Langfuse
Langfuse is an open-source LLM engineering platform that provides observability, analytics, evaluations, prompt management, and experimentation features to help teams debug, analyze and improve their LLM applications.
https://langfuse.com?ref=aipure
Product Information
Updated:Jun 16, 2025
Langfuse Monthly Traffic Trends
Langfuse saw a slight 2.5% decline in traffic to 331.8K visits. Without recent direct product updates, this decline could be attributed to normal market fluctuations or increased competition in the LLM analytics space.
What is Langfuse
Langfuse is a comprehensive platform designed specifically for Language Learning Model (LLM) engineering and development. As an open-source solution backed by Y Combinator, it offers essential tools for managing and optimizing LLM applications. The platform integrates seamlessly with popular frameworks like OpenAI SDK, LlamaIndex, Langchain, and more, while maintaining high security standards with SOC 2 Type II and ISO 27001 certifications. Users can choose between a managed cloud offering or self-host the platform, with most core features available under an MIT license.
Key Features of Langfuse
Langfuse is an open-source LLM engineering platform that provides comprehensive tools for observability, analytics, and experimentation of LLM applications. It offers features like tracing, evaluation, prompt management, and metrics collection to help developers debug and improve their LLM applications. The platform integrates with popular frameworks like OpenAI, LangChain, and LlamaIndex, while supporting multiple programming languages through its SDKs.
Comprehensive Observability: Captures full context of LLM applications including LLM inference, embedding retrieval, API usage, and system interactions to help pinpoint problems
Quality Measurement & Analytics: Enables attaching scores to production traces through model-based evaluations, user feedback, manual labeling, and custom metrics to measure quality over time
Prompt Management: Provides tools for managing and versioning prompts, allowing teams to experiment with different versions and track their performance
Multi-modal Support: Fully supports tracing of multi-modal LLM applications, including text, images, audio, and attachments with configurable storage options
Use Cases of Langfuse
RAG Pipeline Optimization: Teams can evaluate and monitor their Retrieval-Augmented Generation pipelines using Ragas integration for reference-free evaluations
Enterprise LLM Development: Large organizations like Khan Academy and Twilio use Langfuse to monitor and improve their production LLM applications
Collaborative Development: Development teams can work together using features like code sharing, real-time collaboration, and version control integration for faster issue resolution
Pros
Open-source with MIT license for core features
Extensive integration support with popular LLM frameworks
Enterprise-grade security with SOC 2 Type II and ISO 27001 certification
Active community and regular feature updates
Cons
Some peripheral features require commercial licensing
Requires setup of additional infrastructure for certain features like media storage
How to Use Langfuse
1. Create Langfuse Account: Sign up for a Langfuse account at cloud.langfuse.com or self-host using Docker
2. Get API Keys: Go to project settings and create a new set of API keys (LANGFUSE_SECRET_KEY and LANGFUSE_PUBLIC_KEY)
3. Install SDK: Install the Langfuse SDK using pip: pip install langfuse
4. Set Environment Variables: Set your Langfuse credentials as environment variables: LANGFUSE_SECRET_KEY, LANGFUSE_PUBLIC_KEY, and LANGFUSE_HOST
5. Initialize Langfuse Client: Create a Langfuse client instance in your code: from langfuse import Langfuse; langfuse = Langfuse()
6. Instrument Your Application: Add tracing to your LLM calls using either automated integrations (OpenAI, Langchain, LlamaIndex) or manual instrumentation with @observe decorator
7. Create Traces: Create traces to log LLM interactions including prompts, completions, and metadata using langfuse.trace() or automated integrations
8. Add Scoring (Optional): Implement scoring to evaluate quality of outputs using langfuse.score() or automated evaluation tools like RAGAS
9. View Analytics: Access the Langfuse dashboard to view traces, metrics, costs, latency and quality scores
10. Manage Prompts (Optional): Use the Prompt Management feature to version and update prompts via the Langfuse UI
Langfuse FAQs
Langfuse is an open-source LLM engineering platform that provides observability, analytics, and experimentation features for LLM applications. It helps teams collaboratively debug, analyze, and iterate on their LLM applications.
Official Posts
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Analytics of Langfuse Website
Langfuse Traffic & Rankings
399.8K
Monthly Visits
#76757
Global Rank
#532
Category Rank
Traffic Trends: Oct 2024-Jun 2025
Langfuse User Insights
00:05:36
Avg. Visit Duration
8.04
Pages Per Visit
34.32%
User Bounce Rate
Top Regions of Langfuse
US: 18.14%
DE: 12.24%
CN: 7.12%
IN: 5.76%
KR: 5.2%
Others: 51.54%
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