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Langfuse

Open-source LLM observability — tracing, evaluation, and prompt management

Evals & ObservabilityFreeOpen source

Langfuse is an open-source LLM engineering platform for tracing, evaluation, and prompt management. Self-host it for complete data control or use Langfuse Cloud. Works with LangChain, LlamaIndex, OpenAI SDK, and any custom LLM stack via a simple Python/JavaScript SDK.

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37,343 GitHub stars source
as of 2026-11-12

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What we know

Open sourceYessource, checked 2026-11-12
PricingFreefrom our original listing, not yet re-checked
GitHub stars37,360source, checked 2026-11-12

Why it matters

  • The only major LLM observability platform that is both fully open source (MIT) and self-hostable — critical for teams with data residency requirements.
  • Framework-agnostic: trace any LLM app with three lines of Python, regardless of the framework.
  • Built-in prompt management with versioning means your prompts are versioned, testable, and rollback-able like code.
  • Evaluation pipeline lets you systematically measure output quality on real production data.

Key capabilities

  • Distributed tracing: Capture nested traces of every LLM call, retrieval, tool use, and agent step.
  • Prompt management: Version, compare, and deploy prompts from the Langfuse UI with rollback support.
  • Datasets and evaluation: Create evaluation datasets from production traces; run automated evals with custom scorers.
  • User feedback: Collect thumbs up/down or custom scores from end users and attach to traces.
  • Cost tracking: Per-request token counts and cost in USD across all LLM providers.
  • Latency analytics: P50/P95/P99 latency for each model and trace type.
  • Sessions: Group related traces into conversation sessions for user-level analysis.
  • SDK support: Python and TypeScript/JavaScript SDKs; OpenAI drop-in proxy; LangChain/LlamaIndex integrations.

Technical notes

  • License: MIT — fully open source at github.com/langfuse/langfuse
  • Self-hosted deployment: Docker Compose; Kubernetes Helm chart; Railway/Render one-click deploy
  • Backend: Next.js + Prisma; PostgreSQL + ClickHouse for analytics
  • SDKs: Python (pip install langfuse); JS/TS (npm install langfuse)
  • Integrations: LangChain, LlamaIndex, OpenAI SDK, Anthropic SDK, LiteLLM, Haystack, Instructor
  • Langfuse Cloud pricing: Free (50K obs/mo); Team $59/mo; Enterprise custom
  • Founded: 2023 by Max Deichmann and Marc Klingen; Berlin/San Francisco; backed by YC

Ideal for

  • Teams with strict data privacy requirements who need self-hosted LLM observability with no data leaving their infrastructure.
  • Engineering teams who want systematic prompt versioning and production quality monitoring for LLM apps.
  • Companies building multi-step LLM pipelines who need to debug failures by replaying exact production traces.

Not ideal for

  • Teams exclusively using LangChain who want zero-config setup — LangSmith requires one env variable.
  • Small hobby projects where the operational overhead of self-hosting isn't worth it.
  • Teams primarily needing model training experiment tracking — Weights & Biases is better for that.

See also

  • LangSmith — LangChain's native observability platform, cloud-only but tighter LangChain integration.
  • Helicone — Lightweight LLM proxy with observability, simpler setup.
  • Weights & Biases — ML experiment tracking with W&B Weave for LLM tracing.

FAQ

What is Langfuse?

Langfuse is an open-source LLM observability platform. It traces every LLM call, chain step, and agent action in your app, records them with metadata (tokens, latency, cost), and provides a dashboard for analysis. It also handles prompt version management, evaluation datasets, and user feedback collection.

Is Langfuse free?

Langfuse is fully open source under MIT license — completely free to self-host with all features and unlimited usage. Langfuse Cloud has a free tier (50,000 observations/month), Team plan ($59/mo), and Enterprise (custom). Self-hosting on a $20/month server is a common cost-effective setup.

How does Langfuse compare to LangSmith?

Both are LLM tracing and evaluation platforms with similar features. Langfuse is open source and self-hostable — better for data privacy requirements and teams who want to avoid vendor lock-in. LangSmith has tighter LangChain ecosystem integration and is cloud-only. Langfuse has a more generous free tier; LangSmith is more turnkey for LangChain users.

Does Langfuse work without LangChain?

Yes. Langfuse works with any Python or JavaScript app. You can use the `@observe` decorator or manual span creation to trace any LLM call — OpenAI SDK, Anthropic SDK, custom HTTP calls, or any framework. LangChain and LlamaIndex have first-class integration but are not required.

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Listed 2026-02-22 · updated 2026-09-11