OpenTelemetry

PromptLayer natively supports OpenTelemetry (OTEL), the industry-standard observability framework. You can send traces from any OpenTelemetry-compatible SDK or Collector directly to PromptLayer — no PromptLayer SDK required.

This is ideal when:

• Your framework isn’t listed on the Telemetry Integrations page
• You already have an OpenTelemetry pipeline and want to add PromptLayer as a destination
• You want vendor-neutral instrumentation

If you’re using a supported framework like the Vercel AI SDK, OpenAI Agents SDK, or Claude Code, see the Telemetry Integrations page for framework-specific setup — those integrations handle the OTEL configuration for you.

How It Works

PromptLayer exposes an OTLP/HTTP endpoint at:

https://api.promptlayer.com/v1/traces

Any OpenTelemetry SDK or Collector can export traces to this endpoint. Spans that include GenAI semantic convention attributes are automatically converted into PromptLayer request logs.

Setup

Configure your OpenTelemetry SDK to export traces to PromptLayer using the OTLP/HTTP exporter.

Python

from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.sdk.resources import Resource
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

# Install required packages:
# pip install opentelemetry-sdk opentelemetry-exporter-otlp-proto-http

exporter = OTLPSpanExporter(
    endpoint="https://api.promptlayer.com/v1/traces",
    headers={"X-API-KEY": "your-promptlayer-api-key"},
)

provider = TracerProvider(
    resource=Resource.create({"service.name": "my-llm-app"})
)
provider.add_span_processor(BatchSpanProcessor(exporter))

# Use the tracer to create spans
tracer = provider.get_tracer("my-llm-app")

GenAI Semantic Conventions

Spans that use GenAI semantic conventions are automatically parsed into PromptLayer request logs. Add these attributes to your LLM call spans:

Attribute	Description
gen_ai.request.model	Model name (e.g. gpt-4, claude-sonnet-4-20250514)
gen_ai.provider.name	Provider (e.g. openai, anthropic)
gen_ai.operation.name	Operation type (chat, text_completion, embeddings)
gen_ai.usage.input_tokens	Input token count
gen_ai.usage.output_tokens	Output token count
gen_ai.input.messages	Request messages
gen_ai.output.messages	Response messages
gen_ai.request.temperature	Temperature parameter
gen_ai.request.max_tokens	Max tokens parameter
gen_ai.response.finish_reasons	Finish reasons

Event-Based Conventions

PromptLayer also supports the newer event-based GenAI semantic conventions where message content is sent as span events rather than span attributes. This format is used by frameworks like LiveKit and newer versions of OpenTelemetry GenAI instrumentation.

The following event types are recognized:

Event Name	Description
gen_ai.system.message	System message
gen_ai.user.message	User message
gen_ai.assistant.message	Assistant message (including tool calls)
gen_ai.tool.message	Tool/function result message
gen_ai.choice	Model response/choice

Event attributes like gen_ai.system.message.content, gen_ai.user.message.content, and tool call data are automatically extracted and mapped to PromptLayer request logs.

When both attribute-based messages (gen_ai.input.messages) and event-based messages are present on the same span, attribute-based messages take priority.

Linking to Prompt Templates

You can associate OTEL spans with prompt templates in your PromptLayer workspace by setting custom span attributes:

Attribute	Type	Description
promptlayer.prompt.name	string	Name of the prompt template in your workspace
promptlayer.prompt.version	integer	Specific version number to link (optional)

Python

from opentelemetry import trace

tracer = trace.get_tracer("my-llm-app")

with tracer.start_as_current_span("llm-call") as span:
    # Link this span to a prompt template
    span.set_attribute("promptlayer.prompt.name", "my-prompt")
    span.set_attribute("promptlayer.prompt.version", 3)

    # Add GenAI attributes
    span.set_attribute("gen_ai.request.model", "gpt-4")
    span.set_attribute("gen_ai.provider.name", "openai")

    # ... make your LLM call ...

Attaching User Identity & Metadata

You can attach searchable metadata — including end-user identity and conversation IDs — to the request logs generated from your spans. This is the OpenTelemetry-native equivalent of the PromptLayer SDK’s track.metadata(), with no extra REST call required.

PromptLayer recognizes two kinds of span attributes for metadata.

Standard OpenTelemetry attributes

If your instrumentation already follows OpenTelemetry conventions, these are picked up automatically — no PromptLayer-specific attributes needed:

Attribute	Mapped to	Description
user.id	user_id metadata	End-user identity (current OpenTelemetry attribute)
enduser.id	user_id metadata	End-user identity (deprecated OpenTelemetry attribute, supported as a fallback)
gen_ai.conversation.id	conversation_id metadata	Conversation / session / thread identifier (OpenTelemetry GenAI attribute)
session.id	conversation_id metadata	Conversation identifier (common alias, supported as a fallback)

PromptLayer custom metadata

For arbitrary key/value metadata, use the promptlayer.metadata. namespace. Each attribute becomes a metadata key on the request log — for example, promptlayer.metadata.tenant becomes a tenant metadata key.

Attribute	Mapped to
promptlayer.metadata.<key>	<key> metadata

Python

with tracer.start_as_current_span("llm-call") as span:
    # Standard OpenTelemetry attributes
    span.set_attribute("user.id", "customer-42")
    span.set_attribute("gen_ai.conversation.id", "conv_abc123")

    # Arbitrary PromptLayer metadata
    span.set_attribute("promptlayer.metadata.tenant", "acme-corp")
    span.set_attribute("promptlayer.metadata.environment", "production")

    # ... make your LLM call ...

An explicit promptlayer.metadata.<key> always takes precedence over a standard attribute mapped to the same key. For example, if a span has both user.id and promptlayer.metadata.user_id, the promptlayer.metadata.user_id value wins.

Metadata is attached to the request log generated from the span, so set these attributes on your LLM call spans. To apply metadata across an entire trace, set the attributes as resource attributes — they apply to every span in the export.

Using an OpenTelemetry Collector

If you’re already running an OpenTelemetry Collector, you can add PromptLayer as an additional exporter in your Collector config:

exporters:
  otlphttp/promptlayer:
    endpoint: "https://api.promptlayer.com"
    headers:
      X-API-Key: "${PROMPTLAYER_API_KEY}"

service:
  pipelines:
    traces:
      exporters: [otlphttp/promptlayer]

This lets you fan out traces to PromptLayer alongside your existing observability backends (Datadog, New Relic, Jaeger, etc.) without changing your application code.

Content Types

The endpoint accepts both binary protobuf (application/x-protobuf, recommended) and JSON (application/json) encodings. Both support Content-Encoding: gzip.

Next Steps

• OTLP Ingest Traces API Reference — full endpoint documentation
• Telemetry Integrations — framework-specific setups (Vercel AI SDK, OpenAI Agents, Claude Code)
• Traces — PromptLayer SDK native tracing with @traceable and wrapWithSpan