What is LangChain?
LangChain is a modular open-source framework for building applications powered by Large Language Models (LLMs). A model on its own (like OpenAI’s GPT-4 or Anthropic’s Claude) only sees the text it is given. It keeps no memory between calls, cannot reach live data, and cannot take actions unless an application provides those capabilities.
LangChain fills this gap. It provides the building blocks to connect LLMs to external data sources, conversation state, and tools. By standardizing these integration interfaces, LangChain helps developers build systems such as Retrieval-Augmented Generation (RAG) pipelines and AI agents that work through multi-step problems and take actions.
Core Architectural Components
LangChain breaks AI application development into reusable, interchangeable abstractions.
Prompts and Models
Rather than hardcoding string inputs, LangChain uses Prompt Templates. These templates allow developers to dynamically inject user queries, historical context, and explicit constraints into a structured format before sending it to the model. The framework provides a common chat model interface, so an organization can switch LLM providers (e.g., from OpenAI to a self-hosted Llama 3 model) with small code changes, which reduces lock-in to any one provider.
Memory
LLMs are stateless. To build conversational agents, the application must send the relevant chat history back to the model on every turn. Earlier LangChain versions provided Memory classes for this; since LangChain 1.0, conversation state is handled by the LangGraph runtime with checkpointers. Applications can keep a window of recent messages or store past interactions in a vector database and retrieve only the relevant ones, keeping the prompt within the model’s context limit.
The LangChain Expression Language (LCEL)
As applications grow, hand-written glue code between steps becomes hard to maintain. LangChain introduced the LangChain Expression Language (LCEL), a declarative syntax for composing pipelines with the pipe (|) operator. LCEL chains support synchronous, asynchronous, batch, and streaming execution through the same interface, and independent steps can run in parallel.
Retrieval-Augmented Generation (RAG)
A model on its own cannot access private company data. LangChain is one of the most widely used frameworks for building RAG pipelines to solve this problem.
When a user asks a specific question (e.g., “What does our EMEA travel policy say about upgrades?”), the LangChain pipeline converts the question into an embedding and runs a semantic search against a vector store (like Pinecone or Milvus). It retrieves the most relevant internal documents, inserts them into the Prompt Template, and instructs the LLM to answer using only the provided context. This reduces hallucinations and grounds answers in the organization’s own documents, though it does not prevent every error.
Agents and Tool Calling
LangChain also provides an agent framework. Instead of running a fixed sequence of steps, an Agent uses the LLM to decide which steps to take to reach a goal. In LangChain 1.0, agents are created with create_agent, which runs on the LangGraph runtime.
LangChain equips the Agent with a defined set of Tools. A tool might be a Python REPL, a calculator, a web scraper, or a SQL connector. When a user makes a request, the Agent evaluates it. If it needs more information, it decides to call a Tool (e.g., generating and running a SQL query against an Apache Iceberg table). It reviews the returned data, decides whether it is enough, and repeats this ReAct (Reason and Act) loop until it can answer or reaches a step limit.
Integration with the Data Lakehouse
In data architectures, LangChain can act as the application layer above the open Data Lakehouse. Organizations build LangChain agents that query analytical engines like Dremio or Trino through governed connections.
By giving the Agent tools that work through the Semantic Layer, organizations let business users ask analytical questions in natural language. The Agent reads catalog metadata, generates SQL, sends the query to the engine, and interprets the results, connecting AI applications to enterprise data under the same access rules as other users.
Summary of Technical Value
LangChain helped move LLM development from experimental scripts toward structured software engineering. By providing standard abstractions for prompts, data retrieval, and tool calling, it helps organizations build and deploy AI applications. It remains a widely used framework for connecting language models to the data in the enterprise lakehouse.
Learn More
To learn more about the Data Lakehouse, read The Lakehouse Built for Everyone by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.