The article introduces Spring AI Alibaba DeepResearch, a Java‑based system that automates end‑to‑end research by integrating multi‑agent workflows, configurable RAG pipelines, data ingestion, observability, and dynamic report generation, and provides detailed deployment and extension guidance.
This article walks through constructing a LangGraph graph that encapsulates three agents—task planning, web search, and report generation—into a DeepResearch application, then shows how to package and deploy the backend and frontend so users can interact with the system via a web UI.
This guide walks through deploying Alibaba's open‑source Tongyi‑DeepResearch 30B MoE model locally, configuring FastAPI and A2A interfaces, implementing a ReAct‑style agent with LangGraph, setting up research tools, and testing the full UI‑API‑Agent pipeline via CLI and Streamlit.
Amid the rapid rise of generative AI, this article examines the limitations of large language models and explains how Retrieval‑Augmented Generation (RAG), followed by the advanced paradigms DeepSearch and DeepResearch, progressively enhance knowledge handling through dynamic retrieval, multi‑agent reasoning, and autonomous research capabilities.
The article argues that the next wave of AI agents will shift from brittle, prompt‑driven workflows like Manus to truly autonomous, model‑centric agents trained with reinforcement learning and reasoning, exemplified by OpenAI's DeepResearch and Anthropic's Claude Sonnet 3.7, while the API‑driven market model collapses.
