Langfuse
Open-source LLM engineering platform — tracing and observability, prompt management, evaluations, datasets, and a playground for teams building AI applications
Langfuse is an open-source LLM engineering platform that helps teams develop, monitor, evaluate, and debug the AI features inside their product — tracing every LLM call, versioning prompts, running evaluations, and testing on datasets, all self-hostable or on its hosted cloud. It has 31,154 GitHub stars as of July 2026, is MIT-licensed at its core, built in TypeScript, and came out of Y Combinator's W23 batch (it became part of ClickHouse in January 2026). For a founder shipping an AI product it answers a real question — "is my model actually doing what I think, and getting better?" — but it improves the product, not its reach. Langfuse can tell you your AI got sharper; it can't tell anyone that your product exists.
What Langfuse is
Langfuse (github.com/langfuse/langfuse) is an open-source platform for the engineering work behind an AI feature. A normal app has logs and error tracking; an LLM-powered app has a fuzzier problem — the model's output changes, prompts drift, and "is this good?" is a judgment call rather than a green check. Langfuse gives that work a home: you instrument your app, and it traces each LLM call, stores and versions your prompts, runs evaluations, and lets you test changes against saved datasets. It's built in TypeScript, MIT-licensed at its core, can be self-hosted in minutes or run on Langfuse Cloud, and came out of Y Combinator's W23 batch.
- Tracing and observability — see every LLM call, retrieval, and agent step behind a user session, so you can debug why a response went wrong.
- Prompt management — version and iterate on prompts centrally instead of hard-coding them, without adding latency to your app.
- Evaluations and datasets — score outputs with LLM-as-a-judge, code checks, or human labels, and benchmark a change against a saved test set before you ship it.
- Open-source and self-hostable — run it on your own infrastructure, with a hosted cloud and paid enterprise tier if you'd rather not.
Where Langfuse fits a founder's stack
For the growing number of founders shipping AI-native products — the ones building with a kit like Open SaaS or a SaaS starter kit, then wiring an LLM into the core experience — Langfuse fills a real gap: it makes the AI part observable and improvable instead of a black box. That matters, because an AI feature that quietly degrades is a retention leak you can't see. If you're building with AI, being able to measure and tighten model quality is genuinely part of the job.
But it's worth being precise about what that leverage touches. Langfuse operates on the inside of your product — the quality of what your AI does once someone is already using it. It says nothing about the harder, separate problem of getting those someones in the first place.
What it doesn't do — and how to grow what you build
Langfuse can tell you your AI got sharper this week; it can't tell a single new person that your product exists. Better traces don't write your positioning, rank your pages, post in the communities your users live in, or follow up with someone who tried you once. That is a different discipline from LLM engineering, and it's the one that decides whether the polished AI product you're instrumenting ever gets used.
- Measures the product — but the first users still come from launches, content, communities, and outreach you do deliberately.
- Improves model quality — but quality only compounds once people are in the door; marketing a product you built with AI is its own loop.
- Instruments what exists — but distribution is the scarce input for an AI-native startup, and no observability tool creates it.
This is where AgentCeres — the AI Growth Officer at agentceres.com fits alongside a tool like Langfuse. AgentCeres is a managed AI marketing team: specialists research your market and draft the SEO, social, and outreach that bring people to the product — with a human approving anything that goes out. Langfuse helps you build an AI product worth using; AgentCeres helps you get it used. The two sit on opposite sides of the same goal — one makes the product better, the other makes it known.
FAQ
- Is Langfuse free?
- Langfuse is open source and MIT-licensed at its core, so you can self-host it for free and own your data. It also offers Langfuse Cloud, a hosted version with a free tier and paid plans, plus enterprise features for larger teams. Check the repository and langfuse.com for current limits and which features sit in the paid tiers.
- Is Langfuse open source?
- Yes — the core is public at github.com/langfuse/langfuse under the MIT license and can be self-hosted, which is the main reason teams pick it over a fully closed observability tool. Some advanced enterprise features sit under a separate commercial license, the common open-core pattern, but the platform is genuinely usable and self-hostable for free.
- Do I need Langfuse if I'm just adding one AI feature?
- Not on day one. If you have a single prompt and it works, plain logging is enough. Reach for Langfuse once the AI part gets hard to reason about — multiple prompts, agents, or retrieval steps, or a nagging sense that quality is drifting and you can't see where. It earns its place when "is the model still good?" becomes a question you need real evidence to answer.
- How is Langfuse different from a tool like Umami?
- They measure different layers. Umami and product analytics tell you how people move through your app — pages, signups, retention. Langfuse tells you how well the AI inside your app is performing — traces, prompt versions, eval scores. One watches user behavior; the other watches model behavior. A team shipping an AI product often wants both, plus the marketing that brings users to measure in the first place.
You built it. Now grow it.
AgentCeres is a managed AI marketing team — specialists draft the SEO, social, and outreach that fill your links, you approve what ships. 14-day free trial, from $19/month.