What is the key difference between Waggle Mcp and Memora?

Waggle Mcp belongs to the Knowledge & Memory category with local stdio execution, while Memora belongs to the Knowledge & Memory category with local stdio execution.

How do I install Waggle Mcp or Memora 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 Memora free or paid?

Waggle Mcp operates under a Free / Open Source model (No auth required). Memora operates under a BYOK (Pay Provider Direct) model (API Key required).

Which MCP server has more tools and community activity?

Waggle Mcp lists 35 tools with 2 views and 34 GitHub stars. Memora lists 6 tools with 2 views and 594 GitHub stars.

Can I use both Waggle Mcp and Memora 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.

How do I install Waggle Mcp or Memora in Claude Desktop, Cursor, or Windsurf?

Both servers follow the standard Model Context Protocol configuration format. Simply copy the JSON block from the configuration section above and paste it into your AI client's mcpServers configuration object (for instance in claude_desktop_config.json or ~/.cursor/mcp.json ), then completely restart or refresh the application.

Are Waggle Mcp and Memora free to use, or do they require API keys?

Waggle Mcp is listed under a Free / Open Source model with No auth required . Memora operates under a BYOK (Pay Provider Direct) model with API Key required . If environment variables are required (such as API keys), be sure to define them under the env key in your MCP client's configuration file.

Can I run both Waggle Mcp and Memora at the same time in my AI client?

Yes! MCP clients support multi-server orchestration. You can include both abhigyan-shekhar-waggle-mcp and agentic-mcp-tools-memora as distinct keys inside the single mcpServers object in your configuration file. Your AI assistant will automatically select and call the appropriate tool from either server during chat sessions.

Which MCP server has higher directory engagement and quality ratings?

On AllMCPs, Waggle Mcp has a Quality Score of 57 /100 ( Good ) with 2 views, 0 installs, and 34 GitHub stars. Memora holds a Quality Score of 53 /100 ( Good ) with 2 views, 0 installs, and 594 GitHub stars.

Side-by-Side Model Context Protocol Comparison

Waggle Mcp vs Memora

In-depth architectural comparison of the Waggle Mcp and Memora 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: 57/100 (Good) | Auth: No auth required

Memora

Knowledge & Memory · Local stdio

Quality: 53/100 (Good) | Auth: API Key required

Verdict Summary: Choose Waggle Mcp if you need specialized Knowledge & Memory tools running via a local process. Choose Memora 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 Memora when:

You need dedicated capabilities in the Knowledge & Memory domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: API Key required (BYOK (Pay Provider Direct)).
You have access to required keys: MEMORA_DB_PATH, MEMORA_STORAGE_URI, CLOUDFLARE_API_TOKEN, AWS_PROFILE, AWS_ENDPOINT_URL, MEMORA_CLOUD_ENCRYPT, MEMORA_ALLOW_ANY_TAG, MEMORA_GRAPH_PORT.

Feature & Specification Comparison

Specification	Waggle Mcp Abhigyan-Shekhar Knowledge & Memory	Memora agentic-mcp-tools 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 with knowledge graph visualization, semantic/hybrid search, cloud sync (S3/R2), and cross-session context management.
Category & Scope

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"
      }
    }
  }
}

Memora Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "agentic-mcp-tools-memora": {
      "command": "npx",
      "args": [
        "-y",
        "wrangler"
      ],
      "env": {
        "MEMORA_DB_PATH": "YOUR_MEMORA_DB_PATH_HERE",
        "MEMORA_STORAGE_URI": "YOUR_MEMORA_STORAGE_URI_HERE",
        "CLOUDFLARE_API_TOKEN": "YOUR_CLOUDFLARE_API_TOKEN_HERE",
        "AWS_PROFILE": "YOUR_AWS_PROFILE_HERE",
        "AWS_ENDPOINT_URL": "YOUR_AWS_ENDPOINT_URL_HERE",
        "MEMORA_CLOUD_ENCRYPT": "YOUR_MEMORA_CLOUD_ENCRYPT_HERE",
        "MEMORA_ALLOW_ANY_TAG": "YOUR_MEMORA_ALLOW_ANY_TAG_HERE",
        "MEMORA_GRAPH_PORT": "YOUR_MEMORA_GRAPH_PORT_HERE"
      }
    }
  }
}

Frequently Asked Questions

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

More alternatives to Waggle Mcp More alternatives to Memora Knowledge & Memory category hub