What is the key difference between Waggle MCP and Mnemos?

Waggle MCP belongs to the Knowledge & Memory category with local stdio execution, while Mnemos belongs to the Knowledge & Memory category with local stdio execution.

How do I install Waggle MCP or Mnemos in Claude Desktop or Cursor?

Copy the mcpServers JSON configuration snippet provided on this page into your claude_desktop_config.json or ~/.cursor/mcp.json file and restart your AI client.

Are Waggle MCP and Mnemos free or paid?

Waggle MCP operates under a Free / Open Source model (No auth required). Mnemos operates under a Free / Open Source model (No auth required).

Which MCP server has more tools and community activity?

Waggle MCP lists 35 tools with 2 views and 42 GitHub stars. Mnemos lists 9 tools with 2 views and 22 GitHub stars.

Can I use both Waggle MCP and Mnemos together in the same MCP host?

Yes! MCP hosts like Claude Desktop, Cursor, Windsurf, Cline, and VS Code support configuring multiple servers under the mcpServers object simultaneously.

Side-by-Side Model Context Protocol Comparison

Waggle MCP vs Mnemos

In-depth architectural comparison of the Waggle MCP and Mnemos 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.

At a Glance & Executive Verdict

Waggle MCP

Knowledge & Memory · Local stdio

Quality: 55/100 (Good) | Auth: No auth required

Mnemos

Knowledge & Memory · Local stdio

Quality: 59/100 (Good) | Auth: No auth required

Verdict Summary: Choose Waggle MCP if you need specialized Knowledge & Memory tools running via a local process. Choose Mnemos if your workspace requires Knowledge & Memory integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.

Which MCP Server Should You Choose?

Choose Waggle MCP 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).
You have access to required keys: WAGGLE_MODEL, PYTHONUTF8.
Primary tools included: observe_conversation, query_graph, prime_context.

Explore Waggle MCP Details

Choose Mnemos 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).
Primary tools included: mnemos_store, mnemos_search, mnemos_context.

Explore Mnemos Details

Feature & Specification Comparison

Specification	Waggle MCP Abhigyan-Shekhar Knowledge & Memory	Mnemos s60yucca Knowledge & Memory
Summary	Persistent graph memory for AI agents. Drop a conversation turn in via observeconversation() and facts are auto-extracted, stored as typed graph nodes with local semantic embeddings (no API key). Supports temporal queries ("what did we decide last week?"), conflict detection, and context priming. One-command setup with waggle-mcp init. SQLite locally, Neo4j in production.	Persistent memory engine for AI coding agents. Stores architecture decisions, bug root causes, and project conventions across sessions. Single Go binary with embedded SQLite, FTS5 search, context assembly within token budgets, and autopilot setup for Claude Code, Kiro, and Cursor.

Tools & Capabilities Breakdown

Waggle MCP Tools (35)

observe_conversation

After any turn containing a decision, preference, constraint, correction, or project fact. Persists the verbatim turn first, then extracts graph nodes. Returns `turn_id`, `verbatim_stored`, `nodes_extracted`, `edges_inferred`.

query_graph

Before answering questions that may depend on prior context. Hybrid retrieval (graph + verbatim transcript) by default. Supports `as_of` for point-in-time queries.

prime_context

At the start of a new session to hydrate context from the most relevant scoped memories.

graph_diff

When the user asks what changed recently.

aggregate_graph

Broad filtered subgraph for map-reduce tasks. Use when you want a large scoped slice rather than high-precision top-K. Supports `node_types`, `tags`, `as_of`, `include_invalidated`.

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).

Waggle MCP Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "abhigyan-shekhar-waggle-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "abhigyan-shekhar-waggle-mcp"
      ],
      "env": {
        "WAGGLE_MODEL": "YOUR_WAGGLE_MODEL_HERE",
        "PYTHONUTF8": "YOUR_PYTHONUTF8_HERE"
      }
    }
  }
}

Mnemos Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "s60yucca-mnemos": {
      "command": "npx",
      "args": [
        "-y",
        "@s60yucca/mnemos"
      ]
    }
  }
}

Frequently Asked Questions

Waggle MCP is categorized under Knowledge & Memory and uses a local stdio subprocess. In contrast, Mnemos belongs to Knowledge & Memory using local stdio subprocess. Select Waggle MCP when you need capabilities focused on knowledge & memory and Mnemos when you require tools for knowledge & memory.

More alternatives to Waggle MCP More alternatives to Mnemos Knowledge & Memory category hub