Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Communication & Collaboration
AgentMesh
Provides end-to-end encrypted, authenticated, and forward-secret messaging between AI agents with cryptographic identities and tamper-proof delivery.
skill openclawclawhub Free
Provides end-to-end encrypted, authenticated, and forward-secret messaging between AI agents with cryptographic identities and tamper-proof delivery.
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Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
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- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md, examples/01_simple_chat.py, examples/02_multi_agent.py, examples/03_persistent_keys.py, examples/04_llm_agents.py
Validation
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- Review SKILL.md after the package is downloaded.
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New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.1
Provenance
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- cerbug45
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Documentation
AgentMesh SKILL.md
WhatsApp-style end-to-end encrypted messaging for AI agents. GitHub: https://github.com/cerbug45/AgentMesh | Author: cerbug45
What Is AgentMesh?
AgentMesh gives every AI agent a cryptographic identity and lets agents exchange messages that are: PropertyMechanismEncryptedAES-256-GCM authenticated encryptionAuthenticatedEd25519 digital signatures (per message)Forward-secretX25519 ECDH ephemeral session keysTamper-proofAEAD authentication tagReplay-proofNonce + counter deduplicationPrivateThe Hub (broker) never sees message contents No TLS certificates. No servers required for local use. One pip install.
Requirements
Python 3.10 or newer pip
Option 1 โ Install from GitHub (recommended)
pip install git+https://github.com/cerbug45/AgentMesh.git
Option 2 โ Clone and install locally
git clone https://github.com/cerbug45/AgentMesh.git cd AgentMesh pip install .
Option 3 โ Development install (editable, with tests)
git clone https://github.com/cerbug45/AgentMesh.git cd AgentMesh pip install -e ".[dev]" pytest # run all tests
Verify installation
python -c "import agentmesh; print(agentmesh.__version__)" # โ 1.0.0
Quick Start (5 minutes)
from agentmesh import Agent, LocalHub hub = LocalHub() # in-process broker alice = Agent("alice", hub=hub) # keys generated automatically bob = Agent("bob", hub=hub) @bob.on_message def handle(msg): print(f"[{msg.recipient}] โ {msg.sender}: {msg.text}") alice.send("bob", text="Hello, Bob! This is end-to-end encrypted.") Output: [bob] โ alice: Hello, Bob! This is end-to-end encrypted.
Agent
An Agent is an AI agent with a cryptographic identity (two key pairs): Ed25519 identity key โ signs every outgoing message X25519 exchange key โ used for ECDH session establishment from agentmesh import Agent, LocalHub hub = LocalHub() alice = Agent("alice", hub=hub) # See the agent's fingerprint (share out-of-band to verify identity) print(alice.fingerprint) # โ a1b2:c3d4:e5f6:g7h8:i9j0:k1l2:m3n4:o5p6
Hub
A Hub is the message router. It stores public key bundles (for discovery) and routes encrypted envelopes. It cannot decrypt messages. HubUse caseLocalHubSingle Python process (demos, tests, notebooks)NetworkHubMulti-process / multi-machine (production)
Message
@bob.on_message def handle(msg): msg.sender # str โ sender agent_id msg.recipient # str โ recipient agent_id msg.text # str โ shortcut for msg.payload["text"] msg.type # str โ shortcut for msg.payload["type"] (default: "message") msg.payload # dict โ full decrypted payload msg.timestamp # int โ milliseconds since epoch
Sending messages with extra data
alice.send( "bob", text = "Run this task", task_id = 42, priority = "high", data = {"key": "value"}, ) All keyword arguments beyond text are included in msg.payload.
Chaining handlers
# Handler as decorator @alice.on_message def handler_one(msg): ... # Handler as lambda alice.on_message(lambda msg: print(msg.text)) # Multiple handlers โ all called in registration order alice.on_message(log_handler) alice.on_message(process_handler)
Persistent keys
Save keys to disk so an agent has the same identity across restarts: alice = Agent("alice", hub=hub, keypair_path=".keys/alice.json") File is created on first run (new keys). File is loaded on subsequent runs (same keys = same fingerprint). Store this file securely โ it contains the private key.
Peer discovery
# List all agents registered on the hub peers = alice.list_peers() # โ ["bob", "carol", "dave"] # Check agent status print(alice.status()) # { # "agent_id": "alice", # "fingerprint": "a1b2:โฆ", # "active_sessions": ["bob"], # "known_peers": ["bob"], # "handlers": 2 # }
1. Start the hub server
On the broker machine (or in its own terminal): # Option A โ module python -m agentmesh.hub_server --host 0.0.0.0 --port 7700 # Option B โ entry-point (after pip install) agentmesh-hub --host 0.0.0.0 --port 7700
2. Agents connect from anywhere
# Machine A from agentmesh import Agent, NetworkHub hub = NetworkHub(host="192.168.1.10", port=7700) alice = Agent("alice", hub=hub) # Machine B (different process / different computer) from agentmesh import Agent, NetworkHub hub = NetworkHub(host="192.168.1.10", port=7700) bob = Agent("bob", hub=hub) bob.on_message(lambda m: print(m.text)) alice.send("bob", text="Cross-machine encrypted message!")
Network hub architecture
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ NetworkHubServer โ โ Stores public bundles. Routes encrypted envelopes. โ โ Cannot read message contents. โ โโโโโโโโโโโโโโโโโโโโโโโโฌโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ TCP (newline-delimited JSON) โโโโโโโโโโโโโผโโโโโโโโโโโโ โ โ โ Agent A Agent B Agent C (encrypted) (encrypted) (encrypted)
Cryptographic stack
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ Application layer (dict payload) โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ Ed25519 signature (sender authentication) โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ AES-256-GCM (confidentiality + integrity) โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ HKDF-SHA256 key derivation (directional keys) โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ X25519 ECDH (shared secret / forward secrecy) โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Security properties
AttackDefenceEavesdroppingAES-256-GCM encryptionMessage tamperingAES-GCM authentication tag (AEAD)ImpersonationEd25519 signature on every messageReplay attackNonce + monotonic counter deduplicationKey compromiseX25519 ephemeral sessions (forward secrecy)Hub compromiseHub stores only public keys; cannot decrypt
What the Hub can see
โ Agent IDs (to route messages) โ Public key bundles (required for discovery) โ Metadata: sender, recipient, timestamp, message counter โ Message contents (always encrypted) โ Payload data (always encrypted)
Examples
FileWhat it showsexamples/01_simple_chat.pyTwo agents, basic send/receiveexamples/02_multi_agent.pyCoordinator + 4 workers, task distributionexamples/03_persistent_keys.pyKeys saved to disk, identity survives restartexamples/04_llm_agents.pyLLM agents (OpenAI / any API) in a pipeline Run any example: python examples/01_simple_chat.py
Agent(agent_id, hub=None, keypair_path=None, log_level=WARNING)
MethodDescriptionsend(recipient_id, text="", **kwargs)Send encrypted messagesend_payload(recipient_id, payload: dict)Low-level sendon_message(handler)Register message handler (decorator or call)connect(peer_id)Pre-establish session (optional, auto-connects)connect_with_bundle(bundle)P2P: connect using public bundle directlylist_peers()List all peer IDs on the hubstatus()Dict with agent statefingerprintHuman-readable hex identity fingerprintpublic_bundleDict with public keys (share with peers)
LocalHub()
MethodDescriptionregister(agent)Register an agent (called automatically)deliver(envelope)Route an encrypted envelopeget_bundle(agent_id)Get a peer's public bundlelist_agents()List all registered agent IDsmessage_count()Number of messages routed
NetworkHub(host, port=7700)
Same interface as LocalHub, but communicates with a NetworkHubServer over TCP.
NetworkHubServer(host="0.0.0.0", port=7700)
MethodDescriptionstart(block=True)Start listening (block=False for background thread)
Low-level crypto (advanced)
from agentmesh.crypto import ( AgentKeyPair, # key generation, serialisation, fingerprint CryptoSession, # encrypt / decrypt perform_key_exchange,# X25519 ECDH โ CryptoSession seal, # sign + encrypt (high-level) unseal, # decrypt + verify (high-level) CryptoError, # raised on any crypto failure )
CryptoError: Replay attack detected
You are sending the same encrypted envelope twice. Each call to send() produces a fresh envelope โ do not re-use envelopes.
CryptoError: Authentication tag mismatch
The envelope was modified in transit. Check that your transport does not corrupt binary data (use JSON-safe base64).
ValueError: Peer 'xxx' not found on hub
The recipient has not registered with the hub yet. Ensure both agents are created with the same hub instance (LocalHub) or connected to the same hub server (NetworkHub).
RuntimeError: No hub configured
You created Agent("name") without a hub. Pass hub=LocalHub() or hub=NetworkHub(...) to the constructor.
Contributing
git clone https://github.com/cerbug45/AgentMesh.git cd AgentMesh pip install -e ".[dev]" pytest -v Issues and PRs welcome at https://github.com/cerbug45/AgentMesh/issues
License
MIT ยฉ cerbug45 โ see LICENSE
Category context
Messaging, meetings, inboxes, CRM, and teammate communication surfaces.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
4 Scripts2 Docs
- SKILL.md
- README.md
- examples/01_simple_chat.py
- examples/02_multi_agent.py
- examples/03_persistent_keys.py
- examples/04_llm_agents.py