Langfuse

Why It Matters

LLM observability shouldn’t lock you into a vendor or require sending production data to third parties. Langfuse offers enterprise-grade tracing and evaluation while remaining open-source and self-hostable. For teams with data residency requirements or cost constraints, this matters.

The key insight: you need to see inside your LLM applications to improve them. What prompts are being sent? How long do responses take? Which model versions perform better? Langfuse answers these questions without requiring you to trust a third party with your data.

For AI engineers, Langfuse represents the open alternative to LangSmith. Both solve the same problem (LLM observability) but Langfuse can run entirely in your infrastructure. This makes it attractive for regulated industries, privacy-sensitive applications, or teams that prefer self-hosted tools.

How It Works

Langfuse captures and organizes LLM application data:

1. Trace Collection Instrument your code to send traces to Langfuse. Each trace captures the full execution flow, including prompts, completions, tool calls, and retrieved documents.

2. Trace Analysis Explore traces in the web UI. Filter by metadata, search by content, identify patterns in successes and failures.

3. Prompt Management Version and deploy prompts through Langfuse. Track which prompt versions are in production and compare their performance.

4. Evaluation and Scoring Attach scores to traces, including model-based evaluation, heuristic checks, or human feedback. Aggregate scores to measure quality over time.

Implementation Basics

Deploying and using Langfuse:

Cloud or Self-Hosted Use Langfuse Cloud for quick start, or deploy with Docker Compose or Kubernetes for self-hosting. The self-hosted version has all features.

SDK Integration SDKs available for Python, JavaScript, and other languages. Works with any LLM framework, including LangChain, LlamaIndex, and raw API calls.

OpenAI-Compatible Proxy Route OpenAI API calls through Langfuse’s proxy for zero-code instrumentation. Existing applications get observability without code changes.

Trace Structure Organize traces hierarchically: a root trace contains generations (LLM calls), spans (operations), and events (logs). Attach metadata for filtering.

Datasets and Experiments Create evaluation datasets, run experiments comparing configurations, and track results. Export data for offline analysis.

Cost and Usage Track token usage and estimated costs. Set up alerts for budget thresholds or error rate spikes.

Langfuse integrates with Vercel AI SDK, Instructor, and many other tools. The open-source model means you can extend or customize as needed.