Langfuse Overview

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https://github.com/langfuse/langfuse/assets/2834609/a94062e9-c782-4ee9-af59-dee6370149a8

Develop

• LLM Observability: Instrument your app and start ingesting traces to Langfuse (Quickstart, Integrations Tracing)
• Langfuse UI: Inspect and debug complex logs (Demo, Tracing)
• Prompt Management: Manage, version and deploy prompts from within Langfuse (Prompt Management)
• Prompt Engineering: Test and iterate on your prompts with the LLM Playground

Monitor

• LLM Analytics: Track metrics (cost, latency, quality) and gain insights from dashboards & data exports (Analytics)
• LLM Evaluations: Collect and calculate scores for your LLM completions (Scores & Evaluations)
  • Run (Model-based evaluations) and LLM-as-a-Judge within Langfuse
  • Collect user feedback (User Feedback)
  • Manually score LLM outputs in Langfuse (Manual Scores)

Test

• Experiments: Track and test app behaviour before deploying a new version
  • Datasets let you test expected in and output pairs and benchmark performance before deploying (Datasets)
  • Track versions and releases in your application (Experimentation, Prompt Management)

Get started

Langfuse Cloud

Managed deployment by the Langfuse team, generous free-tier (hobby plan), no credit card required.

» Langfuse Cloud

Self-Hosting Open Source LLM Observability with Langfuse

Localhost (docker)

# Clone repository
git clone https://github.com/langfuse/langfuse.git
cd langfuse

# Run server and database
docker compose up -d

→ Learn more about deploying locally

Self-host (docker)

Langfuse is simple to self-host and keep updated. It currently requires only a single docker container and a postgres database. → Self Hosting Instructions

Templated deployments: Railway, GCP, AWS, Azure, Kubernetes and others

Get Started

API Keys

You need a Langfuse public and secret key to get started. Sign up here and find them in your project settings.

Ingesting Data · Instrumenting Your Application · LLM Observability with Langfuse

Note: We recommend using our fully async, typed SDKs that allow you to instrument any LLM application with any underlying model. They are available in Python (Decorators) & JS/TS. The SDKs will always be the most fully featured and stable way to ingest data into Langfuse.

See the → Quickstart to integrate Langfuse.

LLM Observability Integrations

Integration	Supports	Description
SDK	Python, JS/TS	Manual instrumentation using the SDKs for full flexibility.
OpenAI	Python, JS/TS	Automated instrumentation using drop-in replacement of OpenAI SDK.
Langchain	Python, JS/TS	Automated instrumentation by passing callback handler to Langchain application.
LlamaIndex	Python	Automated instrumentation via LlamaIndex callback system.
Haystack	Python	Automated instrumentation via Haystack content tracing system.
LiteLLM	Python, JS/TS (proxy only)	Use any LLM as a drop in replacement for GPT. Use Azure, OpenAI, Cohere, Anthropic, Ollama, VLLM, Sagemaker, HuggingFace, Replicate (100+ LLMs).
Vercel AI SDK	JS/TS	TypeScript toolkit designed to help developers build AI-powered applications with React, Next.js, Vue, Svelte, Node.js.
API		Directly call the public API. OpenAPI spec available.

Packages integrated with Langfuse:

Name	Description
Instructor	Library to get structured LLM outputs (JSON, Pydantic)
Dify	Open source LLM app development platform with no-code builder.
Ollama	Easily run open source LLMs on your own machine.
Mirascope	Python toolkit for building LLM applications.
Flowise	JS/TS no-code builder for customized LLM flows.
Langflow	Python-based UI for LangChain, designed with react-flow to provide an effortless way to experiment and prototype flows.

Questions and feedback

Ideas and roadmap

• Roadmap
• GitHub Discussions
• Feature Requests

Support and feedback

In order of preference the best way to communicate with us:

• GitHub Discussions (preferred): Contribute ideas, support requests and report bugs
• Discord: community support
• Privately: contact at langfuse dot com

Contributing to Langfuse

• Vote on Ideas
• Raise and comment on Issues
• Open a PR - see CONTRIBUTING.md for details on how to setup a development environment.

License

This repository is MIT licensed, except for the ee folders. See LICENSE and docs for more details.

Misc

GET API to export your data

GET routes to use data in downstream applications (e.g. embedded analytics). You can also access them conveniently via the SDKs (docs).

Security & Privacy

We take data security and privacy seriously. Please refer to our Security and Privacy page for more information.

Telemetry

By default, Langfuse automatically reports basic usage statistics of self-hosted instances to a centralized server (PostHog).

This helps us to:

1. Understand how Langfuse is used and improve the most relevant features.
2. Track overall usage for internal and external (e.g. fundraising) reporting.

None of the data is shared with third parties and does not include any sensitive information. We want to be super transparent about this and you can find the exact data we collect here.

You can opt-out by setting TELEMETRY_ENABLED=false.