LangChain Interview Questions (with Explanations)

About this course

Ace your next LLM & Agentic AI/ML Engineer interview with 500 scenario-based practice questions — each with a detailed explanation. This question bank is carefully crafted to match what hiring teams at companies building agent systems actually test for: real engineering decisions, not textbook definitions. It covers the full spectrum of topics you'll face, including: multi-agent orchestration, stateful workflows, tool calling, memory systems, checkpointing, streaming, human-in-the-loop patterns, production deployment, and more.

Sample Questions1. You are designing a LangGraph system where a Researcher agent gathers data and a Writer agent drafts a report. What is the most appropriate graph structure?2. In a multi-agent LangGraph system, what happens if two parallel branches both modify the same state field that has no reducer defined?3. What is the difference between the Store (cross-thread memory) and the Checkpointer (within-thread memory) in LangGraph?4. You want to ensure a tool always returns a string to the LLM, even if it encounters an error. What is the recommended approach?5. Your chatbot uses a summarization strategy for long conversations. How do you implement the RemoveMessage pattern in LangGraph?6. How does the Functional API's @entrypoint differ from StateGraph in terms of checkpointing granularity?7. An LLM returns multiple tool_calls in a single response. How does the standard ToolNode handle this?8. You want a LangGraph agent where one tool's output is required as input for another tool. How should this be orchestrated?9. What is 'durable execution' in LangGraph, and how does it differ from simple retry logic?10. How do you implement selective human-in-the-loop: automatic execution for low-risk tools, approval required for high-risk tools?

Topics Covered:

• Foundations

Chat Models & LLM Interface — ChatM

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