# Langfuse Langfuse is an open-source LLM observability platform. Traces, evals, prompt management, datasets, scores — the same surface area as [[LangSmith]], but you can run the whole thing on your own server. SDKs are MIT; the server is MIT for the core and commercial for some enterprise extras. This is the one I reach for when I want LLM-app observability without sending prompts and traces to a third party. ## What it gives you - **Tracing** — nested traces of LLM calls, tools, retrievals, and arbitrary spans. Framework-agnostic, with first-class integrations for LangChain, LlamaIndex, OpenAI SDK, Anthropic SDK, and OpenTelemetry. - **Prompt management** — versioned prompts with environments (production, staging) and pull-by-label from code. Built for the "edit prompt without redeploy" workflow. - **Datasets + evals** — pull traces into datasets, run experiments, compare runs side by side. LLM-as-judge, code-based scorers, or human feedback. - **Sessions + users** — group traces by session ID or user ID for conversation-level analysis. ## When I'd reach for it - You want LLM observability and you want to self-host. - You need a real prompt management layer (versions, labels, environments) and don't want to roll your own. - You're comparing LangSmith but the hosted-only default is a dealbreaker. ## Trade-offs - **Self-hosting has a cost.** Postgres, ClickHouse, Redis, queue workers — it's a real deployment, not a single binary. Their cloud removes that pain (and pays the team). - **The UI is good but not flashy.** Optimized for engineers debugging traces, not for execs reading dashboards. - **Some advanced features are cloud-only** (SSO, advanced RBAC). Read the matrix before you commit to self-host. ## Alternatives - [[LangSmith]] — closed source, polished, tied to LangChain's brand. - [[Helicone]] — open source too, but proxy-first. Easier 1-line install; less prompt-management depth. - [[MLflow]] — wider scope (classic ML + LLM); LLM features are newer and less specialized. - [[Edgee]] — observability via gateway pattern, optimized for cost/routing more than evals. ## References - Website: https://langfuse.com/ - GitHub: https://github.com/langfuse/langfuse - Docs: https://langfuse.com/docs ## Related - [[LangSmith]] - [[Helicone]] - [[LLM Monitoring]] - [[AI Observability]] - [[MLflow]] - [[Edgee]] - [[LangChain]] - [[LangGraph]]