Langfuse
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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.
Hyperview is the No.1 cloud-based Data Center Infrastructure Management (DCIM) platform designed to optimize and streamline the operations of modern data centers. Our state-of-the-art software platform empowers businesses to effectively manage their data center assets, reduce energy consumption, minimize downtime, and enhance overall performance.
The platform is used further to manage and monitor capacity, rack and floor space, asset lifecycles, asset health, power, energy, and temperature. Core features include Asset Management, Power Monitoring, Energy Management, Environmental Monitoring, and Capacity Planning, Carbon Footprint Reporting, and 3D Visualization.
Langfuse HyperviewBased on our record, Langfuse seems to be more popular. 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 / 19 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
Helicone AI - Open-source LLM Observability for Developers
Cyclr - Powerful SaaS integration toolkit for SaaS developers - create, amplify, manage and publish native integrations from within your app with Cyclr's flexible Embedded iPaaS.
LangSmith - Build and deploy LLM applications with confidence
Investor List - A searchable, crowdsourced list of over 1k investors
LangChain - Framework for building applications with LLMs through composability
Investor Scout - A database of 45k+ investors to raise your seed round ๐ฐ