This is Part 2 of Closing the execution gap — a series on building jhansi.io, a cloud sandbox for AI-generated code.

The first question I got after shipping persistent sandboxes was predictable:

"Great — but do I still have to pip install everything myself?"

Yes. You did. That was embarrassing.

If the pitch is "run AI-generated code with zero friction," making users manage deps manually is a contradiction. For regulated teams it's worse: every new package is a supply-chain review. Friction kills adoption.

So v0.3 fixes it.

The problem with dependencies in sandboxes

Every sandbox starts as a clean container. Upload main.py, hit run, and you get:

ModuleNotFoundError: No module named 'requests'

The naive fix is to install at exec time. But downloading from PyPI on every run is slow, expensive, and brittle. pandas + numpy is a 40s cold start. Run that 100 times and your AI agent burns budget before it does anything useful.

The right fix: install once, persist forever.

Install once, persist forever

jhansi.io gives every sandbox a persistent workspace — a folder that survives across runs. In v0.3, dependencies live there too.

• First exec: we detect deps, install to /sandbox/deps, run your code.
• Second exec: deps are already there. Cold start drops dramatically. This matters for AI agents. Humans tolerate a 30s install. Agents that try 5 approaches to solve a task can't. Workspace-scoped cache means failed attempt #1 pays the install tax. Attempts #2–5 run instantly.

First exec — install + run

$ curl -X POST .../sandboxes/sb_abc123/exec -d '{"filename": "main.py"}'
{
"output": "Installing requests==2.31.0...\n200\n"
}

Second exec — just run

$ curl -X POST .../sandboxes/sb_abc123/exec -d '{"filename": "main.py"}'
{
"output": "200\n"
}

That's the difference between "AI is too slow" and "AI is faster than a junior dev."

Manifest first, auto-detect as fallback

How do we know what to install? Both approaches, in the right order.

If you provide a manifest, we trust you. You know your deps better than any static analyser. If you don't, we fall back to auto-detection.

Priority for Python:

1. pyproject.toml → pip install
2. requirements.txt → pip install -r
3. Neither → pipreqs scan pipreqs isn't just import requests → requests. It knows import cv2 means opencv-python, import sklearn means scikit-learn. You don't have to remember.

Using a manifest isn't just faster — it's auditable. Auditors can diff your pinned deps between runs. Auto-detect is for prototyping. More on auditability in Part 5.

Four languages, four strategies

AI doesn't just write Python. jhansi.io handles the four languages LLMs generate most:

Each language keeps its own idioms. We don't impose a universal abstraction. Workspace-scoping means one sandbox's torch==2.1.0 can't poison another's torch==1.13. No dependency hell across AI runs.

The trust boundary

One decision worth documenting: we don't vet what gets installed.

Egress is restricted to official registries — PyPI, npm, Maven Central, proxy.golang.org — and nothing else. No arbitrary domains. What you install from those registries is your responsibility.

The contract is simple:

jhansi.io guarantees isolation. You own your code.

This is the same model as AWS Lambda or Cloud Run. We contain the blast radius. We don't audit your imports.

SBOM per exec — a full list of every package installed, with versions and licenses — is on the roadmap. Today we contain. Tomorrow we curate.

What's next

Two things didn't make v0.3:

• Streaming output — dep installs can take 30s. Right now you wait. Soon you'll see output live and know exactly why torch is taking forever.
• Missing import detection — if your manifest forgets a package, you get an ImportError today. We should surface the unlisted import in the response. Coming soon. Next in the series: Isolation — you can pip install safely now. But can you stop that package from exfiltrating your AWS credentials? What "hard-sandboxed" actually means, why Docker isn't enough, and the attacks most sandboxes miss.

jhansi.io is open source (Apache 2.0) at github.com/jhansi-io. Follow the series on Dev.to, LinkedIn, and X.