Get Started with Prompt Management

This guide walks you through creating and using a prompt with Langfuse. If you’re looking to understand what prompt management is and why it matters, check out the Prompt Management Overview first. For details on how prompts are structured in Langfuse and how it works in the background, see Core Concepts.

Get API keys

Create Langfuse account or self-host Langfuse.
Create new API credentials in the project settings.

Create a prompt

Use the agent mode of your editor to create prompts in Langfuse.

Install the Langfuse Skill:

# Cursor plugin
/add-plugin langfuse
 
# skills CLI
npx skills add langfuse/skills --skill "langfuse"
 
# Manual: clone and symlink
git clone https://github.com/langfuse/skills.git /path/to/langfuse-skills
ln -s /path/to/langfuse-skills/skills/langfuse ~/.skills/langfuse

Ask the agent to create your prompt:

Create a prompt in Langfuse called "movie-critic" that takes a movie name and critic level as variables.

Use the prompt in your code

Use the agent mode of your editor to write the code to fetch Langfuse prompts in your application.

Install the Langfuse Skill:

# Cursor plugin
/add-plugin langfuse
 
# skills CLI
npx skills add langfuse/skills --skill "langfuse"
 
# Manual: clone and symlink
git clone https://github.com/langfuse/skills.git /path/to/langfuse-skills
ln -s /path/to/langfuse-skills/skills/langfuse ~/.skills/langfuse

Ask the agent to integrate prompt fetching:

Fetch the "movie-critic" prompt, production version, from Langfuse and use it in my application.

Get API keys

Create Langfuse account or self-host Langfuse.
Create new API credentials in the project settings.

Create a prompt

Use the Langfuse UI to create a new prompt or update an existing one. You’ll need to select the prompt type, you can’t change this afterwards.

pip install langfuse

Add your Langfuse credentials as environment variables so the SDK knows which project to create the prompt in.

.env

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

Use the Python SDK to create a new prompt or update an existing one.

# 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
)

If you already have a prompt with the same name, the prompt will be added as a new version.

npm i @langfuse/client

Add your Langfuse credentials as environment variables so the SDK knows which project to create the prompt in.

.env

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();

Use the JS/TS SDK to create a new prompt or update an existing one.

// 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
});

If you already have a prompt with the same name, the prompt will be added as a new version.

Use the Public API to create a new prompt or update an existing one.

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}}?" }
    ]
  }'

API Reference

If you have prompts in your existing codebase, you can migrate them to Langfuse programmatically.

Using the Langfuse Skill

Install the Langfuse Skill:

# Cursor plugin
/add-plugin langfuse
 
# skills CLI
npx skills add langfuse/skills --skill "langfuse"
 
# Manual: clone and symlink
git clone https://github.com/langfuse/skills.git /path/to/langfuse-skills
ln -s /path/to/langfuse-skills/skills/langfuse ~/.skills/langfuse

Ask the agent to migrate your prompts:

Migrate the hardcoded prompts in this codebase to Langfuse prompt management.

Using the API

You can write a script that reads your existing prompts and creates them in Langfuse using the Public API. This is ideal for bulk migrations or CI/CD integration.

API Reference

Things to look out for

Langfuse uses a specific syntax for variables, prompt references, and message placeholders. Make sure to update your prompts to use the correct format, if you want to use Langfuse’s dynamic rendering capabilities.

Use the prompt in your code

At runtime, you can fetch the prompt from Langfuse. We recommend using the production label to fetch the version intentionally chosen for production. Learn more about control (versions/labels) here.

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

Below are code examples for both a text type prompt and a chat type prompt. Learn more about prompt types here.

Text prompt

# 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?"

Chat prompt

# 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();

Below are code examples for both a text type prompt and a chat type prompt. Learn more about prompt types here.

Text prompt

// 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?"

Chat prompt

// 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?"}]

Use the Public API to fetch a prompt at runtime. By default, the prompt labeled production is returned.

curl "https://cloud.langfuse.com/api/public/v2/prompts/movie-critic?label=production" \
  -u "your-public-key:your-secret-key"

For fetching a specific version instead of a label:

curl "https://cloud.langfuse.com/api/public/v2/prompts/movie-critic?version=1" \
  -u "your-public-key:your-secret-key"

API Reference

pip install langfuse openai

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

Below are code examples for both a text type prompt and a chat type prompt. Learn more about prompt types here.

Text prompt

# 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}]
)

Chat 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
)

Example notebook

Example Cookbook

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());

Below are code examples for both a text type prompt and a chat type prompt. Learn more about prompt types here.

Text prompt

// 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 }],
});

Chat prompt

// 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()

Below are code examples for both a text type prompt and a chat type prompt. Learn more about prompt types here.

These examples contain variables. As Langfuse and Langchain process input variables of prompt templates differently ({} instead of {{}}), we provide the prompt.get_langchain_prompt() method to transform the Langfuse prompt into a string that can be used with Langchain’s PromptTemplate. You can pass optional keyword arguments to prompt.get_langchain_prompt(**kwargs) in order to precompile some variables and handle the others with Langchain’s PromptTemplate.

Text prompt

# 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'))

Chat prompt

# 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())

Example notebook

Example Cookbook

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

Below are code examples for both a text type prompt and a chat type prompt. Learn more about prompt types here.

Text prompt

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

Chat prompt

// 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])
);

Example notebook

Example Cookbook.

Use Langfuse Prompt Management with the Vercel AI SDK.

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();

Below are code examples for both a text type prompt and a chat type prompt. Learn more about prompt types here.

Text prompt

// 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,
  },
});

Chat prompt

// 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,
  },
});

Not seeing your latest version? This might be because of the caching behavior. See prompt caching for more details.

Not seeing what you expected?

I'm not seeing the latest version of my prompt. Why?

Next steps

Now that you’ve used your first prompt, there are a couple of things we recommend you do next to make the most of Langfuse Prompt Management:

Link prompts to traces to analyze performance by prompt version
Use version control and labels to manage deployments across environments

Looking for something specific? Take a look under Features for guides on specific topics.

Overview Concepts

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