Building AI Agents in Go

If you want to build AI agents in Go, there are a few Agent SDKs and frameworks available in 2026 that make it easier to integrate with LLMs, tools, and multi-agent workflows.

Below is a runnable Go example using a modern Agent SDK pattern. I’ll show you a minimal agent that can receive a prompt, call an LLM API, and return a response.

Example: Minimal AI Agent in Go

package main

import (
    "context"
    "fmt"
    "log"
    "os"
    "time"

    "github.com/ingenimax/agent-sdk-go/agent"
    "github.com/ingenimax/agent-sdk-go/llm"
)

func main() {
    // Load API key from environment variable
    apiKey := os.Getenv("OPENAI_API_KEY")
    if apiKey == "" {
        log.Fatal("Please set the OPENAI_API_KEY environment variable")
    }

    // Create a new LLM client (example: OpenAI GPT model)
    llmClient, err := llm.NewOpenAI(apiKey, llm.WithModel("gpt-4o-mini"))
    if err != nil {
        log.Fatalf("Failed to create LLM client: %v", err)
    }

    // Create an agent with a simple reasoning function
    myAgent := agent.New("helper-agent",
        agent.WithLLM(llmClient),
        agent.WithSystemPrompt("You are a helpful assistant that answers concisely."),
    )

    // Context with timeout for safety
    ctx, cancel := context.WithTimeout(context.Background(), 15*time.Second)
    defer cancel()

    // Run the agent with a user query
    response, err := myAgent.Run(ctx, "Explain the difference between concurrency and parallelism in Go.")
    if err != nil {
        log.Fatalf("Agent error: %v", err)
    }

    fmt.Println("Agent Response:")
    fmt.Println(response)
}

How This Works

  • agent-sdk-go – A Go framework for building AI agents with modular tools, memory, and reasoning loops.
  • LLM Client – Connects to an LLM provider (OpenAI in this example).
  • Agent – Wraps the LLM with a system prompt and optional tools.
  • Run – Executes the reasoning loop and returns the answer.

Installation

go get github.com/Ingenimax/agent-sdk-go

Features of Modern Go Agent SDKs

  • Tool Integration – Agents can call APIs, databases, or custom functions.
  • Multi-Agent Workflows – Agents can hand off tasks to other agents.
  • Memory – Store and recall conversation history.
  • Streaming – Get partial responses in real time.
  • Concurrency – Use Go’s goroutines for parallel tool calls.