Bento Documentation

The OTel transport is the SpanProcessor the analytics layer (bentolabs_sdk.analytics) sits on top of. Wire it into an OpenTelemetry pipeline directly when you already have one configured, and skip the analytics layer entirely. This is the path when a TracerProvider is already in place and a second one isn’t welcome, when batching, sampling, or resource attributes need to stay under your control, or when a framework like LangChain, LlamaIndex, or the OpenAI Agents SDK already emits OTel spans that need a destination. For the ergonomic track_ai / begin / decorator surface without managing OTel objects, use the analytics layer instead.

Install

pip install bentolabs-sdk opentelemetry-api opentelemetry-sdk

Usage

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider

from bentolabs_sdk import BentoLabsSpanProcessor

provider = TracerProvider()
provider.add_span_processor(BentoLabsSpanProcessor())
trace.set_tracer_provider(provider)

tracer = trace.get_tracer("example")
with tracer.start_as_current_span("agent.run") as span:
    span.set_attribute("input.value", "Summarize the incident")
    span.set_attribute("output.value", "The incident is resolved.")

provider.force_flush()
provider.shutdown()

BentoLabsSpanProcessor batches spans with OpenTelemetry defaults and POSTs them to ${base_url}/v1/traces with Authorization: Bearer bl_pk_....

What ends up in the dashboard

These OTel attributes map to the same dashboard columns the analytics layer fills in. Set them on your spans:

OTel attribute	Lands in	Set via
`span.name`	`spans.name`	`tracer.start_span(name)`
`gen_ai.user.id`	`traces.user_id`	`span.set_attribute(...)`
`gen_ai.conversation.id`	`traces.session_id`	`span.set_attribute(...)`
`gen_ai.request.model`	`spans.model`	`span.set_attribute(...)`
`gen_ai.system`	`spans.provider`	`span.set_attribute(...)`
`input.value`	`spans.input`	`span.set_attribute(...)`
`output.value`	`spans.output`	`span.set_attribute(...)`
`openinference.span.kind="tool"`	`spans.kind = "tool"`	`span.set_attribute(...)`

See Attributes for the full table.

Wire the exporter directly

To wire a custom SpanProcessor (a SimpleSpanProcessor for tests, or a BatchSpanProcessor with custom limits), use BentoLabsTraceExporter directly:

from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor

from bentolabs_sdk import BentoLabsTraceExporter

provider = TracerProvider()
provider.add_span_processor(SimpleSpanProcessor(BentoLabsTraceExporter()))

Errors at boot

BentoLabsSpanProcessor and BentoLabsTraceExporter both resolve options at construction time, which can raise BentoAuthError:

from bentolabs_sdk import BentoAuthError, BentoLabsSpanProcessor

try:
    processor = BentoLabsSpanProcessor(base_url="http://localhost:8080")
except BentoAuthError as exc:
    if exc.code == "missing_api_key":
        # telemetry disabled or misconfigured
        pass
    raise

See Configuration → Errors for the full set of error codes.

Get started

Integrations

Manual tracking

Advanced

OTel transport

Install

Usage

What ends up in the dashboard

Wire the exporter directly

Errors at boot

​Install

​Usage

​What ends up in the dashboard

​Wire the exporter directly

​Errors at boot

Install

Usage

What ends up in the dashboard

Wire the exporter directly

Errors at boot