Desktop Computer Automation

CRITICAL RULES — VIOLATIONS WILL BREAK THE WORKFLOW:

1. Never run midscene commands in the background. Each command must run synchronously so you can read its output (especially screenshots) before deciding the next action. Background execution breaks the screenshot-analyze-act loop.
2. Run only one midscene command at a time. Wait for the previous command to finish, read the screenshot, then decide the next action. Never chain multiple commands together.
3. Allow enough time for each command to complete. Midscene commands involve AI inference and screen interaction, which can take longer than typical shell commands. A typical command needs about 1 minute; complex act commands may need even longer.
4. Always report task results before finishing. After completing the automation task, you MUST proactively summarize the results to the user — including key data found, actions completed, screenshots taken, and any relevant findings. Never silently end after the last automation step; the user expects a complete response in a single interaction.
5. Only minimize windows, never close them unless explicitly asked. When you need to dismiss or get a window out of the way, minimize it instead of closing it. Do not close any app or window unless the user explicitly asks you to do so.

Control your desktop (macOS, Windows, Linux) using npx -y @midscene/computer@1. Each CLI command maps directly to an MCP tool — you (the AI agent) act as the brain, deciding which actions to take based on screenshots.

What act Can Do

Inside a single act call on desktop, Midscene can move the mouse, click, double-click, right-click, drag items, type or clear text, scroll, press single keys or keyboard shortcuts, and work through multi-step interactions on whatever is visible on the selected display.

Prerequisites

Midscene requires models with strong visual grounding capabilities. The following environment variables must be configured — either as system environment variables or in a .env file in the current working directory (Midscene loads .env automatically):

MIDSCENE_MODEL_API_KEY="your-api-key"
MIDSCENE_MODEL_NAME="model-name"
MIDSCENE_MODEL_BASE_URL="https://..."
MIDSCENE_MODEL_FAMILY="family-identifier"

Example: Gemini (Gemini-3-Flash)

MIDSCENE_MODEL_API_KEY="your-google-api-key"
MIDSCENE_MODEL_NAME="gemini-3-flash"
MIDSCENE_MODEL_BASE_URL="https://generativelanguage.googleapis.com/v1beta/openai/"
MIDSCENE_MODEL_FAMILY="gemini"

Example: Qwen 3.5

MIDSCENE_MODEL_API_KEY="your-aliyun-api-key"
MIDSCENE_MODEL_NAME="qwen3.5-plus"
MIDSCENE_MODEL_BASE_URL="https://dashscope.aliyuncs.com/compatible-mode/v1"
MIDSCENE_MODEL_FAMILY="qwen3.5"
MIDSCENE_MODEL_REASONING_ENABLED="false"
# If using OpenRouter, set:
# MIDSCENE_MODEL_API_KEY="your-openrouter-api-key"
# MIDSCENE_MODEL_NAME="qwen/qwen3.5-plus"
# MIDSCENE_MODEL_BASE_URL="https://openrouter.ai/api/v1"

Example: Doubao Seed 2.0 Lite

MIDSCENE_MODEL_API_KEY="your-doubao-api-key"
MIDSCENE_MODEL_NAME="doubao-seed-2-0-lite"
MIDSCENE_MODEL_BASE_URL="https://ark.cn-beijing.volces.com/api/v3"
MIDSCENE_MODEL_FAMILY="doubao-seed"

Commonly used models: Doubao Seed 2.0 Lite, Qwen 3.5, Zhipu GLM-4.6V, Gemini-3-Pro, Gemini-3-Flash.

If the model is not configured, ask the user to set it up. See Model Configuration for supported providers.

Commands

Connect to Desktop

npx -y @midscene/computer@1 connect
npx -y @midscene/computer@1 connect --displayId <id>

Connect via RDP

Use RDP mode to drive a remote Windows desktop instead of the local machine. Providing --host switches connect to RDP and routes every subsequent command (act, tap, take_screenshot, assert, disconnect) through the RDP helper binary bundled with @midscene/computer. The local mouse/keyboard is not touched.

Minimum example:

npx -y @midscene/computer@1 connect \
  --host rdp.example.com \
  --username Administrator \
  --password "$RDP_PASSWORD"

All RDP options for connect (RDP mode is activated when --host is set; the other flags are optional):

• --host <fqdn-or-ip> — RDP host. Required to enter RDP mode.
• --port <number> — RDP port (default 3389).
• --username <user> — RDP user account.
• --password <secret> — RDP password. Prefer reading from an environment variable, secrets manager, or interactive prompt; never paste it into a shared transcript.
• --domain <domain> — Active Directory / NTLM domain.
• --security-protocol <auto|tls|nla|rdp> — Security protocol negotiation. Defaults to auto.
• --ignore-certificate — Skip TLS certificate validation. Use only for trusted dev hosts with self-signed certs.
• --admin-session — Attach to the admin/console session (equivalent to mstsc /admin).
• --desktop-width <px> and --desktop-height <px> — Request a specific remote desktop resolution. The actual size is whatever the RDP server negotiates back; e.g. requesting 1024x768 against a host that pins 1280x720 will land on 1280x720. Confirm the negotiated size with listdisplays --host ... --username ... --password ... after connect.

Notes specific to RDP mode:

• --displayId and --headless are ignored in RDP mode. A connected RDP session always exposes a single virtual display whose size is whatever the server negotiated.
• Two display-listing commands exist and behave differently — pick the right one:
  • list_displays (with underscore, platform tool) — enumerates local physical displays only. Does not accept RDP flags. Useless after an RDP connect.
  • listdisplays (no underscore, action tool) — accepts the same RDP flags as connect/take_screenshot/etc. In RDP mode it returns the negotiated virtual display, e.g. [{ "id": "...", "name": "RDP 10.70.86.26:3389 (1280x720)", "primary": true }]. Use this to verify the actual resolution.
• The RDP transport uses a native helper binary shipped inside @midscene/computer. If you see RDP helper binary not found errors, the optional bin/<platform>/rdp-helper was stripped from your install — reinstall the package or unpack a fresh tarball.
• Treat RDP credentials as secrets: do not commit .env files containing --password to the repo; prefer export RDP_PASSWORD=... in the current shell and reference it as --password "$RDP_PASSWORD".
• Latency expectations: every CLI invocation is a fresh node process, so each command re-establishes the RDP session. Budget roughly:
  • connect / take_screenshot / keyboardpress / scroll: ~5 s (node startup + RDP TLS+NLA handshake + first frame).
  • act / assert / tap --locate: ~5 s + AI inference + any planned sub-actions; expect 8–20 s end-to-end for typical interactions.
  • The RDP handshake itself is ~700 ms; the rest is unavoidable cold-start cost in the CLI shape.
• Connect failure diagnostics: when connect fails, the first line of stderr is the actionable error (e.g. connect_failed: Failed to connect to RDP server: ERRCONNECT_LOGON_FAILURE: Logon failed.). The subsequent stack trace is diagnostic noise — read the first line, then check credentials/network. Common ERRCONNECT_* causes:
  • LOGON_FAILURE — bad username/password/domain.
  • CONNECT_TRANSPORT_FAILED — host unreachable or RDP port blocked. Verify with nc -zv <host> 3389.
  • TLS_CONNECT_FAILED — TLS handshake rejected. Try --ignore-certificate for self-signed dev hosts, or pin --security-protocol nla.

After connect --host ... succeeds, the rest of the workflow (act, tap --locate, assert, take_screenshot, listdisplays, report-tool, disconnect) is identical to local mode — just remember to pass the same --host/--username/--password/--ignore-certificate flags to every subsequent command, since each CLI invocation is stateless and reconnects.

List Displays

npx -y @midscene/computer@1 list_displays

Take Screenshot

npx -y @midscene/computer@1 take_screenshot

After taking a screenshot, read the saved image file to understand the current screen state before deciding the next action.

Perform Action

Use act to interact with the computer and get the result. It autonomously handles all UI interactions internally — clicking, typing, scrolling, waiting, and navigating — so you should give it complex, high-level tasks as a whole rather than breaking them into small steps. Describe what you want to do and the desired effect in natural language:

# specific instructions
npx -y @midscene/computer@1 act --prompt "type hello world in the search field and press Enter"
npx -y @midscene/computer@1 act --prompt "drag the file icon to the Trash"

# or target-driven instructions
npx -y @midscene/computer@1 act --prompt "search for the weather in Shanghai using the Chrome browser, tell me the result"

Assert Current Screen State

Use assert to verify that the current screen satisfies a natural language condition. It does not perform UI actions; it checks the visible screen state and passes only when the assertion is true. Use this for validation, QA checks, and final state verification after act.

npx -y @midscene/computer@1 assert --prompt "there is a login button visible"
npx -y @midscene/computer@1 assert --prompt "the active window shows a saved confirmation message"
npx -y @midscene/computer@1 assert --displayId 1 --prompt "the file picker is open"

By default a failed assertion throws an AI-generated reason. Pass --message to throw a custom error message instead, which is useful for surfacing the intended outcome in QA and CI logs.

npx -y @midscene/computer@1 assert \
  --prompt "the export completed dialog is visible" \
  --message "the export should finish after clicking Save"

When the assertion needs to compare against a reference image (icon, logo, screenshot), pass --image for the URL/path and --image-name for its display name. Each --image may be an http(s) link, a data: URI, or a local file path. Repeat both flags in matching order when you need to attach more than one image. Add --convertHttpImage2Base64 true when the model cannot reach the URL directly. Requires @midscene/computer@1.9.0+.

npx -y @midscene/computer@1 assert \
  --prompt "the active window matches the supplied reference screenshot" \
  --image "https://example.com/reference.png" \
  --image-name "reference" \
  --convertHttpImage2Base64 true

# or with a local file
npx -y @midscene/computer@1 assert \
  --prompt "the visible icon matches the supplied logo" \
  --image "./fixtures/logo.png" \
  --image-name "logo"

# multiple reference images — pair --image and --image-name by order
npx -y @midscene/computer@1 assert \
  --prompt "the active window matches both the icon and the logo" \
  --image "./fixtures/icon.png" --image-name "icon" \
  --image "./fixtures/logo.png" --image-name "logo"

Record and Assert Transient UI

Use a recording when the state to verify may disappear before a current-screen assertion runs, such as a toast, loading banner, animation, or transition:

# Terminal 1: keep this foreground command running
npx -y @midscene/computer@1 record start --displayId 1 \
  --output ./save-observation.json

# Terminal 2, while Terminal 1 records
npx -y @midscene/computer@1 act --displayId 1 \
  --prompt "click the Save button"

# Send Ctrl+C to Terminal 1 and wait for the saved-path message, then assert
npx -y @midscene/computer@1 assert --displayId 1 \
  --record ./save-observation.json \
  --prompt "a saved confirmation appeared"

Pass local display or RDP target flags and --output to record start, then wait for Recording. Press Ctrl+C to stop and save. Keep the recorder as a foreground process in its dedicated terminal; never add shell &. Perform the interaction manually or from a second terminal, send Ctrl+C to the recorder, and wait until it prints the saved path before asserting. Repeat target flags on actions and the final assert. Optional capture flags are --interval-ms, --max-frames, and --watchdog-ms; --max-frames caps sampled frames, and the manifest may contain one additional final representative frame. The default watchdog finalizes and saves the recording after five minutes, while --watchdog-ms 0 disables that safety limit. The output is a JSON manifest plus an adjacent <name>.frames image directory, not an encoded video or archive. The manifest contains relative JPEG/PNG paths and no base64 image bodies. Keep or move the JSON file and image directory together, and pass the JSON path to assert --record. Use ordinary assert without --record when only the current screen matters.

Use a Reference Image for Precise Targeting

When the user provides a screenshot, icon, logo, or reference image and wants an exact visual match, prefer tap --locate instead of a generic act --prompt. Pass --locate as JSON. The prompt describes the target, images supplies named reference images, and convertHttpImage2Base64: true is useful when the image URL may not be directly accessible to the model.

npx -y @midscene/computer@1 tap --locate '{
  "prompt": "tap the area contains the image",
  "images": [
    {
      "name": "target image",
      "url": "https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png"
    }
  ],
  "convertHttpImage2Base64": true
}'

The same locate JSON shape also works for other commands that accept a locate parameter.

Disconnect

npx -y @midscene/computer@1 disconnect

Consume Report Files

The generated HTML report is recommended for human reading first. It includes step-by-step execution details and replay videos for each operation, which makes it much easier to understand what happened and troubleshoot problems.

If another skill or tool needs to consume the report, first convert it with report-tool from the same platform CLI package. Prefer Markdown for LLM-based workflows. Use JSON when the report needs to be processed programmatically.

npx -y @midscene/computer@1 report-tool --action to-markdown --htmlPath ./midscene_run/report/.../index.html --outputDir ./output-markdown
npx -y @midscene/computer@1 report-tool --action split --htmlPath ./midscene_run/report/.../index.html --outputDir ./output-data

Workflow Pattern

Since CLI commands are stateless between invocations, follow this pattern:

1. Connect to establish a session
2. Health check — observe the output of the connect command. If connect already performed a health check (screenshot and mouse movement test), no additional check is needed. If connect did not perform a health check, do one manually: take a screenshot and verify it succeeds, then move the mouse to a random position (act --prompt "move the mouse to a random position") and verify it succeeds. If either step fails, stop and troubleshoot before continuing. Only proceed to the next steps after both checks pass without errors.
3. Launch the target app and take screenshot to see the current state, make sure the app is launched and visible on the screen.
4. Execute action using act to perform the desired action or target-driven instructions. Use assert for the resulting screen state, or keep record start --output ... running in a dedicated terminal during transient-state workflows, stop it with Ctrl+C, and then use assert --record.
5. Disconnect when done
6. Report results — summarize what was accomplished, present key findings and data extracted during the task, and list any generated files (screenshots, logs, etc.) with their paths

Best Practices

1. Always run a health check first: After connecting, observe the output of the connect command. If connect already performed a health check (screenshot and mouse movement test), no additional check is needed. If it did not, do one manually: take a screenshot and move the mouse to a random position. Both must succeed (no errors) before proceeding with any further operations. This catches environment issues early.
2. Bring the target app to the foreground before using this skill: For best efficiency, bring the app to the foreground using other means (e.g., open -a <AppName> on macOS, start <AppName> on Windows) before invoking any midscene commands. Then take a screenshot to confirm the app is actually in the foreground. Only after visual confirmation should you proceed with UI automation using this skill. Avoid using Spotlight, Start menu search, or other launcher-based approaches through midscene — they involve transient UI, multiple AI inference steps, and are significantly slower.
3. Be specific about UI elements: Instead of vague descriptions, provide clear, specific details. Say "the yellow minimize button in the top-left corner of the Safari window" instead of "the button".
4. Describe locations when possible: Help target elements by describing their position (e.g., "the icon in the top-right corner of the menu bar", "the third item in the left sidebar").
5. Never run in background: Every midscene command must run synchronously — background execution breaks the screenshot-analyze-act loop.
6. Check for multiple displays: If you launched an app but cannot see it on the screenshot, the app window may have opened on a different display. Use list_displays to check available displays. You have two options: either move the app window to the current display, or use connect --displayId <id> to switch to the display where the app is.
7. Batch related operations into a single act command: When performing consecutive operations within the same app, combine them into one act prompt instead of splitting them into separate commands. For example, "search for X, click the first result, and scroll down to see more details" should be a single act call, not three. This reduces round-trips, avoids unnecessary screenshot-analyze cycles, and is significantly faster.
8. Set up PATH before running (macOS): On macOS, some commands (e.g., system_profiler) may not be found if the PATH is incomplete. Before running any midscene commands, ensure the PATH includes the standard system directories:

   export PATH="/usr/sbin:/usr/bin:/bin:/sbin:$PATH"
   

   Copy
   This prevents screenshot failures caused by missing system utilities.
9. Choose the right verification window: Use assert --prompt "..." for the current screen. For a toast, banner, animation, or transition, run record start --output ... in a dedicated terminal, perform the interaction, stop recording with Ctrl+C, wait for the saved-path message, then pass the artifact to assert --record.
10. Always report results after completion: After finishing the automation task, you MUST proactively present the results to the user without waiting for them to ask. This includes: (1) the answer to the user's original question or the outcome of the requested task, (2) key data extracted or observed during execution, (3) screenshots and other generated files with their paths, (4) a brief summary of steps taken. Do NOT silently finish after the last automation command — the user expects complete results in a single interaction.
11. Prefer tap --locate when a reference image is provided: If the user shares a screenshot, icon, or logo and wants that exact visual target, use tap --locate with a multimodal locate JSON object such as { "prompt": "...", "images": [...] } instead of relying only on act --prompt.

Example — Context menu interaction:

npx -y @midscene/computer@1 act --prompt "right-click the file icon and select Delete from the context menu"
npx -y @midscene/computer@1 take_screenshot

Example — Dropdown menu:

npx -y @midscene/computer@1 act --prompt "open the File menu and click New Window"
npx -y @midscene/computer@1 take_screenshot

Improve Precision (Deep Locate / Deep Think)

Two optional global flags help when Midscene struggles with a task. Put them anywhere in the command (before or after the sub-command); once set, the relevant operations use them by default, so you don't pass a per-call parameter.

• --deep-locate — spends an extra round of visual reasoning to pinpoint the target element. Use it when an action interacts with the wrong spot (location drift / offset). It applies to every operation that locates an element, including tap --locate and the locating that happens inside act.
• --deep-think — plans act with deeper reasoning (richer context and sub-goal decomposition). Use it for complex, multi-step act instructions; it only affects planning.

Both trade a little speed for better results, and you can combine them.

# more accurate element location (helps act's internal locating too)
npx -y @midscene/computer@1 act --deep-locate --prompt "click the tiny red close button in the top-left of the window"

# deeper planning for a complex, multi-step act
npx -y @midscene/computer@1 act --deep-think --prompt "open the Export dialog, choose PDF, and save it to the Desktop"

# combine both
npx -y @midscene/computer@1 act --deep-locate --deep-think --prompt "open Preferences and switch to the Advanced tab"

Troubleshooting

macOS: Accessibility Permission Denied

Your terminal app does not have Accessibility access:

1. Open System Settings > Privacy & Security > Accessibility
2. Add your terminal app and enable it
3. Restart your terminal app after granting permission

macOS: Xcode Command Line Tools Not Found

xcode-select --install

API Key Not Set

Check .env file contains MIDSCENE_MODEL_API_KEY=<your-key>.

macOS: Screenshot Fails with system_profiler Not Found

If take_screenshot fails with an error like system_profiler: command not found, the PATH environment variable is likely incomplete. Fix it by running:

export PATH="/usr/sbin:/usr/bin:/bin:/sbin:$PATH"

Then retry the screenshot command.

macOS: Screenshot Returns a Black Screen

If take_screenshot returns a completely black image, the Mac is likely locked (e.g. screen is at the login/lock window). This is a system-level restriction — macOS prohibits capturing the screen contents while the session is locked, so there is no workaround at the application level.

Recommended fix: Use a screensaver instead of locking the screen. A screensaver keeps the user session active and unlocked, allowing screenshots to capture normally.

1. Open System Settings > Lock Screen
2. Set "Require password after screen saver begins or display is turned off" to a longer delay (or turn it off during automation)
3. Optionally configure a screensaver under System Settings > Screen Saver so the display still dims after inactivity without locking

AI Cannot Find the Element

1. Take a screenshot to verify the element is actually visible
2. Use more specific descriptions (include color, position, surrounding text)
3. Ensure the element is not hidden behind another window

@midscene/* Dependency Version Outdated

• Check local versions: npm ls @midscene/computer @midscene/core @midscene/shared (or pnpm why @midscene/computer).
• Check latest versions: npm view @midscene/computer version, npm view @midscene/core version, npm view @midscene/shared version.
• Upgrade dependencies: npm i @midscene/computer@latest @midscene/core@latest @midscene/shared@latest.