PatternPad
MagicPattern
Patterninja
Pattern Monster
SVG Stripe Generator
Patternizer
SVGeez
Doodad.dev's Pattern Generator
Langfuse
Helicone AI
LangSmith
LangChain
Braintrust.dev
Openlayer
PromptLayer
Portkey
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 PatternPad. 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.
Design beautiful custom patterns effortlessly with PatternPad. - Source: dev.to / over 1 year ago
PatternPad: It generates graphical patterns based on a variety of parameters. This results in an endless number of variations. You can choose from popular styles or create your own individual pattern. - Source: dev.to / about 2 years ago
That's an SVG pattern in a CSS background-image property, the exact line of code is here. If memory serves me correctly, I used this site to generate the pattern: https://patternpad.com/. Source: over 4 years 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 / 26 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 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 / about 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 / 2 months ago
MagicPattern - The best design toolbox with 10+ tools for anyone
Helicone AI - Open-source LLM Observability for Developers
Patterninja - Create patterns online
LangSmith - Build and deploy LLM applications with confidence
Pattern Monster - Pattern Monster is a pattern maker app to create vector patterns for your projects
LangChain - Framework for building applications with LLMs through composability