LangChain
The agent framework that outlived the hype cycle
What it is
The original LLM application framework, now consolidated around durable agent runtimes: abstractions for tools, retrieval, memory, and multi-step agents across every major model provider, in Python and JavaScript.
Why it's interesting
Still the most-adopted way to build agents, and its 1.x consolidation around LangGraph marks the ecosystem's real shift — from prompt chains to long-running, stateful agent systems.
Use cases
- Provider-agnostic agent and RAG applications
- Tool-using agents with durable state
- Standardizing LLM plumbing across a team
Who it's for
Application developers; enterprises standardizing an agent stack
Setup
Easy. Python 3.9+ or Node; API keys for your model providers. Easy to install, sizeable API surface to master.
Limitations & cautions
Abstraction-heavy by design, with a history of API churn between major versions. Learn the primitives before adopting the whole tower.
Editorial takeaway
Not the newest thing in the room anymore — which is exactly why it's safe to build on.