Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
- Major internal restructuring: migrated all core source files from the top level to src/agentmesh/. - All example scripts moved from project root to examples/ directory. - Test files relocated to tests/ directory. - Updated packaging and references to support the new source layout. - Deprecated legacy top-level Python files; all code organization now follows standard Python packaging conventions.
What This Skill Does
AgentMesh gives AI agents a cryptographic identity and lets them exchange messages with end-to-end encryption. Each message is signed with Ed25519, encrypted with AES-256-GCM, and uses X25519 ephemeral keys for forward secrecy. A central hub routes messages without ever seeing their contents.
Unlike standard TLS-based messaging, the hub never has access to message contents, so a compromised broker cannot leak agent communication.
When to use it
- Coordinating distributed AI workers without exposing task data to the broker
- Running multi-agent pipelines across separate machines
- Verifying message authenticity between agents using cryptographic fingerprints
- Persisting agent identities across process restarts with saved keypairs
- Building LLM agent pipelines with encrypted inter-agent communication
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: 'Set up two agents to exchange an encrypted message in the same Python process'
- 1Creates a LocalHub instance as the in-process message broker
- 2Creates two Agent instances with auto-generated keypairs registered on the hub
- 3Registers a message handler on bob using the on_message decorator
- 4Calls alice.send('bob', text='Hello, Bob!') to dispatch an encrypted message
- 5Handler fires and prints the decrypted message with sender and recipient fields
[bob] <- alice: Hello, Bob!