TokenMix Research Lab · 2026-04-25

OpenLLMetry: OpenTelemetry for LLMs Explained (2026)

OpenLLMetry is Traceloop's open-source extension to OpenTelemetry designed specifically for LLM application observability. Apache 2.0 licensed, non-intrusive instrumentation, works with Datadog, New Relic, Sentry, Honeycomb, and any OpenTelemetry-compatible backend. Supports Python, TypeScript, Go, and Ruby. Pre-built instrumentations for OpenAI, Anthropic, Cohere, Pinecone, LangChain, and Haystack. This guide covers what OpenLLMetry adds on top of standard OpenTelemetry, installation, integration with existing APM stacks, and when to pick it vs purpose-built LLM observability platforms. Verified April 2026.

Table of Contents

• What OpenLLMetry Is
• Why OpenTelemetry for LLMs
• Installation and Setup
• Supported Integrations
• Supported LLM Providers and Model Routing
• Exporting to Your APM Stack
• Comparison: OpenLLMetry vs Native LLM Observability
• When to Pick OpenLLMetry
• Known Limitations
• FAQ

What OpenLLMetry Is

OpenLLMetry is a set of extensions to OpenTelemetry specifically for LLM and generative AI applications. Think of it as OpenTelemetry's LLM-shaped adapter layer.

Key attributes:

Value proposition: if your engineering org already uses OpenTelemetry for APM (most do), OpenLLMetry adds LLM tracing without introducing a new observability stack. One protocol, one export pipeline, extended with LLM-specific metadata.

Why OpenTelemetry for LLMs

Most LLM observability platforms (Langfuse, Helicone, LangSmith) have proprietary data pipelines. You send data to their backend; you read from their UI.

OpenTelemetry is different — it's a protocol for instrumentation data. Your app emits traces in OTel format; you choose which backend receives them.

Benefits of OTel-native LLM observability:

• Backend freedom. Switch from Datadog to New Relic without changing app code
• Unified view. LLM calls appear in the same APM pipeline as your database queries, HTTP requests, background jobs
• Existing tooling reuse. If your team knows Datadog, use it for LLM traces too
• No vendor lock-in at the SDK layer

Trade-off: OTel backends are general-purpose. Dedicated LLM observability platforms (Langfuse, LangSmith) often have better LLM-specific features (prompt versioning, evals, detailed token analysis).

Installation and Setup

Python (via traceloop-sdk):

pip install traceloop-sdk

Minimal integration:

from traceloop.sdk import Traceloop

Traceloop.init(
    app_name="my-llm-app",
    api_key="your-traceloop-key",  # optional, for Traceloop cloud
)

# Your existing OpenAI / Anthropic / etc. calls now automatically traced
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
    model="gpt-5.4",
    messages=[{"role": "user", "content": "Hello"}],
)

TypeScript:

npm install @traceloop/node-server-sdk

import { initialize } from "@traceloop/node-server-sdk";

initialize({
  appName: "my-llm-app",
  apiKey: "your-key",
});

Go, Ruby: similar patterns via respective SDKs.

Key detail: instrumentation is auto-applied via monkey-patching at init time. You don't decorate every LLM call manually.

Supported Integrations

OpenLLMetry auto-instruments:

LLM providers:

• OpenAI (and OpenAI-compatible endpoints)
• Anthropic Claude
• Cohere
• Google (Gemini, Vertex AI)
• AWS Bedrock
• Azure OpenAI
• Replicate

Vector databases:

• Pinecone
• Chroma
• Qdrant
• Weaviate

Frameworks:

• LangChain (Python + JS)
• LlamaIndex
• Haystack

Effectively: if you use common LLM ecosystem tools, OpenLLMetry likely has instrumentation.

Supported LLM Providers and Model Routing

OpenLLMetry works with any OpenAI-compatible endpoint, which means it works with:

• Direct OpenAI
• Direct Anthropic
• Aggregators like TokenMix.ai
• Self-hosted OpenAI-compatible servers (vLLM, SGLang)

Through TokenMix.ai, you access Claude Opus 4.7, GPT-5.5, DeepSeek V4-Pro, Kimi K2.6, Gemini 3.1 Pro, and 300+ other models via one API key. OpenLLMetry instruments these uniformly — you see unified traces across providers, attributed per model, with cost tracking based on model-specific pricing.

Configuration stays OpenAI-standard:

from openai import OpenAI

client = OpenAI(
    api_key="your-tokenmix-key",
    base_url="https://api.tokenmix.ai/v1",
)

# OpenLLMetry auto-traces this call regardless of which model you use
response = client.chat.completions.create(
    model="claude-opus-4-7",
    messages=[...],
)

Traces captured: input tokens, output tokens, latency, model name, prompt content (if PII-redaction not configured), completion content.

Exporting to Your APM Stack

This is OpenLLMetry's killer feature. Export to any OTel-compatible backend:

Datadog:

Traceloop.init(
    app_name="my-app",
    exporter_type="otlp",
    otlp_endpoint="http://datadog-agent:4318",
)

New Relic:

Traceloop.init(
    app_name="my-app",
    exporter_type="otlp",
    otlp_endpoint="https://otlp.nr-data.net:4317",
    otlp_headers={"api-key": "your-nr-key"},
)

SigNoz, Honeycomb, Sentry, Grafana Tempo: similar patterns — configure OTLP endpoint and auth.

Traceloop cloud (Traceloop's own backend):

Traceloop.init(app_name="my-app", api_key="your-traceloop-key")

Pick based on existing infrastructure.

Comparison: OpenLLMetry vs Native LLM Observability

Rule of thumb: OpenLLMetry excels at making LLM traces appear in your existing APM. Purpose-built LLM platforms excel at LLM-specific features (prompts, evals).

Common pattern: use both — OpenLLMetry for APM integration, Langfuse for prompt management, combined via dual-export.

When to Pick OpenLLMetry

Strong fit:

• Your team already uses Datadog/New Relic/Honeycomb for APM
• Want unified observability (LLM traces in same pipeline as app traces)
• Don't want to learn yet another platform UI
• Open-source preference with flexibility to switch backends
• Multi-language stacks (Python + TypeScript + Go)

Weak fit:

• No existing APM investment (start with Langfuse or Helicone instead)
• Need prompt management as core use case (Langfuse has this; OpenLLMetry doesn't)
• Small team without APM expertise (dedicated LLM observability is simpler)
• Very LLM-heavy stack where general APM won't render LLM specifics well

Known Limitations

1. No built-in prompt management. If you need versioning, A/B testing of prompts — use Langfuse on top or alternative.

2. No built-in evaluation framework. OpenLLMetry captures traces; evaluations require separate tooling (DeepEval, Ragas, etc.).

3. APM backends weren't designed for LLMs. Token counts, prompt text, completion text look like generic span attributes in Datadog/New Relic. Less rich than dedicated LLM UIs.

4. PII concerns. Default instrumentation captures prompts and completions. Configure filtering before emitting to shared APM.

5. Instrumentation auto-patching can conflict. Rare edge cases where multiple instrumentation libraries patch same modules differently.

6. Protocol overhead. OTel adds small latency (~1-5ms per span). Negligible for LLM calls which take seconds; may matter for latency-critical pipelines.

FAQ

Is Traceloop the same as OpenLLMetry?

Traceloop is the company. OpenLLMetry is their open-source project. Traceloop also offers a cloud backend (similar to Langfuse/LangSmith) but you can use OpenLLMetry without Traceloop cloud.

Can I use OpenLLMetry without Traceloop cloud?

Yes. Export to any OTel-compatible backend (Datadog, New Relic, Jaeger, self-hosted). Traceloop cloud is optional.

Does it work with LangChain?

Yes, auto-instrumented. LangChain calls appear as traces with chain structure preserved.

What's the overhead?

~1-5ms per span, plus network overhead for export. Negligible compared to LLM inference time (seconds).

Can I sample to reduce volume?

Yes, standard OTel sampling applies. Sample 1%, 10%, 100% as needed for cost control.

Does it support multimodal LLM tracing?

Yes. Images, audio inputs captured as metadata on spans. Some APM backends render these better than others.

How does it compare to pgai or OpenTelemetry semantic conventions for GenAI?

OTel semantic conventions for GenAI are evolving — OpenLLMetry aligned with these. Over time, OpenLLMetry's instrumentation may converge with official OTel spec.

Can I use it with AWS Bedrock?

Yes. Auto-instrumented. Token counts and model info captured for Claude / Llama / Titan / other Bedrock models.

Is this production-ready at scale?

Yes. Used in production at Fortune 500 companies via Traceloop. OpenTelemetry itself is enterprise-scale proven.

Where can I see unified LLM + APM dashboards?

Your APM backend. In Datadog, create LLM-specific dashboards filtered by openllmetry.* attributes. In SigNoz, use GenAI-specific views. Via TokenMix.ai, provider-level cost and latency roll up in the aggregator dashboard while OpenLLMetry captures per-request spans in your APM.

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Author: TokenMix Research Lab | Last Updated: April 25, 2026 | Data Sources: OpenLLMetry GitHub, Traceloop OpenLLMetry docs, Introducing OpenLLMetry (Traceloop blog), Dynatrace OpenLLMetry knowledge base, Langfuse OpenLLMetry Integration, TokenMix.ai OpenLLMetry-compatible routing