Langfuse

Langfuse is the observability layer most LLM apps are missing. It traces every call your app makes — prompts, completions, tool calls, latency, token cost — and gives you evals, datasets, and prompt management on top, so you can answer "why did it do that?" without re-running the whole thing by hand.

What you get

• Tracing — nested spans across chains, agents, and tool calls, with cost and latency attached to each step.
• Evals — run model-based or custom scorers over datasets so prompt changes get measured instead of vibed.
• Prompt management — version prompts outside your codebase and roll them forward without a deploy.
• Integrations — OpenTelemetry, LangChain, LlamaIndex, LiteLLM, and the OpenAI SDK, so instrumenting is usually a few lines.

Good for

Anyone running an LLM feature in production, or anyone whose API bill is growing faster than their understanding of it. Self-host the whole thing or use the managed cloud; the core is MIT-adjacent open source and the project came out of YC W23.

Repo: github.com/langfuse/langfuse.