AI Tools•SOTAAZ Lab•March 30, 2026•KR

AgentScope RAG + Memory Architecture — Building Knowledge-Based Agents

Build knowledge-based agents with KnowledgeBase, vector stores (Qdrant/Milvus), and ReMe long-term memory.

AgentScope RAG + Memory Architecture — Building Knowledge-Based Agents

An agent that can reason and use tools is powerful. An agent that can also search your documents and remember past interactions is transformative.

In this post, we'll add RAG (Retrieval-Augmented Generation) and long-term memory to AgentScope agents — turning them into knowledge workers that improve over time.

Series: Part 1: Getting Started | Part 2: Multi-Agent | Part 3: MCP Integration | Part 4 (this post) | Part 5: Realtime Voice | Part 6: Production

1. RAG Overview

AgentScope's RAG system is built around two abstractions:

`KnowledgeBase` — orchestrates document ingestion, chunking, embedding, and retrieval
`SimpleKnowledge` — a lightweight wrapper for ad-hoc text snippets

Documents → Reader → Chunks → Embedding → Vector Store
                                              ↓
User Query → Embedding → Similarity Search → Top-K Results → LLM

Install RAG dependencies:

The rest of this series is paid

✓ The whole “AgentScope multi-agent” series: 5 posts in English, 5 in Korean
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AgentScope RAG + Memory Architecture — Building Knowledge-Based Agents#

1. RAG Overview#

The rest of this series is paid

AgentScope RAG + Memory Architecture — Building Knowledge-Based Agents

1. RAG Overview