MRRA Documentation

MRRA (Mobility Retrieve-and-Reflect Agent) is an advanced mobility prediction framework that combines Large Language Models, Graph-based Retrieve-and-Reflect Generation (GraphRAG), real-time context services (MCP), and multi-agent collaboration technologies to provide a complete solution for intelligent mobility analysis.

🌟 Key Features

• 🧠 Intelligent Reasoning: Progressive interpretable decision-making process based on ReAct framework
• 🔍 Knowledge Graph Retrieval: GraphRAG technology deeply mines historical mobility patterns
• 🌐 Real-time Context Services: Integrated MCP protocol for real-time geographic, weather and external data
• 🤖 Multi-Agent Collaboration: Reflection mechanisms and inter-agent collaboration optimize predictions
• 🔌 Pluggable Architecture: Support for multiple LLM providers, retrieval systems and MCP servers
• 📊 Flexible Data Processing: Support for multiple trajectory data formats and preprocessing pipelines
• ⚡ High Performance: Parallel processing, asynchronous operations and intelligent caching optimization
• ✅ Robustness Guarantee: Progressive prediction ensures usable results at every step

🎯 Supported Prediction Tasks

• Next Location Prediction: Predict user's next location based on historical trajectories
• Trajectory Generation: Generate complete mobility trajectories for future time periods
• Long-term Prediction: Long-term mobility pattern prediction

Quick Start

How It Works

MRRA analyzes trajectory data from various sources and generates three types of outputs:

1. 🎯 Location Predictions - Next location predictions with confidence scores
2. 🧠 Reasoning Process - Interpretable AI reasoning and decision paths
3. 📊 Analysis Results - Complete prediction results with metadata

Architecture Overview

MRRA Intelligent Mobility Analysis Framework
├── 🎯 API Layer: AgentBuilder and high-level interfaces
├── 🧠 Core Layer: Type system, configuration management, protocol definitions
├── 🔌 Module Layer: Pluggable component ecosystem
│   ├── 🤖 LLM Providers: OpenAI, Qwen, SiliconFlow, DeepInfra
│   ├── 🔍 Retrieval Systems: GraphRAG, semantic search, vector indexing
│   └── 🌐 MCP Clients: Amap, weather services, real-time POI queries
├── 🛠️ Tools Layer: Geographic computation, time processing, data preprocessing
└── 📊 Data Layer: Trajectory batching, state management, prediction results

Use Cases

• Smart City Planning - Predict population movement patterns
• Transportation Optimization - Optimize routes and schedules
• Location-Based Services - Improve recommendation systems
• Urban Analytics - Understand mobility patterns and trends
• Research Applications - Academic research in mobility prediction