Architecture

Polyglot's architecture is built around a central principle: controlled memory management for AI research workflows. Every component is designed to preserve, enhance, and leverage conversational memory across different AI models and research sessions.

Core Architecture Philosophy

Memory-Centric Design

Traditional AI chat interfaces treat conversations as isolated sessions. Polyglot treats them as accumulated research memory that grows more valuable over time:

  • Persistent Context: Conversations maintain full context across browser sessions and model switches

  • Memory Evolution: Research insights accumulate and evolve through multiple interactions

  • Knowledge Integration: RAG documents and MCP tools become part of persistent memory

  • Cross-Model Continuity: Memory context transfers seamlessly between different AI models

Research-First Architecture

Every architectural decision prioritizes research workflows over generic chat functionality:

  • Comparative Studies: Architecture supports running identical prompts across multiple models with consistent context

  • Long-term Projects: System design accommodates research spanning weeks or months with growing knowledge bases

  • Knowledge Synthesis: Built-in support for integrating external documents and tools into AI conversations

  • Privacy Control: Research data remains local-first with optional synchronization

System Architecture Layers

┌─────────────────────────────────────────────────────────────┐
│                     Research Interface Layer                │
│  ┌─────────────────┐  ┌─────────────────┐  ┌──────────────┐ │
│  │  Conversation   │  │   Comparative   │  │  Knowledge   │ │
│  │   Management    │  │   Analysis      │  │ Integration  │ │
│  └─────────────────┘  └─────────────────┘  └──────────────┘ │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│                    Memory Management Layer                  │
│  ┌─────────────────┐  ┌─────────────────┐  ┌──────────────┐ │
│  │ Memory Context  │  │ Research State  │  │ Cross-Model  │ │
│  │  Preservation   │  │   Tracking      │  │   Context    │ │
│  └─────────────────┘  └─────────────────┘  └──────────────┘ │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│                   Knowledge Integration Layer                │
│  ┌─────────────────┐  ┌─────────────────┐  ┌──────────────┐ │
│  │  RAG Document  │  │  MCP Tool       │  │  Semantic    │ │
│  │   Processing    │  │  Integration    │  │   Search     │ │
│  └─────────────────┘  └─────────────────┘  └──────────────┘ │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│                    AI Provider Integration                   │
│  ┌─────────────────┐  ┌─────────────────┐  ┌──────────────┐ │
│  │ Multi-Provider  │  │ Context Transfer│  │ Performance  │ │
│  │   API Layer     │  │  & Adaptation   │  │  Monitoring  │ │
│  └─────────────────┘  └─────────────────┘  └──────────────┘ │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│                     Storage & Persistence                   │
│  ┌─────────────────┐  ┌─────────────────┐  ┌──────────────┐ │
│  │ Local Storage   │  │ Memory Context  │  │  Optional    │ │
│  │   (IndexedDB)   │  │   Management    │  │   Sync       │ │
│  └─────────────────┘  └─────────────────┘  └──────────────┘ │
└─────────────────────────────────────────────────────────────┘

Key Architectural Components

Memory Management System

Purpose: Preserve and transfer conversation context across models and sessions

Components:

  • Context Snapshots: Full conversation state with memory markers and research insights

  • Memory Markers: Extracted insights, decisions, and research findings that persist

  • Cross-Model Translation: Adapting memory context for different AI model capabilities

  • Research State Tracking: Long-term project context and hypothesis evolution

Research Benefits:

  • Switch from GPT-4 to Claude mid-conversation without losing context

  • Build on previous research sessions with accumulated knowledge

  • Track insight evolution over weeks or months of research

  • Compare model performance with identical memory context

Knowledge Integration Framework

Purpose: Integrate external documents and tools into AI conversations

Components:

  • RAG Processing: Document chunking, embedding, and semantic search

  • MCP Integration: Tool access and external data source connections

  • Context Weaving: Intelligent integration of knowledge into conversation flow

  • Citation Tracking: Maintain provenance of information sources

Research Benefits:

  • Ground AI responses in your specific research documents and data

  • Access real-time information through connected tools and APIs

  • Maintain citation trails for research reproducibility

  • Build comprehensive knowledge bases that enhance AI capabilities

Multi-Model Research Platform

Purpose: Enable seamless switching between AI models for comparative research

Components:

  • Provider Abstraction: Unified interface for OpenAI, Anthropic, Google, and local models

  • Context Adaptation: Intelligent context transfer between different model architectures

  • Performance Monitoring: Consistent metrics across all AI providers

  • Comparative Analytics: Side-by-side analysis of model responses

Research Benefits:

  • Run identical experiments across multiple AI models

  • Identify model-specific strengths and biases in controlled conditions

  • Build model-agnostic research workflows

  • Generate comparative analyses with consistent methodology

Local-First Storage Architecture

Purpose: Maintain complete data control while enabling optional collaboration

Components:

  • IndexedDB Persistence: Browser-based storage for all research data

  • Encryption Layer: Client-side encryption for sensitive research content

  • Sync Protocol: Optional server synchronization with privacy controls

  • Conflict Resolution: Intelligent merging of research data across devices

Research Benefits:

  • Complete ownership of research data and conversations

  • Offline-capable research environment

  • Optional collaboration while maintaining privacy control

  • Multi-device access to research projects

Research Workflow Architecture

Comparative Study Workflow

Research Question
       ↓
┌─────────────────┐
│ Baseline Setup  │ ← Identical context and prompts
└─────────────────┘
       ↓
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Model A Test  │    │   Model B Test  │    │   Model C Test  │
│   (with memory  │    │   (with memory  │    │   (with memory  │
│    context)     │    │    context)     │    │    context)     │
└─────────────────┘    └─────────────────┘    └─────────────────┘
       ↓                       ↓                       ↓
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│  Response A +   │    │  Response B +   │    │  Response C +   │
│ Updated Memory  │    │ Updated Memory  │    │ Updated Memory  │
└─────────────────┘    └─────────────────┘    └─────────────────┘
       ↓                       ↓                       ↓
       └───────────────────────┼───────────────────────┘
                              ↓
                 ┌─────────────────────────┐
                 │  Comparative Analysis   │
                 │  + Memory Integration   │
                 │  + Research Synthesis   │
                 └─────────────────────────┘

Long-Term Research Workflow

Project Initiation
       ↓
┌─────────────────┐
│ Knowledge Base  │ ← RAG documents, MCP tools
│   Integration   │
└─────────────────┘
       ↓
┌─────────────────┐
│ Session 1:      │ ← Initial conversations with memory markers
│ Foundation      │
└─────────────────┘
       ↓
┌─────────────────┐
│ Session 2:      │ ← Builds on Session 1 memory + new insights
│ Development     │
└─────────────────┘
       ↓
┌─────────────────┐
│ Session N:      │ ← Accumulated memory + evolved understanding
│ Synthesis       │
└─────────────────┘
       ↓
┌─────────────────┐
│ Research        │ ← Export findings with full provenance
│ Export          │
└─────────────────┘

Data Flow Architecture

Memory Context Flow

User Input → Context Assembly → Model Processing → Response + Memory Update
     ↑              ↓                    ↓                    ↓
     └──────── Memory Markers ←── Response Analysis ←── Context Integration
                    ↓
              Research State Update
                    ↓
            Cross-Session Persistence

Context Assembly Process:

  1. Current Conversation: Recent message history and immediate context

  2. Memory Markers: Persistent insights and research findings from previous sessions

  3. Knowledge Base: Relevant RAG documents and MCP tool results

  4. Research Context: Project goals, methodology, and accumulated understanding

  5. Model History: Previous model interactions and comparative context

Knowledge Integration Flow

External Knowledge → Processing → Integration → Context Enhancement
       ↓                ↓            ↓              ↓
   RAG Documents    Chunking +    Semantic      Enhanced AI
   MCP Tools        Embedding     Search        Responses
   External APIs    Indexing      Ranking       Research Context

Integration Process:

  1. Document Processing: Semantic chunking and embedding generation

  2. Tool Integration: MCP server connections and capability mapping

  3. Context Weaving: Intelligent integration into conversation flow

  4. Citation Management: Maintaining provenance and research integrity

Performance and Scalability

Memory Management Performance

Optimization Strategies:

  • Context Caching: Pre-loaded memory contexts for instant model switching

  • Semantic Indexing: Fast retrieval of relevant memory markers and knowledge

  • Incremental Updates: Efficient memory marker updates without full context reload

  • Compression: Lossless compression of conversation data with integrity preservation

Scalability Targets:

  • Context Retrieval: Sub-100ms for any conversation or memory marker

  • Model Switching: Sub-second model changes with full context preservation

  • Knowledge Search: Real-time semantic search across large document collections

  • Research Projects: Support for projects spanning months with gigabytes of data

Storage Architecture Scaling

Local Storage Management:

  • Intelligent Archiving: Automatic compression of older conversations with research preservation

  • Priority-Based Retention: Critical research data preserved, temporary data cleaned

  • Memory Optimization: Efficient storage patterns for long-term research projects

  • Backup Integration: Seamless export/import for data migration and backup

Optional Cloud Scaling:

  • Selective Sync: Granular control over what data synchronizes across devices

  • Privacy-Preserving Sync: End-to-end encryption with zero-knowledge architecture

  • Collaborative Research: Multi-researcher coordination with individual privacy preservation

  • Infrastructure Agnostic: Deploy sync server on any infrastructure

Security and Privacy Architecture

Local-First Security Model

Data Sovereignty:

  • Local Processing: All sensitive operations happen on user devices

  • Optional Sync: Cloud synchronization is opt-in with granular controls

  • Encryption: End-to-end encryption for any data that leaves the device

  • Zero-Knowledge: Sync servers cannot access conversation content or research data

Research Data Protection:

  • Compartmentalization: Research projects can be isolated for sensitive work

  • Access Controls: Fine-grained permissions for collaborative research

  • Audit Trails: Complete logging of data access and modifications

  • Data Retention: User-controlled retention policies for research compliance

Privacy-Preserving Collaboration

Multi-Researcher Support:

  • Individual Privacy: Personal conversations remain private in collaborative projects

  • Shared Insights: Aggregated research findings can be shared while preserving individual data

  • Anonymization: Contribution tracking with identity protection

  • Selective Sharing: Granular control over what research components are shared

This architecture enables Polyglot to function as a comprehensive AI research environment while maintaining the simplicity and performance of local-first design.

PreviousQuick StartNextClient Storage

Last updated