GitHub Metrics
GitWrapped
Contributions for GitHub
GitPrime
CodersRank
Waydev
Code Time
Sixtyfour
Langfuse
Helicone AI
LangSmith
LangChain
Openlayer
Braintrust.dev
Portkey
PromptLayer
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 GitHub Metrics. 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.
This tutorial shows you how to create a fully automated GitHub profile README using GitHub Metrics with custom SVGs and GitHub Actions. - Source: dev.to / about 1 year ago
Metrics this will generate a detailed stats infographic based on your GitHub Profile. - Source: dev.to / about 2 years ago
Another GitHub profile using lowlighter/metrics with a slightly different setup. - Source: dev.to / almost 3 years ago
Using projects like this is an easy way to make your Github profile really standout. Source: over 3 years ago
Lowlighter/metrics is a GitHub repo you will fall in love with if you adore easy-to-use upgrading capabilities for your GitHub README.md through GitHub Actions. - Source: dev.to / about 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 / 9 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 / 27 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
GitWrapped - View/Share how you contributed to Github over the years
Helicone AI - Open-source LLM Observability for Developers
Contributions for GitHub - Show your GitHub contributions graph on your iOS Devices
LangSmith - Build and deploy LLM applications with confidence
GitPrime - GitPrime uses data from any Git based code repository to give management the software engineering metrics needed to move faster and optimize work patterns.
LangChain - Framework for building applications with LLMs through composability