Python FS shim

Python code inside ws.execute('python3 ...') can open(), os.listdir(), pathlib.Path() etc. against any registered Mirage mount. The shim routes those calls through Mirage’s mount layer (RAM, S3, Linear, GDocs, Slack, anything you’ve registered).

# inside python3
import json
team = json.load(open('/linear/teams/eng/team.json'))   # reads through Linear's API
open('/ram/notes.txt', 'w').write('hello')              # writes flush back to RAMResource

What works

open('/s3/data.csv').read()
open('/linear/teams/eng/team.json').read()
open('/gdocs/owned/MyDoc.gdoc.json').read()

The first read fetches via the resource and caches in MEMFS. Subsequent reads are sync.

open('/ram/out.txt', 'w').write('hello')
open('/s3/icon.png', 'wb').write(png_bytes)

On close(), bytes flush back through the resource’s write op.

import os
for entry in os.listdir('/linear/teams/'):
    print(entry)

# Synthesized paths work too, Gmail materializes date dirs on access:
for msg_dir in os.listdir('/gmail/INBOX/2026-05-03/'):
    print(msg_dir)

The first listdir of a synthesized path triggers a one-time bridge fetch.

from pathlib import Path
Path('/gdocs/owned/MyDoc.gdoc.json').read_text()

import glob
for f in glob.glob('/ram/*.txt'):
    print(f)

from PIL import Image
Image.new('RGB', (4, 4), color='red').save('/ram/icon.png')

import json
json.dump({'k': 'v'}, open('/ram/cfg.json', 'w'))

import numpy as np
np.save('/ram/arr.npy', np.arange(10))

Anything that goes through Python’s open() works (PIL, numpy, pandas, json, pickle).

# Read from one mount, write to another, same Python code:
import os, json

out = []
for t in os.listdir('/linear/teams/')[:5]:
    team = json.load(open(f'/linear/teams/{t}/team.json'))
    out.append({'name': team['name']})

json.dump(out, open('/ram/summary.json', 'w'), indent=2)

What doesn’t work

Case	Why	Workaround
C extensions calling `fopen` directly, `sqlite3.connect('/ram/db.sqlite')`, `h5py.File('/ram/x.h5')`	They bypass Python’s `open()` and the shim never sees them	Use a local copy, or stream bytes via stdin
External edits show up live, someone else edits the GDoc while you’re reading	Once a path is in MEMFS the shim doesn’t re-fetch it	Currently no API; must `unmount` + `addMount`
Huge mounts, preloading a 100GB S3 bucket	Every byte lives in MEMFS until close	Mount a narrower prefix
Concurrent writers, two Python processes write the same path	Last `close()` wins, no conflict detection	Out of scope for v1
Browser-only resources from Node, OPFS	OPFS isn’t a thing in Node	Run in a browser-side Mirage

How it works (one paragraph)

Pyodide’s in-memory FS (MEMFS) is the sync facade. At addMount, Mirage walks the prefix and copies files into MEMFS. Python reads hit MEMFS directly, fast, no network. On a miss (path not yet in MEMFS but the prefix is registered), the shim catches the error, calls back through the bridge via JSPI to fetch it, populates MEMFS, and retries. Writes are intercepted on close() and flushed back through Mirage’s write op.

Python open() / listdir()
        │
        ▼
   Pyodide MEMFS  ──hit──►  bytes
        │
       miss
        │
        ▼
   run_sync(_mirage_bridge.list/fetch)
        │
        ▼
   populate MEMFS, retry

Quick start (TypeScript)

import { Workspace, RAMResource, S3Resource, MountMode } from '@struktoai/mirage-node'

const ws = new Workspace({}, { mode: MountMode.WRITE })
ws.addMount('/ram', new RAMResource(), MountMode.WRITE)
ws.addMount('/s3', new S3Resource({ bucket: 'my-bucket', region: 'us-east-1' }), MountMode.READ)

await ws.fs.writeFile('/ram/in.json', '{"hello":"world"}')

const r = await ws.execute(`python3 -c '
import json
print(json.load(open("/ram/in.json"))["hello"])
'`)
console.log(r.stdoutText)  // "world"

See the full demo at examples/typescript/pyodide/vfs.ts.

Python packages (PIL, numpy, pandas, …)

Pyodide ships CPython, but third-party packages aren’t loaded until you import them. Mirage scans the code you run for import statements and auto-fetches matching packages on demand, so from PIL import Image, import numpy as np, import pandas as pd all just work the first time. Subsequent calls hit Pyodide’s package cache. If you need to opt out (e.g., to keep workspace startup lean):

new Workspace({}, { python: { autoLoadFromImports: false } })

Resource compatibility

Resource	Status
RAM, OPFS (browser), Disk	✅
S3, R2, GCS, OCI, Supabase	✅ (narrow the prefix to keep the working set small)
Linear, GDocs, GSheets, GSlides, GDrive	✅
GitHub, Redis	✅
Gmail (including synthesized date dirs)	✅ (lazy-fetched on first access)
Slack	⚠️ preloads channel histories; large workspaces may be slow

Runtime requirements

The shim uses JSPI to bridge sync Python calls to async JS.

Chrome / Edge 137+ (May 2025): works out of the box
Firefox: behind javascript.options.wasm_js_promise_integration
Node 24+: pass --experimental-wasm-jspi (vitest config already does this)
Cloudflare Workers: Python Workers with JSPI runtime

Without JSPI, reads of preloaded files still work but writes throw RuntimeError: Cannot stack switch on close().

To run examples directly:

node --experimental-wasm-jspi --import tsx/esm examples/typescript/pyodide/vfs.ts

Errors you might see

Symptom	What it means
`FileNotFoundError [Errno 44]`	Path isn’t in MEMFS and lazy fetch failed too. Check the preceding `console.warn: mirage lazy: ...` for the real reason (auth, network, bad path).
`console.warn: mirage lazy: list <path> failed`	The lazy backfill called `_mirage_bridge.list` and got rejected. Followed by `FileNotFoundError`.
`RuntimeError: Cannot stack switch`	JSPI not enabled. Pass the Node flag or upgrade browser.
`console.warn: mirage preload: skipping <path>`	A single entry failed during initial preload, others continued. Usually harmless (synthetic listing entries).

Getting Started

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Resources

Runtimes

Python FS shim

What works

What doesn’t work

How it works (one paragraph)

Quick start (TypeScript)

Python packages (PIL, numpy, pandas, …)

Resource compatibility

Runtime requirements

Errors you might see

See also

​What works

​What doesn’t work

​How it works (one paragraph)

​Quick start (TypeScript)

​Python packages (PIL, numpy, pandas, …)

​Resource compatibility

​Runtime requirements

​Errors you might see

​See also

What works

What doesn’t work

How it works (one paragraph)

Quick start (TypeScript)

Python packages (PIL, numpy, pandas, …)

Resource compatibility

Runtime requirements

Errors you might see

See also