Send OpenLLMetry traces to Bronto

OpenLLMetry is a collection of OpenTelemetry instrumentations maintained by Traceloop. It adds tracing for LLM provider SDKs, agent frameworks, and vector databases that may not be covered by the official OpenTelemetry contrib packages. Use OpenLLMetry when you need broader automatic coverage—for example, Anthropic instrumentation in Node.js—or want framework-level workflow and task spans. If an official OTel contrib instrumentation covers your provider, prefer it for the most direct alignment with the latest OTel GenAI semantic conventions.

OpenLLMetry is built on OpenTelemetry, but attribute coverage varies by language, instrumentation, and release. Current JavaScript provider instrumentations use current fields such as gen_ai.provider.name, gen_ai.operation.name, and gen_ai.usage.input_tokens / gen_ai.usage.output_tokens. Older releases and some Python, framework, workflow, or task spans may also emit legacy or traceloop.* attributes. Keep OpenLLMetry updated and verify the fields emitted by the version you deploy.

Prerequisites

A Bronto API key with ingestion permissions—see API Keys
An application that uses a supported LLM provider or framework
Python or Node.js; see OpenLLMetry’s current support matrix before choosing an instrumentation
Optional: an OTel Collector configured using Connect OpenTelemetry to Bronto

Choose an integration approach

OpenLLMetry provides a bundled SDK and individual instrumentation packages:

Use the bundled SDK for the simplest setup. It detects supported libraries and configures tracing and export.
Use individual instrumentations when your application already owns its OTel TracerProvider, processors, and exporters. This avoids creating a second provider and lets OpenLLMetry spans follow your existing sampling and export configuration.

Do not enable two instrumentations for the same provider call. For example, choose either the OTel contrib OpenAI instrumentation or OpenLLMetry’s OpenAI instrumentation, otherwise a single request can produce duplicate spans.

Node.js

Install the bundled SDK:

npm install @traceloop/node-server-sdk

Initialize it before importing any LLM provider or framework module so that OpenLLMetry can patch the module as it loads:

import * as traceloop from "@traceloop/node-server-sdk";

traceloop.initialize({ appName: "llm-service" });

// Import provider SDKs only after OpenLLMetry is initialized.
const { Anthropic } = await import("@anthropic-ai/sdk");

If you already manage the OTel SDK, install only the provider instrumentation you need. For example:

npm install @traceloop/instrumentation-anthropic

Register it with the same NodeSDK or registerInstrumentations configuration as your other OTel instrumentations. See Node.js: GenAI semantic conventions for the shared SDK setup.

Python

Install and initialize the bundled SDK before making provider calls:

pip install --upgrade traceloop-sdk

from traceloop.sdk import Traceloop

Traceloop.init(app_name="llm-service")

OpenLLMetry also publishes individual opentelemetry-instrumentation-* packages. If your application already configures a global OTel TracerProvider, use the individual package and the normal OTel auto-instrumentation flow instead of initializing a second SDK. See Python: GenAI semantic conventions.

Send traces to Bronto

Through an OTel Collector

Point OpenLLMetry at the Collector’s OTLP/HTTP receiver. TRACELOOP_BASE_URL is a base endpoint; OpenLLMetry appends /v1/traces:

export TRACELOOP_BASE_URL="http://otel-collector:4318"

Configure the Collector to forward traces to Bronto as described in Connect OpenTelemetry to Bronto. The Collector is recommended in production because it centralizes credentials, batching, sampling, retries, and fan-out.

Directly to Bronto

Set the Bronto ingestion base URL and API-key header. Do not include /v1/traces in TRACELOOP_BASE_URL because OpenLLMetry adds it automatically.

export TRACELOOP_BASE_URL="https://ingestion.eu.bronto.io"
export TRACELOOP_HEADERS="x-bronto-api-key=<YOUR_API_KEY>"

export TRACELOOP_BASE_URL="https://ingestion.us.bronto.io"
export TRACELOOP_HEADERS="x-bronto-api-key=<YOUR_API_KEY>"

TRACELOOP_HEADERS is required because Bronto authenticates with x-bronto-api-key; TRACELOOP_API_KEY sends a bearer token and is intended for the Traceloop service.

Control prompt and response capture

OpenLLMetry captures prompts, completions, and embeddings on spans by default. These values may contain personal data, credentials, proprietary context, or large payloads. Disable content capture globally unless you have intentionally approved collecting it:

export TRACELOOP_TRACE_CONTENT=false

In Node.js you can instead configure traceContent: false:

traceloop.initialize({
  appName: "llm-service",
  traceContent: false,
});

This setting does not remove model metadata or token usage. It suppresses prompt, response, and embedding content collected by OpenLLMetry instrumentations.

What you get

On current provider spans, query the OTel GenAI semantic-convention attributes that the provider response makes available:

Attribute	What it represents
`gen_ai.provider.name`	Model provider, such as `anthropic` or `openai`
`gen_ai.operation.name`	Operation type, such as `chat` or `text_completion`
`gen_ai.request.model` / `gen_ai.response.model`	Requested and returned model names
`gen_ai.request.max_tokens`	Configured output-token limit
`gen_ai.request.temperature` / `gen_ai.request.top_p`	Sampling parameters
`gen_ai.response.finish_reasons`	Reasons generation stopped
`gen_ai.usage.input_tokens` / `gen_ai.usage.output_tokens`	Input and output token counts
`gen_ai.usage.cache_read.input_tokens` / `gen_ai.usage.cache_creation.input_tokens`	Prompt-cache token counts, when reported
`gen_ai.system_instructions`	System instructions, when content capture is enabled
`gen_ai.input.messages` / `gen_ai.output.messages`	Structured messages, when content capture is enabled

OpenLLMetry may add fields outside this list, including gen_ai.usage.total_tokens, provider-specific extensions, and traceloop.* workflow or task attributes. These can be useful, but they are not a portable substitute for the current OTel fields above.

Verify in Bronto

Open Log Search and filter on the service name or standard GenAI fields. For example, find Anthropic calls with token usage:

"$gen_ai.provider.name" = 'anthropic'
AND "$gen_ai.usage.input_tokens" > 0

To compare usage across providers, create a visualization using Sum, Median, or P75, P90, P95, or P99 on $gen_ai.usage.input_tokens or $gen_ai.usage.output_tokens, grouped by $gen_ai.provider.name. See LLM Observability for the complete query and aggregation examples.

Troubleshooting

No Node.js provider spans: initialize OpenLLMetry before importing the provider SDK. Module patching cannot instrument an SDK that was loaded first.
Duplicate model spans: disable the overlapping contrib or OpenLLMetry instrumentation so only one package instruments each provider.
No traces after a short-lived script exits: temporarily use disableBatch: true in Node.js or disable_batch=True in Python while testing, or shut down the SDK cleanly before exit.
404 from direct export: remove /v1/traces from TRACELOOP_BASE_URL; OpenLLMetry appends the signal path.
Queries do not match: inspect a span from the installed package. Upgrade older OpenLLMetry releases and base shared dashboards on the current gen_ai.* fields listed above.

Getting Started

MCP

AI Investigations

LLM Observability

LLM-Powered Parsing

AI Integrations

Send OpenLLMetry traces to Bronto

Prerequisites

Choose an integration approach

Node.js

Python

Send traces to Bronto

Through an OTel Collector

Directly to Bronto

Control prompt and response capture

What you get

Verify in Bronto

Troubleshooting

References

​Prerequisites

​Choose an integration approach

​Node.js

​Python

​Send traces to Bronto

​Through an OTel Collector

​Directly to Bronto

​Control prompt and response capture

​What you get

​Verify in Bronto

​Troubleshooting

​References

Prerequisites

Choose an integration approach

Node.js

Python

Send traces to Bronto

Through an OTel Collector

Directly to Bronto

Control prompt and response capture

What you get

Verify in Bronto

Troubleshooting

References