Fovux Kit
About
Python toolkit and application suite for Fovux workflows, MCP integrations, developer utilities, and structured automation across local projects.
Details
- Author
- oaslananka
- GitHub stars
- 1
- Downloads
- 209
Jump to
- End-to-end YOLO lifecycle: From raw image to optimized ONNX/TensorRT edge artifact, Fovux manages the complexity
- Reproducible local runs: Consistent configurations that you can share, compare, and audit
- Export and edge inference focus: Export your models seamlessly to production-ready formats
- Privacy-first by default: Fovux contains no hidden telemetry
- Runs Dashboard: Monitor training metrics, GPU usage, and epoch progress in real-time
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Fovux KitCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Follow the repository README to install the server and add its MCP configuration to your client.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"fovux kit": {
"fovux-kit": {
"command": "uv",
"args": [
"tool",
"install",
"fovux-mcp"
]
}
}
}
}
McpServers
{
"fovux-kit": {
"command": "uv",
"args": [
"tool",
"install",
"fovux-mcp"
]
}
}
Fovux
<p align="center">
<strong>Local-first YOLO workbench for edge AI.</strong>
</p>
<p align="center">
<a href="https://www.buymeacoffee.com/oaslananka">
</a>
</p>
What is Fovux?
Fovux is a local-first vision workbench for YOLO datasets, training, evaluation, export, and edge inference. It combines the Python backend package fovux-mcp 1.4.0, the npm wrapper fovux-mcp 1.4.0, and the VS Code companion Fovux Studio 1.3.0 to streamline the computer vision engineering lifecycle.
Why developers use it
- Local-first: No hosted control plane required. Your datasets and models stay entirely on your local machine or trusted infrastructure.
- End-to-end YOLO lifecycle: From raw image to optimized ONNX/TensorRT edge artifact, Fovux manages the complexity.
- MCP-native automation: Exposes the tool surface over MCP stdio for agent clients, while Fovux Studio uses a separate local HTTP/SSE API for dashboards and guarded UI workflows.
- VS Code Studio: Visual workflows directly in your editor for tracking runs, visualizing performance, and evaluating datasets.
- Reproducible local runs: Consistent configurations that you can share, compare, and audit.
- Export and edge inference focus: Export your models seamlessly to production-ready formats.
- Privacy-first by default: Fovux contains no hidden telemetry.
60-second quickstart
Ensure you have Python 3.12 through 3.14 installed. Install the backend globally using uv:
uv tool install fovux-mcp
fovux doctor
fovux-mcp is the primary CLI alias used by Fovux Studio and MCP clients. The shorter fovux
alias points to the same Typer application for direct terminal use.
npm users can install the wrapper package with npm install -g fovux-mcp; it delegates to
the matching Python package through uvx.
Initialize your Fovux environment and start the MCP server:
fovux-mcp serve --http
Install the VS Code extension, open the command palette (Ctrl+Shift+P), and type Fovux: Start Training... to begin your first run.
Install
Using uv (Recommended)
uv tool install fovux-mcp
Using npm
npm install -g fovux-mcp
Fovux Studio (VS Code Extension)
Search for Fovux Studio in the VS Code Marketplace or Open VSX, or install via the CLI:
code --install-extension oaslananka.fovuxstudiokit
MCP client configuration
To connect an MCP desktop client to Fovux, add the following to your MCP client configuration (mcp_config.json):
{
"mcpServers": {
"fovux": {
"command": "fovux-mcp",
"args": ["serve"]
}
}
}
Fovux Studio
Fovux Studio provides a visual layer over your Fovux environment directly inside VS Code:
- Runs Dashboard: Monitor training metrics, GPU usage, and epoch progress in real-time.
- Dataset Inspector: Analyze your YOLO annotations and locate missing labels.
- Export Wizard: Convert your models to ONNX, TensorRT, or TFLite with optimal shapes.
- Timeline & Compare: Diff your runs to understand regression or progress.
Use the VS Code Command Palette (Cmd/Ctrl+Shift+P) and type Fovux: to discover available commands.
Core tools
Fovux MCP 1.4.0 exposes 47 local tools across dataset inspection, validation, active learning, training, evaluation, export, quantization, inference, benchmarking, run management, policy/audit, and support-bundle workflows.
The generated complete tool list lives in fovux-mcp/README.md and the MkDocs site; CI now fails if a registered tool is missing from docs, the schema snapshot, policy metadata, Studio mappings, or the MkDocs navigation.
Architecture
Fovux separates concerns across three core components:
1. Fovux Core: The underlying Python engine interfacing with YOLO and local hardware.
2. Fovux MCP Server: The stdio MCP server exposing Fovux Core to AI agents, plus the Fovux Studio local API/custom REST+SSE bridge used by Fovux Studio. A standards-compliant Streamable HTTP MCP endpoint is tracked separately in the v1.4.0 - MCP Conformance & Agent Safety milestone.
3. Fovux Studio: The React/TypeScript VS Code extension for human interaction.
Read more about the architecture in the docs
Security and privacy
Fovux is built for enterprise privacy. No telemetry is collected by default. Data stays exactly where you put it, and no analytics payloads are sent to external services unless you explicitly configure third-party integrations (like W&B).
CI/CD and release model
Fovux maintains a secure GitHub Actions release model in this repository:
- oaslananka/fovux-kit: The source of truth for code, issues, pull requests, CI, and releases.
- .github/workflows: The active CI, security, release, and registry publishing workflows.
- Protected GitHub environments gate PyPI, npm, Marketplace, and Open VSX publishing.
All releases are created by release-please from Conventional Commits, gated by CI, and published from GitHub Actions with checksums, SBOMs, and provenance.
Repository operations
Repository operations, runtime compatibility, branch protection, developer bootstrap, and the release
process are documented in docs/repository-operations.md,
docs/runtime-compatibility.md,
docs/development.md,
docs/branch-protection.md, and
docs/release-process.md. Local environment variable names are listed in
.env.example; publishing credentials remain in protected GitHub Actions secrets.
For a fresh development checkout on Linux/macOS:
scripts/bootstrap-dev.sh --install-deps --hooks
task ci
Roadmap
- v1.3.1 - Stabilization & Documentation Truth: documentation drift, local DX, registry verification, release metadata, and fail-fast quality gates.
- v1.4.0 - MCP Conformance & Agent Safety: official MCP transport decision, conformance tests, schema snapshots, and agent approval safety.
- v1.5.0 - Studio Workflow & Dataset Intelligence: guided dataset→training→evaluation→export workflows and Studio e2e smoke coverage.
- v1.6.0 - Edge Export & Deployment Intelligence: current export matrix, target profiles, benchmark reproducibility, and quantization workflow.
- v2.0.0 - Extensibility, Supply Chain & Ecosystem Readiness: plugin/API stability, trusted publishing, attestations, threat model, and marketplace release evidence.
Contributing
We welcome contributions! Please read our Contributing Guidelines to get started.
License
Fovux is released under the Apache-2.0 License.
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