Kronk

May 23, 2026 · View on GitHub

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Kronk

This project lets you use Go for hardware accelerated local inference with llama.cpp and whisper.cpp directly integrated into your Go applications via the yzma and bucky modules. Kronk provides a high-level API that feels similar to using an OpenAI compatible API.

This project also provides a model server for chat completions, responses, messages, embeddings, reranking, and audio transcription. The server is compatible with OpenWebUI, OpenCode, and the Claude Code project.

To see all the documentation, clone the project and run the Kronk Model Server:

$ make kronk-server

$ make website

You can also install Kronk, run the Kronk Model Server, and open the browser to localhost:11435

On macOS or Linux with Homebrew:

$ brew tap ardanlabs/kronk
$ brew install kronk

$ kronk server start

Or with Go:

$ go install github.com/ardanlabs/kronk/cmd/kronk@latest

$ kronk server start

Read the Manual to learn more about running the Kronk Model Server.

Project Status

Sometimes there are breaking changes to llama.cpp that require an update to yzma and Kronk. Here are some of the known compatible versions:

As of May 15th, 2026 please use version b9163 until we can fix the problems with b9165+

You can use this environment variable: export KRONK_LIB_VERSION=b9163

llama.cpp	yzma	kronk
b8864	v1.12.0	1.23.1
b8865+	v1.13.0	1.23.2
b9180+	v1.14.0	1.25.8

Owner Information

Name:     Bill Kennedy
Company:  Ardan Labs
Title:    Managing Partner
Email:    bill@ardanlabs.com
BlueSky:  https://bsky.app/profile/goinggo.net
LinkedIn: www.linkedin.com/in/william-kennedy-5b318778/
Twitter:  https://x.com/goinggodotnet

Install Kronk

The recommended way to install Kronk on macOS or Linux is with Homebrew:

$ brew tap ardanlabs/kronk
$ brew install kronk

$ kronk --help

To upgrade later:

$ brew upgrade kronk

You can also install via Go on any supported platform:

$ go install github.com/ardanlabs/kronk/cmd/kronk@latest

$ kronk --help

Issues/Features

Here is the existing Issues/Features for the project and the things being worked on or things that would be nice to have.

If you are interested in helping in any way, please send an email to Bill Kennedy.

Architecture

The architecture of Kronk is designed to be simple and scalable.

Watch this video to learn more about the project and the architecture.

SDK

The Kronk SDK allows you to write applications that can diectly interact with local open source GGUF models (supported by llama.cpp) that provide inference for text and media (vision and audio). The Bucky SDK provides the same surface for speech-to-text via whisper.cpp — see the Bucky chapter.

Check out the examples section below.

Models

Kronk uses models in the GGUF format supported by llama.cpp. You can find many models in GGUF format on Hugging Face (over 147k at last count):

models?library=gguf&sort=trending

Support

Kronk currently has support for over 94% of llama.cpp functionality thanks to yzma. See the yzma ROADMAP.md for the complete list.

You can use multimodal models (image/audio) and text language models with full hardware acceleration on Linux, on macOS, and on Windows.

OS	CPU	GPU
Linux	amd64, arm64	CUDA, Vulkan, HIP, ROCm, SYCL
macOS	arm64	Metal
Windows	amd64	CUDA, Vulkan, HIP, SYCL, OpenCL

Whenever there is a new release of llama.cpp, the tests for yzma are run automatically. Kronk runs tests once a day and will check for updates to llama.cpp. This helps us stay up to date with the latest code and models.

API Examples

There are examples in the examples direction:

The first time you run these programs the system will download and install the model and libraries.

AGENT - This example shows you how to write a small coding agent.

make example-agent

AUDIO - This example shows you how to execute a simple prompt against an audio model.

make example-audio

BUCKY - This example shows you how to transcribe an audio file with the bucky SDK (whisper.cpp under the hood). See the manual chapter Bucky (Audio Transcription) for the full subsystem reference.

make example-bucky

CHAT - This example shows you how to chat with the chat-completion api.

make example-chat

CONCURRENCY - This example shows you how to leverage concurrency using vision models.

make example-concurrency

EMBEDDING - This example shows you a basic program using Kronk to perform an embedding operation.

make example-embedding

GRAMMAR - This example shows how to use GBNF grammars to constrain model output.

make example-grammar

POOL - This example shows you how to use the pool package to manage multipl models in memory at the same time.

make example-pool

QUESTION - This example shows you how to ask a simple question with the chat-completion api.

make example-question

RAG - This example shows you a complete RAG application.

make example-rag

RERANK - This example shows you how to use a rerank model.

make example-rerank

RESPONSE - This example shows you how to chat with the response api.

make example-question

VISION - This example shows you how to execute a simple prompt against a vision model.

make example-vision

YZMA - This example shows you how to use the yzma api at it's basic level.

make example-yzma

You can find more examples in the ArdanLabs AI training repo at Example13.

Sample API Program - Question Example

package main

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

	"github.com/ardanlabs/kronk/sdk/kronk"
	"github.com/ardanlabs/kronk/sdk/kronk/model"
	"github.com/ardanlabs/kronk/sdk/tools/defaults"
	"github.com/ardanlabs/kronk/sdk/tools/libs"
	"github.com/ardanlabs/kronk/sdk/tools/models"
)

const modelSource = "unsloth/Qwen3-0.6B-Q8_0"

func main() {
	if err := run(); err != nil {
		fmt.Printf("\nERROR: %s\n", err)
		os.Exit(1)
	}
}

func run() error {
	mp, err := installSystem()
	if err != nil {
		return fmt.Errorf("unable to installation system: %w", err)
	}

	krn, err := newKronk(mp)
	if err != nil {
		return fmt.Errorf("unable to init kronk: %w", err)
	}

	defer func() {
		fmt.Println("\nUnloading Kronk")
		if err := krn.Unload(context.Background()); err != nil {
			fmt.Printf("failed to unload model: %v", err)
		}
	}()

	if err := question(krn); err != nil {
		fmt.Println(err)
	}

	return nil
}

func installSystem() (models.Path, error) {
	ctx, cancel := context.WithTimeout(context.Background(), 15*time.Minute)
	defer cancel()

	libs, err := libs.New(
		libs.WithVersion(defaults.LibVersion("")),
	)
	if err != nil {
		return models.Path{}, err
	}

	if _, err := libs.Download(ctx, kronk.FmtLogger); err != nil {
		return models.Path{}, fmt.Errorf("unable to install llama.cpp: %w", err)
	}

	// -------------------------------------------------------------------------

	mdls, err := models.New()
	if err != nil {
		return models.Path{}, fmt.Errorf("unable to init models: %w", err)
	}

	mp, err := mdls.Download(ctx, kronk.FmtLogger, modelSource)
	if err != nil {
		return models.Path{}, fmt.Errorf("unable to install model: %w", err)
	}

	return mp, nil
}

func newKronk(mp models.Path) (*kronk.Kronk, error) {
	fmt.Println("loading model...")

	if err := kronk.Init(); err != nil {
		return nil, fmt.Errorf("unable to init kronk: %w", err)
	}

	krn, err := kronk.New(
		model.WithModelFiles(mp.ModelFiles),
	)
	if err != nil {
		return nil, fmt.Errorf("unable to create inference model: %w", err)
	}

	fmt.Print("- system info:\n\t")
	for k, v := range krn.SystemInfo() {
		fmt.Printf("%s:%v, ", k, v)
	}
	fmt.Println()

	fmt.Println("- contextWindow  :", krn.ModelConfig().ContextWindow())
	fmt.Printf("- k/v            : %s/%s\n", krn.ModelConfig().CacheTypeK, krn.ModelConfig().CacheTypeV)
	fmt.Println("- flashAttention :", krn.ModelConfig().FlashAttention)
	fmt.Println("- nBatch         :", krn.ModelConfig().NBatch())
	fmt.Println("- nuBatch        :", krn.ModelConfig().NUBatch())
	fmt.Println("- modelType      :", krn.ModelInfo().Type)
	fmt.Println("- isGPT          :", krn.ModelInfo().IsGPTModel)
	fmt.Println("- template       :", krn.ModelInfo().Template.FileName)
	fmt.Println("- grammar        :", krn.ModelConfig().DefaultParams.Grammar != "")
	fmt.Println("- nSeqMax        :", krn.ModelConfig().NSeqMax())
	fmt.Println("- vramTotal      :", krn.ModelInfo().VRAMTotal/(1024*1024), "MiB")
	fmt.Println("- slotMemory     :", krn.ModelInfo().SlotMemory/(1024*1024), "MiB")
	fmt.Println("- modelSize      :", krn.ModelInfo().Size/(1000*1000), "MB")
	fmt.Println("- imc            :", krn.ModelConfig().IncrementalCache())
	if n := krn.ModelConfig().PtrNGpuLayers; n != nil {
		fmt.Println("- nGPULayers     :", *n)
	} else {
		fmt.Println("- nGPULayers     : all")
	}

	return krn, nil
}

func question(krn *kronk.Kronk) error {
	ctx, cancel := context.WithTimeout(context.Background(), 120*time.Second)
	defer cancel()

	question := "Hello model"

	fmt.Println()
	fmt.Println("QUESTION:", question)
	fmt.Println()

	d := model.D{
		"messages": model.DocumentArray(
			model.TextMessage(model.RoleUser, question),
		),
		"temperature": 0.7,
		"top_p":       0.9,
		"top_k":       40,
		"max_tokens":  2048,
	}

	ch, err := krn.ChatStreaming(ctx, d)
	if err != nil {
		return fmt.Errorf("chat streaming: %w", err)
	}

	// -------------------------------------------------------------------------

	var reasoning bool

	for resp := range ch {
		switch resp.Choices[0].FinishReason() {
		case model.FinishReasonError:
			return fmt.Errorf("error from model: %s", resp.Choices[0].Delta.Content)

		case model.FinishReasonStop:
			return nil

		default:
			if resp.Choices[0].Delta.Reasoning != "" {
				reasoning = true
				fmt.Printf("\u001b[91m%s\u001b[0m", resp.Choices[0].Delta.Reasoning)
				continue
			}

			if reasoning {
				reasoning = false
				fmt.Println()
				continue
			}

			fmt.Printf("%s", resp.Choices[0].Delta.Content)
		}
	}

	return nil
}

This example can produce the following output:

$ make example-question
go run examples/question/main.go
download-libraries: check libraries version information: arch[arm64] os[darwin] processor[cpu]
download-libraries: check llama.cpp installation: arch[arm64] os[darwin] processor[cpu] latest[b8189] current[b8189]
download-libraries: already installed: latest[b8189] current[b8189]
download-model: model-url[https://huggingface.co/unsloth/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B-Q8_0.gguf] proj-url[] model-id[Qwen3-0.6B-Q8_0]:
download-model: waiting to check model status...:
download-model: model already exists:
loading model...
- system info:
	ACCELERATE:on, REPACK:on, MTL:EMBED_LIBRARY, CPU:NEON, ARM_FMA:on, FP16_VA:on, DOTPROD:on, LLAMAFILE:on,
- contextWindow: 8196
- k/v          : q8_0/q8_0
- nBatch       : 2048
- nuBatch      : 512
- modelType    : dense
- isGPT        : false
- template     : tokenizer.chat_template

QUESTION: Hello model

Okay, the user just said "Hello model." I need to respond appropriately. Since I'm an AI assistant, my initial response is friendly and helpful. Let me start by acknowledging their greeting. I should make sure to use a friendly tone and offer assistance. Maybe add something about being here to help with anything they need. Keep it simple and conversational. Let me check if there's any additional context needed, but since they just said hello, a basic reply should suffice.

! How can I assist you today? 😊
Unloading Kronk

Travel Schedule

Come find me in any of these cities or events this year. I will be giving workshops and talks about Kronk

Dates	Event	Location	Comments
Jan 29th - 2nd	AI Plumbers Fringe, FOSDEM	Brussels, Belgium	Talk
Mar 4th - 5th	Ardan Connect	São Paulo, Brazil	Workshop
Apr 20th - 25th	Gophercamp 2026	Brno, Czech Republic	Workshop, Talk
Apr 27th - 29th	AI Dev 26	San Francisco, USA	Attendee
May 17th - 23rd	Gophercon Signapore	Singapore	Workshop, Talk
Jun 1st - 5th	Crusoe Corporate Training	San Francisco, USA	Workshop
Jun 8th - 12th	Genetec Corporate Training	Montreal, Canada	Workshop
Jun 14th - 19th	GopherCon EU	Berlin, Germany	Workshop, Talk
JULY	Summer Vacation	Huntsville, AL	Rest
Aug 3rd - 6th	GopherCon USA	Seattle, Washington	Workshop, Talk
Aug 11th - 13th	GopherCon UK	London, England	Workshop, Talk
Sep 1st - 4th	GopherCon LATAM	Florianópolis, Brazil	Workshop, Talk
Oct 12th - 18th	GopherCon Africa	Kenya, East Africa	Workshop, Talk
Oct 29th - 4th	GoLab (GopherCon Italy)	Bologna, Italy	Workshop, Talk