LangChain

LangChain Core Cheat Sheet

Runnables, LCEL composition and the streaming interface

Modern LangChain is one interface — the Runnable — and a pipe operator for composing them. Learning that pair covers most of the library.

Intermediate 1 min read 13 Entries Version 1.0 Sabir Updated 1
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The Runnable interface

Method Does Returns
invoke(input) Runs once One output
batch(inputs) Runs many, in parallel where possible A list of outputs
stream(input) Runs once, yielding as it goes An iterator of chunks
astream_events(input) Streams every internal step An iterator of typed events
with_retry() Wraps with a retry policy A Runnable
with_fallbacks() Adds alternatives on failure A Runnable

Composition

Construct Meaning
a | b Pipe: a's output becomes b's input
RunnableParallel({"x": a, "y": b}) Run both on the same input, return a dict
RunnablePassthrough() Forward the input unchanged — used to keep the original alongside a result
RunnableLambda(fn) Lift any plain function into the chain
RunnableBranch(...) Conditional routing
.bind(**kwargs) Pin arguments — model parameters, tools — onto a step

Composition is what buys streaming

Anything built by piping Runnables gets streaming, batching, async and tracing for free, because every link implements the same interface. Dropping to plain Python inside a step is allowed — but that step becomes a black box that cannot stream, which is usually why a chain "suddenly stopped streaming".

Code examples

An LCEL chain that streams

Everything built by piping Runnables inherits streaming, batching and async for free.

Python chain.py Download 
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableParallel, RunnablePassthrough

prompt = ChatPromptTemplate.from_template(
    "Answer using ONLY this context.\n\n{context}\n\nQuestion: {question}"
)

chain = (
    RunnableParallel(
        context=retriever | (lambda docs: "\n\n".join(d.page_content for d in docs)),
        question=RunnablePassthrough(),      # keep the original question
    )
    | prompt
    | model
    | StrOutputParser()
)

for chunk in chain.stream("What changed in the refund policy?"):
    print(chunk, end="", flush=True)

Frequently asked questions

Why did my chain stop streaming?
Almost always a step that dropped to plain Python. Anything not implementing the Runnable interface is a black box the streaming machinery cannot see through.
Do I need LangChain at all?
For a single model call, no — the provider SDK is simpler. It earns its place when you want composition, retries, fallbacks and tracing across many steps without writing that layer yourself.

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