Questions
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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?
- 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?
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?