strukto-ai/mirage#dsh

Mirage is a Unified Virtual File System for AI Agents: it mounts services and data sources like S3, Google Drive, Slack, Gmail, and Redis side-by-side as one filesystem. Any LLM that already knows bash can read, grep, and pipe across every backend out of the box, with zero new vocabulary.

ws = Workspace(
    {
        "/tmp":   (RAMResource(), MountMode.EXEC),
        "/redis": (RedisResource(url=redis_url), MountMode.WRITE),
        "/slack": (SlackResource(SlackConfig(token=slack_bot_token)), MountMode.EXEC),
    },
    # monty captures python, so scripts run sandboxed inside the workspace
    runtimes=[MontyRuntime(captures=["python", "python3"]), "vfs"],
)

# one grep sweeps every source
await ws.execute("grep -rln session /redis /tmp")

# run a script that lives in Slack, file the report into Redis
await ws.execute(
    "python3 /slack/channels/general__C0.../files/example__F0....py > /redis/report.txt"
)

# install a typed CLI under a head word: dispatched by name, not by path,
# and discoverable through `man`, `type` and `which` like any other program
ws.register_cli("slack", SLACK, {"token": slack_bot_token})
await ws.execute('slack send-message --channel general --text "report is up"')

About

• One interface instead of N SDKs and M MCPs. Every service speaks the same filesystem semantics, and pipelines compose across services as naturally as on a local disk.
• Around 50 built-in backends: RAM, Disk, Redis, S3 / R2 / OCI / Supabase / GCS, Gmail / GDrive / GDocs / GSheets / GSlides, GitHub / Linear / Notion / Trello, Slack / Discord / Email, MongoDB / GridFS / Postgres / LanceDB / Qdrant, SSH, and more, mounted side-by-side under a single root.
• Portable workspaces: clone, snapshot, and version a workspace; agent runs move between machines without restarting or reconfiguring the system.
• Embeddable: the Python and TypeScript SDKs run in-process inside FastAPI, Express, browser apps, or any async runtime; no separate process required.
• Agent integrations: OpenAI Agents SDK, Vercel AI SDK, LangChain, Pydantic AI, CAMEL, and OpenHands via the SDKs; coding agents through native adapters, installable plugins, MCP, or FUSE.

Architecture

Installation

• Python ≥ 3.11 for the mirage-ai package and the mirage CLI
• Node.js ≥ 20 for the TypeScript SDK
• macOS or Linux (FUSE-based mounts require platform support)

Python

uv add mirage-ai    # installs the `mirage` library and the `mirage` CLI binary

TypeScript

npm install @struktoai/mirage-node      # Node.js servers and CLIs
npm install @struktoai/mirage-browser   # browser / edge runtimes
npm install @struktoai/mirage-agents    # OpenAI / Vercel AI / LangChain / Mastra adapters

Both runtime packages pull in @struktoai/mirage-core automatically.

CLI

curl -fsSL https://strukto.ai/mirage/install.sh | sh
# or
npm install -g @struktoai/mirage-cli
# or
uvx mirage-ai
# or
npx @struktoai/mirage-cli

Quickstart

Python

from mirage import Workspace
from mirage.resource.ram import RAMResource
from mirage.resource.s3 import S3Config, S3Resource

ws = Workspace({
    "/data": RAMResource(),
    "/s3":   S3Resource(S3Config(bucket="my-bucket")),
})

await ws.execute("cp /s3/report.csv /data/report.csv")
await ws.execute("grep alert /s3/data/log.jsonl | wc -l")

await ws.snapshot("demo.tar")

TypeScript

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

const ws = new Workspace({
  '/data': new RAMResource(),
  '/s3':   new S3Resource({ bucket: 'my-bucket' }),
})

await ws.execute('cp /s3/report.csv /data/report.csv')
await ws.execute('grep alert /s3/data/log.jsonl | wc -l')

await ws.snapshot('demo.tar')

CLI

mirage workspace create ws.yaml --id demo
mirage execute   --workspace_id demo --command "cp /s3/report.csv /data/report.csv"
mirage provision --workspace_id demo --command "cat /s3/data/large.jsonl"
mirage workspace snapshot demo demo.tar
mirage workspace load demo.tar --id demo-restored

Agent Frameworks

Mirage plugs into agent frameworks as a sandbox or tool layer. POSIX operations such as read can also be customized per resource and filetype: Mirage ships no filetype renderers, so a format renders however you register it, and a command registered for one resource and extension wins over the generic one.

Cache

Every Workspace has a two-layer cache so repeated work against remote backends hits local state instead of the network:

• Index cache: listings and metadata. The first directory walk hits the API; later ones serve from the index until the TTL expires (default 10 minutes).
• File cache: object bytes. The first read streams from origin; later pipelines read from cache (default 512 MB).

Both layers default to in-process RAM with zero setup. A Redis store shares cache state across workers, processes, and machines:

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

const ws = new Workspace(
  { '/s3': new S3Resource({ bucket: 'my-bucket' }) },
  {
    cache: new RedisFileCacheStore({ url: 'redis://localhost:6379/0', cacheLimit: '8GB' }),
    index: { type: 'redis', url: 'redis://localhost:6379/0', ttl: 600 },
  },
)

See the cache docs for the full miss/hit lifecycle.

Contributors

Thanks to everyone who has contributed to Mirage.