
TablePlus
DBeaver
DataGrip
Navicat
DbVisualizer
Beekeeper Studio
phpMyAdmin
Sequel Pro
Langfuse
Helicone AI
LangSmith
LangChain
Openlayer
Braintrust.dev
Portkey
LastMile AI
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.
TablePlus
LangfuseBased on our record, TablePlus should be more popular than Langfuse. It has been mentiond 67 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.
TablePlus is a polished native database client known for speed, clean design, and direct data editing. It supports relational databases such as MySQL, PostgreSQL, SQLite, and others, with apps across macOS, Windows, Linux, and iOS. - Source: dev.to / about 1 month ago
Best part? Itโs standard Postgres. Any tool that speaks Postgres can connect, TablePlus, Retool, Cloudflare Hyperdrive, pgAdmin, even other ORMs. - Source: dev.to / 7 months ago
If you want something sleeker than DBeaver, TablePlus is a beautiful database client. Its free tier is limited but plenty for small dev projects. - Source: dev.to / 11 months ago
For simpler use-cases I've used both https://dataflare.app/ and https://tableplus.com/ with success. They are much quicker and lighter to start-up, browse some tables and run some queries. - Source: Hacker News / about 1 year ago
Things I use and have Black Friday - * https://tableplus.com/. - Source: Hacker News / over 1 year 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 / 11 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
DBeaver - DBeaver - Universal Database Manager and SQL Client.
Helicone AI - Open-source LLM Observability for Developers
DataGrip - Tool for SQL and databases
LangSmith - Build and deploy LLM applications with confidence
Navicat - Powerful database management & design tool for Win, Mac & Linux. With intuitive GUI, user manages MySQL, MariaDB, SQL Server, SQLite, Oracle & PostgreSQL DB easily.
LangChain - Framework for building applications with LLMs through composability