ai-agent-interview-guide: A Chinese AI Agent Interview Prep Repository With Three Backend Implementations

What ai-agent-interview-guide actually is

This is a preparation repository, not a library you import. The README describes a closed loop: a learning roadmap, a question bank, a hands-on project, a resume template, STAR interview scripts and a mock Q&A set. The intended reader is someone moving into an AI Agent role, and the README says it is written for beginners (面向小白).

The scope is unusually wide for a single repo. The question bank is split into nine modules: basic concepts, core frameworks such as ReAct and Plan-and-Execute, RAG, tool calling, memory systems, multi-agent systems, large model fundamentals, engineering practice and prompt engineering. The README puts the total at more than 200 questions with answers, and the per-module table lists counts ranging from 17 to 29. Separately, docs/06-面试问答集 is described as over 100 project-level questions with STAR answers.

The three language versions matter more than the question count. Python uses FastAPI, LangChain, Milvus and Redis. Java uses Spring Boot 3, Spring AI, MyBatis Plus and Milvus. Go uses Gin, a self-built framework, Milvus and Redis. Each targets a different hiring pool, and the README names them explicitly: AI and algorithm roles for Python, Java backend for Java, Go backend and cloud native for Go.

One thing to be clear about up front: all documentation is in Chinese. The repository name is English, the code is Python, Java and Go, but the prose you would study from is Chinese. If your interview is in English, the question bank loses most of its value and you are left with the three projects.

The architecture the projects are built to demonstrate

The README includes an architecture diagram that is the real centerpiece, because it is what an interviewer would ask you to draw. A request enters through an API gateway, passes intent recognition, and reaches an Agent orchestrator. The orchestrator fans out to four agent types: a ReAct agent running a thought-action-observation loop, a planning agent that decomposes tasks, a RAG agent for retrieval and generation, and a reflection agent for quality checks.

Underneath that sits a support layer with six named components. Retrieval is multi-path: vector search, BM25, hybrid retrieval and RRF fusion. Memory is split into short-term (a Redis sliding window) and long-term (a vector database). Tools cover search, a calculator and database queries, plus MCP. Model routing handles multiple candidates with a three-state circuit breaker and automatic degradation. Tracing records every step, and a document ETL pipeline parses PDF and Word files, chunks them, vectorizes them and writes them to the store.

This is a deliberate design choice. The README states the projects aim at the architectural complexity of nageoffer/ragent, an existing open source project. That means the code is meant to be large enough to survive follow-up questions about circuit breakers, RRF fusion or memory eviction, rather than a minimal demo.

The trade-off is that a study project carrying this much surface area is hard to verify quickly. Nothing in the README documents test coverage, a benchmark, or a comparison against a simpler baseline. If you plan to put this on a resume, the burden is on you to understand each component well enough to explain why it is there, because the repository provides the structure and the questions but not evidence that the structure performs better than a single retrieval path.

Installing the Python project and making a first request

The README marks the Python version as the recommended starting point. It expects a local Milvus and Redis, since both appear in the stack, and it expects you to supply your own model API key through an environment file.

Start by entering the project directory and copying the environment template:

cd project-python
cp .env.example .env

Open .env and fill in your API key. The README does not enumerate the variable names beyond this instruction, so read .env.example to see which keys are expected before starting the server.

Then install dependencies and launch the API with uvicorn:

pip install -r requirements.txt
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

The README gives this exact command, including port 8000 and the app.main:app entry point. If it starts cleanly, the FastAPI service is listening on that port and you can send a request to it. The README does not document specific endpoints or a sample request body, so the route list is something you discover from the application code or the interactive API docs that FastAPI exposes by default.

The Java and Go versions follow the same pattern with their own toolchains. Java builds with Maven and runs a jar named agent-platform-1.0.0.jar; Go builds a binary from ./cmd/server and runs it directly. The README does not give environment setup steps for either beyond these commands, and it does not document how the Java or Go service reads its model credentials.

Where the repository is thin

The question bank is the strongest part and the least verifiable. Counts are stated in a table, but there is no index of individual questions in the README, no difficulty rating and no indication of which answers include code. You cannot tell from the repository description whether a given question has a two-line answer or a worked example.

Operational documentation is the weakest area. The README shows how to start each service and nothing about stopping, upgrading, migrating a vector collection, or rotating an API key. There is no documented rollback procedure, no health check endpoint, and no note on what happens when Milvus or Redis is unavailable at startup. For a study project that is acceptable; for anyone tempted to run it as a service, those gaps are the first thing to close.

The maintenance signal is mixed. The repository is not archived, and the last push was on 2026-04-01. That is roughly five and a half months before the date of this article, so the code has not moved recently, and there are no releases in the repository. Nothing here suggests abandonment, but nothing suggests a fast-moving project either. Treat the material as a fixed corpus rather than a living one.

Licensing deserves a specific note. The project is MIT, which is permissive for the code. The README adds a separate sentence saying the interview material and study documents are for learning reference and should not be used commercially. That sentence sits outside the MIT grant and its enforceability is unclear, so if you intend to reuse the question bank in a paid product, read the LICENSE file and consider the distinction between the code and the prose.

How it compares with building your own agent from a framework's docs

The obvious alternative is to skip the repository and go straight to a framework: LangChain or LangGraph for Python, Spring AI for Java, or one of the Go agent libraries. That path gives you current APIs and official documentation, and it is what most working engineers do.

The difference is what you get out of it. A framework teaches you the API surface; it does not tell you which questions an interviewer will ask about RAG chunking strategy or why a circuit breaker belongs in a model router. This repository inverts the order. You start from the question set, and the projects exist to give you something concrete to point at when you answer.

There is a cost to that inversion. Framework documentation is updated with each release, while a question bank is only as current as its last push, and this one was last pushed on 2026-04-01. Topics like MCP and model routing move quickly. If your target role is at a company using a specific stack, official docs will beat a study repository on accuracy every time.

The honest framing is that these are complements. Use the repository to find out what you do not know, then use the framework's own documentation to learn the current answer. The repository's nine-module structure is a reasonable map of the field; the framework docs are the terrain.

Cost, maintenance and what you are signing up for

The repository itself costs nothing and ships under MIT. The real cost is the runtime dependencies. All three projects use Milvus, and Python and Go also use Redis. Those are services you run somewhere, and the README does not describe a Docker Compose file or any container setup, so provisioning is on you.

Model calls are the other recurring cost, and it is unquantified. The README tells you to put an API key in .env but gives no token budget, no caching note and no estimate of what a full walkthrough of the projects would consume. Model routing with a three-state circuit breaker and automatic degradation implies multiple model candidates, which implies multiple credentials, though the README does not spell out the configuration.

Upgrade cost is low in the sense that there is little to upgrade: no releases exist, so there is no version to pin and no changelog to track. You would pull the default branch and read the diff. For a study repository that is fine. For anything you intend to keep running, the absence of releases and of a documented upgrade path means you own that problem entirely.

On the licence, the split between MIT code and a non-commercial note on the study material is the one thing worth reading carefully before reuse. This is not legal advice; if the distinction matters to you, that is a question for someone qualified to answer it.

Editorial conclusion

Adopt it if you are preparing for a Chinese-market AI Agent or backend role and want question banks plus a runnable FastAPI, Spring Boot or Gin project to talk about. Skip it if you need English material or a production framework, because this is a study repository whose projects exist to be explained in an interview. Before relying on it, verify three things: that the docs/01-面试八股文 module list matches the topics in your target job posting, that project-python actually starts with your own API key in .env, and that you can defend the architecture diagram in the README without reading from it.

Frequently asked questions

Official sources

1. bcefghj/ai-agent-interview-guide on GitHub
2. Issues
3. License: MIT
4. README