Langfuse (GitHub) is an open-source LLM engineering platform. It includes features such as traces, evals, and prompt management to help you debug and improve your LLM app.
- Capture the complete context of execution, including API calls, context, prompts, parallelism, and more
- Monitor model usage and associated costs
- Gather user feedback effectively
- Detect and identify low-quality outputs
- Create fine-tuning and testing datasets
These guides offer detailed instructions for integrating Langfuse with Mistral AI using Python. By following these steps, you will learn how to effectively analyze and trace interactions with Mistral's language models, improving the transparency, debuggability, and performance monitoring of your AI-powered applications.
| Guides | Description |
|---|---|
| 1. Cookbook: Mistral AI SDK Integration (Python) | This cookbook provides step-by-step examples of integrating Langfuse with the Mistral AI SDK (v1) in Python. By following these examples, you'll learn how to seamlessly log and trace interactions with Mistral's language models, enhancing the transparency, debuggability, and performance monitoring of your AI-driven applications. |
| 2. Cookbook: Monitoring LlamaIndex + Mistral Applications with PostHog and Langfuse (Python) | This cookbook shows you how to build a RAG (Retrieval-Augmented Generation) application with LlamaIndex and Mistral models, observe the steps with Langfuse, and analyze the data in PostHog. |
If you have any feedback or requests, please create a GitHub Issue or share your idea with the community on Discord.