Base Image
What every Tenki Sandbox session ships with out of the box, and how to add your own tools.
Every Tenki Sandbox session starts from the same prepared base image, so common
runtimes and tools are already present the moment a session reaches READY. The
essentials need no install-and-warm-up step. This page lists what ships by default and
how to add anything else.
The base is a standard Ubuntu 24.04 LTS microVM. You log in as the tenki user
(uid 1000) with passwordless sudo, so you can apt install, pip install, or
npm install anything not listed here.
Language runtimes
| Runtime | Version | Notes |
|---|---|---|
| Python | 3.12 | Ubuntu 24.04 system Python, with pip |
| Node.js | 24.14.0 | Installed via nvm; npm included |
| Bun | 1.3.14 | Fast JavaScript/TypeScript runtime and bundler |
Package managers & language tooling
| Tool | Version | Purpose |
|---|---|---|
| pip | latest | Python package installer |
| uv | 0.11.28 | Fast Python installer and resolver |
| npm | bundled | Node package manager (with Node.js) |
| pnpm | 10.20.0 | Fast, disk-efficient Node package manager |
TypeScript (tsc) | 5.9.3 | TypeScript compiler |
| ts-node | 10.9.2 | Run TypeScript directly |
| typescript-language-server | 5.3.0 | TypeScript/JavaScript LSP |
Coding agents
Tenki Sandbox bakes in the major terminal coding agents so you can drive them directly inside a session:
| Agent | Command | Version |
|---|---|---|
| Claude Code | claude | 2.1.209 |
| OpenAI Codex | codex | 0.144.4 |
| opencode | opencode | 1.17.20 |
Python data & ML libraries
Preinstalled into the system Python so data and analysis workloads run without a setup step:
| Category | Package | Version |
|---|---|---|
| Data core | numpy | 2.4.1 |
| Data core | pandas | 2.3.3 |
| Data core | matplotlib | 3.10.8 |
| Data core | scipy | 1.17.0 |
| Data core | seaborn | 0.13.2 |
| Data core | pillow | 12.1.0 |
| Data core | requests | 2.32.5 |
| Data core | beautifulsoup4 | 4.14.3 |
| ML | scikit-learn | 1.8.0 |
| ML | opencv-python-headless | 4.13.0.90 |
Heavier deep-learning frameworks (PyTorch, TensorFlow, Transformers, and similar) are not preinstalled. They tend to be large and GPU-oriented, so it's best to pin them per workload. Install them per session or bake them into a template.
Common CLI tools
Available on PATH in every session:
- Version control & GitHub:
git,gh - HTTP & transfer:
curl,wget,rsync - Search & data:
ripgrep(rg),jq,sqlite3 - Archives:
tar,zip/unzip,zstd,bzip2,xz - Build:
build-essential(gcc/make),pkg-config - SSH:
openssh-client,openssh-server - Inspect:
file,less,lsof
Checking what's installed
Because you have sudo, you can inspect anything from inside a session:
python3 --version
node --version
claude --version
python3 -c "import numpy, pandas, sklearn; print(numpy.__version__, pandas.__version__)"
dpkg -l | grep -i ripgrepAdding your own tools
For a one-off session, just install what you need:
pip install polars
sudo apt-get update && sudo apt-get install -y ffmpeg
npm install -g some-cliIf every session should start with the same extra tools, define them once in a template instead of installing on each session — the template build captures the prepared environment as a snapshot so new sessions start ready.