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Architecture Navigation

Core Architecture

DocumentDescriptionFocus Area
System OverviewHigh-level architecture & design principlesComponents, interactions, data flow
Request FlowRequest processing pipeline deep diveProcessing stages, memory management
BenchmarksPerformance benchmarks & optimizationMetrics, scaling, optimization
ConcurrencyWorker pools & threading modelGoroutines, channels, resource isolation

Internal Systems

DocumentDescriptionFocus Area
Plugin SystemHow plugins work internallyPlugin lifecycle, interfaces, execution
MCP SystemModel Context Protocol internalsTool discovery, execution, integration
Design DecisionsArchitecture rationale & trade-offsWhy we built it this way, alternatives

Architecture at a Glance

High-Performance Design Principles

  • Asynchronous Processing - Channel-based worker pools eliminate blocking
  • Memory Pool Management - Object reuse minimizes garbage collection
  • Provider Isolation - Independent resources prevent cascade failures
  • Plugin-First Architecture - Extensible without core modifications
  • Connection Optimization - HTTP/2, keep-alive, intelligent pooling

System Components Overview

Processing Flow: Transport → Router → Plugins → MCP → Workers → Providers

Key Performance Characteristics

MetricPerformanceDetails
Throughput10,000+ RPSSustained high-load performance
Latency11-59μs overheadMinimal processing overhead
MemoryOptimized poolingObject reuse minimizes GC pressure
Reliability100% success rateUnder 5000 RPS sustained load

Architectural Features

  • Provider Isolation - Independent worker pools prevent cascade failures
  • Memory Optimization - Channel, message, and response object pooling
  • Extensible Hooks - Plugin system for custom logic injection
  • MCP Integration - Native tool discovery and execution system
  • Built-in Observability - Prometheus metrics without external dependencies

Core Concepts

Request Lifecycle

  1. Transport receives request (HTTP/SDK)
  2. Router selects provider and manages load balancing
  3. Plugin Manager executes pre-processing hooks
  4. MCP Manager discovers and prepares available tools
  5. Worker Pool processes request with dedicated provider workers
  6. Memory Pools provide reusable objects for efficiency
  7. Plugin Manager executes post-processing hooks
  8. Transport returns response to client

Scaling Strategies

  • Vertical Scaling - Increase pool sizes and buffer capacities
  • Horizontal Scaling - Deploy multiple instances with load balancing
  • Provider Scaling - Independent worker pools per provider
  • Memory Scaling - Configurable object pool sizes

Extension Points

  • Plugin Hooks - Pre/post request processing
  • Custom Providers - Add new AI service integrations
  • MCP Tools - External tool integration
  • Transport Layers - Multiple interface options (HTTP, SDK, gRPC planned)