AgenticGoKit

⚠️ ALPHA RELEASE - This project is in active development. APIs may change and features are still being stabilized. Use in production at your own risk. We welcome feedback and contributions!

Production-ready Go framework for building intelligent multi-agent AI systems

The most productive way to build AI agents in Go. AgenticGoKit provides a unified, streaming-first API for creating intelligent agents with built-in workflow orchestration, tool integration, and memory management. Start with simple single agents and scale to complex multi-agent workflows.

Why Choose AgenticGoKit?

• vnext APIs: Modern, streaming-first agent interface with comprehensive error handling
• Real-time Streaming: Watch your agents think and respond in real-time
• Multi-Agent Workflows: Sequential, parallel, and DAG orchestration patterns
• High Performance: Compiled Go binaries with minimal overhead
• Rich Integrations: Memory providers, tool discovery, MCP protocol support
• Zero Dependencies: Works with OpenAI, Ollama, Azure OpenAI out of the box

---

Quick Start

Option 1: Use vnext APIs (Recommended)

Start building immediately with the modern vnext API:

package main

import (
    "context"
    "fmt"
    "log"
    
    "github.com/kunalkushwaha/agenticgokit/core/vnext"
)

func main() {
    // Create a chat agent with Ollama
    agent, err := vnext.NewChatAgent("assistant", 
        vnext.WithLLM("ollama", "gemma3:1b", "http://localhost:11434"),
    )
    if err != nil {
        log.Fatal(err)
    }

    // Basic execution
    result, err := agent.Run(context.Background(), "Explain Go channels in 50 words")
    if err != nil {
        log.Fatal(err)
    }
    
    fmt.Println("Response:", result.Content)
    fmt.Printf("Duration: %.2fs | Tokens: %d\n", result.Duration.Seconds(), result.TokensUsed)
}

Option 2: Use CLI Generator (Scaffolding)

Generate complete projects with agentcli:

# Install CLI
go install github.com/kunalkushwaha/agenticgokit/cmd/agentcli@latest

# Create project with scaffolding
agentcli create my-agent --template basic
cd my-agent

# Set up environment 
export AZURE_OPENAI_API_KEY=your-key
export AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/

# Run generated code
go run .

Streaming Workflows

Watch your multi-agent workflows execute in real-time:

package main

import (
    "context"
    "fmt" 
    "log"
    
    "github.com/kunalkushwaha/agenticgokit/core/vnext"
)

func main() {
    // Create specialized agents
    researcher, _ := vnext.NewResearchAgent("researcher")
    analyzer, _ := vnext.NewDataAgent("analyzer")
    
    // Build workflow
    workflow, _ := vnext.NewSequentialWorkflow(&vnext.WorkflowConfig{
        Timeout: 300 * time.Second,
    })
    workflow.AddStep(vnext.WorkflowStep{Name: "research", Agent: researcher})
    workflow.AddStep(vnext.WorkflowStep{Name: "analyze", Agent: analyzer})
    
    // Execute with streaming
    stream, _ := workflow.RunStream(context.Background(), "Research Go best practices")
    
    for chunk := range stream.Chunks() {
        if chunk.Type == vnext.ChunkTypeDelta {
            fmt.Print(chunk.Delta) // Real-time token streaming!
        }
    }
    
    result, _ := stream.Wait()
    fmt.Printf("\nComplete: %s\n", result.Content)
}

Core Features

vnext APIs (Production-Ready)

• Unified Agent Interface: Single API for all agent operations
• Real-time Streaming: Watch tokens generate in real-time
• Multi-Agent Workflows: Sequential, parallel, DAG orchestration
• Memory & RAG: Built-in persistence and retrieval
• Tool Integration: MCP protocol, function calling

Project Generation

• CLI Scaffolding: Generate complete projects with agentcli create
• Multiple Templates: Chat, research, RAG, workflow patterns
• Configuration: TOML-based with environment overrides
• Visualization: Auto-generated Mermaid workflow diagrams

Choose Your Path

Recommended: Direct vnext API Usage

Start coding immediately with clean, modern APIs:

import "github.com/kunalkushwaha/agenticgokit/core/vnext"

// Single agent
agent, _ := vnext.NewChatAgent("bot")
result, _ := agent.Run(ctx, "Hello world")

// Streaming agent  
stream, _ := agent.RunStream(ctx, "Write a story")
for chunk := range stream.Chunks() { /* real-time output */ }

// Multi-agent workflow
workflow, _ := vnext.NewSequentialWorkflow(config)
stream, _ := workflow.RunStream(ctx, input) 

Alternative: Project Scaffolding

Generate complete projects with configuration:

my-agent/                 # Generated by agentcli create
├── main.go              # Entry point with vnext APIs
├── agentflow.toml       # Configuration 
├── go.mod               # Dependencies
├── agents/              # Custom agent implementations
└── docs/                # Generated diagrams

# agentflow.toml - Configuration for generated projects
[orchestration]
mode = "sequential" 
timeout_seconds = 300

[llm]
provider = "ollama"
model = "gemma3:1b"

[memory]
provider = "local"
enable_rag = true

Examples & Templates

Direct vnext Examples

// Basic chat agent
agent, _ := vnext.NewChatAgent("helper")

// With custom configuration  
agent, _ := vnext.NewChatAgent("helper",
    vnext.WithLLM("ollama", "gemma3:1b", "http://localhost:11434"),
    vnext.WithMemory(vnext.EnableRAG()),
    vnext.WithTools("web_search", "calculator"),
)

// Workflow orchestration
workflow, _ := vnext.NewParallelWorkflow(config)
workflow.AddStep(vnext.WorkflowStep{Name: "research", Agent: researchAgent})
workflow.AddStep(vnext.WorkflowStep{Name: "fact-check", Agent: factChecker}) 

CLI Templates (agentcli create)

agentcli create my-bot --template basic           # Simple chat agent
agentcli create research-team --template research  # Multi-agent research
agentcli create kb-system --template rag-system   # Knowledge base + RAG
agentcli create workflow --template chat-system   # Conversational workflows

Provider Support

// Works with any LLM provider
vnext.WithLLM("openai", "gpt-4", "")                    // OpenAI
vnext.WithLLM("azure", "gpt-4", "https://your.azure")  // Azure OpenAI  
vnext.WithLLM("ollama", "gemma3:1b", "http://localhost:11434") // Local Ollama

Learning Resources

Working Examples (examples/)

• vnext Streaming Workflow - Real-time multi-agent workflows
• Simple Agent - Basic agent setup
• Multi-Agent Collaboration - Team coordination
• RAG Knowledge Base - Memory & retrieval
• MCP Tool Integration - Dynamic tool discovery

Documentation

• vnext API Reference - Complete API documentation
• Workflow Streaming Guide - Real-time execution
• Migration Guide - Upgrade from legacy APIs
• Performance Benchmarks - Overhead analysis

Development

# Clone and build
git clone https://github.com/kunalkushwaha/agenticgokit.git
cd agenticgokit
make build

# Run tests
make test

# Run examples
cd examples/01-simple-agent
go run .

Resources

• Website: www.agenticgokit.com
• Documentation: docs.agenticgokit.com
• Examples: examples/
• Discussions: GitHub Discussions
• Issues: GitHub Issues

Contributing

We welcome contributions! See docs/contributors/ContributorGuide.md for getting started.

License

Apache 2.0 - see LICENSE for details.