OpenTelemetry-native

AI Observability, Monitoring and Evaluation Framework

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OpenLIT SDK is a monitoring framework built on top of OpenTelemetry that gives your complete Observability for your AI stack, from LLMs to vector databases, with just one line of code with tracing and metrics. It also allows you to send the generated traces and metrics to your existing monitoring tools like Grafana, New Relic, and more.

This project proudly follows and maintains the Semantic Conventions with the OpenTelemetry community, consistently updating to align with the latest standards in Observability.

• 🔎 Auto Instrumentation: Works with 12+ LLM providers, vector databases, and AI frameworks with just one line of code.
• 🔭 OpenTelemetry-Native Observability SDKs: Vendor-neutral SDKs that can send traces and metrics to your existing observability tool like Prometheus and Jaeger.
• 💲 Cost Tracking for Custom and Fine-Tuned Models: Pass custom pricing files for accurate budgeting of custom and fine-tuned models.
• 🚀 Support for OpenLIT Features: Includes support for prompt management and secrets management features available in OpenLIT.

• ✅ OpenTelemetry Collector
• ✅ Prometheus + Tempo
• ✅ Prometheus + Jaeger
• ✅ Grafana Cloud
• ✅ New Relic
• ✅ Elastic
• ✅ HyperDX
• ✅ DataDog
• ✅ SigNoz
• ✅ OneUptime
• ✅ Dynatrace
• ✅ OpenObserve
• ✅ Highlight.io

The following metrics are supported across all integrations:

• genaiClientUsageTokens: Histogram for total input/output tokens used.
• genaiClientOperationDuration: Histogram for GenAI operation duration.
• genaiServerTbt: Histogram for time per output token after the first token.
• genaiServerTtft: Histogram for time to first token for successful responses.
• genaiRequests: Counter for number of GenAI requests.
• genaiPromptTokens: Counter for number of prompt tokens processed.
• genaiCompletionTokens: Counter for number of completion tokens processed.
• genaiReasoningTokens: Counter for number of reasoning thought tokens processed.
• genaiCost: Histogram for distribution of GenAI request costs (USD).

These metrics allow you to monitor usage, performance, and cost for supported GenAI operations.

npm install openlit

Open your command line or terminal and run:

npm install openlit

Integrate OpenLIT into your AI applications by adding the following lines to your code.

import Openlit from 'openlit';

Openlit.init();

Configure the telemetry data destination as follows:

💡 Info: If the otlp_endpoint or OTEL_EXPORTER_OTLP_ENDPOINT is not provided, the OpenLIT SDK will output traces directly to your console, which is recommended during the development phase.

import Openlit from 'openlit';

Openlit.init({ 
  otlpEndpoint: 'YOUR_OTEL_ENDPOINT',
  otlpHeaders: 'YOUR_OTEL_ENDPOINT_AUTH'
});

import Openlit from "openlit"

Openlit.init()

export OTEL_EXPORTER_OTLP_ENDPOINT = "YOUR_OTEL_ENDPOINT"
export OTEL_EXPORTER_OTLP_HEADERS = "YOUR_OTEL_ENDPOINT_AUTH"

Now that your LLM observability data is being collected and sent to configured OpenTelemetry destination, the next step is to visualize and analyze this data. This will help you understand your LLM application's performance and behavior and identify where it can be improved.

If you want to use OpenLIT's Observability Dashboard to monitor LLM usage—like cost, tokens, and user interactions—please check out our Quickstart Guide.

If you're sending metrics and traces to other observability tools, take a look at our Connections Guide to start using a pre-built dashboard we have created for these tools.

Below is a detailed overview of the configuration options available, allowing you to adjust OpenLIT's behavior and functionality to align with your specific observability needs:

Below are the parameters for use with the SDK for OpenLIT Prompt Hub for prompt management:

Below are the parameters for use with the SDK for OpenLIT Vault for secret management:

Evaluate trace attributes against the OpenLIT Rule Engine to retrieve matching rules and associated entities (contexts, prompts, evaluation configurations).

import Openlit from 'openlit';

const result = await Openlit.evaluateRule({
  entityType: 'context',
  fields: {
    'gen_ai.system': 'openai',
    'gen_ai.request.model': 'gpt-4',
    'service.name': 'my-app',
  },
  includeEntityData: true,
});

if (!('err' in result)) {
  console.log('Matching rules:', result.matchingRuleIds);
  console.log('Entities:', result.entities);
}

Attach numeric, boolean, or categorical scores and user feedback to a GenAI span. When called inside an instrumented LLM request, the score auto-attaches to the active span.

import Openlit from 'openlit';

Openlit.init();

Openlit.logScore({ name: 'user_feedback', value: true, comment: 'Helpful response' });
Openlit.logScore({ name: 'quality', value: 0.85, metadata: { reviewer: 'human' } });
Openlit.logScore({ name: 'category', value: 'accurate' });

Whether it's big or small, we love contributions 💚. Check out our Contribution guide to get started

Unsure where to start? Here are a few ways to get involved:

• Join our Slack or Discord community to discuss ideas, share feedback, and connect with both our team and the wider OpenLIT community.

Your input helps us grow and improve, and we're here to support you every step of the way.

Connect with the OpenLIT community and maintainers for support, discussions, and updates:

• 🌟 If you like it, Leave a star on our GitHub
• 🌍 Join our Slack or Discord community for live interactions and questions.
• 🐞 Report bugs on our GitHub Issues to help us improve OpenLIT.
• 𝕏 Follow us on X for the latest updates and news.