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Overview

Integration Overview

Integrate your application with Langfuse to explore production traces and metrics.

Objective:

  1. Capture traces of your application
  2. Add scores to these traces to measure/evaluate quality of outputs

There are currently six main ways to integrate with Langfuse:

Main Integrations

IntegrationSupportsDescription
SDKPython, JS/TSManual instrumentation using the SDKs for full flexibility.
OpenAIPython, JS/TSAutomated instrumentation using drop-in replacement of OpenAI SDK.
LangchainPython, JS/TSAutomated instrumentation by passing callback handler to Langchain application.
LlamaIndexPythonAutomated instrumentation via LlamaIndex callback system.
HaystackPythonAutomated instrumentation via Haystack content tracing system.
LiteLLMPython, 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).
Vercel AI SDKJS/TSTypeScript toolkit designed to help developers build AI-powered applications with React, Next.js, Vue, Svelte, Node.js.
APIDirectly call the public API. OpenAPI spec available.

Packages integrated with Langfuse

NameDescription
InstructorLibrary to get structured LLM outputs (JSON, Pydantic)
DifyOpen source LLM app development platform with no-code builder.
OllamaEasily run open source LLMs on your own machine.
MirascopePython toolkit for building LLM applications.
FlowiseJS/TS no-code builder for customized LLM flows.
LangflowPython-based UI for LangChain, designed with react-flow to provide an effortless way to experiment and prototype flows.

Unsure which integration to choose? Ask us on Discord or in the chat.

End to end examples

If you want to see how things work together, you can look at the end-to-end examples below. They are Jupyter notebooks that you can easily run in Google Colab or locally.

Generally, we recommend reading the get started guides for each integration first.

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