Get Started with Prompt Management

pip install langfuse

LANGFUSE_SECRET_KEY = "sk-lf-..." LANGFUSE_PUBLIC_KEY = "pk-lf-..." LANGFUSE_HOST = "https://cloud.langfuse.com" # 🇪🇺 EU region # LANGFUSE_HOST = "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"], # directly promote to production  config={  "model": "gpt-4o",  "temperature": 0.7,  "supported_languages": ["en", "fr"],  }, # optionally, add configs (e.g. model parameters or model tools) or tags )   # 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"], # directly promote to production  config={  "model": "gpt-4o",  "temperature": 0.7,  "supported_languages": ["en", "fr"],  }, # optionally, add configs (e.g. model parameters or model tools) or tags )

npm i @langfuse/client

// Create a text prompt await langfuse.prompt.create({  name: "movie-critic",  type: "text",  prompt: "As a {{criticlevel}} critic, do you like {{movie}}?",  labels: ["production"], // directly promote to production  config: {  model: "gpt-4o",  temperature: 0.7,  supported_languages: ["en", "fr"],  }, // optionally, add configs (e.g. model parameters or model tools) or tags });   // 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"], // directly promote to production  config: {  model: "gpt-4o",  temperature: 0.7,  supported_languages: ["en", "fr"],  }, // optionally, add configs (e.g. model parameters or model tools) or tags });

curl https://cloud.langfuse.com/api/public/v2/prompts \  --request POST \  --header 'Content-Type: application/json' \  --data '{  "type": "chat",  "name": "",  "prompt": [  {  "type": "chatmessage",  "role": "",  "content": ""  }  ],  "config": null,  "labels": [  ""  ],  "tags": [  ""  ],  "commitMessage": null }'  

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

# Get current `production` version of a text prompt 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?"

# Get current `production` version of a chat prompt 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?"}]

# Get specific version prompt = langfuse.get_prompt("movie-critic", version=1)   # Get specific label prompt = langfuse.get_prompt("movie-critic", label="staging")   # Get latest prompt version. The 'latest' label is automatically maintained by Langfuse. prompt = langfuse.get_prompt("movie-critic", label="latest")

# Raw prompt including {{variables}}. For chat prompts, this is a list of chat messages. prompt.prompt   # Config object prompt.config

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

// Get current `production` version 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?"

// Get current `production` version of a chat prompt 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?"}]

// Get specific version of a prompt (here version 1) const prompt = await langfuse.prompt.get("movie-critic", {  version: 1 });   // Get specific label const prompt = await langfuse.prompt.get("movie-critic", {  label: "staging", });   // Get latest prompt version. The 'latest' label is automatically maintained by Langfuse. const prompt = await langfuse.prompt.get("movie-critic", {  label: "latest", });

// Raw prompt including {{variables}}. For chat prompts, this is a list of chat messages. prompt.prompt;   // Config object prompt.config;

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

# Get current `production` version 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'))

# Get current `production` version of a chat prompt 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())

# Get specific version prompt = langfuse.get_prompt("movie-critic", version=1)   # Get specific label prompt = langfuse.get_prompt("movie-critic", label="staging")   # Get latest prompt version. The 'latest' label is automatically maintained by Langfuse. prompt = langfuse.get_prompt("movie-critic", label="latest")

# Raw prompt including {{variables}}. For chat prompts, this is a list of chat messages. prompt.prompt   # Config object prompt.config

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]) );

// Get specific version of a prompt (here version 1) const prompt = await langfuse.prompt.get("movie-critic", {  version: 1 });   // Get specific label const prompt = await langfuse.prompt.get("movie-critic", {  label: "staging", });   // Get latest prompt version. The 'latest' label is automatically maintained by Langfuse. const prompt = await langfuse.prompt.get("movie-critic", {  label: "latest", });

// Raw prompt including {{variables}}. For chat prompts, this is a list of chat messages. prompt.prompt;   // Config object prompt.config;

from langfuse import observe, get_client   langfuse = get_client()   @observe(as_type="generation") def nested_generation():  prompt = langfuse.get_prompt("movie-critic")    langfuse.update_current_generation(  prompt=prompt,  )   @observe() def main():  nested_generation()   main()

from langfuse import get_client   langfuse = get_client()   prompt = langfuse.get_prompt("movie-critic")   with langfuse.start_as_current_generation(  name="movie-generation",  model="gpt-4o",  prompt=prompt ) as generation:  # Your LLM call here  generation.update(output="LLM response")

import { LangfuseClient } from "@langfuse/client"; import { startObservation } from "@langfuse/tracing";   const prompt = new LangfuseClient().prompt.get("my-prompt");   startObservation(  "llm",  {  prompt,  },  { asType: "generation" }, );

import { LangfuseClient } from "@langfuse/client"; import { updateActiveObservation } from "@langfuse/tracing";   const langfuse = new LangfuseClient();   startActiveObservation(  "llm",  async (generation) => {  const prompt = langfuse.prompt.get("my-prompt");  generation.update({ prompt });  },  { asType: "generation" }, );

import { LangfuseClient } from "@langfuse/client"; import { observe } from "@langfuse/tracing";   const langfuse = new LangfuseClient();   const callLLM = async (input: string) => {  const prompt = langfuse.prompt.get("my-prompt");    updateActiveObservation({ prompt }, { asType: "generation" });    return await invokeLLM(input); };   export const observedCallLLM = observe(callLLM);

from langfuse.openai import openai from langfuse import get_client   langfuse = get_client()   prompt = langfuse.get_prompt("calculator")   openai.chat.completions.create(  model="gpt-4o",  messages=[  {"role": "system", "content": prompt.compile(base=10)},  {"role": "user", "content": "1 + 1 = "}],  langfuse_prompt=prompt )

import { observeOpenAI } from "@langfuse/openai"; import OpenAI from "openai";   const langfusePrompt = await langfuse.prompt.get("prompt-name"); // Fetch a previously created prompt   const res = await observeOpenAI(new OpenAI(), {  langfusePrompt, }).completions.create({  prompt: langfusePrompt.prompt,  model: "gpt-4o",  max_tokens: 300, });

from langfuse import get_client from langfuse.langchain import CallbackHandler from langchain_core.prompts import ChatPromptTemplate, PromptTemplate from langchain_openai import ChatOpenAI, OpenAI   langfuse = get_client()   # Initialize the Langfuse handler langfuse_handler = CallbackHandler()

langfuse_text_prompt = langfuse.get_prompt("movie-critic")   ## Pass the langfuse_text_prompt to the PromptTemplate as metadata to link it to generations that use it langchain_text_prompt = PromptTemplate.from_template(  langfuse_text_prompt.get_langchain_prompt(),  metadata={"langfuse_prompt": langfuse_text_prompt}, )   ## Use the text prompt in a Langchain chain llm = OpenAI() completion_chain = langchain_text_prompt | llm   completion_chain.invoke({"movie": "Dune 2", "criticlevel": "expert"}, config={"callbacks": [langfuse_handler]})

langfuse_chat_prompt = langfuse.get_prompt("movie-critic-chat", type="chat")   ## Manually set the metadata on the langchain_chat_prompt to link it to generations that use it langchain_chat_prompt = ChatPromptTemplate.from_messages(  langfuse_chat_prompt.get_langchain_prompt() )   langchain_chat_prompt.metadata = {"langfuse_prompt": langfuse_chat_prompt}   ## or use the ChatPromptTemplate constructor directly. ## Note that using ChatPromptTemplate.from_template led to issues in the past ## See: https://github.com/langfuse/langfuse/issues/5374 langchain_chat_prompt = ChatPromptTemplate(  langfuse_chat_prompt.get_langchain_prompt(),  metadata={"langfuse_prompt": langfuse_chat_prompt} )   ## Use the chat prompt in a Langchain chain chat_llm = ChatOpenAI() chat_chain = langchain_chat_prompt | chat_llm   chat_chain.invoke({"movie": "Dune 2", "criticlevel": "expert"}, config={"callbacks": [langfuse_handler]})

import { LangfuseClient } from "@langfuse/client"; import { CallbackHandler } from "@langfuse/langchain";   import { PromptTemplate } from "@langchain/core/prompts"; import { ChatOpenAI, OpenAI } from "@langchain/openai";   const langfuseHandler = new CallbackHandler({  secretKey: "sk-lf-...",  publicKey: "pk-lf-...",  baseUrl: "https://cloud.langfuse.com", // 🇪🇺 EU region  // baseUrl: "https://us.cloud.langfuse.com", // 🇺🇸 US region });   const langfuse = new Langfuse();

const langfuseTextPrompt = await langfuse.prompt.get("movie-critic"); // Fetch a previously created text prompt   // Pass the langfuseTextPrompt to the PromptTemplate as metadata to link it to generations that use it const langchainTextPrompt = PromptTemplate.fromTemplate(  langfuseTextPrompt.getLangchainPrompt() ).withConfig({  metadata: { langfusePrompt: langfuseTextPrompt }, });   const model = new OpenAI(); const chain = langchainTextPrompt.pipe(model);   await chain.invoke({ movie: "Dune 2", criticlevel: "expert" }, { callbacks: [langfuseHandler] });  

const langfuseChatPrompt = await langfuse.prompt.get(  "movie-critic-chat",  {  type: "chat",  } ); // type option infers the prompt type as chat (default is 'text')   const langchainChatPrompt = ChatPromptTemplate.fromMessages(  langfuseChatPrompt.getLangchainPrompt().map((m) => [m.role, m.content]) ).withConfig({  metadata: { langfusePrompt: langfuseChatPrompt }, });   const chatModel = new ChatOpenAI(); const chatChain = langchainChatPrompt.pipe(chatModel);   await chatChain.invoke({ movie: "Dune 2", criticlevel: "expert" }, { callbacks: [langfuseHandler] });

import { generateText } from "ai"; import { LangfuseClient } from "@langfuse/client";   const langfuse = new LangfuseClient();   const fetchedPrompt = await langfuse.prompt.get("my-prompt");   const result = await generateText({  model: openai("gpt-4o"),  prompt: fetchedPrompt.prompt,  experimental_telemetry: {  isEnabled: true,  metadata: {  langfusePrompt: fetchedPrompt.toJSON(),  },  }, });