OpenTelemetry

Already instrumented? Point your exporter at AgentPing and you are done. AgentPing accepts standard OTLP/HTTP trace exports, protobuf or JSON, and maps them onto runs and steps. No new SDK, no code changes.

The endpoint

POST https://eu.ingest.agentping.io/v1/traces

Use the host matching your data residency (eu or us). Authenticate with your team API key (apk_...) as a Bearer token; exporters that cannot set Authorization can send X-AgentPing-Key instead. Per-agent ping_ tokens do not work here; OTel traffic can create agents, so it needs the team key.

Generic exporter setup

Any OpenTelemetry SDK works with two environment variables:

export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT="https://eu.ingest.agentping.io/v1/traces"
export OTEL_EXPORTER_OTLP_TRACES_HEADERS="authorization=Bearer apk_eu_..."

How traces map to runs

OpenTelemetry	AgentPing
Trace	Run (one run per trace; re-exports are deduplicated)
Root span	The run record; its name becomes the run's goal
Span with model/provider attributes	LLM call step, with tokens and server-side cost
Span with a tool name	Tool call step
Any other span	Step
`service.name`	The agent (created automatically on first sight)

LLM calls are recognised across emitter conventions, not just the canonical gen_ai.* set. AgentPing reads the model from gen_ai.request.model, gen_ai.response.model, OpenInference's llm.model_name, or the Vercel AI SDK's ai.model.id; the provider from gen_ai.system, llm.provider, or ai.model.provider; and token counts from any of gen_ai.usage.input_tokens / output_tokens, the older prompt_tokens / completion_tokens, OpenInference's llm.token_count.*, or Vercel's ai.usage.*. An OpenInference span tagged openinference.span.kind = LLM or TOOL is mapped even without gen_ai.* attributes.

Two attributes are worth setting explicitly:

agentping.agent on the resource overrides the agent identity when service.name is not what you want on the dashboard.
agentping.goal on the root span tells evaluations what the run was supposed to achieve. Setting it materially improves evaluation quality; without it the root span's name is used.

Run completion

OTel delivers spans in batches with no end-of-run signal, so a run ingested this way completes once its root span has arrived and no new span has landed for two minutes. Costs, health checks, and evaluations then run exactly as they do for SDK-reported runs.

Limits

Span attributes AgentPing does not map are kept with the step, up to 8KB per step; beyond that the largest attributes are dropped and the step is flagged, never rejected. Prompt and completion attributes count as step content with the usual 64KB allowance.

Verified emitters

Anything that speaks OTLP/HTTP works; each needs only the endpoint and header configuration above. These are the ones we test against recorded fixtures, so an upstream attribute rename is caught by us, not by you:

Emitter	What to set	Mapped from
OpenLLMetry / Traceloop	`TRACELOOP_BASE_URL` to the endpoint, plus the auth header	`gen_ai.*`
Pydantic AI	Enable instrumentation, point the OTLP exporter at the endpoint	`gen_ai.*`
Vercel AI SDK	`experimental_telemetry: { isEnabled: true }` plus an OTLP exporter	`ai.*`
OpenInference (Arize Phoenix, OpenAI Agents SDK)	OTLP exporter to the endpoint	`openinference.span.kind`, `llm.*`
LangGraph (via OpenLLMetry's LangChain instrumentation)	OTLP exporter to the endpoint	`gen_ai.*`, node spans as steps

CrewAI and LlamaIndex are supported through their OpenLLMetry / OpenInference instrumentation by the same path.