Langfuse
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Langfuse is an open-source LLM engineering platform designed to empower developers by providing insights into user interactions with their LLM applications. We offer tools that help developers understand usage patterns, diagnose issues, and improve application performance based on real user data. By integrating seamlessly into existing workflows, Langfuse streamlines the process of monitoring, debugging, and optimizing LLM applications. Our platform's robust documentation and active community support make it easy for developers to leverage Langfuse for enhancing their LLM projects efficiently. Whether you're troubleshooting interactions or iterating on new features, Langfuse is committed to simplifying your LLM development journey.
Features: Zero Configuration - Works out of the box Multiple Display Modes - Table, Grid, and Masonry layouts Advanced Interactions - Column drag-and-drop, resizing, expandable rows Smart Auto-Generation - Columns, filters, and searches auto-generated Responsive Design - Mobile-first approach Performance Optimized - Virtual scrolling, debounced search Type Safe - Full TypeScript support State Persistence - User preferences saved automatically Composable UI - Override UI components, icons, and styles Predictable Filters - Structured search and field-level filters
Langfuse
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Tablefront's answer:
It's a zero-config setup, you feed it your data and it sets all the defaults for you so you get a beautiful looking table that fits your data automatically, with all the features activated. So you start off with something that already works and looks great, then you can configure, override and customize as you wish with full power.
Tablefront's answer:
Simplicity, speed and advanced interactions are there from moment zero. No hassle, no learning curve. Just plug-and-play to get you to a production-grade working version. Then you still have full power to customize any part of it if you wish.
Tablefront's answer:
Web developers with a focus on data and visualizing it in a beautiful way. UX obsessed designers.
Tablefront's answer:
We built it for our selves in order to develop our data-heavy B2B products, it's currently used in production in multiple systems and we were so happy with it we had to put it out there.
Based on our record, Langfuse seems to be more popular. It has been mentiond 28 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
In this project we will build a Python banking assistant agent using Strands Agents and make it observable and continuously evaluated using Langfuse โ step by step. - Source: dev.to / 14 days ago
Langfuse is the open-source standard for LLM observability. It traces every LLM interaction โ prompts, completions, latency, token usage, cost โ and provides the tooling to debug, evaluate, and optimize LLM applications in production. Think of it as "Datadog for LLM calls" with a focus on prompt engineering workflows. - Source: dev.to / about 1 month ago
You're monitoring production traffic. You need Langfuse / Phoenix / Helicone / Braintrust for that. Online eval is a different problem class: implicit feedback, drift detection, hallucination rates on your data, not on HellaSwag. - Source: dev.to / about 1 month ago
Gateway or proxy attribution. A reverse proxy in front of the model-provider API records the request, computes the cost, and exposes per-customer breakdowns. Open-source options include Helicone, LiteLLM, Langfuse, and OpenLLMetry. Hosted equivalents serve as the AI cost observability layer for teams that want centralized visibility: LangSmith, Datadog LLM Observability, Arize Phoenix. Adds a network hop.... - Source: dev.to / about 1 month ago
Same approach works with Langfuse, Phoenix, Braintrust, or your existing OTel pipeline โ the metadata.userId pattern is the universal part. - Source: dev.to / about 2 months ago
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