netclode

agent
Guvenlik Denetimi
Uyari
Health Uyari
  • No license — Repository has no license file
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Community trust — 108 GitHub stars
Code Gecti
  • Code scan — Scanned 12 files during light audit, no dangerous patterns found
Permissions Gecti
  • Permissions — No dangerous permissions requested
Purpose
This tool is a self-hosted, cloud-based coding agent. It uses microVM sandboxes managed via Kubernetes to let you run AI coding tasks from a native iOS or macOS app, featuring deep GitHub integration and local LLM support.

Security Assessment
The overall risk is Medium. The agent operates in "full yolo mode," which explicitly includes root access, shell execution, and Docker within its isolated Kata microVMs. It makes extensive network requests, connecting to LLM APIs, S3 storage for JuiceFS, and GitHub. The architecture actively mitigates risk by preventing API keys from entering the sandbox, instead injecting them securely via a proxy. A light code scan found no hardcoded secrets or dangerous patterns.

Quality Assessment
The project is actively maintained, with its most recent push occurring today. It has earned just over 100 GitHub stars, indicating a small but present level of community validation. However, it completely lacks a license file, meaning the legal terms of use and distribution are undefined, which may restrict its suitability for business environments.

Verdict
Use with caution due to its intentionally privileged sandbox execution, the inherent risks of self-hosting complex cloud infrastructure, and the complete absence of a formal software license.
SUMMARY

Self hosted cloud coding agent with k3s + kata containers + cloud hypervisor microVMs + tailscale + any harness + a nice iOS app

README.md

Netclode

netclode

Self-hosted coding agent with microVM sandboxes and a native iOS and macOS app.

Netclode iOS App Netclode macOS App

Why I built this

I wanted a self-hosted Claude Code environment I can use from my phone, with the UX I actually want. The existing cloud coding agents were a bit underwhelming when I tried them, so I built my own!

I wrote a blog post about how it works: Building a self-hosted cloud coding agent.

What makes it nice

  • Full yolo mode - Docker, root access, install anything. The microVM handles isolation
  • Local inference with Ollama - Run models on your own GPU, nothing leaves your machine
  • Tailnet integration - Preview URLs, port forwarding, access to my infra through Tailscale
  • JuiceFS for storage - Storage offloaded to S3. Paused sessions cost nothing but storage
  • Live terminal access - Drop into the sandbox shell from the app
  • Session history - Auto-snapshots after each turn. Roll back workspace and chat to any previous point
  • GitHub integration - Clone private repos, push commits, create PRs. Per-repo scoped tokens generated on demand via a GitHub App
  • GitHub Bot - @mention on PRs/issues to spin up a sandbox and get a response as a comment. Auto-reviews dependency update PRs from Dependabot/Renovate
  • Multiple SDKs & providers - Claude Code, OpenCode, Copilot, Codex SDKs with Anthropic, OpenAI, Mistral, Ollama, and more
  • Secrets can't be stolen - API keys never enter the sandbox. A proxy injects them on the fly for allowed hosts

How it works

flowchart LR
    subgraph CLIENT["Client"]
        APP["iOS / macOS<br/><sub>SwiftUI</sub>"]
    end

    subgraph VPS["VPS - k3s"]
        TS["Tailscale Ingress<br/><sub>TLS - HTTP/2</sub>"]
        CP["Control Plane<br/><sub>Go</sub>"]
        BOT["GitHub Bot<br/><sub>Go</sub>"]
        REDIS[("Redis<br/><sub>Sessions</sub>")]
        POOL["agent-sandbox<br/><sub>Warm Pool</sub>"]
        JFS[("JuiceFS")]

        subgraph SANDBOX["Sandbox - Kata VM<br/><sub>Cloud Hypervisor</sub>"]
            AGENT["Agent<br/><sub>Claude / OpenCode / Copilot / Codex SDK</sub>"]
            DOCKER["Docker"]
        end
    end

    GH["GitHub Webhooks"]
    S3[("S3")]
    LLM["LLM APIs"]

    APP <-->|"Connect RPC<br/>HTTPS/H2"| TS
    TS <-->|"Connect RPC<br/>h2c"| CP
    GH -->|"Webhooks"| BOT
    BOT <-->|"Connect RPC<br/>h2c"| CP
    CP <-->|"Redis Streams"| REDIS
    CP <-->|"Connect RPC<br/>gRPC/h2c"| AGENT
    POOL -.->|"allocate"| SANDBOX
    JFS <--> SANDBOX
    JFS <-->|"POSIX on S3"| S3
    AGENT --> LLM

The control plane grabs a pre-booted Kata VM from the warm pool (so it's instant), forwards prompts to the agent SDK inside, and streams responses back. Redis persists events so clients can reconnect without losing anything.

When pausing, the VM is deleted but JuiceFS keeps everything in S3: workspace, installed tools, Docker images, SDK session. Resume mounts the same storage and the conversation continues as if nothing happened. Dozens of paused sessions cost practically nothing.

Stack

Layer Technology Purpose
Host Linux VPS + Ansible Provisioned via playbooks
Orchestration k3s Lightweight Kubernetes, nice for single-node
Isolation Kata Containers + Cloud Hypervisor MicroVM per agent session
Storage JuiceFS → S3 POSIX filesystem on object storage
State Redis (Streams) Real-time, streaming session state
Network Tailscale Operator VPN to host, ingress, sandbox previews
API Protobuf + Connect RPC Type-safe, gRPC-like, streams
Control Plane Go Session and sandbox orchestration
Agent TypeScript/Node.js SDK runner inside sandbox
GitHub Bot Go Webhook-driven bot for @mentions and dep reviews
Secret Proxy Go Injects API keys outside the sandbox
Local LLM Ollama Optional, local models on GPU
Client SwiftUI (iOS 26) Native iOS/macOS app
CLI Go Debug client for development

Project structure

netclode/
├── clients/
│   ├── ios/              # iOS/Mac app (SwiftUI)
│   └── cli/              # Debug CLI (Go)
├── services/
│   ├── control-plane/    # Session orchestration (Go)
│   ├── agent/            # SDK runner (Node.js)
│   │   └── auth-proxy/   # Adds SA token to requests (Go)
│   ├── github-bot/       # GitHub webhook bot (Go)
│   └── secret-proxy/     # Injects real API keys (Go)
├── proto/                # Protobuf definitions
├── infra/
│   ├── ansible/          # Server provisioning
│   └── k8s/              # Kubernetes manifests
└── docs/

Getting started

See docs/deployment.md for full setup. I tried to make it as easy as possible: ideally a single playbook run.

Quick version:

  1. Provision a VPS with nested virtualization support
  2. Run Ansible playbooks to provision the server
  3. Configure secrets (API keys, S3 credentials, Tailscale OAuth)
  4. Deploy k8s manifests
  5. Connect via Tailscale and you're good to go

Docs

Demo

All videos from the blog post:

Warm pool instant start

No cold start, sandboxes are pre-booted

Session pause & resume

Older sessions are automatically paused to save resources. Resume brings everything back instantly

Local inference with Ollama

Run models on your own GPU

CLI shell

Instant sandbox access from the terminal, inspired by sprites.dev

Git diff view
Diff view with multi-repo support

Live terminal
Drop into the sandbox shell from iOS

Speech input
Speech recognition for prompts

Tailscale port preview
Expose sandbox ports to the tailnet

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