Here are five foundational LangChain and LangGraph concepts every AI engineer should understand.
A chain is a sequence of steps where the output of one step becomes the input of the next.
Example:
User Question
↓
Retrieve Documents
↓
LLM Generates Answer
↓
Format Response
Think of it as a pipeline for AI tasks.
LLMs only know what was in their training data.
Tools let them interact with the outside world.
Examples include:
Instead of only generating text, the AI can now do things.
Memory allows an AI to remember information across interactions.
Without memory:
User: My name is Sam.
...
User: What's my name?
AI: I don't know.
With memory:
AI: Your name is Sam.
Memory can be:
An agent doesn't follow a fixed workflow.
Instead, it:
Example:
User:
"Find the latest exchange rate and calculate how much 250 USD is in KES."
Agent:
→ Search exchange rate
→ Use calculator
→ Return final answer
The workflow is dynamic rather than predetermined.
Traditional chains are linear.
LangGraph introduces graphs, where execution can branch, loop, , or resume.
Example:
Start
│
▼
Understand Task
┌────┴────┐
▼ ▼
Search Web Query Database
│ │
└────┬────┘
▼
Evaluate Results
┌────┴────┐
Good? No
│ │
▼ │
Final Answer │
│
▼
Try Another Tool
This makes LangGraph ideal for building autonomous AI agents that can recover from errors, make decisions, and manage complex workflows.
If you're learning modern AI engineering, mastering these five concepts will give you a strong foundation for building production-ready AI applications.