TOREN

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Observability ​

How-to: see what your agents are doing.

OpenTelemetry (built in, vendor-neutral) ​

Toren emits standard OTel spans for every orchestrator tick (toren.run.tick), task (toren.task), model call (toren.llm, with gen_ai.request.model), and tool call (toren.tool). There is no toren dashboard by design, register any OTel SDK/exporter in your process and the spans flow to whatever backend you already run:

ts
import { NodeTracerProvider } from "@opentelemetry/sdk-trace-node";
// configure your exporter, then:
provider.register();   // toren picks it up automatically (tracer is resolved per call)

No SDK registered → spans are no-ops with zero overhead.

Any OTLP backend works, including the LLM-specific ones: point an OTLP exporter at Langfuse's or LangSmith's OpenTelemetry endpoint (each documents its OTLP URL and auth headers) and Toren's traces appear there, with gen_ai.usage.input_tokens / gen_ai.usage.output_tokens on every model-call span so their cost views populate. One deliberate boundary: prompts and responses are not span attributes. Payload content stays in your Postgres event log, so nothing sensitive rides to a third-party trace backend by default; the trace shows shape, timing, models, and spend, and the log holds the words.

The event log is the deep trace ​

Every run's full history, prompts, responses, tool args/results, token usage, timings, is queryable SQL in the agent's schema. toren jobs show <runId> gives the summary; store.read(runId, stream) gives everything. This is the audit trail regulated deployments need, with no extra system.

Cost per run, from the log ​

toren jobs show <runId> (and GET /runs/:id as usage) rolls up every model call recorded in the run: calls, tokens in/out, an estimated dollar cost, and the number the runtime exists for: how many completed calls a resume replayed from the log instead of re-buying, and what that was worth. Prices come from a built-in table; extend or override it with TOREN_MODEL_PRICES (JSON, USD per million tokens, e.g. {"openai/gpt-5.5":{"in":1.25,"out":10}}). Models without a price report tokens and skip the dollars rather than inventing them.

Gaps (planned) ​

Fleet dashboards and cross-run analytics are on the roadmap.