How does LlamaIndex ChatEngine differ from QueryEngine?

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Quick reference

TL;DR

QueryEngine is stateless retrieve and synthesize; ChatEngine adds conversation memory and can rewrite underspecified follow-ups into standalone queries before retrieval.

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Easy to grasp

Picture a research assistant at a library. The QueryEngine version is the desk attendant. You walk up, ask one question, they pull a book, read you the relevant page, you leave. Every visit starts fresh; they do not remember you. The ChatEngine version is a personal researcher you book by the hour. They remember what you asked last time. When you say 'and what about its side effects?', they know 'its' means the drug you discussed three minutes ago, and they rewrite the question into something they can actually search before pulling the next book. The difference is memory and follow-up rewriting, not the books or the search.

Key concepts

RAG LLM Frameworks

Quick scan · 2–4 min

Everything important, quickly.

4 min: QueryEngine vs ChatEngine, the three ChatEngine modes, the antecedent-resolution failure mode, and how to pick a mode.

Side-by-side

Axis	QueryEngine	ChatEngine
State	Stateless per call	Conversation memory across turns
Turn handling	Literal query → retrieve → synthesize	Mode-dependent (condense / context / react)
Follow-up rewriting	No	Yes in condense_question mode
Tool-calling option	No (just retrieve)	Yes in react mode
Cost per turn	1 retrieval + 1 synthesis	Up to 1 rewrite + 1 retrieval + 1 synthesis
Best fit	Search box, single-turn Q&A	Multi-turn chatbot with referential follow-ups

Real-world examples

Real products, models, and research that use this idea.

LlamaIndex's documentation chatbot uses ChatEngine in `condense_question` mode because doc questions chain into 'how does this interact with X' follow-ups.
Notion AI and Linear's assistant patterns rely on the agentic mode equivalent. Retrieval as a tool, not as the default action per turn.
QueryEngine alone backs many 'search-over-PDFs' single-turn demos where each query is independent.
LangChain's ConversationalRetrievalChain solves the same problem with a similar two-step rewrite then retrieve flow under different naming.

Interviewer follow-ups

What an interviewer would ask next. Try answering before peeking at the approach.

QHow would you decide between condense_question and react?

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QWhat happens to the condensed query in condense_question mode if the history is misleading?

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QCould you implement condense_question yourself on top of QueryEngine?

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60-second cheat sheet

The night-before-the-interview bullets. Scan these on the way to the call.

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References

Primary sources. Skim if you want the original framing.