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Langfuse in 2025: The Best Way to Monitor and Improve Your LLM Applications

  • Philip Moses
  • Aug 1
  • 4 min read
In today’s fast-moving world of AI and large language models (LLMs), just building smart applications isn’t enough—you also need to monitor, evaluate, and improve them regularly. That’s where Langfuse comes in.

Langfuse is a modern platform that helps teams get full visibility into their LLM-based applications. It gives you tools to track what’s happening, evaluate results with human feedback, and integrate everything easily using their Public API.

In this blog, you’ll learn:

  • How Langfuse’s Public API helps you build custom workflows and connect it to your tools

  • Why Human Annotation is a game-changer for evaluating LLM outputs

  • How teams can combine automation and human feedback to create better AI experiences

The Langfuse Public API: Connect and Automate with Full Control

Langfuse is designed to work well with your existing setup. Its Public API gives you full access to all your Langfuse data and features—meaning you’re not just limited to the web dashboard. You can automate processes, create custom dashboards, or plug Langfuse into your app pipeline easily.


Where to Access the API

Langfuse offers different endpoints based on region and compliance needs:

This ensures data stays where it’s supposed to—and complies with data protection rules.


How to Authenticate

Authentication is simple and secure. You just use your:

  • Public Key as the username

  • Secret Key as the password

These are available in your Langfuse project settings.

Example using curl:

bashCopyEditcurl -u public-key:secret-key https://cloud.langfuse.com/api/public/projects

Developer Tools That Help

Langfuse provides lots of useful resources for developers:

SDKs: Simplify Development in Your Favorite Language

Instead of writing raw API calls, you can use Langfuse SDKs available in:

Python:

pythonCopyEditfrom langfuse import get_clientlangfuse = get_client()langfuse.api.trace.get(trace_id)# Async version await langfuse.async_api.trace(trace_id)

JavaScript / TypeScript:

typescriptCopyEditimport { Langfuse } from "langfuse"const langfuse = new Langfuse()await langfuse.api.traceGet(traceId)

Java:

javaCopyEditLangfuseClient client = LangfuseClient.builder()    .url("https://cloud.langfuse.com")    .credentials("pk-lf-...", "sk-lf-...")    .build();PromptMetaListResponse prompts = client.prompts().list();

These SDKs make your life easier by handling caching, retries, and simplifying common tasks.

Ingesting Data and Exporting It Later

Although the API lets you send trace data, Langfuse recommends using OpenTelemetry to send traces—this aligns with industry observability standards.

For exporting data, you can:

  • Manually export from the UI

  • Schedule automatic exports to cloud storage

This flexibility helps you manage data your way.

Human Annotation: Because AI Still Needs Human Judgment

Automation is great, but some things need human insight—especially when judging the quality of AI-generated content. That’s why Langfuse includes a powerful Human Annotation feature.

With it, your team can manually score LLM outputs, helping you evaluate quality, catch edge cases, and build trust in your system.


Why Human Annotation Matters

Here’s what it brings to the table:

  • Collaboration: Multiple team members can review and score traces

  • Consistency: Use ScoreConfigs to ensure everyone follows the same evaluation standards

  • Better Evaluation: Especially useful when you’re testing new features or use cases

  • Benchmarking: Use human scores as a baseline to compare automated evaluations

How Human Annotation Works

Step 1: Set Up ScoreConfigs

Before anything else, you need to define how you want to score—categorical (e.g., good/average/bad), numerical, or binary (yes/no).


Step 2: Annotate a Single Trace

You can do this inside the Langfuse UI:

  1. Go to a trace or observation

  2. Click Annotate

  3. Choose your scoring criteria

  4. Add scores

  5. View the results in the Scores tab


Step 3: Use Annotation Queues for Larger Projects

Need to scale manual reviews? Use Annotation Queues to organize and manage evaluation tasks.

Create a Queue:

  1. Go to Human Annotation > New queue

  2. Add a name and description

  3. Choose ScoreConfigs

  4. Click Create queue


Add Traces to a Queue:

  • Bulk Add: Filter and select multiple traces → Actions → Add to queue

  • Single Add: From the trace page, click Annotate → Add to queue

Process the Queue:

  • Open a queue

  • Score each trace using the Annotate Card

  • Click Complete + Next to move forward

Automate Annotation with the Public API

Langfuse also lets you manage queues through the API—so you can automate the entire evaluation flow. While not every endpoint is detailed publicly, this shows Langfuse is serious about offering full control to developers.


Final Thoughts: Langfuse = Full Visibility + Human Insight

Langfuse combines two powerful ideas:

  1. Automation through APIs and SDKs

  2. Human evaluation for quality and nuance

With Langfuse, you can build smarter LLM apps, track performance, and continuously improve—all in one place.

Whether you’re a developer looking for seamless integration or a team lead focused on quality, Langfuse gives you the tools to succeed in 2025 and beyond.

🛠️ Want to Deploy Langfuse Without the Hassle?

That’s where House of FOSS steps in.

At House of FOSS, we make open-source tools like Langfuse plug-and-play for businesses of all sizes. Whether you're building an AI product, monitoring prompts, or evaluating LLM outputs — we help you deploy, scale, and manage Langfuse with zero friction.

✅ Why Choose House of FOSS?


🧩 Custom Setup – We tailor Langfuse to your exact observability and evaluation needs.

🕒 24/7 Support – We're here when you need us.

💰 Save up to 60% – Cut SaaS costs, not performance.

🛠️ Fully Managed – We handle security, scaling, and updates.


⚡ Bonus: With House of FOSS, deploying Langfuse is as easy as installing an app on your phone. No configs. No setup stress. Just click, install, and start monitoring.


 
 
 

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