Get Started with Prompt Management

pip install langfuse

LANGFUSE_SECRET_KEY = "sk-lf-..." LANGFUSE_PUBLIC_KEY = "pk-lf-..." LANGFUSE_BASE_URL = "https://cloud.langfuse.com" # 🇪🇺 EU region # LANGFUSE_BASE_URL = "https://us.cloud.langfuse.com" # 🇺🇸 US region

# Create a text prompt langfuse.create_prompt(  name="movie-critic",  type="text",  prompt="As a {{criticlevel}} movie critic, do you like {{movie}}?",  labels=["production"] # optionally, directly promote to production )   # Create a chat prompt langfuse.create_prompt(  name="movie-critic-chat",  type="chat",  prompt=[  { "role": "system", "content": "You are an {{criticlevel}} movie critic" },  { "role": "user", "content": "Do you like {{movie}}?" },  ],  labels=["production"] # optionally, directly promote to production )

npm i @langfuse/client

LANGFUSE_SECRET_KEY = "sk-lf-..." LANGFUSE_PUBLIC_KEY = "pk-lf-..." LANGFUSE_BASE_URL = "https://cloud.langfuse.com" # 🇪🇺 EU region # LANGFUSE_BASE_URL = "https://us.cloud.langfuse.com" # 🇺🇸 US region

import { LangfuseClient } from "@langfuse/client";   const langfuse = new LangfuseClient();

// Create a text prompt await langfuse.prompt.create({  name: "movie-critic",  type: "text",  prompt: "As a {{criticlevel}} critic, do you like {{movie}}?",  labels: ["production"] // optionally, directly promote to production });   // Create a chat prompt await langfuse.prompt.create({  name: "movie-critic-chat",  type: "chat",  prompt: [  { role: "system", content: "You are an {{criticlevel}} movie critic" },  { role: "user", content: "Do you like {{movie}}?" },  ],  labels: ["production"] // optionally, directly promote to production });

curl -X POST "https://cloud.langfuse.com/api/public/v2/prompts" \  -u "your-public-key:your-secret-key" \  -H "Content-Type: application/json" \  -d '{  "type": "chat",  "name": "movie-critic",  "prompt": [  { "role": "system", "content": "You are an {{criticlevel}} movie critic" },  { "role": "user", "content": "Do you like {{movie}}?" }  ]  }'  

from langfuse import get_client   # Initialize Langfuse client langfuse = get_client()

# By default, the production version is fetched. prompt = langfuse.get_prompt("movie-critic")   # Insert variables into prompt template compiled_prompt = prompt.compile(criticlevel="expert", movie="Dune 2") # -> "As an expert movie critic, do you like Dune 2?"

# By default, the production version of a chat prompt is fetched. chat_prompt = langfuse.get_prompt("movie-critic-chat", type="chat") # type arg infers the prompt type (default is 'text')   # Insert variables into chat prompt template compiled_chat_prompt = chat_prompt.compile(criticlevel="expert", movie="Dune 2") # -> [{"role": "system", "content": "You are an expert movie critic"}, {"role": "user", "content": "Do you like Dune 2?"}]

import { LangfuseClient } from "@langfuse/client";   // Initialize the Langfuse client const langfuse = new LangfuseClient();

// By default, the production version of a text prompt is fetched. const prompt = await langfuse.prompt.get("movie-critic");   // Insert variables into prompt template const compiledPrompt = prompt.compile({  criticlevel: "expert",  movie: "Dune 2", }); // -> "As an expert movie critic, do you like Dune 2?"

// By default, the production version of a chat prompt is fetched. const chatPrompt = await langfuse.prompt.get("movie-critic-chat", {  type: "chat", }); // type option infers the prompt type (default is 'text')   // Insert variables into chat prompt template const compiledChatPrompt = chatPrompt.compile({  criticlevel: "expert",  movie: "Dune 2", }); // -> [{"role": "system", "content": "You are an expert movie critic"}, {"role": "user", "content": "Do you like Dune 2?"}]

pip install langfuse openai

import openai from langfuse import get_client   # Initialize Langfuse client langfuse = get_client()

# By default, the production version of a text prompt is fetched. prompt = langfuse.get_prompt("movie-critic")   # Compile the prompt with variables compiled_prompt = prompt.compile(criticlevel="expert", movie="Dune 2")   # Use with OpenAI - prompt is a string completion = openai.chat.completions.create(  model="gpt-4o",  messages=[{"role": "user", "content": compiled_prompt}] )

# By default, the production version of a chat prompt is fetched. chat_prompt = langfuse.get_prompt("movie-critic-chat", type="chat")   # Compile the prompt with variables - returns a list of message dicts compiled_chat_prompt = chat_prompt.compile(criticlevel="expert", movie="Dune 2")   # Use with OpenAI - prompt is a list of messages completion = openai.chat.completions.create(  model="gpt-4o",  messages=compiled_chat_prompt )

npm install @langfuse/openai openai

import { observeOpenAI } from "@langfuse/openai"; import { LangfuseClient } from "@langfuse/client"; import OpenAI from "openai";   // Initialize Langfuse client const langfuse = new LangfuseClient();   // Wrap OpenAI client const openai = observeOpenAI(new OpenAI());

// By default, the production version of a text prompt is fetched. const prompt = await langfuse.prompt.get("movie-critic", {  type: "text", });   // Compile the prompt with variables const compiledPrompt = prompt.compile({  criticlevel: "expert",  movie: "Dune 2", });   // Use with OpenAI - prompt is a string const completion = await openai.chat.completions.create({  model: "gpt-4o",  messages: [{ role: "user", content: compiledPrompt }], });

// By default, the production version of a chat prompt is fetched. const chatPrompt = await langfuse.prompt.get("movie-critic-chat", {  type: "chat", });   // Compile the prompt with variables - returns an array of messages const compiledChatPrompt = chatPrompt.compile({  criticlevel: "expert",  movie: "Dune 2", });   // Use with OpenAI - prompt is an array of messages const completion = await openai.chat.completions.create({  model: "gpt-4o",  messages: compiledChatPrompt, });

from langfuse import Langfuse from langchain_core.prompts import ChatPromptTemplate   # Initialize Langfuse client langfuse = Langfuse()

# By default, the production version of a text prompt is fetched. langfuse_prompt = langfuse.get_prompt("movie-critic")   # Example using ChatPromptTemplate langchain_prompt = ChatPromptTemplate.from_template(langfuse_prompt.get_langchain_prompt())   # Example using ChatPromptTemplate with pre-compiled variables. langchain_prompt = ChatPromptTemplate.from_template(langfuse_prompt.get_langchain_prompt(strictness='tough'))

# By default, the production version of a chat prompt is fetched. langfuse_prompt = langfuse.get_prompt("movie-critic-chat", type="chat")   # Create a Langchain ChatPromptTemplate from the Langfuse prompt chat messages langchain_prompt = ChatPromptTemplate.from_messages(langfuse_prompt.get_langchain_prompt())

import { LangfuseClient } from "@langfuse/client"; import { ChatPromptTemplate } from "@langchain/core/prompts";   const langfuse = new LangfuseClient();

// Get current `production` version const langfusePrompt = await langfuse.prompt.get("movie-critic");   // Example using ChatPromptTemplate const promptTemplate = PromptTemplate.fromTemplate(  langfusePrompt.getLangchainPrompt() );

// Get current `production` version of a chat prompt const langfusePrompt = await langfuse.prompt.get(  "movie-critic-chat",  { type: "chat" } );   // Example using ChatPromptTemplate const promptTemplate = ChatPromptTemplate.fromMessages(  langfusePrompt.getLangchainPrompt().map((msg) => [msg.role, msg.content]) );

npm install @langfuse/client ai

import { generateText } from "ai"; import { openai } from "@ai-sdk/openai"; import { LangfuseClient } from "@langfuse/client";   // Initialize Langfuse client const langfuse = new LangfuseClient();

// By default, the production version of a text prompt is fetched. const prompt = await langfuse.prompt.get("movie-critic", {  type: "text", });   // Compile the prompt with variables const compiledPrompt = prompt.compile({  criticlevel: "expert",  movie: "Dune 2", });   // Use with Vercel AI SDK const result = await generateText({  model: openai("gpt-4o"),  prompt: compiledPrompt,  experimental_telemetry: {  isEnabled: true,  }, });

// By default, the production version of a chat prompt is fetched. const chatPrompt = await langfuse.prompt.get("movie-critic-chat", {  type: "chat", });   // Compile the prompt with variables - returns an array of messages const compiledChatPrompt = chatPrompt.compile({  criticlevel: "expert",  movie: "Dune 2", });   // Use with Vercel AI SDK const result = await generateText({  model: openai("gpt-4o"),  messages: compiledChatPrompt,  experimental_telemetry: {  isEnabled: true,  }, });