LangChain

// Overview

LangChain is an open-source framework for building agents and applications powered by large language models. It gives you a standard interface over models, embeddings, vector stores, retrievers, and tools, so you can wire these pieces together into chains and agents instead of writing provider-specific glue code for each one.

It is aimed at developers who want to connect an LLM to their own data and external systems without locking into a single model vendor. Because the abstractions are consistent across providers, you can swap one model for another as your needs change, and you can work at a high level for a quick start or drop down to lower-level components for fine-grained control.

As an app framework in the LLM orchestration space, LangChain sits at the center of a wider ecosystem: LangGraph for controllable agent workflows, Deep Agents for higher-level agent patterns, LangSmith for evaluation and debugging, and LangChain.js for JavaScript and TypeScript projects.

// What it does

• Standard interface across chat models, embeddings, vector stores, and retrievers, so you can swap providers without rewriting your app
• A large library of third-party integrations for model providers, tools, vector stores, and retrievers
• Real-time data augmentation: connect LLMs to internal and external data sources
• Modular, component-based design for rapid prototyping and iteration
• Flexible abstraction layers, from high-level chains to low-level components
• Pairs with LangGraph for agent orchestration and LangSmith for evals, observability, and debugging

// Getting started

Install the package, then initialize a chat model and call it. The README's example targets a hosted model, so you will need that provider's API key set in your environment.

uv add langchain

from langchain.chat_models import init_chat_model

model = init_chat_model("openai:gpt-5.5")
result = model.invoke("Hello, world!")

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Build a retrieval-augmented chatbot that answers from your own documents using LangChain's retrievers and vector store integrations
• Prototype and compare different model providers behind a single interface to find the best fit for a task
• Connect an LLM to external tools and data sources to build an agent that takes actions
• Stand up a production LLM app with evaluation and observability via the LangSmith integration

// How LangChain compares

LangChain alongside other open-source app frameworks tools AI/TLDR tracks, ranked by GitHub stars.