval)
+
+
+def wrap_agent_as_judge_eval(AgentAsJudgeEval: Any) -> Any:
+ """Manually patch an AgentAsJudgeEval class for tracing."""
+ return AgentAsJudgeEvalPatcher.wrap_target(AgentAsJudgeEval)
+
+
+def wrap_reliability_eval(ReliabilityEval: Any) -> Any:
+ """Manually patch a ReliabilityEval class for tracing."""
+ return ReliabilityEvalPatcher.wrap_target(ReliabilityEval)
+
+
+def wrap_performance_eval(PerformanceEval: Any) -> Any:
+ """Manually patch a PerformanceEval class for tracing."""
+ return PerformanceEvalPatcher.wrap_target(PerformanceEval)
+
+
+def wrap_eval_suite(suite_module: Any) -> Any:
+ """Manually patch the ``agno.eval.suite`` module for tracing."""
+ return EvalSuitePatcher.wrap_target(suite_module)
diff --git a/py/src/braintrust/integrations/agno/test_agno_evals.py b/py/src/braintrust/integrations/agno/test_agno_evals.py
new file mode 100644
index 000000000..99a036679
--- /dev/null
+++ b/py/src/braintrust/integrations/agno/test_agno_evals.py
@@ -0,0 +1,489 @@
+# pyright: reportPrivateUsage=false
+# pyright: reportMissingParameterType=false
+# pyright: reportUnknownMemberType=false
+# pyright: reportUnknownParameterType=false
+# pyright: reportUnknownVariableType=false
+# pyright: reportUnknownArgumentType=false
+"""Tracing coverage for agno's eval framework (``agno.eval``).
+
+Every eval type is exercised against real agno objects and recorded provider traffic.
+The modules imported at the top exist in every version of the agno matrix; the two
+that do not — ``agent_as_judge`` (agno >= 2.4) and ``suite`` (agno >= 2.9) — are
+imported per test so the older sessions skip just those.
+"""
+
+import asyncio
+
+import pytest
+from braintrust import logger
+from braintrust.integrations.agno import eval_experiments, setup_agno, wrap_reliability_eval
+from braintrust.test_helpers import find_span_by_name, find_spans_by_type, init_test_exp, init_test_logger
+
+from ._test_agno_helpers import PROJECT_NAME
+
+
+agno_agent = pytest.importorskip("agno.agent")
+agno_openai = pytest.importorskip("agno.models.openai")
+accuracy_module = pytest.importorskip("agno.eval.accuracy")
+reliability_module = pytest.importorskip("agno.eval.reliability")
+performance_module = pytest.importorskip("agno.eval.performance")
+
+MODEL = "gpt-4o-mini"
+
+
+@pytest.fixture
+def memory_logger():
+ init_test_logger(PROJECT_NAME)
+ with logger._internal_with_memory_background_logger() as bgl:
+ assert not bgl.pop(), "spans leaked in from a previous test"
+ yield bgl
+
+
+@pytest.fixture(scope="module", autouse=True)
+def setup_wrapper():
+ setup_agno(project_name=PROJECT_NAME)
+ yield
+
+
+@pytest.fixture(autouse=True)
+def suite_experiments_off():
+ """Keep suite rows in logs unless a test opts into experiment routing.
+
+ ``eval_experiments`` defaults to on, which would have a suite run call the real
+ ``braintrust.init()``.
+ """
+ eval_experiments.configure(False)
+ try:
+ yield
+ finally:
+ eval_experiments.configure(None)
+
+
+def _names(spans):
+ return [span["span_attributes"]["name"] for span in spans]
+
+
+def _spans_named(spans, prefix):
+ return [span for span in spans if span["span_attributes"]["name"].startswith(prefix)]
+
+
+def _make_agent(*, name="Math Agent", tools=None):
+ return agno_agent.Agent(
+ name=name,
+ model=agno_openai.OpenAIChat(id=MODEL),
+ instructions="Answer with the final number only.",
+ tools=tools,
+ )
+
+
+# ---------------------------------------------------------------------------
+# AccuracyEval
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.vcr
+def test_accuracy_eval_logs_score_and_nests_agent(memory_logger):
+ evaluation = accuracy_module.AccuracyEval(
+ name="Multiplication Eval",
+ model=agno_openai.OpenAIChat(id=MODEL),
+ agent=_make_agent(),
+ input="What is 10*5?",
+ expected_output="50",
+ num_iterations=1,
+ )
+ result = evaluation.run(print_summary=False, print_results=False)
+ assert result is not None
+ assert result.avg_score is not None
+
+ spans = memory_logger.pop()
+ eval_span = find_span_by_name(spans, "Multiplication Eval")
+
+ assert eval_span["span_attributes"]["type"] == "eval"
+ assert eval_span["context"]["span_origin"]["instrumentation"]["name"] == "agno-auto"
+ assert eval_span["input"] == "What is 10*5?"
+ assert eval_span["expected"] == "50"
+ assert eval_span["output"]
+ assert eval_span["scores"]["accuracy"] == pytest.approx(result.avg_score / 10)
+ assert 0.0 <= eval_span["scores"]["accuracy"] <= 1.0
+
+ metadata = eval_span["metadata"]
+ assert metadata["eval_type"] == "accuracy"
+ assert metadata["eval_name"] == "Multiplication Eval"
+ assert metadata["num_iterations"] == 1
+ assert metadata["agent_name"] == "Math Agent"
+ assert metadata["evaluator_model"] == MODEL
+ assert metadata["avg_score"] == result.avg_score
+ assert metadata["iteration_scores"] == [result.results[0].score]
+
+ # The iteration's judge call is a scorer span under the eval row.
+ score_span = find_span_by_name(spans, "accuracy")
+ assert score_span["span_attributes"]["type"] == "score"
+ assert score_span["span_attributes"]["purpose"] == "scorer"
+ assert score_span["span_parents"] == [eval_span["span_id"]]
+ assert score_span["scores"]["accuracy"] == pytest.approx(result.results[0].score / 10)
+ assert score_span["output"]["reason"]
+
+ # The agent under test runs inside the eval row, not as its own trace.
+ agent_span = find_span_by_name(spans, "Math Agent.run")
+ assert agent_span["span_parents"] == [eval_span["span_id"]]
+
+
+@pytest.mark.vcr
+def test_accuracy_eval_arun_logs_score(memory_logger):
+ evaluation = accuracy_module.AccuracyEval(
+ name="Async Multiplication Eval",
+ model=agno_openai.OpenAIChat(id=MODEL),
+ agent=_make_agent(),
+ input="What is 10*5?",
+ expected_output="50",
+ num_iterations=1,
+ )
+ result = asyncio.run(evaluation.arun(print_summary=False, print_results=False))
+ assert result is not None
+
+ spans = memory_logger.pop()
+ eval_span = find_span_by_name(spans, "Async Multiplication Eval")
+ assert eval_span["span_attributes"]["type"] == "eval"
+ assert eval_span["scores"]["accuracy"] == pytest.approx(result.avg_score / 10)
+
+ score_span = find_span_by_name(spans, "accuracy")
+ assert score_span["span_parents"] == [eval_span["span_id"]]
+
+
+@pytest.mark.vcr
+def test_accuracy_eval_run_with_output_skips_agent(memory_logger):
+ evaluation = accuracy_module.AccuracyEval(
+ name="Given Output Eval",
+ model=agno_openai.OpenAIChat(id=MODEL),
+ agent=_make_agent(),
+ input="What is 10*5?",
+ expected_output="50",
+ )
+ result = evaluation.run_with_output(output="50", print_summary=False, print_results=False)
+ assert result is not None
+
+ spans = memory_logger.pop()
+ eval_span = find_span_by_name(spans, "Given Output Eval")
+ assert eval_span["scores"]["accuracy"] == pytest.approx(result.avg_score / 10)
+ assert eval_span["output"] == "50"
+
+ # No agent ran: the judge is the only model traffic under the row.
+ assert not _spans_named(spans, "Math Agent.")
+
+
+# ---------------------------------------------------------------------------
+# AgentAsJudgeEval (agno >= 2.4)
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.vcr
+def test_agent_as_judge_eval_numeric_score(memory_logger):
+ judge_module = pytest.importorskip("agno.eval.agent_as_judge")
+
+ judge = judge_module.AgentAsJudgeEval(
+ name="Tone Judge",
+ model=agno_openai.OpenAIChat(id=MODEL),
+ criteria="The response is polite and mentions renewable energy.",
+ scoring_strategy="numeric",
+ threshold=7,
+ )
+ result = judge.run(
+ input="Tell me about renewable energy.",
+ output="Certainly! Renewable energy comes from sources like wind and solar power.",
+ )
+ assert result is not None
+ assert result.results
+
+ spans = memory_logger.pop()
+ eval_span = find_span_by_name(spans, "Tone Judge")
+ assert eval_span["span_attributes"]["type"] == "eval"
+ assert eval_span["input"] == "Tell me about renewable energy."
+ assert eval_span["scores"]["judge"] == pytest.approx(result.results[0].score / 10)
+ assert eval_span["output"]["passed"] == result.results[0].passed
+
+ metadata = eval_span["metadata"]
+ assert metadata["eval_type"] == "agent_as_judge"
+ assert metadata["scoring_strategy"] == "numeric"
+ assert metadata["threshold"] == 7
+ assert metadata["criteria"] == "The response is polite and mentions renewable energy."
+
+ # A single-pair judge run grades exactly what the row already describes, so it gets
+ # no redundant scorer span -- only the judge's own model call nests under it.
+ assert "judge" not in _names(spans)
+ assert find_span_by_name(spans, "Agent.run")["span_parents"] == [eval_span["span_id"]]
+
+
+@pytest.mark.vcr
+def test_agent_as_judge_eval_batch_scores_each_case(memory_logger):
+ judge_module = pytest.importorskip("agno.eval.agent_as_judge")
+
+ judge = judge_module.AgentAsJudgeEval(
+ name="Politeness Judge",
+ model=agno_openai.OpenAIChat(id=MODEL),
+ criteria="The response is polite.",
+ scoring_strategy="binary",
+ )
+ result = judge.run(
+ cases=[
+ {"input": "Say hello politely.", "output": "Hello! How may I help you today?"},
+ {"input": "Say hello politely.", "output": "what do you want"},
+ ]
+ )
+ assert result is not None
+ assert len(result.results) == 2
+
+ spans = memory_logger.pop()
+ eval_span = find_span_by_name(spans, "Politeness Judge")
+ assert eval_span["metadata"]["num_cases"] == 2
+ assert eval_span["metadata"]["pass_rate"] == result.pass_rate
+ assert eval_span["scores"]["judge"] == pytest.approx(result.pass_rate / 100)
+
+ # Batch mode keeps a scorer span per graded pair.
+ pair_spans = _spans_named(spans, "judge")
+ assert len(pair_spans) == 2
+ for span, evaluation in zip(pair_spans, result.results):
+ assert span["span_attributes"]["type"] == "score"
+ assert span["span_parents"] == [eval_span["span_id"]]
+ assert span["scores"]["judge"] == (1.0 if evaluation.passed else 0.0)
+ assert span["output"]["reason"]
+
+
+@pytest.mark.vcr
+def test_agent_as_judge_post_hook_scores_the_agent_row(memory_logger):
+ judge_module = pytest.importorskip("agno.eval.agent_as_judge")
+
+ judge = judge_module.AgentAsJudgeEval(
+ name="Quality Check",
+ model=agno_openai.OpenAIChat(id=MODEL),
+ criteria="The response answers the question directly.",
+ scoring_strategy="binary",
+ )
+ agent = agno_agent.Agent(
+ name="Post Hook Agent",
+ model=agno_openai.OpenAIChat(id=MODEL),
+ instructions="Answer in one short sentence.",
+ post_hooks=[judge],
+ )
+ response = agent.run("What is the capital of France?")
+ assert response.content
+
+ spans = memory_logger.pop()
+ agent_span = find_span_by_name(spans, "Post Hook Agent.run")
+ judge_span = find_span_by_name(spans, "Quality Check")
+
+ # The verdict is mirrored onto the agent's own row, so the trace is scored where a
+ # reader (and an experiment summary) looks for it.
+ assert agent_span["scores"]["judge"] in (0.0, 1.0)
+ assert judge_span["scores"]["judge"] == agent_span["scores"]["judge"]
+ assert judge_span["span_parents"] == [agent_span["span_id"]]
+
+
+# ---------------------------------------------------------------------------
+# ReliabilityEval
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.vcr
+def test_reliability_eval_scores_tool_calls(memory_logger):
+ calculator = pytest.importorskip("agno.tools.calculator")
+
+ agent = _make_agent(name="Calculator Agent", tools=[calculator.CalculatorTools()])
+ response = agent.run("What is 10*5? Use your tools.")
+ memory_logger.pop() # the agent run is its own trace; assert on the eval below
+
+ evaluation = reliability_module.ReliabilityEval(
+ name="Calculator Reliability",
+ agent_response=response,
+ expected_tool_calls=["multiply"],
+ )
+ result = evaluation.run(print_results=False)
+ assert result is not None
+
+ spans = memory_logger.pop()
+ eval_span = find_span_by_name(spans, "Calculator Reliability")
+ assert eval_span["span_attributes"]["type"] == "eval"
+ assert eval_span["input"]["expected_tool_calls"] == ["multiply"]
+ assert eval_span["output"]["eval_status"] == result.eval_status
+ assert eval_span["scores"]["reliability"] == (1.0 if result.eval_status == "PASSED" else 0.0)
+ assert eval_span["metadata"]["eval_type"] == "reliability"
+
+
+# ---------------------------------------------------------------------------
+# PerformanceEval
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.vcr
+def test_performance_eval_logs_metrics_and_suppresses_child_spans(memory_logger):
+ agent = _make_agent(name="Perf Agent")
+
+ def run_agent():
+ return agent.run("What is 2+2?")
+
+ evaluation = performance_module.PerformanceEval(
+ name="Agent Latency",
+ func=run_agent,
+ warmup_runs=0,
+ num_iterations=2,
+ measure_memory=False,
+ )
+ result = evaluation.run(print_summary=False, print_results=False)
+ assert result is not None
+ assert len(result.run_times) == 2
+
+ spans = memory_logger.pop()
+ # One row for the eval; the measured iterations do not each produce a trace.
+ assert _names(spans) == ["Agent Latency"]
+
+ eval_span = spans[0]
+ assert eval_span["span_attributes"]["type"] == "eval"
+ assert eval_span["metrics"]["avg_run_time"] == pytest.approx(result.avg_run_time)
+ assert eval_span["metrics"]["p95_run_time"] == pytest.approx(result.p95_run_time)
+ assert eval_span["output"]["median_run_time"] == pytest.approx(result.median_run_time)
+
+ metadata = eval_span["metadata"]
+ assert metadata["eval_type"] == "performance"
+ assert metadata["func"] == "run_agent"
+ assert metadata["num_iterations"] == 2
+ assert metadata["measure_memory"] is False
+
+
+# ---------------------------------------------------------------------------
+# Eval suites (agno >= 2.9)
+# ---------------------------------------------------------------------------
+
+
+async def multiply(a: int, b: int) -> str:
+ """Multiply two numbers.
+
+ Args:
+ a: the first number
+ b: the second number
+ """
+ return str(a * b)
+
+
+def _suite_cases(suite):
+ # An async tool is awaited on the event loop. A sync tool would run on agno's worker
+ # thread, where the memory background logger (a threading.local override) does not
+ # apply, so its span would escape to the real API.
+ agent = _make_agent(name="Suite Calculator Agent", tools=[multiply])
+ return (
+ suite.Case(
+ name="multiplies_with_tool",
+ agent=agent,
+ input="What is 10*5? Use your tools.",
+ tags=("smoke",),
+ criteria="States that the answer is 50.",
+ expected_tool_calls=("multiply",),
+ ),
+ suite.Case(
+ name="answers_without_tool",
+ agent=agent,
+ input="Say the word 'hello' and nothing else.",
+ criteria="The response is the word hello.",
+ ),
+ )
+
+
+@pytest.mark.vcr
+def test_eval_suite_logs_one_row_per_case(memory_logger):
+ suite = pytest.importorskip("agno.eval.suite")
+
+ cases = _suite_cases(suite)
+ result = suite.run_cases(cases, judge_model=agno_openai.OpenAIChat(id=MODEL))
+ assert result.total == 2
+
+ spans = memory_logger.pop()
+ rows = find_spans_by_type(spans, "eval")
+ assert _names(rows) == ["multiplies_with_tool", "answers_without_tool"]
+
+ tool_case, plain_case = rows
+ assert tool_case["input"] == "What is 10*5? Use your tools."
+ assert tool_case["tags"] == ["smoke"]
+ assert set(tool_case["scores"]) == {"judge", "reliability"}
+ assert tool_case["scores"]["judge"] in (0.0, 1.0)
+ assert tool_case["metadata"]["eval_type"] == "suite_case"
+ assert tool_case["metadata"]["case_name"] == "multiplies_with_tool"
+ assert tool_case["metadata"]["expected_tool_calls"] == ["multiply"]
+ assert "multiply" in tool_case["metadata"]["tools_called"]
+ assert tool_case["metadata"]["judge_reason"]
+ assert tool_case["output"]
+
+ # A case without a reliability check is scored by the judge alone.
+ assert set(plain_case["scores"]) == {"judge"}
+ assert "tags" not in plain_case
+
+ # The suite's own judge/reliability evals render as scorer spans inside the case.
+ scorer_spans = find_spans_by_type(spans, "score")
+ assert "judge" in _names(scorer_spans)
+ assert "reliability" in _names(scorer_spans)
+ for span in scorer_spans:
+ assert span["span_attributes"]["purpose"] == "scorer"
+
+ agent_spans = _spans_named(spans, "Suite Calculator Agent.")
+ assert agent_spans, f"no agent span under the case rows. Available: {_names(spans)}"
+ assert agent_spans[0]["span_parents"] == [tool_case["span_id"]]
+
+
+@pytest.mark.vcr
+def test_eval_suite_routes_rows_to_an_experiment(memory_logger, monkeypatch):
+ suite = pytest.importorskip("agno.eval.suite")
+
+ opened = {}
+
+ def fake_init(**kwargs):
+ opened.update(kwargs)
+ return init_test_exp("agno-suite", project_name=PROJECT_NAME)
+
+ monkeypatch.setattr(eval_experiments, "init", fake_init)
+ eval_experiments.configure(True)
+
+ cases = _suite_cases(suite)[:1]
+ result = suite.run_cases(cases, tag="smoke", judge_model=agno_openai.OpenAIChat(id=MODEL))
+ assert result.total == 1
+
+ # The experiment inherits the project already in scope, and carries what ran.
+ assert opened["project"] == PROJECT_NAME
+ assert opened["set_current"] is False
+ assert opened["metadata"] == {
+ "source": "agno",
+ "eval_type": "suite",
+ "num_cases": 1,
+ "tag": "smoke",
+ }
+
+ spans = memory_logger.pop()
+ row = find_span_by_name(spans, "multiplies_with_tool")
+ assert row["span_parents"] is None or row["span_parents"] == []
+ assert row["scores"]["judge"] in (0.0, 1.0)
+
+
+# ---------------------------------------------------------------------------
+# Patcher wiring (no provider traffic)
+# ---------------------------------------------------------------------------
+
+
+def test_eval_patchers_are_registered_and_idempotent():
+ from braintrust.integrations.agno.integration import AgnoIntegration
+
+ available = AgnoIntegration.available_patchers()
+ for expected in (
+ "agno.eval.accuracy",
+ "agno.eval.agent_as_judge",
+ "agno.eval.reliability",
+ "agno.eval.performance",
+ "agno.eval.suite",
+ ):
+ assert expected in available
+
+ # wrapt hands out a fresh BoundFunctionWrapper per attribute access, so identity is
+ # not the idempotency signal -- a second layer of wrapping is.
+ original = accuracy_module.AccuracyEval.run.__wrapped__
+ assert AgnoIntegration.setup()
+ assert accuracy_module.AccuracyEval.run.__wrapped__ is original
+ assert not hasattr(original, "__wrapped__"), "setup() must not wrap an already patched target twice"
+
+ # The manual wrap_*() helpers are no-ops once auto-instrumentation has run.
+ assert wrap_reliability_eval(reliability_module.ReliabilityEval) is reliability_module.ReliabilityEval
+ assert reliability_module.ReliabilityEval.run.__wrapped__ is not None
diff --git a/py/src/braintrust/integrations/agno/tracing.py b/py/src/braintrust/integrations/agno/tracing.py
index 6263cf2f7..2fb45d231 100644
--- a/py/src/braintrust/integrations/agno/tracing.py
+++ b/py/src/braintrust/integrations/agno/tracing.py
@@ -1,4 +1,6 @@
+import contextvars
import time
+from contextlib import contextmanager
from inspect import isawaitable
from typing import Any
@@ -8,12 +10,45 @@
_INSTRUMENTATION = "agno-auto"
+_SUPPRESSED: contextvars.ContextVar[bool] = contextvars.ContextVar("braintrust_agno_suppressed", default=False)
-def start_span(*args, **kwargs):
+
+@contextmanager
+def suppress_spans():
+ """Skip agno instrumentation entirely for the duration of the block.
+
+ ``PerformanceEval`` calls the measured function ``warmup_runs + num_iterations``
+ times (60 by default). Tracing all of those would bury the eval's own row under
+ dozens of identical child traces, so the agno patchers hand straight through to
+ the wrapped method while this is set, building no payloads and retaining no
+ stream chunks (see ``_AgnoFunctionWrapperPatcher``). Spans from other
+ integrations (openai, anthropic, ...) are not agno's to suppress and still
+ appear under the eval's row.
+ """
+ token = _SUPPRESSED.set(True)
+ try:
+ yield
+ finally:
+ _SUPPRESSED.reset(token)
+
+
+def spans_suppressed() -> bool:
+ """Whether agno instrumentation is currently suppressed."""
+ return _SUPPRESSED.get()
+
+
+def start_span(*args, parent_object: Any | None = None, **kwargs):
+ """Start a span stamped as agno-instrumented.
+
+ ``parent_object`` starts the span on an explicit parent (an experiment, say)
+ rather than on whatever the ambient span/experiment/logger resolution picks, so
+ both routes keep the instrumentation stamp.
+ """
internal = dict(kwargs.get("internal") or {})
internal.setdefault("instrumentation", _INSTRUMENTATION)
kwargs["internal"] = internal
- return _bt_start_span(*args, **kwargs)
+ start = _bt_start_span if parent_object is None else parent_object.start_span
+ return start(*args, **kwargs)
from braintrust.span_types import SpanTypeAttribute
@@ -29,8 +64,17 @@ def omit(obj: dict[str, Any], keys: list[str]):
return {k: v for k, v in obj.items() if k not in keys}
-def clean(obj: dict[str, Any]) -> dict[str, Any]:
- return {k: v for k, v in obj.items() if v is not None}
+def bound_args(args: Any, kwargs: Any, names: tuple[str, ...]) -> dict[str, Any]:
+ """Resolve a wrapped method's arguments by name, positional or keyword.
+
+ Cheaper and more forgiving than binding the real signature per call, which the
+ wrappers here deliberately avoid.
+ """
+ bound: dict[str, Any] = dict(zip(names, args))
+ for name in names:
+ if name in kwargs:
+ bound[name] = kwargs[name]
+ return bound
# Keys the SDK-integrations spec routes into metadata rather than the span input.
diff --git a/py/src/braintrust/integrations/auto_test_scripts/test_auto_agno.py b/py/src/braintrust/integrations/auto_test_scripts/test_auto_agno.py
index 93af9a2a0..7cdfa01a3 100644
--- a/py/src/braintrust/integrations/auto_test_scripts/test_auto_agno.py
+++ b/py/src/braintrust/integrations/auto_test_scripts/test_auto_agno.py
@@ -72,6 +72,43 @@ def check_wrapped(klass, private_method, public_method, required=True):
"FunctionCall.aexecute should be wrapped"
)
+# Eval classes (agno.eval). These live in submodules the eval package imports lazily,
+# so this also verifies auto_instrument imports and patches them in a fresh process.
+# agent_as_judge (agno >= 2.4) and suite (agno >= 2.9) are absent in older versions.
+import importlib
+
+
+def check_eval_targets_wrapped(module_path, targets):
+ try:
+ module = importlib.import_module(module_path)
+ except ImportError:
+ print(f"{module_path} not present in this agno version, skipped")
+ return
+ for target in targets:
+ obj = module
+ for part in target.split("."):
+ obj = getattr(obj, part)
+ assert hasattr(obj, "__wrapped__"), f"{module_path}.{target} should be wrapped"
+ print(f"{module_path} wrapped: {', '.join(targets)}")
+
+
+check_eval_targets_wrapped(
+ "agno.eval.accuracy",
+ ["AccuracyEval.run", "AccuracyEval.arun", "AccuracyEval.evaluate_answer", "AccuracyEval.aevaluate_answer"],
+)
+check_eval_targets_wrapped(
+ "agno.eval.agent_as_judge",
+ [
+ "AgentAsJudgeEval.run",
+ "AgentAsJudgeEval.arun",
+ "AgentAsJudgeEval.post_check",
+ "AgentAsJudgeEval.async_post_check",
+ ],
+)
+check_eval_targets_wrapped("agno.eval.reliability", ["ReliabilityEval.run", "ReliabilityEval.arun"])
+check_eval_targets_wrapped("agno.eval.performance", ["PerformanceEval.run", "PerformanceEval.arun"])
+check_eval_targets_wrapped("agno.eval.suite", ["arun_cases", "_arun_case"])
+
# 4. Make API call and verify spans
with autoinstrument_test_context("test_auto_agno", integration="agno") as memory_logger:
from agno.models.openai import OpenAIChat