Langfuse

Langfuse is an open-source observability and analytics platform designed specifically for large language model (LLM) applications.

It provides robust tooling to trace, debug, evaluate, and monitor LLM-based workflows, offering developers a deeper understanding of model behavior in production environments. Langfuse plays a critical role in improving the reliability, performance, and efficiency of AI-driven products.

Key Features:

End-to-End Tracing and Monitoring:
Langfuse captures detailed traces of LLM calls, including inputs, outputs, intermediate steps, latency, cost, and user feedback. This enables teams to identify bottlenecks, monitor performance, and resolve issues efficiently.

Prompt and Model Evaluation:
The platform allows for the comparison of different prompts and model versions using both human and automated evaluation metrics. This supports iterative prompt engineering and model fine-tuning in real-world applications.

Analytics and Reporting:
Langfuse provides dashboards and custom metrics to analyze token usage, success rates, and latency. This visibility is essential for optimizing cost and performance in production systems.

A/B Testing and Experimentation:
Integrated version control and experimentation features enable developers to run A/B tests on different prompt strategies or model configurations. Results are tracked and visualized for data-driven decision-making.

Broad Integration Support:
Langfuse offers SDKs and native integrations for widely used frameworks such as LangChain, OpenAI, and LlamaIndex. It supports Python, TypeScript, and other common development environments.

Applications:

Langfuse is designed for AI teams and developers working with LLMs across use cases such as customer support automation, AI agents, chatbots, and content generation. It helps improve prompt reliability, reduce costs, enhance user experience, and accelerate deployment cycles.