Langfuse
Helicone AI
LangSmith
LangChain
PromptLayer
Humanloop
Braintrust.dev
Openlayer
KubeMastery
KodeKloud
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.
I wanted to learn Kubernetes, but the options were either "build a local lab" or pay for expensive classes built around cloud environments. I wanted something you could just open and learn from. So I built a browser-based cluster simulation with lessons to follow along.
It behaves like Kubernetes, but it's just JavaScript under the hood. Enough to run kubectl commands, create resources, and watch their lifecycle play out. There's also a live cluster visualizer so you can see your nodes, Pods, and containers in real time.
Once you've covered the concepts, you can jump into CKA drills. Each drill is a standalone, time-boxed scenario that mirrors the real exam format: a broken cluster state, a concrete task, and a pass-or-fail outcome.
The goal isn't to simulate the exam. It's to build the reflex of diagnosing and fixing things quickly, so that when you sit the real exam, the commands and the reasoning are already automatic.
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Based on our record, Langfuse seems to be more popular. It has been mentiond 29 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.
Langfuse and LangSmith exist for this. Use them. The 30 minutes you spend setting up observability saves you the 87 hours you'd spend debugging blind. - Source: dev.to / 2 days ago
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
Helicone AI - Open-source LLM Observability for Developers
KodeKloud - We are a fast growing EdTech startup on trending technologies in IT and Cloud Computing.
LangSmith - Build and deploy LLM applications with confidence
LangChain - Framework for building applications with LLMs through composability
PromptLayer - The first platform built for prompt engineers
Humanloop - Train state-of-the-art language AI in the browser