LangChain & LangGraph Interview Questions Interview Guide

Sample Answer:

I built a research agent with 8 tools (search, calculate, summarize). Main challenge: agent looping and token budget. I added max iterations, summarized long context, and implemented selective tool use. System now stable in production.

Sample Answer:

Enable verbose logging to see each step, inspect intermediate outputs, test prompts in isolation, check tool definitions for clarity. Use LangSmith to trace execution, identify bottlenecks in callbacks.

Sample Answer:

Migrated from custom state machine to LangGraph. Explicit state transitions eliminated edge cases, visual graphs clarified workflows, and built-in persistence simplified recovery and replay.

Sample Answer:

Design tool descriptions clearly to help LLM choose. Implement tool tagging by category, provide examples in prompt. Use function calling models for deterministic routing in critical paths.

Sample Answer:

Sequential: execute steps in order. Router: pick one path based on condition. Conditional: branch on input. Choose sequential for simple pipelines, router for mutually exclusive paths, conditional for complex logic.

Sample Answer:

Use ConversationBufferMemory for short chats, SummaryMemory for long chats (compress history), EntityMemory for tracking specific facts. Combine in hybrid model for production systems.

Sample Answer:

Compress prompts, summarize conversation history, cache retrieved documents, use lower-cost models for preprocessing. Monitor token budget throughout chain execution.