Langfuse
Helicone AI
LangSmith
LangChain
Openlayer
Braintrust.dev
Portkey
PromptLayer
Apache Struts
Spring Framework
Grails
Spark Mail
Play Framework
Eclipse Jetty
Eclipse RAP
Vaadin Framework
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.
Langfuse Apache StrutsBased on our record, Langfuse seems to be a lot more popular than Apache Struts. While we know about 28 links to Langfuse, we've tracked only 2 mentions of Apache Struts. 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 / 4 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 / 23 days 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
The Apache Struts website (https://struts.apache.org/) offers tutorials and other resources for learning about the Apache Struts framework. Source: over 3 years ago
3) Struts 2 - Also a popular java based framework. Backed by the Apache Foundation and built to easily integrate with Spring. This is the easiest choice when converting from a Struts 1 framework to a more modern and secure framework. - Source: dev.to / over 4 years ago
Helicone AI - Open-source LLM Observability for Developers
Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.
LangSmith - Build and deploy LLM applications with confidence
Grails - An Open Source, full stack, web application framework for the JVM
LangChain - Framework for building applications with LLMs through composability
Spark Mail - Spark helps you take your inbox under control. Instantly see whatโs important and quickly clean up the rest. Spark for Teams allows you to create, discuss, and share email with your colleagues