OpenInference TypeSafe AI Instrumentation

Python auto-instrumentation library for the TypeSafe AI Python SDK (typesafe-sdk).

Calls to TypeSafeClient.system_one and AsyncTypeSafeClient.system_one are traced and exported as OpenInference DECISION spans. A decision model scores or selects among the candidate options supplied in the request instead of generating free-form text, which is exactly what System One does: a request sends a state plus a map of typed questions (Noul, Choice, Score) and returns one typed answer per question, so the span records:

• input.value: the request body (state, model, questions) as JSON
• llm.invocation_parameters: the call configuration, meaning the model and any extra_body fields
• output.value: the response body (model, answers, usage) as JSON
• llm.request.model_name (for example jev-latest) and llm.response.model_name (the resolved model, for example jev-1.13.0)
• llm.token_count.prompt, llm.token_count.completion, and llm.token_count.total

A System One call is not a chat exchange, so the state and the answers are recorded only as input.value and output.value, not as llm.input_messages / llm.output_messages.

The state and the questions are recorded only in input.value, so TraceConfig(hide_inputs=True) keeps the whole request — caller data and question instructions alike — off the span, and hide_outputs=True does the same for the answers. llm.invocation_parameters holds no request content, only the model and any extra_body fields; mask it with hide_llm_invocation_parameters if those are sensitive.

These traces are fully OpenTelemetry compatible and can be sent to an OpenTelemetry collector for viewing, such as Arize Phoenix or Arize AX.

Supported Features

• Synchronous and asynchronous clients (TypeSafeClient and AsyncTypeSafeClient)
• All three question primitives, passed as SDK objects or raw dictionaries
• Suppressing tracing via suppress_tracing()
• Context attribute propagation (using_session, using_user, using_attributes, metadata, tags)
• Sensitive-data masking via TraceConfig (e.g. hide_inputs, hide_outputs)

Requires typesafe-sdk >= 0.6.0.

Installation

pip install openinference-instrumentation-typesafe

Quickstart

pip install openinference-instrumentation-typesafe typesafe-sdk arize-phoenix opentelemetry-sdk opentelemetry-exporter-otlp

Start Phoenix as a collector (default http://localhost:6006), then:

from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor

from openinference.instrumentation.typesafe import TypeSafeAIInstrumentor

endpoint = "http://127.0.0.1:6006/v1/traces"
tracer_provider = TracerProvider()
tracer_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter(endpoint)))

TypeSafeAIInstrumentor().instrument(tracer_provider=tracer_provider)

Make a System One call. Set the TYPESAFE_API_KEY environment variable with your key.

from typesafe_sdk import Choice, Noul, Score, TypeSafeClient

client = TypeSafeClient()
response = client.system_one(
    state={"document": "I was charged twice. Please fix this ASAP."},
    questions={
        "billing": Noul(instructions="Is this ticket about billing?"),
        "tone": Choice(
            instructions="What is the customer's tone?",
            criteria={"calm": None, "frustrated": None, "angry": None},
        ),
        "urgency": Score(
            instructions="How urgent is this ticket?",
            criteria=["can wait", "this week", "today"],
        ),
    },
)
print(response.nouls["billing"].noul)
print(response.choices["tone"].choice)
print(response.scores["urgency"].score)

Runnable examples, including async usage and context attributes, are in the examples/ directory.

More Info

• More info on OpenInference and Phoenix
• How to customize spans to track sessions, metadata, etc.
• How to account for private information and span payload customization