Flow AI Agent

Flow AI Agents

Flow AI is a platform for building, deploying, and monetizing AI agents tailored for the Web3 ecosystem and their community. It enables users to gather insights from onchain and offchain data and run complex transactions.

✨ Features

• AI Agent Marketplace: Build and trade AI agents that can interact with various data sources and perform tasks.
• Custom Dashboards: Create dashboards with rich insights to streamline team alignment and processes.
• Data Integration: Integrate with a wide range of on-chain and off-chain Web3 datasets and tools.
• Contract Analysis Reports: Discover information about contract funders, their balances, and more.
• Community Engagement: Enhance onboarding and education for communities through automated reporting and shared insights. 

💰 Pricing

To charge users, define events in JSON format and save them on the Apify platform. Here is an example schema:

To set up the PPE model:

1. Go to your Actor's Publication settings.
2. Set the Pricing model to Pay per event.
3. Add the pricing schema (see .actor/pay_per_event.json for a complete example).

🔧 How It Works

This template implements a proxy server that can connect to either a stdio-based or SSE-based MCP server and expose it via SSE transport. Here's how it works:

Server types

1. Stdio Server (StdioServerParameters):
   • Spawns a local process that implements the MCP protocol over stdio.
   • Configure using the command parameter to specify the executable and the args parameter for additional arguments.
   • Optionally, use the env parameter to pass environment variables to the process.

Example:

MCP_SERVER_PARAMS = StdioServerParameters(
    command='uv',
    args=['run', 'arxiv-mcp-server'],
    env={'YOUR_ENV_VAR', os.getenv('YOUR-ENV-VAR')},  # Optional environment variables
)

2. SSE Server (SseServerParameters):
   • Connects to a remote MCP server via SSE transport.
   • Configure using the url parameter to specify the server's endpoint.
   • Optionally, use the headers parameter to include custom headers (e.g., for authentication) and the auth parameter for additional authentication mechanisms.

Example:

MCP_SERVER_PARAMS = SseServerParameters(
    url='https://mcp.apify.com/sse',
    headers={'Authorization':  os.getenv('YOUR-AUTH-TOKEN')},  # Replace with your authentication token
)

• Tips:
  • Ensure the remote server supports SSE transport and is accessible from the Actor's environment.
  • Use environment variables to securely store sensitive information like tokens or API keys.

Environment variables:

Environment variables can be securely stored and managed at the Actor level on the Apify platform. These variables are automatically injected into the Actor's runtime environment, allowing you to:

• Keep sensitive information like API keys secure.
• Simplify configuration by avoiding hardcoded values in your code.

Proxy implementation

The proxy server (ProxyServer class) handles:

• Creating a Starlette web server with SSE endpoints (/sse and /messages/)
• Managing connections to the underlying MCP server
• Forwarding requests and responses between clients and the MCP server
• Handling charging through the actor_charge_function

Key components:

• ProxyServer: Main class that manages the proxy functionality
• create_proxy_server: Creates an MCP server instance that proxies requests
• charge_mcp_operation: Handles charging for different MCP operations

MCP operations

The proxy supports all standard MCP operations:

• list_tools(): List available tools
• call_tool(): Execute a tool with arguments
• list_prompts(): List available prompts
• get_prompt(): Get a specific prompt
• list_resources(): List available resources
• read_resource(): Read a specific resource

Each operation can be configured for charging in the PPE model.

📚 Resources

• What is Anthropic's Model Context Protocol?
• How to use MCP with Apify Actors
• Apify MCP server
• Apify MCP server documentation
• Apify MCP client
• Model Context Protocol documentation
• TypeScript tutorials in Academy
• Apify SDK documentation

Getting started

For complete information see this article. In short, you will:

1. Build the Actor
2. Run the Actor

Pull the Actor for local development

If you would like to develop locally, you can pull the existing Actor from Apify console using Apify CLI:

1. Install apify-cli

   Using Homebrew

   $brew install apify-cli
   

   Using NPM

   $npm -g install apify-cli
   

2. Pull the Actor by its unique <ActorId>, which is one of the following:

   • unique name of the Actor to pull (e.g. "apify/hello-world")
   • or ID of the Actor to pull (e.g. "E2jjCZBezvAZnX8Rb")

   You can find both by clicking on the Actor title at the top of the page, which will open a modal containing both Actor unique name and Actor ID.

   This command will copy the Actor into the current directory on your local machine.

   $apify pull <ActorId>
   

Documentation reference

To learn more about Apify and Actors, take a look at the following resources:

• Apify SDK for JavaScript documentation
• Apify SDK for Python documentation
• Apify Platform documentation
• Join our developer community on Discord