LangChain Builder
Generate LangChain code templates
Generated Code
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
llm = ChatOpenAI(model="gpt-4o-mini")
prompt = ChatPromptTemplate.from_template("Summarize the following text: {input}")
chain = prompt | llm | StrOutputParser()
result = chain.invoke({"input": "Your input text"})
print(result)Related Tools
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What is LangChain?
LangChain is a popular Python framework for building applications with LLMs. It provides abstractions for chaining prompts, managing conversation memory, integrating tools, and building complex AI workflows.
This builder generates ready-to-use LangChain code templates for common patterns. Select your chain type, customize prompts, and copy the generated Python code.
Chain Types
Simple Chain
Single prompt → LLM → output. Best for straightforward tasks like summarization or translation.
Sequential Chain
Multiple steps where each output feeds the next. Great for multi-stage processing.
Router Chain
Conditional routing to different chains based on input. Useful for multi-intent handling.
FAQ
Do I need LangChain?
Not always. For simple API calls, direct SDK usage is fine. LangChain shines for complex workflows, agents, and RAG systems.
What's the pipe (|) operator?
LangChain Expression Language (LCEL) uses | to chain components. It's similar to Unix pipes—data flows left to right.