Langfuse
Helicone AI
LangSmith
LangChain
Openlayer
Braintrust.dev
Portkey
LastMile AI
JSON Server
JSON Placeholder
Postman
mocki Fake JSON API
MockAPI
WireMock Cloud
ExpressJS
soapUI
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
JSON ServerBased on our record, JSON Server should be more popular than Langfuse. It has been mentiond 45 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 / 13 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 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
We'll be using json-server to create the REST API that we'll fetch data from. In the root of the project, create a db.json file with the contents. - Source: dev.to / about 2 years ago
Our backend will be little more than a two-way translation layer between the database and the user interface (UI). Later in this post we will identify other responsibilities of a backend but our implementation will be kept simple to demonstrate the fundamental machinery and concepts. It is worth noting the backend comes in two parts, web server and application server. Both json-server and Express are able to... - Source: dev.to / about 3 years ago
JSON-Server creates fake REST API with a minimum amount of configuration, it provides a simple way to create mock RESTful APIs and easily define the required endpoints, allows easy definition of the data schema in a JSON file and can serve as a reference for each figure in the project. - Source: dev.to / about 3 years ago
I thought about usingJson Server (hosting the repo with the words on Github to begin with), Googlesheets, or maybe Firestore (i would prefer not to use it ,to avoid extra costs just in case it gets a reasonable amount of users). It isnt a big app so I just want a simple solution for storing the words and fetching them. Source: about 3 years ago
First, I didn't create a backend API for this example, but I used a fake API to test. I created it with json-server and json-server-auth. They are two npm packages that use a JSON file as a database and expose the database in an API. You can find more about json-server in its documentation and about json-server-auth here. - Source: dev.to / over 3 years ago
Helicone AI - Open-source LLM Observability for Developers
JSON Placeholder - JSON Placeholder is a modern platform that provides you online REST API, which you can instantly use whenever you need any fake data.
LangSmith - Build and deploy LLM applications with confidence
Postman - The Collaboration Platform for API Development
LangChain - Framework for building applications with LLMs through composability
mocki Fake JSON API - mocki Fake JSON API is an advanced platform that offers you to create API for personal use or testing purposes.