How to use Langfuse: what you can do with it

3 use cases

• Self-host LLM tracing in five minutes

  Run Langfuse locally with Docker Compose and start sending traces from your app.

  • REPO langfuse/langfuse
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  Self-host langfuse/langfuse locally with Docker Compose, then add tracing to my <app> using the <OpenAI or Langchain> integration. Send a few test requests and show me the trace for the slowest one.

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• Version and test prompts outside your code

  Move prompts into Langfuse Prompt Management and iterate on them in the LLM Playground.

  • REPO langfuse/langfuse
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  Using langfuse/langfuse, move the prompts in <file> into Prompt Management, update my code to fetch them through the SDK, and set up the playground so I can test a new version against <example trace>.

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• Score your app's answers with evals and datasets

  Build a dataset of test cases and score outputs with LLM-as-a-judge, code evaluators and user feedback.

  • REPO langfuse/langfuse
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  Using langfuse/langfuse, create a dataset from <examples>, run it against <current model> and <new model>, and score both with an LLM-as-a-judge evaluator for <criteria>. Summarise which version wins and on which cases.

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Quick start with Langfuse

1. Check you have what it needs: Docker and Docker Compose for local self-hosting, or a Langfuse Cloud account; The Python or JS/TS SDK, or a supported framework integration; For production: a VM, Kubernetes with Helm, or Terraform on AWS, Azure or GCP.
2. Open langfuse/langfuse on GitHub and read the README and license.
3. Clone the repo with git clone --depth=1 https://github.com/langfuse/langfuse.git and cd langfuse
4. Start it with docker compose up
5. Instrument your app with the Python or JS/TS SDK, or a drop-in integration such as OpenAI, Langchain or LlamaIndex
6. Open the Langfuse UI and inspect the traces for a request that went wrong

These steps come from our top use case, Self-host LLM tracing in five minutes, which also has a copy-paste agent prompt.

Using the Deduped MCP, get the setup steps for langfuse/langfuse

claude mcp add --transport http deduped https://dedupedai.com/mcp

Best AI repos for Software and DevOps

About this repo

• Who it is forTeams shipping LLM apps or agents who need to see what each call did, manage prompts and measure quality over time.
• Why it is hot+509 stars this week, 37k total, talked about in 2 AI sources we track
• LanguageTypeScript
• Sourcegithub.com/langfuse/langfuse · Back to Hot repos

Requirements

• Docker and Docker Compose for local self-hosting, or a Langfuse Cloud account
• The Python or JS/TS SDK, or a supported framework integration
• For production: a VM, Kubernetes with Helm, or Terraform on AWS, Azure or GCP

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