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Memory and knowledge · MCP server

Waggle MCP: MCP server, setup and security

Waggle MCP is an open-source MCP server published on GitHub that lets an AI agent store conversation facts and detect conflicts through temporal graph queries. Its repository has 44 stars and its last commit is from October 23, 2026 and it is licensed under Apache-2.0.

Score
66/100
Adoption (stars) 16/40
Maintenance 30/30
Framework (licence, registry, official) 20/30
Actively maintained

What Waggle MCP does

According to its repository: “Persistent graph memory that extracts facts from conversations into typed nodes with local embeddings, supporting temporal queries and conflict detection.”

The Model Context Protocol (MCP) is a standard plug between an AI agent and an outside service: once the server is connected, its tools show up in the agent, which decides when to call them. Waggle MCP is listed under Memory and knowledge, a category of 250 servers in the directory, where it ranks #111 by stars.

Topics declared by the repository: agent-memory, ai-agents, graph-memory, knowledge-graph, llm-memory, python.

Installing and connecting Waggle MCP

Distribution modes declared in the official MCP registry:

  • PyPI package (Python, started with uvx or pip)

It works with any MCP-compatible client (Claude Desktop, Claude Code, Cursor, VS Code, Codex and most recent agents). You add one entry to the client's MCP configuration: server name, start command or URL, environment variables. Project language: Python. Listed systems: macOS, Windows, Linux.

Security: what to check

Connecting an MCP server means installing software that talks straight to your agent: it runs with your rights and the model trusts its tool descriptions. The risk therefore depends on what it can touch and how far its code can be trusted.

None of the risk signals we track (system access, secrets, missing licence, abandonment) applies to Waggle MCP. That does not replace reading the code.

Good practice: grant only the rights needed (least privilege), require human approval for irreversible actions, and run the AI agent security check.

Project activity and maintenance

Created on April 12, 2026, the Abhigyan-Shekhar/Waggle-mcp repository has 44 stars and 138 forks, with 171 open issues. It has more stars than 50% of the servers in the directory. It received changes this month. Version published in the registry: 0.1.8 (April 21, 2026).

Stars measure attention, not quality: a recent, heavily promoted project can collect them fast, while a useful but niche server will have few.

Context7Context7 : 63.6K63.6KHindsightHindsight : 53.4K53.4KCogneeCognee : 32.7K32.7KSkill SeekersSkill Seekers : 15.2K15.2KHonchoHoncho : 7.8K7.8KBasic memoryBasic memory : 4.3K4.3KNoteDiscoveryNoteDiscovery : 2.8K2.8KMemantoMemanto : 2.3K2.3KWaggle MCPWaggle MCP : 4444
GitHub stars of the most followed servers in the category (log scale); Waggle MCP in orange. Checked October 25, 2026.

Verdict

Waggle MCP is a reasonable choice; check the points below before connecting it.

Strengths

  • Active project: recent commit
  • Listed in the official MCP registry
  • Permissive licence (Apache-2.0)

Frequently asked questions

How do I install Waggle MCP?

Waggle MCP is distributed as PyPI package (Python, started with uvx or pip). Add it to your client's MCP configuration (Claude Desktop, Cursor, VS Code…) following the README of Abhigyan-Shekhar/Waggle-mcp.

Is Waggle MCP free?

The code is published under the Apache-2.0 licence and can be used free of charge.

Is Waggle MCP safe?

No risk signal found (system access, secrets, licence, abandonment). As with any MCP server, keep rights minimal and read the code before sensitive use.

Is it an official server?

Nothing says so: it is a project published by Abhigyan-Shekhar on GitHub. Check who maintains it before giving it access.

What are the alternatives to Waggle MCP?

In the same category: Context7, Basic memory, Hindsight, and Cognee. Context7 and Basic memory score higher on our grid.

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Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score