Technologies Coding Problems Bookmarks Learning Paths

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What are middlewares in langchain? | Middleware | langchain Interview Questions | NextRound.in

Questions

1 of 1

1What are middlewares in langchain?

langchain

Context Engineering

Human in the loop

01 / 01

What are middlewares in langchain?

Difficulty: 5/10

middleware pattern, chain customization, error handling

In LangChain, middleware is a powerful mechanism introduced in version 1.0 that lets you customize and control your AI agent's internal workflow. It operates by providing a set of hooks to run custom logic at specific stages of the agent's core loop, such as before or after the model is called or a tool is executed

Middleware provides a way to more tightly control what happens inside the agent. Middleware is useful for the following:

1
Tracking agent behavior with logging, analytics, and debugging.
2
Transforming prompts, tool selection, and output formatting.
3
Adding retries, fallbacks, and early termination logic.
4
Applying rate limits, guardrails, and PII detection.

Add middleware by passing them to createAgent:

Scenario Questions

0-2 years experience

1We need to log every prompt that goes to the LLM. How would you add a middleware in LangChain to do that?
2If a middleware you wrote returns None for the modified input, what will happen when the chain runs?

2-5 years experience

1Your chain sometimes hits rate‑limit errors. You added a retry middleware but the chain still crashes. What could be wrong?

Follow-up Questions

How would you unit‑test a custom middleware?
What are the trade‑offs of placing a logging middleware before versus after the LLM call?
Can you describe how middleware interacts with LangChain's callback system?

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2Explain why the order of a caching middleware and a logging middleware matters, and which order you would choose.

3How would you skip a particular middleware for inputs that match a certain pattern?

5-8 years experience

1Design a middleware stack for a multi‑tenant SaaS product that uses LangChain, ensuring per‑tenant rate limiting, isolation, and observability.
2Discuss the performance impact of chaining many middlewares and how you would mitigate added latency.
3If a middleware needs to keep state across multiple chain invocations, how would you implement it safely?

8+ years experience

1Your team is migrating from a custom LLM orchestration layer to LangChain. How would you move existing request/response interceptors into LangChain middleware with minimal downtime?
2What governance policies would you establish so multiple teams can safely add new middlewares to a shared LangChain codebase?
3A security‑critical middleware is becoming a bottleneck at scale. How would you refactor it to keep the security guarantees while improving throughput?