neuronum 2026.6.1

Neuronum SDK

Neuronum SDK

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About

Neuronum is a private data network that enables distributed AI agents to connect and communicate securely across devices with built-in end-to-end encryption, identity, routing, data mapping and delivery by simple function calls.

You focus on building your agent's logic. Neuronum handles the rest.

⚠️ Development Status: The Neuronum SDK is currently in beta and is not production-ready. It is intended for development, testing, and experimental purposes only. Do not use in production environments or for critical applications.

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Requirements

• Python >= 3.8

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Installation

Set up and activate a virtual environment:

python3 -m venv ~/neuronum-venv
source ~/neuronum-venv/bin/activate

Install the Neuronum SDK:

pip install neuronum

Note: Always activate this virtual environment (source ~/neuronum-venv/bin/activate) before running any neuronum commands.

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Cell

A Cell is your address used to send and receive data on the Neuronum network. You can think of it like a unique digital identity.

Example IDs: acme.com::cell [e-mail]::cell

Create a Cell:

neuronum create-cell

This generates your Cell ID, public/private key pair, and a 12-word mnemonic recovery phrase. Your Cell credentials are stored locally at ~/.neuronum/.env.

Connect your Cell to a device using your 12-word mnemonic:

neuronum connect-cell

View the connected Cell ID:

neuronum view-cell

Disconnect Cell credentials from this device:

neuronum disconnect-cell

Delete your Cell permanently from the network:

neuronum delete-cell

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Agent

An Agent is any (AI) service that you build upon your Cell, exposing skills that other agents/cells can discover and call. Each agent has its own configuration, handles, and logic.

Initialize a new Agent:

neuronum init-agent

Business cells can choose between two agent types:

Task Agent — service-level agents that register on the Neuronum network, receive a unique agent_id, and are discoverable by other cells. The agent folder is named agent_<agent_id> and includes agent.py, model.py, and agent.config.

Business Agent — compliance-level agents that run locally within your business infrastructure without network registration. The agent folder is named business_agent and includes agent.py, model.py, and agent.html.

agent.config (inspired by Google's A2A protocol Agent Card):

{
  "agent_meta": {
    "agent_id": "019d8671-22c8-7a91-9fa7-8eb46d85969b",
    "version": "1.0.0",
    "name": "Q&A Agent",
    "description": "An agent that returns answers to natural language prompts",
    "audience": "private",
    "logo": "https://neuronum.net/static/logo_new.png"
  },
  "skills": [
    {
      "handle": "get_answer",
      "description": "Ask a question and get an answer.",
      "examples": [
        "What is the capital of France?",
        "Explain quantum mechanics simply."
      ],
      "stream": false,
      "input_schema": {
        "properties": {
          "query": {
            "type": "string",
            "description": "The user request"
          },
          "context": {
            "type": "string",
            "description": "Optional background information"
          }
        },
        "required": [
          "query"
        ]
      }
    }
  ],
  "auth": ["api_key"],
  "legals": {
    "terms": "https://url_to_your/legals",
    "privacy_policy": "https://url_to_your/legals"
  }
}

Field	Description
agent_meta.agent_id	Auto-generated. Do not change
agent_meta.version	Version of your agent. Update as needed
agent_meta.name	Display name of your agent
agent_meta.description	What your agent does
agent_meta.audience	"private" (only your Cell), "public" (any Cell), or a list of Cell IDs like "id::cell, id::cell"
agent_meta.logo	URL to your agent's logo
skills	List of skills your agent exposes. Add, remove, or modify as needed
skills[].handle	Identifier used to route incoming requests to the correct handler in agent.py
skills[].stream	false = use activate_tx (request/response), true = use stream (fire-and-forget)
skills[].description	What the skill does
skills[].examples	Example prompts or inputs
skills[].input_schema	JSON Schema defining the expected input fields
auth	Authentication parameters required to invoke the agent. Can be an empty array if no authentication is required
legals	Links to your terms of service and privacy policy

Start your Agent:

neuronum start-agent

or

neuronum start-agent -d

Stop your Agent:

neuronum stop-agent

Update your Agent after changing an agent.config file:

neuronum update-agent

Delete your Agent

neuronum delete-agent

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Methods

Cells interact using six methods:

Method	Description
list_cells()	List all Neuronum Cells
list_agents()	List all Agents built on Neuronum
stream(data, cell_id)	Send data to a Cell (fire-and-forget)
activate_tx(data, cell_id)	Send a request and wait for a response
sync()	Listen for incoming transmissions
tx_response(tx_id, data, public_key)	Send an encrypted response back

All data is end-to-end encrypted. The network handles routing, key exchange, and delivery. You just send and receive.

Connecting to the network: Use async with Cell(network="testnet.neuronum.net") as cell to connect. This reads your Cell credentials from ~/.neuronum/.env and establishes a connection to the specified Neuronum network. Omitting the network parameter defaults to testnet.neuronum.net.

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Quick Example

List Cells

import asyncio
from neuronum import Cell

async def main():
    async with Cell(network="testnet.neuronum.net") as cell:
        cells = await cell.list_cells()
        print(cells)

asyncio.run(main())

List Agents

import asyncio
from neuronum import Cell

async def main():
    async with Cell(network="testnet.neuronum.net") as cell:
        agents = await cell.list_agents()
        print(agents)

asyncio.run(main())

Stream data (fire-and-forget)

import asyncio
from neuronum import Cell

async def main():
    async with Cell(network="testnet.neuronum.net") as cell:
        await cell.stream(
          {"msg": "Ping"},
          "receiver_cell_id"
        )

asyncio.run(main())

Send data & wait for response

import asyncio
from neuronum import Cell

async def main():
    async with Cell(network="testnet.neuronum.net") as cell:
        tx_response = await cell.activate_tx(
          {"msg": "Ping"},
          "receiver_cell_id"
        )
        print(tx_response)

asyncio.run(main())

Receive data & send response

import asyncio
from neuronum import Cell

async def main():
    async with Cell(network="testnet.neuronum.net") as cell:
        async for tx in cell.sync():
            data = tx.get("data", {})

            await cell.tx_response(
                tx.get("tx_id"),
                {"msg": "Pong"},
                data.get("public_key", "")
            )

asyncio.run(main())

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TX (Transmitter) Object

When you receive data via sync(), each transmission arrives as a TX object:

{
    "tx_id": "bfd2a0d009c6f784ec97c41d3738a24e0e5ac8f1",
    "time": "1772923393",
    "sender": "1uRQdV593S91E3T2-Vj_29mxBJoI7Cvxxg6dNFDVfv4::cell",
    "data": {
        "msg": "Ping",
        "public_key": "-----BEGIN PUBLIC KEY-----\n..."
    }
}

Field	Description
tx_id	Unique payload ID generated from the encrypted data context and timestamp
time	Unix timestamp of the transmission
sender	The sender's Cell ID
data	The decrypted payload, including the sender's public key for responding via tx_response()

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Full Documentation

For the complete SDK reference including the E2EE protocol, visit the Neuronum Docs.