RAGFlow ships with a built-in [Langfuse](https://langfuse.com) integration so that you can **inspect and debug every retrieval and generation step** of your RAG pipelines in near real-time.
Langfuse stores traces, spans and prompt payloads in a purpose-built observability backend and offers filtering and visualisations on top.
:::info NOTE
• RAGFlow **≥ 0.18.0** (contains the Langfuse connector)
• A Langfuse workspace (cloud or self-hosted) with a _Project Public Key_ and _Secret Key_
:::
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## 1. Collect your Langfuse credentials
1. Sign in to your Langfuse dashboard.
2. Open **Settings ▸ Projects** and either create a new project or select an existing one.
3. Copy the **Public Key** and **Secret Key**.
4. Note the Langfuse **host** (e.g. `https://cloud.langfuse.com`). Use the base URL of your own installation if you self-host.
> The keys are _project-scoped_: one pair of keys is enough for all environments that should write into the same project.
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## 2. Add the keys to RAGFlow
RAGFlow stores the credentials _per tenant_. You can configure them either via the web UI or the HTTP API.
1. Log in to RAGFlow and click your avatar in the top-right corner.
2. Select **API ▸ Scroll down to the bottom ▸ Langfuse Configuration**.
3. Fill in you Langfuse **Host**, **Public Key** and **Secret Key**.
([Example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/0bde9629-4251-4386-b583-26101b8e7561?timestamp=2025-05-09T19%3A15%3A37.797Z&display=details&observation=823997d8-ac40-40f3-8e7b-8aa6753b499e))
:::tip NOTE
Use Langfuse's diff view to compare prompt versions or drill down into long-running retrievals to identify bottlenecks.