# pip install -qU langchain "langchain[anthropic]" from langchain.agents import create_agent def get_weather(city: str) -> str: """Get weather for a given city.""" return f"It's always sunny in {city}!" agent = create_agent( model="claude-sonnet-4-5-20250929", tools=[get_weather], system_prompt="You are a helpful assistant", ) # Run the agent agent.invoke( {"messages": [{"role": "user", "content": "what is the weather in sf"}]} ) See the Installation instructions and Quickstart guide to get started building your own agents and applications with LangChain.

April 14, 2024 - mkdir prompt-templates cd prompt-templates python3 -m venv .venv touch prompt-templates.py pip install python-dotenv langchain langchain-openai

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

Prompt templates provide us with a reusable way to generate prompts using a base prompt structure. This helps standardize the structure and content of prompts. In LangChain, we can use the PromptTemplate() function and the from_template() function ...

The prompt template classes in Langchain are built to make constructing prompts with dynamic inputs easier. Of these classes, the simplest is the PromptTemplate.

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.

Use to create flexible templated prompts for chat models. from langchain_core.prompts import ChatPromptTemplate template = ChatPromptTemplate( [ ("system", "You are a helpful AI bot.

"Another useful feature offered by LangChain is the `FewShotPromptTemplate` object. This is ideal for what we'd call *few-shot learning* using our prompts.\n",

Exceptions: Common LangChain exception types. Language models: Base interfaces for language models. Serialization: Components for serialization and deserialization. Output parsers: Parsing model outputs. Prompts: Prompt templates and related utilities.

It accepts a set of parameters from the user that can be used to generate a prompt for a language model. The template can be formatted using either f-strings (default) or jinja2 syntax. ... Prefer using template_format=”f-string” instead of template_format=”jinja2”, or make sure to ...

Exceptions: Common LangChain exception types. Language models: Base interfaces for language models. Serialization: Components for serialization and deserialization. Output parsers: Parsing model outputs. Prompts: Prompt templates and related utilities.

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