Member-only story
LangChain Middleware: The Missing Layer Between Your Agent and Production
How to intercept, control, and extend AI agents without touching the logic that actually matters
You build an agent. It works. Then production happens.
The model API times out randomly. You need retry logic. Users are blowing past token limits. You need to compress history. A tool is about to send an email and nobody approved it. You need a human checkpoint before that fires.
The instinct is to fix this inside the agent. So you add a try/except block here, a message counter there, a conditional approval check somewhere else. Two weeks later, your agent is doing three jobs: the actual task, the infrastructure, and keeping all of it from falling apart at the seams.
This is the problem middleware exists to solve.
You do not fix production concerns by stuffing them into the agent. You pull them out into their own layer.
LangChain 1.0 introduced a formal middleware system for exactly this. A clean layer that hooks into the agent loop, handles operational concerns, and never touches the core logic. The agent stays focused. The plumbing lives somewhere else.
