nageoffer/ragent

Health v2 · maintenance only

Adoption not part of the score

3779 stars · 763 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Ragent AI is a production-grade Java platform for building Agentic RAG applications, covering document ingestion, hybrid retrieval (vector, keyword, knowledge graph, web search), intent recognition, query rewriting, session memory, and MCP tool calling. It is also positioned as a hands-on learning project for Java backend developers transitioning into AI engineering.

Use cases

• build an enterprise RAG chatbot over internal documents
• implement hybrid retrieval with vector, keyword, and knowledge graph recall
• add MCP tool calling to an LLM agent in Java
• manage conversation memory with token cost control
• learn how production RAG systems are engineered end to end
• add rate limiting and fair queuing in front of LLM services
• trace and evaluate RAG answers with source attribution

When to choose

• you are a Java/Spring developer building a RAG or agent system in production
• you need a full pipeline from document ingestion to grounded answers with tracing
• you want a realistic, enterprise-level reference project for learning AI engineering

When to avoid

• you need a Python-based RAG stack like LangChain or LlamaIndex
• you only need a lightweight demo or a simple vector search wrapper
• you want a managed SaaS RAG service rather than a self-hosted platform

Facets

application · maturity active

rag agent-framework mcp search-engine chatbot llm-inference prompt-engineering web-framework large-language-models chatbots developer-tools education jvm self-hosted agentic-rag spring-ai hybrid-retrieval knowledge-base rerank session-memory interview-preparation enterprise retrieval-augmented-generation ai-agents natural-language-processing web-server docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="nageoffer/ragent")