rudimentall1/agentic-wallet-guardian-v3
by Various
[](https://glama.ai/mcp/servers/rudimentall1/agentic-wallet-guardian-v3) 🐍 🏠 - Self-hosted security and policy layer for AI agents interacting with blockchain wallets. Agents sub
MCP
rudimentall1/agentic-wallet-guardian-v3
Added 3 Sept 2026
Overview
Self-hosted security and policy layer for AI agents that interact with blockchain wallets. It provides a policy gate between agent actions and wallet operations. Written in Python and designed to run in the user's own environment.
Best for
Best for
Developers who want a self-managed policy and security layer for AI agents touching blockchain wallets.
Use cases
- Enforce allowlist rules on agent wallet operations
- Add a policy checkpoint before blockchain transactions
- Gate AI agent calls to wallet functionality
Notes
Self-hosted security and policy layer for AI agents that interact with blockchain wallets. It provides a policy gate between agent actions and wallet operations. Written in Python and designed to run in the user’s own environment.
1 stars on GitHub. Last updated 2026-08-30. Licensed MIT.
Use cases
- Enforce allowlist rules on agent wallet operations
- Add a policy checkpoint before blockchain transactions
- Gate AI agent calls to wallet functionality
Pros
- Self-hosted, so sensitive wallet logic stays in your control
- Python-based, easy to integrate with common agent tooling
- Narrowly focused security layer for a specific risk
Cons
- Very early stage project with minimal adoption (1 star)
- Limited community support and likely sparse documentation
- Requires manual deployment and tuning for production use
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Self-hosted, so sensitive wallet logic stays in your control
- Python-based, easy to integrate with common agent tooling
- Narrowly focused security layer for a specific risk
Cons
- Very early stage project with minimal adoption (1 star)
- Limited community support and likely sparse documentation
- Requires manual deployment and tuning for production use
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