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Overview
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https://github.com/langfuse/langfuse/assets/2834609/a94062e9-c782-4ee9-af59-dee6370149a8
Develop
Monitor
- Analytics: Track metrics (cost, latency, quality) and gain insights from dashboards & data exports (Analytics)
- Evals: Collect and calculate scores for your LLM completions (Scores & Evaluations)
Test
- Experiments: Track and test app behaviour before deploying a new version
Get started
Langfuse Cloud
Managed deployment by the Langfuse team, generous free-tier (hobby plan), no credit card required.
» Langfuse Cloud
Localhost (docker)
# Clone repository
git clone https://github.com/langfuse/langfuse.git
cd langfuse
# Run server and database
docker compose up -d
→ Learn more about deploying locally
Self-host (docker)
Langfuse is simple to self-host and keep updated. It currently requires only a single docker container.
→ Self Hosting Instructions
Templated deployments: Railway, GCP Cloud Run, AWS Fargate, Kubernetes and others
Get Started
API Keys
You need a Langfuse public and secret key to get started. Sign up here and find them in your project settings.
Ingesting Data · Instrumenting Your Application
Note: We recommend using our fully async, typed SDKs that allow you to instrument any LLM application with any underlying model. They are available in Python (Decorators) & JS/TS. The SDKs will always be the most fully featured and stable way to ingest data into Langfuse.
You may want to use another integration to get started quickly or implement a use case that we do not yet support. However, we recommend to migrate to the Langfuse SDKs over time to ensure performance and stability.
See the → Quickstart to integrate Langfuse.
Integrations
| Integration | Supports | Description |
|---|
| SDK | Python, JS/TS | Manual instrumentation using the SDKs for full flexibility. |
| OpenAI | Python, JS/TS | Automated instrumentation using drop-in replacement of OpenAI SDK. |
| Langchain | Python, JS/TS | Automated instrumentation by passing callback handler to Langchain application. |
| LlamaIndex | Python | Automated instrumentation via LlamaIndex callback system. |
| Haystack | Python | Automated instrumentation via Haystack content tracing system. |
| LiteLLM | Python, JS/TS (proxy only) | Use any LLM as a drop in replacement for GPT. Use Azure, OpenAI, Cohere, Anthropic, Ollama, VLLM, Sagemaker, HuggingFace, Replicate (100+ LLMs). |
| API | | Directly call the public API. OpenAPI spec available. |
Packages that integrate with Langfuse:
| Name | Description |
|---|
| Instructor | Library to get structured LLM outputs (JSON, Pydantic) |
| Mirascope | Python toolkit for building LLM applications. |
| AI SDK by Vercel | Typescript SDK that makes streaming LLM outputs super easy. |
| Flowise | JS/TS no-code builder for customized LLM flows. |
| Langflow | Python-based UI for LangChain, designed with react-flow to provide an effortless way to experiment and prototype flows. |
Questions and feedback
Ideas and roadmap
Support and feedback
In order of preference the best way to communicate with us:
Contributing to Langfuse
License
This repository is MIT licensed, except for the ee folders. See LICENSE and docs for more details.
Misc
GET API to export your data
GET routes to use data in downstream applications (e.g. embedded analytics). You can also access them conveniently via the SDKs (docs).
Security & Privacy
We take data security and privacy seriously. Please refer to our Security and Privacy page for more information.
Telemetry
By default, Langfuse automatically reports basic usage statistics of self-hosted instances to a centralized server (PostHog).
This helps us to:
- Understand how Langfuse is used and improve the most relevant features.
- Track overall usage for internal and external (e.g. fundraising) reporting.
None of the data is shared with third parties and does not include any sensitive information. We want to be super transparent about this and you can find the exact data we collect here.
You can opt-out by setting TELEMETRY_ENABLED=false.
