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analytics

langfuse: AI engineering insights

Get AI metrics and observability with langfuse, an open source platform for startup founders, with 30k+ GitHub stars
intermediate⏱ 30 minutes💵 Free (self-hosted)
31,991 stars3,419 forksTypeScriptQuality 9/10Updated 7/28/2026100% free · open source
What it is

Develops and operates large-language-models with tools for evaluation, observability, and metrics.

What you can make with it

Automations like: when a new customer signs up, add them to a database and send a welcome message.

How it helps

langfuse provides a platform for efficient LLM operations, streamlining tasks and saving time.

Real use case example

"A founder uses langfuse to monitor LLM performance and optimize it for better results. First, they install langfuse and set up OpenTelemetry for metrics and observability. Next, they create a prompt management system and configure it to integrate with the LLM. Finally, they run a series of tests to fine-tune the model's performance. With langfuse, they're able to scale their LLM operations efficiently and make data-driven decisions."

If you're new

If you're new to AI engineering, start with langfuse when you need to set up and manage a simple LLM system.

If you're senior

Senior engineers use langfuse for custom LLM deployments and complex operations, leveraging its integration capabilities.

Common confusion cleared up

langfuse is not just a development tool, but also a platform for operation and optimization.

Pairs with
stripe webhooknotion databaseOpenTelemetry
Why we list it on WorkflowStacks: It's a free and open-source solution, making it a cost-effective option for users.
What it does

Langfuse is an open-source platform for large language model (LLM) engineering, providing observability, metrics, and management tools for LLM development and deployment.

Install / run
git clone https://github.com/langfuse/langfuse.git && cd langfuse && npm install
When to use it
  • •When you need to monitor and optimize the performance of your LLMs
  • •When you want to manage and version your prompts and datasets for LLMs
  • •When you need to integrate your LLMs with other tools and platforms like OpenTelemetry, Langchain, or OpenAI SDK
Quick start
  1. 1Run 'npm run dev' to start the Langfuse development server
  2. 2Configure your LLM and dataset settings in the 'config.ts' file
  3. 3Use the 'langfuse playground' to test and refine your prompts and LLM configurations
  4. 4Explore the 'langfuse metrics' dashboard to monitor your LLM's performance and optimize its settings
  5. 5Integrate Langfuse with your preferred LLM library, such as Langchain or OpenAI SDK, by following the documentation in the 'integrations' folder
Ready-to-paste prompt
Run 'npm run eval -- --model <your-llm-model> --prompt <your-prompt-text>' to evaluate your LLM's performance on a specific prompt
Heads up: Make sure you have a compatible version of Node.js (14 or higher) and TypeScript installed, as Langfuse is built with TypeScript and relies on Node.js for its development server and build process
Saves to your device

Topics

analytics
autogen
evaluation
langchain
large-language-models
llama-index
llm
llm-evaluation
llm-observability
llmops
monitoring
observability
open-source
openai
playground
prompt-engineering
prompt-management
self-hosted
ycombinator
What's inside — free to inspect
No purchase needed

Read the entire source before you build — unlike paid marketplaces that hide it behind a buy button.

33
top-level files
13
folders
105.6M
repo size
Other
license
Key files
AGENTS.md
package.json
prettier.config.cjs
README.cn.md
README.ja.md
README.kr.md
File tree
.agents/
.devcontainer/
.github/
.husky/
.vscode/
ee/
fern/
packages/
patches/
scripts/
specs/
web/
worker/
.codespellrc
.dockerignore
.env.dev-azure.example
.env.dev-oci.example
.env.dev-redis-cluster.example
.env.dev.example
.env.prod.example
.env.test.example
.gitattributes
.gitignore
.nvmrc
Quick Actions
Details
Creator
langfuse
Language
TypeScript
Category
analytics
Published
5/18/2023

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