Build a Multiagent RAG system with A2A Protocol and LangChain
Introduction
This Oracle Autonomous AI Database solution integrates a multiagent retrieval-augmented generation (RAG) system, known as agentic RAG, to improve communication and performance with open source large language models (LLMs). Agentic RAG changes how users access information across document repositories, websites, and codebases. It extracts insights from unstructured data, automates retrieval at scale, and lets LLMs deliver synthesized, data-informed responses that support faster decision-making.
A key differentiator of this solution is the integration of the Agent2Agent (A2A) protocol, an open standard developed by Google. The A2A protocol facilitates communication and collaboration between agents. Unlike traditional monolithic pipelines, A2A allows independent agent deployment, dynamic discovery via agent cards, and detailed task management, significantly improving scalability and simplifying operations in multiagent systems. The solution uses LangChain integration with Oracle AI Database, simplifying the setup of vector stores and search while providing a flexible framework to orchestrate complex, multiagent chain-of-thought (CoT) reasoning.
The system addresses inherent scaling bottlenecks in multiagent implementations by deploying each agent type (planner, researcher, and synthesizer) on dedicated compute clusters with tailored resource allocation. This approach provides fault isolation and operational efficiency by ensuring workload spikes in one agent type don’t affect others. Furthermore, A2A supports enterprise compliance with customizable security policies, authentication schemes—including JSON Web Token (JWT) and OpenID Connect (OIDC)—and detailed, agent-level audit logging.
Demo
Prerequisites and setup
- Oracle Cloud account—sign-up page
- Oracle AI Database—documentation
- Oracle Autonomous AI Database—documentation
- A2A protocol—documentation
- Gradio—documentation
- LangChain—documentation
- Trafilatura—documentation