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Langfuse

Open-source LLM engineering platform for observability

open sourceupdated Aug 16, 2026
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Langfuse is an open-source LLM engineering platform with 29K+ GitHub stars for tracing, evaluating, and monitoring AI applications. Acquired by ClickHouse, it provides detailed traces of LLM calls, prompt management with versioning, dataset-based evaluation, user feedback collection, and cost tracking. Framework-agnostic with native integrations for LangChain, LlamaIndex, OpenAI SDK, and Vercel AI SDK. Offers both self-hosted deployment and a managed cloud service.

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Langfuse is the most popular open-source LLM observability platform, providing tracing, evaluation, and monitoring for AI applications. With over 29,000+ GitHub stars and acquired by ClickHouse for its analytical capabilities, it has become a critical part of the LLM engineering stack.

The tracing system captures detailed information about every LLM call including inputs, outputs, latency, token usage, and costs. Complex agent workflows with multiple LLM calls are visualized as nested traces, making it easy to debug and optimize multi-step applications.

Prompt management with versioning allows teams to iterate on prompts, track changes, and deploy specific versions to production. Dataset-based evaluation enables systematic testing with custom metrics and LLM-as-judge evaluators. User feedback collection creates ground truth for continuous improvement.

Langfuse is framework-agnostic with native integrations for LangChain, LlamaIndex, OpenAI SDK, Vercel AI SDK, and more. Cost tracking aggregates LLM spending across models and provides breakdowns by user, feature, or time period.

Both self-hosted and managed cloud deployment are available. The open-source version can be deployed via Docker with full feature parity. The managed cloud offers additional convenience with automatic updates and scaling.

Pricing

Hobby free / Core from $29/mo / Pro from $199/mo

Platforms

Web, Self-hosted, Docker, Python, JS/TS SDK

Categories

AI Monitoring & ObservabilityAgent Skills & PromptsTesting & QA

Tags

Open SourceObservabilityPythonTypeScript

Use Cases

Testing & QA AutomationAI-Powered Debugging

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Used in Stacks

Continuous LLM Eval CI Stack: Regression Gates for Every Release

A continuous evaluation workflow that combines Promptfoo matrices, DeepEval assertions, RAGAS retrieval metrics, Langfuse production traces, and garak security probes across pull requests and releases.

varies

AI Agent Testing and Reliability Stack: Trace, Replay, and Guard

A reliability workflow for tool-using agents that combines AgentOps sessions, DeepEval behavioral checks, Langfuse trace-linked scores, Inspect AI benchmarks, and Sentrial runtime detection.

varies

LLM-as-a-Judge Evaluation Stack: Rubrics, Calibration, and Ops

A vendor-neutral judge pipeline using DeepEval for rubrics, Opik for datasets and experiments, Langfuse for production scoring, Arize Phoenix for analysis, and LangSmith as an optional managed operations layer.

varies

RAG Evaluation Pipeline Stack: Metrics, Tracing, and CI

A production-minded RAG evaluation workflow that combines RAGAS and DeepEval metrics with Langfuse traces, Arize Phoenix retrieval analysis, and GitHub Actions release gates.

varies

Production LLM Evaluation Stack

A production LLM evaluation stack should catch regressions before release, probe security failures, and close the loop with real traces and user feedback. This stack combines Promptfoo for CI gates, DeepEval/OpenAI Evals for metric-heavy test suites, and Langfuse or Helicone for observability and production datasets.

varies

LLM Observability Stack

Monitor, trace, and optimize your LLM applications: Langfuse for deep tracing and evaluation, Helicone for request logging and analytics, Portkey for AI gateway routing, and Sentry for error tracking across your full stack.

varies

Comparisons

Opik vs Langfuse: AI Optimization Suite or Open LLM Platform?

Opik and Langfuse are two credible open-source choices for tracing, evaluating, and improving LLM applications and agents. Opik, from Comet, combines observability, test suites, assertions, prompt experiments, production monitoring, and automatic prompt optimization. Langfuse combines agent and application tracing, prompt management, datasets, online and offline evaluation, feedback, and mature self-hosting. **Langfuse is the better default** because it has the broader adoption base, a particularly complete prompt-and-observability workflow, and flexible free or managed deployment. Opik is the better specialist when built-in optimization algorithms and the Comet ecosystem are decisive.

OpikLangfuse

AgentOps vs Langfuse: Agent Sessions or Full LLM Engineering?

AgentOps and Langfuse both help teams understand production AI agents, but AgentOps is centered on agent sessions and events while Langfuse spans agents, general LLM applications, prompt management, datasets, evaluation, and feedback. AgentOps offers a focused path to session replay, timelines, cost and error analysis across popular agent frameworks. **Langfuse is the better overall choice** because it provides comparable tracing plus a broader quality and prompt lifecycle, an MIT-licensed self-hosted edition, and a transparent managed-cloud ladder. AgentOps is still a strong specialist for teams that want agent-specific monitoring with minimal platform breadth.

AgentOpsLangfuse

MLflow vs Langfuse: Full ML Lifecycle or LLM-Native Engineering?

MLflow and Langfuse are both open-source platforms that can trace and evaluate generative AI systems, but they come from different operating centers. MLflow manages the full machine-learning lifecycle, including experiments, models, registry, deployment, and increasingly capable GenAI tracing and evaluation. Langfuse is built specifically for LLM applications and agents, joining traces, prompts, datasets, feedback, and online or offline evaluation. **Langfuse is the better default for an LLM-first team** because its workflows and pricing units match production AI applications directly. MLflow is stronger when a company already runs MLflow or needs one governance layer across classical ML and GenAI.

MLflowLangfuse

Braintrust vs Langfuse: Managed Eval Workflow or Open LLM Platform?

Braintrust and Langfuse both connect tracing, datasets, experiments, scoring, and production feedback, but they make different platform tradeoffs. Braintrust emphasizes a polished managed workflow for evaluation-heavy AI teams, while Langfuse combines observability, prompt management, online and offline evaluation, and a free MIT-licensed self-hosted deployment. **Langfuse is the better overall choice** for most teams because it offers a credible managed cloud path without surrendering deployment control or core features. Braintrust remains attractive when a team prioritizes its integrated experiment and annotation workflow and is comfortable standardizing on the managed product.

BraintrustLangfuse

Phoenix vs Langfuse — Arize AI Observability Platform vs Open-Source LLM Analytics

Phoenix and Langfuse both provide observability for LLM applications but approach the problem from different perspectives. Phoenix by Arize focuses on OpenTelemetry-native tracing with built-in evaluation frameworks and experiment tracking for systematically improving AI quality. Langfuse provides lightweight prompt management, session tracking, and cost analytics through a developer-friendly dashboard with broader framework integrations.

Arize PhoenixLangfuse

OpenLIT vs Langfuse — OpenTelemetry-Native vs Purpose-Built LLM Observability

OpenLIT and Langfuse both provide tracing and evaluation for LLM applications but take architecturally different approaches. Langfuse offers a dedicated observability platform with its own purpose-built dashboard for AI-specific workflows. OpenLIT instruments LLM calls as standard OpenTelemetry spans, routing traces into whatever observability backend teams already operate — Grafana, Datadog, Jaeger, or any OTel-compatible system.

OpenLITLangfuse

Traceloop vs Langfuse — OpenTelemetry-Native LLM Observability vs Dedicated Tracing Platform

Traceloop (OpenLLMetry) and Langfuse both provide LLM application observability, but through different architectural approaches. Traceloop extends the OpenTelemetry standard with LLM-specific instrumentation, sending data to any OTEL backend. Langfuse offers a dedicated tracing platform with prompt management and evaluation built in. This comparison helps teams choose between infrastructure integration and purpose-built LLM analytics.

TraceloopLangfuse

Langfuse vs Helicone — Open-Source LLM Tracing vs Lightweight Observability Proxy

Langfuse and Helicone are the two leading open-source LLM observability platforms, but they differ in architecture and depth. Langfuse provides comprehensive tracing with prompt management, evaluation, and dataset curation. Helicone operates as a lightweight proxy that requires zero code changes — just swap your API base URL. This comparison helps teams choose between deep observability and frictionless integration for their LLM applications.

LangfuseHelicone

Langfuse vs LangSmith — Open-Source vs Commercial LLM Observability Platforms Compared

Langfuse and LangSmith are the leading LLM observability platforms for monitoring, tracing, and evaluating AI applications in production. Langfuse is open-source and self-hostable with a generous free tier, supporting integrations across LangChain, LlamaIndex, OpenAI, and dozens of frameworks. LangSmith is LangChain's commercial platform with zero-config integration for the LangChain ecosystem. Both help developers understand what their LLM applications are doing — the choice depends on your stack and deployment requirements.

LangfuseLangSmith

FAQ

What is Langfuse?

Langfuse is an open-source LLM engineering platform with 29K+ GitHub stars for tracing, evaluating, and monitoring AI applications. Acquired by ClickHouse, it provides detailed traces of LLM calls, prompt management with versioning, dataset-based evaluation, user feedback collection, and cost tracking. Framework-agnostic with native integrations for LangChain, LlamaIndex, OpenAI SDK, and Vercel AI SDK. Offers both self-hosted deployment and a managed cloud service.

Is Langfuse free?

Yes — Langfuse is open source and free to use. Hobby free / Core from $29/mo / Pro from $199/mo

Is Langfuse open source?

Yes — Langfuse is open source.

What are the best Langfuse alternatives?

The top editor-verified Langfuse alternatives are Laminar, Weights & Biases, Braintrust, and more.

How does Langfuse score in our review?

Our hands-on review scores Langfuse 87/100 overall, based on speed, privacy, and developer-experience testing.