Monitor, evaluate & improve
your LLM apps
Langtrace is an open-source observability tool that collects and analyzes traces and metrics to help you improve your LLM apps.
Simple non-intrusive setup
Access the Langtrace SDK with 2 lines of code
from langtrace_python_sdk import langtrace
langtrace.init(api_key=<your_api_key>)Supports popular LLMs, frameworks and vector databases
Why Langtrace?
Open-Source & Secure
Langtrace can be self-hosted and supports OpenTelemetry standard traces, which can be ingested by any observability tool of your choice, resulting in no vendor lock-in.
End-to-end Observability
Get visibility and insights into your entire ML pipeline, whether it is a RAG or a fine-tuned model with traces and logs that cut across framework, vectorDB and LLM requests.
Establish a Feedback Loop
Annotate and create golden datasets with traced LLM interactions, and use them to continuously test and enhance your AI applications. Langtrace includes built-in heuristic, statistical, and model-based evaluations to support this process.
Build and deploy with confidence
Run evaluations to improve the accuracy of your AI application holistically and efficiently.
Track the token usage, costs, and performance of various LLMs simultaneously
Trace requests, detect bottlenecks, and optimize performance with traces.
Develop, test, iterate and version prompts seamlessly.
