Install
openclaw skills install agentmeshProvides end-to-end encrypted, authenticated, and forward-secret messaging between AI agents with cryptographic identities and tamper-proof delivery.
openclaw skills install agentmeshWhatsApp-style end-to-end encrypted messaging for AI agents. GitHub: https://github.com/cerbug45/AgentMesh | Author: cerbug45
AgentMesh gives every AI agent a cryptographic identity and lets agents exchange messages that are:
| Property | Mechanism |
|---|---|
| Encrypted | AES-256-GCM authenticated encryption |
| Authenticated | Ed25519 digital signatures (per message) |
| Forward-secret | X25519 ECDH ephemeral session keys |
| Tamper-proof | AEAD authentication tag |
| Replay-proof | Nonce + counter deduplication |
| Private | The Hub (broker) never sees message contents |
No TLS certificates. No servers required for local use. One pip install.
pippip install git+https://github.com/cerbug45/AgentMesh.git
git clone https://github.com/cerbug45/AgentMesh.git
cd AgentMesh
pip install .
git clone https://github.com/cerbug45/AgentMesh.git
cd AgentMesh
pip install -e ".[dev]"
pytest # run all tests
python -c "import agentmesh; print(agentmesh.__version__)"
# → 1.0.0
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}] ← {msg.sender}: {msg.text}")
alice.send("bob", text="Hello, Bob! This is end-to-end encrypted.")
Output:
[bob] ← alice: Hello, Bob! This is end-to-end encrypted.
An Agent is an AI agent with a cryptographic identity (two key pairs):
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)
# → a1b2:c3d4:e5f6:g7h8:i9j0:k1l2:m3n4:o5p6
A Hub is the message router. It stores public key bundles (for discovery) and routes encrypted envelopes. It cannot decrypt messages.
| Hub | Use case |
|---|---|
LocalHub | Single Python process (demos, tests, notebooks) |
NetworkHub | Multi-process / multi-machine (production) |
@bob.on_message
def handle(msg):
msg.sender # str – sender agent_id
msg.recipient # str – recipient agent_id
msg.text # str – shortcut for msg.payload["text"]
msg.type # str – shortcut for msg.payload["type"] (default: "message")
msg.payload # dict – full decrypted payload
msg.timestamp # int – milliseconds since epoch
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.
# Handler as decorator
@alice.on_message
def handler_one(msg):
...
# Handler as lambda
alice.on_message(lambda msg: print(msg.text))
# Multiple handlers – all called in registration order
alice.on_message(log_handler)
alice.on_message(process_handler)
Save keys to disk so an agent has the same identity across restarts:
alice = Agent("alice", hub=hub, keypair_path=".keys/alice.json")
# List all agents registered on the hub
peers = alice.list_peers() # → ["bob", "carol", "dave"]
# Check agent status
print(alice.status())
# {
# "agent_id": "alice",
# "fingerprint": "a1b2:…",
# "active_sessions": ["bob"],
# "known_peers": ["bob"],
# "handlers": 2
# }
On the broker machine (or in its own terminal):
# Option A – module
python -m agentmesh.hub_server --host 0.0.0.0 --port 7700
# Option B – entry-point (after pip install)
agentmesh-hub --host 0.0.0.0 --port 7700
# 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!")
┌──────────────────────────────────────────────────────┐
│ NetworkHubServer │
│ Stores public bundles. Routes encrypted envelopes. │
│ Cannot read message contents. │
└──────────────────────┬───────────────────────────────┘
│ TCP (newline-delimited JSON)
┌───────────┼───────────┐
│ │ │
Agent A Agent B Agent C
(encrypted) (encrypted) (encrypted)
┌─────────────────────────────────────────────────────┐
│ Application layer (dict payload) │
├─────────────────────────────────────────────────────┤
│ Ed25519 signature (sender authentication) │
├─────────────────────────────────────────────────────┤
│ AES-256-GCM (confidentiality + integrity) │
├─────────────────────────────────────────────────────┤
│ HKDF-SHA256 key derivation (directional keys) │
├─────────────────────────────────────────────────────┤
│ X25519 ECDH (shared secret / forward secrecy) │
└─────────────────────────────────────────────────────┘
| Attack | Defence |
|---|---|
| Eavesdropping | AES-256-GCM encryption |
| Message tampering | AES-GCM authentication tag (AEAD) |
| Impersonation | Ed25519 signature on every message |
| Replay attack | Nonce + monotonic counter deduplication |
| Key compromise | X25519 ephemeral sessions (forward secrecy) |
| Hub compromise | Hub stores only public keys; cannot decrypt |
| File | What it shows |
|---|---|
examples/01_simple_chat.py | Two agents, basic send/receive |
examples/02_multi_agent.py | Coordinator + 4 workers, task distribution |
examples/03_persistent_keys.py | Keys saved to disk, identity survives restart |
examples/04_llm_agents.py | LLM 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)| Method | Description |
|---|---|
send(recipient_id, text="", **kwargs) | Send encrypted message |
send_payload(recipient_id, payload: dict) | Low-level send |
on_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 directly |
list_peers() | List all peer IDs on the hub |
status() | Dict with agent state |
fingerprint | Human-readable hex identity fingerprint |
public_bundle | Dict with public keys (share with peers) |
LocalHub()| Method | Description |
|---|---|
register(agent) | Register an agent (called automatically) |
deliver(envelope) | Route an encrypted envelope |
get_bundle(agent_id) | Get a peer's public bundle |
list_agents() | List all registered agent IDs |
message_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)| Method | Description |
|---|---|
start(block=True) | Start listening (block=False for background thread) |
from agentmesh.crypto import (
AgentKeyPair, # key generation, serialisation, fingerprint
CryptoSession, # encrypt / decrypt
perform_key_exchange,# X25519 ECDH → CryptoSession
seal, # sign + encrypt (high-level)
unseal, # decrypt + verify (high-level)
CryptoError, # raised on any crypto failure
)
CryptoError: Replay attack detectedYou are sending the same encrypted envelope twice.
Each call to send() produces a fresh envelope – do not re-use envelopes.
CryptoError: Authentication tag mismatchThe envelope was modified in transit. Check that your transport does not corrupt binary data (use JSON-safe base64).
ValueError: Peer 'xxx' not found on hubThe 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 configuredYou created Agent("name") without a hub.
Pass hub=LocalHub() or hub=NetworkHub(...) to the constructor.
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
MIT © cerbug45 – see LICENSE