Build LLM Observability Dashboards with Langfuse & Helicone

What You'll Learn

In this tutorial, you will learn how to implement comprehensive LLM observability for your generative AI applications. You will integrate two powerful tools: Langfuse and Helicone. These platforms provide real-time monitoring, logging, and evaluation capabilities. By the end of this guide, you will have a working dashboard that tracks token usage, latency, and custom evaluation metrics.

Observability is critical for production-grade LLM apps. It helps you debug issues, optimize costs, and ensure quality. Without it, you are flying blind when users report hallucinations or slow responses. This project bridges the gap between development and production reliability.

Prerequisites

Before starting, ensure you have the following setup:

• A Python environment (version 3.9 or higher) installed.
• An OpenAI API key for generating LLM responses.
• Accounts on both Langfuse and Helicone.
• Basic familiarity with Python and asynchronous programming concepts.

You do not need prior experience with these specific tools. We will cover all necessary configuration steps in detail. The focus is on practical implementation rather than theoretical deep dives.

Setting Up Your Development Environment

Start by creating a new directory for your project. This keeps your files organized and separate from other work. Use the terminal to create the folder and navigate into it. Consistency in file structure helps prevent import errors later.

Next, initialize a virtual environment. This isolates your project dependencies from your global Python installation. It prevents version conflicts with other projects on your machine. Run the standard commands to create and activate the environment.

Install the required libraries using pip. You need langfuse, helicone, and openai. These packages handle the integration logic and API communication. Installing them together ensures compatibility across the stack.

mkdir llm-observability-project
cd llm-observability-project
python -m venv venv
source venv/bin/activate # On Windows use venv\Scripts\activate
pip install langfuse helicone openai python-dotenv

Create a .env file in your root directory. Store your API keys here securely. Never hardcode secrets directly in your source code. This practice protects your credentials if you share your code publicly.

Add your OpenAI, Langfuse, and Helicone keys to the file. Use the format KEY=VALUE. Load these variables in your script using the dotenv library. This step ensures your application can authenticate with all services automatically.

Integrating Helicone for Request Logging

Helicone acts as a proxy layer for your LLM requests. It captures detailed logs of every interaction with the model. This includes input prompts, output completions, and metadata like timestamps.

To integrate Helicone,

---

📖 Read the full tutorial on AI Tutorials →

🌐 GogoAI Network — Your AI Learning Hub:

• 📰 AI News — Latest AI industry news & analysis
• 📚 AI Tutorials — 2200+ free step-by-step guides
• 🛠️ AI Tool Navigator — Discover 250+ AI tools
• 💡 AI Prompts — Free prompt library for ChatGPT & Claude

Comments

[Void Stitch]

Useful walkthrough. One gap we hit in production is tenant chargeback once traces are in Langfuse: metadata keys (for example organizationId) are easy to attach, but dashboard breakdowns still cannot slice by metadata.*

Langfuse issue #12614 (opened 2026-03-16, updated 2026-05-14) tracks this exact blocker. In your setup, how are you producing per-tenant cost/latency views today: pre-aggregating outside Langfuse, or using a workaround inside dashboard widgets?

*If this changed recently, would love the exact field path/config that works.