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Orchestre Architecture Overview

High-Level Architecture

Core Concepts

MCP Server Architecture

Orchestre operates as an MCP (Model Context Protocol) server that provides:

  1. Dynamic Prompts: 20 essential prompts that adapt to your project context
  2. MCP Tools: 8 specialized tools for project initialization, analysis, and visual context capture
  3. Resource System: Contextual access to project knowledge and patterns

How It Works

  1. User Interaction: You invoke prompts through Claude Desktop/Code using the MCP protocol
  2. Context Discovery: Orchestre analyzes your project structure and existing patterns
  3. Adaptive Behavior: Prompts adjust their behavior based on discovered context
  4. Tool Execution: MCP tools perform specific operations when needed

Prompt System Architecture

Dynamic Context Adaptation

Template-Aware Behavior

Prompts automatically adapt based on your project template:

  • MakerKit Projects: Uses Supabase conventions, understands team/billing structure
  • Cloudflare Projects: Optimizes for edge runtime, uses Workers-specific APIs
  • React Native Projects: Mobile-first patterns, handles platform-specific code

State-Based Intelligence

Prompts understand your project's lifecycle:

  • MVP Phase: Focus on core features, skip premature optimizations
  • Scaling Phase: Include performance optimizations, add monitoring
  • Production Phase: Emphasize security, error handling, observability

Resource System

URI Scheme

Orchestre uses a custom URI scheme for resource access:

orchestre://[resource-type]/[path]

Examples:

  • orchestre://project/prompts.json - Project configuration
  • orchestre://memory/features/auth - Feature-specific knowledge
  • orchestre://template/makerkit-nextjs/patterns - Template patterns
  • orchestre://knowledge/insights - Discovered insights

Resource Types

TypePurposeExample Usage
projectProject-specific configurationCurrent prompt state, settings
memoryContextual knowledge storageFeature documentation, decisions
templateTemplate-specific resourcesPatterns, conventions, examples
knowledgeDiscovered patterns and insightsCode patterns, best practices

Memory System Architecture

Distributed Knowledge

Knowledge Lifecycle

  1. Discovery: Prompts analyze existing code and documentation
  2. Storage: Knowledge stored in contextual CLAUDE.md files
  3. Evolution: Documentation evolves with the codebase
  4. Retrieval: Future prompts access accumulated knowledge

Tool Architecture

Available Tools

  1. initialize_project: Smart template initialization

    • Clones templates or custom repositories
    • Sets up project structure
    • Installs dependencies
  2. analyze_project: AI-powered requirement analysis

    • Uses Gemini for complexity assessment
    • Identifies technical requirements
    • Suggests appropriate approach
  3. generate_plan: Adaptive implementation planning

    • Creates phased development plans
    • Considers project complexity
    • Optimizes for parallelization
  4. install_commands: Orchestre setup

    • Creates .orchestre directory
    • Generates prompts.json configuration
    • Sets up memory templates

Tool Integration

Security Considerations

API Key Management

  • API keys stored as environment variables
  • Never committed to version control
  • Validated before tool execution

File System Access

  • Scoped to project directory
  • No arbitrary file system access
  • Safe path resolution

Memory Isolation

  • Project memories isolated
  • No cross-project data leakage
  • Git-friendly text format

Performance Characteristics

Response Times

  • Prompts: < 100ms (in-memory)
  • Tools (local): < 1s (file I/O)
  • Tools (AI): 2-7s (API calls)
  • Resource access: < 50ms

Scalability

  • Stateless server design
  • Minimal memory footprint
  • Efficient file system access
  • Cached template data

Future Architecture Considerations

Planned Enhancements

  1. Vector embeddings for semantic memory search
  2. Graph-based knowledge relationships
  3. Multi-modal support for diagrams/images
  4. Distributed team knowledge sharing

Extension Points

  • Custom resource providers
  • Additional AI model support
  • Plugin architecture for tools
  • Custom prompt templates

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