LangChain & LangGraph Concepts You Should Know

Here are five foundational LangChain and LangGraph concepts every AI engineer should understand.

1. Chains (The AI Workflow)

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.

2. Tools (Giving AI Superpowers)

LLMs only know what was in their training data.

Tools let them interact with the outside world.

Examples include:

• Search the web
• Query SQL databases
• Call REST APIs
• Execute Python code
• Read PDFs
• Send emails

Instead of only generating text, the AI can now do things.

3. Memory (Remembering Conversations)

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:

• Short-term (current conversation)
• Long-term (saved facts)
• Semantic memory (knowledge)
• Episodic memory (past interactions)

4. Agents (Reason + Choose Tools)

An agent doesn't follow a fixed workflow.

Instead, it:

1. Understands the goal
2. Decides what to do
3. Chooses the right tool
4. Executes it
5. Evaluates the result
6. Repeats until the task is complete

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.

5. Graphs (LangGraph's Superpower)

Traditional chains are linear.

LangGraph introduces graphs, where execution can branch, loop, pause, 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.

A Simple Way to Remember

• LangChain = LLM + tools + workflows
• Agents = LangChain + reasoning + tool selection
• LangGraph = Agents + state + loops + branching + durable execution

If you're learning modern AI engineering, mastering these five concepts will give you a strong foundation for building production-ready AI applications.