openlit 0.0.2

OpenTelemetry-native Auto instrumentation library for monitoring LLM Applications, facilitating the integration of observability into your GenAI-driven projects

OpenLIT: OpenTelemetry-native Observability for LLMs

OpenLIT Python SDK is an OpenTelemetry-native Auto instrumentation library for monitoring LLM Applications, facilitating the integration of observability into your GenAI-driven projects. Designed with simplicity and efficiency, OpenLIT offers the ability to embed observability into your GenAI-driven projects effortlessly using just a single line of code.

Whether you're directly using LLM Libraries like OpenAI, Anthropic or building complex RAG Agents using Langchain, OpenLIT seamlessly integrates observability into your applications, ensuring enhanced performance and reliability across diverse scenarios.

This project adheres to the Semantic Conventions proposed by the OpenTelemetry community. You can check out the current definitions here.

What can be Auto Instrumented?

LLMs

• ✅ OpenAI
• ✅ Anthropic
• ✅ Cohere
• ✅ Mistral
• ✅ Azure OpenAI
• ✅ HuggingFace Transformers

Vector DBs

• ✅ ChromaDB
• ✅ Pinecone

Frameworks

• ✅ Langchain
• ✅ LiteLLM

Supported Destinations

• ✅ OpenTelemetry Collector
• ✅ Grafana Cloud
• ✅ Grafana Tempo
• ✅ DataDog
• ✅ New Relic
• ✅ SigNoz
• ✅ Dynatrace
• ✅ OpenObserve

💿 Installation

pip install openlit

⚡ Quick Integration

import openlit
openlit.init()

By default, OpenLIT directs traces and metrics straight to your console. To forward telemetry data to an HTTP OTLP endpoint, such as the OpenTelemetry Collector, set the otlp_endpoint parameter with the desired endpoint. Alternatively, you can configure the endpoint by setting the OTEL_EXPORTER_OTLP_ENDPOINT environment variable as recommended in the OpenTelemetry documentation.

To send telemetry to OpenTelemetry backends requiring authentication, set the otlp_headers parameter with its desired value. Alternatively, you can configure the endpoint by setting the OTEL_EXPORTER_OTLP_HEADERS environment variable as recommended in the OpenTelemetry documentation.

Configuration

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"	Yes
application_name	Identifies the name of your application.	"default"	Yes
tracer	An instance of OpenTelemetry Tracer for tracing operations.	None	No
meter	An OpenTelemetry Metrics instance for capturing metrics.	None	No
otlp_endpoint	Specifies the OTLP endpoint for transmitting telemetry data.	None	No
otlp_headers	Defines headers for the OTLP exporter, useful for backends requiring authentication.	None	No
disable_batch	A flag to disable batch span processing, favoring immediate dispatch.	False	No
trace_content	Enables tracing of content for deeper insights.	True	No
disabled_instrumentors	List of instrumentors to disable. Choices: ["openai", "anthropic", "langchain", "cohere", "mistral", "transformers", "chroma", "pinecone"].	None	No
disable_metrics	If set, disables the collection of metrics.	False	No

🌱 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 channel 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 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.