Everything You Need to Know About LangChain (Before Starting an AI Project) – Part 1
A new series to understand and apply the LangChain framework in AI projects
If you've been keeping up with the world of artificial intelligence, you've probably heard of LangChain.
This open-source framework has been gaining traction among developers and enthusiasts for making it easier to build intelligent applications with language models (LLMs).
But what exactly is LangChain?
LangChain is not a language model, but rather an orchestration layer. It connects the LLM to external tools (such as web search, databases, or internal systems), conversation memory, business rules, and other components that turn simple prompts into robust and useful applications.
This is the first post in a new series about LangChain! We’ll explore what it is, how it works, and why it’s been making such a big impact on the development of generative AI solutions.
What is LangChain?
LangChain is an open-source framework designed to simplify the development of applications that use language models like GPT-4, Claude, Llama, and others. Created by Harrison Chase in 2022, it was built to make the process of integrating these powerful AI models into real-world applications easier and more structured.
Think of LangChain as an “orchestrator” that connects various AI components, data sources, and external services, allowing you to build complex solutions in a modular and organized way. It abstracts much of the technical complexity involved in orchestrating multiple AI systems.
How does it work?
LangChain is based on several core concepts that work together:
1. Chains
Chains are sequences of operations that process information in a structured manner. A chain can range from a simple query to an LLM to complex workflows involving multiple data sources and intermediate steps.
2. Prompt Templates
The framework provides a robust system for managing and customizing prompts. This allows developers to create reusable and dynamic templates, making it easier to maintain and optimize interactions with language models.
3. Memory
One of the main challenges when working with LLMs is maintaining context across different interactions. LangChain addresses this through memory systems that allow applications to "remember" previous conversations and stay coherent over time.
4. Agents
Agents are components that can make decisions about which tools to use and in what order, based on the context of the conversation. They make applications more autonomous and capable of solving complex tasks.
5. Tools
The framework makes it easy to integrate external tools, such as APIs, databases, calculators, and other services—greatly expanding the capabilities of AI-powered applications.
Why use LangChain?
Before LangChain, building applications with LLMs required a lot of custom code and deep technical knowledge. This framework democratizes the process by offering:
Simplicity: Dramatically reduces the amount of code needed to create sophisticated AI applications
Modularity: Lets you combine different components flexibly, making experimentation and iteration easier
Standardization: Provides a consistent interface for working with different LLM providers
Active Community: With a growing user base, there’s continuous evolution and support from the community
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Practical Examples – Getting Started with LangChain
Let’s start with a simple example to understand how LangChain works in practice by creating a basic chatbot using OpenAI’s model.
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage
import os
# Set your OpenAI API key
os.environ["OPENAI_API_KEY"] = "your-key-here"
# Create a chat model instance
chat = ChatOpenAI(temperature=0.3)
# Send a message and print the response
response = chat([HumanMessage(content="Hello, LangChain! How do you work?")])
print(response.content)This code shows the basics: creating a chat model, sending a user message, and receiving a response generated by OpenAI’s model.
To use Google’s Gemini model, the process is similar:
from langchain_google_genai import ChatGoogleGenerativeAI
import os
# Set your Google API key
os.environ["GOOGLE_API_KEY"] = "your-key-here"
# Create the chat instance
chat = ChatGoogleGenerativeAI(model="models/gemini-1.5-flash", temperature=0.3)
# Send messages just like in the previous example
...🚀 To access example code using models from OpenAI, Gemini, HuggingFace, and Ollama, visit our Colab Notebooks section — with ready-to-run notebooks! Look for Langchain-first-steps.ipynb.
Use Cases
LangChain can be used in a variety of applications, such as:
Intelligent chatbots: Creating virtual assistants that maintain context and can access external information
Document analysis: Systems that can read, understand, and answer questions about large volumes of text
Process automation: Agents that perform complex tasks by combining multiple tools
Recommendation systems: Applications that use AI to suggest personalized content
Ecosystem
The project has evolved rapidly and now includes:
LangGraph: An extension for building more complex, state-based workflows
LangGraph Platform: A full platform for deploying, monitoring, and managing LangGraph applications in production (not open-source)
LangSmith: A platform for debugging and monitoring applications (not open-source)
LangChain Expression Language (LCEL): A declarative language for building complex chains
LangServe (discontinued)
Conclusion and Next Steps
LangChain is one of the most promising tools for turning large language models into real, useful applications.
In the next posts of this series, we’ll dive into hands-on examples of RAG with LangChain, adding memory and conversation history, calling agents, and creating a chatbot interface using Streamlit.
If you're interested in developing AI applications, it's definitely worth exploring LangChain to bring your ideas to life quickly and efficiently.
This is the first part of our LangChain series. Stay tuned for the upcoming posts, where we’ll explore how to build applications with RAG, memory, interfaces (Streamlit), and agent calls!