LangChain: A Developer's Overview

Published on February 8, 2026

LangChain is a framework for building applications that use large language models (LLMs). It provides a standard way to compose prompts, model calls, retrieval, and tools so you can build chatbots, agents, and RAG (retrieval-augmented generation) pipelines without writing the same glue code repeatedly.

What problem it solves

Integrating LLMs into applications usually means calling APIs, managing prompts, handling context windows, and chaining multiple steps (retrieval, reasoning, tools). Doing this from scratch leads to repeated glue code and inconsistent patterns. LangChain provides a standard way to compose LLM calls with prompts, memory, external data (e.g. vector stores), and tools (APIs, code execution), so you can build chatbots, agents, and RAG pipelines without reimplementing common patterns.

How it works at a high level

LangChain models an application as a graph or a linear chain of steps. Each step is a "node" (e.g. a prompt template, an LLM call, a retriever, or a tool). You define the flow, and the runtime runs the steps, passing outputs as inputs to the next step. Built-in integrations handle connection to OpenAI, Anthropic, local models, vector stores, and document loaders. The framework also standardizes interfaces for "runnables" (units of work), so you can swap providers or add middleware (e.g. logging, tracing) in one place.

Key features

Chains and agents – Compose prompts, LLMs, tools, and retrieval into reusable flows. Agents can decide which tool to call next.
Document loaders and text splitters – Ingest PDFs, web pages, and other sources. Split text into chunks suitable for embedding and retrieval.
Vector stores and retrievers – Store and query embeddings. Plug in Pinecone, Chroma, FAISS, and others for RAG.
Memory – Conversation and entity memory so agents can keep context across turns.
Tool use – Define tools (e.g. search, calculator, API calls) and let the LLM choose when to call them (ReAct-style or function-calling).
LangSmith (optional) – Observability, debugging, and evaluation for chains and agents.

Where it fits in the AI/ML stack

LangChain sits between your application and one or more LLM providers (OpenAI, Anthropic, open-source models). It does not replace the model or the embedding API. It orchestrates them. Typical stack: app → LangChain (chains/agents) → LLM API + optional vector store + optional tools. It is often used with Python for backend services and prototypes. JS/TS support exists for Node and browser. It complements ML frameworks (e.g. PyTorch) by focusing on application-level composition, not training.

When to use it

You need RAG (retrieval-augmented generation) with documents or knowledge bases.
You are building a chatbot or agent that uses tools (search, APIs, code).
You want to try multiple LLM providers or swap models without rewriting orchestration logic.
You need prompt templating, memory, or evaluation and prefer a standard library over ad-hoc code.

When not to use it

You only need a single direct call to an LLM API. A thin client is enough.
You require minimal dependencies or a very small runtime. LangChain pulls in many integrations.
You need full control over prompts and execution order. The abstraction may get in the way.
Your stack is not Python or JS/TS. Community support is strongest there.

Alternatives

LlamaIndex – Focused on data indexing and RAG. Strong for document QA and retrieval-centric apps.
Semantic Kernel (Microsoft) – Plugin and orchestration layer for LLMs. Good fit for .NET and Azure.
Direct API + custom code – Using OpenAI/Anthropic SDKs and your own orchestration when you need minimal abstraction.
CrewAI, AutoGen – Multi-agent frameworks. Consider these if you need several agents collaborating more than you need LangChain's chain-centric model.