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AI Agents · Lesson

LangChain Architecture: Models, Prompts, Chains

The three building blocks: ChatModels, PromptTemplates, and chains that compose them.

LangChain Architecture: Models, Prompts, Chains is a free AI Agents lesson on CoddyKit — lesson 1 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the AI Agents learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Why LangChain?

LangChain is the most popular Python framework for building LLM apps. It provides:

  • Adapters for every major LLM provider
  • Loaders and parsers for many data sources
  • Memory, agents, and chains primitives
  • Vector store integrations

Critics call it bloated; supporters love its breadth. We focus on the core that has stabilised: LCEL.

Three Core Concepts

  1. Models — LLMs and ChatModels you call
  2. Prompts — templates that produce messages
  3. Chains — compositions that pipe prompts -> models -> parsers

Models

A unified interface across providers:

from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic

openai_model = ChatOpenAI(model='gpt-4o-mini', temperature=0.2)
claude_model = ChatAnthropic(model='claude-sonnet-4-5')

resp = openai_model.invoke('Hello!')
print(resp.content)

Prompt Templates

Reusable prompts with variables:

from langchain.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages([
    ('system', 'You are a {role} assistant.'),
    ('user', '{question}')
])

messages = prompt.invoke({'role': 'SQL', 'question': 'How do I delete rows?'})
print(messages.to_messages())

Chains: The Pipe Operator

Combine a prompt + model + parser with |:

from langchain.schema.output_parser import StrOutputParser

chain = prompt | openai_model | StrOutputParser()
result = chain.invoke({'role': 'SQL', 'question': 'How do I delete rows?'})
print(result)

Output Parsers

Convert raw model output to Python types:

from langchain.output_parsers import PydanticOutputParser
from pydantic import BaseModel

class Plan(BaseModel):
    steps: list[str]

parser = PydanticOutputParser(pydantic_object=Plan)
chain = prompt | model | parser

Runnables and the Runnable Protocol

Every chain element is a Runnable with:

  • .invoke(input) — synchronous
  • .ainvoke(input) — async
  • .stream(input) — token streaming
  • .batch(inputs) — parallel batches

The pipe operator composes them.

Streaming

for chunk in chain.stream({'role': 'helpful', 'question': 'Tell a story.'}):
    print(chunk, end='', flush=True)

Batch

results = chain.batch([
    {'role': 'SQL', 'question': 'Q1'},
    {'role': 'SQL', 'question': 'Q2'}
])
# Runs in parallel.

Configuration at Runtime

Override model parameters per call:

result = chain.invoke(
    {'role': 'helpful', 'question': '...'},
    config={'configurable': {'temperature': 0.0}}
)

LangChain Hub

Pull community-curated prompts:

from langchain import hub
rag_prompt = hub.pull('rlm/rag-prompt')

When NOT to Use LangChain

For very simple agents (one model call, one tool), LangChain is over-engineered. Write the call directly. Use LangChain when you need composability, retries, observability, and tested integrations.

Chain Operator

What does the | operator do in LangChain?

Recap

Models + Prompts + Chains, glued by |. Next: loaders and vector stores in the LangChain ecosystem.

Frequently asked questions

Is the “LangChain Architecture: Models, Prompts, Chains” lesson free?

Yes — the full text of “LangChain Architecture: Models, Prompts, Chains” is free to read here on the web, and the AI Agents course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the AI Agents course, upgrade to CoddyKit PRO.

What will I learn in “LangChain Architecture: Models, Prompts, Chains”?

The three building blocks: ChatModels, PromptTemplates, and chains that compose them. You practise AI Agents with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start AI Agents?

No prior experience is required. AI Agents on CoddyKit is structured for beginners through advanced learners; this is — lesson 1 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “LangChain Architecture: Models, Prompts, Chains” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this AI Agents lesson?

Yes. Every AI Agents lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. LangChain Architecture: Models, Prompts, Chains
  2. Loaders, Splitters and Vector Stores
  3. LCEL (LangChain Expression Language)
  4. Building a RAG Chain End-to-End
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