LangChain

LangChain is an open source framework for building AI agents and LLM-powered applications. It gives developers a set of modular building blocks: model integrations, prompt templates, memory, tools, and chains. These can be composed into complete, production-ready systems without writing everything from scratch. For engineers building agents that reason, use tools, and connect to external data, LangChain cuts weeks of boilerplate down to a handful of lines of code.

How LangChain works

LangChain sits between your code and the language model. Instead of calling an LLM directly and manually wiring everything around it, you compose components that handle each part of the workflow. Here is what those components are:

• Model interface: A unified wrapper that lets you call any LLM (OpenAI, Anthropic, Hugging Face, and 1,000+ others) with the same code. Swap providers without rewriting your application.
• Prompt templates: Reusable, parameterized prompt structures that keep your inputs consistent and testable across different model calls.
• Chains: Sequences of steps where the output of one component feeds into the next. A chain might load a document, split it, embed it, search it, then pass the relevant chunks to the LLM. Everything runs in order, in a single call.
• Agents: The decision-making layer. An agent uses an LLM to decide, at each step, which tool to call and in what order. It loops until the task is done. LangChain agents run on LangGraph’s durable runtime, which gives them built-in persistence, checkpointing, and human-in-the-loop support.
• Memory: Modules that store conversation history so the model can reference earlier messages. LangChain offers several types, from a buffer that stores the full history to a summarization memory that compresses older turns.
• Tools: Connectors that let the agent call external APIs, run code, search the web, or query databases as part of its reasoning loop.

One core pattern you will use constantly is RAG, short for Retrieval-Augmented Generation. RAG is a technique where you give the LLM access to a knowledge base (your PDFs, internal docs, databases) so it can retrieve relevant facts before generating a response. LangChain makes this straightforward: load documents, split them into chunks, convert chunks into embeddings (numerical representations that capture meaning), store those embeddings in a vector database, and retrieve the closest matches at query time.

What you can build with LangChain

LangChain suits any developer who needs an LLM to do more than answer a single question. It connects the LLM to data, tools, and multi-step logic.

• RAG question-answering system: An internal search tool that ingests your company’s PDFs, Notion pages, or database records, converts them into a searchable vector index, and answers employee questions with cited sources. LangChain handles every stage of the LangChain RAG pipeline from document loading to retrieval.
• Conversational support agent: A customer-facing chatbot that remembers the thread of a conversation, pulls answers from a product knowledge base, and escalates when it hits the edge of what it knows. LangChain’s memory modules handle the session context.
• Multi-step research agent: A tool that takes a user’s question, searches the web, reads relevant pages, cross-references findings, and drafts a sourced report. The agent decides its own steps using LangChain’s ReAct loop.
• Document summarization pipeline: A system that ingests long contracts, reports, or legal documents, splits them into chunks, and produces a structured summary. Useful when documents exceed an LLM’s context window.
• Code review or generation assistant: A developer tool that understands a codebase, answers questions about it, suggests refactors, or auto-generates boilerplate for specified patterns.
• Data quality agent: A pipeline that monitors incoming data streams, validates records against predefined rules, and flags or corrects violations automatically before they reach downstream systems.