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How to self-host LLM tracing in five minutes with Langfuse

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

Short answer: Clone the repo with git clone --depth=1 https://github.com/langfuse/langfuse.git and cd langfuse; then start it with docker compose up; Instrument your app with the Python or JS/TS SDK, or a drop-in integration such as OpenAI, Langchain or LlamaIndex. All steps.

disk. Docker's default json-file logging does not rotate, so a long-running instance can fill the disk unless you configure log rotation.

Who it is forA developer whose agent gives odd answers and who cannot see which prompt, retrieval or tool call went wrong.

The problemWithout tracing, debugging an LLM app means print statements and guesswork.

The outcomeA local Langfuse instance showing each LLM call, retrieval step and agent action in your app.

What you need first

  • 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

Steps

  1. Clone the repo with git clone --depth=1 https://github.com/langfuse/langfuse.git and cd langfuse
  2. Start it with docker compose up
  3. Instrument your app with the Python or JS/TS SDK, or a drop-in integration such as OpenAI, Langchain or LlamaIndex
  4. Open the Langfuse UI and inspect the traces for a request that went wrong
  5. Configure Docker log rotation, since the default json-file driver does not rotate logs

Prompt for your agent

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.

Caveat. The local Docker Compose setup is for trying it out; the README recommends Kubernetes with Helm for production.

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