name: langfuse-observability
description: Instrument LLM applications with Langfuse tracing. Use when setting up Langfuse, adding observability to LLM calls, or auditing existing instrumentation.

Langfuse Observability

Instrument LLM applications with Langfuse tracing, following best practices and tailored to your use case.

When to Use

• Setting up Langfuse in a new project
• Auditing existing Langfuse instrumentation
• Adding observability to LLM calls

Workflow

1. Assess Current State

Check the project:
- Is Langfuse SDK installed?
- What LLM frameworks are used? (OpenAI SDK, LangChain, LlamaIndex, Vercel AI SDK, etc.)
- Is there existing instrumentation?

No integration yet: Set up Langfuse using a framework integration if available. Integrations capture more context automatically and require less code than manual instrumentation.

Integration exists: Audit against baseline requirements below.

2. Verify Baseline Requirements

Every trace should have these fundamentals:

Framework integrations (OpenAI, LangChain, etc.) handle model name, tokens, and observation types automatically. Prefer integrations over manual instrumentation.

Docs: https://langfuse.com/docs/tracing

3. Explore Traces First

Once baseline instrumentation is working, encourage the user to explore their traces in the Langfuse UI before adding more context:

"Your traces are now appearing in Langfuse. Take a look at a few of them—see what data is being captured, what's useful, and what's missing. This will help us decide what additional context to add."

This helps the user:
- Understand what they're already getting
- Form opinions about what's missing
- Ask better questions about what they need

4. Discover Additional Context Needs

Determine what additional instrumentation would be valuable. Infer from code when possible, only ask when unclear.

Infer from code:

Only ask when not obvious from code:

• "How do you know when a response is good vs bad?" → Determines scoring approach
• "What would you want to filter by in a dashboard?" → Surfaces non-obvious tags
• "Are there different user segments you'd want to compare?" → Customer tiers, plans, etc.

Additions and their value:

These are NOT baseline requirements—only add what's relevant based on inference or user input.

5. Guide to UI

After adding context, point users to relevant UI features:

• Traces view: See individual requests
• Sessions view: See grouped conversations (if session_id added)
• Dashboard: Build filtered views using tags
• Scores: Filter by quality metrics

Framework Integrations

Prefer these over manual instrumentation:

Full list: https://langfuse.com/docs/integrations

Always Explain Why

When suggesting additions, explain the user benefit:

"I recommend adding session_id to your traces.

Why: This groups messages from the same conversation together.
You'll be able to see full conversation flows in the Sessions view,
making it much easier to debug multi-turn interactions.

Learn more: https://langfuse.com/docs/tracing-features/sessions"

Common Mistakes