Top 4 Open Source Alternatives to Langfuse

Langfuse is a popular open-source platform for LLM observability that provides real-time monitoring, logs, and performance analytics for large language models.

While powerful, it has limitations. The interface can be complex for non-technical users, and enterprise features like data lake integrations are currently limited or non-existent.

Let's explore some open-source alternatives that may better match your requirements.

1. Lunary

Lunary is a complete platform for LLM developers that provides a feature set similar to Langfuse, including powerful tools for observability, prompt management, and assessment.

In addition to the features provided by Langfuse, Lunary lets you:

• Replay user chats and track their feedback
• Understand where your LLMs are hallucinating and RAG pipelines failing
• Categorize chats into topics
• An integration that takes 2min to setup
• Manage promots with non-technical teammates
• Create custom dashboards for advanced reporting
• Identify inflammatory language, negative emotion, PII leaking

Get started with Lunary for free and enjoy using it until your daily log count reaches 1000, making it ideal for experimentation and prototyping.

2. Helicone

Helicone is another open-source tool with robust and scalable solution for LLM observability compared to Langfuse, particularly for organizations dealing with high-volume applications or complex workflows.

Its feature set, while more limited, includes:

• Advanced caching of LLM responses.
• Custom properties for detailed analysis, and robust security measures.
• A simple proxy to URL for monitoring OpenAI calls
• Better cost analytics than Langfuse segmented by users and agents.

Helicone offers an easy start with its one-line integration and comprehensive feature set.

Both Helicone and Langfuse offer free tiers, making them accessible for small projects or initial testing.

3. LangWatch

LangWatch allows you to track, monitor, guardrail and evaluate your LLMs apps for measuring quality and alert on issues.

Differentiator features of Langwatch are:

• Easily shift through conversations, see topics being discussed and annotate and score messages for improvement.
• Debug, Build datasets and prompt engineer on the playground and run batch evaluations
• Track conversation metrics and give full user and quality analytics, cost tracking, build custom dashboards.
• Integrate it back on your own platform for reporting to your customers.

If your preferred programming language or platform is not directly supported by the existing LangWatch libraries, you can use the REST API with curl to send trace data. So you don’t have to rely on SDKs

4. Phoenix By Arize

Phoenix is a great tool for teams that need to keep an eye on how their LLM models are working. It does many of the same things as Langfuse, but it has extra features that make it better for more advanced uses.

Here’s what Phoenix can do:

• Understand model predictions with detailed explanations, showing how and why decisions are made.
• Monitor model performance changes to catch problems early, before they escalate.
• Test model changes on specific datasets to predict performance before going live.
• Track predictions from start to finish to find performance issues and root causes.

Hosted Phoenix is free for all developers. They will add a paid tier in the future which increases data retention and also gives developers access to more storage.

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Each tool offers distinct features to debug LLM-based applications, allowing teams to choose the solution that best fits their operational and analytical needs.

For developers and companies building GenAI chatbots needing to understand user behavior, Lunary might be the top choice, ideal for teams prioritizing ease of use and in-depth analytics.

Meanwhile, Helicone suits high-volume applications, LangWatch excels in conversation evaluation, and Phoenix offers advanced model explainability and performance tracking.