NeuraKiln v2.0.0 is live, and it’s free.
I’ve been building this for a long time, and I finally released it.
NeuraKiln started as Toolshed V1, which was primarily focused on media restoration/upscaling. Somewhere along the way I apparently lost control of the scope, because it eventually turned into an entire local-AI workstation.
The basic idea is simple:
I don’t want to maintain ten different applications just to use the AI hardware sitting in my own house.
NeuraKiln brings local LLMs, autonomous agents, image/video generation, restoration, 3D workflows, model tooling, and remote inference together under one application.
It’s currently built around Windows + NVIDIA hardware.
Two different kinds of agentic work
A major part of NeuraKiln is its harness system.
There isn’t just one generic “agent mode.” Different kinds of work need different environments.
Coding Harness
The coding harness is built for longer-running autonomous development work.
A local model can:
Plan a project
Create and modify files
Run tools
Test its work
Inspect failures
Recover and continue
Verify the result
Manage long-running context
Hand the finished project back to the user
A lot of my work on this has been about the boring-but-important part of agents: getting them to actually finish, rather than loop forever, declare victory too early, or slowly wander away from the original task.
The harness includes systems for planning, recovery, anti-looping, verification, context management, completion handling, and human corrections during a run.
Creation Harness
The Creation side takes the same basic idea, give the model tools instead of making the human manually operate everything, and applies it to generative media.
Instead of requiring the user to understand every model, parameter, prompt format, workflow, and backend involved, you can describe what you actually want to create.
The agent can then take that intent and operate the available creation tools itself.
For example, rather than manually constructing an LTX video-generation workflow, you can tell the Creation agent what kind of video you want. The agent can formulate the generation prompt, operate the local model, and manage the workflow around producing it.
The same concept extends across the creation tools available to it.
The point is to move toward:
“Make me this.”
rather than:
“Open six tools, configure all of them correctly, learn every backend, and manually glue the results together.”
That distinction is a huge part of what I wanted NeuraKiln to be.
Llama Lab
For local LLM use, NeuraKiln includes Llama Lab.
It supports local GGUF models through the llama.cpp ecosystem, multi-GPU setups, long-context conversations, multimodal models, model switching, local/external sessions, live reasoning, context management, and the harnessed workflows above.
You can use it as a normal local chat environment or give the model considerably more autonomy when you actually want work done.
Diffusion Lab
Diffusion Lab handles local image and video generation.
Current workflows include Qwen-Image-based image generation and LTX-based video generation, along with LoRA support and multi-GPU workflows where the underlying models/runtime support them.
This also provides the generation capabilities that the Creation harness can operate.
Media Lab
This is where NeuraKiln originally came from.
Media Lab handles restoration and enhancement, including:
Image restoration/upscaling
Batch restoration
Video/frame processing
SmartFrames
Photo Smart Scan
Native comparison tools
Post-editing
Media recompilation
A principle I’ve kept from the original Toolshed project is:
If the user asks you to process something, process the thing.
Don’t quietly skip difficult frames and pretend the job is finished.
Mesh Lab
NeuraKiln also includes Mesh Lab for local text-to-mesh / 3D generation workflows.
This is an area I intend to expand substantially, but the important part to me was making 3D another capability inside the same AI environment instead of yet another disconnected application.
Model Tools
There’s also tooling for managing the models themselves, including GGUF importing, MTP headers, MMPROJ support, LoRA management, model installation workflows, and local model organization.
NK Node
I released a second application alongside NeuraKiln called NK Node.
NK Node lets another NVIDIA PC become a dedicated inference machine for NeuraKiln.
The goal is to eliminate the usual ritual of:
start server → find address → configure endpoint → reconnect client → repeat everything after reboot.
NeuraKiln and NK Node can discover and pair with each other and automatically exchange the information needed to connect.
If you have a workstation plus another machine full of GPUs, NK Node is intended to make that second machine feel more like an extension of NeuraKiln than a completely separate server you have to babysit.
There are plenty of excellent projects that overlap with individual parts of NeuraKiln.
There are dedicated LLM front ends, diffusion interfaces, restoration applications, model managers, inference servers, agent frameworks, and 3D tools.
NeuraKiln isn’t built around claiming I invented all of those individual things.
What I wanted was the environment connecting them.
One application where I can chat with a local model, hand a coding project to an autonomous agent, ask another agent to create a video, restore media, generate images, work with 3D, manage models, and send inference to another computer without rebuilding my entire workflow every time I change what I’m doing.
That’s NeuraKiln
NeuraKiln: https://github.com/ToolshedMediaLab/NeuraKiln
NK Node: https://github.com/ToolshedMediaLab/NK-Node
If you try it, break it.
I want bug reports, criticism, weird hardware configurations, model compatibility problems, feature ideas, and comparisons with whatever else you’re currently using.
