LangChain Architecture and Core Abstractions
Understand LangChain's component model including LLMs, ChatModels, PromptTemplates, OutputParsers, and Runnables, and how they compose into pipelines.
LangChain Architecture and Core Abstractions is a free AI Engineering Academy 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 Engineering Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
What LangChain Actually Is
LangChain is a framework for building applications powered by language models. Rather than writing raw API calls and glue code yourself, LangChain provides reusable components that snap together into pipelines. It supports multiple LLM providers (OpenAI, Anthropic, Cohere, local models) through a unified interface, so switching providers requires minimal code changes.
The Component Model Overview
LangChain organizes its building blocks into distinct component categories. LLMs and ChatModels wrap model providers. PromptTemplates manage dynamic prompt construction. OutputParsers transform raw model text into structured Python objects. Runnables are the universal interface that ties everything together into composable pipelines.
LLMs vs ChatModels
LangChain distinguishes between two model interfaces. LLM takes a string prompt and returns a string completion — the older text-completion style. ChatModel takes a list of messages (system, human, AI) and returns a message object. Modern OpenAI, Anthropic, and Gemini models are all ChatModels. You should prefer ChatModel for new applications.
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, SystemMessage
llm = ChatOpenAI(model='gpt-4o-mini')
response = llm.invoke([
SystemMessage(content='You are a helpful assistant.'),
HumanMessage(content='What is LangChain?')
])
print(response.content)PromptTemplates: Dynamic Prompts
PromptTemplate lets you define a prompt with placeholder variables that get filled in at runtime. ChatPromptTemplate does the same for chat-style prompts with multiple messages. Templates separate the prompt structure from the runtime values, making prompts reusable, testable, and easy to version-control.
from langchain_core.prompts import ChatPromptTemplate
template = ChatPromptTemplate.from_messages([
('system', 'You are an expert in {domain}.'),
('human', 'Explain {concept} in simple terms.')
])
# Fill in variables at runtime
messages = template.invoke({'domain': 'machine learning', 'concept': 'embeddings'})
print(messages)OutputParsers: Shaping Model Output
OutputParsers convert the raw string output from a model into structured Python types. StrOutputParser simply extracts the content string. JsonOutputParser parses JSON. PydanticOutputParser validates against a Pydantic schema. Parsers also generate format instructions that you can inject into prompts to guide the model toward the expected format.
from langchain_core.output_parsers import StrOutputParser, JsonOutputParser
from pydantic import BaseModel
class MovieReview(BaseModel):
title: str
rating: int
summary: str
parser = JsonOutputParser(pydantic_object=MovieReview)
# parser.get_format_instructions() returns JSON schema instructions
print(parser.get_format_instructions()[:200])The Runnable Interface
Every LangChain component implements the Runnable interface, which defines three key methods: invoke() for synchronous single calls, stream() for token-by-token streaming, and batch() for processing multiple inputs in parallel. Because every component is a Runnable, they can all be composed uniformly using the pipe operator.
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model='gpt-4o-mini')
# Single call
result = llm.invoke('What is 2+2?')
# Streaming tokens
for chunk in llm.stream('Tell me a joke.'):
print(chunk.content, end='', flush=True)
# Batch processing
results = llm.batch(['What is Paris?', 'What is Rome?'])Composing with the Pipe Operator
The pipe operator (|) is syntactic sugar for creating RunnableSequence objects. When you write prompt | llm | parser, LangChain creates a chain where the output of each step flows into the input of the next. This is the core of LCEL — LangChain Expression Language — and replaces the older verbose chain classes.
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
prompt = ChatPromptTemplate.from_template('Summarize this in one sentence: {text}')
llm = ChatOpenAI(model='gpt-4o-mini')
parser = StrOutputParser()
# Chain composed with pipe operator
chain = prompt | llm | parser
result = chain.invoke({'text': 'LangChain is a framework for LLM applications...'})
print(result)Document Loaders and Text Splitters
For RAG applications, LangChain provides Document Loaders that read files in various formats (PDF, Word, HTML, CSV) and return a list of Document objects with page_content and metadata fields. Text Splitters then divide long documents into smaller chunks suitable for embedding and retrieval.
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
loader = PyPDFLoader('report.pdf')
docs = loader.load() # List of Document objects
splitter = RecursiveCharacterTextSplitter(
chunk_size=500,
chunk_overlap=50
)
chunks = splitter.split_documents(docs)
print(f'Split {len(docs)} pages into {len(chunks)} chunks')Vector Stores as Runnables
LangChain wraps vector databases (Chroma, Pinecone, pgvector) as VectorStore objects that expose a similarity_search() method. You can convert any VectorStore into a Retriever Runnable with .as_retriever(), making it composable in LCEL chains. The retriever takes a query string and returns the top-k most relevant documents.
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(chunks, embeddings)
# Convert to Retriever Runnable
retriever = vectorstore.as_retriever(
search_type='similarity',
search_kwargs={'k': 5}
)
results = retriever.invoke('How does LangChain work?')
for doc in results:
print(doc.page_content[:100])Callbacks and Observability
LangChain's callback system lets you hook into every event in a chain's execution: when an LLM starts, when tokens stream, when a chain finishes, when a tool is called. Built-in callbacks include StdOutCallbackHandler for console logging. Third-party integrations like LangSmith attach automatically via environment variables, capturing full execution traces without code changes.
from langchain.callbacks import StdOutCallbackHandler
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
model='gpt-4o-mini',
callbacks=[StdOutCallbackHandler()]
)
# Now every call logs input/output automatically
# Or enable LangSmith tracing globally:
import os
os.environ['LANGCHAIN_TRACING_V2'] = 'true'
os.environ['LANGCHAIN_API_KEY'] = 'your-key'Memory and State in Chains
By default, LangChain chains are stateless — each invocation is independent. To add conversational memory, you wrap a chain with a memory component that stores and injects conversation history. RunnableWithMessageHistory is the LCEL-native way to add memory, taking a get_session_history function that loads history by session ID.
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_community.chat_message_histories import ChatMessageHistory
store = {} # session_id -> ChatMessageHistory
def get_session_history(session_id: str):
if session_id not in store:
store[session_id] = ChatMessageHistory()
return store[session_id]
with_history = RunnableWithMessageHistory(chain, get_session_history)
response = with_history.invoke(
{'input': 'Hi, my name is Alice'},
config={'configurable': {'session_id': 'user-1'}}
)Quick Check
Test your understanding of LangChain Architecture and Core Abstractions.
Lesson Recap
In this lesson you learned: LangChain's component model organizes LLMs, PromptTemplates, OutputParsers, and Retrievers as Runnables with a unified interface, the pipe operator (|) composes Runnables into sequential chains via LCEL, and callbacks enable observability at every step without changing business logic. Next up we explore building chains with LCEL in depth.
Frequently asked questions
Is the “LangChain Architecture and Core Abstractions” lesson free?
Yes — the full text of “LangChain Architecture and Core Abstractions” is free to read here on the web, and the AI Engineering Academy 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 Engineering Academy course, upgrade to CoddyKit PRO.
What will I learn in “LangChain Architecture and Core Abstractions”?
Understand LangChain's component model including LLMs, ChatModels, PromptTemplates, OutputParsers, and Runnables, and how they compose into pipelines. You practise AI Engineering Academy 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 Engineering Academy?
No prior experience is required. AI Engineering Academy 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 and Core Abstractions” 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 Engineering Academy lesson?
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All lessons in this course
- LangChain Architecture and Core Abstractions
- Building Chains with LCEL
- Branching and Parallel Chains
- Streaming Output in LangChain