LangChain Cheat Sheet
Build LLM applications with chains, agents, tools, retrievers, and LangGraph orchestration using the modern LangChain Expression Language.
Build a Chain with LCEL
Compose a prompt, model, and output parser using the pipe operator.
from langchain_core.prompts import ChatPromptTemplatefrom langchain_core.output_parsers import StrOutputParserfrom langchain_anthropic import ChatAnthropicprompt = ChatPromptTemplate.from_messages([ ("system", "You are a concise technical assistant."), ("human", "{question}"),])model = ChatAnthropic(model="claude-sonnet-4-5", temperature=0)chain = prompt | model | StrOutputParser()result = chain.invoke({"question": "What is a monad?"})print(result)
RAG Chain with a Retriever
Wire a vector store retriever into a chain that grounds answers in retrieved context.
from langchain_community.vectorstores import Chromafrom langchain_core.runnables import RunnablePassthroughretriever = Chroma(persist_directory="./db", embedding_function=embeddings).as_retriever(k=4)def format_docs(docs): return "\n\n".join(d.page_content for d in docs)rag_chain = ( {"context": retriever | format_docs, "question": RunnablePassthrough()} | prompt | model | StrOutputParser())answer = rag_chain.invoke("How does the refund policy work?")
Tool-Calling Agent
Bind Python functions as tools and let the model decide when to call them.
from langchain_core.tools import toolfrom langgraph.prebuilt import create_react_agent@tooldef get_weather(city: str) -> str: """Look up the current weather for a city.""" return f"It is sunny in {city}"agent = create_react_agent(model, tools=[get_weather])response = agent.invoke({"messages": [("human", "What's the weather in Austin?")]})print(response["messages"][-1].content)
Streaming Responses
Stream tokens from a chain instead of waiting for the full completion.
for chunk in chain.stream({"question": "Summarize the CAP theorem."}): print(chunk, end="", flush=True)# async streamingasync for chunk in chain.astream({"question": "Summarize the CAP theorem."}): print(chunk, end="", flush=True)
Core Building Blocks
The primary abstractions you compose to build a LangChain application.
- Runnable- unified interface (invoke/stream/batch) implemented by prompts, models, parsers
- PromptTemplate / ChatPromptTemplate- parameterized prompt with variable substitution
- Retriever- fetches relevant documents given a query string
- Memory / checkpointer- persists conversation state across turns (LangGraph)
- Tool- a callable the model can invoke with structured arguments
- LangGraph- graph-based orchestration for stateful, multi-step agents
Prefer LangGraph's create_react_agent over the older AgentExecutor for anything new — it gives you explicit state, checkpointing, and human-in-the-loop interrupts that the legacy agent classes never supported well.
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