agentsmcp 0.1.1

Context-sync protocol for AI agents. Every agent has a mailbox. No agent ever starts cold.

AgentMailbox — Python SDK

Python client for AgentMailbox, the context-sync protocol for AI agents. Mirrors the JS SDK feature-for-feature.

Install

pip install agentsmcp

The PyPI distribution is named agentsmcp (matching the npm SDK); the import name stays agentmailbox:

from agentmailbox import AgentMailbox

For local development from a clone:

pip install -e ".[dev]"

Requires Python 3.10+.

Start the server

The Python SDK is a pure HTTP client — it talks to the AgentMailbox Node server. Start it first:

cd ~/agentmailbox
npm start

Async usage

import asyncio
from agentmailbox import AgentMailbox

async def main():
    async with AgentMailbox("researcher@demo", server="http://localhost:3000") as a:
        await a.connect()
        result = await a.send(
            "writer@demo",
            {"task": "summarize diffusion models"},
            context_snapshot={"step": "research_complete", "paper_count": 2},
        )
        print(result.thread_id)

asyncio.run(main())

Multi-agent (CC/BCC/ReplyAll)

async with AgentMailbox("orchestrator@demo", server=SERVER) as orchestrator:
    await orchestrator.connect()
    sent = await orchestrator.send(
        "researcher@demo",
        {"task": "find 50 papers on diffusion models"},
        cc=["writer@demo"],
        bcc=["logger@demo"],
        context_snapshot={"step": "task_dispatched", "priority": "high"},
    )

async with AgentMailbox("researcher@demo", server=SERVER) as researcher:
    await researcher.connect()
    inbound = await researcher.receive()
    # inbound.context.snapshot → {"step": "task_dispatched", "priority": "high"}
    # inbound.messages[0].cc   → ["writer@demo"]
    # inbound.messages[0].bcc  → None  (stripped from non-sender's view)

    await researcher.reply_all(
        sent.thread_id,
        {"result": "found 50 papers"},
        context_snapshot={"step": "research_complete", "paper_count": 50},
    )

Run the bundled demos:

python examples/basic.py
python examples/multi_agent.py

Sync usage

For scripts and notebooks that aren't async-friendly:

from agentmailbox import AgentMailboxSync

agent = AgentMailboxSync("researcher@demo")
agent.connect()
result = agent.send("writer@demo", {"task": "..."})
print(result.thread_id)

AgentMailboxSync drives a fresh event loop per call via asyncio.run. It's convenient but slower than the async client.

API

All methods return typed dataclasses (SendResult, ReceiveResult, Thread, ContextFrame, Message, ParticipantRole, Context). No untyped dicts in the public surface.

Method	HTTP
connect()	POST /agents/register
send(to, payload, ...)	POST /messages/send
reply_all(thread_id, payload)	POST /messages/reply-all
unread()	GET /mailbox/:id/unread
receive(from_agent=None)	GET /mailbox/:id/unread + filter
threads()	GET /mailbox/:id
sync(thread_id)	GET /threads/:id/sync?as=:id
participants(thread_id)	GET /threads/:id/participants?as=
mark_read(thread_id)	POST /mailbox/:id/read

Exceptions

• AgentMailboxError — base class. .status_code set when raised from HTTP.
• NotFoundError — 404
• ServerError — 5xx
• ConnectionError — server unreachable

License

MIT