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.

	Integrations
Python	OpenAI Agents SDK, LangChain, Pydantic AI, CAMEL, OpenHands, Agno
TypeScript	Vercel AI SDK, OpenAI Agents SDK, LangChain, Mastra
Coding agents	Claude Code, Codex, DeepSeek Harness, Grok Build, OpenCode, Pi

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.