Subscribers#

This page explains how subscribers consume lifecycle events without changing runtime execution.

What Subscribers Are#

Subscribers are consumers of the NeMo Relay event stream. They receive emitted lifecycle events and use them for observation, forwarding, export, or analysis.

How Subscribers Relate to Events#

Events describe what happened. Subscribers are the components that watch those events.

That separation matters:

  • The runtime can emit one canonical event stream

  • Many subscribers can consume that same stream

  • Observability behavior stays downstream from execution semantics

Registration Levels#

Middleware and subscribers can be registered at different levels depending on their lifetime and visibility.

Global Subscribers#

Global subscribers remain active process-wide until they are removed.

Scope-Local Subscribers#

Scope-local subscribers are owned by one active scope and disappear when that scope closes.

Plugin-Installed Subscribers#

Plugins can install subscribers as reusable, configuration-driven runtime components.

What Subscribers Consume#

Subscribers consume the canonical event stream. They do not define the event model. They react to it.

This lets plain subscribers, exporters, and tracing adapters share one runtime source of truth.

Common Subscriber Roles#

Subscribers are commonly used for in-process observation, counters, debugging, and exporter handoff.

In-Process Observation#

Some subscribers stay inside the process and power custom logging, analytics, or debugging logic.

Host Integration Event JSON#

For host integrations that need a serialized event payload, use the event object’s canonical JSON helpers instead of reconstructing payloads from native attributes. Python subscribers can call event.to_dict() or event.to_json() from the callback while still using the normal subscriber registration API.

This pattern is useful when an agent runtime, framework adapter, or plugin host already has its own lifecycle hooks but wants NeMo Relay to be the shared telemetry representation. The host integration maps those hooks into NeMo Relay scopes, LLM calls, tool calls, or marks. NeMo Relay emits the canonical ATOF event stream, and each subscriber chooses whether to consume the native event object, the canonical JSON helper, or an exporter-specific translation.

        flowchart
    Host[Host Integration]

    subgraph NeMoFlow[NeMo Relay]
        direction TB
        Binding[Binding API]
        Core[Rust Core Runtime]
        Events[Canonical ATOF Event Stream]
        Observer[In-Process Subscriber]
        Json[Canonical Event JSON]
        Exporters[Exporter Subscribers]
        Backends[JSONL / ATIF / OTLP]

        Binding -->|emits scopes, tools, LLMs, marks| Core
        Core --> Events
        Events --> Observer
        Observer -->|to_dict / to_json / JSON| Json
        Events --> Exporters
        Exporters --> Backends
    end

    Host -->|maps lifecycle hooks| Binding
    Json -. host consumes canonical telemetry .-> Host

The important boundary is that subscribers do not define the event schema. They receive the runtime event and may serialize it through the binding helper when they need a stable JSON payload. Exporter subscribers, such as the ATOF JSONL exporter, consume the same event stream and serialize the same canonical event shape for their target backend.

Forwarding and Export#

Some subscribers translate the event stream into external formats or transport it to another system.

Analytics and Diagnostics#

Some subscribers derive measurements, trajectories, or diagnostics from the event stream without affecting execution behavior.

Built-In Subscriber Examples#

These examples show how built-in subscriber patterns relate to custom subscribers and exporters.

Custom Subscribers#

A plain custom subscriber is the right choice when you want in-process handling of the canonical event stream.

Agent Trajectory Interchange Format (ATIF) Exporter#

The Agent Trajectory Interchange Format (ATIF) exporter collects lifecycle events and emits trajectory artifacts for offline analysis, replay, or debugging.

Agent Trajectory Observability Format (ATOF) JSONL Exporter#

The Agent Trajectory Observability Format (ATOF) JSONL exporter writes the canonical event stream to a native filesystem path as one raw ATOF event per line.

OpenTelemetry Subscriber#

The OpenTelemetry subscriber maps runtime events into OTLP traces for tracing backends.

OpenInference Subscriber#

The OpenInference subscriber maps runtime events into OTLP traces using OpenInference semantics for model-centric observability.

Detailed setup, configuration, and API shape for these subscribers belongs in Observability. For configuration-driven setup, use the built-in observability plugin to install ATOF, ATIF, OpenTelemetry, and OpenInference subscribers from one plugin component.

Practical Guidance#

Use these practices when applying the concept in application or integration code.

  • Use a plain subscriber when you want in-process custom behavior.

  • Use event.to_dict() or event.to_json() when a host runtime or exporter needs the canonical event JSON shape in-process.

  • Use a scope-local subscriber when the observation should disappear with the owning scope.

  • Use a plugin-installed subscriber when the behavior should be reusable and config-driven.

  • Use an exporter-oriented subscriber when the event stream should leave the process.