Langchain
docs.langchain.com › oss › python › langchain › overview
LangChain overview - Docs by LangChain
# 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.
Medium
medium.com › @ssmaameri › prompt-templates-in-langchain-efb4da260bd3
Prompt Templates in LangChain. Do you ever get confused by Prompt… | by Sami Maameri | Medium
April 14, 2024 - mkdir prompt-templates cd prompt-templates python3 -m venv .venv touch prompt-templates.py pip install python-dotenv langchain langchain-openai
Send multiple parameters through prompt template
I found the same issue. Every single example out there seems to use a single input, which is kind of ridiculous. Like, what is the point in that case? There doesn't appear to just be a section of docs that fully explains what is going on here. This is a stripped down example of a starting point where I was playing around with an agent with a tool to gather research. The idea would be to give it inputs directly. Now, I can't easily explain why this has to be so complicated. I get LCEL and get what the first piece of the agent defined below is doing, but this seems like such a confusing way of handling this. There has got to be a better way than just adding a bunch of seemingly random entries to a dict. What seems to be happening here is that each entry in the dict is executed with the data passed into the invoke method. Instead of just passing a string, pass a dict, then use a lambda function to get the data from the dict that is passed into it. agent_prompt = """You are content researcher and writer...""" article_prompt_1 = """Write a title and intro paragraph after researching for: Topic: {topic} Merchant: {merchant} """ prompt = ChatPromptTemplate.from_messages( [ ("system", agent_prompt), ("user", article_prompt_1), MessagesPlaceholder(variable_name="agent_scratchpad"), ] ) llm = ChatOpenAI( openai_api_key="", temperature=0 ) tools = [research_merchant_topic] llm_with_tools = llm.bind_tools(tools) agent = ( { "topic": lambda x: x["topic"], "merchant": lambda x: x["merchant"], "agent_scratchpad": lambda x: format_to_openai_tool_messages( x["intermediate_steps"] ), } | prompt | llm_with_tools | parse ) agent_executor = AgentExecutor( agent=agent, tools=[research_merchant_topic], verbose=True ) agent_executor.invoke({"merchant": "target", "topic": "return policy"}) More on reddit.com
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March 13, 2024Value of prompt templates
One reason is that they have validation built in. I had the same question and explored their source code. These are basically wrappers o ln top of formatter class from standard lib and pydantic data models. The validation part is the key value add. More on reddit.com
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June 25, 2023LLama2 prompt template
I have implemented the llama 2 llm using langchain and it need to customise the prompt template, you can't just use the key of {history} for conversation. Currently langchain api are not fully supported the llm other than openai.
More on reddit.com 2
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March 24, 2022Why have Prompt Templates?
It’s probably to keep passing original context to not hallucinate much? More on reddit.com
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April 17, 2023Videos
29:56
Prompt Templating and Techniques in LangChain - YouTube
18:14
How to Use Chat Models & Prompt Templates | LangChain Tutorial ...
06:51
LangChain Tutorial (JS) #2: LLM Chains and Prompt Templates - YouTube
17:50
LangChain Tutorial | Creating Effective Prompt Templates with ...
LangChain: Prompt Templates, Parsers and Chaining for ...
Mirascope
mirascope.com › blog › langchain-prompt-template
A Guide to Prompt Templates in LangChain | Mirascope
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
Codecademy
codecademy.com › article › getting-started-with-lang-chain-prompt-templates
Getting Started with LangChain Prompt Templates | Codecademy
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 ...
LangChain.js
v03.api.js.langchain.com › classes › _langchain_core.prompts.PromptTemplate.html
PromptTemplate | LangChain.js
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
LangChain
python.langchain.com › api_reference › core › prompts › langchain_core.prompts.prompt.PromptTemplate.html
PromptTemplate — 🦜🔗 LangChain documentation
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.
Comet
comet.com › home › llmops › introduction to prompt templates in langchain
Introduction to Prompt Templates in LangChain - Comet
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.
LangChain
python.langchain.com › api_reference › core › prompts.html
prompts — 🦜🔗 LangChain documentation
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.
Pinecone
pinecone.io › learn › series › langchain › langchain-prompt-templates
Prompt Engineering and LLMs with Langchain | Pinecone
The prompt template classes in Langchain are built to make constructing prompts with dynamic inputs easier. Of these classes, the simplest is the PromptTemplate.
Reddit
reddit.com › r/langchain › send multiple parameters through prompt template
r/LangChain on Reddit: Send multiple parameters through prompt template
March 13, 2024 -
Hi All, How can I send multiple parameters through my prompt, for example,
from langchain_core.prompts import PromptTemplate
template = """Use the following pieces of context to answer the question at the end.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Use three sentences maximum and keep the answer as concise as possible.
Always say "thanks for asking!" at the end of the answer.
{context}
Question: {question}
Helpful Answer:"""
custom_rag_prompt = PromptTemplate.from_template(template)
rag_chain = (
{"context": retriever | format_docs, "question": RunnablePassthrough()}
| custom_rag_prompt
| llm
| StrOutputParser()
)
rag_chain.invoke("What is Task Decomposition")How to add more parameters?
Top answer 1 of 3
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I found the same issue. Every single example out there seems to use a single input, which is kind of ridiculous. Like, what is the point in that case? There doesn't appear to just be a section of docs that fully explains what is going on here. This is a stripped down example of a starting point where I was playing around with an agent with a tool to gather research. The idea would be to give it inputs directly. Now, I can't easily explain why this has to be so complicated. I get LCEL and get what the first piece of the agent defined below is doing, but this seems like such a confusing way of handling this. There has got to be a better way than just adding a bunch of seemingly random entries to a dict. What seems to be happening here is that each entry in the dict is executed with the data passed into the invoke method. Instead of just passing a string, pass a dict, then use a lambda function to get the data from the dict that is passed into it. agent_prompt = """You are content researcher and writer...""" article_prompt_1 = """Write a title and intro paragraph after researching for: Topic: {topic} Merchant: {merchant} """ prompt = ChatPromptTemplate.from_messages( [ ("system", agent_prompt), ("user", article_prompt_1), MessagesPlaceholder(variable_name="agent_scratchpad"), ] ) llm = ChatOpenAI( openai_api_key="", temperature=0 ) tools = [research_merchant_topic] llm_with_tools = llm.bind_tools(tools) agent = ( { "topic": lambda x: x["topic"], "merchant": lambda x: x["merchant"], "agent_scratchpad": lambda x: format_to_openai_tool_messages( x["intermediate_steps"] ), } | prompt | llm_with_tools | parse ) agent_executor = AgentExecutor( agent=agent, tools=[research_merchant_topic], verbose=True ) agent_executor.invoke({"merchant": "target", "topic": "return policy"})
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You can do it using the following: template = """Answer the question based only on the following context. Answer in a way that is suitable for {audience}: {context} Question: {question} """ custom_rag_prompt= PromptTemplate.from_template(prompt) rag_chain = ( {"context":{ "sentence": itemgetter("sentence")} | RunnableLambda(retriever)| RunnableLambda(format_docs), "question": itemgetter("question") | RunnablePassthrough(), "audience": itemgetter("audience") | RunnablePassthrough() } | custom_rag_prompt | llm | StrOutputParser() ) rag_chain.invoke({"question":"What is Task Decomposition","audience":"10 year olds."})
LangChain
api.python.langchain.com › en › latest › prompts › langchain_core.prompts.prompt.PromptTemplate.html
langchain_core.prompts.prompt.PromptTemplate — 🦜🔗 LangChain 0.2.17
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 ...
Reddit
reddit.com › r/langchain › value of prompt templates
r/LangChain on Reddit: Value of prompt templates
June 25, 2023 -
Can someone articulate why langchain prompt templates are so much more valuable that just working with f-strings? They are basically f-strings, but wrapped in a class.. so I can specify the parameters to the prompt at runtime instead of redeclaring the variables again on separate lines. ..so it saves me a couple lines I guess?
Top answer 1 of 3
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One reason is that they have validation built in. I had the same question and explored their source code. These are basically wrappers o ln top of formatter class from standard lib and pydantic data models. The validation part is the key value add.
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I prefer Guidance. Microsoft is doing something right for once.
Kaggle
kaggle.com › code › youssef19 › langchain-prompt-templates
LangChain Prompt Templates | Kaggle
April 14, 2024 - Explore and run machine learning code with Kaggle Notebooks | Using data from No attached data sources
Aurelio
aurelio.ai › learn › langchain-prompts
Prompt Templating and Techniques in LangChain | Aurelio AI
January 5, 2025 - From this, we can see that each tuple provided when using ChatPromptTemplate.from_messages becomes an individual prompt template itself. Within each of these tuples, the first value defines the role of the message, which is typically system, human, or ai. Using these tuples is shorthand for the following, more explicit code: ... from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate prompt_template = ChatPromptTemplate.from_messages([ SystemMessagePromptTemplate.from_template(prompt), HumanMessagePromptTemplate.from_template("{query}"), ])
LinkedIn
linkedin.com › pulse › how-use-prompt-templates-langchain-mohammad-jazim-vdedc
How to Use Prompt Templates in LangChain
We cannot provide a description for this page right now
Readthedocs
langchain-contrib.readthedocs.io › en › latest › prompts › chained.html
Chained Prompt Template — langchain-contrib 0.0.4 documentation
You can also chain arbitrary chat prompt templates or message prompt templates together. Plain strings are intepreted as Human messages. ... from langchain.prompts.chat import ChatPromptTemplate, SystemMessagePromptTemplate template = ChainedPromptTemplate([ SystemMessagePromptTemplate.from_template("You have access to {tools}."), ChatPromptTemplate.from_messages([ SystemMessagePromptTemplate.from_template("Your objective is to answer human questions."), ]), "Tell me: {question}?", ]) template.format_prompt(tools="Search", question="how high is Everest").to_messages()