Architecture Overview - Maxim Docs

Core Architecture

Document	Description	Focus Area
System Overview	High-level architecture & design principles	Components, interactions, data flow
Request Flow	Request processing pipeline deep dive	Processing stages, memory management
Benchmarks	Performance benchmarks & optimization	Metrics, scaling, optimization
Concurrency	Worker pools & threading model	Goroutines, channels, resource isolation

Internal Systems

Document	Description	Focus Area
Plugin System	How plugins work internally	Plugin lifecycle, interfaces, execution
MCP System	Model Context Protocol internals	Tool discovery, execution, integration
Design Decisions	Architecture rationale & trade-offs	Why 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

Metric	Performance	Details
Throughput	10,000+ RPS	Sustained high-load performance
Latency	11-59μs overhead	Minimal processing overhead
Memory	Optimized pooling	Object reuse minimizes GC pressure
Reliability	100% success rate	Under 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

Transport receives request (HTTP/SDK)
Router selects provider and manages load balancing
Plugin Manager executes pre-processing hooks
MCP Manager discovers and prepares available tools
Worker Pool processes request with dedicated provider workers
Memory Pools provide reusable objects for efficiency
Plugin Manager executes post-processing hooks
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)

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