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rudimentall1/agentic-wallet-guardian-v3

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[](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

#agent-security #blockchain-security-solutions #ethereum #policy-engine #risk-engine #transaction-security #wallet-security-seeds #web3

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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