Langfuse
Helicone AI
LangSmith
LangChain
Openlayer
Braintrust.dev
Portkey
LastMile AI
JournalX
TraderSync
Moodfol.io
Quantro
UltraTrader
Stonk Journal
Mantis Trading Journal
ProfitHelper.app
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.
JournalX is a professional trading journal built for serious, active traders who want a clear feedback loop on their performance. It brings every trade, plan, and review into one centralized platform.
JournalX syncs your trades and translates them into actionable performance dashboards. By tracking P&L, expectancy, win rate, profit factor, drawdown, and R-multiples across stocks, options, futures, and crypto, you can immediately see what is and isn't working.
Where JournalX goes further is discipline. Rule-based Gameplans, pre-trade planning, and linked notes keep your strategy tied strictly to your actual execution. An integrated AI assistant allows you to query and analyze your own trading data on the fly.
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Based 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 / 17 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 / about 2 months ago
Helicone AI - Open-source LLM Observability for Developers
TraderSync - Biometric trading journal to trade without emotion
LangSmith - Build and deploy LLM applications with confidence
Moodfol.io - Moodfol.io is the fastest trading journal that helps you log trades, tag emotions and strategies, and uncover the patterns behind your performance - so you can trade with discipline and clarity.
LangChain - Framework for building applications with LLMs through composability
Quantro - Track trades.
