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Portkey is an AI gateway with an OpenAI-compatible SDK. The OpenInference Portkey instrumentor captures each chat completion made through the Portkey client as an OTLP span — model, token usage, and messages. Point the exporter at AgentMark and the traces arrive normalized. The example below is Python.

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Setup

1

Install the instrumentor and the OTLP exporter

pip install openinference-instrumentation-portkey \
  opentelemetry-sdk opentelemetry-exporter-otlp-proto-http
2

Point the exporter at AgentMark and instrument Portkey

Use your AgentMark API key and app id from project settings.
from openinference.instrumentation.portkey import PortkeyInstrumentor
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

provider = TracerProvider()
provider.add_span_processor(
    BatchSpanProcessor(
        OTLPSpanExporter(
            endpoint="https://api.agentmark.co/v1/traces",
            headers={
                "Authorization": "<YOUR_API_KEY>",  # raw key, no "Bearer" prefix
                "X-Agentmark-App-Id": "<YOUR_APP_ID>",
            },
        )
    )
)

PortkeyInstrumentor().instrument(tracer_provider=provider)
3

Run your calls

Use the Portkey client’s chat.completions.create as usual. Each call arrives in AgentMark as a span, grouped into a trace. See Traces and logs.

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What AgentMark captures

Portkey spans use the OpenInference attribute conventions — model, token usage, input and output messages, tool calls, settings, and span kind are all mapped onto AgentMark’s normalized trace fields, and token counts feed cost tracking. See OpenInference for the full attribute mapping.

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Next steps

OpenInference

How AgentMark reads OpenInference attributes

Traces and logs

Explore traces once they arrive

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Have questions?

Reach out any time: