mscp 0.1.3

The Model Smart Contract Protocol (MSCP) is a standard protocol that enables LLM applications to interact with EVM-compatible networks.

Model Smart Contract Protocol (MSCP)

A standard protocol that enables LLM applications to interact with EVM-compatible networks.

Features

Component as a service
AI Agent interacts with the network by operating different components.

Fast integration
Component-based design makes it easier to build workflows and accelerates the development of AI applications.

Unified interaction
Use consistent rules and protocols to standardize the calls to contracts with different functions and ensure the consistency of AI interactions.

Dynamic expansion
AI Agent can add custom onchain components with greater flexibility.

EVM compatibility
It can interact with multiple EVM-compatible network contracts at the same time, and has greater adaptability in handling tasks in complex scenarios.

Decentralization
Access component capabilities without permission, share onchain data, and provide persistent services and information verification.

Architecture

MSCP consists of three parts:

Component: This is a smart contract that complies with ERC-7654.

Connectors: This is used to establish a connection with a component and convert its methods into tool functions that can be interacted with by the LLM.
In addition, it supports customization, you can customize different Connectors according to different smart contracts.

Chat2Web3: This is a router that dynamically calls the connector based on the response from the LLM.

Quick Start

Install

pip install mscp

Set up environment variables

Please refer to .env.example file, and create a .env file with your own settings. You can use two methods to import environment variables.

Deploy Component Smart Contract

Here is a simple component example.sol that you can deploy on any network.

Integrate MSCP into your AI application

from openai import OpenAI
from eth_account import Account
from mscp import Chat2Web3
from mscp.connectors import ERC7654Connector
from dotenv import load_dotenv
import os

load_dotenv()
# Create a connector to connect to the component
component_connector = ERC7654Connector(
    "http://localhost:8545",  # RPC of the component network
    "0x0E2b5cF475D1BAe57C6C41BbDDD3D99ae6Ea59c7",  # component address
    Account.from_key(os.getenv("EVM_PRIVATE_KEY")),
)

# Create a Chat2Web3 instance
chat2web3 = Chat2Web3([component_connector])

# Create a client for OpenAI
client = OpenAI(api_key=os.getenv("OPENAI_KEY"), base_url=os.getenv("OPENAI_API_BASE"))

# Set up the conversation
messages = [
    {
        "role": "user",
        "content": "What is the user's name and age? 0x8241b5b254e47798E8cD02d13B8eE0C7B5f2a6fA",
    }
]

# Add the chat2web3 to the tools
params = {
    "model": "gpt-3.5-turbo",
    "messages": messages,
    "tools": chat2web3.functions,
}

# Start the conversation
response = client.chat.completions.create(**params)

# Get the function message
func_msg = response.choices[0].message

# fliter out chat2web3 function
if func_msg.tool_calls and chat2web3.has(func_msg.tool_calls[0].function.name):

    # execute the function from llm
    function_result = chat2web3.call(func_msg.tool_calls[0].function)

    messages.extend(
        [
            func_msg,
            {
                "role": "tool",
                "tool_call_id": func_msg.tool_calls[0].id,
                "content": function_result,
            },
        ]
    )

    # Model responds with final answer
    response = client.chat.completions.create(model="gpt-3.5-turbo", messages=messages)

    print(response.choices[0].message.content)

Integrations

Integrate MSCP into Aser Agent

Aser is a minimalist, modular, and versatile AI agent framework. You can assemble an agent with just a few lines of code. Learn more about aser agent here.

# Create a Connector instance for the component
component_connector = Connector(
    "http://127.0.0.1:8545",  # RPC URL
    "0x0E2b5cF475D1BAe57C6C41BbDDD3D99ae6Ea59c7",  # component contract address
    Account.from_key(os.getenv("EVM_PRIVATE_KEY"))  # Load account from private key in environment variable
)

# Initialize Chat2Web3 with the component connector
chat2web3 = Chat2Web3([component_connector])

# Create an Agent instance with chat2web3
agent = Agent(name="chat2web3", model="gpt-4o", chat2web3=chat2web3)

# Use the agent to chat and get the user's name and age by passing an address
response = agent.chat("What is the user's name and age?0x8241b5b254e47798E8cD02d13B8eE0C7B5f2a6fA")

# Print the response from the agent
print(response)

Integrate smart contracts through Custom Connector

Developers can customize Connector functions to adapt to different contracts. Please refer to the following steps:

1. Depoly Your Contract.
Deploy your custom contract. Here is a CustomConnectorContract.sol as example that you can deploy directly.

2. Implement the Connector.
Developers can implement interface abstraction to customize different connectors

from abc import ABC, abstractmethod

class AbstractConnector(ABC):
    def __init__(self, rpc, address, account, type):
        self.rpc = rpc
        self.address = address
        self.account = account
        self.type = type

    @abstractmethod
    def call_function(self, function):
        pass

    @abstractmethod
    def get_functions(self):
        pass

You can refer to the custom_connector.

3. Add the Connector to Chat2Web3.
Add the implemented connector to the Chat2Web3 instance.

chat2web3 = Chat2Web3([custom_connector])

You can refer to the custom_connector_example.

Integrate ERC8004 Connector

ERC8004 is a standard interface for smart contracts that enables agents to discover and and establish trust through reputation and validation.

• ERC8004
• ERC8004 Smart Contracts by ChaosChain
• ERC8004 Identity Connector

This example demonstrates agent identity registration

from aser import Agent
from mscp import Chat2Web3
from mscp.connectors.erc8004 import ERC8004IdentityConnector
from eth_account import Account
import os

# Load account from environment variable
account = Account.from_key(os.getenv("EVM_PRIVATE_KEY"))

# Initialize ERC8004 identity connector with RPC, contract address, and account
identity_connector = ERC8004IdentityConnector(
    "http://127.0.0.1:8545",
    "0x8e0E422Ad7BdAbB4e90Edfdd0424039434e38e42",
    account
)

# Create Chat2Web3 instance with the identity connector
chat2web3 = Chat2Web3([identity_connector])

# Create an Agent instance, specifying name, model, and chat2web3
agent = Agent(name="chat2web3", model="gpt-4o", chat2web3=chat2web3)

# Send a chat request to create a new agent with domain and address info
response = agent.chat(
    f"""
    create a newAgent
    agentDomain: http://www.ame.network
    agentAddress: {account.address}
    """
)

# Print the response from the agent
print(response)