Grok Bot vs Meta Muse (2026)

Plugins connect Gmail, Notion, Slack and other services under the account used to sign in, added from Connect apps in the desktop sidebar, the mobile app or an in chat card. Beyond plugins, a Bot logs in once to tools such as Zendesk on its own computer and works inside them the way a person would, including tools that are hard to navigate. Bots can also claim their own address on mail.grokbot.com to receive, search and send email.

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Connectors reach email, calendars, the car and the home, Instagram, Facebook and Meta ads, each built with the service provider's API and paired with a skill that tells Muse how to use it well. Muse for Small Business adds Asana, Box, Canva, Dropbox, Figma, Granola, HighLevel, QuickBooks, Klaviyo, Lovable, Notion, Shopify, Slack, Stripe and Zoom. For anything else, Muse can write a custom connector of its own, and it calls tools through command line programs and skills it can build in its VM.

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A user creates several Bots, one per project or function, and gathers them in a group chat, where they answer in the thread, take requests addressed to one Bot with an @mention, and message each other directly to hand off work. Bots carry a project from start to end, launch subagents and cloud agents, and run routines a user can teach by doing a workflow once while a Bot follows along. Because all of a user's Bots share one computer, files, browser and logins, a handoff keeps its context.

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Muse breaks a goal into an actionable plan and runs several long tasks at once, launching swarms of subagents and handling concurrent subagents and cron jobs inside its VM. The model behind it was trained for long trajectories and multi agent coordination. A Goals tab tracks every goal it is pursuing and the plan to reach it, and a user can interrupt or pile on new tasks without waiting for the last one to finish.

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A routine tells a Bot when to run a workflow, either on a schedule or when something happens, such as a Slack message, a GitHub, Linear, Sentry or PagerDuty event, an email or a webhook call. Routines run in the cloud while the user's laptop is closed and can be paused or resumed from the Bot's details. Users also message Bots from macOS, Windows and the iOS app, talk to them by voice, and email them at their own address.

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Once a goal or task is set, Muse keeps working on a schedule and in response to relevant events, takes the next step and follows up while the app is closed. It messages first when background work turns up something new or needing a decision, holding proactive messages to a high bar by default, and the user can switch that off or dial it up or down. Requests come in through the iOS and Android apps and the web, all connecting straight to the user's own VM.

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Bots work from the files on their shared computer, the services connected through plugins and the tools they sign in to, reading what each task needs as it runs, and a Team Bot can carry a description and secrets its owner sets up for the whole team. There is no curated knowledge base or search index that a team maintains for its Bots.

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Every file a user puts into Muse and everything it generates is stored on the user's own VM, the system of record for the account, and connectors bring in mail, calendars and business tools as tasks need them. For a business, connecting Facebook, Instagram and Meta ads means Muse starts out understanding the business and picks up its voice. Answers come from those files and live connections, with no separately managed knowledge base for a team to curate.

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Each Bot keeps context on how its user works and updates its own memory as it learns, such as noting that a customer only signs annual contracts and who approves them, and a user can make a standing rule by asking a Bot to remember it. Bots also learn from one another, and the shared computer keeps files, browser state and logins between runs while chats are stored separately. A user gets no retention period for a Bot's memory and no way to review or remove a single entry.

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Muse's memory persists across conversations and lives in files on the user's own VM, where the user can inspect, edit and download it alongside everything else Muse keeps. Durable application state sits in a separate database on the same machine. How long memory is kept, and whether a single memory can be removed without wiping the rest, is left unsaid.

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When an action needs approval, the conversation shows the proposed operation and its inputs, and the user picks allow once, always allow, which can save a matching rule, or deny, with the same controls on iPhone.

Auto Review, an independent review model, checks risky actions before they run, covering shell commands, plugin calls, computer use, changes to routines and triggers, and subagent launches, and it lets them proceed, holds them for approval or stops them.

Members can add their own check first rules, and Enterprise admins can enforce Auto Review with team rules members cannot switch off.

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Every connector action passes through Sentinel, which checks it against the user's connector policy and allows it, denies it or stops for approval. Approval cards appear in the app's own interface, outside the conversation, with clear accept and reject actions, and grants are scoped to one connector and use, lasting once, for a session, for a task, for a set time or permanently. By default Muse browses freely and stops for anything hard to undo, such as writing an email or making a purchase, and the user can change that balance.

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Grok Bot uses Cursor's sign in, auth and Privacy Mode, with SAML or OIDC single sign on on Teams Premium, and SCIM, the MCP allowlist and organization computer management on Enterprise. Admins control network destinations, team secrets, setup scripts, sudo on Bot computers and action recording, and API keys go into masked secret cards that keep the value out of chat and files. Anysphere, the company behind Cursor, holds ISO 27001 and ISO 42001 certification from Schellman with Grok Bot in the current scope, and data is deleted or returned within 30 days of written direction under the DPA.

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Each user's VM splits the agent's runtime cell, mapped to an unprivileged host user, from security services that run outside it.

Privilege separated workers hold connector credentials, an authentication service mints surrogate tokens so the agent never sees real ones, and Sentinel is the sole permission authority for connector actions and network egress, with least privilege enforced between processes.

Agentic red teaming and a public bug bounty back the design, paying up to $300,000 and up to $130,000 for a prompt injection that affects one user. Muse carries no SOC 2 report or other attestation, and a Confidential VM that would keep Meta itself out of user data is still with testers.

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Enterprise teams get audit logs and OpenTelemetry export of Bot activity, and admins can switch on action recording for team computers. In every plan, the conversation shows each operation awaiting approval with its inputs, and Teams plans add shared usage analytics. Usage and spending views count Bot work as Grok Bot usage, without naming the model behind each request.

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A snippet under the Muse avatar shows what it is working on, and tapping the avatar opens its full activity log alongside every permission the user has approved. The Goals tab lists everything Muse is tracking and its plan to get there, and each approval card names the exact action and its purpose before it runs. The log and the permission history live inside the app, next to the settings that control them.

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Each member gets one hosted microVM in the US, reached through Cursor's servers, with internet traffic leaving through static egress addresses shared across customers. Enterprise teams can route a computer's traffic through a member's desktop, install their own networking client or set proxies and TLS inspection, which connects the cloud computer to private networks without moving it. There is no region choice or deployment inside a customer's own environment.

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Every user gets a dedicated, isolated VM in Meta's cloud, which holds their files, memory and credentials, and limited data leaves it for inference and telemetry. A user cannot choose where that VM runs, and there is no private or self hosted deployment.

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A marketplace offers ready made Bots, each built for one role, among them Sales Outbound, Talent Scout, Paid Media, Expense Manager, Product Performance, Bug Reproduction, Account Health and Chief of Staff. Sales Outbound, for one, researches accounts overnight, ranks contacts by intent and drafts email and LinkedIn messages in the user's voice for review. A Bot's identity, skills and routines can be shared as a public link for others to copy, owners can publish a Team Bot that every teammate chats with privately, and Teams plans add a team marketplace for skills and plugins.

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Each connector ships with a skill Meta wrote on how to get the most out of it, and Muse for Small Business adds a named collection of skills and connectors for owners, covering content and ads, customer replies, cash flow and inventory. An Ideas tab and onboarding tips suggest what Muse could take on based on the user's goals. All of this shapes how the single agent works, and there are no separate prebuilt agents or task templates to pick from a catalog.

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For each task, Grok Bot picks the model most likely to do it well, an xAI model or a third party model from a provider on the subprocessor list, and the choice can change from one request to the next. Users cannot see, choose or block a model, there is no model picker, and a team's Cursor model allowlist does not apply to Grok Bot.

Sourcecursor.com/help/grok-bot/modelsread 2026-10-12

Meta trained the model at Muse's core itself, tuned for tool calling through command line programs and skills, long context and multi agent coordination, and a separate set of Meta safety models inspects inference. Conversations and tool trajectories are sanitized and used to train new checkpoints by default. A user cannot choose another model.

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Outside systems reach a Bot through a routine started by a webhook call, with the routine's own URL and key shown on its details. That is the one inbound path. There is no public API or SDK for creating Bots, sending them work or reading their results, and MCP plugins point outward to other services.

Sourcecursor.com/help/grok-bot/routinesread 2026-10-12

Muse is reached through its own iOS, Android and web clients, which connect straight to the user's VM. Muse can extend itself by writing connectors and skills, but outside software has no API or MCP server through which to call it.

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Auto Review judges each risky action as it happens, a guardrail on behavior, and a taught routine is saved from a single demonstration. Neither tests a Bot's output. There are no test cases, quality checks or comparisons of a Bot's results before or after a change.

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Meta hardened Muse through internal use, agentic red teaming and a bug bounty, and Muse judges whether a finished background result merits a notification before it pings the user. Both work on the vendor's product. A user has no way to test a goal, check the agent's output against cases or compare changes.

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Bots share a persistent cloud computer with files, a browser, installed apps and saved logins, sign in to a team's apps and websites and use them the way a person does, including tools that are hard to navigate. When a site blocks cloud traffic, the Bot hands the step to the user or routes its traffic through the user's desktop, and with permission it can run commands on that desktop, asking before each one by default. Settings can update, recover or reset the cloud computer, keeping saved Bots, files and logins.

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Each user's Muse lives on a dedicated Linux VM with a browser, storage, CPU and memory, where it browses the web, fills forms, shops and books, and compiles code it writes. Its web activity appears as the user's own, and every network request it makes passes through Sentinel. Browsing is one of the headline capabilities, with critical steps such as a purchase held for approval.

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