Langfuse is an open source AI engineering platform for agent evals and observability. It is one open platform for every AI call your company makes and every agent your teams build: track usage and costs, trace and evaluate agents, and use production data to ship better AI. Use Langfuse Cloud or self-host in minutes; Langfuse is battle-tested at scale.
Watch a quick demo of the project here:
🧑💻 We're hiring
Interested in contributing to Langfuse full-time? Langfuse is growing fast (we doubled the team in the last 6 months). Since January 2026 we're part of ClickHouse. We hire remotely, with offices in Berlin, San Francisco, Amsterdam, and New York.
See open roles →
✨ Core Features
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AI Observability: Instrument your app and start ingesting traces to Langfuse, thereby tracking LLM calls and other relevant logic in your app such as retrieval, embedding, or agent actions. Inspect and debug complex logs and user sessions. Built on OpenTelemetry. Try the interactive demo to see this in action.
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Prompt Management helps you centrally manage, version control, and collaboratively iterate on your prompts. Thanks to strong caching on server and client side, you can iterate on prompts without adding latency to your application. The LLM Playground lets you test prompts and model configurations; when you see a bad result in tracing, you can jump straight to the playground to iterate on it.
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Skill Management (beta) brings versioning and labels to agent skills: a SKILL.md with task instructions plus optional supporting files. Promote or roll back versions without changing application code, and link skill versions to the traces they produced.
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Evaluations are key to the AI agent development workflow, and Langfuse adapts to your needs. It supports LLM-as-a-judge, code evaluators, user feedback collection, manual labeling, and custom evaluation pipelines via APIs/SDKs. Use Datasets and Experiments to build test sets and benchmarks for pre-deployment testing and continuous improvement.
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AI Gateway (beta) gives your organization one endpoint for calls to your model providers. Keep provider credentials in one place, hand out per-user or service account gateway keys, approve models centrally, track AI spend per user, key, and model, and record every call as a Langfuse trace.
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Metrics and Dashboards: Track latency, cost, and quality across any dimension with custom dashboards, the metrics API, and alerts.
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Comprehensive API: Langfuse is frequently used to power bespoke AI engineering workflows while using the building blocks provided by Langfuse via the API. An OpenAPI spec, typed SDKs for Python and JS/TS, the Langfuse CLI, and the Langfuse Agent Skill for coding agents are available.
📦 Deploy Langfuse
Langfuse Cloud
Managed deployment by the Langfuse team, generous free tier, no credit card required.
Sign up for Langfuse Cloud →
Self-Host Langfuse
Run Langfuse on your own infrastructure:
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Docker Compose: Run Langfuse on your own machine or a single virtual machine in 5 minutes. The guide also covers configuring Docker log rotation so container logs don't fill up the disk.
# Get a copy of the latest Langfuse repository
git clone --depth=1 https://github.com/langfuse/langfuse.git
cd langfuse
# Run the langfuse docker compose
docker compose up
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Kubernetes (Helm): Run Langfuse on a Kubernetes cluster using Helm. This is the preferred production deployment.
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Terraform Templates: AWS, Azure, GCP
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Community-maintained: Render, Railway
See self-hosting documentation to learn more about architecture and configuration options.
[!TIP]
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🔌 Integrations
Langfuse is built on OpenTelemetry, so any OpenTelemetry-instrumented application, framework, or collector can send traces to Langfuse.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
