Claude Code Remote Control vs Screencap: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Claude Code Remote Control and Screencap — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Claude Code Remote Control
Anthropic
Remote Control lets you continue a local Claude Code session from any phone, tablet, or browser while the session runs entirely on your machine.
Key features
- Local Execution Preservation: Remote Control keeps the Claude Code session running on your machine so code, logs, local MCP servers, and toolchains remain accessible and no session data is moved to Anthropic-managed cloud infrastructure.
- Multi-Device Handoff: Connects the claude.ai/code web interface or the Claude mobile app to an active local session, allowing you to start work on a desktop and continue from a phone, tablet, or another browser.
- Manual and Automatic Activation: Start a remote session per task with the CLI command `claude remote-control` or the `/remote-control` command, or enable it automatically for all sessions via the `/config` setting.
- One-Session-per-Instance Model: Each Claude Code instance supports a single remote session at a time; running multiple instances provides isolated environments and independent remote sessions.
- Privacy-First Connection & Authentication: Remote Control uses an authenticated handshake tied to an Anthropic account session token and documents connection and security controls to prevent unauthorized access.
- Local Toolchain Integration: Because the session runs locally, Claude retains access to local services (databases, servers, custom tools, MCP integrations) for debugging, testing, and agentic tasks that depend on on-prem resources.
- Clear Cloud vs Local Semantics: Documentation contrasts Remote Control (local execution) with Claude Code on the web (cloud execution) so users choose based on need for local resources or ease of kicking off tasks without local setup.
- Connect claude.ai/code or Claude mobile app (iOS/Android) to a local Claude Code session
- Start remote sessions via CLI (claude remote-control) or in-session command (/remote-control)
- Session runs locally on user's machine; local resources (MCP servers, tools, repo state) remain available
- Option to enable Remote Control automatically for all sessions via /config
- One remote session per Claude Code instance; multiple instances each support their own session
- Research-preview access gating (Pro and Max plans only); not available on Team/Enterprise
- Security handshake requiring Anthropic account session token (current limitation for some integrations like AWS Bedrock)
- Integrates with community tools using WebSocket/PTY, QR-code pairing, tmux for persistence, and tunnels (Cloudflare) for remote access
Best for
- Continue coding on mobile: Start a coding task at your desk and seamlessly continue edits, prompts, and commands from a phone or tablet while the session runs on your workstation.
- Monitor long-running agentic tasks: Check progress, send new instructions, or terminate long-running tests, builds, or automation tasks from anywhere without moving artifacts to the cloud.
- Secure on-prem development: Work with sensitive repositories, internal databases, or proprietary services while ensuring data and execution remain on local infrastructure.
- Debug with local resources: Trigger tests, inspect logs, and run debugging commands that require local services (databases, caches, internal APIs) accessible only from your machine.
- Device handoff for collaboration: Quickly hand off a session between devices (desktop → mobile or vice versa) to review diffs, make quick fixes, or demonstrate behavior during discussions.
- Resume interrupted work: Reconnect to an existing local Claude Code session from another device to continue a paused workflow without losing state or context.
- Monitor and steer long-running local tasks (e.g., builds, tests, data processing) from a phone
- Continue an in-progress coding session on a different device without moving code or environment to the cloud
- Receive notifications and remotely send follow-up commands to local sessions
- Access local development servers and tools remotely while preserving local credentials and environment
- Integrate with messaging platforms or custom tooling (community projects show Email, Telegram, LINE, desktop notifications)
Screencap
Proteus Computer Use
Local-first macOS screen recorder that captures, labels, and indexes team workflows so knowledge stays searchable and private.
Key features
- Local-First Capture: Recordings live in ~/.screencap on your Mac and never leave unless you explicitly share them.
- On-Device Task Segmentation: An on-device model breaks long recordings into labeled tasks like payroll runs, expense approvals, or CRM data entry.
- Full-Text Search of Workflows: Every spoken word and on-screen moment is indexed so any past workflow can be surfaced months later by search.
- Privacy-Enforced Recording: Password managers and banking apps are cut before a frame is written; email and chat are masked in real time.
- MCP Context Snapshots: While recording, Screencap queries connected MCP servers to capture the exact Gusto/Attio/Linear/Notion record on screen inside the video.
- Deliberate Sharing With PII Scrubbing: Every shared copy is scrubbed of names, secrets, and PII, and only the recordings you pick ever leave the machine.
- Source-Available Codebase: The full capture engine, encryption, agent, and anonymizer are public on GitHub under PolyForm Noncommercial 1.0.0.
Best for
- Team Onboarding: Assemble ordered collections of real workflow recordings so new teammates learn exactly how work is actually done.
- Institutional Knowledge Capture: Preserve the tacit steps behind payroll runs, reconciliations, and quarterly reports as searchable video.
- Ops Documentation: Replace stale wikis by pointing teammates at labeled task recordings that stay current with the real system.
- Compliance-Sensitive Recording: Capture back-office work in banking, finance, and HR without leaking passwords, account balances, or PII.
- Computer-Use Dataset Contribution: Optionally donate reviewed, scrubbed recordings to a public dataset for training open computer-use models.