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Architecture Overview

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System Philosophy

1. Modularity: Every component is designed as a standalone, pluggable module with well-defined interfaces
2. Scalability: From single-file edits to enterprise deployments, the architecture scales seamlessly
3. Observability: Built-in monitoring, tracing, and telemetry at every layer
4. Extensibility: Plugin architecture for agents, tools, providers, and integrations
5. Security-First: Multi-layered security with approval systems, sandboxing, and audit trails

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High-Level Architecture

┌─────────────────────────────────────────────────────────────────┐
│                        CLI Interface Layer                       │
│  ┌──────────────┐  ┌──────────────┐  ┌─────────────────────┐  │
│  │   Terminal   │  │   Web UI     │  │   Background API    │  │
│  │   Interface  │  │   Dashboard  │  │     Server          │  │
│  └──────────────┘  └──────────────┘  └─────────────────────┘  │
└──────────────────────────┬──────────────────────────────────────┘
                           │
┌──────────────────────────┴──────────────────────────────────────┐
│                    Orchestration Layer                           │
│  ┌──────────────────┐  ┌──────────────────┐  ┌───────────────┐ │
│  │   Main           │  │   Streaming      │  │   VM          │ │
│  │   Orchestrator   │  │   Orchestrator   │  │   Orchestrator│ │
│  └──────────────────┘  └──────────────────┘  └───────────────┘ │
└──────────────────────────┬──────────────────────────────────────┘
                           │
┌──────────────────────────┴──────────────────────────────────────┐
│                       Agent System                               │
│  ┌─────────────┐  ┌─────────────┐  ┌──────────────────────┐   │
│  │   Universal │  │ Specialized │  │   Virtualized        │   │
│  │   Agent     │  │   Agents    │  │   Agents             │   │
│  └─────────────┘  └─────────────┘  └──────────────────────┘   │
└──────────────────────────┬──────────────────────────────────────┘
                           │
┌──────────────────────────┴──────────────────────────────────────┐
│                      Service Layer                               │
│  ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌────────┐ ┌──────────┐  │
│  │ Agent   │ │Planning │ │ Memory  │ │  LSP   │ │  Tool    │  │
│  │ Service │ │ Service │ │ Service │ │Service │ │ Service  │  │
│  └─────────┘ └─────────┘ └─────────┘ └────────┘ └──────────┘  │
└──────────────────────────┬──────────────────────────────────────┘
                           │
┌──────────────────────────┴──────────────────────────────────────┐
│                    Infrastructure Layer                          │
│  ┌─────────┐ ┌──────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
│  │   AI    │ │  Memory  │ │ Context │ │  Event  │ │ Monitor │ │
│  │Provider │ │ Provider │ │   RAG   │ │  System │ │  System │ │
│  └─────────┘ └──────────┘ └─────────┘ └─────────┘ └─────────┘ │
└─────────────────────────────────────────────────────────────────┘

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Core Components

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1. Orchestration Layer

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Main Orchestrator

• Purpose: Top-level coordinator for the entire system
• Responsibilities:
  • Service lifecycle management
  • Global error handling and recovery
  • Resource allocation and cleanup
  • Graceful shutdown coordination
• Location: /src/cli/main-orchestrator.ts

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Streaming Orchestrator

• Purpose: Real-time message processing and agent coordination
• Responsibilities:
  • Message queue management
  • Agent lifecycle (spawn, monitor, terminate)
  • Real-time streaming output
  • Context management and token tracking
  • Cognitive AI pipeline integration
• Location: /src/cli/streaming-orchestrator.ts

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VM Orchestrator

• Purpose: Manages virtualized agent execution environments
• Responsibilities:
  • Container lifecycle management
  • VM session coordination
  • Resource isolation
  • Cross-environment communication
• Location: /src/cli/virtualized-agents/vm-orchestrator.ts

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2. Agent System

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Universal Agent

• File operations and code editing
• Git operations
• Command execution
• Project analysis
• Natural language understanding

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Specialized Agents

• React Expert: React component development and optimization
• Next.js Expert: Next.js application development
• Backend Expert: API and server-side development
• DevOps Expert: Deployment, CI/CD, containerization
• Code Review Agent: Code quality analysis and security auditing
• AI SDK Integrator: Vercel AI SDK integration
• Framer Motion Expert: Animation and interaction design

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Virtualized Agents

• Sandboxed execution
• Custom runtime configurations
• Enhanced security
• Resource limits

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3. Service Layer

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Agent Service

• Agent registration and discovery
• Task queueing and scheduling
• Concurrent agent management
• Agent capability matching
• Location: /src/cli/services/agent-service.ts

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Planning Service

• Multi-step plan generation
• Task decomposition
• Execution flow management
• Progress tracking
• Location: /src/cli/services/planning-service.ts

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Memory Service

• Conversation history management
• Context persistence
• Semantic memory storage
• Memory retrieval and search
• Location: /src/cli/services/memory-service.ts

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LSP Service

• Language Server Protocol integration
• Code intelligence (completions, diagnostics, references)
• Multi-language support
• IDE-quality code understanding
• Location: /src/cli/services/lsp-service.ts

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Tool Service

• Tool registration and discovery
• Tool execution coordination
• Permission management
• Tool metadata and categorization
• Location: /src/cli/services/tool-service.ts

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4. Middleware System

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Middleware Manager

• Priority-based execution
• Timeout handling
• Retry logic
• Metrics collection
• Location: /src/cli/middleware/middleware-manager.ts

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Built-in Middleware

• Validation Middleware: Input validation and sanitization
• Audit Middleware: Operation logging and compliance
• Logging Middleware: Structured logging
• Performance Middleware: Performance tracking and optimization
• Security Middleware: Authorization and rate limiting

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5. Infrastructure Layer

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AI Providers

• Anthropic: Claude models with reasoning support
• OpenAI: GPT models
• Google: Gemini models
• Ollama: Local models
• OpenRouter: Multi-provider gateway
• Vercel AI SDK: Unified AI interface

• Automatic model routing based on task complexity
• Cost optimization
• Fallback mechanisms
• Token budget management

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Context & RAG System

• Workspace Indexing: Automatic codebase analysis and indexing
• Semantic Search: Vector-based code search with ChromaDB
• Embeddings: Code and documentation embeddings
• Token Management: Intelligent context window optimization
• Cache System: Multi-tier caching (semantic, API, tool results)

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Event System

• Event Bus: Centralized event distribution
• Event Types:
  • Agent events (spawn, complete, error)
  • Tool events (execute, result)
  • System events (startup, shutdown)
  • User events (input, approval)
• Subscribers: Logging, monitoring, UI updates, webhooks

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Monitoring System

• OpenTelemetry: Distributed tracing and metrics
• Prometheus: Metrics export and aggregation
• Sentry: Error tracking and performance monitoring
• Alerting: Multi-channel alert delivery (Slack, Discord)
• Health Checks: System health monitoring
• Location: /src/cli/monitoring/

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6. Tool System

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Core Tools

• File Operations: Read, write, edit, multi-edit, JSON patch
• Search: Glob pattern matching, grep, find files
• Git Operations: Status, diff, commit, branch management
• Command Execution: Bash commands with safety checks
• Code Analysis: LSP-based code intelligence

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Integration Tools

• Browser Automation: Playwright integration, Browserbase
• Web3: GOAT SDK, Coinbase AgentKit, wallet operations
• Figma: Design file access and manipulation
• Image Generation: AI image generation
• CAD/3D: Text-to-CAD, G-code generation

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Tool Security

• Approval System: User approval for high-risk operations
• Sandboxing: Isolated execution environments
• Rate Limiting: Operation frequency controls
• Audit Trail: Complete operation logging

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Data Flow

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Request Processing Flow

User Input
    ↓
Input Queue (debouncing, batching)
    ↓
Streaming Orchestrator
    ↓
Middleware Chain (validation, logging, audit)
    ↓
Agent Selection (capability matching)
    ↓
Agent Execution (streaming)
    ↓
Tool Invocation (with approval if needed)
    ↓
Result Processing
    ↓
UI Update (real-time streaming)
    ↓
Memory Storage (persistence)

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Context Flow

User Request
    ↓
Workspace Context Manager
    ↓
┌──────────────────────────────────────┐
│ Context Gathering                     │
│  - File tree                          │
│  - Git status                         │
│  - Recent changes                     │
│  - LSP diagnostics                    │
│  - Semantic search results            │
└──────────────────────────────────────┘
    ↓
Token Budget Manager (optimization)
    ↓
AI Provider (with optimized context)
    ↓
Streaming Response
    ↓
Context Update (incremental)

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Configuration Architecture

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Configuration Layers

1. Default Configuration: Built-in sensible defaults
2. User Configuration: ~/.nikcli/config.yaml
3. Project Configuration: .nikcli/config.yaml
4. Environment Variables: Runtime overrides
5. CLI Flags: Command-line overrides

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Configuration Manager

• Schema validation (Zod)
• Type-safe access
• Hot reloading
• Migration support
• Environment variable interpolation

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State Management

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State Categories

1. Session State: Current conversation, context, active agents
2. Persistent State: Configuration, history, cached data
3. Transient State: Streaming buffers, temporary files
4. Distributed State: Multi-agent coordination (Redis)

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State Synchronization

• Lock-based Coordination: AsyncLock for critical sections
• Event-driven Updates: Event emitters for state changes
• Snapshot System: Point-in-time state capture
• State Recovery: Automatic recovery from crashes

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Extension Points

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1. Custom Agents

class CustomAgent extends BaseAgent {
  async execute(task: string): Promise<Result> {
    // Custom logic
  }
}

agentService.registerAgent({
  name: 'custom-agent',
  handler: customAgentHandler
});

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2. Custom Tools

class CustomTool extends BaseTool {
  async execute(args: any): Promise<ToolExecutionResult> {
    // Tool implementation
  }
}

toolRegistry.registerTool('custom-tool', new CustomTool());

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3. Custom Middleware

class CustomMiddleware implements BaseMiddleware {
  async execute(req, next, ctx): Promise<MiddlewareResponse> {
    // Pre-processing
    const response = await next();
    // Post-processing
    return response;
  }
}

middlewareManager.register(new CustomMiddleware());

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4. Custom Providers

class CustomAIProvider implements AIProvider {
  async generateCompletion(prompt: string): Promise<Completion> {
    // Provider implementation
  }
}

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Performance Characteristics

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Scalability Metrics

• Concurrent Agents: Up to 10 agents (configurable)
• File Operations: Handles repositories with 100K+ files
• Context Window: Up to 200K tokens (model-dependent)
• Streaming Latency: < 50ms time-to-first-token
• Memory Footprint: ~100-500MB typical usage

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Optimization Strategies

1. Lazy Loading: Components loaded on-demand
2. Incremental Context: Only changed files re-indexed
3. Result Caching: Multi-tier cache (semantic, API, tool)
4. Token Optimization: Intelligent context pruning
5. Parallel Processing: Concurrent tool execution
6. Stream Processing: Real-time output without buffering

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Reliability & Resilience

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Error Handling

• Structured Errors: Categorized, typed error classes
• Error Recovery: Automatic retry with exponential backoff
• Graceful Degradation: Fallback to reduced functionality
• Error Reporting: Sentry integration for production monitoring

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Fault Tolerance

• Circuit Breakers: Prevent cascading failures
• Timeout Management: Per-operation timeout controls
• Resource Limits: Memory and CPU constraints
• Health Checks: Continuous system health monitoring

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Security Architecture

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Defense in Depth

1. Input Validation: Zod schemas at all boundaries
2. Approval System: User confirmation for risky operations
3. Sandboxing: Isolated execution environments
4. Rate Limiting: Operation frequency controls
5. Audit Logging: Complete operation history
6. Token Security: Secure credential management

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Security Policies

• Execution Policies: Configurable operation restrictions
• File Access Control: Workspace-scoped file operations
• Network Policies: Outbound connection controls
• Resource Quotas: CPU, memory, storage limits

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Deployment Models

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Local Development

• Single-process CLI
• In-memory state
• Local file system
• SQLite for persistence

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Background Mode

• Daemon process (nikd)
• API server on localhost
• Redis for state
• WebSocket for real-time updates

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Enterprise Deployment

• Kubernetes orchestration
• Distributed agents
• PostgreSQL database
• External monitoring (Prometheus, Grafana)
• Multi-tenant support

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Technology Stack

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Core Technologies

• Runtime: Node.js 18+ / Bun
• Language: TypeScript 5.x
• Package Manager: pnpm
• Build Tool: esbuild / Bun

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Key Dependencies

• AI SDK: Vercel AI SDK
• Vector DB: ChromaDB
• Cache: Upstash Redis
• Telemetry: OpenTelemetry
• Error Tracking: Sentry
• LSP: vscode-languageserver-node
• UI: Blessed, Ink (React for terminal)
• Web3: GOAT SDK, Coinbase AgentKit

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Future Architecture Roadmap

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Planned Enhancements

1. Distributed Agent Network: Multi-machine agent collaboration
2. Plugin Marketplace: Community-contributed extensions
3. GraphQL API: Structured API for integrations
4. WebAssembly Tools: High-performance tool execution
5. Federated Learning: Privacy-preserving model improvements
6. Multi-Modal Support: Image, audio, video processing
7. Real-time Collaboration: Multi-user sessions

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Related Documentation

• Layered Design - Deep dive into architectural layers
• Event System - Event-driven architecture details
• Streaming Architecture - Real-time streaming implementation
• Security Model - Security architecture and policies
• Performance - Performance optimization strategies

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Conclusion