strukto-ai/mirage#dsh

Mirage is a Virtual Terminal for AI Agents: one shell over data, CLIs and runtimes. Unix tools reach every service, and grep runs on each service's own search, such as Gmail's or Slack's. Mirage routes each command to the runtime that fits it, so most lines run in-process and only heavy work reaches a sandbox, which cuts infrastructure cost and tokens. The terminal itself is light: a workspace takes about 100 KB of memory and each new session about 10 KB, so one process serves many agents at once. Profiles decide what an agent can run and even what it can see, and scriptable policies refuse anything those rules cannot express.

Here is an example of launching Mirage inside an application:

ws = Workspace(
    {
        "/s3":    (S3VFS(S3Config(bucket="my-bucket")), MountMode.WRITE),
        "/slack": (SlackVFS(SlackConfig(token=slack_bot_token)), MountMode.EXEC),
        "/gmail": (GmailVFS(GmailConfig(**google_credentials)), MountMode.READ),
    },
    # Install the Slack CLI, run by name and listed by `man` and `which` like any program
    clis={"slack": (SLACK, {"token": slack_bot_token})},
    # the analyst profile hides /s3/secrets and denies posting to Slack and curl
    profiles={
        "analyst": {
            "paths": {"hide": ["/s3/secrets"]},
            "commands": {"deny": ["slack send-message", "curl"]},
        },
    },
    # The e2b sandbox is a machine; the remote runtime runs programs on it
    machines={"box": {"provider": "e2b", "sandbox_id": sandbox_id, "mount_type": "ssh"}},
    runtimes=[
        MontyRuntime(captures=["python", "python3"]),
        RemoteRuntime(captures=["python", "python3"], machine="box"),
    ],
    # route the underlying computation: numpy lines run on the machine, the rest on monty
    route_policy=lambda ctx: "remote" if "numpy" in ctx.line else None,
)

# Add an agent using the analyst profile
alice = await ws.session("alice", profile="analyst")

# one grep sweeps S3, Slack and Gmail, on each service's own search where it has one
await alice.shell("grep -rlw invoice /s3 /slack /gmail")

# run a script that lives in Slack on monty, file the report into S3
await alice.shell("python3 /slack/channels/general__C0123/2026-10-21/files/report__F0456.py > /s3/report.txt")

# numpy is not in monty, so the route policy sends this line to e2b
await alice.shell("python3 -c 'import numpy; print(numpy.arange(10).sum())'")

And what alice sees in the shell:

$ grep -rlw invoice /s3 /slack /gmail
/s3/billing/2026-10.csv
/gmail/INBOX/2026-10-21/Invoice_1042__18f3a9c2b7d1e0f4.gmail.json
$ ls /s3
billing
report.txt
$ cat /s3/secrets/aws.txt
cat: /s3/secrets/aws.txt: No such file or directory
$ slack send-message --channel general --text "report is up"
slack: Permission denied

About

• Unified virtual terminal interface, not N SDKs and M MCPs. Every backend speaks the same filesystem semantics, so pipelines compose across services.
• A virtual filesystem over every source. S3, Google Drive, Slack, Gmail, Redis and the rest mount side by side under one root, so an agent reaches all of them through a unified interface with the unix tools it already knows, like ls, grep, find and jq.
• Virtual command line tools (CLIs). git, slack and ntn are answered by Mirage itself, so an agent drives the service with nothing installed, across different runtimes and machines, and one tool can be virtualized into two or more, each under its own name with its own credentials.
• Routed, dynamic runtimes. Python, JavaScript and any other command can be sent to a configured runtime, in process, sandboxed or remote, which decouples computation from storage and lets either change without touching the other.
• The virtualized Mirage shell. It binds the filesystem, the CLIs and the runtimes into one command line, so pipes, redirection, variables, jobs and history work across all three.
• Lightweight by design. A workspace, the virtual terminal itself, takes about 100 KB of memory and each new session about 10 KB, so one process serves many agents without a container or VM per agent.
• Profiles designed for agents. allow, ask and deny govern commands and CLIs, while hide and show govern files and folders, so a hidden path is not merely unreadable but absent from the filesystem the agent sees.
• A scriptable policy engine. A policy script can prohibit any dangerous action before it runs, and the same stack gates every VFS op and session write, so neither a file nor an environment variable leaks.
• Notifications wired into the VFS and agents. External changes become an event stream on the mount, so a new Slack reply surfaces as a change to the chat file in the virtual filesystem, and the agent reacts to it instead of rescanning the tree.

Virtual Filesystem

Everything Mirage "mounts" as one unified virtual filesystem for AI agents. Each service sits side-by-side under a single root and answers the same POSIX semantics.

	VFS
Object Storage
Files and Documents
Messaging and Work
Databases and Data Platforms
Observability
Local and Remote

Agents reach it through the Python and TypeScript SDKs, the mirage CLI, or a real mountpoint over FUSE and FSKit, then work it with the unix tools they already know, like ls, grep, find and jq.

Virtual Command Line Tool

These command line tools are virtualized: Mirage answers git, slack or ntn itself, so an agent drives the service without that program being installed on the machine. Each one mimics the real tool, so an agent that knows the CLI needs nothing new. Because they are virtual, the same tool can be installed more than once under different names, each with its own credentials, so every agent gets exactly the accounts it is given.

	CLIs
Code
Communication
Work and Data

Virtual Runtime

Runtimes are virtualized the same way, and not only for coding languages. Any command on the line can be redirected to a configured runtime, so Python might run in-process with Monty while another command, say kubectl, is sent to a remote machine over SSH. Which runtime serves a given line can be decided by a scripted runtime router.

	Runtimes
Python
JavaScript
Machines

Security

A profile decides what a session may run and what it may see. Commands are governed by customizable allow, ask and deny rules, and paths by hide and show, so a hidden file is not merely unreadable but absent from the filesystem the agent sees. For anything those rules cannot express, a profile can name a policy script that runs at the gate on every command and answers allow, deny or ask itself, though like every rule it can only restrict and never grant. Separately, a host can register its own policies on the policy engine, an ordered stack the workspace consults on every command, VFS op and session write. See the policy docs.

Service Credentials

Service credentials integrate with the stores secrets already live in, including AWS Secrets Manager, 1Password and dotenv, so an environment variable in Mirage can resolve straight to a credential held in one of them.

	Sources
Built in
Custom

Installation

• Python ≥ 3.12 for the mirage-ai package and the mirage CLI
• Node.js 22.20+ on Node 22, 24.12+ on Node 24, or 25+ for the TypeScript SDK

Python

uv add mirage-ai              # installs the `mirage` library and the `mirage` CLI binary
uv add 'mirage-ai[openai]'    # OpenAI Agents adapter; each agent framework is an extra
uv add 'mirage-ai[monty]'     # Monty, the in-process Python runtime the examples run on

TypeScript

npm install @struktoai/mirage-node      # Node.js servers and CLIs
npm install @struktoai/mirage-browser   # browser / edge runtimes
npm install @struktoai/mirage-vercel    # Vercel AI adapter; each agent framework has its own package

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

CLI

npm install -g @struktoai/mirage-cli
# or
uvx --from mirage-ai mirage
# or
npx @struktoai/mirage-cli

To get started, see the quickstarts for Python, TypeScript and the CLI.

Scriptable Policy

A profile can name policy scripts for what its rules cannot express. A script's pre_command takes the command, can open the files it names, and returns a deny, an ask or None. This one disables rm, and a Python script whose code deletes files:

def deletes(path):
    try:
        co

…