Shadowgraph vs Screenpipe

In-depth architectural comparison of the Shadowgraph and Screenpipe MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.

Which MCP Server Should You Choose?

Choose Shadowgraph when:

• You need dedicated capabilities in the Knowledge & Memory domain.
• You prefer local stdio subprocess transport architecture.
• Your security boundary fits: No auth required (Free / Open Source).

Explore Shadowgraph Details

Choose Screenpipe when:

• You need dedicated capabilities in the Knowledge & Memory domain.
• You prefer local stdio subprocess transport architecture.
• Your security boundary fits: No auth required (Free / Open Source).

Explore Screenpipe Details

Feature & Specification Comparison

Specification	Shadowgraph LiLara-AI Knowledge & Memory	Screenpipe Knowledge & Memory
Summary	Technical Preview / Early Access. Local-first decision memory for AI agents that stores choices, rejected alternatives and their reasons, failed attempts, outcomes, provenance, and caller-invoked review conditions in a local file. Stdio MCP server; install from GitHub with Node.js 20+.	Local-first workflow memory for AI agents. screenpipe lets MCP clients search selected screen, audio, app, and meeting context and turn real work into cited notes, SOPs, workflow reports, and automation candidates.
Category & Scope

Tools & Capabilities Breakdown

Ready-to-Paste Client Configurations

Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).

{
  "mcpServers": {
    "lilara-ai-shadowgraph": {
      "command": "npx",
      "args": [
        "-y",
        "lilara-ai-shadowgraph"
      ]
    }
  }
}

{
  "mcpServers": {
    "screenpipe": {
      "command": "npx",
      "args": [
        "-y",
        "screenpipe"
      ]
    }
  }
}

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