LangChain & GenAI · Module 8

Module 8: AI Agents & Memory

Master AI Agents and Memory in LangChain — ReAct reasoning loops, custom tools, create_react_agent, AgentExecutor, all major memory classes, structured chat agents, LangGraph, and LangSmith debugging.

⏱ 55 Min Read • Module 8 of 8 • Updated: May 2026

AI Agents represent the ultimate evolution of Large Language Model applications. Instead of static pipelines, agents use the LLM as a reasoning engine that dynamically determines a sequence of actions, selects external tools (such as web search or mathematical calculators), interprets tool results, and iteratively solves complex goals. This module covers the ReAct loop, custom tool creation, memory strategies, multi-agent frameworks, and systematic testing.

Day 15

AI Agents & Custom Tools

Why this matters

Agents let LLMs choose tools (search, APIs, calculators) — LangChain wraps ReAct-style loops.

Agents loop: LLM decides which tool to call, observes result, repeats until done.

from langchain.agents import create_react_agent, AgentExecutor
from langchain_core.tools import tool

@tool
def search_docs(query: str) -> str:
    """Search internal documentation."""
    return retriever.invoke(query)[0].page_content

Common mistakes

Hard-coding API keys in source instead of environment variables.
Passing raw strings where ChatPromptTemplate expects message tuples.
Skipping text splitting before embedding large PDFs (context overflow).

Interview checkpoints

Q: Explain agents & tools in LangChain. A: One-sentence definition + one API name.
Q: Common bug? A: Keys, message format, or missing split/embed step.

Practice

Basic: Sketch a minimal agents & tools snippet.
Intermediate: Run a notebook cell demonstrating Agents & Tools.
Advanced: Break Agents & Tools intentionally and interpret the error.

Recap

You can explain agents & tools clearly.
You know one mistake to avoid.
You see how this connects to the next lesson.

Next: Memory & LangSmith

Day 16

Memory, Multi-Agent & LangSmith

Why this matters

Memory and LangSmith tracing make agents debuggable and production-ready across sessions.

Memory (buffer, summary, entity) persists state across turns. LangSmith traces every chain step for debugging and eval datasets.

Track complete: GenAI → LangChain core → prompts → chains → documents → vectors → RAG → agents. Next: LangGraph for multi-agent workflows.

Common mistakes

Hard-coding API keys in source instead of environment variables.
Passing raw strings where ChatPromptTemplate expects message tuples.
Skipping text splitting before embedding large PDFs (context overflow).

Interview checkpoints

Q: Explain memory & langsmith in LangChain. A: One-sentence definition + one API name.
Q: Common bug? A: Keys, message format, or missing split/embed step.

Practice

Basic: Sketch a minimal memory & langsmith snippet.
Intermediate: Run a notebook cell demonstrating Memory & LangSmith.
Advanced: Break Memory & LangSmith intentionally and interpret the error.

Recap

You can explain memory & langsmith clearly.
You know one mistake to avoid.
You see how this connects to the next lesson.

Next: Back to LangChain Hub

← RAG Systems Back to LangChain Hub →