LangChain Cheatsheet: LCEL, Agents, RAG and LangGraph

# pip — install only what you need; provider packages are split out
pip install langchain langchain-core langchain-community langgraph
pip install langchain-openai langchain-anthropic   # providers
pip install langchain-chroma langchain-pinecone    # vector stores

# env
export OPENAI_API_KEY=sk-...
export ANTHROPIC_API_KEY=sk-...
export LANGSMITH_API_KEY=ls-...   # optional, for tracing

from langchain_core.prompts import (
    ChatPromptTemplate,
    FewShotChatMessagePromptTemplate,
)

examples = [{"q": "2+2", "a": "4"}, {"q": "3+3", "a": "6"}]
ex_prompt = ChatPromptTemplate.from_messages([("human", "{q}"), ("ai", "{a}")])
few_shot = FewShotChatMessagePromptTemplate(
    example_prompt=ex_prompt,
    examples=examples,
)

from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser

rag = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

from langchain_community.document_loaders import WebBaseLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_chroma import Chroma
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough

# 1 · Load + split
docs = WebBaseLoader(["https://example.com/about"]).load()
chunks = RecursiveCharacterTextSplitter(
    chunk_size=1000, chunk_overlap=150,
).split_documents(docs)

# 2 · Embed + store
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
store = Chroma.from_documents(chunks, embeddings)
retriever = store.as_retriever(search_kwargs={"k": 4})

# 3 · Compose chain
prompt = ChatPromptTemplate.from_messages([
    ("system", "Answer using only the context.\n\n{context}"),
    ("human", "{question}"),
])
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)

rag_chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

# 4 · Run
print(rag_chain.invoke("What does this company do?"))

from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from langchain.agents import create_agent
from langgraph.checkpoint.memory import MemorySaver

@tool
def multiply(a: int, b: int) -> int:
    """Multiply two integers."""
    return a * b

agent = create_agent(
    ChatOpenAI(model="gpt-4o-mini"),
    tools=[multiply],
    checkpointer=MemorySaver(),
)

config = {"configurable": {"thread_id": "demo"}}
result = agent.invoke({"messages": [("user", "What is 21 * 2?")]}, config)
print(result["messages"][-1].content)