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Pure Golang AI Agents

May 4, 2026 | 09:57

This article explains how to build a local Go-based AI agent using Ollama and simple tool calling, including a web search tool and a datetime helper. Building local AI agents with Go and Ollama is about combining a small, self-contained Go program with a locally hosted language model so your applications can reason, use tools, and answer questions without depending on cloud services.

Prerequisite knowledge

  • Go fundamentals: packages, modules, structs, interfaces, slices, maps, and error handling
  • Experience using the Go toolchain: go run, go build, and basic project layout
  • Working with HTTP in Go: net/http clients, requests, responses, and timeouts
  • JSON handling in Go: encoding/decoding with encoding/json, struct tags, and working with golang maps
  • Familiarity with REST APIs: request/response patterns, status codes, and authentication basics
  • Understanding of AI language models: prompts, messages, and multi-turn chat workflows
  • Conceptual grasp of tool/function calling in AI agents (LLMs invoking external functions or APIs to get data or perform actions)
  • Comfort with the command line: running binaries, setting environment variables, and reading logs
  • Ability to read technical documentation (API references, model docs, and configuration guides)

Introduction

Building AI agents with Go and Ollama offers a compelling alternative to Python-based frameworks and cloud-dependent solutions. This stack gives you complete control over your infrastructure while maintaining simplicity. Go compiles to a single binary with no runtime dependencies, and Ollama runs models locally with no API keys or external services required. The result is an agent that can be deployed anywhere, from production servers to edge devices, with predictable costs, low latency, and full data privacy.

Go’s strong standard library makes agent development straightforward. JSON marshaling, HTTP clients, and concurrency primitives are built-in, so you can focus on agent logic rather than managing dependencies. Cross-compilation is trivial, meaning you write once and deploy to any platform. Combined with Ollama’s simple REST API and local model execution, you get a lightweight, portable agent architecture that’s easy to understand and modify.

This article walks through building a small research agent that demonstrates the core pattern: the agent takes a user question, decides when it needs external information, calls tools to gather data, and synthesizes a grounded answer. We’ll implement web search via SearXNG and a clock tool, showing how the model autonomously decides which tools to use and when. By the end, you’ll have a complete working agent and understand how to extend it with your own tools.

Use Cases and Advantages for Go-based Agents

  • Running a Go agent on a Raspberry Pi or other edge device with a lightweight binary and minimal runtime overhead.
  • Concurrent tool calls using goroutines, making it easy to execute multiple searches, API requests, or data fetches in parallel.
  • Lightning-fast execution that can be cross-compiled for all platforms (Windows/Linux/macOS/ARM devices) while maintaining a single code base.
  • Lower infrastructure costs: because Go is compiled and memory-efficient, you can run the same agent workload on significantly smaller, cheaper compute instances compared to interpreted languages.
  • Future-proof orchestration: with the rise of frameworks that have native support for the Model Context Protocol (MCP), Go is optimized for “agentic” workflows rather than just data science.
  • Infinite prototyping at no cost: since Ollama runs on your local GPU/CPU, you can run thousands of agent reasoning loops for testing without worrying about token costs or hitting cloud provider rate limits.

AI Agent Architecture in Go

Before diving into our specific example, let’s understand how AI agents are generally structured in Go and why this architecture works well.

The Core Agent Pattern

An AI agent differs from a simple chatbot in one key way: autonomy. Rather than just responding to prompts, agents can decide to take actions, gather information, and iterate toward a solution. This requires three main components working together:

  1. The Model (LLM): The reasoning engine that interprets user requests, decides which tools to call, and synthesizes final answers. With Ollama, this runs locally via a simple REST API.
  2. Tools: Functions the agent can execute to interact with the world, such as searching the web, reading files, querying databases, calling APIs, or performing calculations. Each tool has a schema describing its name, purpose, and parameters.
  3. The Agent Loop: The orchestration layer that mediates between the model and tools. It sends the conversation history and tool schemas to the model, executes any requested tool calls, appends results back to the conversation, and repeats until the model produces a final answer.

Why Go Excels at This Pattern

Go’s design aligns naturally with agent architecture:

  • Structs for schemas: Tool definitions, message formats, and API payloads map cleanly to Go structs with JSON tags, making serialization trivial.
  • HTTP client in stdlib: No external dependencies needed to communicate with Ollama’s REST API or other web services.
  • Goroutines for concurrency: When the model requests multiple tools simultaneously, goroutines make parallel execution straightforward without complex async/await patterns.
  • Type safety: The compiler catches mismatches between tool schemas and implementations, reducing runtime errors.
  • Single binary: The entire agent compiles to one executable you can copy to any target system.

The Agent Request Flow

Here’s how a typical agent request flows through the system:

User Question
     ↓
[Agent Loop]
     ↓
Send: {messages, tool_definitions} → [Ollama Model]
     ↓
Receive: assistant message with tool_calls or final answer
     ↓
If tool_calls present:
  Execute each tool → collect results
  Append tool results to messages
  Loop back to Ollama
     ↓
If no tool_calls:
  Return final answer to user

This loop continues until either the model produces a final text response or a maximum iteration count is reached. Each round preserves the full conversation history so the model has context about what tools it called and what they returned.

Tool Definition and Execution

Tools in Go are defined with two parts:

  1. Schema: A JSON structure sent to the model describing the tool’s name, purpose, and parameters. This follows a format similar to OpenAPI or JSON Schema.
  2. Implementation: A Go function that executes when the model requests the tool. It receives the arguments the model provides and returns a string result that gets appended to the conversation.

The separation between schema and implementation is crucial: the schema teaches the model when and how to use the tool, while the implementation determines what actually happens when it’s invoked.

Data Flow with Go Types

Go’s type system makes the data flow explicit and safe, for example:

User Input (string)
  → OllamaMessage{Role: "user", Content: input}
  → OllamaChatRequest{Model, Messages, Tools}
  → JSON marshaling → HTTP POST
  → OllamaChatResponse{Message}
  → OllamaMessage{ToolCalls: [...]}
  → Execute matching Go functions
  → OllamaMessage{Role: "tool", Content: result}
  → Back to OllamaChatRequest

Every step has a clear type definition, making the code self-documenting and reducing bugs.

Now let’s see this architecture in action by building a concrete research agent.

Building a Research Agent: High-Level Architecture

At a high level, the agent works like this:

  1. Read the user’s question from CLI args or stdin.
  2. Define the tools the model is allowed to call.
  3. Send messages and tool definitions to Ollama’s /api/chat.
  4. If the model asks to call tools, execute them in Go.
  5. Append the tool results back into the conversation.
  6. Repeat a few rounds until the model returns a final answer.

This “loop between model and tools” pattern is the essence of modern agents.

Full code

Here is the complete program we’ll walk through:

package main

import (
    "bufio"
    "bytes"
    "encoding/json"
    "fmt"
    "io"
    "net/http"
    "os"
    "strings"
    "time"
)

// This struct is for receiving Ollama messages back from the REST API
type OllamaMessage struct {
    Role      string           `json:"role"`
    Content   string           `json:"content,omitempty"`
    ToolCalls []OllamaToolCall `json:"tool_calls,omitempty"`
    ToolName  string           `json:"tool_name,omitempty"`
}

// OllamaFunction represents a tool call payload from the assistant.
// It contains the name of the tool and the arguments the model wants the tool to execute.
type OllamaFunction struct {
    Name      string         `json:"name"`
    Arguments map[string]any `json:"arguments,omitempty"`
}

// OllamaToolCall wraps an OllamaFunction in the structure used by Ollama assistant messages.
// This is what appears under the assistant's "tool_calls" field.
type OllamaToolCall struct {
    Function OllamaFunction `json:"function"`
}

// OllamaFuncSchema defines the tool schema sent to Ollama when registering available tools.
// It includes the tool name, a description, and JSON schema for the expected parameters.
type OllamaFuncSchema struct {
    Name        string         `json:"name"`
    Description string         `json:"description"`
    Parameters  map[string]any `json:"parameters"`
}

// OllamaToolDef describes a tool definition that can be used by the model.
// It declares the tool type and the function schema describing its arguments.
type OllamaToolDef struct {
    Type     string           `json:"type"`
    Function OllamaFuncSchema `json:"function"`
}

// OllamaChatRequest is the request body sent to the Ollama /api/chat endpoint.
// It contains the model, the chat history messages, available tools, and stream settings.
type OllamaChatRequest struct {
    Model    string          `json:"model"`
    Messages []OllamaMessage `json:"messages"`
    Tools    []OllamaToolDef `json:"tools,omitempty"`
    Stream   bool            `json:"stream"`
}

// OllamaChatResponse models the response returned by Ollama /api/chat.
// It contains the latest message from the model and a completion flag.
type OllamaChatResponse struct {
    Message OllamaMessage `json:"message"`
    Done    bool          `json:"done"`
}

func main() {
    cfg := struct {
        ModelID   string
        OllamaURL string
        SearXNG   string
    }{
        ModelID:   "gemma4",
        OllamaURL: "http://127.0.0.1:11434",
        SearXNG:   "http://127.0.0.1:8080",
    }

    query := ""
    if len(os.Args) > 1 {
        query = strings.Join(os.Args[1:], " ")
    } else {
        fmt.Print("Enter a question: ")
        scanner := bufio.NewScanner(os.Stdin)
        if scanner.Scan() {
            query = strings.TrimSpace(scanner.Text())
        }
        if err := scanner.Err(); err != nil {
            panic(err)
        }
        if query == "" {
            query = "What are the latest best practices for secure password storage?"
        }
    }

    tools := []OllamaToolDef{
        {
            Type: "function",
            Function: OllamaFuncSchema{
                Name:        "searxng_search",
                Description: "Search the web using local SearXNG and return top result titles, URLs, and snippets.",
                Parameters: map[string]any{
                    "type": "object",
                    "properties": map[string]any{
                        "query": map[string]any{
                            "type":        "string",
                            "description": "Search query",
                        },
                    },
                    "required": []string{"query"},
                },
            },
        },
        {
            Type: "function",
            Function: OllamaFuncSchema{
                Name:        "get_current_datetime",
                Description: "Get the current date and time.",
                Parameters: map[string]any{
                    "type":       "object",
                    "properties": map[string]any{},
                },
            },
        },
    }

    messages := []OllamaMessage{
        {Role: "system", Content: "You are a research agent. Use the tools provided for web research and checking the date/time, and do not invent facts."},
        {Role: "user", Content: query},
    }

    for round := 0; round < 3; round++ {
        resp, err := ollamaChat(cfg.ModelID, cfg.OllamaURL, messages, tools)
        if err != nil {
            panic(err)
        }

        if len(resp.Message.ToolCalls) == 0 {
            fmt.Println("=== Final answer ===")
            fmt.Println(strings.TrimSpace(resp.Message.Content))
            return
        }

        messages = append(messages, OllamaMessage{Role: "assistant", Content: resp.Message.Content, ToolCalls: resp.Message.ToolCalls})

        for _, call := range resp.Message.ToolCalls {
            switch call.Function.Name {
            case "searxng_search":
                searchQuery, _ := call.Function.Arguments["query"].(string)
                result := searxngSearch(cfg.SearXNG, searchQuery)
                messages = append(messages, OllamaMessage{Role: "tool", ToolName: "searxng_search", Content: result})
            case "get_current_datetime":
                result := time.Now().Format(time.RFC3339)
                messages = append(messages, OllamaMessage{Role: "tool", ToolName: "get_current_datetime", Content: result})
            default:
                fmt.Printf("unsupported tool: %s\n", call.Function.Name)
                return
            }
        }
    }

    fmt.Println("No final answer after tool loop.")
}

func ollamaChat(modelID, ollamaURL string, messages []OllamaMessage, tools []OllamaToolDef) (OllamaChatResponse, error) {
    reqBody := OllamaChatRequest{
        Model:    modelID,
        Messages: messages,
        Tools:    tools,
        Stream:   false,
    }

    b, err := json.Marshal(reqBody)
    if err != nil {
        return OllamaChatResponse{}, err
    }

    resp, err := http.Post(strings.TrimRight(ollamaURL, "/")+"/api/chat", "application/json", bytes.NewReader(b))
    if err != nil {
        return OllamaChatResponse{}, err
    }
    defer resp.Body.Close()

    if resp.StatusCode != http.StatusOK {
        body, _ := io.ReadAll(resp.Body)
        return OllamaChatResponse{}, fmt.Errorf("ollama error %d: %s", resp.StatusCode, string(body))
    }

    var chatResp OllamaChatResponse
    if err := json.NewDecoder(resp.Body).Decode(&chatResp); err != nil {
        return OllamaChatResponse{}, err
    }
    return chatResp, nil
}

func searxngSearch(searxngURL, query string) string {
    req, err := http.NewRequest("GET", strings.TrimRight(searxngURL, "/")+"/search", nil)
    if err != nil {
        return fmt.Sprintf("search request error: %v", err)
    }

    q := req.URL.Query()
    q.Set("q", query)
    q.Set("format", "json")
    req.URL.RawQuery = q.Encode()

    client := &http.Client{Timeout: 15 * time.Second}
    resp, err := client.Do(req)
    if err != nil {
        return fmt.Sprintf("search request error: %v", err)
    }
    defer resp.Body.Close()

    var data struct {
        Results []struct {
            Title   string `json:"title"`
            URL     string `json:"url"`
            Content string `json:"content"`
        } `json:"results"`
    }
    if err := json.NewDecoder(resp.Body).Decode(&data); err != nil {
        return fmt.Sprintf("search decode error: %v", err)
    }

    if len(data.Results) == 0 {
        return "No search results found."
    }

    var out strings.Builder
    for i, item := range data.Results {
        if i >= 5 {
            break
        }
        fmt.Fprintf(&out, "Title: %s\nURL: %s\nSnippet: %s\n---\n", item.Title, item.URL, item.Content)
    }
    return out.String()
}

The rest of the article breaks the code into focused sections, showing each relevant snippet first and then explaining how it works.

Modeling messages and tools

// This struct is for receiving Ollama messages back from the REST API
type OllamaMessage struct {
    Role      string           `json:"role"`
    Content   string           `json:"content,omitempty"`
    ToolCalls []OllamaToolCall `json:"tool_calls,omitempty"`
    ToolName  string           `json:"tool_name,omitempty"`
}

// OllamaFunction represents a tool call payload from the assistant.
// It contains the name of the tool and the arguments the model wants the tool to execute.
type OllamaFunction struct {
    Name      string         `json:"name"`
    Arguments map[string]any `json:"arguments,omitempty"`
}

// OllamaToolCall wraps an OllamaFunction in the structure used by Ollama assistant messages.
// This is what appears under the assistant's "tool_calls" field.
type OllamaToolCall struct {
    Function OllamaFunction `json:"function"`
}

// OllamaFuncSchema defines the tool schema sent to Ollama when registering available tools.
// It includes the tool name, a description, and JSON schema for the expected parameters.
type OllamaFuncSchema struct {
    Name        string         `json:"name"`
    Description string         `json:"description"`
    Parameters  map[string]any `json:"parameters"`
}

// OllamaToolDef describes a tool definition that can be used by the model.
// It declares the tool type and the function schema describing its arguments.
type OllamaToolDef struct {
    Type     string           `json:"type"`
    Function OllamaFuncSchema `json:"function"`
}

// OllamaChatRequest is the request body sent to the Ollama /api/chat endpoint.
// It contains the model, the chat history messages, available tools, and stream settings.
type OllamaChatRequest struct {
    Model    string          `json:"model"`
    Messages []OllamaMessage `json:"messages"`
    Tools    []OllamaToolDef `json:"tools,omitempty"`
    Stream   bool            `json:"stream"`
}

// OllamaChatResponse models the response returned by Ollama /api/chat.
// It contains the latest message from the model and a completion flag.
type OllamaChatResponse struct {
    Message OllamaMessage `json:"message"`
    Done    bool          `json:"done"`
}

These types mirror Ollama’s chat and tool-calling JSON structure so Go can encode and decode data cleanly. OllamaMessage is the core unit: it holds the role (system, user, assistant, or tool), the text Content, optional ToolCalls that the assistant wants to execute, and the ToolName used when sending tool results back.

OllamaFunction and OllamaToolCall represent what the model sends when it wants a tool to run: a function name plus arguments. OllamaFuncSchema and OllamaToolDef describe what tools are available and how they should be called. These schemas are sent with the request so the model knows what it can do.

Finally, OllamaChatRequest and OllamaChatResponse wrap up the data for the /api/chat endpoint: you send a model name, message history, and tool definitions; you receive the latest assistant message and a flag indicating whether the response is done.

Config and input handling

func main() {
    cfg := struct {
        ModelID   string
        OllamaURL string
        SearXNG   string
    }{
        ModelID:   "gemma4",
        OllamaURL: "http://127.0.0.1:11434",
        SearXNG:   "http://127.0.0.1:8080",
    }

    query := ""
    if len(os.Args) > 1 {
        query = strings.Join(os.Args[1:], " ")
    } else {
        fmt.Print("Enter a question: ")
        scanner := bufio.NewScanner(os.Stdin)
        if scanner.Scan() {
            query = strings.TrimSpace(scanner.Text())
        }
        if err := scanner.Err(); err != nil {
            panic(err)
        }
        if query == "" {
            query = "What are the latest best practices for secure password storage?"
        }
    }

The program starts by defining a simple inline config struct for the model ID and URLs of Ollama and SearXNG. These values are hard-coded for clarity, but in a real project you could switch them to environment variables or flags.

Next, it determines the user’s query. If arguments were passed on the command line, it joins them into a single string; otherwise it prompts for a question on stdin. The interactive fallback makes the agent easy to test by hand. If the user just hits enter, it falls back to a default security-related question so you always have something meaningful to run.

Defining tools and the initial messages

    tools := []OllamaToolDef{
        {
            Type: "function",
            Function: OllamaFuncSchema{
                Name:        "searxng_search",
                Description: "Search the web using local SearXNG and return top result titles, URLs, and snippets.",
                Parameters: map[string]any{
                    "type": "object",
                    "properties": map[string]any{
                        "query": map[string]any{
                            "type":        "string",
                            "description": "Search query",
                        },
                    },
                    "required": []string{"query"},
                },
            },
        },
        {
            Type: "function",
            Function: OllamaFuncSchema{
                Name:        "get_current_datetime",
                Description: "Get the current date and time.",
                Parameters: map[string]any{
                    "type":       "object",
                    "properties": map[string]any{},
                },
            },
        },
    }

    messages := []OllamaMessage{
        {Role: "system", Content: "You are a research agent. Use the tools provided for web research and checking the date/time, and do not invent facts."},
        {Role: "user", Content: query},
    }

Here the agent declares the tools it wants the model to be able to call. Each tool is described as a “function” with a name, natural-language description, and a JSON-schema-like Parameters object that defines its expected arguments. In this example:

  • searxng_search accepts a single string query.
  • get_current_datetime accepts no arguments.

Ollama uses these definitions when deciding whether and how to call tools. The descriptions also help steer the model toward the right tool for a given user request.

The initial messages slice sets up the conversation. The system message defines the agent’s job: perform research using tools and avoid inventing facts. The user message contains the actual query collected from the command line or stdin. That message history is what you’ll send into the chat endpoint.

The agent loop

    for round := 0; round < 3; round++ {
        resp, err := ollamaChat(cfg.ModelID, cfg.OllamaURL, messages, tools)
        if err != nil {
            panic(err)
        }

        if len(resp.Message.ToolCalls) == 0 {
            fmt.Println("=== Final answer ===")
            fmt.Println(strings.TrimSpace(resp.Message.Content))
            return
        }

        messages = append(messages, OllamaMessage{Role: "assistant", Content: resp.Message.Content, ToolCalls: resp.Message.ToolCalls})

        for _, call := range resp.Message.ToolCalls {
            switch call.Function.Name {
            case "searxng_search":
                searchQuery, _ := call.Function.Arguments["query"].(string)
                result := searxngSearch(cfg.SearXNG, searchQuery)
                messages = append(messages, OllamaMessage{Role: "tool", ToolName: "searxng_search", Content: result})
            case "get_current_datetime":
                result := time.Now().Format(time.RFC3339)
                messages = append(messages, OllamaMessage{Role: "tool", ToolName: "get_current_datetime", Content: result})
            default:
                fmt.Printf("unsupported tool: %s\n", call.Function.Name)
                return
            }
        }
    }

    fmt.Println("No final answer after tool loop.")
}

This for loop is the heart of the agent. Each iteration represents one “round” of interaction with the model.

  • First, it calls ollamaChat, passing the model ID, Ollama URL, the current messages, and the tool definitions.
  • If the returned assistant message has no ToolCalls, the model is done: the program prints a final answer and exits.
  • If there are tool calls, the assistant message (including its ToolCalls) is appended to the conversation history to preserve the model’s reasoning.

Then the program walks through each requested tool call:

  • For searxng_search, it extracts the query argument, calls searxngSearch in Go, and appends a tool role message with the result.
  • For get_current_datetime, it returns the current time in RFC3339 format and appends that as a tool role message.

After all tool calls are handled, the loop repeats, sending the expanded history back to the model so it can integrate the tool results. The loop is capped at three rounds as a safety limit.

Calling Ollama’s chat API

func ollamaChat(modelID, ollamaURL string, messages []OllamaMessage, tools []OllamaToolDef) (OllamaChatResponse, error) {
    reqBody := OllamaChatRequest{
        Model:    modelID,
        Messages: messages,
        Tools:    tools,
        Stream:   false,
    }

    b, err := json.Marshal(reqBody)
    if err != nil {
        return OllamaChatResponse{}, err
    }

    resp, err := http.Post(strings.TrimRight(ollamaURL, "/")+"/api/chat", "application/json", bytes.NewReader(b))
    if err != nil {
        return OllamaChatResponse{}, err
    }
    defer resp.Body.Close()

    if resp.StatusCode != http.StatusOK {
        body, _ := io.ReadAll(resp.Body)
        return OllamaChatResponse{}, fmt.Errorf("ollama error %d: %s", resp.StatusCode, string(body))
    }

    var chatResp OllamaChatResponse
    if err := json.NewDecoder(resp.Body).Decode(&chatResp); err != nil {
        return OllamaChatResponse{}, err
    }
    return chatResp, nil
}

ollamaChat is a focused helper around the /api/chat endpoint. It builds an OllamaChatRequest with the model name, messages, tools, and Stream: false to request a non-streaming response.

It marshals that struct to JSON, posts it to OLLAMA_URL/api/chat, and checks for a successful status code. On error it returns a descriptive fmt.Errorf that includes both the status and body text; on success it decodes the JSON into OllamaChatResponse.

Keeping this logic in its own function keeps main easier to read and lets you reuse the same call pattern if you later expose this agent behind an HTTP server.

Implementing the SearXNG Search Tool

func searxngSearch(searxngURL, query string) string {
    req, err := http.NewRequest("GET", strings.TrimRight(searxngURL, "/")+"/search", nil)
    if err != nil {
        return fmt.Sprintf("search request error: %v", err)
    }

    q := req.URL.Query()
    q.Set("q", query)
    q.Set("format", "json")
    req.URL.RawQuery = q.Encode()

    client := &http.Client{Timeout: 15 * time.Second}
    resp, err := client.Do(req)
    if err != nil {
        return fmt.Sprintf("search request error: %v", err)
    }
    defer resp.Body.Close()

    var data struct {
        Results []struct {
            Title   string `json:"title"`
            URL     string `json:"url"`
            Content string `json:"content"`
        } `json:"results"`
    }
    if err := json.NewDecoder(resp.Body).Decode(&data); err != nil {
        return fmt.Sprintf("search decode error: %v", err)
    }

    if len(data.Results) == 0 {
        return "No search results found."
    }

    var out strings.Builder
    for i, item := range data.Results {
        if i >= 5 {
            break
        }
        fmt.Fprintf(&out, "Title: %s\nURL: %s\nSnippet: %s\n---\n", item.Title, item.URL, item.Content)
    }
    return out.String()
}

This helper wraps a SearXNG instance behind the searxng_search tool. It constructs a GET request to /search with the user’s query and format=json, sends it with a 15-second timeout, and decodes the response into a small anonymous struct representing the results array.

If there are no results, it returns a simple message. Otherwise, it formats up to five results into a compact plain-text list with title, URL, and a snippet. That text is what the model sees when it reads the tool role message, which is usually enough for it to synthesize an answer.

Cross-Platform Deployment

One of Go’s standout features for agent deployment is effortless cross-compilation. You can build binaries for any operating system and CPU architecture from a single development machine by setting environment variables before running go build:

# Linux on x86_64
GOOS=linux GOARCH=amd64 go build -o agent-linux-amd64

# Linux on ARM64 (Raspberry Pi, Graviton, etc.)
GOOS=linux GOARCH=arm64 go build -o agent-linux-arm64

# Windows on x86_64
GOOS=windows GOARCH=amd64 go build -o agent.exe

# macOS on Apple Silicon
GOOS=darwin GOARCH=arm64 go build -o agent-macos-arm64

For pure Go programs like this one (no cgo), cross-compilation is this simple. This matters for AI agents because you may want the same agent to run on laptops, headless servers, homelab nodes, and small ARM devices without maintaining separate codebases.

Running the Agent

Before running the agent, ensure you have:

  1. Ollama running locally: Install and start Ollama
  2. A model downloaded: Pull a model that supports tool calling (ollama pull gemma4)
  3. SearXNG instance: Set up a local SearXNG instance at http://127.0.0.1:8080 for web search capabilities

Once your dependencies are ready, build and run the agent:

# Build the agent
go build -o agent main.go

# Run with a question as an argument
./agent "What are the latest developments in quantum computing?"

# Or run interactively
./agent

The agent will use the model to determine which tools to call, execute them, and synthesize a grounded answer based on real-time search results.

=== Final answer ===
Based on recent research, the developments in quantum computing are accelerating rapidly, focusing on three main areas: improving hardware stability, developing smarter algorithms, and demonstrating clearer signs of "quantum advantage" (where a quantum computer solves a problem exponentially faster than the best classical computer).

Here is a summary of the latest key developments:
### 🔬 Hardware Advancements and Qubit Stability

*   **Majorana Qubits:** There has been significant progress in addressing qubit stability. Scientists have developed new methods to read the hidden states of **Majorana qubits**. These qubits are notable because they store information in paired quantum modes that are inherently resistant to environmental noise, a major hurdle in quantum computing.

*   **Advanced Modeling and Fabrication:** Researchers are pushing the boundaries of physical design by using supercomputers (like those with thousands of GPUs) to simulate every physical detail of quantum chips *before* fabrication. This allows for better prediction and
engineering of signal travel within the quantum hardware.

*   **General Hardware Improvement:** The field is marked by the continuous development of novel hardware architectures, moving theoretical concepts...
...
...

Extending the Agent

This example provides a foundation you can build on:

  • Add more tools: File system operations, database queries, API calls to other services
  • Implement streaming: Use Stream: true to display responses as they arrive
  • Parallel tool execution: Use goroutines to call multiple tools concurrently
  • Better error handling: Add retry logic, fallbacks, and more detailed error messages
  • Configuration: Move hardcoded values to environment variables or config files
  • Memory: Add conversation persistence to maintain context across runs

The core pattern loop between model and tools remains the same regardless of complexity.

Conclusion

Building AI agents with Go and Ollama proves that you don’t need a heavy cloud stack or Python dependency chain to create useful, autonomous systems. Go gives you a clean, type-safe runtime for defining tool schemas, managing message flow, and orchestrating the model loop, while Ollama lets you keep the reasoning engine local and under your control.

This example demonstrated the core agent pattern: send the user question, expose a small set of tools, let the model request tool calls, execute those calls in Go, and feed the results back until a grounded answer emerges. That loop is the real power behind modern agents, especially when the tools are simple, well-defined, and easy to extend.

If you want to take this further, the next step is to add more domain-specific tools, improve tool result formatting, and introduce better error handling or parallel tool execution. With Go and Ollama, you have a lightweight, portable foundation that scales from local prototypes to production-ready agents.

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