Lesson 1: LangChain — Chains, Runnables & LCEL

LangChain is the most widely used AI application framework. This lesson covers what actually matters for building production systems: LCEL pipe syntax, chain composition, and custom runnables — skipping the cruft.

What LangChain Actually Is

LangChain is a composition library. Its value is in standardizing how LLMs, prompts, retrievers, tools, and parsers connect to each other — so you can swap components without rewriting the integration code. LCEL (LangChain Expression Language) is the composable core of modern LangChain.

LCEL is LangChain's pipe syntax: compose components with | operator. The result is a Runnable that can be invoked, streamed, or batched uniformly.

from langchain_anthropic import ChatAnthropic
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

model = ChatAnthropic(model="claude-haiku-4-5-20251001")

# LCEL chain: prompt | model | parser
chain = (
    ChatPromptTemplate.from_template("Translate to French: {text}")
    | model
    | StrOutputParser()
)

# Invoke
result = chain.invoke({"text": "Hello, how are you?"})
print(result)  # → "Bonjour, comment allez-vous?"

# Stream token by token
for chunk in chain.stream({"text": "The weather is nice today"}):
    print(chunk, end="", flush=True)

# Batch (parallel)
results = chain.batch([
    {"text": "Good morning"},
    {"text": "Good night"},
    {"text": "See you later"},
])

When to Use LangChain vs. Raw SDK

Use LangChain when	Use raw SDK when
Building a RAG pipeline (retrievers are first-class)	Simple 1–2 step LLM calls
Need streaming + batching + caching consistently	Maximum control and minimal dependencies
Composing 5+ steps that need easy observability	Learning how LLMs work at the protocol level
Integrating with LangSmith for tracing	The LangChain abstraction adds more confusion than value

LangSmith: Observability

# Set these env vars — LangChain auto-instruments all calls
import os
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "your-langsmith-key"
os.environ["LANGCHAIN_PROJECT"] = "my-rag-app"

# Now every chain.invoke() is automatically traced:
# - Input/output for every step
# - Token counts and latency
# - Error traces with full context
# All visible in the LangSmith UI at smith.langchain.com

In the next lesson, we move from stateless chains to stateful agents using LangGraph — where the flow of execution depends on the model's decisions.