Adapters & on-ramps

from aevyra_witness.runtime import span
from aevyra_origin import diagnose_pipeline
from aevyra_origin.llm import anthropic_llm

@span("classify")
def classify(text): ...

@span("retrieve")
def retrieve(topic): ...

@span("answer", optimize=True, prompt_id="answer_v1")
def answer(q, docs): ...

def my_agent(q):
    topic = classify(q)
    return answer(q, retrieve(topic))

result = diagnose_pipeline(
    my_agent, "how do I get a refund?",
    judge=my_judge,
    rubric="Accurate and grounded in policy.",
    llm=anthropic_llm(),
)

from pathlib import Path
from aevyra_witness.adapters import from_openclaw_jsonl
from aevyra_origin import Origin
from aevyra_origin.llm import anthropic_llm

lines = Path("run_2026_04_21.jsonl").read_text().splitlines()
trace = from_openclaw_jsonl(lines, ideal="expected output")

origin = Origin(llm=anthropic_llm())
result = origin.diagnose(trace=trace, score=0.4, rubric=rubric)

trace = from_openclaw_jsonl(lines, optimize_prompt_ids=["planner", "responder"])

from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter
from aevyra_witness.adapters import from_otel_spans
from aevyra_origin import Origin
from aevyra_origin.llm import anthropic_llm

exporter = InMemorySpanExporter()
# ... configure your OTel TracerProvider with this exporter, run your agent ...
spans = exporter.get_finished_spans()
trace = from_otel_spans(spans)

origin = Origin(llm=anthropic_llm())
result = origin.diagnose(trace=trace, score=0.4, rubric=rubric)

from aevyra_origin import Origin
from aevyra_origin.llm import anthropic_llm

origin = Origin(llm=anthropic_llm())
result = origin.diagnose(
    trace=my_trace,
    score=0.4,
    rubric="Accurate, grounded in the policy docs.",
)
print(result.render())

aevyra-origin diagnose trace.json \
  --score 0.4 \
  --rubric rubric.txt \
  --model anthropic/claude-sonnet-4-5