Langfuse
Helicone AI
LangSmith
LangChain
PromptLayer
Braintrust.dev
Portkey
Openlayer
React Bootstrap
Ant Design
Material UI
React Rainbow Components
Chakra UI
Blueprint
Semantic UI
Ayro UI
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.
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Based on our record, Langfuse should be more popular than React Bootstrap. 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 / about 1 month 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 2 months 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 / 2 months 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 / 2 months 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 / 3 months ago
My recommendation would be to learn using design systems and UI Kits that take care of it for you right away. My choice would be MDBootstrap React for Bootstrap, and Tailwind Elements for Tailwind. Source: about 3 years ago
Check out something like MDB UI Kit (for react) - this is much more robust & consistent than any Tailwind kit out there. Source: over 3 years ago
Check this out: Https://mdbootstrap.com/docs/b5/react/. Source: about 5 years ago
Helicone AI - Open-source LLM Observability for Developers
Ant Design - An enterprise-class UI design language and React implementation with a set of high-quality React components, one of best React UI library for enterprises
LangSmith - Build and deploy LLM applications with confidence
Material UI - A CSS Framework and a Set of React Components that Implement Google's Material Design
LangChain - Framework for building applications with LLMs through composability
React Rainbow Components - Build your web application in a snap.
