What is OpenLLMetry?

OpenLLMetry (short for "Open Large Language Model Telemetry") is an open source observability toolkit built on top of OpenTelemetry that offers specialized instrumentation for large language model (LLM) applications. It extends OpenTelemetry's standard tracing, metrics, and logging capabilities by capturing LLM-specific data points — such as model name and version, prompt and completion tokens, temperature parameters, latency, and errors. It enables developers to monitor, debug, and optimize LLM workflows across diverse observability backends.

LLMs from providers like OpenAI or tools like HuggingFace and LangChain are now widely used across industries. As adoption grows, keeping track of how these models perform and how they're used is becoming increasingly important.

AI model observability involves monitoring, tracing, and logging a model's metrics, inputs, and outputs in production. This enables organizations to:

• Evaluate model performance and reliability. Ensuring models respond accurately and within acceptable latency thresholds is fundamental for user trust.
• Monitor resource consumption. Observing computational resources and cost drivers is particularly important in cloud deployments.
• Detect data quality issues or drift. Monitor the nature of incoming data and how it may affect model accuracy over time.
• Increase explainability. Track model versions, parameters, and deployment context to audit or troubleshoot unexpected results.
• Ensure security and compliance. Monitor for potential problems, such as prompt injection attacks, inappropriate use, or data leakages.

Without comprehensive visibility, problems in LLM-powered applications can remain undetected until they cause business or reputational impact. As regulatory requirements for responsible and transparent AI continue to advance, robust observability becomes a regulatory necessity.

OpenLLMetry is specifically designed for LLM observability, addressing the unique challenges of monitoring AI models in production. It extends OpenTelemetry (the open standard for observability) by adding AI-aware instrumentation that captures model-specific data such as prompt details, token usage, and model parameters. OpenLLMetry provides standardization across various AI frameworks, such as OpenAI, HuggingFace, and LangChain, among others, and integrates seamlessly with observability platforms like Dynatrace. This comprehensive approach makes it particularly well-suited for organizations seeking to monitor their LLM applications alongside their broader infrastructure.

Traditionally, observability in software engineering relies on mature solutions for monitoring traces, metrics, and logs. OpenTelemetry has emerged as the open standard in this space, allowing for unified instrumentation and collection across diverse programming languages and platforms. In many runtime environments, such as Java or .NET, out-of-the-box instrumentation can deliver high-level application traces with ease.

However, observing AI models, particularly those written in Python and orchestrated by AI-specific libraries, introduces the following unique challenges:

• Generic telemetry lacks AI context. OpenTelemetry's default auto-instrumentation captures general-purpose spans and resource usage, but it does not record AI-specific attributes. Critical information like the LLM's name, version, prompt and completion token counts, temperature parameters, and prompt input are not captured by default.
• Fragmented tooling across frameworks. The AI ecosystem is heterogeneous, with multiple frameworks, model providers, and orchestration libraries, which makes standardizing observability across them difficult.
• Integrated end-to-end visibility. Organizations want to analyze AI model data alongside application-level and infrastructure traces for a holistic view, but integrations are not straightforward.

OpenLLMetry fills the gaps of standard observability tools in the LLM and broader AI context. By building on the industry standard, OpenTelemetry, it introduces a focused software development kit (SDK) that understands AI workloads and enhances the depth and context of the information being collected.

OpenLLMetry adds an AI-aware layer to observability. Instead of recording only generic details such as execution times, it extracts and normalizes LLM-specific information each time a model is invoked. For example, when a user invokes a GPT-4 model using LangChain, the system records the specific model version, the prompt used, parameter choices such as temperature and max tokens, and token consumption metrics.

An e-commerce company could use OpenLLMetry to monitor their product recommendation chatbots across different model versions, tracking response times under 500ms, token usage per request, and ensuring no sensitive configuration parameters are exposed in model inputs or outputs.

OpenLLMetry provides direct support for key AI frameworks and services that underpin modern AI application development. This includes OpenAI for API-based model calls, HuggingFace for transformer-based models, Pinecone for vector databases and retrieval-augmented generation, and LangChain for orchestrating multi-step, prompt-based workflows. By providing decorators and instrumentation targeted to these frameworks, OpenLLMetry ensures that similar data is captured regardless of underlying technologies, simplifying cross-team and cross-system analysis.

A SaaS company employing both OpenAI API calls for English language tasks and HuggingFace-hosted models for multilingual features can gather consistent metrics and performance data regardless of where or how the underlying models are hosted or invoked.

OpenLLMetry sends data in a standardized format using the OpenTelemetry protocol, which works with popular monitoring tools like Dynatrace. This lets product teams view LLM traces, logs, and custom metrics alongside their existing infrastructure dashboards — providing a single source of truth for operational insights and troubleshooting.

A banking firm using observability platforms to monitor end-to-end customer journeys can include AI-powered chatbot interactions within these traces, directly correlating application slowdowns or inaccurate responses with backend AI model calls and their parameters.

OpenLLMetry can help organizations meet AI transparency requirements by logging key details like model versions, prompts, input/output sizes, and randomness settings. This makes it easier to audit how decisions were made and ensure compliance. In regulated fields like healthcare, where explainability is critical, it provides clear, traceable records of which model generated each response and the parameters that shaped it.

Consider an application that delivers concise business profiles, using OpenAI models via LangChain. The system accepts the company name and desired profile length, constructs a prompt, sends it to the LLM, and returns the generated summary.

OpenLLMetry can be used in applications like this executive summary generator. When a user requests a company profile, OpenLLMetry automatically records metadata such as:

• Model provider and version: "gpt-4o-mini" as used via OpenAI.
• Prompt parameters: The company name, the prompt template, and length restriction (50 tokens).
• Model hyperparameters: Temperature (0.7), affecting response variability.
• Token usage: Number of prompt and completion tokens for cost and efficiency tracking.
• Latency and outcome: Request duration and result metadata.

This data can be sent to observability platforms, where product managers can visualize trends in latency, track costs associated with API usage by monitoring average tokens consumed per request, and ensure the AI system remains performant and predictable.

If a specific request yields an unexpectedly vague or off-topic answer, engineers can trace every step — from prompt crafting to model selection and output — enabling targeted debugging and remedial actions.

Traceloop is the organization that maintains OpenLLMetry. OpenLLMetry is an open source SDK that extends OpenTelemetry for AI model observability. Traceloop serves as the steward of this open source project, developing and maintaining the toolkit that helps organizations monitor and observe their AI models in production environments.

Yes, OpenLLMetry is free and open source, distributed under the Apache 2.0 license. This means organizations can freely use, modify, and distribute the software to meet their specific needs. As an open source project, it's accessible to the community without licensing costs, though there may be costs associated with the observability platforms like Dynatrace that you connect it to for visualization and analysis.

OpenLLMetry addresses the growing challenge of observing AI models in production by extending the widely adopted OpenTelemetry standard to capture essential LLM-focused metrics, parameters, and context. Its open source SDK supports integration with popular frameworks and observability platforms, delivering consistent, actionable insights for product managers, AI engineers, and compliance teams.

As organizations increasingly rely on AI for critical business operations, the need for deep, reliable, and compliant observability continues to grow. OpenLLMetry enables this transparency, helping businesses manage AI systems with the same confidence as the rest of their digital infrastructure.