Effortless LLM Observability: Integrating Langfuse with GenAI Starter

Introduction

Building production-grade LLM applications means more than just getting answers – it’s about understanding, debugging, and optimizing your chains, agents, and RAG workflows. That’s where Langfuse comes in: a modern observability platform for LLMs, tracing every step and making your AI systems transparent and tunable.

In this post, I’ll show you how the GenAI Starter project makes Langfuse integration a breeze – whether you’re running locally or in the cloud.

Why Observability Matters for LLMs

Debugging: See exactly how your chains, retrievers, and agents behave.
Performance: Identify bottlenecks and optimize prompts or model selection.
Transparency: Trace every step, every tool call, and every intermediate result.
Collaboration: Share traces with your team for faster iteration.

Plug-and-Play Langfuse Integration

The GenAI Starter repo uses Docker Compose profiles to make Langfuse optional and easy to enable. Here’s how it works:

1. Opt-In with a Single Command

Want observability? Just run:

docker compose --profile=langfuse up -d

docker compose --profile=langfuse up -d

This spins up all Langfuse services (web, worker, DB, Redis, Minio, Clickhouse) alongside your LLM stack.

2. Flexible Configuration

Add your Langfuse credentials to .env – local or cloud, your choice:

Local Example:

LANGFUSE_SECRET_KEY=sk-lf-...
LANGFUSE_PUBLIC_KEY=pk-lf-...
LANGFUSE_BASE_URL=http://host.docker.internal:3001

LANGFUSE_SECRET_KEY=sk-lf-...
LANGFUSE_PUBLIC_KEY=pk-lf-...
LANGFUSE_BASE_URL=http://host.docker.internal:3001

Cloud Example:

LANGFUSE_SECRET_KEY=sk-lf-...   # from your cloud dashboard
LANGFUSE_PUBLIC_KEY=pk-lf-...   # from your cloud dashboard
LANGFUSE_BASE_URL=https://cloud.langfuse.com

LANGFUSE_SECRET_KEY=sk-lf-...   # from your cloud dashboard
LANGFUSE_PUBLIC_KEY=pk-lf-...   # from your cloud dashboard
LANGFUSE_BASE_URL=https://cloud.langfuse.com

If you leave these unset, Langfuse is disabled – no errors, no fuss.

3. Zero-Friction Code Changes

Langfuse is injected as a callback handler into every example chain, agent, and retriever. Here’s the pattern:

# In src/app.py
if langfuse_enabled:
    langfuse_handler = CallbackHandler()
else:
    langfuse_handler = None

# Passed to every example:
handle_arxiv(st, model_name, langfuse_handler=langfuse_handler)

# In src/app.py
if langfuse_enabled:
    langfuse_handler = CallbackHandler()
else:
    langfuse_handler = None

# Passed to every example:
handle_arxiv(st, model_name, langfuse_handler=langfuse_handler)

And in your chains:

result = chain.invoke({"question": search_query},
    config={"callbacks": [langfuse_handler] if langfuse_handler else None})

result = chain.invoke({"question": search_query},
    config={"callbacks": [langfuse_handler] if langfuse_handler else None})

No global imports, no code duplication – just clean dependency injection.

What You Get: Real-Time Tracing

With Langfuse enabled, every LLM call, chain step, and agent action is logged and visualized:

Trace Graphs: See the full execution path, including intermediate steps.
Prompt & Response Logging: Inspect inputs, outputs, and errors.
Performance Metrics: Track latency, token usage, and more.

Open http://localhost:3001 (or your cloud dashboard) and watch your app in action.

Example: Debugging a RAG Chain

Suppose your RAG workflow isn’t returning good answers. With Langfuse, you can:

See which retriever was called and what docs were returned.
Inspect the prompt sent to the LLM.
View the final answer and all intermediate steps.

No more guessing—just actionable insights.

Conclusion

Langfuse turns your LLM app from a black box into a transparent, debuggable system. With GenAI Starter, adding observability is as simple as flipping a switch – no refactoring, no headaches.

Ready to level up your LLM workflows?
Clone GenAI Starter, enable Langfuse, and start building with confidence.

Further Reading: