B — Some risk signals Graded October 16, 2026Minor concerns worth a quick check.Based on public adoption and transparency signals as of October 16, 2026. Our opinion, not a code audit or security guarantee.
AI agent skill · Memory & knowledge
Everywhere
On-screen aware AI assistant for your desktop. Uses current app context, multiple LLMs, and MCP tools to help you act across apps.
Bottom line: Signal-based grade (4/7 points): very widely starred on GitHub, public source code. This reflects adoption and transparency, not a code audit.
- Popularity score
- 43/100
- Rank
- Not ranked: outside the 500 most popular tools
- GitHub stars
- 6.3k
What it does
Everywhere is a desktop AI assistant that maintains awareness of your current application context and uses multiple LLMs alongside MCP tools to help you perform actions across different apps without switching windows. It acts as an on-screen copilot that understands what you're working on and can execute tasks across your application ecosystem.
Who it’s for
Software developers and power users who want seamless AI assistance across their desktop workflow. Technical teams building or integrating AI agent capabilities into their tool stack.
Copy-paste setup
Check these steps against the project’s own instructions before running anything.
Verify against the repo README at https://github.com/Sylinko/Everywhere (opens external site) for the most current installation instructions. The project appears to be a desktop application; check the repository for platform-specific setup (likely involves cloning, installing dependencies with npm/yarn, and configuring LLM credentials and MCP server connections).
Risks
- Credential exposure risk: Desktop-wide app context access means API keys for LLMs and MCP tools are accessible to the agent; compromise could exfiltrate multiple service credentials
- Prompt injection via app content: Malicious text in open applications could manipulate the AI agent into unintended actions across your app ecosystem
- Cross-app action execution: Broad permission to act across applications increases blast radius of agent errors or adversarial prompts
- Supply chain: Depends on multiple LLM providers and MCP connectors; vulnerabilities in any dependency could compromise the system
- Data exfiltration: On-screen context awareness means sensitive information from any open app may be sent to external LLM APIs depending on configuration
How to stay secure
Least-privilege permissions
- Restrict API keys: Only give the AI agent access to API keys (authentication credentials for cloud services) it actually needs. Remove unused keys regularly and rotate them monthly.
- Limit app access: Configure which desktop applications the agent can monitor and control—disable access to apps containing sensitive data like banking software or email if not essential.
- Separate credential storage: Store API keys in a dedicated password manager rather than in the agent's configuration files; never hardcode credentials in settings.
- Sandbox LLM access: Use read-only or limited-scope API tokens for external AI services; prevent the agent from accessing billing, user management, or admin functions.
Network isolation
- VPN or firewall rules: Route the agent's traffic through a VPN or restrict outbound connections to known, approved LLM provider domains only.
- Disable screen capture for sensitive contexts: Configure the agent to skip screen analysis when you're using banking apps, healthcare portals, or password managers.
- Monitor data transmission: Ensure sensitive on-screen content (financial data, personal health info, trade secrets) is not sent to external AI APIs—keep processing local when possible.
- Segment your network: If possible, run the agent on a separate user account or isolated network segment so it cannot access other sensitive systems if compromised.
Monitoring
- Enable audit logging: Turn on detailed logs of what the agent sees, what actions it takes, and what data it sends externally; review weekly.
- Track API usage: Monitor your LLM provider and MCP tool usage dashboards for unexpected spikes or unfamiliar requests that could indicate compromise.
- Set alerts for unusual actions: Configure alerts if the agent attempts to modify files, send emails, or access applications outside its normal pattern.
- Credential access alerts: Enable notifications whenever the agent requests or accesses stored API keys or passwords.
Red flags to watch for
- Unexpected application behavior: If apps crash, behave strangely, or take actions you didn't request, immediately disconnect the agent and review logs.
- Suspicious credential usage: Check your LLM and MCP tool account activity for logins from unfamiliar locations or times you weren't using the agent.
- Performance degradation: Sudden slowness, excessive disk/network activity, or battery drain while the agent runs may indicate unauthorized data exfiltration.
- Prompt injection attempts: If you notice the agent misinterpreting instructions or acting against your explicit requests after you've pasted text from untrusted sources, disable the agent and update it immediately.
Alternatives
- Claude Desktop with MCP – Official Anthropic tool for local MCP server integration with context awareness
- Codeium Windsurf – IDE-focused AI agent with multi-file and multi-tool capabilities
- Cursor – AI-native code editor with cross-file context and integrated agent features