
Langfuse
Helicone AI
LangSmith
LangChain
Openlayer
Braintrust.dev
Portkey
LastMile AI
Pl@ntNet
PictureThis
iNaturalist
Garden Answers
Gardenia
HortusFox
iPflanzen
Plant Parent
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
Pl@ntNetBased on our record, Langfuse should be more popular than Pl@ntNet. 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 / 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
There are a number of phone apps that will identify trees from a picture. I personally prefer plantnet.org (non-profit entity / no ads or tracking). Source: about 4 years ago
You can also go directly to plantnet.org and perform the same check. Source: over 4 years ago
Get the app from plantnet.org. It's developed by a non-profit consortium of European organizations. I promise it's completely ad free and won't terrorize you in any way. Source: over 4 years ago
You could scrape them off the plantnet.org site. But unless your problem is purely academic you could skip creating your own engine and just use their API. Source: over 4 years ago
Helicone AI - Open-source LLM Observability for Developers
PictureThis - Instantly identify your plants
LangSmith - Build and deploy LLM applications with confidence
iNaturalist - iNaturalist is known as one of the most popular nature applications that helps you to identify the animals, plants, insects, and lots of other things with just a single click.
LangChain - Framework for building applications with LLMs through composability
Garden Answers - Garden Answers is an online plant identification application that allows you to get detailed information about any plants or flowers in your garden.