agent-coworker 0.3.1

Peer-to-peer AI agent collaboration over XMTP — discover, call, and collaborate across the open internet with E2E encryption.

English | 中文

CoWorker Protocol

Peer-to-peer AI agent collaboration over XMTP

     

MCP connects agents to tools. A2A connects agents in enterprises.
CoWorker connects agents across the open internet — peer-to-peer, E2E encrypted, zero cost.

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Who is this for?

You already have an agent — Claude Code, Cursor, a custom bot, a CrewAI pipeline. Now you want to collaborate with someone else's agent to get something done together. But:

• You don't know how to decompose the goal across two agents
• You can't see what's happening during the collaboration
• You don't want to expose your prompts, code, or data to do it
• You don't have a shared server and don't want to set one up

CoWorker handles all of this. Any Python agent becomes a collaboration node in one line. Goals auto-decompose. A dashboard shows everything. Skills stay private. No server needed.

Why CoWorker?

Existing agent protocols force you into a single process, a shared network, or a central broker. CoWorker is different — three design choices set it apart:

1. Group Trust Collaboration

Multiple humans and AI agents work together in one encrypted group — with trust tiers visible to everyone.

# Create a group with humans and bots
group = agent.create_group(
    name="Research Sprint",
    members=["alice_invite_code", "bob_invite_code"]
)

# Everyone sees all messages — trust badges show each member's tier
group.send("Let's start the research on quantum computing")

The built-in dashboard shows the full interaction: who said what, which skills were called, each member's trust level — all in one view.

Group chat — humans + bots, trust badges visible	Per-peer trust tier management

2. Skill-as-API with Auto Trust Downgrade

Share what your agent can do, not how it does it. Peers only see input/output schema — never your code, prompts, or logic.

@agent.skill("translate",
             description="Translate text between languages",
             input_schema={"text": "str", "to_lang": "str"},
             output_schema={"translated": "str"},
             min_trust_tier=1)  # Only KNOWN+ peers can see this skill
def translate(text: str, to_lang: str) -> dict:
    # Your proprietary implementation stays private
    # Peers only know: "translate" takes text+lang, returns translated text
    return {"translated": do_translate(text, to_lang)}

Trust is temporary by design. When a collaboration goal (OKR) completes, the peer's trust automatically steps down:

Before collaboration:  PRIVILEGED (3) — full skill access
OKR completed:         → INTERNAL (2) — auto-downgraded
Next OKR completed:    → KNOWN (1)    — further downgraded

Skill Visibility Control: You choose which skills to expose. Hidden skills are invisible to peers — they can't even tell they exist.

coworker skills configure          # interactive toggle
coworker skills expose translate   # expose one skill
coworker skills hide admin         # hide one skill
coworker skills preview --peer-tier known  # preview what peers see

3. Peer-to-Peer, No Server, Zero Cost

There is no CoWorker server. Each agent runs independently. Communication happens directly over XMTP — E2E encrypted, NAT-traversing, works across any network.

Your machine                          Collaborator's machine
┌──────────────────┐                 ┌──────────────────┐
│  Python Agent    │                 │  Python Agent    │
│  + Dashboard     │                 │  + Dashboard     │
│  + XMTP Bridge   │                 │  + XMTP Bridge   │
└────────┬─────────┘                 └────────┬─────────┘
         │                                     │
         └─────── XMTP Network ───────────────┘
              E2E encrypted, NAT traversal
              No central server, no API keys
              No cost, no rate limits

• No registration — pip install and go
• No API keys — cryptographic identity, keys never leave your machine
• No server costs — run on your laptop, your Raspberry Pi, anywhere
• No port forwarding — XMTP handles NAT traversal
• Privacy by default — invite codes contain no sensitive addresses

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Quick Start

pip install agent-coworker
coworker init --name my-agent    # generates identity + installs XMTP bridge
coworker bridge start            # connect to XMTP network
coworker demo                    # connect to our demo bot & test skills

China mainland

pip install agent-coworker -i https://pypi.tuna.tsinghua.edu.cn/simple

Try the Demo Bot

The fastest way to experience CoWorker — connect to icy, our always-online demo bot:

coworker demo

# → Discovers icy's skills: about, translate, search, ping
# → Calls icy.about('general') — E2E encrypted
# → Calls icy.translate('Hello world', to_lang='zh')
# → Calls icy.search('coworker protocol')
# → All without seeing icy's source code!

Create an Agent with Skills

from agent_coworker import Agent

agent = Agent("my-bot")

@agent.skill("summarize", description="Summarize text",
             input_schema={"text": "str"},
             output_schema={"summary": "str"})
def summarize(text: str) -> dict:
    return {"summary": text[:200]}

agent.serve()  # XMTP listener + dashboard on :8090

Share Your Invite Code

coworker invite
#   Agent:  my-bot
#
#   Invite code:  eyJuIjoibXktYm90Ii...
#   Short ID:     my-bot-b36cd7f6
#
#   Your collaborator runs:
#     coworker connect eyJuIjoibXktYm90Ii...

Invite codes are privacy-preserving — they contain only your agent name and an XMTP routing ID. No sensitive addresses exposed.

Connect and Collaborate

agent2 = Agent("caller")

# Connect using invite code
peer = agent2.connect("eyJuIjoibXktYm90Ii...")
print(peer["skills"])  # Only shows skills you're trusted to see

# Call a remote skill — E2E encrypted
result = agent2.call("eyJuIjoibXktYm90Ii...", "summarize", {"text": "Hello!"})

# Or set a goal and let agents coordinate
agent2.collaborate("eyJuIjoibXktYm90Ii...", "Research AI agents and write a report")
# → discovers skills, builds OKR, executes steps, auto-downgrades trust when done

---

Monitor Dashboard

agent.serve() launches a React dashboard at http://localhost:8090.

Live activity feed & real-time stats	Cross-network OKR tracking

Activity feed, team/peer management, OKR tracking, workflow insights, group chat, skill visibility toggle, metering & receipts. Auto-detects browser language (Chinese / English).

Comparison

	CoWorker	MCP	A2A	CrewAI / AutoGen
Connects	Agent ↔ Agent	Agent ↔ Tool	Agent ↔ Agent	Agent ↔ Agent
Network	Open internet	Local	Enterprise HTTP	Single process
Encryption	E2E (XMTP MLS)	Transport-only	Enterprise TLS	None
NAT traversal	Yes	No	Infra-dependent	No
Central server	None	MCP server	Discovery service	Runtime host
Skill privacy	Input/output only	Full exposure	Schema-based	Full exposure
Skill visibility	Owner-controlled toggle	None	None	None
Trust management	4-tier + auto-downgrade	None	Enterprise IAM	None
Cost	Zero	Server costs	Infra costs	Compute costs
Dependencies	Zero (stdlib)	Varies	HTTP stack	Heavy

Privacy & Trust

UNTRUSTED (0)  → Can ping, sees NO skills
KNOWN (1)      → Can see/call exposed skills, propose plans
INTERNAL (2)   → Context queries, deep collaboration
PRIVILEGED (3) → Full access — must be granted manually

Default: UNTRUSTED (deny by default)
After OKR: auto-downgrade (PRIVILEGED → INTERNAL → KNOWN)
Transport: E2E encrypted (XMTP MLS, forward secrecy)
Identity: cryptographic, locally generated, never transmitted
Invite codes: contain routing ID only, no sensitive addresses

CLI

coworker init --name my-agent    # generate identity + install bridge
coworker bridge start            # start XMTP bridge
coworker bridge stop             # stop bridge
coworker demo                    # connect to demo bot & test skills
coworker invite                  # generate privacy-preserving invite code
coworker connect <invite-code>   # connect to a peer
coworker status                  # show agent status
coworker skills list             # show skill visibility
coworker skills configure        # toggle which skills peers can see
coworker trust list              # show trust overrides
coworker trust set <peer> known  # grant trust

Examples

examples/
├── 01_minimal.py           # Bare-bones agent
├── 02_collaboration.py     # In-process collaboration
├── 03_discover_skills.py   # Discover a peer's skills
├── 04_remote_skill_call.py # Call a remote skill
├── 05_collaborate.py       # Goal-based collaboration
├── 06_serve_with_dashboard.py  # Agent with monitoring dashboard
├── 07_nanobot_adapter.py   # Bridge Nanobot skills
├── 08_openclaw_adapter.py  # Bridge OpenClaw skills
└── icy_demo_bot.py         # Demo bot source code

Cross-Network Proof

Tested between two independent agents on different continents:

Agent	Location	Network
ziway	Beijing, China	China Telecom
icy	San Francisco, USA	Alibaba Cloud

discover, about, translate, search — all skills called successfully via XMTP E2E encrypted messaging. No IP addresses, no port forwarding, no shared server.

Roadmap

• Skill Visibility Control — owner chooses which skills to expose
• Privacy-preserving invite codes — no addresses in invite codes
• Demo bot — coworker demo for instant onboarding
• MCP bridge — expose CoWorker skills as MCP tools
• TypeScript SDK
• On-chain agent registry (ERC-8004)
• Gossip-based peer discovery
• XMTP production network

Contributing

See CONTRIBUTING.md.

Citation

@software{coworker_protocol,
  title  = {CoWorker Protocol: Peer-to-Peer Agent Collaboration over XMTP},
  author = {Zhao, Ziway and Liu, Dantong and Ding, Xizhi},
  year   = {2026},
  url    = {https://github.com/ZiwayZhao/agent-coworker}
}

License

MIT

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_{Built with XMTP for the open agent internet.}
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