Langtrace.ai Howto
Langtrace is an open-source observability tool for monitoring, evaluating, and optimizing large language model applications with real-time insights and detailed performance metrics.
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Sign up for Langtrace: Go to https://langtrace.ai/signup to create an account and generate an API key for your project.
Install the Langtrace SDK: Install the Langtrace SDK in your project using pip install langtrace-python-sdk for Python or npm install langtrace-js-sdk for JavaScript.
Initialize Langtrace in your code: Import and initialize Langtrace at the beginning of your script, before any LLM module imports: from langtrace_python_sdk import langtrace; langtrace.init(api_key='<LANGTRACE_API_KEY>')
Integrate with your LLM application: Langtrace will automatically trace LLM, VectorDB, and framework-level requests once initialized. No additional code changes are needed for basic tracing.
View traces in dashboard: Log into the Langtrace web dashboard to view and analyze the automatically generated traces and metrics for your LLM application.
Annotate and evaluate: Use the dashboard to manually annotate traces, create golden datasets, and run automated evaluations on your LLM outputs.
Set up continuous monitoring: Configure alerts and ongoing evaluations to continuously monitor and improve your LLM application's performance over time.
Langtrace.ai FAQs
Langtrace.ai is an open-source observability tool that collects and analyzes traces and metrics to help monitor, evaluate, and improve LLM (Large Language Model) applications. It provides end-to-end visibility into ML pipelines, including RAG systems and fine-tuned models.
Langtrace.ai Monthly Traffic Trends
Langtrace.ai received 9.3k visits last month, demonstrating a Significant Decline of -27.6%. Based on our analysis, this trend aligns with typical market dynamics in the AI tools sector.
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