Googles Open-Source Stack: Introducing AI Agents That Reflect and Research Beyond Basic Retrieval

We have all grown accustomed to the fact that large language models tend to «hallucinate.» To combat this, the RAG (Retrieval-Augmented Generation) method was developed, which allows the model to find answers in provided documents rather than making them up. However, the issue with most RAG systems is their straightforwardness: they simply retrieve the first relevant snippet and use it as an answer. This often results in a rewritten Wikipedia article instead of an in-depth analysis.

Recently, Google released an open-source project called Gemini Fullstack LangGraph. Essentially, it serves as a template for creating an AI agent that goes beyond mere searching and conducts a mini-research project with reflection and self-critique. Let’s dive into its underlying architecture.

On the surface, it’s a rather typical full-stack project: the frontend is built with React, while the backend is based on Python with FastAPI. However, the core of the project lies in its backend architecture, which utilizes LangGraph. This isn’t just a straightforward series of LLM calls; it’s a complex state graph that transforms passive information retrieval into an active research process.

Frontend: React (with Vite), Tailwind CSS, Shadcn UI.

Backend: LangGraph, FastAPI, Google Gemini.

Production: Docker, Redis (for Pub/Sub and streaming), PostgreSQL (to store states, threads, and queues).

This project is more than just a demo; it’s a production-ready template that can be used as a foundation for your product. However, the real value lies not in the code itself, but in the methodology employed.

Unlike traditional RAG models, which operate on a «find and answer» basis, this agent functions like a meticulous research scientist. The entire process revolves around an iterative cycle:

Hypothesis Generation (Search Queries). Upon receiving a user’s question, the agent, utilizing Gemini, formulates several initial search queries to cover various aspects of the topic.

Data Collection. Using the Google Search API, it gathers information based on those queries.

Reflection and Gap Analysis. Here’s where the magic happens. The agent doesn’t rush to generate an answer. Instead, it analyzes the information collected and poses critical questions to itself: “Is this data sufficient for a complete answer? Are all terms clarified? Are there any inconsistencies? What additional information do I need to find?” This step is crucial.

Iterative Refinement. If the agent concludes that the data is lacking, it formulates new, more detailed queries to fill in the identified “knowledge gaps.” It then returns to step 2.

Answer Synthesis. The cycle continues until the agent decides it has gathered enough information. Only then does it begin to generate the final, comprehensive answer, supporting it with citations from all the utilized sources.

This approach significantly differs from basic vector database searches.

Cost. Each step in this cycle constitutes a call to Gemini. The iterative reflection and refinement can lead to a quite high cost for a single answer.

Speed. It’s evident that such a multi-step process will operate considerably slower than a simple RAG. For real-time chats, this could pose a challenge.

Complexity. Configuring and debugging such an intricate state graph is no trivial task.

In conclusion, we encounter a classic dilemma. Is such a level of complexity necessary for most tasks? It seems that for about 90% of typical Q&A scenarios, this approach might be clear over-engineering. However, for the remaining 10% of inquiries where depth and accuracy are more critical than speed and cost, this methodology could prove beneficial.