Architecture

Agno-Go follows a clean, modular architecture designed for simplicity, efficiency, and extensibility.

Core Philosophy

Simple, Efficient, Scalable

Overall Architecture

┌─────────────────────────────────────────┐
│          Application Layer              │
│  (CLI Tools, Web API, Custom Apps)      │
└──────────────┬──────────────────────────┘
               │
┌──────────────▼──────────────────────────┐
│         Core Abstractions               │
│  ┌─────────┐  ┌──────┐  ┌──────────┐   │
│  │  Agent  │  │ Team │  │ Workflow │   │
│  └─────────┘  └──────┘  └──────────┘   │
└──────────────┬──────────────────────────┘
               │
┌──────────────▼──────────────────────────┐
│        Foundation Layer                  │
│  ┌────────┐ ┌───────┐ ┌──────┐         │
│  │ Models │ │ Tools │ │Memory│ ...     │
│  └────────┘ └───────┘ └──────┘         │
└─────────────────────────────────────────┘

Core Interfaces

1. Model Interface

type Model interface {
    // Synchronous invocation
    Invoke(ctx context.Context, req *InvokeRequest) (*ModelResponse, error)

    // Streaming invocation
    InvokeStream(ctx context.Context, req *InvokeRequest) (<-chan ResponseChunk, error)

    // Metadata
    GetProvider() string
    GetID() string
}

2. Toolkit Interface

type Toolkit interface {
    Name() string
    Functions() map[string]*Function
}

type Function struct {
    Name        string
    Description string
    Parameters  map[string]Parameter
    Handler     func(context.Context, map[string]interface{}) (interface{}, error)
}

3. Memory Interface

type Memory interface {
    Add(message types.Message) error
    GetMessages() []types.Message
    Clear() error
}

Component Details

Agent

File: pkg/agno/agent/agent.go

Autonomous AI entity that:

• Uses LLM for reasoning
• Can call tools
• Maintains conversation memory
• Validates inputs/outputs with hooks

Key Methods:

New(config Config) (*Agent, error)
Run(ctx context.Context, input string) (*RunOutput, error)
ClearMemory()

Team

File: pkg/agno/team/team.go

Multi-agent collaboration with 4 coordination modes:

1. Sequential - Agents work one after another
2. Parallel - All agents work simultaneously
3. LeaderFollower - Leader delegates to followers
4. Consensus - Agents discuss until agreement

Workflow

File: pkg/agno/workflow/workflow.go

Step-based orchestration with 5 primitives:

1. Step - Execute agent or function
2. Condition - Branch based on context
3. Loop - Iterate with exit condition
4. Parallel - Execute steps concurrently
5. Router - Dynamic routing

Models

Directory: pkg/agno/models/

LLM provider implementations:

• openai/ - OpenAI GPT models
• anthropic/ - Anthropic Claude models
• ollama/ - Ollama local models
• deepseek/, gemini/, modelscope/ - Other providers

Tools

Directory: pkg/agno/tools/

Extensible toolkit system:

• calculator/ - Math operations
• http/ - HTTP requests
• file/ - File operations
• search/ - Web search

AgentOS Production Server

Directory: pkg/agentos/

Production-ready HTTP server with:

• RESTful API endpoints
• Session management
• Agent registry
• Health monitoring
• CORS support
• Request timeout handling

Architecture:

┌─────────────────────┐
│   HTTP Handlers     │
│  (API Endpoints)    │
└──────────┬──────────┘
           │
┌──────────▼──────────┐
│  Agent Registry     │
│  (Thread-safe map)  │
└──────────┬──────────┘
           │
┌──────────▼──────────┐
│ Session Manager     │
│  (In-memory store)  │
└──────────┬──────────┘
           │
┌──────────▼──────────┐
│  Agent Instances    │
│  (Runtime agents)   │
└─────────────────────┘

Design Patterns

1. Interface-Based Design

All core components use interfaces for flexibility:

type Model interface { /* ... */ }
type Toolkit interface { /* ... */ }
type Memory interface { /* ... */ }

2. Composition Over Inheritance

Agents compose models, tools, and memory:

type Agent struct {
    Model    Model
    Toolkits []Toolkit
    Memory   Memory
    // ...
}

3. Context Propagation

All operations accept context.Context for cancellation and timeouts:

func (a *Agent) Run(ctx context.Context, input string) (*RunOutput, error)

4. Error Wrapping

Consistent error handling with wrapped errors:

if err != nil {
    return nil, fmt.Errorf("failed to run agent: %w", err)
}

Performance Optimizations

1. Low Allocation Count

• Minimal heap allocations (8-9 per agent)
• Pre-allocated slices
• String interning where appropriate

2. Efficient Memory Layout

type Agent struct {
    ID           string   // 16B
    Name         string   // 16B
    Model        Model    // 16B (interface)
    // Total: ~112B struct + heap allocations
}

3. Goroutine Safety

• No global state
• Thread-safe by design
• Lock-free where possible

Concurrency Model

Agent Concurrency

// Safe to create multiple agents concurrently
for i := 0; i < 100; i++ {
    go func() {
        ag, _ := agent.New(config)
        output, _ := ag.Run(ctx, input)
    }()
}

Team Parallel Mode

// Agents run in parallel goroutines
team := team.New(team.Config{
    Mode: team.ModeParallel,
    Agents: agents,
})

Workflow Parallel Step

// Steps execute concurrently
workflow.NewParallel("tasks", []Primitive{
    step1, step2, step3,
})

Extensibility Points

1. Custom Models

Implement Model interface:

type MyModel struct{}

func (m *MyModel) Invoke(ctx context.Context, req *InvokeRequest) (*ModelResponse, error) {
    // Custom implementation
}

2. Custom Tools

Extend BaseToolkit:

type MyToolkit struct {
    *toolkit.BaseToolkit
}

func (t *MyToolkit) RegisterFunctions() {
    t.RegisterFunction(&Function{
        Name: "my_function",
        Handler: t.myHandler,
    })
}

3. Custom Memory

Implement Memory interface:

type MyMemory struct{}

func (m *MyMemory) Add(msg types.Message) error {
    // Custom storage
}

Testing Strategy

Unit Tests

• Each package has *_test.go files
• Mock implementations for interfaces
• Table-driven tests

Integration Tests

• End-to-end workflow tests
• Multi-agent scenarios
• Real API integration tests

Benchmark Tests

• Performance benchmarks in *_bench_test.go
• Memory allocation tracking
• Concurrency stress tests

Dependencies

Core Dependencies

• Go Standard Library - Most functionality
• No heavy frameworks - Lightweight design

Optional Dependencies

• LLM provider SDKs (OpenAI, Anthropic, etc.)
• Vector database clients (ChromaDB)
• HTTP client libraries

Future Architecture

Planned Enhancements

1. Streaming Support - Real-time response streaming
2. Plugin System - Dynamic tool loading
3. Distributed Agents - Multi-node deployment
4. Advanced Memory - Persistent storage, vector memory

Best Practices

1. Use Interfaces

var model models.Model = openai.New(...)

2. Handle Errors

if err != nil {
    return fmt.Errorf("operation failed: %w", err)
}

3. Use Contexts

ctx, cancel := context.WithTimeout(context.Background(), 30*time.Second)
defer cancel()

4. Keep It Simple

Follow the KISS principle - don't over-engineer.

References

• Performance Benchmarks
• Deployment Guide
• API Reference
• Source Code