Introduction to LangChain Chains
Understand the concept of chains in LangChain and how they facilitate multi-step operations with LLMs.
Introduction to LangChain Chains is a free AI Agents with LangChain & Autonomous Workflows 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 with LangChain & Autonomous Workflows learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
What are LangChain Chains?
Welcome to LangChain Chains! Imagine you have a complex task for an AI, like writing a blog post or summarizing a long document. A single command to a Large Language Model (LLM) might not be enough.
This is where 'Chains' come in. They help you break down complex AI tasks into smaller, manageable steps, executed in a specific order. Think of them as a blueprint for AI workflows.
Why We Need Chains
An LLM is powerful, but for multi-step problems or interactions requiring specific formatting, you need more structure. Chains provide this structure by:
- Enabling multi-step reasoning: Allowing the LLM to process information iteratively.
- Connecting components: Linking LLMs with prompts, output parsers, or other tools.
- Building structured workflows: Ensuring tasks are performed in a predefined sequence, making complex applications manageable.
Chains: A Sequential Flow
At its core, a chain is about sequential processing. The output from one step automatically becomes the input for the next step. It's like an assembly line for AI tasks.
This allows you to build sophisticated applications by combining different LangChain components and operations in a logical, step-by-step flow.
Key Components in Chains
Chains typically link together various LangChain components. The most common ones you'll encounter are:
- LLMs: The 'brain' that generates text or responses.
- Prompt Templates: Structured instructions that guide the LLM's behavior.
- Output Parsers: Tools to format the LLM's raw text output into a more usable structure (e.g., JSON, lists).
For this lesson, we'll focus on LLMs and Prompt Templates.
The Simplest Chain: LLMChain
The most fundamental chain in LangChain is the LLMChain. It's designed to take an input, apply a PromptTemplate to format it, pass the formatted prompt to an LLM, and get a text output.
It's the basic building block for many more complex interactions and a great starting point for understanding chains.
Setting Up LLM & Prompt
Before we build an LLMChain, let's prepare our ingredients: an LLM and a Prompt Template. We'll use a simple prompt to ask the LLM to say something nice to a person by name.
Remember to replace 'YOUR_OPENAI_API_KEY' with your actual key or set it as an environment variable.
import os
from langchain_openai import ChatOpenAI
from langchain_core.prompts import PromptTemplate
# Set your API key (replace with your actual key or env var)
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"
# 1. Initialize the LLM (e.g., OpenAI's GPT-3.5)
llm = ChatOpenAI(temperature=0.7)
# 2. Define a Prompt Template
# '{name}' is the input variable for this prompt
prompt = PromptTemplate.from_template(
"Hello, my name is {name}. Can you say something nice to me?"
)
print("LLM and Prompt Template are ready!")Creating an LLMChain
Now, let's combine our LLM and Prompt Template into an LLMChain. This chain will take a 'name' as input, format it into the prompt, send it to the LLM, and return the LLM's response.
The LLMChain class from langchain.chains connects these two components seamlessly.
import os
from langchain_openai import ChatOpenAI
from langchain_core.prompts import PromptTemplate
from langchain.chains import LLMChain
# Set your API key (replace with your actual key or env var)
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"
# 1. Initialize the LLM
llm = ChatOpenAI(temperature=0.7)
# 2. Define a Prompt Template
prompt = PromptTemplate.from_template(
"Hello, my name is {name}. Can you say something nice to me?"
)
# 3. Create the LLMChain
# The chain connects the prompt and the LLM
chain = LLMChain(llm=llm, prompt=prompt)
# 4. Invoke the chain with an input
# The input key 'name' must match the prompt variable
response = chain.invoke({"name": "Alice"})
print("Chain invoked successfully!")
print("Response:")
print(response["text"])Understanding Chain Output
Notice that the output from chain.invoke() is a dictionary. For a basic LLMChain, it typically contains:
- The input variables you provided (e.g.,
'name'). 'text': The LLM's generated response based on the prompt.
This structured output makes it easy to extract the LLM's answer and use it in subsequent steps or display it to a user.
Benefits of LLMChain
Even though it's simple, the LLMChain offers significant benefits:
- Encapsulation: It neatly packages the prompt and LLM logic together, making your code modular.
- Readability: It makes your LLM interactions cleaner and easier to understand than raw API calls.
- Foundation: It serves as the fundamental building block for constructing more complex multi-step chains and agents.
It helps organize your LLM interactions efficiently.
Quick Check
You've learned that LangChain Chains help organize multi-step operations. Based on our discussion, which of the following best describes the core purpose of an LLMChain?
Recap: Getting Chained Up!
Great job! In this lesson, you learned about:
- The concept of Chains in LangChain for structuring multi-step AI tasks.
- Why chains are essential for building complex, controlled workflows with LLMs.
- The basic components that typically make up a chain (LLMs, Prompt Templates).
- How to create and run an LLMChain, the simplest chain, by combining a PromptTemplate and an LLM.
Next, we'll explore how to combine multiple LLMChains into more powerful sequential workflows!
Frequently asked questions
Is the “Introduction to LangChain Chains” lesson free?
Yes — the full text of “Introduction to LangChain Chains” is free to read here on the web, and the AI Agents with LangChain & Autonomous Workflows 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 with LangChain & Autonomous Workflows course, upgrade to CoddyKit PRO.
What will I learn in “Introduction to LangChain Chains”?
Understand the concept of chains in LangChain and how they facilitate multi-step operations with LLMs. You practise AI Agents with LangChain & Autonomous Workflows 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 with LangChain & Autonomous Workflows?
No prior experience is required. AI Agents with LangChain & Autonomous Workflows 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 “Introduction to LangChain 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 with LangChain & Autonomous Workflows lesson?
Yes. Every AI Agents with LangChain & Autonomous Workflows 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
- Introduction to LangChain Chains
- Sequential & Simple Chains
- Customizing Chain Logic
- Routing and Conditional Chains