What Is LangChain?

LangChain is an open-source framework (available in Python and JavaScript) designed to help developers create applications powered by large language models (LLMs). It makes it easier to connect models to data sources, add memory, and give agents the ability to take actions.

Core Concepts

• Reasoning: The model’s ability to break down a goal into smaller tasks and decide the next step.
• Actions: Steps the agent can take, such as calling an API, searching a database, or triggering another function.
• Memory: Storing and recalling information from earlier in the conversation or from previous sessions.

These concepts align closely with the technical pillars of agentic AI that I discussed in my Exploring Agentic AI series.

Why Use LangChain?

• Flexibility: You control how the agent reasons, what actions it can take, and how it stores memory.
• Integration: Built-in support for connecting to APIs, databases, and vector stores.
• Community: A large, active developer community and extensive documentation.

Simple Example

Here’s a very simplified Python example of using LangChain to answer a question using an LLM:

from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate

# Create the model
llm = ChatOpenAI(model="gpt-4o", temperature=0)

# Create a prompt
prompt = ChatPromptTemplate.from_template("What are three uses of solar power?")

# Run the model
response = llm.invoke(prompt.format())
print(response.content)

This example doesn’t yet include reasoning, actions, or memory — but it shows how easy it is to get started. From here, you can add tools, custom prompts, and persistence to make a true agent.

How It Fits into the Bigger Picture

If no-code and low-code tools are like building with LEGO kits, LangChain is like having your own workshop — you can create anything you want, but you’ll need to know how to use the tools. For large, complex projects like my SDG ecosystem, LangChain offers the flexibility and control needed to scale.

Coming Next

In the next post, I’ll look at adding memory and context to your agents — a key capability that allows them to learn from the past and make better decisions in the future.