openocto 0.1.2

Any time, any device, anything. AI agent that controls all your terminals across firewalls.

OpenOcto

Any time, any device, anything.

Too many devices? Let Octo handle it.

中文文档

• Work from home, remotely control GPU servers behind firewalls.
• On the go, monitor device status and run commands from your phone.
• Multi-device collaboration and management — leave it to Octo.
• Compatible with Claude Code and MCP.
• Firewall-proof.

How It Works

┌──────────────┐         ┌───────────────┐         ┌──────────────┐
│  Your Agent  │  HTTP   │  Redis Relay  │  HTTP   │  Remote GPU  │
│ (Claude Code)│────────>│  (Upstash)    │<────────│ (octo daemon)│
│              │         │  serverless   │         │              │
│ MCP / CLI    │         │  free tier    │         │  any machine │
└──────────────┘         └───────────────┘         └──────────────┘

• Agent side: sends tasks via MCP tools or CLI commands
• Relay: built-in free relay (or your own Upstash Redis for full privacy)
• Worker side: daemon polls for tasks, executes, streams results back

Both sides only make outbound HTTP requests. No inbound ports, no SSH tunnels, no VPN.

Quick Start

1. Install & configure (local machine)

pip install openocto
octo setup             # AI-guided setup — walks you through everything interactively

Optional extras:

pip install "openocto[qr]"       # QR output for `octo token --qr`
pip install "openocto[nearby]"   # BLE + encrypted nearby transfer
pip install "openocto[metrics]"  # TensorBoard/tfevents parsing helpers

octo setup uses a free LLM — no API key needed. It detects your environment, configures the relay, and optionally sets up an LLM provider, all through conversation.

Prefer manual setup? Use octo init instead.

2. Start a worker (remote machine)

Option A — Install directly on the remote:

pip install openocto
octo join --token "$(octo token)" --name gpu --tags "gpu,cuda"

Run octo token on your local machine first to get a join token. This avoids repeating octo init on every machine.

Option B — Remote has no internet? Use a jump server with --ssh:

# On a jump server that can SSH into the GPU machine:
octo join --token "..." --name gpu --tags "gpu,cuda" --ssh "user@gpu-internal-ip"

3. Use it

octo ls                              # See all terminals
octo run gpu "nvidia-smi"            # Run a command
octo run gpu "python train.py"       # Start training
octo run gpu "python train.py" --notify phone  # Notify phone when done
octo cat gpu /work/model.py          # Read a remote file
octo edit gpu /work/config.py --old "lr=0.001" --new "lr=0.0005"
octo kill gpu                        # Kill running command

4. Connect your AI agent (optional)

Add to .mcp.json in your project:

{
  "mcpServers": {
    "openocto": {
      "command": "octo",
      "args": ["mcp-server"]
    }
  }
}

Claude Code (or any MCP-compatible agent) can now control your remote terminals directly.

Training Notifications

Get notified on your phone when a training job finishes — or fails:

octo run gpu "python train.py" --notify phone
octo run gpu "python train.py" --notify phone --notify-message "Experiment A done"

Works across firewalls. The notification pops up as a system notification on your phone (lock screen visible).

MCP Tools

Tool	Description
remote_ls	List all terminals with status, mode, and tags
remote_run(terminal, command)	Execute a shell command (streaming output)
remote_read(terminal, path)	Read a file with line numbers
remote_edit(terminal, path, old, new)	Edit a file by string replacement
remote_glob(terminal, pattern)	Search files by glob pattern
remote_grep(terminal, pattern)	Search file contents by regex
remote_kill(terminal)	Kill running command
remote_logs(terminal)	View output of current or last task
remote_send(source, target, file)	Transfer files between terminals
remote_metrics(terminal)	GPU status and training metrics
remote_wake / remote_cool	Control polling mode

GPU Metrics

Monitor GPU utilization, VRAM, temperature, and training loss — from CLI, web browser, or phone.

octo metrics gpu                    # One-shot: print GPU status + training metrics
octo metrics --dashboard            # Open web dashboard (all GPU servers)

The daemon reads nvidia-smi and TensorBoard tfevents files. No code changes needed in your training scripts.

File Transfer

octo send local gpu ~/data/dataset.tar.gz --dest /work/data/dataset.tar.gz

Smart routing picks the fastest path:

Route	When	Speed
LAN direct	Same network	Full LAN speed
Redis relay	File < 512KB	Instant
Cloud storage	Large files, different networks	S3 upload → presigned URL → download

Cloud storage setup (optional): octo config --storage — supports Cloudflare R2, AWS S3, or any S3-compatible service.

Configuration

Relay

octo init offers two options:

Option	Setup	Privacy
Free relay (default)	Instant, no account needed	Isolated by random workspace ID
Own Redis	Create free at upstash.com	Full privacy, your own instance

Adding more machines — use a join token instead of repeating octo init:

octo token                    # prints octo://eyJ...
octo token --qr               # or show QR code (for phone)

Cloud Storage (optional)

For large file transfers between terminals on different networks:

octo config --storage         # S3 endpoint, keys, bucket

Works with Cloudflare R2 (free 10GB), AWS S3, MinIO, or any S3-compatible service.

CF Worker Proxy (optional)

For team use with own Redis — deploy the included Cloudflare Worker so members don't need direct Redis credentials. See cf-worker/.

Permissions & Security

Personal mode (default): anyone with the relay credentials has full access. Fine for solo use.

Public mode: for shared environments (lab teams, multi-user setups):

octo network create mylab --public
octo register alice                       # Generate Ed25519 keypair
octo invite gpu --role readwrite          # Share access

Roles: full (shell + kill + edit) / readwrite (read + edit) / readonly (read only)

Every task is signed with the sender's Ed25519 key and verified by the daemon before execution.

Android App

The companion app turns your phone into an OpenOcto node:

• Scan QR code to join a network
• Run as a background daemon
• View GPU metrics with auto-refresh
• Receive training notifications
• Chinese / English language support

Source: android/

Architecture

src/openocto/
├── relay.py              Upstash Redis REST client (atomic Lua CAS)
├── daemon.py             Worker: poll, execute, stream results
├── cli.py                CLI (20+ subcommands)
├── mcp_server.py         MCP server (11 structured tools)
├── storage.py            S3-compatible file transfer (parallel multipart)
├── permissions.py        Role-based ACL engine
├── signing.py            Ed25519 task signing & verification
├── config.py             Config & join token encoding
├── metrics_dashboard.py  GPU metrics web dashboard
└── agent_md.py           CLAUDE.md generator

• Core install stays small — QR, nearby transfer, and metrics parsing are available via optional extras
• Protocol: JSON tasks in Redis, lifecycle PENDING → RUNNING → DONE
• Security: Ed25519 signed tasks, role-based ACL, path validation, atomic CAS updates

CLI Reference

Setup:
  octo setup                             AI-guided setup wizard (recommended)
  octo init                              Manual relay configuration
  octo token [--qr]                      Generate join token
  octo join --name NAME [options]        Start daemon
       --tags T                          Comma-separated tags
       --ssh user@host                   Forward via SSH
       --daemon                          Run in background

Remote Operations:
  octo run TARGET "COMMAND"              Execute command
       --notify TERMINAL                 Notify when done
       --notify-message MSG              Custom notification message
  octo cat TARGET /path                  Read file
  octo edit TARGET /path --old X --new Y Edit file
  octo glob TARGET "**/*.py"             Search files by pattern
  octo grep TARGET "pattern"             Search file contents
  octo logs TARGET [-f] [--tail N]       View task output
  octo kill TARGET                       Kill running command
  octo send SOURCE TARGET FILE           Transfer file
  octo metrics TARGET                    GPU + training metrics
  octo metrics --dashboard               Open web dashboard

Terminal Management:
  octo ls                                List all terminals
  octo wake TARGET                       Fast polling
  octo cool TARGET                       Low-power polling

Network & Permissions:
  octo network create NAME [--public]    Create network
  octo register NAME                     Register device identity
  octo invite TARGET --role ROLE         Generate invite
  octo acl TARGET [--default R]          Manage access

Agent:
  octo agent-md                          Generate CLAUDE.md
  octo mcp-server                        Start MCP server

Troubleshooting

Problem	Solution
"OpenOcto not configured"	Run octo init
Terminal shows "offline"	Daemon not running, check ~/.octo/daemon.log
Command hangs	octo kill TARGET or Ctrl+C
Slow first response	Terminal in cool mode, auto-wakes on task (up to 60s)
pkill -f kills the wrapper	Use bracket trick: pkill -f "[t]rain_script"
Windows: python3 not found	Daemon auto-detects python on Windows
CERTIFICATE_VERIFY_FAILED on Windows / corporate networks	Your Python environment may not trust the system or corporate proxy CA. Try pip install pip-system-certs, then restart the terminal and retry. This is common with Conda/Miniforge behind HTTPS-inspecting firewalls.

License

Apache 2.0