LangChain is the easiest way to start building agents and applications powered by LLMs. With under 10 lines of code, you can connect to OpenAI, Anthropic, Google, and more. LangChain provides a pre-built agent architecture and model integrations to help you get started quickly and seamlessly incorporate LLMs into your agents and applications.

The values to be used to format the prompt template. A promise that resolves to a string which is the formatted prompt. ... Formats the prompt given the input values and returns a formatted prompt value. ... The input values to format the prompt. A Promise that resolves to a formatted prompt value. Inherited from BaseStringPromptTemplate.formatPromptValue · Defined in langchain-core/src/prompts/string.ts:32

Load a prompt template from a json-like object describing it.

This prompt template is responsible for adding a list of messages in a particular place. In the above ChatPromptTemplate, we saw how we could format two messages, each one a string.

April 17, 2025 - Instead of crafting prompts from scratch each time, you use a template with placeholders that dynamically insert relevant information. This ensures consistency, clarity, and better AI-generated responses.

June 30, 2025 - This is handy because you don’t need to manually construct message objects — the template handles it for you. When you’re working with chat-based models, you often want to include conversation history (or some sequence of messages). MessagesPlaceholder acts as a stand-in for a dynamic list of messages you’ll provide at runtime. Imagine we’re building a career coach bot that remembers previous questions and answers: from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder from langchain_core.messages import HumanMessage, AIMessage chat_prompt = ChatPromptTemplate.fro

July 5, 2024 - The core class for handling input prompts in LangChain.js is the PromptTemplate class. The PromptTemplate allows you to create templates that can be dynamically filled with data.

It contains a text string ("the template"), that can take in a set of parameters from the end user and generates a prompt. ... import { PromptTemplate } from "@langchain/core/prompts"; // If a template is passed in, the input variables are inferred ...

import { PromptTemplate } from "npm:@langchain/core/prompts" const langfuseTextPrompt = await langfuse.prompt.get("jokes"); // 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 }, }); We’ll use the native Langfuse Tracing for Langchain JS when executing this chain.

Instead, you can partial the prompt template with the foo value, and then pass the partialed prompt template along and just use that. Below is an example of doing this: import { PromptTemplate } from "langchain/prompts"; const prompt = new ...

April 24, 2025 - These pre-defined recipes can contain instructions, context, few-shot examples, and questions that are appropriate for a particular task. LangChain offers a set of tools for creating and working with prompt templates.

June 27, 2024 - We use the declarative LangChain Expression Language (LCEL) to compose chains. The first element of our chain is the prompt template that has two parameters: instruction and inputText.

September 7, 2025 - Hi! I used to use PromptTemplate to substitute variables in prompts, but now (in the new version of langchain js), as far as I understand, this functionality is no longer available in the regular langchain package. What…

Transform a PromptTemplate to a LangChain.Message. Provide the inputs at the time of transformation to render the final content. Raises an exception if invalid. It renders a PromptTemplate's text by applying the inputs. template = PromptTemplate.new!(%{text: "My name is <%= @user_name %>.", role: :user}) messages = PromptTemplate.to_messages!([template], %{user_name: "Tim"})

August 24, 2025 - Instead of crafting prompts from scratch each time, you use a template with placeholders that dynamically insert relevant information. This ensures consistency, clarity, and better AI-generated responses.

A prompt template string to put after the examples. Implementation of FewShotPromptTemplateInput.suffix · Defined in langchain-core/src/prompts/few_shot.ts:119