openlit

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.

⚡ Features

• 🔎 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.

Auto Instrumentation Capabilities

LLMs
✅ OpenAI
✅ Anthropic
✅ Cohere
✅ Groq
✅ Mistral
✅ Google AI Studio
✅ Together AI
✅ Ollama
✅ AWS Bedrock
✅ Hugging Face
✅ Replicate
✅ Azure OpenAI (via OpenAI SDK)

Vector Databases
✅ Chroma
✅ Pinecone
✅ Qdrant
✅ Milvus

Frameworks
✅ LangChain
✅ LlamaIndex
✅ Vercel AI SDK

Supported Destinations

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

Supported Metrics

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.

💿 Installation

npm install openlit

🚀 Getting Started

Step 1: Install OpenLIT

Open your command line or terminal and run:

npm install openlit

Step 2: Initialize OpenLIT in your Application

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:

Purpose	Parameter/Environment Variable	For Sending to OpenLIT
Send data to an HTTP OTLP endpoint	otlpEndpoint or OTEL_EXPORTER_OTLP_ENDPOINT	"http://127.0.0.1:4318"
Authenticate telemetry backends	otlpHeaders or OTEL_EXPORTER_OTLP_HEADERS	Not required by default

💡 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.

Example

Initialize using Function Arguments

Add the following two lines to your application code:

import Openlit from 'openlit';

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

Initialize using Environment Variables

Add the following two lines to your application code:

import Openlit from "openlit"

Openlit.init()

Then, configure the your OTLP endpoint using environment variable:

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

Step 3: Visualize and Optimize!

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.

Configuration

Observability - Openlit.init()

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:

Argument	Description	Default Value	Required
environment	The deployment environment of the application.	"default"	No
applicationName	Identifies the name of your application.	"default"	No
tracer	An instance of OpenTelemetry Tracer for tracing operations.	undefined	No
otlpEndpoint	Specifies the OTLP endpoint for transmitting telemetry data.	undefined	No
otlpHeaders	Defines headers for the OTLP exporter, useful for backends requiring authentication.	undefined	No
disableBatch	A flag to disable batch span processing, favoring immediate dispatch.	true	No
traceContent	Enables tracing of content for deeper insights.	true	No
disabledInstrumentations	List of instrumentations to disable.	undefined	No
instrumentations	Object of instrumentation modules for manual patching	undefined	No
pricing_json	URL or file path of the pricing JSON file.	https://github.com/openlit/openlit/blob/main/assets/pricing.json	No

OpenLIT Prompt Hub - Openlit.getPrompt()

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

Parameter	Description
url	Sets the OpenLIT URL. Defaults to the OPENLIT_URL environment variable or http://127.0.0.1:3000 if not set.
apiKey	Sets the OpenLIT API Key. Can also be provided via the OPENLIT_API_KEY environment variable.
name	Sets the name to fetch a unique prompt. Use this or promptId.
promptId	Sets the ID to fetch a unique prompt. Use this or name. Optional
version	Sets the version to retrieve a specific prompt. Optional
shouldCompile	Boolean value that compiles the prompt using the provided variables. Optional
variables	Sets the variables for prompt compilation. Optional
metaProperties	Sets the meta-properties for storing in the prompt's access history metadata.

OpenLIT Vault - Openlit.getSecrets()

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

Parameter	Description
url	Sets the Openlit URL. Defaults to the OPENLIT_URL environment variable or http://127.0.0.1:3000 if not set.
apiKey	Sets the OpenLIT API Key. Can also be provided via the OPENLIT_API_KEY environment variable.
key	Sets the key to fetch a specific secret. Optional
tags	Sets the tags for fetching only the secrets that have the mentioned tags assigned. Optional
shouldSetEnv	Boolean value that sets all the secrets as environment variables for the application. Optional

OpenLIT Rule Engine - Openlit.evaluateRule()

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

Parameter	Description
url	Sets the OpenLIT URL. Defaults to the OPENLIT_URL environment variable or http://127.0.0.1:3000.
apiKey	Sets the OpenLIT API Key. Can also be provided via the OPENLIT_API_KEY environment variable.
entityType	Type of entity to match: "context", "prompt", or "evaluation".
fields	Object of trace attributes to evaluate against rules. e.g. { "gen_ai.system": "openai" }
includeEntityData	If true, include full entity data in the response. Default: false. Optional
entityInputs	Optional object of inputs for entity resolution (e.g. prompt variables).

Example

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);
}

🌱 Contributing

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.

💚 Community & Support

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.