Introduction: Multi_Agent_AI—An LLM-Based Multi-Agent Document Processing System

Multi_Agent_AI is a modular AI system whose core functions include automatically recognizing PDF, JSON, and email formats, determining intent via a classification agent, routing to specialized processing agents, and implementing traceable multi-step workflows. This system aims to address the pain points of enterprise heterogeneous document processing, with both scalability and maintainability.

Keywords: multi-agent, LLM, document-processing, workflow-automation, classification

Background and Motivation

In enterprise automation scenarios, there are a large number of heterogeneous document inputs (such as invoice PDFs, API JSONs, customer emails, etc.). Traditional processing requires writing separate parsing logic for each format, which has high maintenance costs and is difficult to scale. The Multi_Agent_AI project leverages the understanding capabilities of LLMs to build a modular system that automatically recognizes, classifies, and routes to specialized agents to address this challenge.

System Architecture and Core Components

Architecture Philosophy

Adopts a three-layer architecture of 'Classification-Routing-Processing'. Adding new document types only requires adding corresponding processing agents, ensuring good scalability.

Core Components

• Classification Agent: Combines heuristic rules and LLMs to determine input formats (PDF/JSON/Email) and business intent, balancing fast recognition and complex scenario processing;
• JSON Agent: Validates structured data schemas and converts formats, ensuring API data quality and system integration;
• Email Agent: Extracts sender information, judges urgency, and structures content for easy import into systems like CRM;
• Shared Memory Layer: Uses Redis/SQLite to store metadata and session IDs, ensuring workflow context continuity and traceability.

Technical Implementation Details

• Development Environment: Python 3.8+, with clear dependency management;
• Shared Memory: Dual-backend design (Redis/SQLite), balancing high performance and lightweight deployment;
• LLM Integration: Reserved OpenAI API interface, supports replacing with open-source models to reduce vendor lock-in risk;
• Code Structure: Modular organization (agents/, memory/, utils/), facilitating collaborative iteration.

Application Scenarios and Value

Applicable Scenarios

Automation scenarios involving multiple document types such as financial bill processing, customer support ticket classification, supply chain document management, etc.

Core Value

Unified architecture reduces redundant development; LLM-driven classification capabilities can adapt to format changes without frequent rule adjustments.

Summary and Outlook

Multi_Agent_AI demonstrates an effective model of combining LLMs with traditional software engineering. It balances flexibility and maintainability through intelligent routing and modular design, serving as a reference architecture example for document processing automation systems.